title: IDENTIFYING OPINION LEADERS USING SOCIAL NETWORK ANALYSIS, A STUDY IN AN EGYPTIAN VILLAGE

abstract: This research used four approaches to identify opinion leaders in two networks in an Egyptian Village. One approach was used for identifying single opinion leaders (network centrality measures), and three approaches were used to identify sets of key players (group centrality, network efﬁciency, and KPP-Pos & KPP-Neg). Social network analysis software (UCINET 6) was used for individual & group centrality measures. The study used the key player software to identify sets of key players under KPP-Pos & KPP-Neg. The study identified different opinion leaders using the four different techniques. This research gives an opportunity for further work on a comprehensive approach to identify key players in social networks to overcome the problem of identifying different leaders by different techniques without an absolute answer for the question concerning which technique will capture the best leaders.


title: Forecast Forex with ANN Using Fundamental Data

abstract: An effective foreign exchange (Forex) trading decision is usually dependent on effective forex forecasting. This paper reports empirical evidence that an artificial neural network (ANN) is applicable to the prediction of foreign exchange rates. The architecture of the network and the related algorithms are described. The effects of the choice of inputs into a neural network model are examined. Except for the normally used time series data and technical indicators, fundamental indicators such as interest rates and gross domestic products are fed into the neural networks to see if any relationship may be captured and improve the predictive capabilities of the model.


title: The Influence of Online Brand Community Characteristics on Community Commitment and Brand Loyalty

abstract: Online community activities are becoming widespread thanks to the expansion of Internet use and the development of information technology. We highlight the importance of online brand, and investigate the relationship between an online community and the online brand. This study examines how online brand community characteristics affect community commitment and brand loyalty and, in particular, how the type of online brand community affects the relationships between the characteristics and community commitment. A survey of 250 respondents revealed that both interaction and reward for the activities significantly influenced the community commitment, while the community commitment was affected by neither information quality nor system quality. The results of analysis also showed that the hosting type of a community has a significant moderating effect and that the community commitment increases brand loyalty. Interpretations and implications of the findings, as well as future research directions, are discussed


title: FIF-IoT: A Forensic Investigation Framework for IoT Using a Public Digital Ledger

abstract: The increased deployment of Internet of Things (IoT) devices will make them targets for attacks. IoT devices can also be used as tools for committing crimes. In this regard, we propose FIF-IoT – a forensic investigation framework using a public digital ledger to find facts in criminal incidents in IoT-based systems. FIF-IoT collects interactions that take place among various IoT entities (clouds, users, and IoT devices) as evidence and store them securely as transactions in a public, distributed and decentralized blockchain network which is similar to the Bitcoin network. Hence, FIF-IoT eliminates a single entity's control over the evidence storage, avoids single-point-offailure on the storage media, and ensures high availability of evidence. FIF-IoT presents a framework that ensures integrity, confidentiality, anonymity, and non-repudiation of the evidence stored in the public digital ledger. Furthermore, FIF-IoT provides a mechanism to acquire evidence from the ledger and to verify the integrity of the obtained evidence. We present a case study of a forensic investigation to demonstrate that FIF-IoT is secure against evidence tampering. We also implement a prototype to evaluate the performance of FIF-IoT.


title: Deep Learning-Based Obstacle Detection and Classification With Portable Uncalibrated Patterned Light

abstract: Autonomous navigation and obstacle avoidance systems are critically relevant and important for visually impaired people, assisted driving applications, and autonomous robots. Even though there has been significant amount of work on obstacle detection and avoidance using LiDAR and camera data, there has not been much effort focusing on providing a lightweight, cost conscious, energy efficient, reliable, and portable solution for the visually impaired. We propose a new method for autonomous obstacle detection and classification, which incorporates a different and novel type of sensor, namely, patterned light field, with camera. The proposed device is small in size, easily carried, as well as low cost. The grid, projected by the patterned light source, is apparent and differentiable as the sensing system is hand carried in natural indoor and outdoor environments over and toward different types of obstacles. Our proposed approach exploits these patterns, without calibration, by employing deep learning techniques, including a convolutional neural network-based classification on individual frames. We further refine our approach by smoothing the frame-based classifications over multiple frames using long short-term memory units. The proposed method provides very promising results with overall detection and classification accuracies of 98.37% for the binary case as well as 95.97% and 92.62% for two different multi-class scenarios. These results represent the average number of sequences correctly detected and classified and were obtained on a sequence-based analysis of over 120 sequences from four different users.


title: AMBER: Adaptive Multi-Batch Experience Replay for Continuous Action Control

abstract: In this paper, a new adaptive multi-batch experience replay scheme is proposed for proximal policy optimization (PPO) for continuous action control. On the contrary to original PPO, the proposed scheme uses the batch samples of past policies as well as the current policy for the update for the next policy, where the number of the used past batches is adaptively determined based on the oldness of the past batches measured by the average importance sampling (IS) weight. The new algorithm constructed by combining PPO with the proposed multi-batch experience replay scheme maintains the advantages of original PPO such as random mini-batch sampling and small bias due to low IS weights by storing the pre-computed advantages and values and adaptively determining the mini-batch size. Numerical results show that the proposed method significantly increases the speed and stability of convergence on various continuous control tasks compared to original PPO.


title: An Improved Method for Prediction of Cancer Prognosis by Network Learning

abstract: Accurate identification of prognostic biomarkers is an important yet challenging goal in bioinformatics. Many bioinformatics approaches have been proposed for this purpose, but there is still room for improvement. In this paper, we propose a novel machine learning-based method for more accurate identification of prognostic biomarker genes and use them for prediction of cancer prognosis. The proposed method specifies the candidate prognostic gene module by graph learning using the generative adversarial networks (GANs) model, and scores genes using a PageRank algorithm. We applied the proposed method to multiple-omics data that included copy number, gene expression, DNA methylation, and somatic mutation data for five cancer types. The proposed method showed better prediction accuracy than did existing methods. We identified many prognostic genes and their roles in their biological pathways. We also showed that the genes identified from different omics data were complementary, which led to improved accuracy in prediction using multi-omics data.


title: Measuring Enjoyment of Computer Game Play

abstract: This paper reports on the development of an instrument designed to measure the enjoyment of computer game play. Despite the enormous technological progress in the field of computer games, enjoyment of computer game play is still not a welldefined construct. Based on Nabi and Krcmar’s (2004) tripartite model of media enjoyment, a survey questionnaire was developed to measure computer game players’ affective, behavioral, and cognitive reactions. Expert consultation, exploratory and confirmatory card sorting sessions were used to refine the instrument. A survey of computer game players was subsequently conducted to test the instrument. Reliabilities and construct validities were analyzed. Findings and their implications were discussed.


title: Continuous Energy Minimization for Multitarget Tracking

abstract: Many recent advances in multiple target tracking aim at finding a (nearly) optimal set of trajectories within a temporal window. To handle the large space of possible trajectory hypotheses, it is typically reduced to a finite set by some form of data-driven or regular discretization. In this work, we propose an alternative formulation of multitarget tracking as minimization of a continuous energy. Contrary to recent approaches, we focus on designing an energy that corresponds to a more complete representation of the problem, rather than one that is amenable to global optimization. Besides the image evidence, the energy function takes into account physical constraints, such as target dynamics, mutual exclusion, and track persistence. In addition, partial image evidence is handled with explicit occlusion reasoning, and different targets are disambiguated with an appearance model. To nevertheless find strong local minima of the proposed nonconvex energy, we construct a suitable optimization scheme that alternates between continuous conjugate gradient descent and discrete transdimensional jump moves. These moves, which are executed such that they always reduce the energy, allow the search to escape weak minima and explore a much larger portion of the search space of varying dimensionality. We demonstrate the validity of our approach with an extensive quantitative evaluation on several public data sets.


title: Mental health and stigma in the medical profession

abstract: Until recently, much of the recent upsurge in interest in physician health has been motivated by concerns about improving patient care and patient safety and reducing medical errors. Increasingly, more attention has turned to examining how the management of mental illness among physicians might be improved within the medical profession and one key direction for change is the reduction of stigma associated with mental illness. I begin this article by presenting a brief overview of the stigma process from the general sociological literature. Next, I provide evidence that illustrates how the stigma of mental illness thrives in the medical profession as a result of the culture of medicine and medical training, perceptions of physicians and their colleagues, and expectations and responses of health care systems and organizations. Lastly, I discuss what needs to change by proposing ways of educating and raising awareness regarding mental illness among physicians, discussing approaches to assessing and identifying mental health concerns for physicians and by examining how safe and confidential support and treatment can be offered to physicians in need. I rely on strategically selected studies to effectively draw attention to and support the central themes of this article.


title: SigniTrend: scalable detection of emerging topics in textual streams by hashed significance thresholds

abstract: Social media such as Twitter or weblogs are a popular source for live textual data. Much of this popularity is due to the fast rate at which this data arrives, and there are a number of global events - such as the Arab Spring - where Twitter is reported to have had a major influence. However, existing methods for emerging topic detection are often only able to detect events of a global magnitude such as natural disasters or celebrity deaths, and can monitor user-selected keywords or operate on a curated set of hashtags only. Interesting emerging topics may, however, be of much smaller magnitude and may involve the combination of two or more words that themselves are not unusually hot at that time. Our contributions to the detection of emerging trends are three-fold first of all, we propose a significance measure that can be used to detect emerging topics early, long before they become "hot tags", by drawing upon experience from outlier detection. Secondly, by using hash tables in a heavy-hitters type algorithm for establishing a noise baseline, we show how to track even all keyword pairs using only a fixed amount of memory. Finally, we aggregate the detected co-trends into larger topics using clustering approaches, as often as a single event will cause multiple word combinations to trend at the same time.


title: BloomStore: Bloom-Filter based memory-efficient key-value store for indexing of data deduplication on flash

abstract: Due to its better scalability, Key-Value (KV) store has superseded traditional relational databases for many applications, such as data deduplication, on-line multi-player gaming, and Internet services like Amazon and Facebook. The KV store efficiently supports two operations (key lookup and KV pair insertion) through an index structure that maps keys to their associated values. The KV store is also commonly used to implement the chunk index in data deduplication, where a chunk ID (SHA1 value computed based on the chunk's content) is a key and its associative chunk metadata (e.g., physical storage location, stream ID) is the value. For a deduplication system, typically the number of chunks is too large to store the KV store solely in RAM. Thus, the KV store maintains a large (hash-table based) index structure in RAM to index all KV pairs stored on secondary storage. Hence, its available RAM space limits the maximum number of KV pairs that can be stored. Moving the index data structure from RAM to flash can possibly overcome the space limitation. In this paper, we propose efficient KV store on flash with a Bloom Filter based index structure called BloomStore. The unique features of the BloomStore include (1) no index structure is required to be stored in RAM so that a small RAM space can support a large number of KV pairs and (2) both index structure and KV pairs are stored compactly on flash memory to improve its performance. Compared with the state-of-the-art KV store designs, the BloomStore achieves a significantly better key lookup performance and roughly the same insertion performance with multiple times less RAM usage based on our experiments with deduplication workloads.


title: Ambient intelligence and smart environments: A state of the art

abstract: Advances in the miniaturization of electronics is allowing computing devices with various capabilities and interfaces to become part of our daily life. Sensors, actuators, and processing units can now be purchased at very affordable prices. This technology can be networked and used with the coordination of highly intelligent software to understand the events and relevant context of a specific environment and to take sensible decisions in real-time or a posteriori.


title: Temporal Glare: Real-Time Dynamic Simulation of the Scattering in the Human Eye

abstract: Glare is a consequence of light scattered within the human eye when looking at bright light sources. This effect can be exploited for tone mapping since adding glare to the depiction of high-dynamic range (HDR) imagery on a low-dynamic range (LDR) medium can dramatically increase perceived contrast. Even though most, if not all, subjects report perceiving glare as a bright pattern that fluctuates in time, up to now it has only been modeled as a static phenomenon. We argue that the temporal properties of glare are a strong means to increase perceived brightness and to produce realistic and attractive renderings of bright light sources. Based on the anatomy of the human eye, we propose a model that enables real-time simulation of dynamic glare on a GPU. This allows an improved depiction of HDR images on LDR media for interactive applications like games, feature films, or even by adding movement to initially static HDR images. By conducting psychophysical studies, we validate that our method improves perceived brightness and that dynamic glare-renderings are often perceived as more attractive depending on the chosen scene.


title: Estimation of Panel Vector Autoregression in Stata: a Package of Programs

abstract: Panel vector autoregression (VAR) models have been increasingly used in applied research. While programs specifically designed to estimate time-series VAR models are often included as standard features in most statistical packages, panel VAR model estimation and inference are often implemented with general-use routines that require some programming dexterity. In this paper, we briefly discuss model selection, estimation and inference of homogeneous panel VAR models in a generalized method of moments (GMM) framework, and present a set of Stata programs to conveniently execute them. We illustrate the pvar package of programs by using standard Stata datasets.


title: SageRobotics: open source framework for symbolic computation of robot models

abstract: This paper presents the SageRobotics framework to symbolically compute robot dynamics. Such framework is open source and is built upon the freely available and open source Sage mathematical software. Given a set of Denavit-Hartenberg parameters describing a serial robot the framework is capable of constructing geometric, kinematic and dynamic models. It offers both Euler-Lagrange and Recursive Newton-Euler formulations for dynamic equation generation, including inverse dynamics, isolated dynamic equation terms and linear to parameters form. The calculation of a minimal base parameter set is also a key feature of this framework. Tools to generate code from symbolic equations are included, enabling the construction of fast numerical functions.


title: A Survey of Provably Secure Searchable Encryption

abstract: We survey the notion of provably secure searchable encryption (SE) by giving a complete and comprehensive overview of the two main SE techniques: searchable symmetric encryption (SSE) and public key encryption with keyword search (PEKS). Since the pioneering work of Song, Wagner, and Perrig (IEEE S&P '00), the field of provably secure SE has expanded to the point where we felt that taking stock would provide benefit to the community. The survey has been written primarily for the nonspecialist who has a basic information security background. Thus, we sacrifice full details and proofs of individual constructions in favor of an overview of the underlying key techniques. We categorize and compare the different SE schemes in terms of their security, efficiency, and functionality. For the experienced researcher, we point out connections between the many approaches to SE and identify open research problems. Two major conclusions can be drawn from our work. While the so-called IND-CKA2 security notion becomes prevalent in the literature and efficient (sublinear) SE schemes meeting this notion exist in the symmetric setting, achieving this strong form of security efficiently in the asymmetric setting remains an open problem. We observe that in multirecipient SE schemes, regardless of their efficiency drawbacks, there is a noticeable lack of query expressiveness that hinders deployment in practice.


Wanderlust: Extracting Semantic Relations from Natural Language Text Using Dependency Grammar Patterns


title: Efficacy and safety of telaprevir with pegylated interferon &agr;‐2a and ribavirin in Japanese patients

abstract: AIM ::: To assess the efficacy and safety of telaprevir (TVR) in combination with pegylated interferon α-2a (PEG-IFNα-2a) and ribavirin (RBV) for treatment-naïve patients and relapsed patients compared to previous TVR-based triple therapy in Japan. ::: ::: ::: METHODS ::: The study group included 35 treatment-naïve (median age, 55 years) and 19 relapsed (median age, 55 years) patients with genotype 1 hepatitis C virus infection. Patients received TVR (750 mg every 8 h) for 12 weeks, in combination with PEG-IFNα-2a and RBV. ::: ::: ::: RESULTS ::: The sustained virological response (SVR24 ) rates for naïve and relapsed patients were 85.7% (30/35) and 94.7% (18/19), respectively. The discontinuation rate of all study drugs due to adverse events was 5.6% (3/54). Among the 54 patients, grade 3 skin disorders and grade 3 anemia (<8.0 g/dL) were reported in 2 (3.7%) and 6 patients (11.1%), respectively. Although the overall safety profiles were similar for the TVR/PEG-IFNα-2a/RBV and TVR/PEG-IFNα-2b/RBV regimens (previous study), the proportion of patients discontinuing all study drugs due to adverse events was lower in the patients treated with the TVR/PEG-IFNα-2a/RBV regimen (3/54, 5.6%) than TVR/PEG-IFNα-2b/RBV regimen (44/267, 16.5%). ::: ::: ::: CONCLUSION ::: Telaprevir in combination with PEG-IFNα-2a/RBV provided a high sustained virological response rate for the treatment of genotype 1 hepatitis C virus in both treatment-naïve and relapsed patients in Japan. Telaprevir-based therapy may provide a useful treatment option for patients who are difficult to treat due to NS5A (Y93, L31) and NS3/4A (D168) variants.


title: Alpha particle and neutron-induced soft error rates and scaling trends in SRAM

abstract: We performed underground real-time tests to obtain alpha particle-induced soft error rates (α-SER) with high accuracies for SRAMs with 180 nm – 90 nm technologies and studied the scaling trend of α-SERs. In order to estimate the maximum permissive rate of alpha emission from package resin, the α-SER was compared to the neutron-induced soft error rate (n-SER) obtained from accelerated tests. We found that as devices are scaled down, the α-SER increased while the n-SER slightly decreased, and that the α-SER could be greater than the n-SER in 90 nm technology even when the ultra-low-alpha (ULA) grade, with the alpha emission rate ≫ 1 × 10−3 cm−2h−1, was used for package resin. We also performed computer simulations to estimate scaling trends of both α-SER and n-SER up to 45 nm technologies, and noticed that the α-SER decreased from 65 nm technology while the n-SER increased from 45 nm technology due to direct ionization from the protons generated in the n + Si nuclear reaction.


CONCEPT AND APPLICATION OF VIRTUAL REALITY HAPTIC TECHNOLOGY : A REVIEW


title: Parallel coordinates: a tool for visualizing multi-dimensional geometry

abstract: A methodology for visualizing analytic and synthetic geometry in R/sup N/ is presented. It is based on a system of parallel coordinates which induces a nonprojective mapping between N-dimensional and two-dimensional sets. Hypersurfaces are represented by their planar images which have some geometrical properties analogous to the properties of the hypersurface that they represent. A point from to line duality when N=2 generalizes to lines and hyperplanes enabling the representation of polyhedra in R/sup N/. The representation of a class of convex and non-convex hypersurfaces is discussed, together with an algorithm for constructing and displaying any interior point. The display shows some local properties of the hypersurface and provides information on the point's proximity to the boundary. Applications are discussed.<<ETX>>


title: Cross-Domain Shoe Retrieval With a Semantic Hierarchy of Attribute Classification Network

abstract: Cross-domain shoe image retrieval is a challenging problem, because the query photo from the street domain (daily life scenario) and the reference photo in the online domain (online shop images) have significant visual differences due to the viewpoint and scale variation, self-occlusion, and cluttered background. This paper proposes the semantic hierarchy of attribute convolutional neural network (SHOE-CNN) with a three-level feature representation for discriminative shoe feature expression and efficient retrieval. The SHOE-CNN with its newly designed loss function systematically merges semantic attributes of closer visual appearances to prevent shoe images with the obvious visual differences being confused with each other; the features extracted from image, region, and part levels effectively match the shoe images across different domains. We collect a large-scale shoe data set composed of 14341 street domain and 12652 corresponding online domain images with fine-grained attributes to train our network and evaluate our system. The top-20 retrieval accuracy improves significantly over the solution with the pre-trained CNN features.


title: Visual masking phenomena with high dynamic range content

abstract: High Dynamic Range (HDR) technology (capture and display), can offer high levels of immersion through a dynamic range that meets and exceeds that of the Human Visual System (HVS). This increase in immersion comes at the cost of higher bitrate requirements, which necessitate the development of efficient HDR-relevant coding solutions. Efficient perception-based compression of HDR imagery requires models that capture accurately the various masking effects experienced by the HVS under HDR conditions, so that bits are not wasted coding redundant imperceptible information. In this paper we present two psychovisual experiments that we carried out with the aid of a high dynamic range display, in order to determine potential differences between Standard Dynamic Range (SDR) and HDR edge masking (EM) and luminance masking (LM) effects. The EM experimental results indicate that the visibility threshold is higher for the case of HDR content than SDR, especially on the dark background side of an edge. The LM experimental results suggest that the HDR visibility threshold is higher compared to SDR for both dark and bright luminance backgrounds.


title: Balancing environmental and industry sustainability: A case study of the US gold mining industry

abstract: Mandatory insurance requirements and/or mitigation fees (royalties) for mining companies may help reduce environmental risk exposure for the federal government. Mining is examined since the Environmental Protection Agency (EPA) Toxic Release Inventory reveals that this sector produces more hazardous waste than any other industrial sector. Although uncommon, environmental expense can exceed hundreds of millions of dollars per development. Of particular concern is the potential for mines to become unfunded Superfund sites. Monte Carlo simulation of risk exposure is used to establish a plausible range of unfunded federal liabilities associated with cyanide-leach gold mining. A model is developed to assess these costs and their impact on both the federal budget and corporate profitability (i.e., industry sustainability), particularly if such costs are borne by offending firms.


title: Full-automatic recognition of various parking slot markings using a hierarchical tree structure

abstract: A full-automatic method for recognizing parking slot markings is proposed. The proposed method recognizes various types of parking slot markings by modeling them as a hierarchical tree structure. This method mainly consists of two processes: bottom-up and top-down. First, the bot- tom-up process climbs up the hierarchical tree structure to excessively generate parking slot candidates so as not to lose the correct slots. This process includes corner detection, junction and slot generation, and type selection procedures. After that, the top-down process confirms the final parking slots by eliminating falsely generated slots, junctions, and corners based on the properties of the parking slot marking type by climbing down the hierarchical tree structure. The proposed method was evaluated in 608 real-world parking situations encompassing a variety of different parking slot markings. The experimental result reveals that the proposed method outperforms the previous semiautomatic method while requiring a small amount of computational costs even though it is fully automatic. © 2013 Society of Photo-Optical Instrumentation Engineers (SPIE) (DOI: 10.1117/1.OE.52.3.037203) Subject terms: driver assist system; parking assist system; parking slot marking recognition; hierarchical tree structure.


title: Does organizing security patterns focus architectural choices?

abstract: Security patterns can be a valuable vehicle to design secure software. Several proposals have been advanced to improve the usability of security patterns. They often describe extra annotations to be included in the pattern documentation. This paper presents an empirical study that validates whether those proposals provide any real benefit for software architects. A controlled experiment has been executed with 90 master students, who have performed several design tasks involving the hardening of a software architecture via security patterns. The results show that annotations produce benefits in terms of a reduced number of alternatives that need to be considered during the selection of a suitable pattern. However, they do not reduce the time spent in the selection process.


title: What Are the Odds? A Practical Guide to Computing and Reporting Bayes Factors

abstract: The purpose of this paper is to provide an easy template for the inclusion of the Bayes factor in reporting experimental results, particularly as a recommendation for articles in the Journal of Problem Solving. The Bayes factor provides information with a similar purpose to the p-value—to allow the researcher to make statistical inferences from data provided by experiments. While the p-value is widely used, the Bayes factor provides several advantages, particularly in that it allows the researcher to make a statement about the alternative hypothesis, rather than just the null hypothesis. In addition, it provides a clearer estimate of the amount of evidence present in the data. Building on previous work by authors such as Wagenmakers (2007), Rouder et al. (2009), and Masson (2011), this article provides a short introduction to Bayes factors, before providing a practical guide to their computation using examples from published work on problem solving. Correspondence: Correspondence concerning this article should be addressed to Andrew F. Jarosz, 1007 W. Harrison St. MC 285, University of Illinois at Chicago, Chicago, IL, 60647, or via email to ajaros5@uic.edu.


title: Statistics review 4: Sample size calculations

abstract: The present review introduces the notion of statistical power and the hazard of under-powered studies. The problem of how to calculate an ideal sample size is also discussed within the context of factors that affect power, and specific methods for the calculation of sample size are presented for two common scenarios, along with extensions to the simplest case.


title: Finding Perceptually Closed Paths in

abstract: Closed or nearly closed regions are an important form of perceptual structure arising both in natural imagery and in many forms of human-created imagery including sketches, line art, graphics, and formal drawings. We present an effective algorithm especially suited for finding perceptually salient, compact closed region structure in hand-drawn sketches and line art. We start with a graph of curvilinear fragments whose proximal endpoints form junctions. The key problem is to manage the search of possible path continuations through junctions in an effort to find paths satisfying global criteria for closure and figural salience. We identify constraints particular to this domain for ranking path continuations through junctions, based on observations of the ways that junctions arise in line drawings. In particular, we delineate the roles of the principle of good continuation versus maximally turning paths. Best-first bidirectional search checks for the cleanest, most obvious paths first, then reverts to more exhaustive search to find paths cluttered by blind alleys. Results are demonstrated on line drawings from several sources including line art, engineering drawings, sketches on whiteboards, as well as contours from photographic imagery.


title: A Large Labeled Corpus for Online Harassment Research

abstract: A fundamental part of conducting cross-disciplinary web science research is having useful, high-quality datasets that provide value to studies across disciplines. In this paper, we introduce a large, hand-coded corpus of online harassment data. A team of researchers collaboratively developed a codebook using grounded theory and labeled 35,000 tweets. Our resulting dataset has roughly 15% positive harassment examples and 85% negative examples. This data is useful for training machine learning models, identifying textual and linguistic features of online harassment, and for studying the nature of harassing comments and the culture of trolling.


FIELD REPORT The TerraMax Autonomous Vehicle


title: A "hitchhiker's" guide to fast and efficient data reconstruction in erasure-coded data centers

abstract: Erasure codes such as Reed-Solomon (RS) codes are being extensively deployed in data centers since they offer significantly higher reliability than data replication methods at much lower storage overheads. These codes however mandate much higher resources with respect to network bandwidth and disk IO during reconstruction of data that is missing or otherwise unavailable. Existing solutions to this problem either demand additional storage space or severely limit the choice of the system parameters. In this paper, we present "Hitchhiker", a new erasure-coded storage system that reduces both network traffic and disk IO by around 25% to 45% during reconstruction of missing or otherwise unavailable data, with no additional storage, the same fault tolerance, and arbitrary flexibility in the choice of parameters, as compared to RS-based systems. Hitchhiker 'rides' on top of RS codes, and is based on novel encoding and decoding techniques that will be presented in this paper. We have implemented Hitchhiker in the Hadoop Distributed File System (HDFS). When evaluating various metrics on the data-warehouse cluster in production at Facebook with real-time traffic and workloads, during reconstruction, we observe a 36% reduction in the computation time and a 32% reduction in the data read time, in addition to the 35% reduction in network traffic and disk IO. Hitchhiker can thus reduce the latency of degraded reads and perform faster recovery from failed or decommissioned machines.


title: Learning Graphs from Signal Observations under Smoothness Prior

abstract: The construction of a meaningful graph plays a crucial role in the success of many graph-based data representations and algorithms, especially in the emerging field of signal processing on graphs. However, a meaningful graph is not always readily available from the data, nor easy to define depending on the application domain. In this paper, we address the problem of graph learning, where we are interested in learning graph topologies, namely, the relationships between data entities, that well explain the signal observations. In particular, we want to infer a graph such that the input data forms graph signals with smooth variations on the resulting topology. To this end, we adopt a factor analysis model for the graph signals and impose a Gaussian probabilistic prior on the latent variables that control these graph signals. We show that the Gaussian prior leads to an efficient representation that favors the smoothness property of the graph signals. We then propose an algorithm for learning graphs that enforce such smoothness property for the signal observations by minimizing the variations of the signals on the learned graph. Experiments on both synthetic and real world data demonstrate that the proposed graph learning framework can efficiently infer meaningful graph topologies from only the signal observations.


title: Representing Sentences as Low-Rank Subspaces

abstract: Sentences are important semantic units of natural language. A generic, distributional representation of sentences that can capture the latent semantics is beneficial to multiple downstream applications. We observe a simple geometry of sentences -- the word representations of a given sentence (on average 10.23 words in all SemEval datasets with a standard deviation 4.84) roughly lie in a low-rank subspace (roughly, rank 4). Motivated by this observation, we represent a sentence by the low-rank subspace spanned by its word vectors. Such an unsupervised representation is empirically validated via semantic textual similarity tasks on 19 different datasets, where it outperforms the sophisticated neural network models, including skip-thought vectors, by 15% on average.


title: A Survey of Automated Layout Techniques for Information Presentations

abstract: Layout refers to the process of determining the sizes and positions of the visual objects that are part of an information presentation. Automated layout refers to the use of a computer program to automate either all or part of the layout process. This field of research lies at the crossroads between artificial intelligence and human computer interaction. Automated layout of presentations is becoming increasingly important as the amount of data that we need to present rapidly overtakes our ability to present it manually. We survey and analyze the techniques used by research systems that have automated layout components and suggest possible areas of future work.


title: Workplace safety: A meta-analysis of the roles of person and situation factors.

abstract: Recent conceptual and methodological advances in behavioral safety research afford an opportunity to integrate past and recent research findings. Building on theoretical models of worker performance and work climate, this study quantitatively integrates the safety literature by meta-analytically examining person- and situation-based antecedents of safety performance behaviors and safety outcomes (i.e., accidents and injuries). As anticipated, safety knowledge and safety motivation were most strongly related to safety performance behaviors, closely followed by psychological safety climate and group safety climate. With regard to accidents and injuries, however, group safety climate had the strongest association. In addition, tests of a meta-analytic path model provided support for the theoretical model that guided this overall investigation. The implications of these findings for advancing the study and management of workplace safety are discussed.


title: A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication

abstract: The large communication overhead has imposed a bottleneck on the performance of distributed Stochastic Gradient Descent (SGD) for training deep neural networks. Previous works have demonstrated the potential of using gradient sparsification and quantization to reduce the communication cost. However, there is still a lack of understanding about how sparse and quantized communication affects the convergence rate of the training algorithm. In this paper, we study the convergence rate of distributed SGD for non-convex optimization with two communication reducing strategies: sparse parameter averaging and gradient quantization. We show that O(1/ ::: √ ::: MK ::: ) convergence rate can be achieved if the sparsification and quantization hyperparameters are configured properly. We also propose a strategy called periodic quantized averaging (PQASGD) that further reduces the communication cost while preserving the O(1/ ::: √ ::: MK ::: ) convergence rate. Our evaluation validates our theoretical results and shows that our PQASGD can converge as fast as full-precision SGD with only 3%−5% communication data size.


Clustering in heterogeneous network and higher order structure


title: Visualizing high dimensional objective spaces for multi-objective optimization: A virtual reality approach

abstract: This paper presents an approach for constructing visual representations of high dimensional objective spaces using virtual reality. These spaces arise from the solution of multi-objective optimization problems with more than 3 objective functions which lead to high dimensional Pareto fronts which are difficult to use. This approach is preliminarily investigated using both theoretically derived high dimensional Pareto fronts for a test problem (DTLZ2) and practically obtained objective spaces for the 4 dimensional knapsack problem via multi-objective evolutionary algorithms like HLGA, NSGA, and VEGA. The expected characteristics of the high dimensional fronts in terms of relative sizes, sequencing, embedding and asymmetry were systematically observed in the constructed virtual reality spaces.


A Catalog of Software Development Rules for Agile Methods


title: Diseases Causing Oral Dryness

abstract: Saliva is important for the maintenance of oral health and also plays an essential role in a number of oral and gastrointestinal functions. Consequently, patients with reduced salivary secretion and changes in their saliva composition are more susceptible to dental caries, oral infections and mucosal lesions and often have symptoms of a dry and sore mouth, burning and itching oral mucosa, difficulties in chewing and swallowing dry foods, impaired sense of taste, difficulty in speaking and problems with acid reflux. These distressing consequences of salivary hypofunction also have a significant negative impact on quality of life and general health status. Several diseases and medical conditions as well as the medications used for treating them are associated with salivary gland hypofunction (objective evidence of diminished salivary output) and xerostomia (subjective sensation of dry mouth). In autoimmune diseases like Sjogren’s syndrome, salivary gland dysfunction is largely related to structural changes in the salivary glands and in endocrine and metabolic disorders mainly related to pathophysiological changes that affect the formation of saliva. Other diseases affect the autonomic outflow pathway involving the salivary gland innervation, the central nervous system and the salivation centre. This chapter reviews systemic diseases and medical conditions associated with salivary gland hypofunction and xerostomia.


title: Agile Millimeter Wave Networks with Provable Guarantees

abstract: There is much interest in integrating millimeter wave radios (mmWave) into wireless LANs and 5G cellular networks to benefit from their multiple GHz of available spectrum. Yet unlike existing technologies, e.g., WiFi, mmWave radios require highly directional antennas. Since the antennas have pencil-beams, the transmitter and receiver need to align their antenna beams before they can communicate. Existing solutions scan the entire space to find the best alignment. Such a process has been shown to introduce up to seconds of delay, and is unsuitable for wireless networks where an access point has to quickly switch between users and accommodate mobile clients. ::: This paper presents Rapid-Link, a new protocol that can find the best mmWave beam alignment without scanning the space. Given all possible directions for setting the antenna beam, Rapid-Link provably finds the optimal direction in logarithmic number of measurements. Further, Rapid-Link works within the existing 802.11ad standard for mmWave LAN, and can support both clients and access points. We have implemented Rapid-Link in a mmWave radio and evaluated it empirically. Our results show that it reduces beam alignment delay by orders of magnitude. In particular, for highly directional mmWave devices operating under 802.11ad, the delay drops from over a second to 2.5 ms.


title: Learning how to explain neural networks: PatternNet and PatternAttribution

abstract: DeConvNet, Guided BackProp, LRP, were invented to better understand deep neural networks. We show that these methods do not produce the theoretically correct explanation for a linear model. Yet they are used on multi-layer networks with millions of parameters. This is a cause for concern since linear models are simple neural networks. We argue that explanation methods for neural nets should work reliably in the limit of simplicity, the linear models. Based on our analysis of linear models we propose a generalization that yields two explanation techniques (PatternNet and PatternAttribution) that are theoretically sound for linear models and produce improved explanations for deep networks.


title: The use of 99mTc-phytate for sentinel node mapping in melanoma, breast cancer and vulvar cancer: a study of 100 cases

abstract: Sentinel node mapping reduces surgical morbidity and allows the use of more accurate tumour staging techniques. Radionuclide studies are preferentially performed using small colloids, which have limited availability in our country. The possibility of using phytate for sentinel node mapping was raised because of the similarity between its biodistribution and that of nanocolloids in the reticulo-endothelial system. In this paper we evaluated the use of (99m)Tc-phytate for sentinel node mapping, correlating the histopathological results with the status of the rest of the lymph node chain in different malignant tumours. A total of 100 patients were studied. group 1 consisted of 62 patients with breast cancer, group 2 of 20 patients with melanoma and group 3 of 18 patients with vulvar carcinoma. Lymph node scintigraphy was carried out after injecting (99m)Tc-phytate subdermally, and the sentinel node projection was marked on the skin. After 18-24 h, intraoperative sentinel node localisation was performed using a gamma probe (combined with visual localisation using patent blue dye) in 75 patients, and lymph node dissection was then carried out. Radionuclide scintigraphy identified the sentinel node in 98% of all studies. Intraoperative detection using the gamma probe was equally efficient: group 1=93% (38/41), group 2=95% (18/19) and group 3=100% (15/15). The sentinel node was involved in 41%, 31% and 20% of cases in groups 1, 2 and 3, respectively. Among the patients with positive nodes, the sentinel node was the only one affected in 53% of group 1, 50% of group 2 and 67% of group 3 cases. The method's negative predictive value was 91% in group 1 and 100% in the other groups. One false-negative study occurred in a patient who had a multifocal tumour and an intraparenchymatous lymph node; another occurred in a patient with a macroscopically affected node found during surgery. There were no side-effects related to the (99m)Tc-phytate. It is concluded that scintigraphic and intraoperative sentinel node identification was satisfactorily performed using (99m)Tc-phytate. The results were comparable to those previously described in the literature using other radiopharmaceuticals. Easy availability and low cost justify the use of phytate in our practice.


title: Short Abstinence from Online Social Networking Sites Reduces Perceived Stress, Especially in Excessive Users

abstract: Abstract Online social networking sites (SNSs), such as Facebook, provide frequent and copious social reinforcers (e.g., “likes”) delivered at variable time intervals. As a result, some SNS users display excessive, maladaptive behaviors on these platforms. Excessive SNS users, and typical users alike, are often aware of their intense use and psychological dependence on these sites, which may lead to elevated stress. In fact, research has demonstrated that use of SNSs alone induces elevated stress. Other research has begun to investigate the effects of short periods of SNS abstinence, revealing beneficial effects on subjective wellbeing. We aligned these two lines of research and hypothesized that a short period of SNS abstinence would induce a reduction in perceived stress, especially in excessive users. The results confirmed our hypothesis and revealed that both typical and excessive SNS users experienced reduction in perceived stress following SNS abstinence of several days. The effects were particularly pronounced in excessive SNS users. The reduction in stress was not associated with academic performance increases. These results indicate a benefit—at least temporarily—of abstinence from SNSs and provide important information for therapists treating patients who struggle with excessive SNS use.


title: Application of Wearable Inertial Sensors and A New Test Battery for Distinguishing Retrospective Fallers from Non-fallers among Community-dwelling Older People

abstract: Considering the challenge of population ageing and the substantial health problem among the elderly population from falls, the purpose of this study was to verify whether it is possible to distinguish accurately between older fallers and non-fallers, based on data from wearable inertial sensors collected during a specially designed test battery. A comprehensive but practical test battery using 5 wearable inertial sensors for multifactorial fall risk assessment was designed. This was followed by an experimental study on 196 community-dwelling Korean older women, categorized as fallers (N1 = 82) and non-fallers (N2 = 114) based on prior history of falls. Six machine learning models (logistic regression, naïve bayes, decision tree, random forest, boosted tree and support vector machine) were proposed for faller classification. Results indicated that compared with non-fallers, fallers performed significantly worse on the test battery. In addition, the application of sensor data and support vector machine for faller classification achieved an overall accuracy of 89.4% with 92.7% sensitivity and 84.9% specificity. These findings suggest that wearable inertial sensor based systems show promise for elderly fall risk assessment, which could be implemented in clinical practice to identify “at-risk” individuals reliably to promote proactive fall prevention.


title: A constant absolute bandwidth tunable bandpass filter based on mixed coupled varactor loaded open ring resonators

abstract: In this paper, a novel analogue tunable bandpass filter with constant absolute bandwidth is presented. The filter is designed based on full wavelength microstrip open ring resonators loaded with varactors. Magnetic dominated mixed coupling is designed to make the coupling coefficient meet the requirement of constant absolute bandwidth response. The filter is designed, fabricated and measured. The measurement shows that the −3 dB bandwidth is from 105MHz to 109MHz while the central frequency of the passband varying from 2.39 GHz to 2.82 GHz. The return loss is better than 15 dB.


title: Implementation of dual-frequency longitudinal slot array antenna on substrate integrated waveguide at X-band

abstract: In this paper, a dual-frequency substrate integrated waveguide (SIW) longitudinal slot array antenna has been implemented with the combination of two 1×4 slot array etched on the top metallic surface of the line. The two arrays resonate at two distinctive frequencies (8.6 GHz & 10.8 GHz) within X band (8 - 12 GHz) with good radiation pattern at both frequencies along with a gain of 7.7 dB and 9 dB respectively. The two arrays have been fed by a single SIW line which reduces the complexity of feeding network. The proposed design shows good potential to control radiation pattern and side lobe level independently at both frequencies.


title: A Field Experimental Design of a Strengths-Based Training to Overcome Academic Procrastination: Short- and Long-Term Effect

abstract: This study reports on the effect of a newly developed four-week strengths-based training ap-proach to overcome academic procrastination, given to first-year elementary teacher education students (N = 54). The training was based on a strengths-based approach, in which elements of the cognitive behavioral approach were also used. The purpose of the training was to pro-mote awareness of the personal strengths of students who experience academic procrastination regularly and to teach them how to use their personal strengths in situations in which they usually tend to procrastinate. With a pretest-posttest control group design (two experimental groups: n = 31, control group: n = 23), the effect of the training on academic procrastination was studied after 1, 11, and 24 weeks. Results of a one-way analysis of covariance revealed a significant short-term effect of the training. In the long term (after 11 and 24 weeks), the scores for academic procrastination for the intervention groups remained stable, whereas the scores for academic procrastination for the control group decreased to the same level as those of the intervention groups. The findings of this study suggest that a strengths-based approach can be helpful to students at an early stage of their academic studies to initiate their individual process of dealing with academic procrastination. The findings for the long term show the importance of measuring the outcomes of an intervention not only shortly after the interven-tion but also in the long term. Further research is needed to find out how the short-term effect can be maintained in the long-term.


title: South Asian chronic lymphocytic leukaemia patients have more rapid disease progression in comparison to White patients.

abstract: Ethnicity has a major impact on the prevalence of chronic lymphocytic leukaemia (CLL) and may also influence disease phenotype. We compared the clinical features of Southern Asian and White CLL patients managed within one UK region. Asians required treatment almost twice as often as Whites (HR, 1.94) and had a shorter time to first treatment (P = 0.0063). This difference remained significant after adjusting for stage, lymphocyte doubling time and IGHV status (P = 0.026). CLL was diagnosed at younger ages in Asians and racial-specific variations in IGHV usage were demonstrated. Our findings indicate that Southern Asian race has a negative impact on CLL phenotype.


title: The attributional Style Questionnaire

abstract: Of current interest are the causal attributions offered by depressives for the good and bad events in their lives. One important attributional account of depression is the reformulated learned helplessness model, which proposes that depressive symptoms are associated with an attributional style in which uncontrollable bad events are attributed to internal (versus external), stable (versus unstable), and global (versus specific) causes. We describe the Attributional Style Questionnaire, which measures individual differences in the use of these attributional dimensions. We report means, reliabilities, intercorrelations, and test-retest stabilities for a sample of 130 undergraduates. Evidence for the questionnaire's validity is discussed. The Attributional Style Questionnaire promises to be a reliable and valid instrument.


title: Toward Practical Homomorphic Evaluation of Block Ciphers Using Prince

abstract: We present the homomorphic evaluation of the Prince block cipher. Our leveled implementation is based on a generalization of NTRU. We are motivated by the drastic bandwidth savings that may be achieved by scheme conversion. To unlock this advantage we turn to lightweight ciphers such as Prince. These ciphers were designed from scratch to yield fast and compact implementations on resource-constrained embedded platforms. We show that some of these ciphers have the potential to enable near practical homomorphic evaluation of block ciphers. Indeed, our analysis shows that Prince can be implemented using only a 24 level deep circuit. Using an NTRU based implementation we achieve an evaluation time of 3.3 s per Prince block – one and two orders of magnitude improvement over homomorphic AES implementations achieved using NTRU, and BGV-style homomorphic encryption libraries, respectively.


title: Freedom of choice, ease of use, and the formation of interface preferences

abstract: How does users' freedom of choice, or the lack thereof, affect interface preferences? The research reported in this article approaches this question from two theoretical perspectives. The first of these argues that an interface with a dominant market share benefits from the absence of competition because users acquire skills that are specific to that particular interface, which in turn reduces the probability that they will switch to a new competitor interface in the future. By contrast, the second perspective proposes that the advantage that a market leader has in being able to install a set of non-transferable skills in its user base is offset by a psychological force that causes humans to react against perceived constraints on their freedom of choice. We test a research model that incorporates the key predictions of these two theoretical perspectives in an experiment involving consequential interface choices. We find strong support for the second perspective, which builds upon the theory of psychological reactance.


title: Design and implementation of an Android host-based intrusion prevention system

abstract: Android has a dominating share in the mobile market and there is a significant rise of mobile malware targeting Android devices. Android malware accounted for 97% of all mobile threats in 2013 [26]. To protect smartphones and prevent privacy leakage, companies have implemented various host-based intrusion prevention systems (HIPS) on their Android devices. In this paper, we first analyze the implementations, strengths and weaknesses of three popular HIPS architectures. We demonstrate a severe loophole and weakness of an existing popular HIPS product in which hackers can readily exploit. Then we present a design and implementation of a secure and extensible HIPS platform---"Patronus." Patronus not only provides intrusion prevention without the need to modify the Android system, it can also dynamically detect existing malware based on runtime information. We propose a two-phase dynamic detection algorithm for detecting running malware. Our experiments show that Patronus can prevent the intrusive behaviors efficiently and detect malware accurately with a very low performance overhead and power consumption.


title: Increasing individual upper alpha power by neurofeedback improves cognitive performance in human subjects

abstract: The hypothesis was tested of whether neurofeedback training (NFT)—applied in order to increase upper alpha but decrease theta power—is capable of increasing cognitive performance. A mental rotation task was performed before and after upper alpha and theta NFT. Only those subjects who were able to increase their upper alpha power (responders) performed better on mental rotations after NFT. Training success (extent of NFT-induced increase in upper alpha power) was positively correlated with the improvement in cognitive performance. Furthermore, the EEG of NFT responders showed a significant increase in reference upper alpha power (i.e. in a time interval preceding mental rotation). This is in line with studies showing that increased upper alpha power in a prestimulus (reference) interval is related to good cognitive performance.


title: Lossless image compression based on Kernel Least Mean Squares

abstract: This paper introduces a novel approach for coding luminance images using kernel-based adaptive filtering and context-adaptive arithmetic coding. This approach tackles the problem that is present in current image and video coders; these coders depend on assumptions of the image and are constrained by the linearity of their predictors. The efficacy of the predictors determines the compression gain. The goal is to create a generic image coder that learns and adapts to the characteristics of the signals and handles nonlinearity in the prediction. Results show that pixel luminance prediction using the Kernel Least Mean Squares (KLMS) yields a significant gain compared to the standard Least Mean Squares algorithm. By coding the residual using a Context-Adaptive Arithmetic Coder (CAAC), the codec is able to outperform the current industry standards of lossless image coding. An average bitrate reduction of more than 2.5% is found for the used test set.


title: Modeling Human Motion Using Binary Latent Variables

abstract: We propose a non-linear generative model for human motion data that uses an undirected model with binary latent variables and real-valued “visible” variables that represent joint angles. The latent and visible variables at each time step receive directed connections from the visible variables at the last few time-steps. Such an architecture makes on-line inference efficient and allows us to use a simple approximate learning procedure. After training, the model finds a single set of parameters that simultaneously capture several different kinds of motion. We demonstrate the power of our approach by synthesizing various motion sequences and by performing on-line filling in of data lost during motion capture.


title: AquaNodes: an underwater sensor network

abstract: This paper describes an underwater sensor network with dual communication and support for sensing and mobility. The nodes in the system are connected acoustically for broadcast communication using an acoustic modem we developed. For higher point to point communication speed the nodes are networked optically using custom built optical modems. We describe the hardware details of the underwater sensor node and the communication and networking protocols. Finally, we present and discuss the results from experiments with this system.


title: Buffer Overflow and Format String Overflow Vulnerabilities

abstract: Buffer overflow vulnerabilities are among the most widespread of security problems. Numerous incidents of buffer overflow attacks have been reported and many solutions have been proposed, but a solution that is both complete and highly practical is yet to be found. Another kind of vulnerability called format string overflow has recently been found and although not as widespread as buffer overflow, format string overflow attacks are no less dangerous.This article surveys representative techniques of exploiting buffer overflow and format string overflow vulnerabilities and their currently available defensive measures. We also describe our buffer overflow detection technique that range checks the referenced buffers at run-time. We augment executable files with the type information of automatic buffers (local variables and parameters of functions) and static buffers (global variables in the data/bss section) and maintain the sizes of allocated heap buffers in order to detect an actual occurrence of buffer overflow. We describe a simple implementation with which we currently protect vulnerable copy functions in the C library.


Nonlinear Ultrasound Simulation Based on Full-Wave Model and Comparisons with KZK


title: Facial smile detection based on deep learning features

abstract: Smile detection from facial images is a specialized task in facial expression analysis with many potential applications such as smiling payment, patient monitoring and photo selection. The current methods on this study are to represent face with low-level features, followed by a strong classifier. However, these manual features cannot well discover information implied in facial images for smile detection. In this paper, we propose to extract high-level features by a well-designed deep convolutional networks (CNN). A key contribution of this work is that we use both recognition and verification signals as supervision to learn expression features, which is helpful to reduce same-expression variations and enlarge different-expression differences. Our method is end-to-end, without complex pre-processing often used in traditional methods. High-level features are taken from the last hidden layer neuron activations of deep CNN, and fed into a soft-max classifier to estimate. Experimental results show that our proposed method is very effective, which outperforms the state-of-the-art methods. On the GENKI smile detection dataset, our method reduces the error rate by 21% compared with the previous best method.


title: Regularized kernel-based Wiener filtering. Application to magnetoencephalographic signals denoising

abstract: We take a new approach in nonlinear Wiener filtering. This approach is based on the theory of reproducing kernel Hilbert spaces (RKHS). By means of the well-known "kernel trick", the arithmetic operations are carried out in the initial space. We show that the solution is given by solving a linear system which may be ill-conditioned. To find a solution for such a problem, we resorted to a kernel principal component analysis (KPCA) method to perform dimensionality reduction in RKHS. A new reduced-rank Wiener filter based on KPCA is thus elaborated. It is applied on magnetoencephalographic (MEG) data for cardiac artifacts extraction.


title: Convolutional Imputation of Matrix Networks

abstract: A matrix network is a family of matrices, with relatedness modeled by a weighted graph. We consider the task of completing a partially observed matrix network. We assume a novel sampling scheme where a fraction of matrices might be completely unobserved. How can we recover the entire matrix network from incomplete observations? This mathematical problem arises in many applications including medical imaging and social networks. ::: To recover the matrix network, we propose a structural assumption that the matrices have a graph Fourier transform which is low-rank. We formulate a convex optimization problem and prove an exact recovery guarantee for the optimization problem. Furthermore, we numerically characterize the exact recovery regime for varying rank and sampling rate and discover a new phase transition phenomenon. Then we give an iterative imputation algorithm to efficiently solve the optimization problem and complete large scale matrix networks. We demonstrate the algorithm with a variety of applications such as MRI and Facebook user network.


title: A virtual electrical test bench for more electrical aircraft architecture verification and energy management development

abstract: The shift towards More Electrical Aircraft is bringing with it many new technologies and a redesign of the existing aircraft electrical network, both of which require thorough testing before their implementation. This led to the creation of the Copper Bird® test bench which recreates the aircraft electrical system and is used for equipment evaluation and validation. This paper details the development of an accompanying virtual electrical test bench; modelling and real-time simulation of the operation of an aircraft electrical system for conducting verification activities over different aircraft electrical architectures and developing energy management strategies. In this paper particular attention is given to the PEM fuel cell model which has been characterized and validated using a scaled version of the electrical test bench. The model shows good accuracy, giving an average error of less than ±350mV and a max error of only ±3%. Fuzzy logic-based energy management was then developed for a fuel cell and battery-based auxiliary power unit with the fuel cell supplying average load power (whilst acting as a current source) and the battery handling load transients and overload situations. The energy management attains its goals of operating the fuel cell at its average power, in order to extend its lifetime, whilst maintaining the battery state of charge between 50% and 90%, also in order to extend its lifetime as well as maintaining its ability to absorb excess generated power.


title: Assessment of lean manufacturing effect on business performance using Bayesian Belief Networks

abstract: This study aims to analyze lean manufacturing effect on financial, non-financial and sustainability performances.Causal maps are used for defining the inter-relation and intra-relation of lean factors.Bayesian Belief Networks are used for linking the lean factors.Effects of various combinations of lean factors on business performance are analyzed.Developed scenarios can be a lead for defining the business strategies based on lean thinking in any industry. The challenge of agility for adopting new business norms creates the need for measuring performance under changing conditions. This study aims to demonstrate the financial and non-financial consequences of implementing different combinations of lean techniques on the business performance. Bayesian Belief Network is used in studying the effects of factors under changing conditions. There are seven lean factors and four achievements studied to analyze the impact on three performance indicators. Bayesian Belief Network is constructed on the lean aspects that stimuli flexibility, reliability, quality and time of operations, which will have positive impacts on the financial, non-financial and sustainability performances of suppliers. A case study is carried out for suppliers in the automotive industry and scenarios with different combinations of lean factors are studied. This study gives a new vision in applying Bayesian network for business performance measures considering both the tangible and intangible results under changing business conditions.


title: FHEW: Bootstrapping Homomorphic Encryption in Less Than a Second

abstract: The main bottleneck affecting the efficiency of all known fully homomorphic encryption (FHE) schemes is Gentry’s bootstrapping procedure, which is required to refresh noisy ciphertexts and keep computing on encrypted data. Bootstrapping in the latest implementation of FHE, the HElib library of Halevi and Shoup (Crypto 2014), requires about six minutes. We present a new method to homomorphically compute simple bit operations, and refresh (bootstrap) the resulting output, which runs on a personal computer in just about half a second. We present a detailed technical analysis of the scheme (based on the worst-case hardness of standard lattice problems) and report on the performance of our prototype implementation.


title: A Morphological Tagger for Korean: Statistical Tagging Combined with Corpus-Based Morphological Rule Application

abstract: This paper describes a novel approach to morphological tagging for Korean, an agglutinative language with a very productive inflectional system. The tagger takes raw text as input and returns a lemmatized and morphologically disambiguated output for each word: the lemma is labeled with a part-of-speech (POS) tag and the inflections are labeled with inflectional tags. Unlike the standard approach to tagging for morphologically complex languages, in our proposed approach the tagging phase precedes the analysis phase. It comprises a trigram-based tagging component followed by a morphological rule application component, obtaining 95% precision and recall on unseen test data.


title: Cryptanalytic attacks on MIFARE classic protocol

abstract: MIFARE Classic is the most widely used contactless smart card in the world. It implements a proprietary symmetric-key mutual authentication protocol with a dedicated reader and a proprietary stream cipher algorithm known as CRYPTO1, both of which have been reverse engineered. The existing attacks in various scenarios proposed in the literature demonstrate that MIFARE Classic does not offer the desired 48-bit security level. The most practical scenario is the card-only scenario where a fake, emulated reader has a wireless access to a genuine card in the on-line stage of the attack. The most effective known attack in the card-only scenario is a differential attack, which is claimed to require about 10 seconds of average on-line time in order to reconstruct the secret key from the card. This paper presents a critical comprehensive survey of currently known attacks on MIFARE Classic, puts them into the right perspective in light of the prior art in cryptanalysis, and proposes a number of improvements. It is shown that the differential attack is incorrectly analyzed and is optimized accordingly. A new attack of a similar, differential type is also introduced. In comparison with the optimized differential attack, it has a higher success probability of about 0.906 and a more than halved on-line time of about 1.8 seconds.


title: Monte Carlo methods for security pricing

abstract: The Monte Carlo approach has proved to be a valuable and flexible computational tool in modern finance. This paper discusses some of the recent applications of the Monte Carlo method to security pricing problems, with emphasis on improvements in efficiency. We first review some variance reduction methods that have proved useful in finance. Then we describe the use of deterministic low-discrepancy sequences, also known as quasi-Monte Carlo methods, for the valuation of complex derivative securities. We summarize some recent applications of the Monte Carlo method to the estimation of partial derivatives or risk sensitivities and to the valuation of American options. We conclude by mentioning other applications.


title: E-commerce adoption in Malaysian Small and Medium Enterprises Practitioner Firms: A revisit on Rogers' model

abstract: The current climate of business necessitates competitions that are often tough and unpredictable. All organizations, regardless their size and scope of operation, are facing severe competitive challenges. In order to cope with this phenomenon, managers are turning to e-commerce in their respective organizations. The present study hinges upon exploring and explaining the different dimensions of the adoption of e-commerce among Small and medium enterprises, based on the Five Factors of Diffusion of Innovation Model derived by Rogers. In this study, however, we employed the survey methods. A questionnaire was distributed to 1,200 managers and employees in the manufacturing, service and agricultural sectors by email; with a response rate of 10%. The results gleamed from this study posits that relative advantage is influential vis-a-vis e-commerce adoption. Trialability and Observability factors affect the level of confidence of management, which in turn, influences e-commerce adoption. Meanwhile, the existing culture of a company affects the resistance of employees, which in turn negatively effects the e-commerce adoption, while complexity does not significantly influence the e-commerce adoption.


title: User Interaction Models for Disambiguation in Programming by Example

abstract: Programming by Examples (PBE) has the potential to revolutionize end-user programming by enabling end users, most of whom are non-programmers, to create small scripts for automating repetitive tasks. However, examples, though often easy to provide, are an ambiguous specification of the user's intent. Because of that, a key impedance in adoption of PBE systems is the lack of user confidence in the correctness of the program that was synthesized by the system. We present two novel user interaction models that communicate actionable information to the user to help resolve ambiguity in the examples. One of these models allows the user to effectively navigate between the huge set of programs that are consistent with the examples provided by the user. The other model uses active learning to ask directed example-based questions to the user on the test input data over which the user intends to run the synthesized program. Our user studies show that each of these models significantly reduces the number of errors in the performed task without any difference in completion time. Moreover, both models are perceived as useful, and the proactive active-learning based model has a slightly higher preference regarding the users' confidence in the result.


SHAvisual: A Visualization Tool for the Secure Hash Algorithm


title: Teaching Machines to Describe Images via Natural Language Feedback

abstract: Robots will eventually be part of every household. It is thus critical to enable algorithms to learn from and be guided by non-expert users. In this paper, we bring a human in the loop, and enable a human teacher to give feedback to a learning agent in the form of natural language. We argue that a descriptive sentence can provide a much stronger learning signal than a numeric reward in that it can easily point to where the mistakes are and how to correct them. We focus on the problem of image captioning in which the quality of the output can easily be judged by non-experts. We propose a hierarchical phrase-based captioning model trained with policy gradients, and design a feedback network that provides reward to the learner by conditioning on the human-provided feedback. We show that by exploiting descriptive feedback our model learns to perform better than when given independently written human captions.


title: The Convoy Model: Explaining Social Relations From a Multidisciplinary Perspective

abstract: Purpose of the Study: Social relations are a key aspect of aging and the life course. In this paper, we trace the scientific origins of the study of social relations, focusing in particular on research grounded in the convoy model. Design and Methods: We first briefly review and critique influential historical studies to illustrate how the scientific study of social relations developed. Next, we highlight early and current findings grounded in the convoy model that have provided key insights into theory, method, policy, and practice in the study of aging. Results: Early social relations research, while influential, lacked the combined approach of theoretical grounding and methodological rigor. Nevertheless, previous research findings, especially from anthropology, suggested the importance of social relations in the achievement of positive outcomes. Considering both life span and life course perspectives and grounded in a multidisciplinary perspective, the convoy model was developed to unify and consolidate scattered evidence while at the same time directing future empirical and applied research. Early findings are summarized, current evidence presented, and future directions projected. Implications: The convoy model has provided a useful framework in the study of aging, especially for understanding predictors and consequences of social relations across the life course.


title: A 0.6 V–2.4 V input, fully integrated reconfigurable switched-capacitor DC-DC converter for energy harvesting sensor tags

abstract: We present a fully integrated, reconfigurable switched-capacitor power supply solution for energy harvesting sensor tags. This converter generates an adjustable regulated output rail (1.2 V typical) from a DC input voltage of 0.6 V–2.4 V or an AC input voltage with amplitude of 0.8 V–2.6 V The converter is implemented in a 0.25-μm CMOS process and features on chip flying capacitors as well as an integrated storage capacitor (5 nF). The peak efficiency of the converter is 76% and it provides a maximum load current of 100 μA. To support large transient loads, the chip also features efficient multi-step charging of an external storage capacitor (up to 10 μF).


An ontology for context-aware pervasive computing environments


title: Contribution of disc degeneration to osteophyte formation in the cervical spine: a biomechanical investigation

abstract: Cervical spine disorders such as spondylotic radiculopathy and myelopathy are often related to osteophyte formation. Bone remodeling experimental–analytical studies have correlated biomechanical responses such as stress and strain energy density to the formation of bony outgrowth. Using these responses of the spinal components, the present study was conducted to investigate the basis for the occurrence of disc-related pathological conditions. An anatomically accurate and validated intact finite element model of the C4-C5-C6 cervical spine was used to simulate progressive disc degeneration at the C5-C6 level. Slight degeneration included an alteration of material properties of the nucleus pulposus representing the dehydration process. Moderate degeneration included an alteration of fiber content and material properties of the anulus fibrosus representing the disintegrated nature of the anulus in addition to dehydrated nucleus. Severe degeneration included decrease in the intervertebral disc height with dehydrated nucleus and disintegrated anulus. The intact and three degenerated models were exercised under compression, and the overall force–displacement response, local segmental stiffness, anulus fiber strain, disc bulge, anulus stress, load shared by the disc and facet joints, pressure in the disc, facet and uncovertebral joints, and strain energy density and stress in the vertebral cortex were determined. The overall stiffness (C4-C6) increased with the severity of degeneration. The segmental stiffness at the degenerated level (C5-C6) increased with the severity of degeneration. Intervertebral disc bulge and anulus stress and strain decreased at the degenerated level. The strain energy density and stress in vertebral cortex increased adjacent to the degenerated disc. Specifically, the anterior region of the cortex responded with a higher increase in these responses. The increased strain energy density and stress in the vertebral cortex over time may induce the remodeling process according to Wolff's law, leading to the formation of osteophytes.


title: Topic classification in social media using metadata from hyperlinked objects

abstract: Social media presents unique challenges for topic classification, including the brevity of posts, the informal nature of conversations, and the frequent reliance on external hyperlinks to give context to a conversation. In this paper we investigate the usefulness of these external hyperlinks for determining the topic of an individual post. We focus specifically on hyperlinks to objects which have related metadata available on the Web, including Amazon products and YouTube videos. Our experiments show that the inclusion of metadata from hyperlinked objects in addition to the original post content improved classifier performance measured with the F-score from 84% to 90%. Further, even classification based on object metadata alone outperforms classification based on the original post content.


title: 3D printed tactile picture books for children with visual impairments: a design probe

abstract: Young children with visual impairments greatly benefit from tactile graphics (illustrations, images, puzzles, objects) during their learning processes. In this paper we present insight about using a 3D printed tactile picture book as a design probe. This has allowed us to identify and engage stakeholders in our research on improving the technical and human processes required for creating 3D printed tactile pictures, and cultivate a community of practice around these processes. We also contribute insight about how our inperson and digital methods of interacting with teachers, parents, and other professionals dedicated to supporting children with visual impairments contributes to research practices.


title: www.jmeds.eu Cryptographic Applications using FPGA Technology

abstract: Cryptographic systems have become a part of our daily life through the need of security of many common activities such as communication, payments, data transfers etc. The best support in design and implementation of cryptographic applications is offered by embedded systems such as ASICs and FPGAs. In the past few years, the increase in performance of FPGAs has made them key components in implementing cryptographic systems. One of the most important parts of the cryptographic systems is the random number generator used. Combinations of PRNG and TRNG are commonly used. A good and efficient TRNG implementation is very important and can be achieved through FPGA technology.


title: Rhinoplasty: The Asymmetric Crooked Nose—An Overview

abstract: There are three reasons why the asymmetric crooked nose is one of the greatest challenges in rhinoplasty surgery. First, the complexity of the problem is not appreciated by the patient nor understood by the surgeon. Patients often see the obvious deviation of the nose, but not the distinct differences between the right and left sides. Surgeons fail to understand and to emphasize to the patient that each component of the nose is asymmetric. Second, these deformities can be improved, but rarely made flawless. For this reason, patients are told that the result will be all "-er words," better, straighter, cuter, but no "t-words," there is no perfect nor straight. Most surgeons fail to realize that these cases represent asymmetric noses on asymmetric faces with the variable of ipsilateral and contralateral deviations. Third, these cases demand a wide range of sophisticated surgical techniques, some of which have a minimal margin of error. This article offers an in-depth look at analysis, preoperative planning, and surgical techniques available for dealing with the asymmetric crooked nose.


title: Assessment of high-heat-flux thermal management schemes

abstract: This paper explores the recent research developments in high-heat-flux thermal management. Cooling schemes such as pool boiling, detachable heat sinks, channel flow boiling, microchannel and mini-channel heat sinks, jet-impingement, and sprays, are discussed and compared relative to heat dissipation potential, reliability, and packaging concerns. It is demonstrated that, while different cooling options can be tailored to the specific needs of individual applications, system considerations always play a paramount role in determining the most suitable cooling scheme. It is also shown that extensive fundamental electronic cooling knowledge has been amassed over the past two decades. Yet there is now a growing need for hardware innovations rather than perturbations to those fundamental studies. An example of these innovations is the cooling of military avionics, where research findings from the electronic cooling literature have made possible the development of a new generation of cooling hardware which promise order of magnitude increases in heat dissipation compared to today's cutting edge avionics cooling schemes.


title: High-efficiency 200 kHz inverter for induction heating applications

abstract: A Mosfet inverter suitable for medium frequency induction heating applications is described.


title: Improving Web Application Firewalls to detect advanced SQL injection attacks

abstract: Injections flaws which include SQL injection are the most prevalent security threats affecting Web applications[1]. To mitigate these attacks, Web Application Firewalls (WAFs) apply security rules in order to both inspect HTTP data streams and detect malicious HTTP transactions. Nevertheless, attackers can bypass WAF's rules by using sophisticated SQL injection techniques. In this paper, we introduce a novel approach to dissect the HTTP traffic and inspect complex SQL injection attacks. Our model is a hybrid Injection Prevention System (HIPS) which uses both a machine learning classifier and a pattern matching inspection engine based on reduced sets of security rules. Our Web Application Firewall architecture aims to optimize detection performances by using a prediction module that excludes legitimate requests from the inspection process.


title: Optimizing Display Advertising in Online Social Networks

abstract: Advertising is a significant source of revenue for most online social networks. Conventional online advertising methods need to be customized for online social networks in order to address their distinct characteristics. Recent experimental studies have shown that providing social cues along with ads, e.g. information about friends liking the ad or clicking on an ad, leads to higher click rates. In other words, the probability of a user clicking an ad is a function of the set of friends that have clicked the ad. In this work, we propose formal probabilistic models to capture this phenomenon, and study the algorithmic problem that then arises. Our work is in the context of display advertising where a contract is signed to show an ad to a pre-determined number of users. The problem we study is the following: given a certain number of impressions, what is the optimal display strategy, i.e. the optimal order and the subset of users to show the ad to, so as to maximize the expected number of clicks? Unlike previous models of influence maximization, we show that this optimization problem is hard to approximate in general, and that it is related to finding dense subgraphs of a given size. In light of the hardness result, we propose several heuristic algorithms including a two-stage algorithm inspired by influence-and-exploit strategies in viral marketing. We evaluate the performance of these heuristics on real data sets, and observe that our two-stage heuristic significantly outperforms the natural baselines.


title: Barycentric Coordinates for Convex Polytopes

abstract: An extension of the standard barycentric coordinate functions for simplices to arbitrary convex polytopes is described. The key to this extension is the construction, for a given convex polytope, of a unique polynomial associated with that polytope. This polynomial, theadjoint of the polytope, generalizes a previous two-dimensional construction described by Wachspress. The barycentric coordinate functions for the polytope are rational combinations of adjoints of various dual cones associated with the polytope.


title: LD-SDS: Towards an Expressive Spoken Dialogue System based on Linked-Data

abstract: In this work we discuss the related challenges and describe an approach towards the fusion of state-of-the-art technologies from the Spoken Dialogue Systems (SDS) and the Semantic Web and Information Retrieval domains. We envision a dialogue system named LD-SDS that will support advanced, expressive, and engaging user requests, over multiple, complex, rich, and open-domain data sources that will leverage the wealth of the available Linked Data. Specifically, we focus on: a) improving the identification, disambiguation and linking of entities occurring in data sources and user input; b) offering advanced query services for exploiting the semantics of the data, with reasoning and exploratory capabilities; and c) expanding the typical information seeking dialogue model (slot filling) to better reflect real-world conversational search scenarios.


title: The Need for Surveillance in Intelligent Transportation Systems

abstract: This article is a continuation of a previous article (see PATH Database reference no. 11197) in which the author examined the the need for traffic surveillance and the parameters that need to be detected. In this article, he identifies one additional parameter vehicle class category, and discusses how some of the traffic related parameters can be measured. He gives an overview of some innovative ways that have been devised by PATH researchers to accurately measure travel time.


title: Feature selection for ranking using boosted trees

abstract: Modern search engines have to be fast to satisfy users, so there are hard back-end latency requirements. The set of features useful for search ranking functions, though, continues to grow, making feature computation a latency bottleneck. As a result, not all available features can be used for ranking, and in fact, much of the time, only a small percentage of these features can be used. Thus, it is crucial to have a feature selection mechanism that can find a subset of features that both meets latency requirements and achieves high relevance. To this end, we explore different feature selection methods using boosted regression trees, including both greedy approaches (selecting the features with highest relative importance as computed by boosted trees; discounting importance by feature similarity and a randomized approach. We evaluate and compare these approaches using data from a commercial search engine. The experimental results show that the proposed randomized feature selection with feature-importance-based backward elimination outperforms greedy approaches and achieves a comparable relevance with 30 features to a full-feature model trained with 419 features and the same modeling parameters.


title: Full-mouth adhesive rehabilitation in case of severe dental erosion, a minimally invasive approach following the 3-step technique.

abstract: A full-mouth adhesive rehabilitation in case of severe dental erosion may present a challenge for both the clinician and the laboratory technician, not only for the multiple teeth to be restored, but also for their time schedule, difficult to be included in a busy agenda of a private practice. Thanks to the simplicity of the 3-step technique, full-mouth rehabilitations become easier to handle. In this article the treatment of a very compromised case of dental erosion (ACE class V) is illustrated, implementing only adhesive techniques. The very pleasing clinical outcome was the result of the esthetic, mechanic and most of all biological success achieved, confirming that minimally invasive dentistry should always be the driving motor of any rehabilitation, especially in patients who have already suffered from conspicuous tooth destruction.


title: Efficient and Autonomous Energy Management Techniques for the Future Smart Homes

abstract: Smart grid with latest technologies provides solid foundation for the implementation of energy management systems at home premises. This paper proposes an autonomous energy management-based cost reduction solution for peak load times using a home energy management system (HEMS). Within a home environment, both the real time and the schedulable appliances are connected with smart meter through HEMS. We formulate an optimization problem under various practical constraints, which is shown to be a mixed integer programming problem that can be solved through a step-wise approach. A novel scheme based on Dijkstra algorithm is proposed, which results in the similar performance to that of the proposed optimal scheme while exhibiting much lower complexity. To further save the computational efforts, a low complexity scheme is also proposed, which produces considerably better results than the non-optimized scheme with the same complexity yet. Simulation results are presented at show the performance and complexity comparison of different proposed solutions and the existing methods.


title: Exploring secondary school students’ understanding and practices of waste management in Ogun State, Nigeria

abstract: This study examined the level of awareness, knowledge and practices of secondary schools students with regard to waste management. Few studies have captured waste management problems in Nigerian educational institutions, particularly the views of students. Using a structured, self-administered questionnaire, 650 students were surveyed from six secondary schools in two of the four educational zones of Ogun State. Data collected were subjected to percentage, mean, standard deviation, t-test and chi-square statistical analyses. Findings revealed that secondary school students from the sampled zones were aware of waste problems on their school compounds, but possessed poor waste management practices. The study showed that propensity for waste management practices differ by sex, class and age of students. Significant relationships were observed between students‘ sex, age and class and their level of awareness, knowledge and practices of waste management.


title: License Plate Recognition using Multi-cluster and Multilayer Neural Networks

abstract: Vehicle license plat recognition has been a much studied research area in many countries. Due to the different types of license plates being used, the requirement of an automatic license plate recognition system is rather different for each country. In this paper, an automatic license plate recognition system is proposed for Malaysian vehicles with standard license plates based on image processing, feature extraction and neural networks. The image-processing library is developed in-house which we referred to as Vision System Development Platform (VSDP). Multi-Cluster approach is applied to locate the license plate at the right position while Kirsch Edge feature extraction technique is used to extract features from the license plates characters which are then used as inputs to the neural network classifier. The neural network model is the standard multilayered perceptron trained using the back-propagation algorithm. The prototyped system has an accuracy of more than 91%, however, suggestions to further improve the system are discussed in this paper based on the analysis of the error.


title: Barriers of Modern Contraceptive Practices among Asian Women: A Mini Literature Review

abstract: Family planning has been cited as essential to the achievement of Millennium Development Goals (MDG). Family planning has a direct impact on women’s health and consequence of each pregnancy. The use of modern contraception among Asian women is less than global average. In Asia a majority of unintended pregnancies are due to using traditional contraceptive or no methods which lead to induced unsafe abortion. Cultural attitudes, lack of knowledge of methods and reproduction, socio demographic factors, and health service barriers are the main obstacles to modern contraceptive practice among Asian women. Culturally sensitive family planning program, reforming health system, and reproductive health education through mass media to create awareness of the benefits of planned parenthood are effective strategies to improve modern contraceptive practice among Asian women.


title: The evolution of episodic memory

abstract: One prominent view holds that episodic memory emerged recently in humans and lacks a “(neo)Darwinian evolution” [Tulving E (2002) Annu Rev Psychol 53:1–25]. Here, we review evidence supporting the alternative perspective that episodic memory has a long evolutionary history. We show that fundamental features of episodic memory capacity are present in mammals and birds and that the major brain regions responsible for episodic memory in humans have anatomical and functional homologs in other species. We propose that episodic memory capacity depends on a fundamental neural circuit that is similar across mammalian and avian species, suggesting that protoepisodic memory systems exist across amniotes and, possibly, all vertebrates. The implication is that episodic memory in diverse species may primarily be due to a shared underlying neural ancestry, rather than the result of evolutionary convergence. We also discuss potential advantages that episodic memory may offer, as well as species-specific divergences that have developed on top of the fundamental episodic memory architecture. We conclude by identifying possible time points for the emergence of episodic memory in evolution, to help guide further research in this area.