title: Sensitivity analysis of multilayer perceptron to input and weight perturbations

abstract: An important issue in the design and implementation of a neural network is the sensitivity of its output to input and weight perturbations. In this paper, we discuss the sensitivity of the most popular and general feedforward neural networks-multilayer perceptron (MLP). The sensitivity is defined as the mathematical expectation of the output errors of the MLP due to input and weight perturbations with respect to all input and weight values in a given continuous interval. The sensitivity for a single neuron is discussed first and an analytical expression that is a function of the absolute values of input and weight perturbations is approximately derived. Then an algorithm is given to compute the sensitivity for the entire MLP. As intuitively expected, the sensitivity increases with input and weight perturbations, but the increase has an upper bound that is determined by the structural configuration of the MLP, namely the number of neurons per layer and the number of layers. There exists an optimal value for the number of neurons in a layer, which yields the highest sensitivity value. The effect caused by the number of layers is quite unexpected. The sensitivity of a neural network may decrease at first and then almost keeps constant while the number increases.


title: Citation Analysis as a Tool in Journal Evaluation: Journals can be ranked by frequency and impact of citations for science policy studies

abstract: As a communications system, the network of journals that play a paramount role in the exchange of scientific and technical information is little understood. Periodically since 1927, when Gross and Gross published their study (1) of references in 1 year’s issues of the Journal of the American Chemical Socie/y, pieces of the network have been illuminated by the work of Bradford (2), Allen (3), Gross and Woodford (4), Hooker (5), Henkle (6), Fussier (7), Brown (8), and others (9). Nevertheless, there is still no map of the journal network as a whok. To date, studies of the network and of the interrelation of its components have been limited in the number of journak, the areas of scientific study, and the periods of time their authors were able to consider, Such shortcomings have not been due to any lack of purpose, insight, or energy on the part of investigators, but to the practical difficulty of compiling and manipulating manually the enormous amount of necessary data. A solution to this problem of data is available in the data base used to produce the Science Citation Index ( SCI ) (10). The coverage of the SCI is international and multidisciplinary; it has grown from 600 journals in 1964 to 2400 journals in 1972, and now includes the world’s most important scientific and technical journals in mow disciplines. The SCI is published quarterly and is cumulated annually and quinquennially, but the data base from which the volumes are compiled is maintained on magnetic tape and is updated weekly. At the end of 1971, this data base contained more than 27 mi[tion references to about 10 million different published items. These references appeared over the past decade in the footnotes and bibliographies of more than 2 million journal articles, communications, letters, and so on. The data base is, thus, not only multidisciplinary, it covers a substantial period of time and, being in machine-readable form, is amenable to extensive manipulation by computer. In 1971, the Institute for Scientfic Information (1S1) decided to undertake a systematic analysis of journal citation patterns across the whole of science and technology. It began by extracting from the data base all references pobIished during the last quarter of 1969 in the 2200 journals then covered by the SCL The resultant sample was about 1 million citations of journals, books, reports, theses, and so forth. To test whether this 3-month sample was representative of the year as a whole, it was matched against another sample made by selecting every 27th reference from the approximately 4 million references collected over the entire year. The two samples were similar enough in scope (number of diflerent items cited) and detail (relative frequency of their citation by different journals) to


title: Shredded Document Reconstruction Based on Intelligent Algorithms

abstract: The technique of shredded documentation reconstruction is widely used in different areas such as military affairs and archaeology. This process, however, can hardly be finished merely by human efforts. Therefore, it is important to develop an effective method basing on intelligent algorithms. Designed for effectively solving this problem, this paper attempts to develop an automatic reconstructing method by discomposing the process into three parts, which are tackled with by different algorithms respectively. Firstly, the document pieces, which are in the first column of the original document, are picked out by the blank-area-searching algorithm. Then follows the searching of the adjacent document piece by the help of rightward-eduction algorithm and hence all rows in the original document are reconstructed. Considering the special cases of 'endless loop' as well as 'false searching' at this stage, the corresponding methods of fixed-distance-based clustering analysis and pattern recognition are given. Lastly, by revising the eduction algorithms into upward and downward directions, all rows can be putted back into the original document in the right order. Following these three steps, the shredded original document can be reconstructed automatically.


title: Likelihood-ratio empirical kernels for i-vector based PLDA-SVM scoring

abstract: Likelihood ratio (LR) scoring in PLDA speaker verification systems only uses the information of background speakers implicitly. This paper exploits the notion of empirical kernel maps to incorporate background speaker information into the scoring process explicitly. This is achieved by training a scoring SVM for each target speaker based on a kernel in the empirical feature space. More specially, given a test i-vector and the identity of the target under test, a score vector is constructed by computing the LR scores of the test i-vector with respect to the target-speaker's i-vectors and a set of background-speakers' i-vectors. While in most situations, only one target-speaker i-vector is available for training the SVM, this paper demonstrates that if the enrollment utterance is sufficiently long, a number of target-speaker i-vectors can be generated by an utterance partitioning and resampling technique, resulting in much better scoring SVMs. Results on NIST 2010 SRE suggests that the idea of incorporating background speaker information into PLDA scoring through training speaker-dependent SVMs together with the utterance partitioning techniques can boost the performance of i-vector based PLDA systems significantly.


Talking To Machines (Statistically Speaking)


title: Learning to Generate Rock Descriptions from Multivariate Well Logs with Hierarchical Attention

abstract: In the shale oil & gas industry, operators are looking toward big data analytics to optimize operations and reduce cost. In this paper, we mainly focus on how to assist operators in understanding the subsurface formation, thereby helping them make optimal decisions. A large number of geology reports and well logs describing the sub-surface have been accumulated over years. Issuing geology reports is more time consuming and depends more on the expertise of engineers than acquiring the well logs. To assist in issuing geology reports, we propose an encoder-decoder-based model to automatically generate rock descriptions in human-readable format from multivariate well logs. Due to the different formats of data, this task differs dramatically from image and video captioning. The challenges are how to model structured rock descriptions and leverage the information in multivariate well logs. To achieve this, we design a hierarchical structure and two forms of attention for the decoder. Extensive validations are conducted on public well data of North Dakota in the United States. We show that our model is effective in generating rock descriptions. The two forms of attention enable the provision of a better insight into relations between well-log types and rock properties with our model from a data-driven perspective.


title: I/O-Cache: A Non-volatile Memory Based Buffer Cache Policy to Improve Storage Performance

abstract: Most computer systems currently consist of DRAM as main memory and hard disk drives (HDDs) as storage devices. Due to the volatile nature of DRAM, the main memory may suffer from data loss in the event of power failures or system crashes. With rapid development of new types of non-volatile memory (NVRAM), such as PCM, Memristor, and STT-RAM, it becomes likely that one of these technologies will replace DRAM as main memory in the not-too-distant future. In an NVRAM based buffer cache, any updated pages can be kept longer without the urgency to be flushed to HDDs. This opens opportunities for designing new buffer cache policies that can achieve better storage performance. However, it is challenging to design a policy that can also increase the cache hit ratio. In this paper, we propose a buffer cache policy, named I/O-Cache, that regroups and synchronizes long sets of consecutive dirty pages to take advantage of HDDs' fast sequential access speed and the non-volatile property of NVRAM. In addition, our new policy can dynamically separate the whole cache into a dirty cache and a clean cache, according to the characteristics of the workload, to decrease storage writes. We evaluate our scheme with various traces. The experimental results show that I/O-Cache shortens I/O completion time, decreases the number of I/O requests, and improves the cache hit ratio compared with existing cache policies.


Loss Functions in Time Series Forecasting


title: Diffusion in Social Networks: A Multiagent Perspective

abstract: In recent years, significant attention has been paid to diffusion in social networks (SNs), which is, factually, the collective behavior of a set of autonomous social actors for interacting on something in SNs (such as opinions, viruses, or innovations). While this subject has been intensively reported, there have been relatively few systematic reviews concerning the typical diffusion elements and models that are relevant to this subject. Because multiagent computing has already been widely envisioned to be a powerful paradigm for modeling the collective interactions of autonomous multientity systems. In this survey, we review diffusion in SNs through a multiagent perspective. First, we review the following essential elements in diffusion: 1) diffusion actors (who will diffuse), which can be understood to be the interacting agents; 2) diffusion media (where to be diffused), which can be understood to be the interaction environments in multiagent systems (MASs); and 3) diffusion contents (what to be diffused), which can be understood to be the interaction objects in MASs. Next, based on varying situations of diffusion elements, we review the representative diffusion models (how to diffuse), which can be understood as the decision-making mechanisms and interaction protocols in MASs. For each class of diffusion elements and models, we summarize the existing studies and discuss the challenges for solving the complex diffusion problems by applying multiagent methodologies. Finally, we discuss the advantages and disadvantages of our multiagent perspective by comparing other typical perspectives (the empirical research perspective and the theoretical perspective in empirical research), and we conclude with suggestions for further research.


Prediction of Aluminum Wheel Distortion under Pothole Impact


title: Pressure ulcers in SCI: does tension stimulate wound healing?

abstract: Pressure ulcers are an immense problem among older and disabled populations. Although there are many studies in the literature about the etiology, interface pressures, natural history, and epidemiology of skin breakdown, there is relatively little information about factors that stimulate the repair of body wall tissues after breakdown. Specifically, there is a paucity of information about the effects of mechanical stress on healing. This is a particularly important consideration for those areas, such as the perineal tissues, that bear large mechanical forces. The purpose of this paper is to discuss the research that addresses skin and tissue repair in response to tension. Two case studies that support the hypothesis that tension stimulates wound healing are presented from the Spinal Cord Injury Center at the Seattle VAMC. After weeks of nonhealing, each of these cases healed within 2 weeks of initiating a range of motion program that applied tension to the nonhealing portion of a myocutaneous flap.


title: Vision-based active safety system for automatic stopping

abstract: Intelligent systems designed to reduce highway fatalities have been widely applied in the automotive sector in the last decade. Of all users of transport systems, pedestrians are the most vulnerable in crashes as they are unprotected. This paper deals with an autonomous intelligent emergency system designed to avoid collisions with pedestrians. The system consists of a fuzzy controller based on the time-to-collision estimate - obtained via a vision-based system - and the wheel-locking probability - obtained via the vehicle's CAN bus - that generates a safe braking action. The system has been tested in a real car - a convertible Citroen C3 Pluriel - equipped with an automated electro-hydraulic braking system capable of working in parallel with the vehicle's original braking circuit. The system is used as a last resort in the case that an unexpected pedestrian is in the lane and all the warnings have failed to produce a response from the driver.


title: BPS: a learning algorithm for capturing the dynamic nature of speech

abstract: A novel backpropagation learning algorithm for a particular class of dynamic neural networks in which some units have a local feedback is proposed. Hence these networks can be trained to respond to sequences of input patterns. This algorithm has the same order of space and time requirements as backpropagation applied to feedforward networks. The authors present experimental results and comparisons with a speech recognition problem. >


title: When Lyrics Outperform Audio for Music Mood Classification: A Feature Analysis

abstract: This paper builds upon and extends previous work on multi-modal mood classification (i.e., combining audio and lyrics) by analyzing in-depth those feature types that have shown to provide statistically significant improvements in the classification of individual mood categories. The dataset used in this study comprises 5,296 songs (with lyrics and audio for each) divided into 18 mood categories derived from user-generated tags taken from last.fm. These 18 categories show remarkable consistency with the popular Russell’s mood model. In seven categories, lyric features significantly outperformed audio spectral features. In one category only, audio outperformed all lyric features types. A fine grained analysis of the significant lyric feature types indicates a strong and obvious semantic association between extracted terms and the categories. No such obvious semantic linkages were evident in the case where audio spectral features proved superior.


title: DAOS and Friends: A Proposal for an Exascale Storage System

abstract: The DOE Extreme-Scale Technology Acceleration Fast Forward Storage and IO Stack project is going to have significant impact on storage systems design within and beyond the HPC community. With phase two of the project starting, it is an excellent opportunity to explore the complete design and how it will address the needs of extreme scale platforms. This paper examines each layer of the proposed stack in some detail along with cross-cutting topics, such as transactions and metadata management. This paper not only provides a timely summary of important aspects of the design specifications but also captures the underlying reasoning that is not available elsewhere. We encourage the broader community to understand the design, intent, and future directions to foster discussion guiding phase two and the ultimate production storage stack based on this work. An initial performance evaluation of the early prototype implementation is also provided to validate the presented design.


1 Intelligent Detection System for e-banking Phishing websites using Fuzzy Data Mining


title: Deep neural networks for cochannel speaker identification

abstract: Speaker identification (SID) in cochannel speech, where two speakers are talking simultaneously over a single recording channel, is a challenging problem. Previous studies address this problem in the anechoic environment under the Gaussian mixture model (GMM) framework. On the other hand, cochannel SID in reverberant conditions has not been addressed. This paper studies cochannel SID in both anechoic and reverberant conditions. We explore deep neural networks (DNNs) for cochannel SID and propose a DNN-based recognition system. Evaluation results demonstrate the proposed DNN-based system outperforms the two state-of-the-art cochannel SID systems in both anechoic and reverberant conditions and various target-to-interferer ratios.


title: Design methodology for the thorax and shoulder of human mimetic musculoskeletal humanoid Kenshiro -a thorax structure with rib like surface -

abstract: To design a robot with humanlike body structure, this paper presents a design methodology for a humanoid upper limb by tendon driven system. We newly designed an upper limb and rib cage like thorax for a musculoskeletal humanoid robot, based on the knowledge of anatomy. The robot consists of muscle, bone, and joint structure based on human and is expected to move flexibly and dynamically. This paper describes how to design such an upper limb and proposes the key mechanical design points, which is “rib surface thorax”, “muscle cushion”, “planar muscle”, and “open type ball joint”. To show that these mechanisms is effective in making a musculoskeletal humanoid robot, we examine the motion range of the robot. One of our goals is to enable robots to do the same movements as humans do through mimicking the human body structure, finding some important elements of human nature. This robots explained in this paper is the prototype for a new life size robot “Kenshiro” project.


title: From the Virtual to the RealWorld: Referring to Objects in Real-World Spatial Scenes

abstract: Predicting the success of referring expressions (RE) is vital for real-world applications such as navigation systems. Traditionally, research has focused on studying Referring Expression Generation (REG) in virtual, controlled environments. In this paper, we describe a novel study of spatial references from real scenes rather than virtual. First, we investigate how humans describe objects in open, uncontrolled scenarios and compare our findings to those reported in virtual environments. We show that REs in real-world scenarios differ significantly to those in virtual worlds. Second, we propose a novel approach to quantifying image complexity when complete annotations are not present (e.g. due to poor object recognition capabitlities), and third, we present a model for success prediction of REs for objects in real scenes. Finally, we discuss implications for Natural Language Generation (NLG) systems and future directions.


Turning Clockwise: Using UML . . .


Dialogue Act Classification Using Language Models


title: Computer Architecture: A Quantitative Approach

abstract: This best-selling title, considered for over a decade to be essential reading for every serious student and practitioner of computer design, has been updated throughout to address the most important trends facing computer designers today. In this edition, the authors bring their trademark method of quantitative analysis not only to high-performance desktop machine design, but also to the design of embedded and server systems. They have illustrated their principles with designs from all three of these domains, including examples from consumer electronics, multimedia and Web technologies, and high-performance computing.


title: Driving Scene Perception Network: Real-time Joint Detection, Depth Estimation and Semantic Segmentation

abstract: As the demand for enabling high-level autonomous driving has increased in recent years and visual perception is one of the critical features to enable fully autonomous driving, in this paper, we introduce an efficient approach for simultaneous object detection, depth estimation and pixel-level semantic segmentation using a shared convolutional architecture. The proposed network model, which we named Driving Scene Perception Network (DSPNet), uses multi-level feature maps and multi-task learning to improve the accuracy and efficiency of object detection, depth estimation and image segmentation tasks from a single input image. Hence, the resulting network model uses less than 850 MiB of GPU memory and achieves 14.0 fps on NVIDIA GeForce GTX 1080 with a 1024x512 input image, and both precision and efficiency have been improved over combination of single tasks.


title: Heart rate variability and its relation to prefrontal cognitive function: the effects of training and detraining

abstract: The aim of the present study was to investigate the relationship between physical fitness, heart rate variability (HRV) and cognitive function in 37 male sailors from the Royal Norwegian Navy. All subjects participated in an 8-week training program, after which the subjects completed the initial cognitive testing (pretest). The subjects were assigned into a detrained group (DG) and a trained group (TG) based on their application for further duty. The DG withdrew from the training program for 4 weeks after which all subjects then completed the cognitive testing again (post-test). Physical fitness, measured as maximum oxygen consumption (VO2(max)), resting HRV, and cognitive function, measured using a continuous performance task (CPT) and a working memory test (WMT), were recorded during the pre-test and the post-test, and the data presented as the means and standard deviations. The results showed no between-group differences in VO2(max) or HRV at the pre-test. The DG showed a significant decrease in VO2(max) from the pre- to the post-test and a lower resting HRV than the TG on the post-test. Whereas there were no between-group differences on the CPT or WMT at the pre-test, the TG had faster reaction times and more true positive responses on tests of executive function at the post-test compared to the pre-test. The DG showed faster reaction times on non-executive tasks at the post-test compared to the pre-test. The results are discussed within a neurovisceral integration framework linking parasympathetic outflow to the heart to prefrontal neural functions.


title: Increased incidence of sepsis and altered monocyte functions in severely injured type A− glucose-6-phosphate dehydrogenase-deficient African American trauma patients

abstract: Objective: To determine whether trauma patients with the common, type A- glucose-6-phosphate dehydrogenase (G6PD) deficiency have an aggravated inflammatory response, increased incidence of septic complications, and/or more profound alterations in leukocyte functions compared with nondeficient trauma patients. Settings: Intensive and surgical care units of a trauma center and flow cytometry and experimental laboratories at a teaching university hospital. Design: Prospective cohort clinical study with measurements on days 2 and 5 postinjury, Monocyte and neutrophil oxidant content, apoptosis, and CD11b expression and plasma cytokine levels were compared between G6PD-deficient and nondeficient patients. Patients: A total of 467 male African American trauma patients were screened for the deficiency. Forty-four type A- 202/376 G6PD-deficient patients were identified and enrolled in the study; 43 nondeficient patients were also enrolled and were matched by age, clinical criteria of injury severity, and type of trauma. Main Results: After severe injury (Injury Severity Score, ≥16), 50% of the deficient and 6.2% of nondeficient patients developed sepsis with positive bacterial blood cultures. In deficient patients, the frequency of bronchial (75%) and wound infections (25%) was also increased compared with nondeficient patients (32% and 0%). The durations of systemic inflammatory response syndrome, Sepsis Syndrome, and days on antibiotics were three times longer in deficient than in nondeficient individuals. However, adult respiratory distress syndrome occurred in 37% of both groups. Anemia was more severe in the deficient than nondeficient patients from day 10 posttrauma. On day 5, the peroxide content was doubled, apoptosis was decreased, and CD11b expression was increased in monocytes from deficient patients compared with cells from nondeficient patients. On day 5, the plasma interleukin (IL)-10 concentration was significantly lower in deficient than nondeficient patients, whereas tumor necrosis factor-α, IL-6, and IL-8 levels were similar. After moderate injuries (Injury Severity Score, 9-16), the deficiency was not associated with adverse clinical effects, and the trauma-induced changes in leukocyte function were similar in deficient and nondeficient patients. Conclusions: The common type A- G6PD deficiency predisposes septic complications and anemia in trauma patients after severe injuries as defined by an Injury Severity Score of ≥16. This adverse clinical course is accompanied by altered monocyte functions manifested as augmented oxidative stress, a decreased apoptotic response, increased cell adhesion properties, and a diminished IL-10 response.


title: Exploring Representation Learning With CNNs for Frame-to-Frame Ego-Motion Estimation

abstract: Visual ego-motion estimation, or briefly visual odometry (VO), is one of the key building blocks of modern SLAM systems. In the last decade, impressive results have been demonstrated in the context of visual navigation, reaching very high localization performance. However, all ego-motion estimation systems require careful parameter tuning procedures for the specific environment they have to work in. Furthermore, even in ideal scenarios, most state-of-the-art approaches fail to handle image anomalies and imperfections, which results in less robust estimates. VO systems that rely on geometrical approaches extract sparse or dense features and match them to perform frame-to-frame (F2F) motion estimation. However, images contain much more information that can be used to further improve the F2F estimation. To learn new feature representation, a very successful approach is to use deep convolutional neural networks. Inspired by recent advances in deep networks and by previous work on learning methods applied to VO, we explore the use of convolutional neural networks to learn both the best visual features and the best estimator for the task of visual ego-motion estimation. With experiments on publicly available datasets, we show that our approach is robust with respect to blur, luminance, and contrast anomalies and outperforms most state-of-the-art approaches even in nominal conditions.


Improve Sentiment Analysis of Citations with Author Modelling


title: How to Use Bitcoin to Design Fair Protocols

abstract: We study a model of fairness in secure computation in which an adversarial party that aborts on receiving output is forced to pay a mutually predefined monetary penalty. We then show how the Bitcoin network can be used to achieve the above notion of fairness in the two-party as well as the multiparty setting (with a dishonest majority). In particular, we propose new ideal functionalities and protocols for fair secure computation and fair lottery in this model.


title: A Wideband High-Gain High-Efficiency Hybrid Integrated Plate Array Antenna for V-Band Inter-Satellite Links

abstract: A wideband high-gain high-efficiency hybrid integrated plate array antenna for inter-satellite links is presented in this paper. This antenna consists of microstrip patches, substrate integrated waveguide (SIW) and waveguide power dividers. A novel feeding structure is proposed to excite the microstrip sub-array with a wideband characteristic. The radiation efficiency of SIW arrays with different sizes is compared by experiment. The hybrid SIW-waveguide feeding topology is optimized to realize high efficiency, low cost and compact configuration at the same time. The array antenna is fabricated through standard multi-layer PCB process and milling technology. Measured results demonstrate about 14.6% of reflection coefficient bandwidth ( $\vert {\rm S}_{11}\vert dB) in the frequency band of 57–66 GHz. The gain fluctuates less than 3 dB within the same band. The 1 dB gain bandwidth is 8.1% within the frequency band of 59–64 GHz. The maximum gain is 39.2 dBi at 59 GHz with the efficiency of 41%.


title: Multi-scale Context Intertwining for Semantic Segmentation

abstract: Accurate semantic image segmentation requires the joint consideration of local appearance, semantic information, and global scene context. In today’s age of pre-trained deep networks and their powerful convolutional features, state-of-the-art semantic segmentation approaches differ mostly in how they choose to combine together these different kinds of information. In this work, we propose a novel scheme for aggregating features from different scales, which we refer to as Multi-Scale Context Intertwining (MSCI). In contrast to previous approaches, which typically propagate information between scales in a one-directional manner, we merge pairs of feature maps in a bidirectional and recurrent fashion, via connections between two LSTM chains. By training the parameters of the LSTM units on the segmentation task, the above approach learns how to extract powerful and effective features for pixel-level semantic segmentation, which are then combined hierarchically. Furthermore, rather than using fixed information propagation routes, we subdivide images into super-pixels, and use the spatial relationship between them in order to perform image-adapted context aggregation. Our extensive evaluation on public benchmarks indicates that all of the aforementioned components of our approach increase the effectiveness of information propagation throughout the network, and significantly improve its eventual segmentation accuracy.


title: Screening and Identification of Diosgenin-Producing Endophytic Bacteria from Paris polyphylla chinensis

abstract: 107 endophytes were isolated.from underground stem tubers of Paris polyphylla chinensis. RZ03 and RZ07 could produce diosgenin were screened through TLC testing. Their morphology, physiology, and biochemistry were characterized and their 16S rDNA sequence were compared with related sequences obtained from GenBank+EMBL+DDBJ, and phylogenetic tree of their 16S rDNA sequence were drawn with ClustalW respectively. The homology between RZ03 and DQ019167 (Exiguobacterium acetylicum) was 99%, therefore, RZ03 was identified as E. acetylicum RZ03. And The homology between RZ07 and DQ207730 (Bacillus subtilis) was 99%, therefore, RZ07 was identified as B. subtilis RZ07.


title: Evaluating the Acceptability of Low-Cost Standing Desks in the Home Environment: An Exploratory Study

abstract: Background: Many studies have attempted to mitigate the negative health consequences of sedentary behavior (SB) in the work environment using standing desks. However, no studies have explored the u...


title: Simplifying Decision Trees: A Survey

abstract: Induced decision trees are an extensively-researched solution to classification tasks. For many practical tasks, the trees produced by tree-generation algorithms are not comprehensible to users due to their size and complexity. Although many tree induction algorithms have been shown to produce simpler, more comprehensible trees (or data structures derived from trees) with good classification accuracy, tree simplification has usually been of secondary concern relative to accuracy, and no attempt has been made to survey the literature from the perspective of simplification. We present a framework that organizes the approaches to tree simplification and summarize and critique the approaches within this framework. The purpose of this survey is to provide researchers and practitioners with a concise overview of tree-simplification approaches and insight into their relative capabilities. In our final discussion, we briefly describe some empirical findings and discuss the application of tree induction algorithms to case retrieval in case-based reasoning systems.


title: DOI:10.1068/b32045 The network analysis of urban streets: a primal approach

abstract: The network metaphor in the analysis of urban and territorial cases has a long tradition, especially in transportation or land-use planning and economic geography. More recently, urban design has brought its contribution by means of the ‘space syntax’ methodology. All these approaches—though under different terms like ‘accessibility’, ‘proximity’, ‘integration’ ‘connectivity’, ‘cost’, or ‘effort’—focus on the idea that some places (or streets) are more important than others because they are more central . The study of centrality in complex systems, however, originated in other scientific areas, namely in structural sociology, well before its use in urban studies; moreover, as a structural property of the system, centrality has never been extensively investigated metrically in geographic networks as it has been topologically in a wide range of other relational networks such as social, biological, or technological ones. After a previous work on some structural properties of the primal graph representation of urban street networks, in this paper we provide an in-depth investigation of centrality in the primal approach as compared with the dual one. We introduce multiple centrality assessment (MCA), a methodology for geographic network analysis, which is defined and implemented on four 1-square-mile urban street systems. MCA provides a different perspective from space syntax in that: (1) it is based on primal, rather than dual, street graphs; (2) it works within a metric, rather than topological, framework; (3) it investigates a plurality of peer centrality indices rather than a single index. We show that, in the MCA primal approach, much more than in the dual approach, some centrality indices nicely capture the ‘skeleton’ of the urban structure that impacts so much on spatial cognition and collective behaviours. Moreover, the distributions of centrality in self-organized cities are different from those in planned cities.


title: Named Entity Recognition in Vietnamese documents

abstract: NamedEntityRecognition (NER) aimstoclassify wordsin a documentintopre-definedtarget entity classes and is now considered to be fundamental for many natural language processing tasks such a si nformation retrieval, machine translation, information extraction and question answering. This paper presents the results of an experiment in which a Support Vector Machine (SVM) based NER model is applied to the Vietnamese language. Though this state of the art machine learning method has been widely applied to NER in several well-studied languages, this is the first time this method has been applied to Vietnamese. In a comparison against Conditional Random Fields (CRFs) the SVM model was shown to outperform CRF by optimizing its feature window size, obtaining an overall F-score of 87.75. The paper also presents a detailed discussion about the characteristics of the Vietnamese language and provides an analysis of the factors which influence performance in this task.


title: Transforming responses to domestic violence in a politically contested environment: The case of Northern Ireland

abstract: Domestic violence is a global phenomenon, but it takes on specific modalities in different cultural and geo-political settings. Drawing on evidence from the Northern Irish case, this article is concerned with exploring the relationship between domestic violence and the international and national sociopolitical context which domestic violence is perpetrated in and responded to. The Northern Irish case reveals a high level of political, religious and ethnic contestation at a societal level, a patriarchal social structure and conservative attitudes, each of which influence experiences of and responses to domestic violence. These factors exist alongside a number of groundbreaking changes to the overall political context and to domestic violence policies in Northern Ireland. This article seeks to explore the impact of these sociopolitical factors and changes on patterns of domestic violence in Northern Ireland over the last thirty years. The article is concerned with identifying how domestic violence responses are shaped by the sociopolitical context, what progress has been made in policy responses to domestic violence and the gaps that remain.


title: Creation of a highly detailed, dynamic, global model and map of science

abstract: The majority of the effort in metrics research has addressed research evaluation. Far less research has addressed the unique problems of research planning. Models and maps of science that can address the detailed problems associated with research planning are needed. This article reports on the creation of an article-level model and map of science covering 16 years and nearly 20 million articles using cocitation-based techniques. The map is then used to define discipline-like structures consisting of natural groupings of articles and clusters of articles. This combination of detail and high-level structure can be used to address planning-related problems such as identification of emerging topics and the identification of which areas of science and technology are innovative and which are simply persisting. In addition to presenting the model and map, several process improvements that result in greater accuracy structures are detailed, including a bibliographic coupling approach for assigning current papers to cocitation clusters and a sequential hybrid approach to producing visual maps from models.


title: Ranking with local regression and global alignment for cross media retrieval

abstract: Rich multimedia content including images, audio and text are frequently used to describe the same semantics in E-Learning and Ebusiness web pages, instructive slides, multimedia cyclopedias, and so on. In this paper, we present a framework for cross-media retrieval, where the query example and the retrieved result(s) can be of different media types. We first construct Multimedia Correlation Space (MMCS) by exploring the semantic correlation of different multimedia modalities, during which multimedia content and co-occurrence information is utilized. We propose a novel ranking algorithm, namely ranking with Local Regression and Global Alignment (LRGA), which learns a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking values of its neighboring points. We propose a unified objective function to globally align the local models from all the data points so that an optimal ranking value can be assigned to each data point. LRGA is insensitive to parameters, making it particularly suitable for data ranking. A relevance feedback algorithm is proposed to improve the retrieval performance. Comprehensive experiments have demonstrated the effectiveness of our methods.


title: Mutual Alignment Transfer Learning

abstract: Training robots for operation in the real world is a complex, time consuming and potentially expensive task. Despite significant success of reinforcement learning in games and simulations, research in real robot applications has not been able to match similar progress. While sample complexity can be reduced by training policies in simulation, such policies can perform sub-optimally on the real platform given imperfect calibration of model dynamics. We present an approach -- supplemental to fine tuning on the real robot -- to further benefit from parallel access to a simulator during training and reduce sample requirements on the real robot. The developed approach harnesses auxiliary rewards to guide the exploration for the real world agent based on the proficiency of the agent in simulation and vice versa. In this context, we demonstrate empirically that the reciprocal alignment for both agents provides further benefit as the agent in simulation can adjust to optimize its behaviour for states commonly visited by the real-world agent.


title: A2U2: A stream cipher for printed electronics RFID tags

abstract: The design of hardware-oriented ciphers has an increasingly important role to play with emerging ubiquitous and pervasive computing devices, such as low cost passive Radio Frequency Identification (RFID) tags. The importance of such ciphers are further highlighted by novel manufacturing technologies, such as printed ink to develop extremely low cost RFID tags. Such developments bring new challenges, especially in terms of providing security, both to protect privacy as well as to enable applications dependent on security, such as e-tickets. In this paper we present a new stream cipher, A2U2, which uses principles of stream cipher design and approaches from block cipher design. Our lightweight cryptographic primitive has taken into consideration the extremely resource limited environment of printed ink tags, to develop a cipher that can be implemented with less than 300 gates, with the added benefit of high throughput provided by stream ciphers.


DHAES: An Encryption Scheme Based on the Diffie-Hellman Problem


title: Cross-cultural Deception Detection

abstract: In this paper, we address the task of cross-cultural deception detection. Using crowdsourcing, we collect three deception datasets, two in English (one originating from United States and one from India), and one in Spanish obtained from speakers from Mexico. We run comparative experiments to evaluate the accuracies of deception classifiers built for each culture, and also to analyze classification differences within and across cultures. Our results show that we can leverage cross-cultural information, either through translation or equivalent semantic categories, and build deception classifiers with a performance ranging between 60-70%.


title: Large margin transductive transfer learning

abstract: Recently there has been increasing interest in the problem of transfer learning, in which the typical assumption that training and testing data are drawn from identical distributions is relaxed. We specifically address the problem of transductive transfer learning in which we have access to labeled training data and unlabeled testing data potentially drawn from different, yet related distributions, and the goal is to leverage the labeled training data to learn a classifier to correctly predict data from the testing distribution. We have derived efficient algorithms for transductive transfer learning based on a novel viewpoint and the Support Vector Machine (SVM) paradigm, of a large margin hyperplane classifier in a feature space. We show that our method can out-perform some recent state-of-the-art approaches for transfer learning on several data sets, with the added benefits of model and data separation and the potential to leverage existing work on support vector machines.


title: A Mechanism for Online Diagnosis of Hard Faults in Microprocessors

abstract: We develop a microprocessor design that tolerates hard faults, including fabrication defects and in-field faults, by leveraging existing microprocessor redundancy. To do this, we must: detect and correct errors, diagnose hard faults at the field deconfigurable unit (FDU) granularity, and deconfigure FDUs with hard faults. In our reliable microprocessor design, we use DIVA dynamic verification to detect and correct errors. Our new scheme for diagnosing hard faults tracks instructions' core structure occupancy from decode until commit. If a DIVA checker detects an error in an instruction, it increments a small saturating error counter for every FDU used by that instruction, including that DIVA checker. A hard fault in an FDU quickly leads to an above-threshold error counter for that FDU and thus diagnoses the fault. For deconfiguration, we use previously developed schemes for functional units and buffers, and we present a scheme for deconfiguring DIVA checkers. Experimental results show that our reliable microprocessor quickly and accurately diagnoses each hard fault that is injected and continues to function, albeit with somewhat degraded performance.


title: Exploiting highly qualified pattern with frequency and weight occupancy

abstract: By identifying useful knowledge embedded in the behavior of search engines, users can provide valuable information for web searching and data mining. Numerous algorithms have been proposed to find the desired interesting patterns, i.e., frequent pattern, in real-world applications. Most of those studies use frequency to measure the interestingness of patterns. However, each object may have different importance in these real-world applications, and the frequent ones do not usually contain a large portion of the desired patterns. In this paper, we present a novel method, called exploiting highly qualified patterns with frequency and weight occupancy (QFWO), to suggest the possible highly qualified patterns that utilize the idea of co-occurrence and weight occupancy. By considering item weight, weight occupancy and the frequency of patterns, in this paper, we designed a new highly qualified patterns. A novel Set-enumeration tree called the frequency-weight (FW)-tree and two compact data structures named weight-list and FW-table are designed to hold the global downward closure property and partial downward closure property of quality and weight occupancy to further prune the search space. The proposed method can exploit high qualified patterns in a recursive manner without candidate generation. Extensive experiments were conducted both on real-world and synthetic datasets to evaluate the effectiveness and efficiency of the proposed algorithm. Results demonstrate that the obtained patterns are reasonable and acceptable. Moreover, the designed QFWO with several pruning strategies is quite efficient in terms of runtime and search space.


title: A Trust Management Framework for Cloud Computing Platforms

abstract: In today's cloud computing platforms, more and more users are now working or collaborating in multi-cloud environment, in which collaborators, clouds, computing nodes may belong to different institutions or organizations. Those different organizations might have their own policies. Security is still a big concern in cloud computing. To help cloud vendors and customers to detect and prevent from being affected by potential attacks, we propose a trust management framework. We consider link/flow's level trust, node's level trust and task/mission's level trust. We show that our trust management framework is able to provide guidance for the administrator or even customers to make decisions, e.g. migrating tasks from suspect nodes to trustworthy nodes, dynamically allocating resource, and managingthe trade-off between the degree of redundancy and the cost of resource.


title: DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension

abstract: We propose DuoRC, a novel dataset for Reading Comprehension (RC) that motivates several new challenges for neural approaches in language understanding beyond those offered by existing RC datasets. DuoRC contains 186,089 unique question-answer pairs created from a collection of 7680 pairs of movie plots where each pair in the collection reflects two versions of the same movie - one from Wikipedia and the other from IMDb - written by two different authors. We asked crowdsourced workers to create questions from one version of the plot and a different set of workers to extract or synthesize answers from the other version. This unique characteristic of DuoRC where questions and answers are created from different versions of a document narrating the same underlying story, ensures by design, that there is very little lexical overlap between the questions created from one version and the segments containing the answer in the other version. Further, since the two versions have different levels of plot detail, narration style, vocabulary, etc., answering questions from the second version requires deeper language understanding and incorporating external background knowledge. Additionally, the narrative style of passages arising from movie plots (as opposed to typical descriptive passages in existing datasets) exhibits the need to perform complex reasoning over events across multiple sentences. Indeed, we observe that state-of-the-art neural RC models which have achieved near human performance on the SQuAD dataset, even when coupled with traditional NLP techniques to address the challenges presented in DuoRC exhibit very poor performance (F1 score of 37.42% on DuoRC v/s 86% on SQuAD dataset). This opens up several interesting research avenues wherein DuoRC could complement other RC datasets to explore novel neural approaches for studying language understanding.


title: Solvent-free microwave extraction of essential oil from aromatic herbs: from laboratory to pilot and industrial scale.

abstract: Abstract Solvent-free microwave extraction (SFME) has been proposed as a green method for the extraction of essential oil from aromatic herbs that are extensively used in the food industry. This technique is a combination of microwave heating and dry distillation performed at atmospheric pressure without any added solvent or water. The isolation and concentration of volatile compounds is performed in a single stage. In this work, SFME and a conventional technique, hydro-distillation HD (Clevenger apparatus), are used for the extraction of essential oil from rosemary ( Rosmarinus officinalis L.) and are compared . This preliminary laboratory study shows that essential oils extracted by SFME in 30 min were quantitatively (yield and kinetics profile) and qualitatively (aromatic profile) similar to those obtained using conventional hydro-distillation in 2 h. Experiments performed in a 75 L pilot microwave reactor prove the feasibility of SFME up scaling and potential industrial applications.


title: Micro-Net: A unified model for segmentation of various objects in microscopy images

abstract: Abstract Object segmentation and structure localization are important steps in automated image analysis pipelines for microscopy images. We present a convolution neural network (CNN) based deep learning architecture for segmentation of objects in microscopy images. The proposed network can be used to segment cells, nuclei and glands in fluorescence microscopy and histology images after slight tuning of input parameters. The network trains at multiple resolutions of the input image, connects the intermediate layers for better localization and context and generates the output using multi-resolution deconvolution filters. The extra convolutional layers which bypass the max-pooling operation allow the network to train for variable input intensities and object size and make it robust to noisy data. We compare our results on publicly available data sets and show that the proposed network outperforms recent deep learning algorithms.


title: Positive Consequences of Conflict on Decision Making: When a Conflict Mindset Facilitates Choice

abstract: Much research has shown that conflict is aversive and leads to increased choice deferral. In contrast, we have proposed that conflict can be beneficial. Specifically, exposure to nonconscious goal conflict can activate a mindset (a set of cognitive procedures) that facilitates the systematic processing of information without triggering the associated costs, such as negative affect and stress. In a conflict mindset, people should be better able to make tradeoffs and resolve choice conflict. We tested this proposition in 4 experiments, and demonstrated that priming conflicting goals before a decision increases choice in domains unrelated to the primed conflict. We further demonstrated that increased choice occurs because people in a conflict mindset process choice information more systematically, and we rule out several alternative explanations for the results.


title: Quickly Boosting Decision Trees- Pruning Underachieving Features Early-

abstract: Boosted decision trees are among the most popular learning techniques in use today. While exhibiting fast speeds at test time, relatively slow training renders them impractical for applications with real-time learning requirements. We propose a principled approach to overcome this drawback. We prove a bound on the error of a decision stump given its preliminary error on a subset of the training data; the bound may be used to prune unpromising features early in the training process. We propose a fast training algorithm that exploits this bound, yielding speedups of an order of magnitude at no cost in the final performance of the classifier. Our method is not a new variant of Boosting; rather, it is used in conjunction with existing Boosting algorithms and other sampling methods to achieve even greater speedups.


COLOR IMAGE SEGMENTATION USING K-MEANS CLASSIFICATION ON RGB HISTOGRAM


title: Enterprise resource planning in reengineering business

abstract: Highlights the need for business process reengineering and impact of IT on enterprises. Presents in detail the evolution, modules, verticals, model, management concerns and network infrastructure, selection of software packages and enterprise preparedness for implementation of enterprise resource planning. Briefly describes the key features of popular ERP packages, viz. MFG/PRO, IFS/AVALON, SAAP, BAAN IV, J.D. Edwards, Marshal (R) and PeopleSoft. Concludes that enterprises definitely attain best business practices by implementing ERP, in an effort to position for success in the twenty‐first century.


title: The origin and evolution of cell types

abstract: In this article, the authors review the current understanding of the molecular mechanisms underlying cell type identity and discuss how new phenotypic features of cell types evolve. They explain how evolutionary lineage differs from developmental lineage and highlight how an evolutionary view of cell type identity can facilitate research in comparative cell biology.


title: Epigenetic and transgenerational reprogramming of brain development

abstract: Neurodevelopmental programming - the implementation of the genetic and epigenetic blueprints that guide and coordinate normal brain development - requires tight regulation of transcriptional processes. During prenatal and postnatal time periods, epigenetic processes fine-tune neurodevelopment towards an end product that determines how an organism interacts with and responds to exposures and experiences throughout life. Epigenetic processes also have the ability to reprogramme the epigenome in response to environmental challenges, such as maternal stress, making the organism more or less adaptive depending on the future challenges presented. Epigenetic marks generated within germ cells as a result of environmental influences throughout life can also shape future generations long before conception occurs.


title: The Impact of Consumer Credit Access on Unemployment

abstract: Unemployed households’ access to unsecured revolving credit (credit cards) nearly quadrupled from about 12 percent to about 45 percent over the last three decades. This paper analyzes how this large increase in revolving credit has impacted the business cycle. The paper develops a general equilibrium business cycle model with search in both the labor market and in the credit market. This generates a very rich and empirically plausible level of heterogeneity in work and credit histories while at the same time permitting a tractable model solution. Calibrating to the observed path of credit use between 1974 and 2012, I nd that the large growth in credit access leads to deeper and longer recessions as well as moderately slower recoveries. Relative to an economy with credit


title: Facial esthetics in adolescents and its relationship to “ideal” ratios and angles

abstract: Introduction: Many guidelines, norms, and ideal ratios and angles for attractive faces have been proposed in the literature. The aim of this study was to test the hypothesis that facial attractiveness in adolescents is related to ideal angles and ratios, as indicated in the literature. Methods: Seventy-six laypeople viewed sets of photographs of 64 adolescents and rated them on a visual analog scale (VAS) from 0 to 100. The facial esthetic value of each subject was calculated as a mean VAS score. Three observers recorded the positions of 61 landmarks, and 45 were found to have acceptable reproducibility. Based on these 45 landmarks, 27 ideal ratios on frontal photographs and 26 ideal angles on lateral photographs were identified in the literature. These ratios and angles were calculated on each photograph, and their deviation from the ideal targets in the literature were determined. Each deviation was related to the VAS score. Results: Two ratios and 3 angles had a significant negative correlation with the VAS scores, indicating that beautiful faces have less deviation from the ideal target than less beautiful faces. Together, these variables explained 28.7% of the variance. Conclusions: Few “ideal” ratios and angles have a significant relationship with facial esthetics in adolescents.


title: Support Vector Regression Based Indoor Location in IEEE 802.11 Environments

abstract: The wide spread of the 802.11-based wireless technology brings about a good opportunity for the indoor positioning system. In this paper, we present a new 802.11-based indoor positioning method using support vector regression (SVR), which consists of offline training stage and online location stage. The model that describes the relations between the position and the received signal strength (RSS) of the mobile device is established at the offline training stage by SVR, and at the online location stage the exact position is determined by this model. Due to the complex indoor environment, RSS is vulnerable and changeable. To address this issue, data filtering rules obtained through statistical analysis are applied at offline training stage to improve the quality of training samples and thus improve the quality of prediction model. At the online location stage, -times continuous measurement is utilized to obtain the high quality RSS input, which guarantees the consistency with the training samples and improves the position accuracy of mobile devices. Performance evaluation shows that the proposed method has a higher positioning accuracy compared with the probability and neutral network method, and the demand for the storage capacity and computing power is also low at the same time.


title: Object Detection Via Structural Feature Selection and Shape Model

abstract: In this paper, we propose an approach for object detection via structural feature selection and part-based shape model. It automatically learns a shape model from cluttered training images without need to explicitly use bounding boxes on objects. Our approach first builds a class-specific codebook of local contour features, and then generates structural feature descriptors by combining context shape information. These descriptors are robust to both within-class variations and scale changes. Through exploring pairwise image matching using fast earth mover's distance, feature weights can be iteratively updated. Those discriminative foreground features are assigned high weights and then selected to build a part-based shape model. Finally, object detection is performed by matching each testing image with this model. Experiments show that the proposed method is very effective. It has achieved comparable performance to the state-of-the-art shape-based detection methods, but requires much less training information.


title: Enabling FPGAs in the cloud

abstract: Cloud computing is becoming a major trend for delivering and accessing infrastructure on demand via the network. Meanwhile, the usage of FPGAs (Field Programmable Gate Arrays) for computation acceleration has made significant inroads into multiple application domains due to their ability to achieve high throughput and predictable latency, while providing programmability, low power consumption and time-to-value. Many types of workloads, e.g. databases, big data analytics, and high performance computing, can be and have been accelerated by FPGAs. As more and more workloads are being deployed in the cloud, it is appropriate to consider how to make FPGAs and their capabilities available in the cloud. However, such integration is non-trivial due to issues related to FPGA resource abstraction and sharing, compatibility with applications and accelerator logics, and security, among others. In this paper, a general framework for integrating FPGAs into the cloud is proposed and a prototype of the framework is implemented based on OpenStack, Linux-KVM and Xilinx FPGAs. The prototype enables isolation between multiple processes in multiple VMs, precise quantitative acceleration resource allocation, and priority-based workload scheduling. Experimental results demonstrate the effectiveness of this prototype, an acceptable overhead, and good scalability when hosting multiple VMs and processes.


title: Femtocell Networks: A Survey

abstract: The surest way to increase the system capacity of a wireless link is by getting the transmitter and receiver closer to each other, which creates the dual benefits of higher-quality links and more spatial reuse. In a network with nomadic users, this inevitably involves deploying more infrastructure, typically in the form of microcells, hot spots, distributed antennas, or relays. A less expensive alternative is the recent concept of femtocells - also called home base stations - which are data access points installed by home users to get better indoor voice and data coverage. In this article we overview the technical and business arguments for femtocells and describe the state of the art on each front. We also describe the technical challenges facing femtocell networks and give some preliminary ideas for how to overcome them.


title: 2-D Human Pose Estimation from Images Based on Deep Learning: A Review

abstract: H150 pose estimation is an important research topic in the field of computer vision and artificial intelligence. This paper focuses on the state-of-art progress of 2-D human pose estimation methods based on deep learning. According to the neural network structure, these methods are classified as single CNN method, Multi-stage CNN method, Multi-branch CNN method, Recurrent Neural Network (RNN) method and Generative Adversarial Networks (GAN) method. We summarize and analyze their attributes and performance. The future development direction is also prospected.


A GLOBAL MEASURE OF PERCEIVED STRESS


title: Controlled switching of power circuit breakers

abstract: This paper presents an overview of importance and significance of controlled switching in complex power networks. The main objective of controlled switching is to avoid transients in the system. In a power system network transients are the redistribution of stored energy among the system capacitance and inductance whenever there is a change of state. So controlled switching is the method to optimize the instant of switching the circuit breaker resulting in minimal energy redistribution.


title: EIGHT MYTHS ABOUT CAUSALITY AND STRUCTURAL EQUATION

abstract: Causality was at the center of the early history of structural equation models (SEMs) which continue to serve as the most popular approach to causal analysis in the social sciences. Through decades of development, critics and defenses of the capability of SEMs to support causal inference have accumulated. A variety of misunderstandings and myths about the nature of SEMs and their role in causal analysis have emerged, and their repetition has led some to believe they are true. Our chapter is organized by presenting eight myths about causality and SEMs in the hope that this will lead to a more accurate understanding. More specifically, the eight myths are the following: (1) SEMs aim to establish causal relations from associations alone, (2) SEMs and regression are essentially equivalent, (3) no causation without manipulation, (4) SEMs are not equipped to handle nonlinear causal relationships, (5) a potential outcome framework is more principled than SEMs, (6) SEMs are not applicable to experiments with randomized treatments, (7) mediation analysis in SEMs is inherently noncausal, and (8) SEMs do not test any major part of the theory against the data. We present the facts that dispel these myths, describe what SEMs can and cannot do, and briefly present our critique of current practice using SEMs. We conclude that the current capabilities of SEMs to formalize and implement causal inference tasks are indispensible; its potential to do more is even greater.


title: Brain-computer interfaces: . . . of movement in paralysis

abstract: The review describes the status of brain–computer or brain–machine interface research. We focus on non-invasive brain–computer interfaces (BCIs) and their clinical utility for direct brain communication in paralysis and motor restoration in stroke. A large gap between the promises of invasive animal and human BCI preparations and the clinical reality characterizes the literature: while intact monkeys learn to execute more or less complex upper limb movements with spike patterns from motor brain regions alone without concomitant peripheral motor activity usually after extensive training, clinical applications in human diseases such as amyotrophic lateral sclerosis and paralysis from stroke or spinal cord lesions show only limited success, with the exception of verbal communication in paralysed and locked-in patients. BCIs based on electroencephalographic potentials or oscillations are ready to undergo large clinical studies and commercial production as an adjunct or a major assisted communication device for paralysed and locked-in patients. However, attempts to train completely locked-in patients with BCI communication after entering the complete locked-in state with no remaining eye movement failed. We propose that a lack of contingencies between goal directed thoughts and intentions may be at the heart of this problem. Experiments with chronically curarized rats support our hypothesis; operant conditioning and voluntary control of autonomic physiological functions turned out to be impossible in this preparation. In addition to assisted communication, BCIs consisting of operant learning of EEG slow cortical potentials and sensorimotor rhythm were demonstrated to be successful in drug resistant focal epilepsy and attention deficit disorder. First studies of non-invasive BCIs using sensorimotor rhythm of the EEG and MEG in restoration of paralysed hand movements in chronic stroke and single cases of high spinal cord lesions show some promise, but need extensive evaluation in well-controlled experiments. Invasive BMIs based on neuronal spike patterns, local field potentials or electrocorticogram may constitute the strategy of choice in severe cases of stroke and spinal cord paralysis. Future directions of BCI research should include the regulation of brain metabolism and blood flow and electrical and magnetic stimulation of the human brain (invasive and non-invasive). A series of studies using BOLD response regulation with functional magnetic resonance imaging (fMRI) and near infrared spectroscopy demonstrated a tight correlation between voluntary changes in brain metabolism and behaviour.


title: Computer aided diagnosis based on medical image processing and artificial intelligence methods

abstract: Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.


The Cyclic Beat Spectrum: Tempo-Related Audio Features for Time-Scale Invariant Audio Identification


title: Practical recommendations for exercise training in patients with COPD

abstract: The aim of this article was to provide practical recommendations to healthcare professionals interested in offering a pulmonary rehabilitation programme for patients with chronic obstructive pulmonary disease (COPD). The latest research findings were brought together and translated into clinical practice. These recommendations focus on the description of useful assessment tests and of the most common exercise modalities for patients with COPD. We provide specific details on the rationale of why and especially how to implement exercise training in patients with COPD, including the prescription of training mode, intensity and duration, as well as suggestions of guidelines for training progression.


title: Automated cyberbullying detection using clustering appearance patterns

abstract: Cyberbullying is an activity of sending threatening messages to insult person. To prevent cyber victimization from the activity is challenging. This paper enhanced the Naive Bayes classifier for extracting the words and examining loaded pattern clustering. The algorithm included two main methods: (1) creating partitions by iteratively relocating from entire datasets into clusters using k-mean clustering and (2) capturing any specific partition with the frequency of words with multinomial model feature vector and drawing the probability of words occurring in a document for predicting the eight classes. The proposed method resulted in increasing accuracy and reliability of an experiment.


title: Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis

abstract: This paper describes an application of fuzzy logic in designing controllers for industrial plants. A Fuzzy Logic is used to synthesise linguistic control protocol of a skilled operator. The method has been applied to pilot scale plants as well as in a practical industrial situation. The merits of this method in its usefulness to control engineering are discussed. This work also illustrates the potential for using fuzzy logic in modelling and decision making. An avenue for further work in this area is described where the need is to go beyond a purely descriptive approach and explore means by which a prescriptive system may be implemented.


title: A model-driven development of web applications using AngularJS framework

abstract: AngularJS is one of the widely used frameworks for modern single-page web application development which is designed to support dynamic views in the applications. To further assist AngularJS developers, this research proposes how the concept of model-driven development can be applied to AngularJS-based development. We propose a UML profile for AngularJS for building a model of an AngularJS web application, and a set of transformations that transform the model into a code template. The developer can then fill in the template to make a complete workable web application. Also, a transformation tool is developed to assist in constructing the code template. Using a case study application, the evaluation in terms of transformation rate shows that the automatically generated code covers 87% of the complete code of the case study, which means it could greatly help reduce development time.


title: Six-step commutation with round robin state machine to alleviate error in hall-effect-sensor reading for BLDC motor control

abstract: Hall-effect sensors are commonly applied in brushless direct current (BLDC) motor control for rotor position detection. By knowing the exact position of the rotor, inverter could supply energy in synchronize to the BLDC motor state. As the motor is categorized as synchronous motor, those sensors are vital. Working out of synchronization not only makes drive system less efficient but also threaten the inverter as current requirement will be increased. Unfortunately, the sensor works in harsh environment where high current, high temperature, and high vibration are exist. Those conditions result in some incorrect position detections. In order to improve the hall-effect position accuracy, in this paper, a simple finite state machine (FSM) called round robin technique is implemented. This technique along with conventional R-C filter in the sensor line successfully suppresses error in the rotor position detection. This results in higher inverter reliability and smoother motor operation.


title: Information technology and economic performance: A critical review of the empirical evidence

abstract: For many years, there has been considerable debate about whether the IT revolution was paying off in higher productivity. Studies in the 1980s found no connection between IT investment and productivity in the U.S. economy, a situation referred to as the productivity paradox. Since then, a decade of studies at the firm and country level has consistently shown that the impact of IT investment on labor productivity and economic growth is significant and positive. This article critically reviews the published research, more than 50 articles, on computers and productivity. It develops a general framework for classifying the research, which facilitates identifying what we know, how well we know it, and what we do not know. The framework enables us to systematically organize, synthesize, and evaluate the empirical evidence and to identify both limitations in existing research and data and substantive areas for future research.The review concludes that the productivity paradox as first formulated has been effectively refuted. At both the firm and the country level, greater investment in IT is associated with greater productivity growth. At the firm level, the review further concludes that the wide range of performance of IT investments among different organizations can be explained by complementary investments in organizational capital such as decentralized decision-making systems, job training, and business process redesign. IT is not simply a tool for automating existing processes, but is more importantly an enabler of organizational changes that can lead to additional productivity gains.In mid-2000, IT capital investment began to fall sharply due to slowing economic growth, the collapse of many Internet-related firms, and reductions in IT spending by other firms facing fewer competitive pressures from Internet firms. This reduction in IT investment has had devastating effects on the IT-producing sector, and may lead to slower economic and productivity growth in the U.S. economy. While the turmoil in the technology sector has been unsettling to investors and executives alike, this review shows that it should not overshadow the fundamental changes that have occurred as a result of firms' investments in IT. Notwithstanding the demise of many Internet-related companies, the returns to IT investment are real, and innovative companies continue to lead the way.


title: Educating engineers for industry 4.0: Virtual worlds and human-robot-teams: Empirical studies towards a new educational age

abstract: The term "Industry 4.0" symbolizes new forms of technology and artificial intelligence within production technologies. Smart robots are going to be the game changers within the factories of the future and will work with humans in indispensable teams within many processes. With this fourth industrial revolution, classical production lines are going through comprehensive modernization, e.g. in terms of in-the-box manufacturing, where humans and machines work side by side in so-called "hybrid teams". Questions about how to prepare for newly needed engineering competencies for the age of Industry 4.0, how to assess them and how to teach and train e.g. human-robot-teams have to be tackled in future engineering education. The paper presents theoretical aspects and empirical results of a series of studies, carried out to investigate the competencies of virtual collaboration and joint problem solving in virtual worlds.


title: TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-Rays

abstract: Chest X-rays are one of the most common radiological examinations in daily clinical routines. Reporting thorax diseases using chest X-rays is often an entry-level task for radiologist trainees. Yet, reading a chest X-ray image remains a challenging job for learning-oriented machine intelligence, due to (1) shortage of large-scale machine-learnable medical image datasets, and (2) lack of techniques that can mimic the high-level reasoning of human radiologists that requires years of knowledge accumulation and professional training. In this paper, we show the clinical free-text radiological reports can be utilized as a priori knowledge for tackling these two key problems. We propose a novel Text-Image Embedding network (TieNet) for extracting the distinctive image and text representations. Multi-level attention models are integrated into an end-to-end trainable CNN-RNN architecture for highlighting the meaningful text words and image regions. We first apply TieNet to classify the chest X-rays by using both image features and text embeddings extracted from associated reports. The proposed auto-annotation framework achieves high accuracy (over 0.9 on average in AUCs) in assigning disease labels for our hand-label evaluation dataset. Furthermore, we transform the TieNet into a chest X-ray reporting system. It simulates the reporting process and can output disease classification and a preliminary report together. The classification results are significantly improved (6% increase on average in AUCs) compared to the state-of-the-art baseline on an unseen and hand-labeled dataset (OpenI).


title: Extracting and Retargeting Color Mappings from Bitmap Images of Visualizations

abstract: Visualization designers regularly use color to encode quantitative or categorical data. However, visualizations “in the wild” often violate perceptual color design principles and may only be available as bitmap images. In this work, we contribute a method to semi-automatically extract color encodings from a bitmap visualization image. Given an image and a legend location, we classify the legend as describing either a discrete or continuous color encoding, identify the colors used, and extract legend text using OCR methods. We then combine this information to recover the specific color mapping. Users can also correct interpretation errors using an annotation interface. We evaluate our techniques using a corpus of images extracted from scientific papers and demonstrate accurate automatic inference of color mappings across a variety of chart types. In addition, we present two applications of our method: automatic recoloring to improve perceptual effectiveness, and interactive overlays to enable improved reading of static visualizations.


title: PERSONALITY PROCESSES AND INDIVIDUAL DIFFERENCES Grit: Perseverance and Passion for Long-Term Goals

abstract: The importance of intellectual talent to achievement in all professional domains is well established, but less is known about other individual differences that predict success. The authors tested the importance of 1 noncognitive trait: grit. Defined as perseverance and passion for long-term goals, grit accounted for an average of 4% of the variance in success outcomes, including educational attainment among 2 samples of adults (N = 1,545 and N = 690), grade point average among Ivy League undergraduates (N = 138), retention in 2 classes of United States Military Academy, West Point, cadets (N = 1,218 and N = 1,308), and ranking in the National Spelling Bee (N = 175). Grit did not relate positively to IQ but was highly correlated with Big Five Conscientiousness. Grit nonetheless demonstrated incremental predictive validity of success measures over and beyond IQ and conscientiousness. Collectively, these findings suggest that the achievement of difficult goals entails not only talent but also the sustained and focused application of talent over time.


Semi Automatic Development of FarsNet ; The Persian WordNet


title: When Coding Style Survives Compilation: De-anonymizing Programmers from Executable Binaries

abstract: The ability to identify authors of computer programs based on their coding style is a direct threat to the privacy and anonymity of programmers. While recent work found that source code can be attributed to authors with high accuracy, attribution of executable binaries appears to be much more difficult. Many distinguishing features present in source code, e.g. variable names, are removed in the compilation process, and compiler optimization may alter the structure of a program, further obscuring features that are known to be useful in determining authorship. We examine programmer de-anonymization from the standpoint of machine learning, using a novel set of features that include ones obtained by decompiling the executable binary to source code. We adapt a powerful set of techniques from the domain of source code authorship attribution along with stylistic representations embedded in assembly, resulting in successful de-anonymization of a large set of programmers. ::: We evaluate our approach on data from the Google Code Jam, obtaining attribution accuracy of up to 96% with 100 and 83% with 600 candidate programmers. We present an executable binary authorship attribution approach, for the first time, that is robust to basic obfuscations, a range of compiler optimization settings, and binaries that have been stripped of their symbol tables. We perform programmer de-anonymization using both obfuscated binaries, and real-world code found "in the wild" in single-author GitHub repositories and the recently leaked Nulled.IO hacker forum. We show that programmers who would like to remain anonymous need to take extreme countermeasures to protect their privacy.


title: Face Recognition Based on Local Binary Patterns with Threshold

abstract: This paper presents a novel and efficient face recognition technique based on Local Binary Pattern (LBP) with threshold for resolving traditional LBP’s weakness of extracting global features. By setting a threshold to enhance the robustness to noise such as light and extract the global features of face preferably. Combining the local features by LBP with global features as the total features of the image is more power discriminating. Principal Component Analysis (PCA) and linear discriminate analysis (LDA) are used to reduce the dimensionality and optimize discriminative recognition respectively. The proposed method is tested and evaluated not only on ORL datasets but also on YALE datasets and yields a recognition rate of 98%, the experimental results show that the method is valid and feasible.


title: Deep neural networks for i-vector language identification of short utterances in cars

abstract: This paper is focused on the application of the Language Identification (LID) technology for intelligent vehicles. We cope with short sentences or words spoken in moving cars in four languages: English, Spanish, German, and Finnish. As the response time of the LID system is crucial for user acceptance in this particular task, speech signals of different durations with total average of 3.8s are analyzed. In this paper, the authors propose the use of Deep Neural Networks (DNN) to model effectively the i-vector space of languages. Both raw i-vectors and session variability compensated i-vectors are evaluated as input vectors to DNNs. The performance of the proposed DNN architecture is compared with both conventional GMM-UBM and i-vector/LDA systems considering the effect of durations of signals. It is shown that the signals with durations between 2 and 3s meet the requirements of this ::: application, i.e., high accuracy and fast decision, in which the proposed DNN architecture outperforms GMM-UBM and i-vector/LDA systems by 37% and 28%, respectively.


title: Y.: Regularized Distance Metric Learning: Theory and Algorithm

abstract: In this paper, we examine the generalization error of regularized distance metric learning. We show that with appropriate constraints, the generalization error of regularized distance metric learning could be independent from the dimensionality, making it suitable for handling high dimensional data. In addition, we present an efficient online learning algorithm for regularized distance metric learning. Our empirical studies with data classification and face recognition show that the proposed algorithm is (i) effective for distance metric learning when compared to the state-of-the-art methods, and (ii) efficient and robust for high dimensional data.


title: Non-iterative RGB-D-inertial Odometry

abstract: This paper presents a non-iterative solution to RGB-D-inertial odometry system. Traditional odometry methods resort to iterative algorithms which are usually computationally expensive or require well-designed initialization. To overcome this problem, this paper proposes to combine a non-iterative front-end (odometry) with an iterative back-end (loop closure) for the RGB-D-inertial SLAM system. The main contribution lies in the novel non-iterative front-end, which leverages on inertial fusion and kernel cross-correlators (KCC) to match point clouds in frequency domain. Dominated by the fast Fourier transform (FFT), our method is only of complexity $\mathcal{O}(n\log{n})$, where $n$ is the number of points. Map fusion is conducted by element-wise operations, so that both time and space complexity are further reduced. Extensive experiments show that, due to the lightweight of the proposed front-end, the framework is able to run at a much faster speed yet still with comparable accuracy with the state-of-the-arts.


title: A SPEC RG Cloud Group's Vision on the Performance Challenges of FaaS Cloud Architectures

abstract: As a key part of the serverless computing paradigm, Function-as-a-Service (FaaS) platforms enable users to run arbitrary functions without being concerned about operational issues. However, there are several performance-related issues surrounding the state-of-the-art FaaS platforms that can deter widespread adoption of FaaS, including sizeable overheads, unreliable performance, and new forms of the cost-performance trade-off. In this work we, the SPEC RG Cloud Group, identify six performance-related challenges that arise specifically in this FaaS model, and present our roadmap to tackle these problems in the near future. This paper aims at motivating the community to solve these challenges together.


title: Cyberbullying Detection on Instagram with Optimal Online Feature Selection

abstract: Cyberbullying has emerged as a large-scale societal problem that demands accurate methods for its detection in an effort to mitigate its detrimental consequences. While automated, data-driven techniques for analyzing and detecting cyberbullying incidents have been developed, the scalability of existing approaches has largely been ignored. At the same time, the complexities underlying cyberbullying behavior (e.g., social context and changing language) make the automatic identification of “the best subset of features” to use challenging. We address this gap by formulating cyberbullying detection as a sequential hypothesis testing problem. Based on this formulation, we propose a novel algorithm to drastically reduce the number of features used in classification. We demonstrate the utility, scalability and responsiveness of our approach using a real-world dataset from Instagram, the online social media platform with the highest percentage of users reporting experiencing cyberbullying. Our approach improves recall by a staggering 700%, while at the same time reducing the average number of features by up to 99.82% compared to state-of-the-art supervised cyberbullying detection methods, learning approaches that require weak supervision, and traditional offline feature selection and dimensionality reduction techniques.


title: Network Properties Revealed Through Matrix Functions

abstract: The emerging field of network science deals with the tasks of modeling, comparing, and summarizing large data sets that describe complex interactions. Because pairwise affinity data can be stored in a two-dimensional array, graph theory and applied linear algebra provide extremely useful tools. Here, we focus on the general concepts of centrality, communicability, and betweenness, each of which quantifies important features in a network. Some recent work in the mathematical physics literature has shown that the exponential of a network's adjacency matrix can be used as the basis for defining and computing specific versions of these measures. We introduce here a general class of measures based on matrix functions, and show that a particular case involving a matrix resolvent arises naturally from graph-theoretic arguments. We also point out connections between these measures and the quantities typically computed when spectral methods are used for data mining tasks such as clustering and ordering. We finish with computational examples showing the new matrix resolvent version applied to real networks.


title: How not to do agile testing

abstract: When our team adopted extreme programming and Scrum, there were a number of challenges. Integrating an agile team in a "traditional" organization created friction and conflict within the team and with management. The team consisted of volunteers new to agile practices. We had difficulties with planning, estimation, task breakdown, managing requirements and working together in a collocated space. However, the biggest challenge the team encountered was not in any of these other areas, but in one that most of the team figured was simple: testing. This paper describes how difficult it was to actually get the various testing practices in place and functioning properly. In particular, the author discusses challenges we faced in coming to a common understanding of the practices, estimating testing effort, allocating the time required to do automated tests in a feature-driven culture, and getting a team of diverse individuals to understand how to apply principles that sound great but are challenging to implement.


title: FiDoop-DP: Data Partitioning in Frequent Itemset Mining on Hadoop Clusters

abstract: Traditional parallel algorithms for mining frequent itemsets aim to balance load by equally partitioning data among a group of computing nodes. We start this study by discovering a serious performance problem of the existing parallel Frequent Itemset Mining algorithms. Given a large dataset, data partitioning strategies in the existing solutions suffer high communication and mining overhead induced by redundant transactions transmitted among computing nodes. We address this problem by developing a data partitioning approach called FiDoop-DP using the MapReduce programming model. The overarching goal of FiDoop-DP is to boost the performance of parallel Frequent Itemset Mining on Hadoop clusters. At the heart of FiDoop-DP is the Voronoi diagram-based data partitioning technique, which exploits correlations among transactions. Incorporating the similarity metric and the Locality-Sensitive Hashing technique, FiDoop-DP places highly similar transactions into a data partition to improve locality without creating an excessive number of redundant transactions. We implement FiDoop-DP on a 24-node Hadoop cluster, driven by a wide range of datasets created by IBM Quest Market-Basket Synthetic Data Generator. Experimental results reveal that FiDoop-DP is conducive to reducing network and computing loads by the virtue of eliminating redundant transactions on Hadoop nodes. FiDoop-DP significantly improves the performance of the existing parallel frequent-pattern scheme by up to 31 percent with an average of 18 percent.


title: Predicting Performance on MOOC Assessments using Multi-Regression Models

abstract: The past few years has seen the rapid growth of data min- ing approaches for the analysis of data obtained from Mas- sive Open Online Courses (MOOCs). The objectives of this study are to develop approaches to predict the scores a stu- dent may achieve on a given grade-related assessment based on information, considered as prior performance or prior ac- tivity in the course. We develop a personalized linear mul- tiple regression (PLMR) model to predict the grade for a student, prior to attempting the assessment activity. The developed model is real-time and tracks the participation of a student within a MOOC (via click-stream server logs) and predicts the performance of a student on the next as- sessment within the course offering. We perform a com- prehensive set of experiments on data obtained from three openEdX MOOCs via a Stanford University initiative. Our experimental results show the promise of the proposed ap- proach in comparison to baseline approaches and also helps in identification of key features that are associated with the study habits and learning behaviors of students.


title: A Parallel Implementation of the 2D Wavelet Transform Using CUDA

abstract: There is a multicore platform that is currently concentrating an enormous attention due to its tremendous potential in terms of sustained performance: the NVIDIA Tesla boards. These cards intended for general-purpose computing on graphic processing units (GPGPUs) are used as data-parallel computing devices. They are based on the Computed Unified Device Architecture (CUDA) which is common to the latest NVIDIA GPUs. The bottom line is a multicore platform which provides an enormous potential performance benefit driven by a non-traditional programming model. In this paper we try to provide some insight into the peculiarities of CUDA in order to target scientific computing by means of a specific example. In particular, we show that the parallelization of the two-dimensional fast wavelet transform for the NVIDIA Tesla C870 achieves a speedup of 20.8 for an image size of 8192x8192, when compared with the fastest host-only version implementation using OpenMP and including the data transfers between main memory and device memory.


title: Understanding the Digital Marketing Environment with KPIs and Web Analytics

abstract: In the practice of Digital Marketing (DM), Web Analytics (WA) and Key Performance Indicators (KPIs) can and should play an important role in marketing strategy formulation. It is the aim of this article to survey the various DM metrics to determine and address the following question: What are the most relevant metrics and KPIs that companies need to understand and manage in order to increase the effectiveness of their DM strategies? Therefore, to achieve these objectives, a Systematic Literature Review has been carried out based on two main themes (i) Digital Marketing and (ii) Web Analytics. The search terms consulted in the databases have been (i) DM and (ii) WA obtaining a result total of n = 378 investigations. The databases that have been consulted for the extraction of data were Scopus, PubMed, PsyINFO, ScienceDirect and Web of Science. In this study, we define and identify the main KPIs in measuring why, how and for what purpose users interact with web pages and ads. The main contribution of the study is to lay out and clarify quantitative and qualitative KPIs and indicators for DM performance in order to achieve a consensus on the use and measurement of these indicators.


title: BeepBeep: A high-accuracy acoustic-based system for ranging and localization using COTS devices

abstract: We present the design and implementation of BeepBeep, a high-accuracy acoustic-based system for ranging and localization. It is a pure software-based solution and uses the most basic set of commodity hardware -- a speaker, a microphone, and some form of interdevice communication. The ranging scheme works without any infrastructure and is applicable to sensor platforms and commercial-off-the-shelf mobile devices. It achieves high accuracy through three techniques: two-way sensing, self-recording, and sample counting. We further devise a scalable and fast localization scheme. Our experiments show that up to one-centimeter ranging accuracy and three-centimeter localization accuracy can be achieved.