title: Parameter Analysis on Fruit Fly Optimization Algorithm

abstract: Fruit fly algorithm is a novel intelligent optimization algorithm based on foraging behavior of the real fruit flies. In order to find optimum solution for an optimization problem, fixed parameters are obtained as a result of manual test in fruit fly algorithm. In this study, it is aimed to find the optimum solution by analyzing the constant parameter concerning the direction of the algorithm instead of manual defining on initialization stage. The study shows an automated approach for finding the related parameter by utilizing grid search algorithm. According to the experimental results, it can be seen that this approach could be used as an alternative way for finding related parameter or other ones in order to achieve optimum model.


title: Extending Impact Analysis in Government Social Media Research: Five Illustrative Cases

abstract: The use of social media by governments is a complex phenomenon that touches upon multiple dimensions, and that involves a wide array of relationships between these dimensions. Existing empirical research on government social media, however, is still mostly focusing on describing isolated aspects of this phenomenon, and falls short of investigating the impacts that these dimensions have on each other. Drawing on a revised framework for classifying existing social media research foci in the categories of management, context, user behavior, user characteristics, platform properties, and effects, we present five empirical cases to illustrate impacts between dimensions of government social media. The empirical findings from the cases extend impact analysis beyond the existing foci, and enable us to propose a research agenda for future research on impacts in government social media.


title: A comparative study of control techniques for three phase PWM rectifier

abstract: In this paper, three different techniques which can be used for control of three phase PWM Rectifier are discussed. Those three control techniques are Direct Power Control, Indirect Power Control or Voltage Oriented Control and Hysteresis Control. The main aim of this paper is to compare and establish the merits and demerits of each technique in various aspects mainly regarding switching frequency hence switching loss, computation and transient state behavior. Each control method is studied in detail and simulated using Matlab/Simulink in order to make the comparison.


title: Supervised Distance Preserving Projections

abstract: In this work, we consider dimensionality reduction in supervised settings and, specifically, we focus on regression problems. A novel algorithm, the supervised distance preserving projection (SDPP), is proposed. The SDPP minimizes the difference between pairwise distances among projected input covariates and distances among responses locally. This minimization of distance differences leads to the effect that the local geometrical structure of the low-dimensional subspace retrieved by the SDPP mimics that of the response space. This, not only facilitates an efficient regressor design but it also uncovers useful information for visualization. The SDPP achieves this goal by learning a linear parametric mapping and, thus, it can easily handle out-of-sample data points. For nonlinear data, a kernelized version of the SDPP is also derived. In addition, an intuitive extension of the SDPP is proposed to deal with classification problems. The experimental evaluation on both synthetic and real-world data sets demonstrates the effectiveness of the SDPP, showing that it performs comparably or superiorly to state-of-the-art approaches.


title: Using data mining techniques for multi-diseases prediction modeling of hypertension and hyperlipidemia by common risk factors

abstract: Research highlights? This paper proposes a two-phase analysis procedure to simultaneously predict hypertension and hyperlipidemia. ? Common risk factors of these diseases picked up by data mining and majority vote. ? This study uses common risk factors to build MARS predictive models for hypertension and hyperlipidemia. Many previous studies have employed predictive models for a specific disease, but fail to note that humans often suffer from not only one disease, but associated diseases as well. Because these associated multiple diseases might have reciprocal effects, and abnormalities in physiological indicators can indicate multiple associated diseases, common risk factors can be used to predict the multiple associated diseases. This approach provides a more effective and comprehensive forecasting mechanism for preventive medicine. This paper proposes a two-phase analysis procedure to simultaneously predict hypertension and hyperlipidemia. Firstly, we used six data mining approaches to select the individual risk factors of these two diseases, and then determined the common risk factors using the voting principle. Next, we used the Multivariate Adaptive Regression Splines (MARS) method to construct a multiple predictive model for hypertension and hyperlipidemia. This study uses data from a physical examination center database in Taiwan that includes 2048 subjects. The proposed analysis procedure shows that the common risk factors of hypertension and hyperlipidemia are Systolic Blood Pressure (SBP), Triglycerides, Uric Acid (UA), Glutamate Pyruvate Transaminase (GPT), and gender. The proposed multi-diseases predictor method has a classification accuracy rate of 93.07%. The results of this paper provide an effective and appropriate methodology for simultaneously predicting hypertension and hyperlipidemia.


Analysis of Depth of Penetration and Bead Width of Shielded Metal Arc Weld under Magnetic Field Applying Artificial Neural Networks


title: The Influence of Personality Traits and Cognitive Load on the Use of Adaptive User Interfaces

abstract: One of the problems adaptive interfaces must solve is the issue of stability---users must be able to complete a familiar task reliably. Split Adaptive Interfaces, where a limited part of the screen contains copies of the interface elements predicted to be of immediate use, are one technique for resolving this difficulty. While prior work demonstrated that Split Adaptive Interfaces improve performance on average, the results of our study demonstrate systematic individual differences in the utilization of the adaptive features, which correlate with the stable user traits of Need for Cognition and Extraversion. Specifically, higher Need for Cognition (a willingness to undertake difficult mental activities) is correlated with increased utilization rates, while higher Extraversion (a general orientation towards seeking gratification from the external world) is negatively correlated with utilization rates. Our results also demonstrate a significant negative correlation between cognitive load induced by a secondary task and the utilization of the adaptive features. This effect, however, is very small (less than two percentage points). Together, these results provide additional evidence of the usefulness of the split adaptive interface approach and a negligible effect of additional cognitive load, but also demonstrate that the approach does not benefit all users equally.


THE ECONOMICS AND PERFORMANCE OF DESALINATION PLANTS


title: Pay for a Sliding Bloom Filter and Get Counting, Distinct Elements, and Entropy for Free

abstract: For many networking applications, recent data is more significant than older data, motivating the need for sliding window solutions. Various capabilities, such as DDoS detection and load balancing, require insights about multiple metrics including Bloom filters, per-flow counting, count distinct and entropy estimation. ::: In this work, we present a unified construction that solves all the above problems in the sliding window model. Our single solution offers a better space to accuracy tradeoff than the state-of-the-art for each of these individual problems! We show this both analytically and by running multiple real Internet backbone and datacenter packet traces.


title: Big Five personality and academic dishonesty: A meta-analytic review

abstract: Abstract Academic dishonesty is widespread within secondary and higher education. It can include unethical academic behaviors such as cheating, plagiarism, or unauthorized help. Researchers have investigated a number of individual and contextual factors in an effort to understand the phenomenon. In the last decade, there has been increasing interest in the role personality plays in explaining unethical academic behaviors. We used meta-analysis to estimate the relationship between each of the Big Five personality factors and academic dishonesty. Previous reviews have highlighted the role of neuroticism and extraversion as potential predictors of cheating behavior. However, our results indicate that conscientiousness and agreeableness are the strongest Big Five predictors, with both factors negatively related to academic dishonesty. We discuss the implications of our findings for both research and practice.


Design and Implementation of Cosine Control Firing Scheme for Single Phase Fully Controlled Bridge Rectifier


title: Precise Distance and Velocity Measurement for Real Time Locating in Multipath Environments Using a Frequency-Modulated Continuous-Wave Secondary Radar Approach

abstract: We present a frequency-modulated continuous-wave secondary radar concept to estimate the offset in time and in frequency of two wireless units and to measure their distance relative to each other. By evaluating the Doppler frequency shift of the radar signals, the relative velocity of the mobile units is measured as well. The distance can be measured with a standard deviation as low as 1 cm. However, especially in indoor environments, the accuracy of the system can be degraded by multipath transmissions. Therefore, we show an extension of the algorithm to cope with multipath propagation. We also present the hardware setup of the measurement system. The system is tested in various environments. The results prove the excellent performance and outstanding reliability of the algorithms presented.


title: F-Score Driven Max Margin Neural Network for Named Entity Recognition in Chinese Social Media

abstract: We focus on named entity recognition (NER) for Chinese social media. With massive unlabeled text and quite limited labelled corpus, we propose a semi-supervised learning model based on B-LSTM neural network. To take advantage of traditional methods in NER such as CRF, we combine transition probability with deep learning in our model. To bridge the gap between label accuracy and F-score of NER, we construct a model which can be directly trained on F-score. When considering the instability of F-score driven method and meaningful information provided by label accuracy, we propose an integrated method to train on both F-score and label accuracy. Our integrated model yields 7.44\% improvement over previous state-of-the-art result.


title: A cross-collection mixture model for comparative text mining

abstract: In this paper, we define and study a novel text mining problem, which we refer to as Comparative Text Mining (CTM). Given a set of comparable text collections, the task of comparative text mining is to discover any latent common themes across all collections as well as summarize the similarity and differences of these collections along each common theme. This general problem subsumes many interesting applications, including business intelligence and opinion summarization. We propose a generative probabilistic mixture model for comparative text mining. The model simultaneously performs cross-collection clustering and within-collection clustering, and can be applied to an arbitrary set of comparable text collections. The model can be estimated efficiently using the Expectation-Maximization (EM) algorithm. We evaluate the model on two different text data sets (i.e., a news article data set and a laptop review data set), and compare it with a baseline clustering method also based on a mixture model. Experiment results show that the model is quite effective in discovering the latent common themes across collections and performs significantly better than our baseline mixture model.


title: Globally Exponential Synchronization and Synchronizability for General Dynamical Networks

abstract: The globally exponential synchronization problem for general dynamical networks is considered in this paper. One quantity will be distilled from the coupling matrix to characterize the synchronizability of the corresponding dynamical networks. The calculation of such a quantity is very convenient even for large-scale networks. The network topology is assumed to be directed and weakly connected, which implies that the coupling configuration matrix can be asymmetric, weighted, or reducible. This assumption is more consistent with the realistic network in practice than the constraint of symmetry and irreducibility. By using the Lyapunov functional method and the Kronecker product techniques, some criteria are obtained to guarantee the globally exponential synchronization of general dynamical networks. In addition, numerical examples, including small-world and scale-free networks, are given to demonstrate the theoretical results. It will be shown that our criteria are available for large-scale dynamical networks.


title: File System Journal Forensics

abstract: Journaling is a relatively new feature of modern file systems that is not yet exploited by most digital forensic tools. A file system journal caches data to be written to the file system to ensure that it is not lost in the event of a power loss or system malfunction. Analysis of journal data can identify which files were overwritten recently. Indeed, under the right circumstances, analyzing a file system journal can reveal deleted files and previous versions of files without having to review the hex dump of a drive. This paper discusses data recovery from ReiserFS and ext3, two popular journaled file systems. It also describes a Java-based tool for analyzing ext3 file system journals and recovering data pertaining to overwritten and deleted files.


Chapter 2 Wireless Power Transfer ( WPT ) for Electric Vehicles ( EVs ) — Present and Future Trends


title: Decubitus ulcers: pathophysiology and primary prevention.

abstract: BACKGROUND ::: Pressure sores are a serious complication of multimorbidity and lack of mobility. Decubitus ulcers have become rarer among bed-ridden patients because of the conscientious use of pressure-reducing measures and increased mobilization. Nonetheless, not all decubitus ulcers can be considered preventable or potentially curable, because poor circulation makes some patients more susceptible to them, and because cognitive impairment can make prophylactic measures difficult to apply. ::: ::: ::: METHODS ::: A systematic literature search was performed in 2004 and 2005 in the setting of a health technology assessment, and a selective literature search was performed in 2009 for papers on the prevention of decubitus ulcers. ::: ::: ::: RESULTS ::: Elderly, multimorbid patients with the immobility syndrome are at high risk for the development of decubitus ulcers, as are paraplegic patients. The most beneficial way to prevent decubitus ulcers, and to treat them once they are present, is to avoid excessive pressure by encouraging movement. At the same time, the risk factors that promote the development of decubitus ulcers should be minimized as far as possible. ::: ::: ::: CONCLUSIONS ::: Malnutrition, poor circulation (hypoperfusion), and underlying diseases that impair mobility should be recognized if present and then treated, and accompanying manifestations, such as pain, should be treated symptomatically. Over the patient's further course, the feasibility, implementation, and efficacy of ulcer-preventing measures should be repeatedly re-assessed and documented, so that any necessary changes can be made. Risk factors for the development of decubitus ulcers should be assessed at the time of the physician's first contact with an immobile patient, or as soon as the patient's condition deteriorates; this is a prerequisite for timely prevention. Once the risks have been assessed, therapeutic measures should be undertaken on the basis of the patient's individual risk profile, with an emphasis on active encouragement of movement and passive relief of pressure through frequent changes of position.


title: Integrating phase change materials into concrete through microencapsulation using cenospheres

abstract: Abstract Phase change materials (PCMs) can enhance the building energy efficiency through thermal energy storage and thermal regulation. Microencapsulated PCMs (MEPCMs) provide a better utilization of PCMs with building materials. This study proposes a novel method to encapsulate PCMs into cenospheres which are hollow fly ash particles generated in coal burning power plants with size ranging from a few micrometers to hundreds of micrometers. The shell of the cenosphere inherently has some small pores which are sealed by a thin layer of glass-crystalline film. By removing this film through chemical etching, these holes can be exposed, providing paths for PCMs moving into the internal void of cenospheres. A thin layer of silica is coated on the PCM loaded cenospheres to prevent the possible leakage of liquid PCMs. The produced PCM microcapsules are referred to as CenoPCM, which can be directly added into traditional construction and building materials such as concrete to produce thermally active concrete. Prototype thermally active cement mortar integrated with the produced CenoPCM capsules have also been manufactured and characterized for its mechanical and microstructural properties. The characterizations showed that there was only minor reduction in strength and the mortar remained strong enough for building application. From this work, it is found that the produced CenoPCM capsules have great potential to be added into construction materials for reducing energy consumptions in buildings.


title: Semantic web-mining and deep vision for lifelong object discovery

abstract: Autonomous robots that are to assist humans in their daily lives must recognize and understand the meaning of objects in their environment. However, the open nature of the world means robots must be able to learn and extend their knowledge about previously unknown objects on-line. In this work we investigate the problem of unknown object hypotheses generation, and employ a semantic web-mining framework along with deep-learning-based object detectors. This allows us to make use of both visual and semantic features in combined hypotheses generation. Experiments on data from mobile robots in real world application deployments show that this combination improves performance over the use of either method in isolation.


title: Distributed large-scale natural graph factorization

abstract: Natural graphs, such as social networks, email graphs, or instant messaging patterns, have become pervasive through the internet. These graphs are massive, often containing hundreds of millions of nodes and billions of edges. While some theoretical models have been proposed to study such graphs, their analysis is still difficult due to the scale and nature of the data. We propose a framework for large-scale graph decomposition and inference. To resolve the scale, our framework is distributed so that the data are partitioned over a shared-nothing set of machines. We propose a novel factorization technique that relies on partitioning a graph so as to minimize the number of neighboring vertices rather than edges across partitions. Our decomposition is based on a streaming algorithm. It is network-aware as it adapts to the network topology of the underlying computational hardware. We use local copies of the variables and an efficient asynchronous communication protocol to synchronize the replicated values in order to perform most of the computation without having to incur the cost of network communication. On a graph of 200 million vertices and 10 billion edges, derived from an email communication network, our algorithm retains convergence properties while allowing for almost linear scalability in the number of computers.


Enzymatic Amplification of P-Globin Genomic Sequences and Restriction Site Analysis for Diagnosis of Sickle Cell Anemia


Imagination-Based Decision Making with Physical Models in Deep Neural Networks


title: The effect of rhythmic-cued motor imagery on walking, fatigue and quality of life in people with multiple sclerosis: A randomised controlled trial

abstract: Background:Motor imagery and rhythmic auditory stimulation are physiotherapy strategies for walking rehabilitation.Objectives:To investigate the effect of motor imagery combined with rhythmic cueing on walking, fatigue and quality of life (QoL) in people with multiple sclerosis (MS).Methods:Individuals with MS and Expanded Disability Status Scale scores of 1.5–4.5 were randomised into one of three groups: 17 minutes of motor imagery, six times per week, for 4 weeks, with music (A) or metronome cues (B), both with verbal cueing, and (C) controls. Primary outcomes were walking speed (Timed 25-Foot Walk) and distance (6-Minute Walk Test). Secondary outcomes were walking perception (Multiple Sclerosis Walking Scale-12), fatigue (Modified Fatigue Impact Scale) and QoL (Short Form-36 Health Survey, Multiple Sclerosis Impact Scale-29, Euroquol-5D-3L Questionnaire).Results:Of the 112 participants randomised, 101 completed the study. Compared to controls, both interventions significantly improved walking speed, di...


abstract: Lee, M. R. F., Martinez, E. M., Scollan, N. D., Theodorou, M. K. (2003). Plant enzyme mediated lipolysis of Lolium perenne and Trifolium pratense in an in vitro simulated rumen environment. Page 424 in: van der Honing, Y. (Ed.). EAAP Book of abstracts, Volume 9. Wageningen Academic Publishers, ISBN: 978-90-76998-20-6. Book of Abstracts of the Annual Meeting of the European Association for Animal Production (EAPP), Rome, Italy, 30 August-3 September 2003.


title: Flavivirus Entry Receptors: An Update

abstract: Flaviviruses enter host cells by endocytosis initiated when the virus particles interact with cell surface receptors. The current model suggests that flaviviruses use at least two different sets of molecules for infectious entry: attachment factors that concentrate and/or recruit viruses on the cell surface and primary receptor(s) that bind to virions and direct them to the endocytic pathway. Here, we present the currently available knowledge regarding the flavivirus receptors described so far with specific attention to C-type lectin receptors and the phosphatidylserine receptors, T-cell immunoglobulin and mucin domain (TIM) and TYRO3, AXL and MER (TAM). Their role in flavivirus attachment and entry as well as their implication in the virus biology will be discussed in depth.


title: Multiple players tracking and identification using group detection and player number recognition in sports video

abstract: We are interested in the problem of automatically tracking and identifying players in sports video. While there are many automatic multi-target tracking methods, in sports video, it is difficult to track multiple players due to frequent occlusions, quick motion of players and camera, and camera position. We propose tracking method that associates tracklets of a same player using results of player number recognition. To deal with frequent occlusions, we detect human region by level set method and then estimates if it is occluded group region or unoccluded individual one. Moreover, we associate tracklets using the results of player number recognition at each frame by keypoints-based matching with templates from multiple viewpoints, so that final tracklets include occluded region.


title: Combined technique for improving digital image watermarking

abstract: The technique presented in this paper offers better imperceptibility, reduces execution time and improves robustness in comparison to Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and hybrid fusion technique (HFT) digital watermarking techniques. The proposed improved technique (IMD-WC-T) combined both the DWT and DCT subsequently and spread the watermark with PN-sequence with a certain key in the DCT transform after decompose the host image with DWT in level 2 to generate the final watermarked image. Such combination allowed us to exploit the individual advantages derivable from the separate use of the DWT and DCT methods. Results obtained from (IMD-WC-T) showed improved performance in terms of imperceptibility, execution time and certain aspect in robustness (Gama Correction, Histogram Equalization) than that of DWT, DCT, and hybrid fusion technique (HFT), in addition to the quality of recovered watermark image as well. Comparing with HFT, the IMD-WC-T technique guarantees additional imperceptibility, reduced execution time and gave more robustness against Gama Correction and Histogram Equalization.


title: The cost of pressure ulcers in the UK

abstract: OBJECTIVE ::: To estimate the annual cost of treating pressure ulcers in the UK. ::: ::: ::: DESIGN ::: Costs were derived from a bottom-up methodology, based on the daily resources required to deliver protocols of care reflecting good clinical practice. ::: ::: ::: SETTING ::: Health and social care system in the UK. ::: ::: ::: SUBJECTS ::: Patients developing a pressure ulcer. ::: ::: ::: METHODS ::: A bottom-up costing approach is used to estimate treatment cost per episode of care and per patient for ulcers of different grades and level of complications. Also, total treatment cost to the health and social care system in the UK. ::: ::: ::: RESULTS ::: The cost of treating a pressure ulcer varies from pound 1,064 (Grade 1) to pound 10,551 (Grade 4). Costs increase with ulcer grade because the time to heal is longer and because the incidence of complications is higher in more severe cases. The total cost in the UK is pound 1.4- pound 2.1 billion annually (4% of total NHS expenditure). Most of this cost is nurse time. ::: ::: ::: CONCLUSIONS ::: Pressure ulcers represent a very significant cost burden in the UK. Without concerted effort this cost is likely to increase in the future as the population ages. To the extent that pressure ulcers are avoidable, pressure damage may be indicative of clinical negligence and there is evidence that litigation could soon become a significant threat to healthcare providers in the UK, as it is in the USA.


title: Korean text summarization using an aggregate similarity

abstract: In this paper, each document is represented by a weighted graph called a text relationship map. In the graph, each node represents a vector of nouns in a sentence, an undirected link connects two nodes if two sentences are semantically related, and a weight on the link is a value of the similarity between a pair of sentences. The vector similarity can be computed as the inner product between corresponding vector elements. The similarity is based on the word overlap between the corresponding sentences. The importance of a node on the map, called an aggregate similarity, is defined as the sum of weights on the links connecting it to other nodes on the map. In this paper, we present a Korean text summarization system using the aggregate similarity. To evaluate our system, we used two test collections: one collection (PAPER-InCon) consists of 100 papers in the domain of computer science; the other collection (NEWS) is composed of 105 articles in the newspapers. Under the compression rate of 20%, we achieved the recall of 46.6% (PAPER-InCon) and 30.5% (NEWS), and the precision of 76.9% (PAPER-InCon) and 42.3% (NEWS). Experiments show that our system outperforms two commercial systems.


title: Sexual abuse of boys: definition, prevalence, correlates, sequelae, and management.

abstract: OBJECTIVE ::: To clarify the definition of sexual abuse of boys, update estimates of its prevalence, and explore critically its reported correlates, sequelae, and management. ::: ::: ::: DATA SOURCES ::: Studies from 1985 to 1997 were identified using OVID-MEDLINE and OVID-CINAHL. The search terms used were sexual abuse, sexual victimization, and sexual assault. Constraints applied included English language, human male subjects, age younger than 19 years, and North American samples. ::: ::: ::: STUDY SELECTION ::: Publications were included for review if they appeared in peer-reviewed journals; had clear research designs; reported results for at least 20 male subjects; and were not reviews, perspectives, theoretical treatises, editorials, or letters. ::: ::: ::: DATA EXTRACTION ::: Study types and sampling methods were categorized using well-established definitions. Preference was given to studies with large samples, with case-control or cohort designs, and/or with adjustment for effect modifiers or confounders. ::: ::: ::: DATA SYNTHESIS ::: We identified 166 studies representing 149 sexual abuse samples. Studies were methodologically limited and definitions of sexual abuse varied widely. Prevalence estimates varied widely (by definition used and population studied), ranging from 4% to 76%. Boys at highest risk were younger than 13 years, nonwhite, of low socioeconomic status, and not living with their fathers. Perpetrators tended to be known but unrelated males. Abuse frequently occurred outside the home, involved penetration, and occurred more than once. Sequelae included psychological distress, substance abuse, and sexually related problems. Evaluation of management strategies was limited. ::: ::: ::: CONCLUSIONS ::: Sexual abuse of boys appears to be common, underreported, underrecognized, and undertreated. Future study requires clearer definitions of abuse, improved sampling, more rigorous data collection, more sophisticated data analyses, and better assessment of management and treatment strategies. Regardless, health care professionals should be more aware of and sensitive to the possibility of sexual abuse in their male patients.


title: Identification of Colletotrichum spp. associated with fruit rot of Capsicum annuum in North Western Himalayan region of India using fungal DNA barcode markers

abstract: The DNA barcode approach was used to identify and establish association of Colletotrichum species complex with fruit rot disease of chili (Capsicum annuum L.) in North-Western Himalayan region of India. Twenty isolates of five morphologically identified Colletotrichum species collected from commercial chili growing areas were identified using deoxyribonucleic acid (DNA) barcode marker genes, 5.8S ribosomal ribonucleic acid flanking internal transcribed spacers 1 & 2 and β-tubulin gene. Morpho-cultural identification requires expertise to delineate C. gloeosporioides, C. boninense and C. acutatum complexes from each other, as these species possess minute variation in spore shape and size. Ribosomal DNA and β-tubulin sequence analysis along with species-specific marker amplification established the association of seven Colletorichum spp. viz., C. truncatum (syn. Colletotrichum capsici), C. coccodes, C. karstii, C. kahawae, C. nymphaeae, C. fructicola and C. gloeosporioides complex with fruit rot of chili. Phylogenetic analysis of 35 Colletotrichum sequences including authentic type sequences validated the identified sequences with strong bootstrap support. This approach delineated morphologically identified species with great ease into more reliable genotype based speciation of various Colletorichum complexes. The DNA barcode markers have direct implications for plant pathologists in relation to diagnostics in fields and for the purpose of quarantine and disease management.


title: Why is image quality assessment so difficult?

abstract: Image quality assessment plays an important role in various image processing applications. A great deal of effort has been made in recent years to develop objective image quality metrics that correlate with perceived quality measurement. Unfortunately, only limited success has been achieved. In this paper, we provide some insights on why image quality assessment is so difficult by pointing out the weaknesses of the error sensitivity based framework, which has been used by most image quality assessment approaches in the literature. Furthermore, we propose a new philosophy in designing image quality metrics: The main function of the human eyes is to extract structural information from the viewing field, and the human visual system is highly adapted for this purpose. Therefore, a measurement of structural distortion should be a good approximation of perceived image distortion. Based on the new philosophy, we implemented a simple but effective image quality indexing algorithm, which is very promising as shown by our current results.


title: A simplified space vector pulse width modulation algorithm for five level diode clamping inverter

abstract: In this paper, a simplified space vector pulse with modulation (SVPWM) method for the five level diode clamping voltage source inverter is presented. The space vector diagram of the five level inverter is simplified into that of a three level inverter. In turn, the three level inverter space vector diagram is simplified into that of two level inverter. Thus, the algorithm of five level SVPWM becomes very similar to that of conventional two level inverter SVPWM. This method can be extended easily to multi-level inverters above five level


title: Recent views of conceptual structure.

abstract: This article reviews theories of concept structure proposed since the mid-1970s, when the discovery of typicality effects led to the rejection of the view that instances of a concept share necessary and sufficient attributes. To replace that classical view, psychologists proposed the family resemblance and exemplar views (and hybrids of the 2), which argue that instances of a concept share a certain level of overall similarity, rather than necessary and sufficient attributes. These similaritybased views account for much of the typicality data but fail to provide an adequate explanation of the coherence of conceptual categories and of various context effects. Recently proposed explanation-based accounts address these issues but raise further questions about the distinction between concept-specific information and general knowledge and about the relationship between conceptual knowledge and various forms of inference.


The Penn Arabic Treebank: Building a Large-Scale Annotated Arabic Corpus


title: Butter increased total and LDL cholesterol compared with olive oil but resulted in higher HDL cholesterol compared with a habitual diet

abstract: BACKGROUND ::: Butter is known to have a cholesterol-raising effect and, therefore, has often been included as a negative control in dietary studies, whereas the effect of moderate butter intake has not been elucidated to our knowledge. ::: ::: ::: OBJECTIVE ::: We compared the effects of moderate butter intake, moderate olive oil intake, and a habitual diet on blood lipids, high-sensitivity C-reactive protein (hsCRP), glucose, and insulin. ::: ::: ::: DESIGN ::: The study was a controlled, double-blinded, randomized 2 × 5-wk crossover dietary intervention study with a 14-d run-in period during which subjects consumed their habitual diets. The study included 47 healthy men and women (mean ± SD total cholesterol: 5.22 ± 0.90 mmol/L) who substituted a part of their habitual diets with 4.5% of energy from butter or refined olive oil. ::: ::: ::: RESULTS ::: Study subjects were 70% women with a mean age and body mass index (in kg/m²) of 40.4 y and 23.5, respectively. Butter intake increased total cholesterol and LDL cholesterol more than did olive oil intake (P < 0.05) and the run-in period (P < 0.005 and P < 0.05, respectively) and increased HDL cholesterol compared with the run-in period (P < 0.05). No difference in effects was observed for triacylglycerol, hsCRP, insulin, and glucose concentrations. The intake of saturated fatty acids was significantly higher in the butter period than in the olive oil and run-in periods (P < 0.0001). ::: ::: ::: CONCLUSIONS ::: Moderate intake of butter resulted in increases in total cholesterol and LDL cholesterol compared with the effects of olive oil intake and a habitual diet (run-in period). Furthermore, moderate butter intake was also followed by an increase in HDL cholesterol compared with the habitual diet. We conclude that hypercholesterolemic people should keep their consumption of butter to a minimum, whereas moderate butter intake may be considered part of the diet in the normocholesterolemic population.


title: Education Standard for American Physical Education Teachers: Structure,Features and Inspiration

abstract: By the methods of literature review and comparative approach,this paper analyzed American"2008 National Initial Physical Education Teacher Education Standards"from aspects of enactment background,structure and traits. This study gets rid of deficiencies of focusing on standards content in the past studies and hopes to provide a reference for enacting China physical education teacher education standards. Conclusions: American 2008"standards"is standard-based and is constructed on constructivism learning theory with the feature of behaviorism. This"standards"is guided by education theory,mainlined by forming teaching ability,designed by skill,ethics,training,development and constitution. And it incorporates knowledge,skill,capacity and quality of physical education teachers effectively. This study suggests that China should establish the idea of physical education teachers' professional development,confirm the dimension of physical education teachers' education standard,and draw up China physical education teachers' education standard which will be suited to our country's actual conditions as soon as possible.


Predicting Gold Prices


title: Bangla Sentence Correction Using Deep Neural Network Based Sequence to Sequence Learning

abstract: Development in deep neural networks in particular to natural language processing has motivated researchers to apply these techniques in solving challenging problems like machine translation, automatic grammar checking, etc. In this paper, we address the problem of Bangla sentence correction and auto completion using decoder-encoder based sequence-to-sequence recurrent neural network with long short term memory cells. For this purpose, we have constructed a standard benchmark dataset incorporating mis-arrangement of words, missing words and sentence completion tasks. Based on the dataset we have trained our model and achieved 79% accuracy on the test dataset. We have made all our methods and datasets available for future use of the other researchers from: https://github.com/mrscp/bangla-sentence-correction. An online tool have also been developed based on our methods and readily available to use from: http://brl.uiu.ac.bd/s2s.


title: The international epidemiology of child sexual abuse.

abstract: Surveys of child sexual abuse in large nonclinical populations of adults have been conducted in at least 19 countries in addition to the United States and Canada, including 10 national probability samples. All studies have found rates in line with comparable North American research, ranging from 7% to 36% for women and 3% to 29% for men. Most studies found females to be abused at 1 1/2 to 3 times the rate for males. Few comparisons among countries are possible because of methodological and definitional differences. However, they clearly confirm sexual abuse to be an international problem.


title: Smart homes and their users: a systematic analysis and key challenges

abstract: Published research on smart homes and their users is growing exponentially, yet a clear understanding of who these users are and how they might use smart home technologies is missing from a field being overwhelmingly pushed by technology developers. Through a systematic analysis of peer-reviewed literature on smart homes and their users, this paper takes stock of the dominant research themes and the linkages and disconnects between them. Key findings within each of nine themes are analysed, grouped into three: (1) views of the smart home--functional, instrumental, socio-technical; (2) users and the use of the smart home--prospective users, interactions and decisions, using technologies in the home; and (3) challenges for realising the smart home--hardware and software, design, domestication. These themes are integrated into an organising framework for future research that identifies the presence or absence of cross-cutting relationships between different understandings of smart homes and their users. The usefulness of the organising framework is illustrated in relation to two major concerns--privacy and control--that have been narrowly interpreted to date, precluding deeper insights and potential solutions. Future research on smart homes and their users can benefit by exploring and developing cross-cutting relationships between the research themes identified.


title: Functional form of the Parisi overlap distribution for the three-dimensional Edwards-Anderson Ising spin glass

abstract: Recently, it has been conjectured that the statistics of extremes is of relevance for a large class of correlated systems. For certain probability densities this predicts the characteristic large x falloff behavior f(x) ∼exp(-ae x ), a>0. Using a multicanonical Monte Carlo technique, we have measured the Parisi overlap distribution P(q) for the three-dimensional Edward-Anderson Ising spin glass at and below the critical temperature We find that a probability distribution related to extreme-order statistics gives an excellent description of P(q) over about 80 orders of magnitude.


title: A Compact Meander-Line UHF RFID Tag Antenna Loaded With Elements Found in Right/Left-Handed Coplanar Waveguide Structures

abstract: A new planar meander-line antenna for passive UHF radio frequency identification (RFID) tags is presented. Specifically, a meander-line antenna is loaded periodically with coplanar waveguide (CPW) LC elements traditionally found in right/left-handed waveguide structures. It is shown that by using the antenna presented in this letter in a prototype passive UHF RFID tag, effective read ranges up to 4.87 m can be achieved. Many different dielectric substrates and CPW-LC load dimensions are investigated to illustrate how the input impedance, gain, and overall dimensions of the antenna are affected by these structural differences. It is shown that the overall dimension of the meander-line antenna can be reduced by slightly more than 18% with the introduction of the CPW-LC elements to the design. Several of the simulation results are validated by comparison with measurements.


Tackling Spuriousness with Similarity


title: Sentiment Computing for the News Event Based on the Social Media Big Data

abstract: The explosive increasing of the social media data on the Web has created and promoted the development of the social media big data mining area welcomed by researchers from both academia and industry. The sentiment computing of news event is a significant component of the social media big data. It has also attracted a lot of researches, which could support many real-world applications, such as public opinion monitoring for governments and news recommendation for Websites. However, existing sentiment computing methods are mainly based on the standard emotion thesaurus or supervised methods, which are not scalable to the social media big data. Therefore, we propose an innovative method to do the sentiment computing for news events. More specially, based on the social media data (i.e., words and emoticons) of a news event, a word emotion association network (WEAN) is built to jointly express its semantic and emotion, which lays the foundation for the news event sentiment computation. Based on WEAN, a word emotion computation algorithm is proposed to obtain the initial words emotion, which are further refined through the standard emotion thesaurus. With the words emotion in hand, we can compute every sentence’s sentiment. Experimental results on real-world data sets demonstrate the excellent performance of the proposed method on the emotion computing for news events.


Universal Dependencies for Norwegian.


title: A Signal Conditioning Antiwindup Approach for Digital Stationary Frame Current Regulators

abstract: AC current regulators are susceptible to controller windup, i.e., the condition in which the controller dynamic states ramp the controller output up to very large values when the commanded reference cannot be achieved because of inverter saturation. Recovering from saturation can take considerable time once the linear mode has been restored, which degrades the current regulator's transient performance and can also impact on its stability. This paper proposes an improved formulation using the concept of signal conditioning, where windup is avoided by feeding the controller states entirely from the constrained inverter pulsewidth modulation command. Unlike previous approaches, this technique has the benefit of a simplified realization, since the controller transfer functions do not require algebraic reformulation. This makes it particularly suitable for multiple cascaded resonators used in harmonic compensation systems. The technique has been validated using simulation and experimental investigations for a grid-connected inverter.


title: Academic network analysis: a joint topic modeling approach

abstract: We propose a novel probabilistic topic model that jointly models authors, documents, cited authors, and venues simultaneously in one integrated framework, as compared to previous work which embeds fewer components. This model is designed for three typical applications in academic network analysis: the problems of expert ranking, cited author prediction and venue prediction. Experiments based on two real world data sets demonstrate the model to be effective, and it outperforms several state-of-the-art algorithms in all three applications.


title: Quality Management of Learning Management Systems: A User Experience Perspective

abstract: Learning Management Systems (LMS) have been the main vehicle for delivering and managing e-learning courses in educational, business, governmental and vocational learning settings. Since the mid-nineties there is a plethora of LMS in the market with a vast array of features. The increasing complexity of these platforms makes LMS evaluation a hard and demanding process that requires a lot of knowledge, time, and effort. Nearly 50% of respondents in recent surveys have indicated they seek to change their existing LMS primarily due to user experience issues. Yet the vast majority of the extant literature focuses only on LMS capabilities in relation to administration and management of teaching and learning processes. In this study the authors try to build a conceptual framework and evaluation model of LMS through the lens of User Experience (UX) research and practice, an epistemology that is quite important but currently neglected in the elearning domain. They conducted an online survey with 446 learning professionals, and from the results, developed a new UX-oriented evaluation model with four dimensions: pragmatic quality, authentic learning, motivation and engagement, and autonomy and relatedness. Their discussion on findings includes some ideas for future research.


title: Minimal energy storage required for stability of low inertia distributed sources

abstract: Recently there have been extensive research efforts to identify possible adverse effects of distributed sources and power electronics based devices when integrated into existing power grids, where two main challenges are low rotational inertia and stability. This paper studies the dynamics and stability of two simple systems: an ideal power source and a simple synchronous machine, both connected to an infinite bus. The objective in both cases is to determine analytically the minimal storage device that is necessary for stability. One objective of this analysis is educational — to demonstrate the crucial function of local energy storage as part of any power source, and specifically to show that ideal power sources are unstable when no local energy storage is present. Another objective is to approximate the size of local storage devices in practical designs, using simple analytic expressions and limited data.


An Ontological Approach to Handle Multidimensional Schema Evolution for Data Warehouse


title: Cerno: Light-weight tool support for semantic annotation of textual documents

abstract: Enrichment of text documents with semantic metadata reflecting their meaning facilitates document organization, indexing and retrieval. However, most web data remain unstructured because of the difficulty and the cost of manually annotating text. In this work, we present Cerno, a framework for semi-automatic semantic annotation of textual documents according to a domain-specific semantic model. The proposed framework is founded on light-weight techniques and tools intended for legacy code analysis and markup. To illustrate the feasibility of our proposal, we report experimental results of its application to two different domains. These results suggest that light-weight semi-automatic techniques for semantic annotation are feasible, require limited human effort for adaptation to a new domain, and demonstrate markup quality comparable with state-of-the-art methods.


title: Answering Natural Language Questions with Intui3

abstract: Intui3 is one of the participating systems at the fourth evaluation campaign on multilingual question answering over linked data, QALD4. The system accepts as input a question formulated in natural language (in English), and uses syntactic and semantic information to construct its interpretation with respect to a given database of RDF triples (in this case DBpedia 3.9). The interpretation is mapped to the corresponding SPARQL query, which is then run against a SPARQL endpoint to retrieve the answers to the initial question. Intui3 competed in the challenge called Task 1: Multilingual question answering over linked data, which offered 200 training questions and 50 test questions in 7 different languages. It obtained an F-measure of 0.24 by providing a correct answer to 10 of the test questions and a partial answer to 4 of them.


title: Personality and Coping

abstract: Personality psychology addresses views of human nature and individual differences. Biological and goal-based views of human nature provide an especially useful basis for construing coping; the five-factor model of traits adds a useful set of individual differences. Coping—responses to adversity and to the distress that results—is categorized in many ways. Meta-analyses link optimism, extraversion, conscientiousness, and openness to more engagement coping; neuroticism to more disengagement coping; and optimism, conscientiousness, and agreeableness to less disengagement coping. Relations of traits to specific coping responses reveal a more nuanced picture. Several moderators of these associations also emerge: age, stressor severity, and temporal proximity between the coping activity and the coping report. Personality and coping play both independent and interactive roles in influencing physical and mental health. Recommendations are presented for ways future research can expand on the growing understanding of how personality and coping shape adjustment to stress.


title: GENPACK: A Generational Scheduler for Cloud Data Centers

abstract: Cloud data centers largely rely on virtualization to provision resources and host services across their infrastructure. The scheduling problem has been widely studied and is well understood when the resource requirements and the expected lifetime of services are known beforehand. In contrast, when workloads are not known in advance, effective scheduling of services, and more generally system containers, becomes much more complex. In this paper, we propose GENPACK, a framework for system containers scheduling in cloud data centers that leverages principles from generational garbage collection (GC). It combines runtime monitoring of system containers to learn their requirements and properties, and a scheduler that manages different generations of servers. The population of these generations may vary over time depending on the global load, hence they are subject to being shut down when idle to save energy. We implemented GENPACK and tested it in a dedicated data center, showing that it can be up to 23% more energy-efficient that SWARM's built-in scheduling policies on a real-world trace.


title: A tutorial on 5G and the progress in China

abstract: 5G has been developing at high speed since 2012 and has become a global economic driver. In this paper, we offer a survey of 5G covering visions, requirements, roadmap, key technologies, standardization, frequency management, technology trials, industrial ecology, and a list of main 5G contributors. We also point out the contributions to 5G from China, aiming to be ‘globally leading in 5G’ by acting as a main 5G contributor in standardization and promoting/enhancing the Chinese 5G industry. Finally, progress on 5G is reviewed mixed with our rethinking of 5G.


Study of Semantic Models in Identifying Aspects in Customer Reviews


Indra: A Word Embedding and Semantic Relatedness Server.


title: allele-specific methylation in the human genome Implications for genetic studies of complex disease

abstract: Across the genome, outside of a small number of known imprinted genes and regions subject to X-inactivation in females, DNA methylation at CpG dinucleotides is often assumed to be complementary across both alleles in a diploid cell. However, recent findings suggest the reality is more complex, with the discovery that allele-specific methylation (ASM) is a common feature across the genome. A key observation is that the majority of ASM is associated with genetic variation in cis, although a noticeable proportion is also non-cis in nature and mediated, for example, by parental origin. ASM appears to be both quantitative, characterized by subtle skewing of DNA methylation between alleles, and heterogeneous, varying across tissues and between individuals. These findings have important implications for complex disease genetics; whilst cis-mediated ASM provides a functional consequence for non-coding genetic variation, heterogeneous and quantitative ASM complicates the identification of disease-associated loci. We propose that non-cis ASM could contribute toward the 'missing heritability' of complex diseases, rendering certain loci hemizygous and masking the direct association between genotype and phenotype. We suggest that the interpretation of results from genomewide association studies can be improved by the incorporation of epi-allelic information, and that in order to fully understand the extent and consequence of ASM in the human genome, a comprehensive sequencing-based analysis of allelic methylation patterns across tissues and individuals is required.


title: Carotid Artery and Jugular Vein Tracking and Differentiation using Spatiotemporal Analysis

abstract: We have derived and evaluated parameters from ultrasound images of the neck to permit a computer to automatically characterize and differentiate between the carotid artery and jugular vein at image acquisition time during vascular interventions, given manually placed seed points. Our goal is to prevent inadvertent damage to the carotid artery when targeting the jugular vein for catheterization. We used a portable 10 MHz ultrasound system to acquire cross sectional B-mode ultrasound images of these great vessels at 10 fps. An expert user identified the vessels in the first frame by touching the vessels on the screen with his fingertip, and the computer automatically tracked the vessels and calculated a best-fit ellipse for each vessel in each subsequent frame. Vessel location and radii were further analyzed to produce parameters that proved useful for differentiating between the carotid artery and jugular vein. These parameters include relative location of the vessels, distension of the vessel walls, and consistent phase difference between the arterial and venous pulsations as determined by temporal Fourier analysis.


title: Revisiting Fast Practical Byzantine Fault Tolerance

abstract: In this note, we observe a safety violation in Zyzzyva and a liveness violation in FaB. To demonstrate these issues, we require relatively simple scenarios, involving only four replicas, and one or two view changes. In all of them, the problem is manifested already in the first log slot.


title: Gesture is at the cutting edge of early language development

abstract: Children who produce one word at a time often use gesture to supplement their speech, turning a single word into an utterance that conveys a sentence-like meaning ('eat'+point at cookie). Interestingly, the age at which children first produce supplementary gesture-speech combinations of this sort reliably predicts the age at which they first produce two-word utterances. Gesture thus serves as a signal that a child will soon be ready to begin producing multi-word sentences. The question is what happens next. Gesture could continue to expand a child's communicative repertoire over development, combining with words to convey increasingly complex ideas. Alternatively, after serving as an opening wedge into language, gesture could cease its role as a forerunner of linguistic change. We addressed this question in a sample of 40 typically developing children, each observed at 14, 18, and 22 months. The number of supplementary gesture-speech combinations the children produced increased significantly from 14 to 22 months. More importantly, the types of supplementary combinations the children produced changed over time and presaged changes in their speech. Children produced three distinct constructions across the two modalities several months before these same constructions appeared entirely within speech. Gesture thus continues to be at the cutting edge of early language development, providing stepping-stones to increasingly complex linguistic constructions.


WATER QUALITY RESTORATION AND PROTECTION IN STREAMS AND RIVERS.


title: Bag-of-visual-words and spatial extensions for land-use classification

abstract: We investigate bag-of-visual-words (BOVW) approaches to land-use classification in high-resolution overhead imagery. We consider a standard non-spatial representation in which the frequencies but not the locations of quantized image features are used to discriminate between classes analogous to how words are used for text document classification without regard to their order of occurrence. We also consider two spatial extensions, the established spatial pyramid match kernel which considers the absolute spatial arrangement of the image features, as well as a novel method which we term the spatial co-occurrence kernel that considers the relative arrangement. These extensions are motivated by the importance of spatial structure in geographic data. The methods are evaluated using a large ground truth image dataset of 21 land-use classes. In addition to comparisons with standard approaches, we perform extensive evaluation of different configurations such as the size of the visual dictionaries used to derive the BOVW representations and the scale at which the spatial relationships are considered. We show that even though BOVW approaches do not necessarily perform better than the best standard approaches overall, they represent a robust alternative that is more effective for certain land-use classes. We also show that extending the BOVW approach with our proposed spatial co-occurrence kernel consistently improves performance.


title: Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach

abstract: We propose a novel geometric approach for learning bilingual mappings given monolingual embeddings and a bilingual dictionary. Our approach decouples the source-to-target language transformation in...


title: A Survey of Wide Bandgap Power Semiconductor Devices

abstract: Wide bandgap semiconductors show superior material properties enabling potential power device operation at higher temperatures, voltages, and switching speeds than current Si technology. As a result, a new generation of power devices is being developed for power converter applications in which traditional Si power devices show limited operation. The use of these new power semiconductor devices will allow both an important improvement in the performance of existing power converters and the development of new power converters, accounting for an increase in the efficiency of the electric energy transformations and a more rational use of the electric energy. At present, SiC and GaN are the more promising semiconductor materials for these new power devices as a consequence of their outstanding properties, commercial availability of starting material, and maturity of their technological processes. This paper presents a review of recent progresses in the development of SiC- and GaN-based power semiconductor devices together with an overall view of the state of the art of this new device generation.


Challenge: What is the Impact of Bayesian Networks on Learning?


title: Design of a non invasive haptic feedback device for transradial myoelectric upper limb prosthesis

abstract: This paper presents the design of a new haptic feedback device for transradial myoelectric upper limb prosthesis that allows the amputee person to perceive the sensation of force-gripping and object-sliding. The system designed has three mechanical-actuator units to convey the sensation of force, and one vibrotactile unit to transmit the sensation of object sliding. The device designed will be placed on the user's amputee forearm. In order to validate the design of the structure, a stress analysis through Finite Element Method (FEM) is conducted.


title: Enhanced Answer Type Inference From Questions Using Sequential Models

abstract: Question classification is an important step in factual question answering (QA) and other dialog systems. Several attempts have been made to apply statistical machine learning approaches, including Support Vector Machines (SVMs) with sophisticated features and kernels. Curiously, the payoff beyond a simple bag-of-words representation has been small. We show that most questions reveal their class through a short contiguous token subsequence, which we call its informer span. Perfect knowledge of informer spans can enhance accuracy from 79.4% to 88% using linear SVMs on standard benchmarks. In contrast, standard heuristics based on shallow pattern-matching give only a 3% improvement, showing that the notion of an informer is non-trivial. Using a novel multi-resolution encoding of the question's parse tree, we induce a Conditional Random Field (CRF) to identify informer spans with about 85% accuracy. Then we build a meta-classifier using a linear SVM on the CRF output, enhancing accuracy to 86.2%, which is better than all published numbers.


title: Evaluating the influence of YouTube advertising for attraction of young customers

abstract: Nowadays, we have been faced with an increasing number of people who are spending tremendous amounts of time all around the world on YouTube. To date, the factors that persuade customers to accept YouTube advertising as an advertising medium are not yet fully understood. The present paper identified four dimensions towards YouTube advertising (i.e., entertainment, Informativeness, Customization and irritation) which may be affected on advertising value as well as brand awareness, and accordingly on purchase intention of consumers. The conceptual model hypothesizes that ad value strategies are positively associated with brand awareness, which in turn influence perceived usefulness of You Tube and continued purchase behavior. For this study, data were collected from students studying at the Sapienza University of Rome. In total, 315 usable questionnaires were chosen in order to analysis of data for the variables. The results show that entertainment, informativeness and customization are the strongest positive drivers, while irritation is negatively related to YouTube advertising. On the other hand, advertising value through YouTube affects both brand awareness and purchase intention of consumers accordingly. A novel model for the advertisement effectiveness analysis through social media.Brands are advised to note the massive potential for YouTube advertising can provide.Consumers' perception on YouTube advertising has been linked to purchase intention.Customization through YouTube advertising plays a main role on advertisement value.


title: Identification of Block Ciphers using Support Vector Machines

abstract: In this paper, we propose an approach for identification of encryption method for block ciphers using support vector machines. The task of identification of encryption method from cipher text only is considered as a document categorization task. We address the issues in representing a cipher text by a document vector. We consider the common dictionary based method and the class specific dictionary based method for generating a document vector from a cipher text. As the dimension of document vector is large, support vector machines based classifiers are considered for identification of encryption method. We present the performance of the proposed approach for cipher texts generated using five block ciphers.


title: Scalable high performance main memory system using phase-change memory technology

abstract: The memory subsystem accounts for a significant cost and power budget of a computer system. Current DRAM-based main memory systems are starting to hit the power and cost limit. An alternative memory technology that uses resistance contrast in phase-change materials is being actively investigated in the circuits community. Phase Change Memory (PCM) devices offer more density relative to DRAM, and can help increase main memory capacity of future systems while remaining within the cost and power constraints. In this paper, we analyze a PCM-based hybrid main memory system using an architecture level model of PCM.We explore the trade-offs for a main memory system consisting of PCMstorage coupled with a small DRAM buffer. Such an architecture has the latency benefits of DRAM and the capacity benefits of PCM. Our evaluations for a baseline system of 16-cores with 8GB DRAM show that, on average, PCM can reduce page faults by 5X and provide a speedup of 3X. As PCM is projected to have limited write endurance, we also propose simple organizational and management solutions of the hybrid memory that reduces the write traffic to PCM, boosting its lifetime from 3 years to 9.7 years.


title: Management Perceptions of the Importance of Brand Awareness as an Indication of Advertising Effectiveness

abstract: In 1987 Rossiter and Percy wrote “Brand awareness is widely misunderstood and often wrongly measured, even by experi enced managers” (p41). Yet brand awareness is covered in most texts on advertising measurement, it is a central part of the popular hierarchy of-effects advertising model, and marketing managers claim it as an important goal of their communications activities (Kelly 1991). This paper discusses recent theoretical developments which attempt to explain the role which brand awareness plays and then presents empirical findings concerning how Australian managers utilise brand awareness as a measure of marketing and advertising effectiveness.


Automated Machine Learning using Stochastic Algorithm Tuning


title: FDMS with Q-Learning: A Neuro-Fuzzy Approach to Partially Observable Markov Decision Problems

abstract: Finding optimal solutions to Partially Observable Markov Decision Problems is known to be NP-hard. This paper describes a novel neuro-fuzzy approach to obtain fast, robust and easily interpreted solutions by utilizing a combination of several learning techniques including neural networks, fuzzy decision making and Q-learning.


title: Cats and dogs

abstract: We investigate the fine grained object categorization problem of determining the breed of animal from an image. To this end we introduce a new annotated dataset of pets covering 37 different breeds of cats and dogs. The visual problem is very challenging as these animals, particularly cats, are very deformable and there can be quite subtle differences between the breeds. We make a number of contributions: first, we introduce a model to classify a pet breed automatically from an image. The model combines shape, captured by a deformable part model detecting the pet face, and appearance, captured by a bag-of-words model that describes the pet fur. Fitting the model involves automatically segmenting the animal in the image. Second, we compare two classification approaches: a hierarchical one, in which a pet is first assigned to the cat or dog family and then to a breed, and a flat one, in which the breed is obtained directly. We also investigate a number of animal and image orientated spatial layouts. These models are very good: they beat all previously published results on the challenging ASIRRA test (cat vs dog discrimination). When applied to the task of discriminating the 37 different breeds of pets, the models obtain an average accuracy of about 59%, a very encouraging result considering the difficulty of the problem.


title: Scent profiling of Cymbidium ensifolium by electronic nose

abstract: The performance of electronic nose (E-nose) for Chinese Cymbidium scent profiling has been evaluated. Changes in scent profiles of two Cymbidium ensifolium cultivars have been monitored at different flowering stages (initial flowering, full flowering, and terminal flowering) and different times combined with two gas collecting devices. Samples were collected by static headspace (SHS) method. How E-nose can be used for pattern recognition and for studying the releasing of flower scent were proposed. Data obtained were subjected to principal component analysis (PCA) and discriminant function analysis (DFA). PCA was performed on the initially instrumental data to explore the structure of each data set and such result showed that the sensory data contained information related to the cultivar and to time spots. DFA was performed to improve the results, leading to clear separations between the sample groups. Gas collecting device did not seriously affect the result of PCA and DFA. Relative aroma intensity (RAI) was proposed as an alternative concept to compare scent intensity between samples on different time points. These results demonstrate the potential application of the E-nose to evaluate the scent profile of flower.


title: YouTube everywhere: impact of device and infrastructure synergies on user experience

abstract: In this paper we present a complete measurement study that compares YouTube traffic generated by mobile devices (smart-phones,tablets) with traffic generated by common PCs (desktops, notebooks, netbooks). We investigate the users' behavior and correlate it with the system performance. Our measurements are performed using unique data sets which are collected from vantage points in nation-wide ISPs and University campuses from two countries in Europe and the U.S. Our results show that the user access patterns are similar across a wide range of user locations, access technologies and user devices. Users stick with default player configurations, e.g., not changing video resolution or rarely enabling full screen playback. Furthermore it is very common that users abort video playback, with 60% of videos watched for no more than 20% of their duration. We show that the YouTube system is highly optimized for PC access and leverages aggressive buffering policies to guarantee excellent video playback. This however causes 25%-39% of data to be unnecessarily transferred, since users abort the playback very early. This waste of data transferred is even higher when mobile devices are considered. The limited storage offered by those devices makes the video download more complicated and overall less efficient, so that clients typically download more data than the actual video size. Overall, this result calls for better system optimization for both, PC and mobile accesses.


title: Parking Easier by Using Context Information of a Smart City: Enabling Fast Search and Management of Parking Resources

abstract: In the great majority of cities it is difficult and hardly expensive to create more parking spaces for vehicles since they have almost reached its full occupancy. Combining this problem with an inefficient use of parking spaces leads to congestions due to aggregation of parking seekers and regular drivers. Recent advances in low-cost, low-power embedded systems bring the opportunity to develop new applications to solve these problems. In particular, Smart Cities greatly enrich their sustainability by introducing new resource management applications that rely in those constrained devices a significant part of the functionality of the system. The proposed Smart Parking solution consists mainly in the on-site deployment of an IoT solution to monitor and signalize the state of availability of each single parking space, as well as using context information generated by the city and its citizens to provide accurate responses to driver's demands. Furthermore, this system improves the management of parking resources by public authorities, for instance handling groups of parking spaces facilitating the whole city traffic management. The integration of this deployment into an existing live test-bed implies an easy task requiring just the data collection through the available means of the parking spaces availability. At the present time there exist living test-beds which can be used to integrate these new functionalities for experimentation on IoT data level, to gain a better knowledge and understanding of the M2M world, reducing costs, resources, pollution and time.


title: Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank

abstract: UK Biobank is a large-scale prospective epidemiological study with all data accessible to researchers worldwide. It is currently in the process of bringing back 100,000 of the original participants for brain, heart and body MRI, carotid ultrasound and low-dose bone/fat x-ray. The brain imaging component covers 6 modalities (T1, T2 FLAIR, susceptibility weighted MRI, Resting fMRI, Task fMRI and Diffusion MRI). Raw and processed data from the first 10,000 imaged subjects has recently been released for general research access. To help convert this data into useful summary information we have developed an automated processing and QC (Quality Control) pipeline that is available for use by other researchers. In this paper we describe the pipeline in detail, following a brief overview of UK Biobank brain imaging and the acquisition protocol. We also describe several quantitative investigations carried out as part of the development of both the imaging protocol and the processing pipeline.


title: The multi-level perspective on sustainability transitions: Responses to seven criticisms

abstract: The multi-level perspective (MLP) has emerged as a fruitful middle-range framework for analysing socio-technical transitions to sustainability. The MLP also received constructive criticisms. This paper summarises seven criticisms, formulates responses to them, and translates these into suggestions for future research. The criticisms relate to: (1) lack of agency, (2) operationalization of regimes, (3) bias towards bottom-up change models, (4) epistemology and explanatory style, (5) methodology, (6) socio-technical landscape as residual category, and (7) flat ontologies versus hierarchical levels.


title: Location recommendation in location-based social networks using user check-in data

abstract: This paper studies the problem of recommending new venues to users who participate in location-based social networks (LBSNs). As an increasingly larger number of users partake in LBSNs, the recommendation problem in this setting has attracted significant attention in research and in practical applications. The detailed information about past user behavior that is traced by the LBSN differentiates the problem significantly from its traditional settings. The spatial nature in the past user behavior and also the information about the user social interaction with other users, provide a richer background to build a more accurate and expressive recommendation model. Although there have been extensive studies on recommender systems working with user-item ratings, GPS trajectories, and other types of data, there are very few approaches that exploit the unique properties of the LBSN user check-in data. In this paper, we propose algorithms that create recommendations based on four factors: a) past user behavior (visited places), b) the location of each venue, c) the social relationships among the users, and d) the similarity between users. The proposed algorithms outperform traditional recommendation algorithms and other approaches that try to exploit LBSN information. To design our recommendation algorithms we study the properties of two real LBSNs, Brightkite and Gowalla, and analyze the relation between users and visited locations. An experimental evaluation using data from these LBSNs shows that the exploitation of the additional geographical and social information allows our proposed techniques to outperform the current state of the art.


title: Quasar: resource-efficient and QoS-aware cluster management

abstract: Cloud computing promises flexibility and high performance for users and high cost-efficiency for operators. Nevertheless, most cloud facilities operate at very low utilization, hurting both cost effectiveness and future scalability. We present Quasar, a cluster management system that increases resource utilization while providing consistently high application performance. Quasar employs three techniques. First, it does not rely on resource reservations, which lead to underutilization as users do not necessarily understand workload dynamics and physical resource requirements of complex codebases. Instead, users express performance constraints for each workload, letting Quasar determine the right amount of resources to meet these constraints at any point. Second, Quasar uses classification techniques to quickly and accurately determine the impact of the amount of resources (scale-out and scale-up), type of resources, and interference on performance for each workload and dataset. Third, it uses the classification results to jointly perform resource allocation and assignment, quickly exploring the large space of options for an efficient way to pack workloads on available resources. Quasar monitors workload performance and adjusts resource allocation and assignment when needed. We evaluate Quasar over a wide range of workload scenarios, including combinations of distributed analytics frameworks and low-latency, stateful services, both on a local cluster and a cluster of dedicated EC2 servers. At steady state, Quasar improves resource utilization by 47% in the 200-server EC2 cluster, while meeting performance constraints for workloads of all types.


title: Compile-time reflection and metaprogramming for Java

abstract: Java reflection enables us to write reusable programs that are independent of certain classes. However, when runtime performance is a big concern, we propose to use compile-time reflection for writing metaprograms that generate non-reflective class/type specific code, which has lower runtime cost. We proposed both compile-time reflection and metaprogramming for Java, and extended our previous work on pattern-based traits. Pattern-based traits integrate pattern-based reflection with flexible composition of traits. They are capable of pattern-matching over class members and generating expressive code. We add and formalize pattern-based reflection at the statement-level, which enables a meta program to generate statements. We add reified generics for pattern-based traits, which enables a pattern to iterate over any class when traits are instantiated. We implemented an ORM tool (called PtjORM) using pattern-based traits. PtjORM uses compile-time reflection and our benchmark tests show that it has competitive runtime performance compared to the mainstream Java ORM tools.


Clinical Development of Biomarker to Detect Oral Carcinoma in Relation to Genetic Polymorphism at MMP-9


title: Transfer learning for diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data

abstract: Classification of ultrasound (US) kidney images for diagnosis of congenital abnormalities of the kidney and urinary tract (CAKUT) in children is a challenging task. It is desirable to improve existing pattern classification models that are built upon conventional image features. In this study, we propose a transfer learning-based method to extract imaging features from US kidney images in order to improve the CAKUT diagnosis in children. Particularly, a pre-trained deep learning model (imagenet-caffe-alex) is adopted for transfer learning-based feature extraction from 3-channel feature maps computed from US images, including original images, gradient features, and distanced transform features. Support vector machine classifiers are then built upon different sets of features, including the transfer learning features, conventional imaging features, and their combination. Experimental results have demonstrated that the combination of transfer learning features and conventional imaging features yielded the best classification performance for distinguishing CAKUT patients from normal controls based on their US kidney images.


title: Design of TETRA-based dedicated radio communication system for urban rail transit

abstract: Terrestrial Trunked Radio (TETRA) based communication system can provide command dispatching, data transmission and telephone services on one platform. In Rail transit network, communication and short data transmission between fixed and mobile staff can be implemented through digital trunked radio communication system. In this paper, with the requirements of train operation specification, we give the design proposal of dedicated dispatching, network management software and mobile terminal equipments in secondary development mode, achieving a fast, efficient and flexible communication network.


title: A diffusion model account of criterion shifts in the lexical decision task

abstract: Performance in the lexical decision task is highly dependent on decision criteria. These criteria can be influenced by speed versus accuracy instructions and word/nonword proportions. Experiment 1 showed that error responses speed up relative to correct responses under instructions to respond quickly. Experiment 2 showed that responses to less probable stimuli are slower and less accurate than responses to more probable stimuli. The data from both experiments support the diffusion model for lexical decision [Ratcliff, R., Gomez, P., & McKoon, G. (2004a). A diffusion model account of the lexical decision task. Psychological Review, 111, 159–182]. At the same time, the data provide evidence against the popular deadline model for lexical decision. The deadline model assumes that “nonword” responses are given only after the “word” response has timed out—consequently, the deadline model cannot account for the data from experimental conditions in which “nonword” responses are systematically faster than “word” responses.


title: Anaphora Resolution Exercise: an Overview

abstract: Evaluation campaigns have become an established way to evaluate automatic systems which tackle the same task. This paper presents the first edition of the Anaphora Resolution Exercise (ARE) and the lesson s learnt from it. This first edition focused only on English pronominal anaphora and NP coreference, and was organised as an exploratory exercise where various issues were investigated. ARE proposed four different tasks: pronominal anaphora resolution and NP coreference resolution on a predefined set of entities, pronomina l anaphora resolution and NP coreference resolution on raw texts. For each of these tasks different inputs and evaluation metrics were prepared. This paper presents the four tasks, their input data and eva luation metrics used. Even though a large number of researchers in the field expressed their interest to participate, only three institutions took par t in the formal evaluation. The paper briefly presents their results, but does not try to interpret them because in this edition of ARE our aim was not about finding why certain methods are better, but to prepare the ground for a fully-fledged edition.


title: Robust Extreme Multi-label Learning

abstract: Tail labels in the multi-label learning problem undermine the low-rank assumption. Nevertheless, this problem has rarely been investigated. In addition to using the low-rank structure to depict label correlations, this paper explores and exploits an additional sparse component to handle tail labels behaving as outliers, in order to make the classical low-rank principle in multi-label learning valid. The divide-and-conquer optimization technique is employed to increase the scalability of the proposed algorithm while theoretically guaranteeing its performance. A theoretical analysis of the generalizability of the proposed algorithm suggests that it can be improved by the low-rank and sparse decomposition given tail labels. Experimental results on real-world data demonstrate the significance of investigating tail labels and the effectiveness of the proposed algorithm.


title: Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection

abstract: General human action recognition requires understanding of various visual cues. In this paper, we propose a network architecture that computes and integrates the most important visual cues for action recognition: pose, motion, and the raw images. For the integration, we introduce a Markov chain model which adds cues successively. The resulting approach is efficient and applicable to action classification as well as to spatial and temporal action localization. The two contributions clearly improve the performance over respective baselines. The overall approach achieves state-of-the-art action classification performance on HMDB51, J-HMDB and NTU RGB+D datasets. Moreover, it yields state-of-the-art spatio-temporal action localization results on UCF101 and J-HMDB.


title: SMS Based Wireless Home Appliance Control System (HACS) for Automating Appliances and Security

abstract: This paper mainly focuses on the controlling of home appliances remotely and providing security when the user is away from the place. The system is SMS based and uses wireless technology to revolutionize the standards of living. This system provides ideal solution to the problems faced by home owners in daily life. The system is wireless t herefore more adaptable and cost-effective. The HACS system provides security against intrusion as well as automates various home appliances using SMS. The system uses GSM technology thus providing ubiquitous access to the system for security and automated appliance control.


title: A Method of Generating Simultaneous Contoured and Pencil Beams From Single Shaped Reflector Antenna

abstract: This communication deals with simultaneous generation of a contoured and a pencil beam from a single shaped reflector with two feeds. A novel concept of generating a high gain pencil beam from a shaped reflector is presented using focal plane conjugate field matching method. The contoured beam is generated from the shaped reflector by introducing deformations in a parabolic reflector surface. This communication proposes a simple method to counteract the effects of shaping and generate an additional high gain pencil beam from the shaped reflector. This is achieved by using a single feed which is axially and laterally displaced from the focal point. The proposed method is successfully applied to generate an Indian main land coverage contoured beam and a high gain pencil beam over Andaman Islands. The contoured beam with peak gain of 33.05 dBi and the pencil beam with 43.8 dBi peak gain is generated using the single shaped reflector and two feeds. This technique saves mass and volume otherwise would have required for feed cluster to compensate for the surface distortion.


title: Who Do I Look Like? Determining Parent-Offspring Resemblance via Gated Autoencoders

abstract: Recent years have seen a major push for face recognition technology due to the large expansion of image sharing on social networks. In this paper, we consider the difficult task of determining parent-offspring resemblance using deep learning to answer the question "Who do I look like?" Although humans can perform this job at a rate higher than chance, it is not clear how they do it [2]. However, recent studies in anthropology [24] have determined which features tend to be the most discriminative. In this study, we aim to not only create an accurate system for resemblance detection, but bridge the gap between studies in anthropology with computer vision techniques. Further, we aim to answer two key questions: 1) Do offspring resemble their parents? and 2) Do offspring resemble one parent more than the other? We propose an algorithm that fuses the features and metrics discovered via gated autoencoders with a discriminative neural network layer that learns the optimal, or what we call genetic, features to delineate parent-offspring relationships. We further analyze the correlation between our automatically detected features and those found in anthropological studies. Meanwhile, our method outperforms the state-of-the-art in kinship verification by 3-10% depending on the relationship using specific (father-son, mother-daughter, etc.) and generic models.


title: GPS-GSM based inland vessel tracking system for automatic emergency detection and position notification

abstract: In this paper, an upgraded version of vehicle tracking system is developed for inland vessels. In addition to the features available in traditional VTS (Vehicle Tracking System) for automobiles, it has the capability of remote monitoring of the vessel's motion and orientation. Furthermore, this device can detect capsize events and other accidents by motion tracking and instantly notify the authority and/or the owner with current coordinates of the vessel, which is obtained using the Global Positioning System (GPS). This can certainly boost up the rescue process and minimize losses. We have used GSM network for the communication between the device installed in the ship and the ground control. So, this can be implemented only in the inland vessels. But using iridium satellite communication instead of GSM will enable the device to be used in any sea-going ships. At last, a model of an integrated inland waterway control system (IIWCS) based on this device is discussed.


title: Climate control system of a poultry house using sliding mode control

abstract: This work focuses on the application of Sliding Mode Controller with uncertainty learning and control gain estimation techniques to provide the desired climatic conditions in the poultry house. The nonlinear dynamics mathematical model was derived from physical governing equations of the industrial scale system and was validated under the hot and humid climate conditions of Thailand, along with fluctuation of temperature and humidity of the ambient air. The control law was developed by employing Multi-Sliding Modes control scheme. The uncertainty learning and compensation technique were applied to eliminate the unknown nonlinear characteristic and disturbances of the system. The simulation result indicated that the proposed controller was able to improve the control performance of the plant comparing to traditional on/off control, and robust adaptive PI control law provided in previous work. The saturation of control input did not occur while the system response showed fluctuation-free.


title: Random Walk Initialization for Training Very Deep Feedforward Networks

abstract: Training very deep networks is an important open problem in machine learning. One of many difficulties is that the norm of the back-propagated error gradient can grow or decay exponentially. Here we show that training very deep feed-forward networks (FFNs) is not as difficult as previously thought. Unlike when back-propagation is applied to a recurrent network, application to an FFN amounts to multiplying the error gradient by a different random matrix at each layer. We show that the successive application of correctly scaled random matrices to an initial vector results in a random walk of the log of the norm of the resulting vectors, and we compute the scaling that makes this walk unbiased. The variance of the random walk grows only linearly with network depth and is inversely proportional to the size of each layer. Practically, this implies a gradient whose log-norm scales with the square root of the network depth and shows that the vanishing gradient problem can be mitigated by increasing the width of the layers. Mathematical analyses and experimental results using stochastic gradient descent to optimize tasks related to the MNIST and TIMIT datasets are provided to support these claims. Equations for the optimal matrix scaling are provided for the linear and ReLU cases.


A dynamic connection of balanced scorecard applied for the hotel.


title: Modernizing Substation Automation Systems: Adopting IEC Standard 61850 for Modeling and Communication

abstract: There is demand for a substation automation system (SAS) that is simple to integrate and provides high performance and flexibility. The increasing application of intelligent electronic devices (IEDs) with International Electrotechnical Commission (IEC) Standard 61850 capacities provides interoperability and advanced communications capabilities in substation protection, coordination, control, monitoring, metering, and testing. IEC 61850 is the result of the efforts of IEC Technical Committee 57 (TC57) to produce an open standard for substation modeling and communications. The standard has gained popularity and continues to spread throughout the world. In this article, we explore an IEC 61850 application in a typical SA and introduce a real-world SAS modernization case that highlights the benefit of the IEC 61850 in substation monitoring and operation.


title: Cyberculture, cyborg post-modernism and the sociology of virtual reality technologies: Surfing the soul in the information age☆

abstract: The relation between humans and machines has come to assume a central place within the social sciences, particularly in debates about the role of science and about information technologies. Cyberculture plays a key role in these debates, drawing its inspiration in large part from virtual reality systems. This article examines the affinities between two aspects of cyberculture: cyborg post-modernism, which revolves around the notion that the boundaries between humans and machines are becoming irretrievably blurred, and the cyberpunk movement within youth culture with its futuristic ideas about information and communication machines. While cyberculture may be far ahead of the current state of the technology, it is argued here that its new conception of the relation between politics, technology and art is an important reflection of changes within the cultural industries that surround information and communication technologies within advanced societies.


title: Radar sensor signal acquisition and 3D FFT processing for smart mobility surveillance systems

abstract: The paper presents the design and test of a compact multi-channel platform for acquisition and real-time processing of Radar sensor signals. The sensing system includes a multi-channel X-band transceiver, implementing a linear frequency-modulated continuous wave scheme, plus an FPGA processor implementing sensor signal processing chain. The latter includes ROI (Region of Interest Selection), 3D FFT, peak detection, alarm decision logic, data calibration and diagnostic. The FPGA also manages data communication with external hosts. The Radar sensing system allows detecting moving and still targets with covered range of 300 m and a resolution better than 40 cm. The sensor is suited for operations in urban scenarios like surveillance of railroad crossing and road crossing, monitoring of cars parking, with increased robustness vs. LIDAR, camera or ultrasounds techniques. The sensor has a low power consumption and low weight, and hence can be mounted on-board vehicles such as cars or UAVs for aerial surveillance of smart cities.


title: Beam scanning array based on Luneburg lens

abstract: Based on the high gain beam forming and overall spatial beam scanning properties of Luneburg lens, a phased monopole array mounted on the surface of a Luneburg lens is proposed. With controlled distributions of excitation amplitude and phase on four adjacent monopole elements, this monopole array can form continuously scanned beams instead of the conventional discrete beams. To demonstrate this feature, two feed networks with different current distributions are connected to the monopole array to form beam patterns towards the desired angles. Both beam patterns have fairly low side lobes and similarly high gain, indicating that the proposed design has the ability to achieve a much simplified electronically scanned array with high directivity and unchanged beam shape while scanning in all 360 degree range.


FACIAL ANIMATION AND MOTION CAPTURE : KEY ROLE FOR THE COMMUNICATION


title: Photo Aesthetics Analysis via DCNN Feature Encoding

abstract: We propose an automatic framework for quality assessment of a photograph as well as analysis of its aesthetic attributes. In contrast to the previous methods that rely on manually designed features to account for photo aesthetics, our method automatically extracts such features using a pretrained deep convolutional neural network (DCNN). To make the DCNN-extracted features more suited to our target tasks of photo quality assessment and aesthetic attribute analysis, we propose a novel feature encoding scheme, which supports vector machines-driven sparse restricted Boltzmann machines, which enhances sparseness of features and discrimination between target classes. Experimental results show that our method outperforms the current state-of-the-art methods in automatic photo quality assessment, and gives aesthetic attribute ratings that can be used for photo editing. We demonstrate that our feature encoding scheme can also be applied to general object classification task to achieve performance gains.