title: License Plate Recognition From Still Images and Video Sequences: A Survey

abstract: License plate recognition (LPR) algorithms in images or videos are generally composed of the following three processing steps: 1) extraction of a license plate region; 2) segmentation of the plate characters; and 3) recognition of each character. This task is quite challenging due to the diversity of plate formats and the nonuniform outdoor illumination conditions during image acquisition. Therefore, most approaches work only under restricted conditions such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. Numerous techniques have been developed for LPR in still images or video sequences, and the purpose of this paper is to categorize and assess them. Issues such as processing time, computational power, and recognition rate are also addressed, when available. Finally, this paper offers to researchers a link to a public image database to define a common reference point for LPR algorithmic assessment.


title: Novel Method for Border Irregularity Assessment in Dermoscopic Color Images

abstract: Background. One of the most important lesion features predicting malignancy is border irregularity. Accurate assessment of irregular borders is clinically important due to significantly different occurrence in benign and malignant skin lesions. Method. In this research, we present a new approach for the detection of border irregularities, as one of the major parameters in a widely used diagnostic algorithm the ABCD rule of dermoscopy. The proposed work is focused on designing an efficient automatic algorithm containing the following steps: image enhancement, lesion segmentation, borderline calculation, and irregularities detection. The challenge lies in determining the exact borderline. For solving this problem we have implemented a new method based on lesion rotation and borderline division. Results. The algorithm has been tested on 350 dermoscopic images and achieved accuracy of 92% indicating that the proposed computational approach captured most of the irregularities and provides reliable information for effective skin mole examination. Compared to the state of the art, we obtained improved classification results. Conclusions. The current study suggests that computer-aided system is a practical tool for dermoscopic image assessment and could be recommended for both research and clinical applications. The proposed algorithm can be applied in different fields of medical image analysis including, for example, CT and MRI images.


title: Spin-Transfer Torque MRAM (STT-MRAM): Challenges and Prospects

abstract: Spin-transfer torque (STT) switching demonstrated in submicron sized magnetic tunnel junctions (MTJs) has stimulated considerable interest for developments of STT switched magnetic random access memory (STT-MRAM). Remarkable progress in STT switching with MgO MTJs and increasing interest in STTMRAM in semiconductor industry have been witnessed in recent years. This paper will present a review on the progress in the intrinsic switching current density reduction and STT-MRAM prototype chip demonstration. Challenges to overcome in order for STT-MRAM to be a mainstream memory technology in future technology nodes will be discussed. Finally, potential applications of STT-MRAM in embedded and standalone memory markets will be outlined.


title: The Spread of Alcohol Consumption Behavior in a Large Social Network

abstract: Context A person's alcohol use might mirror that of his or her social contacts. Contribution Using the same group of Framingham Heart Study participants who helped to define the associations between social networks and other health behaviors, the researchers found that alcohol use was similar among individuals in a social cluster. Furthermore, changes in a person's alcohol intake over time followed changes in the alcohol intake of their social contacts. Caution Alcohol use was self-reported, and the researchers did not have access to social contacts who were not participating in the study. Implication Changing alcohol use may require intervening with social groups as well as with individuals. The Editors Alcohol use is common in the United States. In 2002, 55% of adults reported having had at least 1 drink in the previous month, and the prevalence of past-month alcohol consumption was somewhat higher for men (62%) than for women (48%) (1). The lifetime prevalence of alcohol use disorders has been measured at 14.6% (1). Excessive alcohol use, either in the form of heavy drinking or binge drinking, increases the risk for numerous health and social problems (2, 3), and approximately 75000 deaths in 2001 were attributable to excessive alcohol use, which makes it the third-leading lifestyle-related cause of death (3). Alcohol consumption behavior has many determinants. Previous studies (3, 4) suggest that biological factors have a significant effect on the progression from experimentation to regular use and that social and cultural factors play a critical role in experimentation with alcohol and the development of drinking patterns over time. Given the social nature of this behavior, it is not surprising that previous work has identified interactions with friends and family members as key factors (48). Although this literature primarily focused on cross-sectional panels, some studies (68) have attempted to test whether social influences act over time. These studies, which focused on peer influence among college students, showed inconsistent results and tended to focus just on pairs of connected persons. The study of social influences on behavior has expanded in recent years to the study of networks of linked individuals over time (9). Recent work in this area has shown that various health-related phenomena, ranging from sexually transmitted diseases to obesity, smoking, and even suicide, may travel along and within social networks (1015). Using a longitudinal, dynamic network of 12067 persons, we analyzed the role of social networks in alcohol use, focusing on 1) whether clusters of heavy drinkers and abstainers existed within the network; 2) whether a person's alcohol consumption behavior was associated with that of his or her social contacts; 3) the extent to which such associations depended on the nature and direction of the social ties (for example, friends of different kinds, siblings, spouses, coworkers, or neighbors); and 4) whether gender affected the spread of alcohol consumption across social ties. Methods Source Data We used data from participants in the Framingham Heart Study (FHS). The FHS is a population-based, longitudinal, observational cohort study that was initiated in 1948 to prospectively investigate risk factors for cardiovascular disease. Four cohorts, who mostly represent different generations linked to an original cohort, are included in the entire FHS. Participant data, collected every 2 to 4 years, includes physical examinations, laboratory tests, noninvasive cardiac and vascular testing, battery testing (such as the Mini-Mental State Examination), questionnaire results, and basic demographic information. For our analyses, we aligned the examination waves for the original cohort with those of the second-generation offspring cohort, which allowed us to treat all participants as having been examined in 7 waves. The offspring cohort, initiated in 1971, is the source of our study's principals, or focal individuals in the network (16). However, we included other FHS participants whom the principals listed as social contacts and refer to them here as contacts. Therefore, even though principals come only from the offspring cohort, contacts are drawn from the entire set of both the original and offspring cohorts. To ascertain social network ties, we created a separate data set that linked individuals through self-described social ties, collected in each of the 7 waves of the study. We could then detect relationships between participants (for example, spouse, sibling, friend, coworker, or neighbor) and observe changes in these ties across time. Either party to a link between 2 people might identify his or her link to the other. This is most relevant to the friend link, which could exist if A nominated B or B nominated A as a friend. We also used complete records of participants' and their contacts' address in each wave since 1971 in our analyses, although we have no information about relationships that participants did not report. For each wave, we could determine who is whose neighbor and the geographic distance between persons (10, 17). Table 1 provides descriptive statistics for the 5124 principals in our sample. Table 1. Summary Statistics for Principals Measures Alcohol consumption was self-reported in all studied waves, with participants reporting their average number of drinks per week over the past year as well as the number of days within the past week during which they consumed alcohol (beer, wine, and liquor). Self-reported data are generally considered a valid and reliable source when assessing alcohol consumption, although recall measures, such as those used in this study, can be subject to recall bias from participants (18). We treated alcohol consumption as a continuous variable in some analyses (for example, number of drinks per day, calculated from participant responses) but conducted others with dichotomous cut-points, defining heavy drinkers as those who averaged more than 1 (for women) or 2 (for men) drinks per day; moderate drinkers as those whose alcohol consumption was less than the cutoff values for heavy drinkers; and abstainers as those who reported no alcohol consumption. We did not use self-reported number of days drinking in the past week as a measure in and of itself but rather as a means to calculate average number of drinks in a day. (These labels do not reflect clinical definitions of alcohol abuse or dependence.) Table 2 shows averages for the study population across time, including age, alcohol consumption, and percentages of abstainers and drinkers. Although the differences in how we measured heavy drinking made it difficult to compare our results with those for other population samples, the other averages for the mean-age groups in each year of the given waves are roughly similar to national averages of alcohol consumption behavior (1, 19, 20). Table 2. Average Age and Alcohol Consumption Behavior, by Examination Statistical Analysis Our first goal was to evaluate whether a person's alcohol consumption behavior was associated with that of his or her social network ties at various degrees of separation. To test this hypothesis, we took an observed clustering of persons (and their alcohol consumption behavior) within the whole network and compared them with 1000 simulated networks with the same topology and overall prevalence of drinking as the observed network, but with the incidence of drinking (for example, at least 1 drink per day) randomly distributed across the nodes (random drinking networks). If clustering occurs in drinking behavior, then the probability that a contact is a drinker given that a principal is a drinker should be higher in the observed network than in the random drinking networks (21). We used the KamadaKawai algorithm, which iteratively repositions nodes to reduce the number of ties that cross each other, to draw the networks (22). Our second goal was to examine the possible determinants of any clustering in alcohol consumption behavior. We considered 3 explanations for nonrandom clustering of alcohol consumption behavior in the network: principals might choose to associate with like contacts (homophily) (23, 24); principals and contacts might share attributes or jointly experience unobserved contemporaneous events that cause their alcohol consumption behavior to covary (omitted variables or confounding); and contacts might exert social influence or peer effects on principals (induction). The availability of dynamic, longitudinal data on both network connections and drinking behavior allowed us to distinguish between interpersonal induction of drinking and homophily (25). Our basic statistical approach involved specifying longitudinal logistic regression models in which a principal's drinking status at time t + 1 is a function of his or her various attributes, such as age, sex, and education; his or her drinking status at time t; and the drinking status of his or her contacts at times t and t + 1 (25). We used generalized estimating equation procedures to account for multiple observations of the same principal across both waves and principalcontact pairings (26). We assumed an independent working correlation structure for the clusters (27). By using a time-lagged dependent variable (lagged to the previous examination) for alcohol consumption, we eliminated serial correlation in the errors (28) (evaluated with a Lagrange multiplier test) and substantially controlled for the principal's genetic endowment and any intrinsic, stable predilection to drink. In addition, the lagged independent variable for a contact's drinking status substantially controlled for homophily (25, 29). The key variable of interest is a contact's alcohol consumption behavior at time t + 1. A significant coefficient on this variable would suggest either that a contact's drinking affects a principal's drinking or that a principal and a contact experience contemporaneous


Auditing Black-Box Models for Indirect Influence


The Solution Path of the Generalized Lasso


title: MPRA Munich Personal RePEc Archive Credit scoring with boosted decision trees

abstract: The enormous growth experienced by the credit industry has led researchers to develop sophisticated credit scoring models that help lenders decide whether to grant or reject credit to applicants. This paper proposes a credit scoring model based on boosted decision trees, a powerful learning technique that aggregates several decision trees to form a classifier given by a weighted majority vote of classifications predicted by individual decision trees. The performance of boosted decision trees is evaluated using two publicly available credit card application datasets. The prediction accuracy of boosted decision trees is benchmarked against two alternative data mining techniques: the multilayer perceptron and support vector machines. The results show that boosted decision trees are a competitive technique for implementing credit scoring models.


title: Manifesting the Cyborg through Techno-Body Modification: From Human-Computer Interaction to Integration

abstract: A community of DIY cyborgs has emerged, known as 'grinders', who practice techno-body modification-the embedding of computing technology into the body. This paper reports on an ethnographic study following GrinderTech, an organization working to design, build and sell these technological artifacts, as it shifts from hacker collective to biotech startup. As technologies are embedded in the body, the boundary between human and machine starts to blur. We find that GrinderTech members, through the design and making of technologies for embedding, do so as a means to move beyond social and gendered binary constructions-or, societal norms that are practiced and performed, and re-enforced through language, as a way of creating power differentials in society, e.g. citizen/scientist and man/woman. Moreover, their motivations for designing and making these devices reflects their desire to re-imagine society. Finally, we re-conceptualize Human-Computer Interaction to include Integration-when technology is embedded in the human body-and discuss the theoretical and design implications of human-computer integration.


title: Toward Comprehensible Software Fault Prediction Models Using Bayesian Network Classifiers

abstract: Software testing is a crucial activity during software development and fault prediction models assist practitioners herein by providing an upfront identification of faulty software code by drawing upon the machine learning literature. While especially the Naive Bayes classifier is often applied in this regard, citing predictive performance and comprehensibility as its major strengths, a number of alternative Bayesian algorithms that boost the possibility of constructing simpler networks with fewer nodes and arcs remain unexplored. This study contributes to the literature by considering 15 different Bayesian Network (BN) classifiers and comparing them to other popular machine learning techniques. Furthermore, the applicability of the Markov blanket principle for feature selection, which is a natural extension to BN theory, is investigated. The results, both in terms of the AUC and the recently introduced H-measure, are rigorously tested using the statistical framework of Demsar. It is concluded that simple and comprehensible networks with less nodes can be constructed using BN classifiers other than the Naive Bayes classifier. Furthermore, it is found that the aspects of comprehensibility and predictive performance need to be balanced out, and also the development context is an item which should be taken into account during model selection.


title: Technical assessment of the CLEERGAS moving grate-based process for energy generation from municipal solid waste

abstract: A technical analysis has been completed for a commercial-scale two-stage gasification-combustion system. The CLEERGAS (Covanta Low Emissions Energy Recovery GASification) process consists of partial combustion and gasification of as-received municipal solid waste (MSW) on a moving grate producing syngas followed by full combustion of the generated syngas in an adjoining chamber and boiler. This process has been in operation since 2009 on a modified 330-tonne day(-1) waste-to-energy (WTE) line in Tulsa, Oklahoma. Material balances determined that the syngas composition is 12.8% H2 and 11.4% CO, the heating value of the gas in the gasifier section is 4098 kJ Nm(-3), and an aggregate molecular formula for the waste is C6H14.5O5. The analysis of gas measurements sampled from the Tulsa unit showed that the gasification-combustion mode fully processed the MSW at an excess air input of only 20% as compared to the 80-100% typically found in conventional WTE moving grate plants. Other important attributes of the CLEERGAS gasification-combustion process are that it has operated on a commercial scale for a period of over two years with 93% availability and utilizes a moving grate technology that is currently used in hundreds of WTE plants around the world.


Leveraging “ big data ” to optimize digital marketing


title: Contingency planning over probabilistic hybrid obstacle predictions for autonomous road vehicles

abstract: This paper presents a novel optimization based path planner that can simultaneously plan multiple contingency paths to account for the uncertain actions of dynamic obstacles. This planner addresses the particular problem of collision avoidance for autonomous road vehicles which are required to safely interact with other vehicles with unknown intentions. The presented path planner utilizes an efficient spline based trajectory representation and fast but accurate collision probability approximations to enable the simultaneous optimization of multiple contingency paths.


title: GSHADE: faster privacy-preserving distance computation and biometric identification

abstract: At WAHC'13, Bringer et al. introduced a protocol called SHADE for secure and efficient Hamming distance computation using oblivious transfer only. In this paper, we introduce a generalization of the SHADE protocol, called GSHADE, that enables privacy-preserving computation of several distance metrics, including (normalized) Hamming distance, Euclidean distance, Mahalanobis distance, and scalar product. GSHADE can be used to efficiently compute one-to-many biometric identification for several traits (iris, face, fingerprint) and benefits from recent optimizations of oblivious transfer extensions. GSHADE allows identification against a database of 1000 Eigenfaces in 1.28 seconds and against a database of 10000 IrisCodes in 17.2 seconds which is more than 10 times faster than previous works.


VIKOR method using interval type two fuzzy sets


title: The “Facebook-self”: characteristics and psychological predictors of false self-presentation on Facebook

abstract: In this study we present and empirically examine a new phenomenon related to social networking sites, such as Facebook, the “false Facebook-self”. Arguably false self-presentation on Facebook is a growing phenomenon, and in extreme cases; i.e., when ones Facebook image substantially deviates from their true image, it may serve as a gateway behavior to more problematic behaviors which may lead to psychological problems and even pathologies. In this study we show that certain users are more vulnerable to such false self-presentation than others. The study involved 258 Facebook users. Applying ANOVA and SEM analyses we show that users with low self-esteem and low trait authenticity are more likely than others to present a Facebook-self which deviates from their true selves. These social-interaction-related traits are influenced by one’s upbringing and the anxious and avoidant attachment styles he or she has developed. Several cases (7.5%) with large gaps between the true and false Facebook-self were detected, which implies that future research should consider the adverse consequences and treatments of high levels of false Facebook-self.


Learning Deep Representations of Time Series Supervisor :


title: Who puts the best "face" forward on Facebook?: Positive self-presentation in online social networking and the role of self-consciousness, actual-to-total Friends ratio, and culture

abstract: We examined possible predictors of positive self-presentation (PSP) on Facebook.Data were collected from college-age Facebook users in the U.S. and South Korea.U.S. Facebook users engaged in PSP to a greater extent than South Korean users.Culture moderated the relationship between public self-consciousness and PSP.Culture moderated the relationship between actual-to-total Friends ratio and PSP. The present research investigated how individual, interpersonal, and cultural variables influence positive self-presentation in online social networking. In particular, we examined the role of self-consciousness, actual-to-total Friends ratio, and culture in positive self-presentation on Facebook. A cross-sectional survey was conducted with college-age participants in the United States (n=183) and South Korea (n=137). Results showed that self-consciousness (public vs. private) and actual-to-total Friends ratio were not significantly associated with positive self-presentation on Facebook; however, culture showed a statistically significant association with positive self-presentation on Facebook, with the U.S. participants engaging in positive self-presentation on Facebook to a greater extent than the South Korean participants. More interestingly, culture significantly moderated the relationship between public self-consciousness and positive self-presentation as well as the relationship between actual-to-total Friends ratio and positive self-presentation. Specifically, positive self-presentation showed a significant positive association with public self-consciousness and a significant negative association with actual-to-total Friends ratio only among the South Korean participants and not among the U.S. participants. Theoretical and practical implications for understanding cross-cultural differences in self-presentation behaviors on social network sites were discussed.


title: Improved solvers for bounded-suboptimal multi-agent path finding

abstract: Multi-Agent Path Finding (MAPF) with the objective to minimize the sum of the travel times of the agents along their paths is a hard combinatorial problem. Recent work has shown that bounded-suboptimal MAPF solvers, such as Enhanced Conflict-Based Search or ECBS(w1) for short, run often faster than optimal MAPF solvers at the cost of incurring a suboptimality factor w1, that is due to using focal search. Other recent work has used experience graphs to guide the search of ECBS(w1) and speed it up, at the cost of incurring a separate suboptimality factor w2, that is due to inflating the heuristic values. Thus, the combination has suboptimality factor w1w2. In this first feasibility study, we develop a bounded-suboptimal MAPF solver, Improved-ECBS or iECBS(w1) for short, that has suboptimality factor w1 rather than w1w2 (because it uses experience graphs to guide its search without inflating the heuristic values) and can run faster than ECBS(w1). We also develop two first approaches for automatically generating experience graphs for a given MAPF instance. Finally, we observe heavy-tailed behavior in the runtimes of these MAPF solvers and develop a simple rapid randomized restart strategy that can increase the success rate of iECBS(w1) within a given runtime limit.


title: Faster Secure Two-Party Computation Using Garbled Circuits

abstract: Secure two-party computation enables two parties to evaluate a function cooperatively without revealing to either party anything beyond the function’s output. The garbled-circuit technique, a generic approach to secure two-party computation for semi-honest participants, was developed by Yao in the 1980s, but has been viewed as being of limited practical significance due to its inefficiency. We demonstrate several techniques for improving the running time and memory requirements of the garbled-circuit technique, resulting in an implementation of generic secure two-party computation that is significantly faster than any previously reported while also scaling to arbitrarily large circuits. We validate our approach by demonstrating secure computation of circuits with over 10 9 gates at a rate of roughly 10 ms per garbled gate, and showing order-of-magnitude improvements over the best previous privacy-preserving protocols for computing Hamming distance, Levenshtein distance, Smith-Waterman genome alignment, and AES.


title: Sexing skulls using discriminant function analysis of visually assessed traits

abstract: The accuracy of sex determinations based on visual assessments of the mental eminence, orbital margin, glabellar area, nuchal area, and mastoid process was tested on a series of 304 skulls of known age and sex from people of European American, African American, and English ancestry as well as on an ancient Native American sample of 156 individuals whose sex could be reliably determined based on pelvic morphology. Ordinal scores of these sexually dimorphic traits were used to compute sex determination discriminant functions. Linear, kth-nearest-neighbor, logistic, and quadratic discriminant analysis models were evaluated based on their capacity to minimize both misclassifications and sex biases in classification errors. Logistic regression discriminant analysis produced the best results: a logistic model containing all five cranial trait scores correctly classified 88% of the modern skulls with a negligible sex bias of 0.1%. Adding age at death, birth year, and population affinity to the model did not appreciably improve its performance. For the ancient Native American sample, the best logistic regression model assigned the correct pelvic sex to 78% of the individuals with a sex bias of only 0.2%. Similar cranial trait frequency distributions were found in same-sex comparisons of the modern African American, European American, and English samples. The sexual dimorphism of these modern people contrasts markedly with that of the ancient Native Americans. Because of such population differences, discriminant functions like those presented in this paper should be used with caution on populations other than those for which they were developed. Am J Phys Anthropol, 2008. © 2008 Wiley-Liss, Inc.


title: Effective implementation of AES-XTS on FPGA

abstract: This paper proposes an effective FPGA implementation for data storage encryption. Throughput, area and power consumption are the most important parameters to evaluate any FPGA implemented design. Our proposed design not only achieves a high throughput but also a considerable amount of throughput/area that is better compared to current implementations. The proposed implementation gives a memory based pipelined architecture. The design achieves a throughput of 5.25 Gb/sec that is relatively better than any other FPGA implementation to date. Xilinx ISE 10.1 is used as a design tool and Verilog HDL is used to code the design.


title: Modeling Citizen Satisfaction with Mandatory Adoption of an E-Government Technology

abstract: While technology adoption is a major stream of research in information systems, few studies have examined the antecedents and consequences of mandatory adoption of technologies. To address this gap, we develop and test a model of mandatory citizen adoption of an e-government technology. Based on a framework that outlines the key stages associated with the launch of technology products, we identify various external factors as antecedents of four key technology adoption variables from the unified theory of acceptance and use of technology (UTAUT), i.e., performance expectancy, effort expectancy, social influence, and facilitating conditions, which ultimately impact citizen satisfaction. The four stages of technology launch and the salient antecedents in each stage are: market preparation stage - awareness; targeting stage - compatibility and self-efficacy; positioning stage - flexibility and avoidance of personal interaction; and execution stage - trust, convenience, and assistance. We test our model in a two-stage survey of 1,179 Hong Kong citizens, before and after they were issued a mandatory smart card to access e-government services. We find that the various factors tied to the different stages in launching the technology predict key technology adoption variables that, in turn, predict citizen satisfaction with e-government technology. We discuss the theoretical and practical implications for governments implementing technologies whose use by citizens is mandated.


title: SPARK : Adapting Keyword Query to Semantic Search

abstract: Semantic search promises to provide more accurate result than present-day keyword search. However, progress with semantic search has been delayed due to the complexity of its query languages. In this paper, we explore a novel approach of adapting keywords to querying the semantic web: the approach automatically translates keyword queries into formal logic queries so that end users can use familiar keywords to perform semantic search. A prototype system named 'SPARK' has been implemented in light of this approach. Given a keyword query, SPARK outputs a ranked list of SPARQL queries as the translation result. The translation in SPARK consists of three major steps: term mapping, query graph construction and query ranking. Specifically, a probabilistic query ranking model is proposed to select the most likely SPARQL query. In the experiment, SPARK achieved an encouraging translation result.


title: Learning compact neural word embeddings by parameter space sharing

abstract: The word embedding vectors obtained from neural word embedding methods, such as vLBL models and SkipGram, have become an important fundamental resource for tackling a wide variety of tasks in the artificial intelligence field. This paper focuses on the fact that the model size of high-quality embedding vectors is relatively large, i.e., more than 1GB. We propose a learning framework that can provide a set of 'compact' embedding vectors for the purpose of enhancing 'usability' in actual applications. Our proposed method incorporates parameter sharing constraints into the optimization problem. These additional constraints force the embedding vectors to share parameter values, which significantly shrinks model size. We investigate the trade-off between quality and model size of embedding vectors for several linguistic benchmark datasets, and show that our method can significantly reduce the model size while maintaining the task performance of conventional methods.


Context-Aware Ranking with Factorization Models


title: Copy-move forgery detection based on deep learning

abstract: Copy-move forgery detection (CMFD) is probably one of the most active research areas within the blind image forensics field. Among existing algorithms, most of them are based on block and key-point methods, or combination of them. Recently, some deep convolutional neural networks methods have been applied in the image classification, image forensic, image hashing retrieval, and so on, which have shown better performance than the traditional method. In the work, a novel copy-move forgery detection method based on convolutional neural network is proposed. The proposed method uses existing trained model from large database as ImageNet, and then adjusts slightly the net structure using small training samples. Experimental results show that the method we proposed obtains satisfactory performance to the forgery image generated automatically by computer with simple image copy-move operation


title: A Speed Hump Sensing Approach to Global Positioning in Urban Cities without Gps Signals

abstract: Outdoor localization is of great importance for driving navigation, attracting many research efforts in past decades. Prevailing GPS achieves meter-level localization accuracy under general outdoor conditions. Yet, GPS service performs poorly in urban canyons where skyscrapers blocks GPS signals and drain mobile phone battery quickly within few hours. In this work, we exploit common city facilities, i.e. speed humps, as an indicator for vehicle location. The key insight is that when the vehicle passes through the speed bump, it experiences significant fluctuations, causing larger acceleration in the vertical direction. On this basis, we design a localization scheme that utilizes the accelerator equipped on modern smart phones to track sequence of speed bumps, which is further transferred into sequence of moving directions of the vehicle, and adopt effective road mapping technology to derive real-time location. As we have concerned, it is the first attempt to exploit the spatiotemporal characteristics generated by the speed humps to recover the trajectory of the travelling route and infer the current position. Experimental results in typical outdoor environment (campus) demonstrate a comparable performance with GPS method, yet achieve lower energy consumption.


title: Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks

abstract: Offline handwriting recognition—the automatic transcription of images of handwritten text—is a challenging task that combines computer vision with sequence learning. In most systems the two elements are handled separately, with sophisticated preprocessing techniques used to extract the image features and sequential models such as HMMs used to provide the transcriptions. By combining two recent innovations in neural networks—multidimensional recurrent neural networks and connectionist temporal classification—this paper introduces a globally trained offline handwriting recogniser that takes raw pixel data as input. Unlike competing systems, it does not require any alphabet specific preprocessing, and can therefore be used unchanged for any language. Evidence of its generality and power is provided by data from a recent international Arabic recognition competition, where it outperformed all entries (91.4% accuracy compared to 87.2% for the competition winner) despite the fact that neither author understands a word of Arabic.


A Comparison of the Heat Transfer Performance of a Hexagonal Pin Fin with Other Types of Pin Fin Heat Sinks


title: Target cells for HIV in the central nervous system: macrophages or glial cells?

abstract: Abstract Infection of foetal or embryonic brain cells and cell lines from human astrocytomas and gliomas with HIV1 derived from T-lymphoma cultures leads to the expression of HIV in about 1 to 2% of the cells in culture. Single-cell cloning of astrocytoma cells shortly after infection resulted in the establishment of persistently HIV1-infected cell lines. These cultures were characterized by low production of virus and moderate intra- and extracellular expression of structural proteins. However, high expression of the nef regulatory protein was found. The virus could be rescued by cocultivation with T cells and primary macrophages giving rise to typical syncytia formation. In contrast to infection with HIV-infected T-lymphoma lines, cocultivation with HIV1-infected primary macrophages or monocytic cell lines induced a reduction in the growth of astrocytes and failed to induce productive infection. These in vitro observations support the hypothesis that astrocytes and glial cells may be a reservoir for HIV in the central nervous system and that macrophages may not carry the virus to the brain, but rather may be infected in the brain after having penetrated the blood-brain barrier.


title: Data Gathering for Gesture Recognition Systems Based on Single Color-, Stereo Color- and Thermal Cameras

abstract: In this paper, we present our results to build an automatic gesture recognition system using different types of cameras to compare them in reference to their features for segmentation. Normally, the images of a mono color camera system are mostly used as input data in the research area of gesture recognition. In comparison to that, the analysis results of a stereo color camera and a thermal camera system are used to determine the advantages and disadvantages of these camera systems. With this basics, a real-time gesture recognition system is build to classify alphabets (A-Z) and numbers (0-9) with an average recognition rate of 98% using Hidden Markov Models (HMM).


Coded strobing photography: Compressive sensing of high speed periodic videos


Neural Content-Collaborative Filtering for News Recommendation


title: Towards an ontology of data mining investigations

abstract: Motivated by the need for unification of the domain of data mining and the demand for formalized representation of outcomes of data mining investigations, we address the task of constructing an ontology of data mining. In this paper we present an updated version of the OntoDM ontology, that is based on a recent proposal of a general framework for data mining and it is aligned with the ontology of biomedical investigations (OBI) . The ontology aims at describing and formalizing entities from the domain of data mining and knowledge discovery. It includes definitions of basic data mining entities (e.g., datatype, dataset, data mining task, data mining algorithm etc.) and allows extensions with more complex data mining entities (e.g. constraints, data mining scenarios and data mining experiments). Unlike most existing approaches to constructing ontologies of data mining, OntoDM is compliant to best practices in engineering ontologies that describe scientific investigations (e.g., OBI ) and is a step towards an ontology of data mining investigations. OntoDM is available at: http://kt.ijs.si/panovp/OntoDM/ .


title: Zero moment point manipulability ellipsoid

abstract: In this paper, we propose zero moment point (ZMP) manipulability ellipsoid as an extension to the existing ZMP balance criterion. The ZMP manipulability ellipsoid was developed by combining the ZMP balance criterion, the humanoid robot dynamics and the manipulability of robotic manipulators. The ZMP manipulability ellipsoid represents the ability of a humanoid robot to instantly move the ZMP from its current ZMP location within the balance criterion. The size and shape of the ZMP manipulability ellipsoid are a function of the joint-torque limitation of a humanoid robot. Thus, the ellipsoid forms an area in which the ZMP can be manipulated instantly, and the larger the ellipsoid, the better the propensity of a humanoid robot will be to recover from an unbalanced situation. We also show that the gravity force of a humanoid robot affects neither the shape nor the translation of the ellipsoid. Furthermore, we show that the ZMP manipulability ellipsoid can aid the humanoid-robot design process by choosing appropriate actuators. Finally, a four degree-of-freedom walking robot was used to illustrate the proposed ZMP manipulability ellipsoid


title: Investigation of electrochemical performance on carbon supported tin-selenium bimetallic anodes in lithium-ion batteries

abstract: Abstract Bimetallic compound, composed of two different metal elements, has emerged as an important class of electrode system. Amorphous carbon materials are widely used in anodes to reduce the internal resistance of electrodes. Therefore, SnSe bimetallic compound uniformly dispersed in acetylene black as a carbon-support has been fabricated for lithium ion batteries by high energy mechanical milling (HEMM) process under argon atmosphere. The SnSe-C composite retains a reversible capacity of 564 mAh g −1 with a coulombic efficiency of 99.8%, at a current rate of 100 mA g −1 after 50 cycles. In the high rate capability test, the SnSe-C composite exhibits the charge capacity of 530 mAh g −1 at 5000 mA g −1 charge rate. Electrochemical impedance spectroscopy (EIS) results indicate that SnSe-C composite shows small increase of surface resistance than that of plain SnSe composite. The enhanced cycle stability of SnSe-C composite can be attributed to the amorphous carbon additive that offers high electrical conductivity as well as a buffer matrix that prevents the volume change during cycling.


title: Rethinking ferrule – a new approach to an old dilemma

abstract: The ‘ferrule effect’ is a long standing, accepted concept in dentistry that is a foundation principle for the restoration of teeth that have suffered advanced structure loss. A review of the literature based on a search in PubMed was performed looking at the various components of the ferrule effect, with particular attention to some of the less explored dimensions that infl uence the effectiveness of the ferrule when restoring severely broken down teeth. These include the width of the ferrule, the effect of a partial ferrule, the infl uence of both, the type of the restored tooth and the lateral loads present as well as the well established 2 mm ferrule height rule. The literature was collaborated and a classifi cation based on risk assessment was derived from the available evidence. The system categorises teeth according to the effectiveness of ferrule effect that can be achieved based on the remaining amount of sound tooth structure. Furthermore, risk assessment for failure can be performed so that the practitioner and patient can better understand the prognosis of restoring a particular tooth. Clinical recommendations were extrapolated and presented as guidelines so as to improve the predictability and outcome of treatment when restoring structurally compromised teeth. The evidence relating to restoring the endodontic treated tooth with extensive destruction is defi cient. This article aims to rethink ferrule by looking at other aspects of this accepted concept, and proposes a paradigm shift in the way it is thought of and utilised.


title: Design of high PSRR folded cascode operational amplifier for LDO applications

abstract: This paper presents a novel CMOS folded cascode operational amplifier that leads to high PSRR and provides gain nearly equal to that of a two stage op-amp. The proposed design is implemented in GPDK 0.18μm CMOS technology. This op-amp uses a folded cascode structure in the output stage combined with the differential amplifier having PMOS input transistors to achieve good input common mode range and lower flicker noise. It has an important feature that it allows the input common mode level close to supply voltage. The proposed topology improves the PSRR of op-amp which can be used for LDO applications. Simulations using Cadence under 1.8 V show a DC gain of 72.0404 dB and a phase margin of 62.4636 degree at a unity gain bandwidth of 13.33 MHz with the power consumption smaller than 0.13 mW along with a PSRR of 72.0966 dB. The layout of the design shows that the area acquired on the chip is approximately equal to 8897.27 μm2.


title: Robust Thruster Fault Diagnosis: Application to the rendezvous phase of the Mars Sample Return mission

abstract: This paper addresses robust fault diagnosis of the chaser's thrusters used for the rendezvous phase of the Mars Sample Return (MSR) mission. The MSR mission is a future exploration mission undertaken jointly by the National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA). The goal is to return tangible samples from Mars atmosphere and ground to Earth for analysis. A residual-based scheme is proposed that is robust against the presence of unknown time-varying delays induced by the thruster modulator unit. The proposed fault diagnosis design is based on Eigenstructure Assignment (EA) and first-order Pade approximation. The resulted method is able to detect quickly any kind of thruster faults and to isolate them using a cross-correlation based test. Simulation results from the MSR "high-fidelity" industrial simulator, provided by Thales Alenia Space, demonstrate that the proposed method is able to detect and isolate some thruster faults in a reasonable time, despite of delays in the thruster modulator unit, inaccurate navigation unit, and spatial disturbances (i.e. J2 gravitational perturbation, atmospheric drag, and solar radiation pressure).


title: Low-light video image enhancement based on multiscale Retinex-like algorithm

abstract: The images taken in low-light environment always lose some details. Given that traditional multi-scale Retinex (MSR) algorithm always appears halo artifact in the edge area of the image, a multiscale Retinex-like is put forward for low-light video image enhancement. The proposed algorithm is operated in HSI color space. For Intensity layer, three Gaussian filters in traditional MSR algorithm are replaced by three guided filters to extract illumination component, then gets the reflection component, the sum of this two components as the new Intensity layer. For the Saturation layer, Gamma correction function is used to enhance Saturation, then the new HSI image is converted to RGB image. Four kinds of numerical methods are adopted to evaluate the images enhanced by different methods. The experimental results show that the proposed algorithm in terms of low-light image, not only makes the picture becomes clearer, but also extracts more image details. Furthermore the algorithm is more efficient.


title: HANDEXOS: Towards an exoskeleton device for the rehabilitation of the hand

abstract: This paper introduces a novel exoskeleton device (HANDEXOS) for the rehabilitation of the hand for post-stroke patients. The nature of the impaired hand can be summarized in a limited extension, abduction and adduction leaving the fingers in a flexed position, so the exoskeleton goal is to train a safe extension motion from the typical closed position of the impaired hand.


title: Design of a simplified compliant anthropomorphic robot hand

abstract: This paper introduces the SCCA Hand, a five motor robot hand developed using a minimalist design approach that focused on grasping abilities in real-world environments. Notable features include a novel bidirectional tendon underactuated finger design that biases actuator force toward finger flexion, an additive manufactured monocoque steel construction that enabled enhanced feature density and a low parts count, low grasp impedance by means of series elastic actuation, shock absorption for protection from impacts, and anthropomorphic speed, strength, and size. In addition to a description of its design, experimental characterizations of speed and force capabilities as well as demonstrated achievement of eight canonical grasps and postures are provided.


title: Re$^3$: Re al-Time Recurrent Regression Networks for Visual Tracking of Generic Objects

abstract: Robust object tracking requires knowledge and understanding of the object being tracked: its appearance, its motion, and how it changes over time. A tracker must be able to modify its underlying model and adapt to new observations. We present Re3, a real-time deep object tracker capable of incorporating temporal information into its model. Rather than focusing on a limited set of objects or training a model at test-time to track a specific instance, we pretrain our generic tracker on a large variety of objects and efficiently update on the fly; Re3 simultaneously tracks and updates the appearance model with a single forward pass. This lightweight model is capable of tracking objects at 150 FPS, while attaining competitive results on challenging benchmarks. We also show that our method handles temporary occlusion better than other comparable trackers using experiments that directly measure performance on sequences with occlusion.


title: Treacher Collins syndrome: clinical implications for the paediatrician—a new mutation in a severely affected newborn and comparison with three further patients with the same mutation, and review of the literature

abstract: UNLABELLED ::: Treacher Collins syndrome (TCS) is the most common and well-known mandibulofacial dysostosis caused by mutations in at least three genes involved in pre-rRNA transcription, the TCOF1, POLR1D and POLR1C genes. We present a severely affected male individual with TCS with a heterozygous de novo frameshift mutation within the TCOF1 gene (c.790_791delAG,p.Ser264GlnfsX7) and compare the clinical findings with three previously unpublished, milder affected individuals from two families with the same mutation. We elucidate typical clinical features of TCS and its clinical implications for the paediatrician and mandibulofacial surgeon, especially in severely affected individuals and give a short review of the literature. ::: ::: ::: CONCLUSION ::: The clinical data of these three families illustrate that the phenotype associated with this specific mutation has a wide intra- and interfamilial variability, which confirms that variable expressivity in carriers of TCOF1 mutations is not a simple consequence of the mutation but might be modified by the combination of genetic, environmental and stochastic factors. Being such a highly complex disease treatment of individuals with TCS should be tailored to the specific needs of each individual, preferably by a multidisciplinary team consisting of paediatricians, craniofacial surgeons and geneticists.


title: An Active 20-MHz to 2.5-GHz UWB Receiver Antenna System Using a TEM Horn

abstract: An ultrawideband receiver antenna system is proposed. The radiating element is a transverse electromagnetic horn antenna, featuring a negative impedance converter at lower frequencies. The voltage standing-wave ratio (VSWR) is less than 2.0 in the bandwidth from 20 MHz to 2.5 GHz. A good agreement between the measurements of the fabricated system and the simulated results is observed. Sample applications include direction finding, broadband communication systems, radar systems, and electromagnetic compatibility measurement systems.


title: Ciphertext-only Cryptanalysis on Hardened Mifare Classic Cards

abstract: Despite a series of attacks, MIFARE Classic is still the world's most widely deployed contactless smartcard on the market. The Classic uses a proprietary stream cipher CRYPTO1 to provide confidentiality and mutual authentication between card and reader. However, once the cipher was reverse engineered, many serious vulnerabilities surfaced. A number of passive and active attacks were proposed that exploit these vulnerabilities. The most severe key recovery attacks only require wireless interaction with a card. System integrators consider such card-only attacks as one of the most serious threat vectors to their MIFARE Classic-based systems, since it allows the adversary to avoid camera detection, which is often present at an access control entrance or public transport gate. However, all card-only attacks proposed in the literature depend on implementation mistakes which can easily be mitigated without breaking backwards compatibility with the existing reader infrastructure. Consequently, many manufactures and system integrators started to deploy "fixed" MIFARE Classic cards which are resilient to such vulnerabilities. However, these countermeasures are rather palliating and inadequate for a cryptographically insecure cipher such as CRYPTO1. In support of this proposition, we present a novel cipher-text card-only attack that exploits a crucial and mandatory step in the authentication protocol and which solely depends on the cryptographic weaknesses of the CRYPTO1 cipher. Hence, in order to avoid this attack, all cards and readers should be upgraded to support an alternative authentication protocol which inherently breaks their backwards compatibility. Our attack requires only a few minutes of wireless interaction with the card, in an uncontrolled environment and can be performed with consumer-grade hardware. The information obtained allows an adversary to drop the computational complexity from 2^48 to approximately 2^30, which enabled us to practically recover a secret key from a hardened MIFARE Classic card in about 5 minutes on an single core consumer laptop.


title: Nature and origin of "squirting" in female sexuality.

abstract: INTRODUCTION ::: During sexual stimulation, some women report the discharge of a noticeable amount of fluid from the urethra, a phenomenon also called "squirting." To date, both the nature and the origin of squirting remain controversial. In this investigation, we not only analyzed the biochemical nature of the emitted fluid, but also explored the presence of any pelvic liquid collection that could result from sexual arousal and explain a massive fluid emission. ::: ::: ::: METHODS ::: Seven women, without gynecologic abnormalities and who reported recurrent and massive fluid emission during sexual stimulation, underwent provoked sexual arousal. Pelvic ultrasound scans were performed after voluntary urination (US1), and during sexual stimulation just before (US2) and after (US3) squirting. Urea, creatinine, uric acid, and prostatic-specific antigen (PSA) concentrations were assessed in urinary samples before sexual stimulation (BSU) and after squirting (ASU), and squirting sample itself (S). ::: ::: ::: RESULTS ::: In all participants, US1 confirmed thorough bladder emptiness. After a variable time of sexual excitation, US2 (just before squirting) showed noticeable bladder filling, and US3 (just after squirting) demonstrated that the bladder had been emptied again. Biochemical analysis of BSU, S, and ASU showed comparable urea, creatinine, and uric acid concentrations in all participants. Yet, whereas PSA was not detected in BSU in six out of seven participants, this antigen was present in S and ASU in five out of seven participants. ::: ::: ::: CONCLUSIONS ::: The present data based on ultrasonographic bladder monitoring and biochemical analyses indicate that squirting is essentially the involuntary emission of urine during sexual activity, although a marginal contribution of prostatic secretions to the emitted fluid often exists.


title: Thoughts on Computational Creativity

abstract: There is a close relationship between computational creativity in the arts and generative art. Prior work is summarized here as to how complexity theory can serve as a theoretical context for generative art. This is extended to show how complexity theory can also illuminate discussions regarding computational creativity. Another prior proposal is also summarized. It suggests that a world-view called “complexism” can reconcile the current differences between the modern culture of science and the postmodern culture of the humanities. This line is also extended to considerations regarding computational creativity. Particular attention is extended here to the issues of authorship, progress, and aesthetic measurement in computational creativity.


title: Feature selection for high-dimensional data

abstract: This book offers a coherent and comprehensive approach to feature subset selection in the scope of classification problems, explaining the foundations, real application problems and the challenges of feature selection for high-dimensional data. The authors first focus on the analysis and synthesis of feature selection algorithms, presenting a comprehensive review of basic concepts and experimental results of the most well-known algorithms. They then address different real scenarios with high-dimensional data, showing the use of feature selection algorithms in different contexts with different requirements and information: microarray data, intrusion detection, tear film lipid layer classification and cost-based features. The book then delves into the scenario of big dimension, paying attention to important problems under high-dimensional spaces, such as scalability, distributed processing and real-time processing, scenarios that open up new and interesting challenges for researchers. The book is useful for practitioners, researchers and graduate students in the areas of machine learning and data mining.


title: Relationship between Visual-Motor Integration and handwriting skills of children in Kindergarten: A Modified replication study

abstract: OBJECTIVE. The purpose of this study was to examine the relationship of performance on the Developmental Test of Visual-Motor Integration (VMI; Beery, 1997) to handwriting legibility in children attending kindergarten. The relationship of using lined versus unlined paper on letter legibility, based on a modified version of the Scale of Children's Readiness in Printing (Modified SCRIPT; Weil & Cunningham Amundson, 1994) was also investigated METHOD. Fifty-four typically developing kindergarten students were administered the VMI; 30 students completed the Modified SCRIPT with unlined paper. 24 students completed the Modified SCRIPT with lined paper. Students were assessed in the first quarter of the kindergarten school year and scores were analyzed using correlational and nonparametric statist cal measures. RESULTS. Strong positive relationshios were found between VMI assessment scores and student's ability to legibly copy letterforms. Students who could copy the first nine forms on the VMI performed significantly better than students who could not correctly copy the first nine VMI forms on both versions of the Modified SCRIPT. CONCLUSION. Visual-motor integration skills were shown to be related to the ability to copy letters legibly. These findings support the research of Well and Cunningham Amundson. Findings from this study also support the conclusion that there is no significant difference in letter writing legibility between students who use paper with or without lines.


title: Two Methods For Wild Variational Inference

abstract: Variational inference provides a powerful tool for approximate probabilistic inference on complex, structured models. Typical variational inference methods, however, require to use inference networks with computationally tractable probability density functions. This largely limits the design and implementation of variational inference methods. We consider wild variational inference methods that do not require tractable density functions on the inference networks, and hence can be applied in more challenging cases. As an example of application, we treat stochastic gradient Langevin dynamics (SGLD) as an inference network, and use our methods to automatically adjust the step sizes of SGLD to maximize its convergence speed, significantly outperforming the hand-designed step size schemes.


title: Interconnection switch on-off state identification of distribution networks based on load measurement

abstract: In this paper, two approaches of interconnection switch on-off state identification of medium voltage distribution network are proposed; one is using weighted least squares (WLS) to estimate the voltage variables of all nodes by measured values of end nodes under given possible topology models, and then using recursive Bayesian approach (RBA) to justify which topology model is the correct one; the second approach is using back-forward sweep (BFS) power flow algorithm to obtain the voltage variables of all nodes, then using the same RBP to identify the topology. The case study of IEEE 33 nodes distribution networks shows that both approaches have a high accuracy, but the BFS has a faster justification, fewer iterations, less computing time and simpler codes.


title: A mapping study on microservice architectures of Internet of Things and cloud computing solutions

abstract: Internet of Things is a fairly new paradigm adopted by the industry, which offers the connectivity, via wireless systems, of all the devices that surround us. One of the challenges of IoT relates to the required resources to store and compute the huge amount of data resulted from devices' connections. Cloud computing is a solution to the IoT challenges; it provides on-demand resources in an easy-to-access manner. Another trend in the enterprise world is the usage of microservice architectures. Being a newly developed paradigm, and although its principles are defined, it is difficult to have a vision of the existing microservice-based research solutions. This paper, through the mapping study methodology, provides an overview of the current state-of-the-art and -practice regarding the usage of microservice architectures by IoT and cloud computing solutions. More specifically, we synthesize the data from 364 selected studies and describe the research types, number of publications and their main venues.


title: Hybrid Trajectory Planning for Autonomous Driving in Highly Constrained Environments

abstract: In this paper, we introduce a novel and efficient hybrid trajectory planning method for autonomous driving in highly constrained environments. The contributions of this paper are fourfold. First, we present a trajectory planning framework that is able to handle geometry constraints, nonholonomic constraints, and dynamics constraints of cars in a humanlike and layered fashion and generate curvature-continuous, kinodynamically feasible, smooth, and collision-free trajectories in real time. Second, we present a derivative-free global path modification algorithm to extract high-order state information in free space for state sampling. Third, we extend the regular state-space sampling method widely used in on-road autonomous driving systems to a multi-phase deterministic state-space sampling method that is able to approximate complex maneuvers. Fourth, we improve collision checking accuracy and efficiency by using a different car footprint approximation strategy and a two-phase collision checking routine. A range of challenging simulation experiments show that the proposed method returns high-quality trajectories in real time and outperforms existing planners, such as hybrid A* and conjugate-gradient descent path smoother in terms of path quality, efficiency, and computation resources used.


title: Image Super-Resolution Using Very Deep Residual Channel Attention Networks

abstract: Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR). However, we observe that deeper networks for image SR are more difficult to train. The low-resolution inputs and features contain abundant low-frequency information, which is treated equally across channels, hence hindering the representational ability of CNNs. To solve these problems, we propose the very deep residual channel attention networks (RCAN). Specifically, we propose a residual in residual (RIR) structure to form very deep network, which consists of several residual groups with long skip connections. Each residual group contains some residual blocks with short skip connections. Meanwhile, RIR allows abundant low-frequency information to be bypassed through multiple skip connections, making the main network focus on learning high-frequency information. Furthermore, we propose a channel attention mechanism to adaptively rescale channel-wise features by considering interdependencies among channels. Extensive experiments show that our RCAN achieves better accuracy and visual improvements against state-of-the-art methods.


title: Automatic Transcription of Polyphonic Piano Music Using Genetic Algorithms, Adaptive Spectral Envelope Modeling, and Dynamic Noise Level Estimation

abstract: This paper presents a new method for multiple fundamental frequency (F0) estimation on piano recordings. We propose a framework based on a genetic algorithm in order to analyze the overlapping overtones and search for the most likely F0 combination. The search process is aided by adaptive spectral envelope modeling and dynamic noise level estimation: while the noise is dynamically estimated, the spectral envelope of previously recorded piano samples (internal database) is adapted in order to best match the piano played on the input signals and aid the search process for the most likely combination of F0s. For comparison, several state-of-the-art algorithms were run across various musical pieces played by different pianos and then compared using three different metrics. The proposed algorithm ranked first place on Hybrid Decay/Sustain Score metric, which has better correlation with the human hearing perception and ranked second place on both onset-only and onset–offset metrics. A previous genetic algorithm approach is also included in the comparison to show how the proposed system brings significant improvements on both quality of the results and computing time.


title: Calibration techniques for digital phased arrays

abstract: Several techniques for calibrating and aligning different aspects of a digital phased array are demonstrated using a 16-element, panelized, vertically-polarized S-band subarray with element-level digitization on both transmit and receive. These techniques make use of the element-level functionality not only to expedite the process of initial alignment and calibration but to enhance the ability of the array to maintain and enforce this calibration over time as RF/analog system variations occur. A method for maintaining element amplitude and phase over time in fully digitized arrays is demonstrated as well as a technique for element-level self-calibration of quadrature mismatches.


title: Software process modeling languages: A systematic literature review

abstract: Context: Organizations working in software development are aware that processes are very important assets as well as they are very conscious of the need to deploy well-defined processes with the goal of improving software product development and, particularly, quality. Software process modeling languages are an important support for describing and managing software processes in software-intensive organizations. Objective: This paper seeks to identify what software process modeling languages have been defined in last decade, the relationships and dependencies among them and, starting from the current state, to define directions for future research. Method: A systematic literature review was developed. 1929 papers were retrieved by a manual search in 9 databases and 46 primary studies were finally included. Results: Since 2000 more than 40 languages have been first reported, each of which with a concrete purpose. We show that different base technologies have been used to define software process modeling languages. We provide a scheme where each language is registered together with the year it was created, the base technology used to define it and whether it is considered a starting point for later languages. This scheme is used to illustrate the trend in software process modeling languages. Finally, we present directions for future research. Conclusion: This review presents the different software process modeling languages that have been developed in the last ten years, showing the relevant fact that model-based SPMLs (Software Process Modeling Languages) are being considered as a current trend. Each one of these languages has been designed with a particular motivation, to solve problems which had been detected. However, there are still several problems to face, which have become evident in this review. This let us provide researchers with some guidelines for future research on this topic.


title: A novel 8T SRAM with minimized power and delay

abstract: In this paper, a novel 8T SRAM cell is proposed which aims at decreasing the delay and lowering the total power consumption of the cell. The threshold voltage variations in the transistor affect the read and write stability of the cell. Also, power dissipation increases with the number of transistors which in turn affects the read and write stability. The proposed 8T SRAM bitcell is designed using 180 nm CMOS, n-well technology with a supply voltage of 1.8 V. The results show that the average delay has been improved by 80 % compared to the conventional 6T cell. The total power is improved by 14.5 % as compared to conventional 6T SRAM cell.


title: Cadec: A corpus of adverse drug event annotations

abstract: Abstract CSIRO Adverse Drug Event Corpus (C adec ) is a new rich annotated corpus of medical forum posts on patient-reported Adverse Drug Events (ADEs). The corpus is sourced from posts on social media, and contains text that is largely written in colloquial language and often deviates from formal English grammar and punctuation rules. Annotations contain mentions of concepts such as drugs, adverse effects, symptoms, and diseases linked to their corresponding concepts in controlled vocabularies, i.e., SNOMED Clinical Terms and MedDRA. The quality of the annotations is ensured by annotation guidelines, multi-stage annotations, measuring inter-annotator agreement, and final review of the annotations by a clinical terminologist. This corpus is useful for studies in the area of information extraction, or more generally text mining, from social media to detect possible adverse drug reactions from direct patient reports. The corpus is publicly available at https://data.csiro.au . 1


title: Real-time Hand Gesture Detection and Classification Using Convolutional Neural Networks

abstract: Real-time recognition of dynamic hand gestures from video streams is a challenging task since (i) there is no indication when a gesture starts and ends in the video, (ii) performed gestures should only be recognized once, and (iii) the entire architecture should be designed considering the memory and power budget. In this work, we address these challenges by proposing a hierarchical structure enabling offline-working convolutional neural network (CNN) architectures to operate online efficiently by using sliding window approach. The proposed architecture consists of two models: (1) A detector which is a lightweight CNN architecture to detect gestures and (2) a classifier which is a deep CNN to classify the detected gestures. In order to evaluate the single-time activations of the detected gestures, we propose to use Levenshtein distance as an evaluation metric since it can measure misclassifications, multiple detections, and missing detections at the same time. We evaluate our architecture on two publicly available datasets - EgoGesture and NVIDIA Dynamic Hand Gesture Datasets - which require temporal detection and classification of the performed hand gestures. ResNeXt-101 model, which is used as a classifier, achieves the state-of-the-art offline classification accuracy of 94.04% and 83.82% for depth modality on EgoGesture and NVIDIA benchmarks, respectively. In real-time detection and classification, we obtain considerable early detections while achieving performances close to offline operation. The codes and pretrained models used in this work are publicly available.


title: A Sparse Probabilistic Model of User Preference Data

abstract: Modern recommender systems rely on user preference data to understand, analyze and provide items of interest to users. However, for some domains, collecting and sharing such data can be problematic: it may be expensive to gather data from several users, or it may be undesirable to share real user data for privacy reasons. We therefore propose a new model for generating realistic preference data. Our Sparse Probabilistic User Preference (SPUP) model produces synthetic data by sparsifying an initially dense user preference matrix generated by a standard matrix factorization model. The model incorporates aggregate statistics of the original data, such as user activity level and item popularity, as well as their interaction, to produce realistic data. We show empirically that our model can reproduce real-world datasets from different domains to a high degree of fidelity according to several measures. Our model can be used by both researchers and practitioners to generate new datasets or to extend existing ones, enabling the sound testing of new models and providing an improved form of bootstrapping in cases where limited data is available.


title: Acoustical and Environmental Robustness in Automatic Speech Recognition

abstract: This dissertation describes a number of algorithms developed to increase the robustness of automatic speech recognition systems with respect to changes in the environment. These algorithms attempt to improve the recognition accuracy of speech recognition systems when they are trained and tested in different acoustical environments, and when a desk-top microphone (rather than a close-talking microphone) is used for speech input. Without such processing, mismatches between training and testing conditions produce an unacceptable degradation in recognition accuracy. ::: Two kinds of environmental variability are introduced by the use of desk-top microphones and different training and testing conditions: additive noise and spectral tilt introduced by linear filtering. An important attribute of the novel compensation algorithms described in this thesis is that they provide joint rather than independent compensation for these two types of degradation. ::: Acoustical compensation is applied in our algorithms as an additive correction in the cepstral domain. This allows a higher degree of integration within SPHINX, the Carnegie Mellon speech recognition system, that uses the cepstrum as its feature vector. Therefore, these algorithms can be implemented very efficiently. Processing in many of these algorithms is based on instantaneous signal-to-noise ratio (SNR), as the appropriate compensation represents a form of noise suppression at low SNRs and spectral equalization at high SNRs. ::: The compensation vectors for additive noise and spectral transformations are estimated by minimizing the differences between speech feature vectors obtained from a "standard" training corpus of speech and feature vectors that represent the current acoustical environment. In our work this is accomplished by minimizing the distortion of vector-quantized cepstra that are produced by the feature extraction module in SPHINX. ::: In this dissertation we describe several algorithms including the SNR-Dependent Cepstral Normalization, (SDCN) and the Codeword-Dependent Cepstral Normalization (CDCN). With CDCN, the accuracy of SPHINX when trained on speech recorded with a close-talking microphone and tested on speech recorded with a desk-top microphone is essentially the same obtained when the system is trained and tested on speech from the desk-top microphone. ::: An algorithm for frequency normalization has also been proposed in which the parameter of the bilinear transformation that is used by the signal-processing stage to produce frequency warping is adjusted for each new speaker and acoustical environment. The optimum value of this parameter is again chosen to minimize the vector-quantization distortion between the standard environment and the current one. In preliminary studies, use of this frequency normalization produced a moderate additional decrease in the observed error rate.


title: Network Effects and Personal Influences: The Diffusion of an Online Social Network

abstract: Abstract This article discusses the diffusion process in an online social network given the individual connections between members. The authors model the adoption decision of individuals as a binary choice affected by three factors: (1) the local network structure formed by already adopted neighbors, (2) the average characteristics of adopted neighbors (influencers), and (3) the characteristics of the potential adopters. Focusing on the first factor, the authors find two marked effects. First, an individual who is connected to many adopters has a greater adoption probability (degree effect). Second, the density of connections in a group of already adopted consumers has a strong positive effect on the adoption of individuals connected to this group (clustering effect). The article also records significant effects for influencer and adopter characteristics. For adopters, specifically, the authors find that position in the entire network and some demographic variables are good predictors of adoption. Similar...


title: Pincer-Search: An Efficient Algorithm for Discovering the Maximum Frequent Set

abstract: Discovering frequent itemsets is a key problem in important data mining applications, such as the discovery of association rules, strong rules, episodes, and minimal keys. Typical algorithms for solving this problem operate in a bottom-up, breadth-first search direction. The computation starts from frequent 1-itemsets (the minimum length frequent itemsets) and continues until all maximal (length) frequent itemsets are found. During the execution, every frequent itemset is explicitly considered. Such algorithms perform well when all maximal frequent itemsets are short. However, performance drastically deteriorates when some of the maximal frequent itemsets are long. We present a new algorithm which combines both the bottom-up and the top-down searches. The primary search direction is still bottom-up, but a restricted search is also conducted in the top-down direction. This search is used only for maintaining and updating a new data structure, the maximum frequent candidate set. It is used to prune early candidates that would be normally encountered in the bottom-up search. A very important characteristic of the algorithm is that it does not require explicit examination of every frequent itemset. We evaluate the performance of the algorithm using well-known synthetic benchmark databases, real-life census, and stock market databases.


title: Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function

abstract: In this paper, we study bidirectional LSTM network for the task of text classification using both supervised and semisupervised approaches. Several prior works have suggested that either complex pretraining schemes using unsupervised methods such as language modeling (Dai and Le 2015; Miyato, Dai, and Goodfellow 2016) or complicated models (Johnson and Zhang 2017) are necessary to achieve a high classification accuracy. However, we develop a training strategy that allows even a simple BiLSTM model, when trained with cross-entropy loss, to achieve competitive results compared with more complex approaches. Furthermore, in addition to cross-entropy loss, by using a combination of entropy minimization, adversarial, and virtual adversarial losses for both labeled and unlabeled data, we report state-of-theart results for text classification task on several benchmark datasets. In particular, on the ACL-IMDB sentiment analysis and AG-News topic classification datasets, our method outperforms current approaches by a substantial margin. We also show the generality of the mixed objective function by improving the performance on relation extraction task.1


Determinants of Economics Students ’ Academic Performance : Case Study of Jimma University , Ethiopia


title: A Selenium based approach to automatic test script generation for refactoring JavaScript code

abstract: During the development process of Web application, two essential phases are software testing and code refactoring. However, automatic testing script plays an important role in test automation. It has been a hot research topic in Web application. In order to refactor the JavaScript code of Web application more conveniently, an approach to automatic script generation from the defined test case is introduced in this paper. First, it describes the test case using customized XML format. Then, since Selenium platform supports multi-browsers testing, a method to transform XML description into test scripts based on Selenium framework is proposed as the emphasis.


title: A review of feature extraction for EEG epileptic seizure detection and classification

abstract: Epileptic seizure is one of the most common neurological diseases around the world. It is clinical symptoms and/or signs due to abnormal excessive or synchronous neuronal activity in the human brain. Electroencephalogram (EEG) that measures the electrical activity of the brain generated by the cerebral cortex nerve cells, is the most utilized test to detect the seizure activities by visual scanning of EEG signal recordings. Many techniques and methods have been proposed and developed to help the neurophysiologists to automatically detect the seizure activities with high accuracy. This paper presents a review of EEG features that have been proposed to characterize the epileptic seizure activities for the purpose of EEG seizure detection and classification. The relevant and discriminate features are analyzed, and their performance are also compared and discussed.


A C 3 D-based Convolutional Neural Network for Frame Dropping Detection in a Single Video Shot Chengjiang


title: Adaptive O-CNN: A Patch-based Deep Representation of 3D Shapes

abstract: We present an Adaptive Octree-based Convolutional Neural Network (Adaptive O-CNN) for efficient 3D shape encoding and decoding. Different from volumetric-based or octree-based CNN methods that represent a 3D shape with voxels in the same resolution, our method represents a 3D shape adaptively with octants at different levels and models the 3D shape within each octant with a planar patch. Based on this adaptive patch-based representation, we propose an Adaptive O-CNN encoder and decoder for encoding and decoding 3D shapes. The Adaptive O-CNN encoder takes the planar patch normal and displacement as input and performs 3D convolutions only at the octants at each level, while the Adaptive O-CNN decoder infers the shape occupancy and subdivision status of octants at each level and estimates the best plane normal and displacement for each leaf octant. As a general framework for 3D shape analysis and generation, the Adaptive O-CNN not only reduces the memory and computational cost, but also offers better shape generation capability than the existing 3D-CNN approaches. We validate Adaptive O-CNN in terms of efficiency and effectiveness on different shape analysis and generation tasks, including shape classification, 3D autoencoding, shape prediction from a single image, and shape completion for noisy and incomplete point clouds.


title: Multifunctional Activity of Polyphenolic Compounds Associated with a Potential for Alzheimer's Disease Therapy fromEcklonia cava

abstract: Five polyphenols were isolated and purified from a brown alga Ecklonia cava. These compounds showed diverse biological activities such as antioxidative, antiinflammatory, and enzyme inhibitory activities. This led us to investigate the potential of these compounds as Alzheimer's disease drugs. All of the compounds showed moderate acetylcholinesterase inhibitory activity in a micromolar range (IC50 from 16.0 to 96.3 μM). For butyrylcholinesterase, a new target for the treatment of Alzheimer's disease, phlorofucofuroeckol-A (PFF-A), showed a particularly potent inhibitory activity (IC50 0.95 μM), which is over 100-fold greater than for acetylcholinesterase. These compounds inhibited glycogen synthase kinase 3 beta, which is related to the formation of hyperphosphorylated tau and generation Aβ. Bieckol and PFF-A inhibited amyloid precursor protein biosynthesis. PFF-A also showed very strong β-secretase inhibitory activity with IC50 of submicromole. These results render these compounds as interesting potential drug candidates for Alzheimer's disease.


title: Anxiety and Depression in COPD: Current Understanding, Unanswered Questions, and Research Needs

abstract: Background Approximately 60 million people in the United States live with one of four chronic conditions: heart disease, diabetes, chronic respiratory disease, and major depression. Anxiety and depression are very common comorbidities in COPD and have significant impact on patients, their families, society, and the course of the disease. Methods We report the proceedings of a multidisciplinary workshop on anxiety and depression in COPD that aimed to shed light on the current understanding of these comorbidities, and outline unanswered questions and areas of future research needs. Results Estimates of prevalence of anxiety and depression in COPD vary widely but are generally higher than those reported in some other advanced chronic diseases. Untreated and undetected anxiety and depressive symptoms may increase physical disability, morbidity, and health-care utilization. Several patient, physician, and system barriers contribute to the underdiagnosis of these disorders in patients with COPD. While few published studies demonstrate that these disorders associated with COPD respond well to appropriate pharmacologic and nonpharmacologic therapy, only a small proportion of COPD patients with these disorders receive effective treatment. Conclusion Future research is needed to address the impact, early detection, and management of anxiety and depression in COPD.


Asynchronous Deep Q-Learning for Breakout with RAM inputs


title: Application of active contour models in medical image segmentation

abstract: Recent developments on medical imaging techniques have brought a completely new research field on image processing. The principal aim is to improve medical diagnosis through segmented images. Techniques have been developed to help for identifying specific structures within a magnetic resonance image: MRI. The active contour methods, these methods are adaptable to the desired features in the image. In our work, we describe two classes of active contour models and discussing application aspects in medical imaging area.


title: Driving Assistance by Deictic Control for a Smart Wheelchair: The Assessment Issue

abstract: In this paper, an assessment of a driving assistance by a deictic command for a smart wheelchair is proposed. This equipment enables the user to move with a series of indications on an interface displaying a view of the environment and bringing about automatic movement of the wheelchair. Two sets of tests were implemented to assess the advantages of this type of assistance compared with conventional wheelchair control. The first set evaluated the performance of the human-machine system that is based on a course time analysis, an observation of users' actions, and an estimation of driving comfort. The second test was implemented to assess the cognitive requirements of the driving task, specifically the attentional and executive processes required when driving in assisted mode. A dual-task method was used to achieve this. The results show that driving assistance brings about a decrease in physical load for the same level of comfort as manual driving, but requires an additional cognitive effort for the user, especially in terms of executive abilities.


title: Reducing OCR errors by combining two OCR systems

abstract: This paper describes our efforts in building a heritage corpus of Alpine texts. We have already digitized the yearbooks of the Swiss Alpine Club from 1864 until 1982. ::: This corpus poses special challenges since the yearbooks are multilingual and vary in orthography and layout. We discuss methods to improve OCR performance and experiment with combining two different OCR programs with the goal to reduce the number of OCR errors. We describe a merging procedure that uses a unigram language model trained on the uncorrected corpus itself to select the best alternative, and report on evaluation results which show that the merging procedure helps to improve OCR quality.


title: Model-Driven Simulations for Computer Vision

abstract: There is a growing interest to utilize Computer Graphics (CG) renderings to generate large scale annotated data in order to train machine learning systems, such as Deep convolutional neural networks, for Computer Vision (CV). However, there has been a long debate on the usefulness of CG generated data for tuning CV systems (even from the 1980's). Especially, the impact of modeling errors and computational rendering approximations, due to choices in the rendering pipeline, on trained CV systems generalization performance is still not clear. In this paper, we take a case study in traffic scenario to empirically analyze the performance degradation when CV systems trained with virtual data are transferred to real data. We: a) discuss a generative model coupled with 3D CAD shapes for scene instance synthesis and, b) explore system performance tradeoffs due to the choice of rendering engine (e.g. Lambertian shader (LS), ray-tracing (RT), and Monte-carlo path tracing (MCPT)) and their respective parameters. DeepLab, that performs semantic segmentation, is chosen as the CV system being evaluated. In our case study, involving traffic scenes, when the CV system is trained with CG data samples (that use MCPT or RT) and augmented with only 10% of real-world training data from CityScapes dataset, the performance levels achieved are comparable to that of training DeepLab with the complete CityScapes dataset. Use of samples from LS degraded the performance of DeepLab by 20%. Physics-based MCPT rendering improved the performance by 6% but at the cost of more than 3 times the rendering time.


title: An Explorative Approach for Crowdsourcing Tasks Design

abstract: Crowdsourcing applications are becoming widespread; they cover very different scenarios, including opinion mining, multimedia data annotation, localised information gathering, marketing campaigns, expert response gathering, and so on. The quality of the outcome of these applications depends on different design parameters and constraints, and it is very hard to judge about their combined effects without doing some experiments; on the other hand, there are no experiences or guidelines that tell how to conduct experiments, and thus these are often conducted in an ad-hoc manner, typically through adjustments of an initial strategy that may converge to a parameter setting which is quite different from the best possible one. In this paper we propose a comparative, explorative approach for designing crowdsourcing tasks. The method consists of defining a representative set of execution strategies, then execute them on a small dataset, then collect quality measures for each candidate strategy, and finally decide the strategy to be used with the complete dataset.


title: CAPTCHA Recognition with Active Deep Learning

abstract: CAPTCHAs are automated tests to tell computers and humans apart. They are designed to be easily solvable by humans, but unsolvable by machines. With Convolutional Neural Networks these tests can also be solved automatically. However, the strength of CNNs relies on the training data that the classifier is learnt on and especially on the size of the training set. Hence, it is intractable to solve the problem with CNNs in case of insufficient training data. We propose an Active Deep Learning strategy that makes use of the ability to gain new training data for free without any human intervention which is possible in the special case of CAPTCHAs. We discuss how to choose the new samples to re-train the network and present results on an auto-generated CAPTCHA dataset. Our approach dramatically improves the performance of the network if we initially have only few labeled training data.


title: Interactions between working memory and visual perception: An ERP/EEG study

abstract: Abstract How do working memory and perception interact with each other? Recent theories of working memory suggest that they are closely linked, and in fact share certain brain mechanisms. We used a sequential motion imitation task in combination with EEG and ERP techniques for a direct, online examination of memory load's influence on the processing of visual stimuli. Using a paradigm in which subjects tried to reproduce random motion sequences from memory, we found a systematic decrease in ERP amplitude with each additional motion segment that was viewed and memorized for later imitation. High-frequency (> 20 Hz) oscillatory activity exhibited a similar position-dependent decrease. When trials were sorted according to the accuracy of subsequent imitation, the amplitude of the ERPs during stimulus presentation correlated with behavioral performance: the larger the amplitude, the more accurate the subsequent imitation. These findings imply that visual processing of sequential stimuli is not uniform. Rather, earlier information elicits stronger neural activity. We discuss possible explanations for this observation, among them competition for attention between memory and perception and encoding of serial order by means of differential activation strengths.


title: The ALSFRSr predicts survival time in an ALS clinic population

abstract: Objective:To determine whether the Amyotrophic Lateral Sclerosis Functional Rating Scale–revised (ALSFRSr), a predictor of survival time in ALS clinical trials, predicts survival time in an ALS clinic population. Methods:The authors prospectively evaluated 267 consecutive patients with ALS at first visit to an ALS clinic using the ALSFRSr and pulmonary function testing. The association of ALSFRSr score at baseline with death or tracheostomy in ALS was examined using Cox proportional hazards models, adjusting for age at baseline, sex, and symptom duration. Results:Of 267 patients with ALS, 103 (39%) reached the endpoint, defined as either death (79 patients) or tracheostomy (24 patients), during a mean follow-up of 1.0 ± 0.7 years. Among the 103 patients who reached the endpoint during follow-up, 77 (75%) had a baseline ALSFRSr score of less than 38 (the median baseline score of all patients), compared to 53 of 164 (32%) who remained alive without tracheostomy. Patients with a total ALSFRSr score below the median had a 4.4-fold increased risk of death or tracheostomy compared to those who scored above the median (HR: 4.38, 95% CI: 2.79 to 6.86, p < 0.001). Both the total ALSFRSr score at baseline (HR: 0.94, 95% CI: 0.91 to 0.98, p < 0.001) and forced vital capacity at baseline (HR: 0.99, 95% CI: 0.98 to 1.00, p = 0.02) were associated with death or tracheostomy when included in the same Cox model. Conclusions:In an ALS clinic population, the total Amyotrophic Lateral Sclerosis Functional Rating Scale–revised score at baseline is a strong predictor of death or tracheostomy independently of forced vital capacity and after adjustment for age at baseline, sex, and symptom duration.


title: Identification of global marine hotspots: sentinels for change and vanguards for adaptation action

abstract: Major changes consistent with the fingerprint of global warming have been reported for nearly every ecosystem on earth. Recently, studies have moved beyond correlation-based inference to demonstrate mechanistic links between warming and biological responses, particularly in regions experiencing rapid change. However, the assessment of climate change impacts and development of adaptation options that humans can undertake are at the earliest stages, particularly for marine systems. Here, we use trends in ocean temperature to characterize regions that can act as natural laboratories or focal points for early learning. These discrete marine ‘hotspots’, where ocean warming is fastest, were identified based on 50 years of historical sea surface temperature data. Persistence of these hotspots into the future was evaluated using global climate models. This analysis provides insights and a starting point for scientists aiming to identify key regions of concern with regard to ocean warming, and illustrates a potential approach for considering additional physical drivers of change such as ocean pH or oxygenation. We found that some hotspot regions were of particular concern due to other non-climate stressors. For instance, many of the marine hotspots occur where human dependence on marine resources is greatest, such as south-east Asia and western Africa, and are therefore of critical consideration in the context of food security. Intensive study and development of comprehensive inter-disciplinary networks based on the hotspot regions identified here will allow earliest testing of management and adaptation pathways, facilitating rapid global learning and implementation of adaptation options to cope with future change.


title: Performance evaluation of network scanning tools with operation of firewall

abstract: As the number of devices connected to the Internet has been exponentially increasing, the degree of threats to those devices and networks has been also increasing. Various network scanning tools, which use fingerprinting techniques, have been developed to make the devices and networks secure by providing the information on its status. However, the tools may be used for malicious purposes. Using network scanning tools, attackers can not only obtain the information of devices such as the name of OS, version, and sessions but also find its vulnerabilities which can be used for further cyber-attacks. In this paper, we compare and analyze the performances of widely used network scanning tools such as Nmap and Nessus. The existing researches on the network scanning tools analyzed a specific scanning tools and they assumed there are only small number of network devices. In this paper, we compare and analyze the performances of several tools in practical network environments with the number of devices more than 40. The results of this paper provide the direction to prevent possible attacks when they are utilized as attack tools as well as the practical understanding of the threats by network scanning tools and fingerprinting techniques.