title: Optimizing SPARQL Query Answering over OWL Ontologies

abstract: The SPARQL query language is currently being extended by the World Wide Web Consortium (W3C) with so-called entailment regimes. An entailment regime defines how queries are evaluated under more expressive semantics than SPARQL's standard simple entailment, which is based on subgraph matching. The queries are very expressive since variables can occur within complex concepts and can also bind to concept or role names. ::: ::: In this paper, we describe a sound and complete algorithm for the OWL Direct Semantics entailment regime. We further propose several novel optimizations such as strategies for determining a good query execution order, query rewriting techniques, and show how specialized OWL reasoning tasks and the concept and role hierarchy can be used to reduce the query execution time. For determining a good execution order, we propose a cost-based model, where the costs are based on information about the instances of concepts and roles that are extracted from a model abstraction built by an OWL reasoner. We present two ordering strategies: a static and a dynamic one. For the dynamic case, we improve the performance by exploiting an individual clustering approach that allows for computing the cost functions based on one individual sample from a cluster. ::: ::: We provide a prototypical implementation and evaluate the efficiency of the proposed optimizations. Our experimental study shows that the static ordering usually outperforms the dynamic one when accurate statistics are available. This changes, however, when the statistics are less accurate, e.g., due to nondeterministic reasoning decisions. For queries that go beyond conjunctive instance queries we observe an improvement of up to three orders of magnitude due to the proposed optimizations.


title: Stability of Networked Control Systems With Uncertain Time-Varying Delays

abstract: In this technical note, a new approach for the stability analysis and controller synthesis of networked control systems (NCSs) with uncertain, time-varying, network delays is presented. Based on the Jordan form of the continuous-time plant, a discrete-time representation of the NCS is derived. Using this model for delays that can be both smaller and larger than the sampling interval, sufficient LMI conditions for stability and feedback stabilization are proposed. The results are illustrated by a typical motion control example.


title: Visual Explanation by High-Level Abduction: On Answer-Set Programming Driven Reasoning about Moving Objects

abstract: We propose a hybrid architecture for systematically computing robust visual explanation(s) encompassing hypothesis formation, belief revision, and default reasoning with video data. The architecture consists of two tightly integrated synergistic components: (1) (functional) answer set programming based abductive reasoning with space-time tracklets as native entities; and (2) a visual processing pipeline for detection based object tracking and motion analysis. ::: We present the formal framework, its general implementation as a (declarative) method in answer set programming, and an example application and evaluation based on two diverse video datasets: the MOTChallenge benchmark developed by the vision community, and a recently developed Movie Dataset.


title: One-class collaborative filtering based on rating prediction and ranking prediction

abstract: One-Class Collaborative Filtering (OCCF) has recently received much attention in recommendation communities due to their close relationship with real industry problem settings. However, the problem with previous research studies on OCCF is that they focused on either rating prediction or ranking prediction, but no concerted research effort has been devoted to developing a recommendation approach that simultaneously optimizes both the ratings and rank of the recommended items. In order to overcome the defects of prior research, a new better unified OCCF approach (UOCCF) based on the newest Collaborative Less-is-More Filtering (CLiMF) approach and the Probabilistic Matrix Factorization (PMF) approach was proposed, which benefits from the ranking-oriented perspective and the rating-oriented perspective by sharing common latent features of users and items in CLiMF and PMF. We also provide an efficient learning algorithm to solve the optimization problem for UOCCF. Experimental results on practical datasets showed that our proposed UOCCF approach outperformed existing OCCF approaches (both ranking-oriented and rating-oriented) over different evaluation metrics, and that the UOCCF approach enjoys the advantage of low complexity and is shown to be linear with the number of observed ratings in a given useritem rating matrix. Because of its high precision and good expansibility, UOCCF is suitable for processing big data, and has wide application prospects in the field of internet information recommendation.


title: Timing attacks on Web privacy

abstract: We describe a class of attacks that can compromise the privacy of users’ Web-browsing histories. The attacks allow a malicious Web site to determine whether or not the user has recently visited some other, unrelated Web page. The malicious page can determine this information by measuring the time the user’s browser requires to perform certain operations. Since browsers perform various forms of caching, the time required for operations depends on the user’s browsing history; this paper shows that the resulting time variations convey enough information to compromise users’ privacy. This attack method also allows other types of information gathering by Web sites, such as a more invasive form of Web “cookies”. The attacks we describe can be carried out without the victim’s knowledge, and most “anonymous browsing” tools fail to prevent them. Other simple countermeasures also fail to prevent these attacks. We describe a way of reengineering browsers to prevent most of them.


title: Faster and Cheaper: Parallelizing Large-Scale Matrix Factorization on GPUs

abstract: Matrix factorization (MF) is used by many popular algorithms such as collaborative filtering. GPU with massive cores and high memory bandwidth sheds light on accelerating MF much further when appropriately exploiting its architectural characteristics. This paper presents cuMF, a CUDA-based matrix factorization library that optimizes alternate least square (ALS) method to solve very large-scale MF. CuMF uses a set of techniques to maximize the performance on single and multiple GPUs. These techniques include smart access of sparse data leveraging GPU memory hierarchy, using data parallelism in conjunction with model parallelism, minimizing the communication overhead among GPUs, and a novel topology-aware parallel reduction scheme. With only a single machine with four Nvidia GPU cards, cuMF can be 6-10 times as fast, and 33-100 times as cost-efficient, compared with the state-of-art distributed CPU solutions. Moreover, cuMF can solve the largest matrix factorization problem ever reported in current literature, with impressively good performance.


title: Unsupervised Learning of Latent Physical Properties Using Perception-Prediction Networks

abstract: We propose a framework for the completely unsupervised learning of latent object properties from their interactions: the perception-prediction network (PPN). Consisting of a perception module that extracts representations of latent object properties and a prediction module that uses those extracted properties to simulate system dynamics, the PPN can be trained in an end-to-end fashion purely from samples of object dynamics. The representations of latent object properties learned by PPNs not only are sufficient to accurately simulate the dynamics of systems comprised of previously unseen objects, but also can be translated directly into human-interpretable properties (e.g., mass, coefficient of restitution) in an entirely unsupervised manner. Crucially, PPNs also generalize to novel scenarios: their gradient-based training can be applied to many dynamical systems and their graph-based structure functions over systems comprised of different numbers of objects. Our results demonstrate the efficacy of graph-based neural architectures in object-centric inference and prediction tasks, and our model has the potential to discover relevant object properties in systems that are not yet well understood.


title: SpotLight: Detecting Anomalies in Streaming Graphs

abstract: How do we spot interesting events from e-mail or transportation logs? How can we detect port scan or denial of service attacks from IP-IP communication data? In general, given a sequence of weighted, directed or bipartite graphs, each summarizing a snapshot of activity in a time window, how can we spot anomalous graphs containing the sudden appearance or disappearance of large dense subgraphs (e.g., near bicliques) in near real-time using sublinear memory? To this end, we propose a randomized sketching-based approach called SpotLight, which guarantees that an anomalous graph is mapped 'far' away from 'normal' instances in the sketch space with high probability for appropriate choice of parameters. Extensive experiments on real-world datasets show that SpotLight (a) improves accuracy by at least 8.4% compared to prior approaches, (b) is fast and can process millions of edges within a few minutes, (c) scales linearly with the number of edges and sketching dimensions and (d) leads to interesting discoveries in practice.


title: Performance of Millimeter Wave Massive MIMO with the Alamouti Code

abstract: Massive MIMO was recently proposed to achieve the advantage of conventional MIMO but on a greater scale. Massive MIMO can provide a much higher capacity without requiring more wireless spectrum. The need for higher data rate led researchers to consider the Millimeter Wave (mmW) band that offers a much larger unused bandwidth. Because of the higher path loss at mmW frequencies, and the poor scattering nature of the mmW channel (fewer paths exist), a hybrid beamforming technique with large antenna arrays and the Alamouti coding scheme are used in this paper to improve the performance of the single-cell mmW massive MIMO systems. Computer simulations have shown that a gain of 20 dB or more can be achieved using the Alamouti code.


title: NILC_USP: Aspect Extraction using Semantic Labels

abstract: This paper details the system NILC USP that participated in the Semeval 2014: Aspect Based Sentiment Analysis task. This system uses a Conditional Random Field (CRF) algorithm for extracting the aspects mentioned in the text. Our work added semantic labels into a basic feature set for measuring the efficiency of those for aspect extraction. We used the semantic roles and the highest verb frame as features for the machine learning. Overall, our results demonstrated that the system could not improve with the use of this semantic information, but its precision was increased.


title: A development of power consumption measurement system for Android smartphones

abstract: Smartphones are going to be more popular over the next five years and smartphone subscriptions will reach 6.1 billion in 2020. Moreover, mobile context-aware technologies and applications will be evolving more rapidly. However, smartphone users are always power-hungry. In order to solve such a problem, many researches for power measurement systems have been existed. But existing research have many limitations. In this paper, we propose to combine (i) the battery consumption information of users and (ii) log analyses, and develop a new power consumption measurement system, called the Log Analysis Consumption Report (LACR), which extends the open source of PowerTutor. The goal of our system is to measure the power consumed by WiFi, which is not supported by PowerTutor. The experiments performed on Galaxy S6 show that our system is much suitable for GalaxyS6 than existing methods.


Toward a framework for human-robot interaction


title: Hand-eye calibration of SCARA robots using dual quaternions

abstract: In SCARA robots, which are often used in industrial applications, all joint axes are parallel, covering three degrees of freedom in translation and one degree of freedom in rotation. Therefore, conventional approaches for the hand-eye calibration of articulated robots cannot be used for SCARA robots. In this paper, we present a new linear method that is based on dual quaternions and extends the work of Daniilid is 1999 (IJRR) for SCARA robots. To improve the accuracy, a subsequent nonlinear optimization is proposed. We address several practical implementation issues and show the effectiveness of the method by evaluating it on synthetic and real data.


title: DynaLog: An automated dynamic analysis framework for characterizing Android applications

abstract: Android is becoming ubiquitous and currently has the largest share of the mobile OS market with billions of application downloads from the official app market. It has also become the platform most targeted by mobile malware that are becoming more sophisticated to evade state-of-the-art detection approaches. Many Android malware families employ obfuscation techniques in order to avoid detection and this may defeat static analysis based approaches. Dynamic analysis on the other hand may be used to overcome this limitation. Hence in this paper we propose DynaLog, a dynamic analysis based framework for characterizing Android applications. The framework provides the capability to analyse the behaviour of applications based on an extensive number of dynamic features. It provides an automated platform for mass analysis and characterization of apps that is useful for quickly identifying and isolating malicious applications. The DynaLog framework leverages existing open source tools to extract and log high level behaviours, API calls, and critical events that can be used to explore the characteristics of an application, thus providing an extensible dynamic analysis platform for detecting Android malware. DynaLog is evaluated using real malware samples and clean applications demonstrating its capabilities for effective analysis and detection of malicious applications.


title: Let's See Your Digits: Anomalous-State Detection using Benford's Law

abstract: Benford's Law explains a curious phenomenon in which the leading digits of "naturally-occurring" numerical data are distributed in a precise fashion. In this paper we begin by showing that system metrics generated by many modern information systems like Twitter, Wikipedia, YouTube and GitHub obey this law. We then propose a novel unsupervised approach called BenFound that exploits this property to detect anomalous system events. BenFound tracks the "Benfordness" of key system metrics, like the follower counts of tweeting Twitter users or the change deltas in Wikipedia page edits. It then applies a novel Benford-conformity test in real-time to identify "non-Benford events". We investigate a variety of such events, showing that they correspond to unnatural and often undesirable system interactions like spamming, hashtag-hijacking and denial-of-service attacks. The result is a technically-uncomplicated and effective "red flagging" technique that can be used to complement existing anomaly-detection approaches. Although not without its limitations, it is highly efficient and requires neither obscure parameters, nor text streams, nor natural-language processing.


title: Neuroimaging and the search for a cure for Alzheimer disease.

abstract: As radiologists, our role in the workup of the dementia patient has long been limited by the sensitivity of our imaging tools and lack of effective treatment options. Over the past 30 years, we have made tremendous strides in understanding the genetic, molecular, and cellular basis of Alzheimer disease (AD). We now know that the pathologic features of AD are present 1 to 2 decades prior to development of symptoms, though currently approved symptomatic therapies are administered much later in the disease course. The search for true disease-modifying therapy continues and many clinical trials are underway. Current outcome measures, based on cognitive tests, are relatively insensitive to pathologic disease progression, requiring long, expensive trials with large numbers of participants. Biomarkers, including neuroimaging, have great potential to increase the power of trials by matching imaging methodology with therapeutic mechanism. One of the most important advances over the past decade has been the development of in vivo imaging probes targeted to amyloid beta protein, and one agent is already available for clinical use. Additional advances include automated volumetric imaging methods to quantitate cerebral volume loss. Use of such techniques in small, early phase trials are expected to significantly increase the number and quality of candidate drugs for testing in larger trials. In addition to a critical role in trials, structural, molecular, and functional imaging techniques can give us a window on the etiology of AD and other neurodegenerative diseases. This combination of developments has potential to bring diagnostic radiology to the forefront in AD research, therapeutic trials, and patient care.


title: Isolation of Human Skin Dendritic Cell Subsets

abstract: Dendritic cells (DCs) are specialized leukocytes with antigen-processing and antigen-presenting functions. DCs can be divided into distinct subsets by anatomical location, phenotype and function. In human, the two most accessible tissues to study leukocytes are peripheral blood and skin. DCs are rare in human peripheral blood (<1 % of mononuclear cells) and have a less mature phenotype than their tissue counterparts (MacDonald et al., Blood. 100:4512-4520, 2002; Haniffa et al., Immunity 37:60-73, 2012). In contrast, the skin covering an average total surface area of 1.8 m(2) has approximately tenfold more DCs than the average 5 L of total blood volume (Wang et al., J Invest Dermatol 134:965-974, 2014). DCs migrate spontaneously from skin explants cultured ex vivo, which provide an easy method of cell isolation (Larsen et al., J Exp Med 172:1483-1493, 1990; Lenz et al., J Clin Invest 92:2587-2596, 1993; Nestle et al., J Immunol 151:6535-6545, 1993). These factors led to the extensive use of skin DCs as the "prototype" migratory DCs in human studies. In this chapter, we detail the protocols to isolate DCs and resident macrophages from human skin. We also provide a multiparameter flow cytometry gating strategy to identify human skin DCs and to distinguish them from macrophages.


' s personal copy Comments and Controversies Ten ironic rules for non-statistical reviewers


title: Incorporating query-specific feedback into learning-to-rank models

abstract: Relevance feedback has been shown to improve retrieval for a broad range of retrieval models. It is the most common way of adapting a retrieval model for a specific query. In this work, we expand this common way by focusing on an approach that enables us to do query-specific modification of a retrieval model for learning-to-rank problems. Our approach is based on using feedback documents in two ways: 1) to improve the retrieval model directly and 2) to identify a subset of training queries that are more predictive than others. Experiments with the Gov2 collection show that this approach can obtain statistically significant improvements over two baselines; learning-to-rank (SVM-rank) with no feedback and learning-to-rank with standard relevance feedback.


title: Design of Compact Wide Stopband Microstrip Low-pass Filter using T-shaped Resonator

abstract: In this letter, a compact microstrip low-pass filter (LPF) using T-shaped resonator with wide stopband is presented. The proposed LPF has capability to remove the eighth harmonic and a low insertion loss of 0.12 dB. The bandstop structure using stepped impendence resonator and two open-circuit stubs are used to design a wide stopband with attenuation level better than −20 dB from 3.08 up to 22 GHz. The proposed filter with −3-dB cutoff frequency of 2.68 GHz has been designed, fabricated, and measured. The operating of the LPF is investigated based on equivalent circuit model. Simulation results are verified by measurement results and excellent agreement between them is observed.


title: The making of Knowledge Cities in Romania

abstract: Abstract The ‘Knowledge City’ can be considered first the supreme objective and then an instrument of knowledge society with its own principles and its distinguishing characteristics and processes. One of the main question remains as how close is the position of Romanian urban areas to the category of knowledge city. Given the characteristics of Romanian cities on culture, science, technology and innovation, and policies in urban, economic and social development, I conclude that Romania must adopt firstly a unified and coherent long term development strategy for a “healthy” economy. The paper will try to emphasize the position possibility of Romanian cities in the category of knowledge city based on the concept of knowledge city, the principles, characteristics and processes of a knowledge city, taking into account the theoretical approach and empirical evidences. In this regard, I will use the background offered by literature and the official statiscal data for analysis to identify the key success factors of few Romanian urban areas which explain the posible ‘candidature’ to the quality of knowledge city. I estimate the analysis to confirm the hypothesis that there are good aspects, but also deficiencies that require major solutions and policy options for designing Romanian cities as emerging knowledge cities. We consider that the paper can be a useful viewpoint, which allows researchers to include other sources of information for researching an in a much more complex approach.


title: Creating Appropriate Trust in Automated Vehicle Systems: A Framework for HMI Design

abstract: While automated vehicle technology progresses, potentially leading to a safer and more efficient traffic environment, many challenges remain within the area of human factors, such as user trust for automated driving (AD) vehicle systems. The aim of this paper is to investigate how an appropriate level of user trust for AD vehicle systems can be created via human–machine interaction (HMI). A guiding framework for implementing trust-related factors into the HMI interface is presented. This trust-based framework incorporates usage phases, AD events, trust-affecting factors, and levels explaining each event from a trust perspective. Based on the research findings, the authors recommend that HMI designers and automated vehicle manufacturers take a more holistic perspective on trust rather than focusing on single, “isolated” events, for example understanding that trust formation is a dynamic process that starts long before a user's first contact with the system, and continues long thereafter. Furthermore, factors-affecting trust change, both during user interactions with the system and over time; thus, HMI concepts need to be able to adapt. Future work should be dedicated to understanding how trust-related factors interact, as well as validating and testing the trust-based framework.


title: Recall Traces: Backtracking Models for Efficient Reinforcement Learning

abstract: In many environments only a tiny subset of all states yield high reward. In these cases, few of the interactions with the environment provide a relevant learning signal. Hence, we may want to preferentially train on those high-reward states and the probable trajectories leading to them. To this end, we advocate for the use of a backtracking model that predicts the preceding states that terminate at a given high-reward state. We can train a model which, starting from a high value state (or one that is estimated to have high value), predicts and sample for which the (state, action)-tuples may have led to that high value state. These traces of (state, action) pairs, which we refer to as Recall Traces, sampled from this backtracking model starting from a high value state, are informative as they terminate in good states, and hence we can use these traces to improve a policy. We provide a variational interpretation for this idea and a practical algorithm in which the backtracking model samples from an approximate posterior distribution over trajectories which lead to large rewards. Our method improves the sample efficiency of both on- and off-policy RL algorithms across several environments and tasks.


Challenges and recent advances in live cell bioimaging


title: Working with young people: the impact of mental health awareness programmes in schools in the UK and Canada

abstract: The persistent and disabling nature of psychiatric stigma has led to the establishment of global programmes to challenge the negative stereotypes and discriminatory responses that generate social disability, but these initiatives are rarely evaluated. This study compares the effectiveness of school-based interventions with young people aged 14-16 aimed at increasing mental health literacy and challenging negative stereotypes associated with severe mental illness in sites in Canada and the UK. In both countries, short educational sessions were delivered involving a facilitator with direct experience of mental illness. Students in Canada at baseline (N=1501) were significantly more aware than those in the UK (N=635) that schizophrenia is not a split personality, that mental illness is prevalent and that it is a myth that people with schizophrenia are any more likely to be violent than members of the general population. Both the UK and Canada programmes had a favourable impact on students factual recall and reported attitudes at first follow-up. The cumulative proportion of students expressing no social distance across 4 key items improved over time in both the UK (N=512) and Canada (N=634). Therefore, this study shows that short educational workshops can produce positive change in young people’s views of mental illness. However, more robust evaluations are needed to assess the long-term impact of both short and integrated whole school approaches to increasing mental health literacy across the globe.


title: Motivated software engineers are engaged and focused, while satisfied ones are happy

abstract: Context -- Motivation and job satisfaction are not the same thing, and although business organization research recognized this a long time ago, in Software Engineering research, we have not. As a result, thirty years of research on motivation in software engineering has produced knowledge on what makes software engineers generally happier, but not about how to increase their motivation. Goal -- In this article, we aim to identify visible signs of a software engineer who is motivated to work. Method -- We describe a field study in which 62 practitioners in Brazil reported their view of "motivation" in the context of their practical work. Data was collected by means of audio-recorded semi-structured interviews, and a thematic analysis was applied to identify the most relevant descriptors of motivation. Results -- Our data reveal that (1) motivated Software Engineers are engaged, focused, and collaborative; and (2) the term "motivation" is used as an umbrella term to cover several distinct organizational behaviours that are not necessarily related to the individual's desire to work. Conclusions -- Without a clear picture of the difference between these two concepts, work-based motivation programs may not be designed effectively to address either turnover or performance issues. Overall, this work indicates the need for a more effective conceptual system to investigate and encourage both job satisfaction and work motivation in software engineering research and practice.


title: A Research Perspective: Artificial Intelligence, Management and Organizations

abstract: In recent years many companies have deployed Artificial Intelligence AI, which has included neural networks, expert systems and voice-recognition systems. Yet managers and developers understand very little about how management and organizations affect or are affected by the technology. Using specific examples from practice and research, this paper discusses the interaction of AI, management and organizations, and describes some methodological approaches and theoretical models for studying those interactions. It provides direction for future research.


title: A Randomized, Double Blind, Placebo-Controlled, Multicenter Phase II Trial of Allisartan Isoproxil in Essential Hypertensive Population at Low-Medium Risk

abstract: BACKGROUND ::: Angiotensin II receptor blockers (ARBs) is a well-tolerated class of antihypertensive agents, exhibiting effective antihypertensive and cardiovascular protective function. The objective of the study was to examine the efficacy and safety of Allisartan Isoproxil, a newly developed, selective, nonpeptide blocker of the angiotensin II type 1 receptor (AT1R), in essential hypertensive patients at low-medium risk. ::: ::: ::: METHODS AND FINDINGS ::: A Phase II prospective, randomized, double-blind, placebo-controlled, multicenter trial comparing Allisartan Isoproxil 240mg versus placebo was conducted in essential hypertensive patients at low-medium risk at 8 sites in China. After a 2-week placebo baseline period, 275 patients received once-daily treatment with Allisartan Isoproxil 240mg or placebo randomly for 8 weeks. Systolic/diastolic blood pressure (SBP/DBP) was measured at week 2, 4 and 8. By the end of treatment, mean reductions from baseline of SBP and DBP in Allisartan Isoproxil and placebo groups were 14.5/10.4 and 8.3/7.7 mmHg, respectively (P<0.01). The rate of effective blood pressure control in Allisartan Isoproxil group was significantly higher than in placebo group at week 4 (61.3% vs 50.0%, P<0.05) and week 8 (67.2% vs 48.6%, P<0.01). In terms of safety and tolerability, there were no report of death and serious adverse event (SAE) in all subjects. There was no difference of frequency between two groups in adverse event (AE) and adverse drug reaction (ADR) (P>0.05). No one withdraw because of an ADR in two groups. 124 patients received additional 56 weeks treatment with Allisartan Isoproxil and 84 of them completed the study. The rate of effective BP control kept up to 80% since week 24. No significant clinical change was observed and ADRs were generally mild or moderate during the long-term study. ::: ::: ::: CONCLUSIONS/SIGNIFICANCE ::: Allisartan Isoproxil 240mg was effective and safe for essential hypertension patients at low-medium risk. ::: ::: ::: TRIAL REGISTRATION ::: http://www.chictr.org/cn/ ChiCTR-TRC-10000886.


title: Fog Computing: Platform and Applications

abstract: Despite the broad utilization of cloud computing, some applications and services still cannot benefit from this popular computing paradigm due to inherent problems of cloud computing such as unacceptable latency, lack of mobility support and location-awareness. As a result, fog computing, has emerged as a promising infrastructure to provide elastic resources at the edge of network. In this paper, we have discussed current definitions of fog computing and similar concepts, and proposed a more comprehensive definition. We also analyzed the goals and challenges in fog computing platform, and presented platform design with several exemplar applications. We finally implemented and evaluated a prototype fog computing platform.


title: A New Approach to Teaching Discrete Mathematics

abstract: ABSTRACT We advocate teaching introductory discrete mathematics by first teaching equational propositional and predicate logic and then using it as tool (and not simply viewing it as an object of study) throughout the study of later topics — e.g., set theory, induction, relations, theory of integers, combinatorics, solving recurrence relations, and modern algebra. The in-depth (6–7 weeks) treatment of logic emphasizes rigorous proofs, strategies for developing proofs, and much practice, so that students develop a skill in formal manipulation. Care is taken to explain all concepts clearly and rigorously. Later topics are dealt with by viewing them as theories — extensions of the predicate calculus. The course should motivate the use of logic as a pervasive tool. It must enlighten, and not stifle and oppress. Our experience shows that this is possible. Anecdotal evidence shows that students come away with less fear of mathematics, proof, and notation, more confidence in their mathematical abilities, an appr...


title: Gender Bias in Leader Evaluations: Merging Implicit Theories and Role Congruity Perspectives

abstract: This research extends our understanding of gender bias in leader evaluations by merging role congruity and implicit theory perspectives. We tested and found support for the prediction that the link between people’s attitudes regarding women in authority and their subsequent gender-biased leader evaluations is significantly stronger for entity theorists (those who believe attributes are fixed) relative to incremental theorists (those who believe attributes are malleable). In Study 1, 147 participants evaluated male and female gubernatorial candidates. Results supported predictions, demonstrating that traditional attitudes toward women in authority significantly predicted a pro-male gender bias in leader evaluations (and progressive attitudes predicted a pro-female gender bias) with an especially strong effect for those with more entity-oriented, relative to incrementally oriented person theories. Study 2 (119 participants) replicated these findings and demonstrated the mediating role of these attitudes in ...


title: A quad 25Gb/s 270mW TIA in 0.13µm BiCMOS with <0.15dB crosstalk penalty

abstract: The push for 100Gb/s optical transport and beyond necessitates electronic components at higher speed and integration level in order to drive down cost, complexity and size of transceivers [1-2]. This requires parallel multi-channel optical transceivers each operating at 25Gb/s and beyond. Due to variations in the output power of transmitters and in some cases different optical paths the parallel receivers have to operate at different input optical power levels. This trend places increasing strain to the acceptable inter-channel crosstalk in integrated multi-channel receivers [3]. Minimizing this cross-talk penalty when all channels are operational is becoming increasingly important in ultra-high throughput optical links.


title: Probabilistic method for selection of power line wire type and cross-section

abstract: The paper presents a comparison of two methodologies for selection of overhead line wire type and cross-section considering market conditions. All existing methodologies which are used to determine optimal cross-section for transmission line wires can be divided into two categories: deterministic and probabilistic (stochastic). This paper presents a new approach for stochastic optimization and compares it to simplified deterministic approach. The proposed method minimizes total annual exploitation and construction costs of the transmission line based on utilization of existing large and easy accessible databases (electricity prices, ambient temperature, load records). The use of the Monte Carlo method's base and above-mentioned databases allows designing a user-friendly algorithm to solve this problem.


title: Video Games and Education: Designing learning systems for an interactive age

abstract: Recently, attention has been paid to computer and video games as a medium for learning. This article provides a way of conceptualizing them as possibility spaces for learning. It provides an overview of two research programs: (1) an after-school program using commercial games to develop deep expertise in game play and game creation, and (2) an in-school program using game techniques to teach science literacy. Although there are yet to be mature examples of gamebased learning programs integrating all of these elements, these programs suggest a future of schooling very different from today's current path.


title: Demographic change and the care of older people: introduction

abstract: The conclusion of the European Commission’s Green Paper "Confronting demographic change: a new solidarity between the generations" (Commission of the European Communities, 2005) was that Europe should actively pursue three ‘essential priorities’. These were: a return to demographic growth; ensuring a balance between the generations; and finding new bridges between the stages of life. The first of these suggested priorities is controversial; all three illustrate the place of population ageing and intergenerational relationships at the centre stage of current policy concerns.


title: Educational Skits Performed by College Students in a Large Technical Writing Class: Can Less Structured Group Assignments Positively Influence the Learning Experience?

abstract: This study compared the attitudes of over 160 undergraduate students toward participating in educational skits when highly structured and less structured skits were used as an interest approach. When students were given fewer instructions and greater creative flexibility, more positive attitudes toward the assignment and technical writing in general resulted. Regardless of class size, beginning a class period reviewing material covered in a previous session is a teaching technique supported by educators across many disciplines (Bean, 1996; Nilson, 2003). The use of an interest approach is one way to review previously covered course content and increase student interest in studying a particular topic (Myers, 2004). For large (> 75 students) university required courses of limited student appeal, increasing student attendance, gaining student attention at the start of each class, and generating student enthusiasm can be accomplished using various interest approaches. Harcum (1991) used rap singing in large (> 300 students), introductory psychology courses as an icebreaker during the first class period and found the instructor composed and performed rap songs helpful in reducing student anxiety and establishing rapport between the instructor and students. Popular, competitive games such as Jeopardy and Pictionary were used as an interest approach in an introductory soil science course and enhanced student enthusiasm for course subject matter and helped students apply soils knowledge in an atypical fashion (Sulzman, 2004). More recently, Donnermeyer et al. (2005) found that distributing candy at the start of large (> 100 students) introductory rural sociology classes and using candy as an interest approach helped encourage student participation and discourage tardiness. Many educational and communication theorists advocate that an interest approach should be relevant to both the topic of presentation and authence (Cheek, Arlington, Rudd, & McGhee, 2000; Osborn & Osborn, 1994). Others suggest that an interest approach should stimulate creativity, arouse curiosity, and connect to other student interests (Myers, 2004). For large, university required technical writing courses, short educational skits on technical writing topics performed by students at the start of each class are the ideal interest approach. Educational skits can make technical writing more relevant to student lives and academic majors, stimulate creativity in those performing the skits, arouse curiosity in those watching the skits, and can help connect class topics to other disciplines. Because students become the teachers, the use of educational skits as an interest approach allows students performing the skits to take personal ownership of course content and helps students watching the skits to review material discussed in a previous class. Educational skits on previously covered course material can be used as a mini-review session and a way to get everyone thinking about course content at the start of class. Particularly for large class settings, witnessing and participating in educational skits performed at the beginning of a class period gives students an incentive for arriving early to class and a reason not to arrive late, thereby interrupting a group performance already in progress. While educational skits allow students to practice course content, instructors also benefit as they are provided opportunities to assess learning and offer feedback. Following the work of Piaget, many students learn best from other students who are able to convey information using language that is less academic and more understandable (Cooper & Robinson, 2000). Educational skits allow students to revisit as a group the information discussed in a previous class and devise a way to creatively review such information so that it is understandable and relevant to their classmates. Although sometimes logistically challenging for students, meeting outside of the classroom to prepare a skit can build new and strengthen existing student relationships. …


title: CRISPR-Cas9 Human Genome Editing: Challenges, Ethical Concerns and Implications

abstract: Genome editing technologies may in the future have therapeutic potential for various incurable diseases: cancer, genetic disorders, HIV/AIDS to mention the most obvious. Genome editing of somatic cells, which is at it various clinical stages, is a promising area of therapeutic development. This year, a group of Chinese researchers led by Junjiu Huang - a gene-function researcher at Sun Yat-sen University in Guangzhou, used complex enzyme-editing tool CRISPR-Cas9 as a therapeutic agent to eradicate the human β-globulin (HBB) gene from the germline of the human embryo. The mutations in HBB gene cause β-thalassaemia (a deadly blood disorder). The research was, however, not completely successful, and had to be abandoned at its preliminary stage. This research was published in the journal Protein and Cell after it was rejected by the journal Nature and Science on ethical grounds. Caution flags have been raised about the use of CRISPR-Cas9 on human germline editing. This research has generated the debate among the world-renowned scientists about the ethical concerns and implications of CRISPR-Cas9 human germline editing. While some members of the scientific community have argued that a moratorium should be called on human germline editing, others have argued that it is unethical to withhold a technology that would eliminate devastating genetic diseases. This paper critically evaluates the challenges, ethical concerns and implications of CRISPR-Cas9 human germ line editing.


title: Features for audio classification

abstract: Four audio feature sets are evaluated in their ability to differentiate five audio classes: popular music, classical music, speech, background noise and crowd noise. The feature sets include low-level signal properties, mel-frequency spectral coefficients, and two new sets based on perceptual models of hearing. The temporal behavior of the features is analyzed and parameterized and these parameters are included as additional features. Using a standard Gaussian framework for classification, results show that the temporal behavior of features is important for automatic audio classification. In addition, classification is better, on average, if based on features from models of auditory perception rather than on standard features.


title: Facts and Figuring: An Experimental Investigation of Network Structure and Performance in Information and Solution Spaces

abstract: Using data from a novel laboratory experiment on complex problem solving in which we varied the structure of 16-person networks, we investigate how an organization's network structure shapes the performance of problem-solving tasks. Problem solving, we argue, involves both exploration for information and exploration for solutions. Our results show that network clustering has opposite effects for these two important and complementary forms of exploration. Dense clustering encourages members of a network to generate more diverse information but discourages them from generating diverse theories; that is, clustering promotes exploration in information space but decreases exploration in solution space. Previous research, generally focusing on only one of those two spaces at a time, has produced an inconsistent understanding of the value of network clustering. By adopting an experimental platform on which information was measured separately from solutions, we bring disparate results under a single theoretical roof and clarify the effects of network clustering on problem-solving behavior and performance. The finding both provides a sharper tool for structuring organizations for knowledge work and reveals challenges inherent in manipulating network structure to enhance performance, as the communication structure that helps one determinant of successful problem solving may harm the other.


title: Dismantling mifare classic

abstract: The mifare Classic is a contactless smart card that is used extensively in access control for office buildings, payment systems for public transport, and other applications. We reverse engineered the security mechanisms of this chip: the authentication protocol, the symmetric cipher, and the initialization mechanism. We describe several security vulnerabilities in these mechanisms and exploit these vulnerabilities with two attacks; both are capable of retrieving the secret key from a genuine reader. The most serious one recovers the secret key from just one or two authentication attempts with a genuine reader in less than a second on ordinary hardware and without any pre-computation. Using the same methods, an attacker can also eavesdrop the communication between a tag and a reader, and decrypt the whole trace, even if it involves multiple authentications. This enables an attacker to clone a card or to restore a real card to a previous state.


title: Unsupervised knowledge acquisition for Extracting Named Entities from speech

abstract: This paper presents a Named Entity Recognition (NER) method dedicated to process speech transcriptions. The main principle behind this method is to collect in an unsupervised way lexical knowledge for all entries in the ASR lexicon. This knowledge is gathered with two methods: by automatically extracting NEs on a very large set of textual corpora and by exploiting directly the structure contained in the Wikipedia resource. This lexical knowledge is used to update the statistical models of our NER module based on a mixed approach with generative models (Hidden Markov Models - HMM) and discriminative models (Conditional Random Field - CRF). This approach has been evaluated within the French ESTER 2 evaluation program and obtained the best results at the NER task on ASR transcripts.


title: Self-Organizing Maps for Fingerprint Image Quality Assessment

abstract: Fingerprint quality assessment is a crucial task which needs to be conducted accurately in various phases in the biometric enrolment and recognition processes. Neglecting quality measurement will adversely impact accuracy and efficiency of biometric recognition systems (e.g. verification and identification of individuals). Measuring and reporting quality allows processing enhancements to increase probability of detection and track accuracy while decreasing probability of false alarms. Aside from predictive capabilities with respect to the recognition performance, another important design criteria for a quality assessment algorithm is to meet the low computational complexity requirement of mobile platforms used in national biometric systems, by military and police forces. We propose a computationally efficient means of predicting biometric performance based on a combination of unsupervised and supervised machine learning techniques. We train a self-organizing map (SOM) to cluster blocks of fingerprint images based on their spatial information content. The output of the SOM is a high-level representation of the finger image, which forms the input to a Random Forest trained to learn the relationship between the SOM output and biometric performance. The quantitative evaluation performed demonstrates that our proposed quality assessment algorithm is a reasonable predictor of performance. The open source code of our algorithm will be posted at NIST NFIQ 2.0 website.


title: A High-Efficiency Wide-Input-Voltage Range Switched Capacitor Point-of-Load DC–DC Converter

abstract: The traditional inductor-based buck converter has been the default design for switched-mode voltage regulators for decades. Switched capacitor (SC) dc-dc converters, on the other hand, have traditionally been used in low-power ( 80% over a load range of 5 mA to 1 A) than surveyed competitive buck converters, while requiring less board area and less costly passive components. The topology and controller enable a wide input range of 7.5-13.5 V. Controls based on feedback and feedforward provide tight regulation under worst case line and load step conditions. This study shows that the SC converter can outperform the buck converter, and thus, the scope of SC converter application can and should be expanded.


title: Prolonged Exposure versus Eye Movement Desensitization and Reprocessing (EMDR) for PTSD rape victims

abstract: This controlled study evaluated the relative efficacy of Prolonged Exposure (PE) and Eye Movement Desensitization and Reprocessing (EMDR) compared to a no-treatment waitlist control (WAIT) in the treatment of PTSD in adult female rape victims (n = 74). Improvement in PTSD as assessed by blind independent assessors, depression, dissociation, and state anxiety was significantly greater in both the PE and EMDR group than the WAIT group (n = 20 completers per group). PE and EMDR did not differ significantly for change from baseline to either posttreatment or 6-month follow-up measurement for any quantitative scale.


title: Hedging your bets: Optimizing accuracy-specificity trade-offs in large scale visual recognition

abstract: As visual recognition scales up to ever larger numbers of categories, maintaining high accuracy is increasingly difficult. In this work, we study the problem of optimizing accuracy-specificity trade-offs in large scale recognition, motivated by the observation that object categories form a semantic hierarchy consisting of many levels of abstraction. A classifier can select the appropriate level, trading off specificity for accuracy in case of uncertainty. By optimizing this trade-off, we obtain classifiers that try to be as specific as possible while guaranteeing an arbitrarily high accuracy. We formulate the problem as maximizing information gain while ensuring a fixed, arbitrarily small error rate with a semantic hierarchy. We propose the Dual Accuracy Reward Trade-off Search (DARTS) algorithm and prove that, under practical conditions, it converges to an optimal solution. Experiments demonstrate the effectiveness of our algorithm on datasets ranging from 65 to over 10,000 categories.


title: Can We Beat DDoS Attacks in Clouds?

abstract: Cloud is becoming a dominant computing platform. Naturally, a question that arises is whether we can beat notorious DDoS attacks in a cloud environment. Researchers have demonstrated that the essential issue of DDoS attack and defense is resource competition between defenders and attackers. A cloud usually possesses profound resources and has full control and dynamic allocation capability of its resources. Therefore, cloud offers us the potential to overcome DDoS attacks. However, individual cloud hosted servers are still vulnerable to DDoS attacks if they still run in the traditional way. In this paper, we propose a dynamic resource allocation strategy to counter DDoS attacks against individual cloud customers. When a DDoS attack occurs, we employ the idle resources of the cloud to clone sufficient intrusion prevention servers for the victim in order to quickly filter out attack packets and guarantee the quality of the service for benign users simultaneously. We establish a mathematical model to approximate the needs of our resource investment based on queueing theory. Through careful system analysis and real-world data set experiments, we conclude that we can defeat DDoS attacks in a cloud environment.


title: Text based graphical password system to obscure shoulder surfing

abstract: Traditional text based password schemes are subjected to dictionary attacks on a very large scale. As a solution, graphical password schemes are a promising alternative to text-based recognition schemes where instead of text, images are chosen for a password. However, these schemes are again affected due to shoulder surfing and less effective due to large dictionary space. This paper focuses on providing a solution based on text-based graphical password techniques, a combination of Deja vu and Moveable Frame scheme. Deja vu is further replaced by text-based images than purely graphics. Our research has two main objectives: 1) To thwart shoulder surfing 2) To minimize the search time of pass images on the login screen. The project was implemented as a resulting android application using Android Studio. Results show the proposed scheme of graphical password system with multiple alphabet images is more recognizable and has less cognitive load on user as compared to image based graphical password schemes. The incorrect login rate for multiple alphabet based images with movable frame was 15% more than graphical password schemes thus a better solution to protect shoulder surfing.


title: Reconfigurable half-width microstrip leaky-wave antenna for fixed-frequency beam scanning

abstract: A new technique to implement fixed-frequency beam scanning using a half-width microstrip leaky-wave antenna (HW-MLWA) is presented. The main beam direction is electronically controlled by changing the reactance between the one edge of a microstrip and the ground. This is achieved through a set of patches periodically placed along the free radiating edge of the microstrip line, with the second edge of the microstrip line is grounded. The patches can be connected to or isolated from the ground using PIN diodes thus facilitating a sub-nanosecond means of controlling the reactance. The full-wave numerical simulations show the ability to control the main beam direction from 21° to 37° at 6.5 GHz within the first quadrant.


title: WASSA-2017 Shared Task on Emotion Intensity

abstract: We present the first shared task on detecting the intensity of emotion felt by the speaker of a tweet. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities using a technique called best--worst scaling (BWS). We show that the annotations lead to reliable fine-grained intensity scores (rankings of tweets by intensity). The data was partitioned into training, development, and test sets for the competition. Twenty-two teams participated in the shared task, with the best system obtaining a Pearson correlation of 0.747 with the gold intensity scores. We summarize the machine learning setups, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful for the task. The emotion intensity dataset and the shared task are helping improve our understanding of how we convey more or less intense emotions through language.


title: Attentional focus of feedback for improving performance of reach-to-grasp after stroke: a randomised crossover study.

abstract: To investigate whether feedback inducing an external focus (EF) of attention (about movement effects) was more effective for retraining reach-to-grasp after stroke compared with feedback inducing an internal focus (IF) of attention (about body movement). It was predicted that inducing an EF of attention would be more beneficial to motor performance.


title: BeThere: 3D mobile collaboration with spatial input

abstract: We present BeThere, a proof-of-concept system designed to explore 3D input for mobile collaborative interactions. With BeThere, we explore 3D gestures and spatial input which allow remote users to perform a variety of virtual interactions in a local user's physical environment. Our system is completely self-contained and uses depth sensors to track the location of a user's fingers as well as to capture the 3D shape of objects in front of the sensor. We illustrate the unique capabilities of our system through a series of interactions that allow users to control and manipulate 3D virtual content. We also provide qualitative feedback from a preliminary user study which confirmed that users can complete a shared collaborative task using our system.


title: BrainSegNet : A Segmentation Network for Human Brain Fiber Tractography Data into Anatomically Meaningful Clusters

abstract: The segregation of brain fiber tractography data into distinct and anatomically meaningful clusters can help to comprehend the complex brain structure and early investigation and management of various neural disorders. We propose a novel stacked bidirectional long short-term memory(LSTM) based segmentation network, (BrainSegNet) for human brain fiber tractography data classification. We perform a two-level hierarchical classification a) White vs Grey matter (Macro) and b) White matter clusters (Micro). BrainSegNet is trained over three brain tractography data having over 250,000 fibers each. Our experimental evaluation shows that our model achieves state-of-the-art results. We have performed inter as well as intra class testing over three patient's brain tractography data and achieved a high classification accuracy for both macro and micro levels both under intra as well as inter brain testing scenario.


Factors for Successful e-Government Adoption : a Conceptual Framework


title: Olmsted syndrome in an Iranian family: report of two new cases.

abstract: Olmsted syndrome is a rare congenital entity characterized by combination of symmetrical, sharply-defined palmoplantar keratoderma with flexion deformities of the digits, periorificial keratosis, perianal involvement, onychodystrophy, and variable leukokeratosis. Herein, we report two new related male patients--the third familial cases of Olmsted syndrome--one with the full-blown spectrum of the syndrome, and the other with early signs and symptoms of the disorder.


title: Efficient Training of Convolutional Neural Nets on Large Distributed Systems

abstract: Deep Neural Networks (DNNs) have achieved im- pressive accuracy in many application domains including im- age classification. Training of DNNs is an extremely compute- intensive process and is solved using variants of the stochastic gradient descent (SGD) algorithm. A lot of recent research has focussed on improving the performance of DNN training. In this paper, we present optimization techniques to improve the performance of the data parallel synchronous SGD algorithm using the Torch framework: (i) we maintain data in-memory to avoid file I/O overheads, (ii) we present a multi-color based MPI Allreduce algorithm to minimize communication overheads, and (iii) we propose optimizations to the Torch data parallel table framework that handles multi-threading. We evaluate the performance of our optimizations on a Power 8 Minsky cluster with 32 nodes and 128 NVidia Pascal P100 GPUs. With our optimizations, we are able to train 90 epochs of the ResNet-50 model on the Imagenet-1k dataset using 256 GPUs in just 48 minutes. This significantly improves on the previously best known performance of training 90 epochs of the ResNet-50 model on the same dataset using 256 GPUs in 65 minutes. To the best of our knowledge, this is the best known training performance demonstrated for the Imagenet- 1k dataset.


title: Rethinking serializable multiversion concurrency control

abstract: Multi-versioned database systems have the potential to significantly increase the amount of concurrency in transaction processing because they can avoid read-write conflicts. Unfortunately, the increase in concurrency usually comes at the cost of transaction serializability. If a database user requests full serializability, modern multi-versioned systems significantly constrain read-write concurrency among conflicting transactions and employ expensive synchronization patterns in their design. In main-memory multi-core settings, these additional constraints are so burdensome that multi-versioned systems are often significantly outperformed by single-version systems. ::: ::: We propose Bohm, a new concurrency control protocol for main-memory multi-versioned database systems. Bohm guarantees serializable execution while ensuring that reads never block writes. In addition, Bohm does not require reads to perform any bookkeeping whatsoever, thereby avoiding the overhead of tracking reads via contended writes to shared memory. This leads to excellent scalability and performance in multi-core settings. Bohm has all the above characteristics without performing validation based concurrency control. Instead, it is pessimistic, and is therefore not prone to excessive aborts in the presence of contention. An experimental evaluation shows that Bohm performs well in both high contention and low contention settings, and is able to dramatically outperform state-of-the-art multi-versioned systems despite maintaining the full set of serializability guarantees.


title: GPU Cluster for High Performance Computing

abstract: Inspired by the attractive Flops/dollar ratio and the incredible growth in the speed of modern graphics processing units (GPUs), we propose to use a cluster of GPUs for high performance scientific computing. As an example application, we have developed a parallel flow simulation using the lattice Boltzmann model (LBM) on a GPU cluster and have simulated the dispersion of airborne contaminants in the Times Square area of New York City. Using 30 GPU nodes, our simulation can compute a 480x400x80 LBM in 0.31 second/step, a speed which is 4.6 times faster than that of our CPU cluster implementation. Besides the LBM, we also discuss other potential applications of the GPU cluster, such as cellular automata, PDE solvers, and FEM.


title: Fuzzy Queries over NoSQL Graph Databases: Perspectives for Extending the Cypher Language

abstract: When querying databases, users often wish to express vague concepts, as for instance asking for the cheap hotels. This has been extensively studied in the case of relational databases. In this paper, we propose to study how such useful techniques can be adapted to NoSQL graph databases where the role of fuzziness is crucial. Such databases are indeed among the fastest-growing models for dealing with big data, especially when dealing with network data (e.g., social networks). We consider the Cypher declarative query language proposed for Neo4j which is the current leader on this market, and we present how to express fuzzy queries.


title: Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches

abstract: We present a method for extracting depth information from a rectified image pair. Our approach focuses on the first stage of many stereo algorithms: the matching cost computation. We approach the problem by learning a similarity measure on small image patches using a convolutional neural network. Training is carried out in a supervised manner by constructing a binary classification data set with examples of similar and dissimilar pairs of patches. We examine two network architectures for this task: one tuned for speed, the other for accuracy. The output of the convolutional neural network is used to initialize the stereo matching cost. A series of post-processing steps follow: cross-based cost aggregation, semiglobal matching, a left-right consistency check, subpixel enhancement, a median filter, and a bilateral filter. We evaluate our method on the KITTI 2012, KITTI 2015, and Middlebury stereo data sets and show that it outperforms other approaches on all three data sets.


title: Sentiment classification for unlabeled dataset using Doc2Vec with JST

abstract: Supervised learning require sentiment labeled corpus for training. But it is hard to apply automatic sentiment classification system to new domain because labeled dataset construction costs a lot of time. Meanwhile, researches using Doc2vec based document representation beat out other sentiment classification researches. However, these document representation methods only represent documents' context or sentiment. In this paper, we proposed supervised learning scheme for unlabeled corpus and also proposed document representation method which can simultaneously represent documents' context and sentiment.


title: Enhancing reliability to boost the throughput over screen-camera links

abstract: With the rapid proliferation of camera-equipped smart devices (e.g., smartphones, pads, tablets), visible light communication (VLC) over screen-camera links emerges as a novel form of near-field communication. Such communication via smart devices is highly competitive for its user-friendliness, security, and infrastructure-less (i.e., no dependency on WiFi or cellular infrastructure). However, existing approaches mostly focus on improving the transmission speed and ignore the transmission reliability. Considering the interplay between the transmission speed and reliability towards effective end-to-end communication, in this paper, we aim to boost the throughput over screen-camera links by enhancing the transmission reliability. To this end, we propose RDCode, a robust dynamic barcode which enables a novel packet-frame-block structure. Based on the layered structure, we design different error correction schemes at three levels: intra-blocks, inter-blocks and inter-frames, in order to verify and recover the lost blocks and frames. Finally, we implement RDCode and experimentally show that RDCode reaches a high level of transmission reliability (e.g., reducing the error rate to 10%) and yields a at least doubled transmission rate, compared with the existing state-of-the-art approach COBRA.


OPERATIONAL RESEARCH : A multidisciplinary discipline


title: RDFPeers: a scalable distributed RDF repository based on a structured peer-to-peer network

abstract: Centralized Resource Description Framework (RDF) repositories have limitations both in their failure tolerance and in their scalability. Existing Peer-to-Peer (P2P) RDF repositories either cannot guarantee to find query results, even if these results exist in the network, or require up-front definition of RDF schemas and designation of super peers. We present a scalable distributed RDF repository (RDFPeers) that stores each triple at three places in a multi-attribute addressable network by applying globally known hash functions to its subject predicate and object. Thus all nodes know which node is responsible for storing triple values they are looking for and both exact-match and range queries can be efficiently routed to those nodes. RDFPeers has no single point of failure nor elevated peers and does not require the prior definition of RDF schemas. Queries are guaranteed to find matched triples in the network if the triples exist. In RDFPeers both the number of neighbors per node and the number of routing hops for inserting RDF triples and for resolving most queries are logarithmic to the number of nodes in the network. We further performed experiments that show that the triple-storing load in RDFPeers differs by less than an order of magnitude between the most and the least loaded nodes for real-world RDF data.


title: Impairment, disability, and the ICIDH (International Classification of Impairments, Disabilities, and Handicaps) model. II: The nature of the underlying condition and patterns of impairment.

abstract: This paper presents a further exploration of the conceptual scheme proposed in the International Classification of Impairments, Disabilities, and Handicaps which links underlying condition, impairment and disability. It examines the proposal that three general patterns of impairment profile might be expected depending on whether the underlying condition affects control (as in stroke or multiple sclerosis), mechanical performance (as in arthritis) or energy (as in cardiorespiratory conditions). Examination of patterns of functional limitation shows more resemblance between conditions in each of these groups than between the groups lending support to the proposal.


title: Predictive Big Data Analytics: A Study of Parkinson’s Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations

abstract: Background ::: A unique archive of Big Data on Parkinson’s Disease is collected, managed and disseminated by the Parkinson’s Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson’s disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data–large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources–all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data. ::: ::: ::: Methods and Findings ::: Collective representation of the multi-source data facilitates the aggregation and harmonization of complex data elements. This enables joint modeling of the complete data, leading to the development of Big Data analytics, predictive synthesis, and statistical validation. Using heterogeneous PPMI data, we developed a comprehensive protocol for end-to-end data characterization, manipulation, processing, cleaning, analysis and validation. Specifically, we (i) introduce methods for rebalancing imbalanced cohorts, (ii) utilize a wide spectrum of classification methods to generate consistent and powerful phenotypic predictions, and (iii) generate reproducible machine-learning based classification that enables the reporting of model parameters and diagnostic forecasting based on new data. We evaluated several complementary model-based predictive approaches, which failed to generate accurate and reliable diagnostic predictions. However, the results of several machine-learning based classification methods indicated significant power to predict Parkinson’s disease in the PPMI subjects (consistent accuracy, sensitivity, and specificity exceeding 96%, confirmed using statistical n-fold cross-validation). Clinical (e.g., Unified Parkinson's Disease Rating Scale (UPDRS) scores), demographic (e.g., age), genetics (e.g., rs34637584, chr12), and derived neuroimaging biomarker (e.g., cerebellum shape index) data all contributed to the predictive analytics and diagnostic forecasting. ::: ::: ::: Conclusions ::: Model-free Big Data machine learning-based classification methods (e.g., adaptive boosting, support vector machines) can outperform model-based techniques in terms of predictive precision and reliability (e.g., forecasting patient diagnosis). We observed that statistical rebalancing of cohort sizes yields better discrimination of group differences, specifically for predictive analytics based on heterogeneous and incomplete PPMI data. UPDRS scores play a critical role in predicting diagnosis, which is expected based on the clinical definition of Parkinson’s disease. Even without longitudinal UPDRS data, however, the accuracy of model-free machine learning based classification is over 80%. The methods, software and protocols developed here are openly shared and can be employed to study other neurodegenerative disorders (e.g., Alzheimer’s, Huntington’s, amyotrophic lateral sclerosis), as well as for other predictive Big Data analytics applications.


Urban Land Use Information Retrieval Based on Scene Classification of Google Street View Images


title: Milepost GCC: Machine Learning Enabled Self-tuning Compiler

abstract: Tuning compiler optimizations for rapidly evolving hardware makes porting and extending an optimizing compiler for each new platform extremely challenging. Iterative optimization is a popular approach to adapting programs to a new architecture automatically using feedback-directed compilation. However, the large number of evaluations required for each program has prevented iterative compilation from widespread take-up in production compilers. Machine learning has been proposed to tune optimizations across programs systematically but is currently limited to a few transformations, long training phases and critically lacks publicly released, stable tools. Our approach is to develop a modular, extensible, self-tuning optimization infrastructure to automatically learn the best optimizations across multiple programs and architectures based on the correlation between program features, run-time behavior and optimizations. In this paper we describe Milepost GCC, the first publicly-available open-source machine learning-based compiler. It consists of an Interactive Compilation Interface (ICI) and plugins to extract program features and exchange optimization data with the cTuning.org open public repository. It automatically adapts the internal optimization heuristic at function-level granularity to improve execution time, code size and compilation time of a new program on a given architecture. Part of the MILEPOST technology together with low-level ICI-inspired plugin framework is now included in the mainline GCC. We developed machine learning plugins based on probabilistic and transductive approaches to predict good combinations of optimizations. Our preliminary experimental results show that it is possible to automatically reduce the execution time of individual MiBench programs, some by more than a factor of 2, while also improving compilation time and code size. On average we are able to reduce the execution time of the MiBench benchmark suite by 11% for the ARC reconfigurable processor. We also present a realistic multi-objective optimization scenario for Berkeley DB library using Milepost GCC and improve execution time by approximately 17%, while reducing compilation time and code size by 12% and 7% respectively on Intel Xeon processor.


title: Process Mining and the ProM Framework: An Exploratory Survey

abstract: In the last decade the field of process mining gained attention from research and practice. There is, however, not much known about the use and the appreciation of the involved techniques and tools, many of which are integrated into the well-known ProM framework. Therefore a questionnaire was sent out to ask people’s opinions about process mining and the ProM framework. This paper reports on the answers and tries to link them to existing knowledge from academic literature and popular articles. It must be seen as a first, exploratory attempt to reveal the adoption of process mining and the actual use of the ProM framework.


title: Deep-Learning-Based Earth Fault Detection Using Continuous Wavelet Transform and Convolutional Neural Network in Resonant Grounding Distribution Systems

abstract: Feature extraction for fault signals is critical and difficult in all kinds of fault detection schemes. A novel simple and effective method of faulty feeder detection in resonant grounding distribution systems based on the continuous wavelet transform (CWT) and convolutional neural network (CNN) is presented in this paper. The time-frequency gray scale images are acquired by applying the CWT to the collected transient zero-sequence current signals of the faulty feeder and sound feeders. The features of the gray scale image will be extracted adaptively by the CNN, which is trained by a large number of gray scale images under various kinds of fault conditions and factors. The features extraction and the faulty feeder detection can be implemented by the trained CNN simultaneously. As a comparison, two faulty feeder detection methods based on artificial feature extraction and traditional machine learning are introduced. A practical resonant grounding distribution system is simulated in power systems computer aided design/electromagnetic transients including DC, the effectiveness and performance of the proposed faulty feeder detection method is compared and verified under different fault circumstances.


title: Feedback-Linearized Inverse Feedforward for Creep, Hysteresis, and Vibration Compensation in AFM Piezoactuators

abstract: In this brief, we study the design of a feedback and feedforward controller to compensate for creep, hysteresis, and vibration effects in an experimental piezoactuator system. First, we linearize the nonlinear dynamics of the piezoactuator by accounting for the hysteresis (as well as creep) using high-gain feedback control. Next, we model the linear vibrational dynamics and then invert the model to find a feedforward input to account vibration - this process is significantly easier than considering the complete nonlinear dynamics (which combines hysteresis and vibration effects). Afterwards, the feedforward input is augmented to the feedback-linearized system to achieve high-precision highspeed positioning. We apply the method to a piezoscanner used in an experimental atomic force microscope to demonstrate the method's effectiveness and we show significant reduction of both the maximum and root-mean-square tracking error. For example, high-gain feedback control compensates for hysteresis and creep effects, and in our case, it reduces the maximum error (compared to the uncompensated case) by over 90%. Then, at relatively high scan rates, the performance of the feedback controlled system can be improved by over 75% (i.e., reduction of maximum error) when the inversion-based feedforward input is integrated with the high-gain feedback controlled system.


title: Supplier evaluation using hybrid multiple criteria decision making approach

abstract: In the face of acute global competition, supplier management is rapidly emerging as a crucial issue to any companies striving for business success and sustainable development. To optimise competitive advantages, a company should incorporate ‘suppliers’ as an essential part of its core competencies. Supplier evaluation, the first step in supplier management, is a complex multiple criteria decision making (MCDM) problem, and its complexity is further aggravated if the highly important interdependence among the selection criteria is taken into consideration. The objective of this paper is to suggest a comprehensive decision method for identifying top suppliers by considering the effects of interdependence among the selection criteria. Proposed in this study is a hybrid model, which incorporates the technique of analytic network process (ANP) in which criteria weights are determined using fuzzy extent analysis, Technique for order performance by similarity to ideal solution (TOPSIS) under fuzzy environment is...


title: Automatic visualization of recursion trees: a case study on generic programming

abstract: Although the principles behind generic programming are already well understood, this style of programming is not widespread and examples of applications are rarely found in the literature. This paper addresses this shortage by presenting a new method, based on generic programming, to automatically visualize recursion trees of functions written in Haskell. Crucial to our solution is the fact that almost any function definition can be automatically factorized into the composition of a fold after an unfold of some intermediate data structure that models its recursion tree. By combining this technique with an existing tool for graphical debugging, and by extensively using Generic Haskell, we achieve a rather concise and elegant solution to this problem.


title: BioASQ: A Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering

abstract: This article provides an overview of BioASQ, a new competition on biomedical semantic indexing and question answering (QA). BioASQ aims to push towards systems that will allow biomedical workers to express their information needs in natural language and that will return concise and user-understandable answers by combining information from multiple sources of different kinds, including biomedical articles, databases, and ontologies. BioASQ encourages participants to adopt semantic indexing as a means to combine multiple information sources and to facilitate the matching of questions to answers. It also adopts a broad semantic indexing and QA architecture that subsumes current relevant approaches, even though no current system instantiates all of its components. Hence, the architecture can also be seen as our view of how relevant work from fields such as information retrieval, hierarchical classification, question answering, ontologies, and linked data can be combined, extended, and applied to biomedical question answering. BioASQ will develop publicly available benchmarks and it will adopt and possibly refine existing evaluation measures. The evaluation infrastructure of the competition will remain publicly available beyond the end of BioASQ.


title: Factors influencing health information technology adoption in Thailand's community health centers: Applying the UTAUT model

abstract: BACKGROUND ::: One of the most important factors for the success of health information technology (IT) implementation is users' acceptance and use of that technology. Thailand has implemented the national universal healthcare program and has been restructuring the country's health IT system to support it. However, there is no national data available regarding the acceptance and use of health IT in many healthcare facilities, including community health centers (CHCs). This study employed a modified Unified Theory of Acceptance and Use of Technology (UTAUT) structural model, to understand factors that influence health IT adoption in community health centers in Thailand and to validate this extant IT adoption model in a developing country health care context. ::: ::: ::: METHODS ::: An observational research design was employed to study CHCs' IT adoption and use. A random sample of 1607 regionally stratified CHC's from a total of 9806 CHCs was selected. Data collection was conducted using a cross-sectional survey by means of self-administered questionnaire with an 82% response rate. The research model was applied using the partial least squares (PLS) path modeling. ::: ::: ::: RESULTS ::: The data showed that people who worked in CHCs exhibited a high degree of IT acceptance and use. The research model analyses suggest that IT acceptance is influenced by performance expectancy, effort expectancy, social influence and voluntariness. Health IT use is predicted by previous IT experiences, intention to use the system, and facilitating conditions. ::: ::: ::: CONCLUSIONS ::: Health IT is pervasive and well adopted by CHCs in Thailand. The study results have implications for both health IT developmental efforts in Thailand and health informatics research. This study validated the UTAUT model in the field context of a developing country's healthcare system and demonstrated that the PLS path modeling works well in a field study and in exploratory research with a complex model.


title: Shape distributions

abstract: Measuring the similarity between 3D shapes is a fundamental problem, with applications in computer graphics, computer vision, molecular biology, and a variety of other fields. A challenging aspect of this problem is to find a suitable shape signature that can be constructed and compared quickly, while still discriminating between similar and dissimilar shapes.In this paper, we propose and analyze a method for computing shape signatures for arbitrary (possibly degenerate) 3D polygonal models. The key idea is to represent the signature of an object as a shape distribution sampled from a shape function measuring global geometric properties of an object. The primary motivation for this approach is to reduce the shape matching problem to the comparison of probability distributions, which is simpler than traditional shape matching methods that require pose registration, feature correspondence, or model fitting.We find that the dissimilarities between sampled distributions of simple shape functions (e.g., the distance between two random points on a surface) provide a robust method for discriminating between classes of objects (e.g., cars versus airplanes) in a moderately sized database, despite the presence of arbitrary translations, rotations, scales, mirrors, tessellations, simplifications, and model degeneracies. They can be evaluated quickly, and thus the proposed method could be applied as a pre-classifier in a complete shape-based retrieval or analysis system concerned with finding similar whole objects. The paper describes our early experiences using shape distributions for object classification and for interactive web-based retrieval of 3D models.


title: ATOM: Efficient Tracking, Monitoring, and Orchestration of Cloud Resources

abstract: The emergence of Infrastructure as a Service framework brings new opportunities, which also accompanies with new challenges in auto scaling, resource allocation, and security. A fundamental challenge underpinning these problems is the continuous tracking and monitoring of resource usage in the system. In this paper, we present ATOM , an efficient and effective framework to automatically track, monitor, and orchestrate resource usage in an Infrastructure as a Service (IaaS) system that is widely used in cloud infrastructure. We use novel tracking method to continuously track important system usage metrics with low overhead, and develop a Principal Component Analysis (PCA) based approach to continuously monitor and automatically find anomalies based on the approximated tracking results. We show how to dynamically set the tracking threshold based on the detection results, and further, how to adjust tracking algorithm to ensure its optimality under dynamic workloads. Lastly, when potential anomalies are identified, we use introspection tools to perform memory forensics on VMs guided by analyzed results from tracking and monitoring to identify malicious behavior inside a VM. We demonstrate the extensibility of ATOM through virtual machine (VM) clustering. The performance of our framework is evaluated in an open source IaaS system.


title: An artificial bee colony approach for clustering

abstract: Clustering is a popular data analysis and data mining technique. In this paper, an artificial bee colony clustering algorithm is presented to optimally partition N objects into K clusters. The Deb's rules are used to direct the search direction of each candidate. This algorithm has been tested on several well-known real datasets and compared with other popular heuristics algorithm in clustering, such as GA, SA, TS, ACO and the recently proposed K-NM-PSO algorithm. The computational simulations reveal very encouraging results in terms of the quality of solution and the processing time required.


title: Active nonrigid ICP algorithm

abstract: The problem of fitting a 3D facial model to a 3D mesh has received a lot of attention the past 15–20 years. The majority of the techniques fit a general model consisting of a simple parameterisable surface or a mean 3D facial shape. The drawback of this approach is that is rather difficult to describe the non-rigid aspect of the face using just a single facial model. One way to capture the 3D facial deformations is by means of a statistical 3D model of the face or its parts. This is particularly evident when we want to capture the deformations of the mouth region. Even though statistical models of face are generally applied for modelling facial intensity, there are few approaches that fit a statistical model of 3D faces. In this paper, in order to capture and describe the non-rigid nature of facial surfaces we build a part-based statistical model of the 3D facial surface and we combine it with non-rigid iterative closest point algorithms. We show that the proposed algorithm largely outperforms state-of-the-art algorithms for 3D face fitting and alignment especially when it comes to the description of the mouth region.


title: A theory of causal learning in children: causal maps and Bayes nets

abstract: We propose that children employ specialized cognitive systems that allow them to recover an accurate causal map of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism.


Equivalent Circuit Model for Double Split Ring Resonators


title: Ultrasensitive, rapid and inexpensive detection of DNA using paper based lateral flow assay

abstract: Sensitive, specific, rapid, inexpensive and easy-to-use nucleic acid tests for use at the point-of-need are critical for the emerging field of personalised medicine for which companion diagnostics are essential, as well as for application in low resource settings. Here we report on the development of a point-of-care nucleic acid lateral flow test for the direct detection of isothermally amplified DNA. The recombinase polymerase amplification method is modified slightly to use tailed primers, resulting in an amplicon with a duplex flanked by two single stranded DNA tails. This tailed amplicon facilitates detection via hybridisation to a surface immobilised oligonucleotide capture probe and a gold nanoparticle labelled reporter probe. A detection limit of 1 × 10-11 M (190 amol), equivalent to 8.67 × 105 copies of DNA was achieved, with the entire assay, both amplification and detection, being completed in less than 15 minutes at a constant temperature of 37 °C. The use of the tailed primers obviates the need for hapten labelling and consequent use of capture and reporter antibodies, whilst also avoiding the need for any post-amplification processing for the generation of single stranded DNA, thus presenting an assay that can facilely find application at the point of need.


title: KNN Model-Based Approach in Classification

abstract: The k-Nearest-Neighbours (kNN) is a simple but effective method for classification. The major drawbacks with respect to kNN are (1) its low efficiency – being a lazy learning method prohibits it in many applications such as dynamic web mining for a large repository, and (2) its dependency on the selection of a “good value” for k. In this paper, we propose a novel kNN type method for classification that is aimed at overcoming these shortcomings. Our method constructs a kNN model for the data, which replaces the data to serve as the basis of classification. The value of k is automatically determined, is varied for different data, and is optimal in terms of classification accuracy. The construction of the model reduces the dependency on k and makes classification faster. Experiments were carried out on some public datasets collected from the UCI machine learning repository in order to test our method. The experimental results show that the kNN based model compares well with C5.0 and kNN in terms of classification accuracy, but is more efficient than the standard kNN.


title: Short-term traffic flow prediction with optimized Multi-kernel Support Vector Machine

abstract: Accurate prediction of the traffic state can help to solve the problem of urban traffic congestion, providing guiding advices for people's travel and traffic regulation. In this paper, we propose a novel short-term traffic flow prediction algorithm, which is based on Multi-kernel Support Vector Machine (MSVM) and Adaptive Particle Swarm Optimization (APSO). Firstly, we explore both the nonlinear and randomness characteristic of traffic flow, and hybridize Gaussian kernel and polynomial kernel to constitute the MSVM. Secondly, we optimize the parameters of MSVM with a novel APSO algorithm by considering both the historical and real-time traffic data. We evaluate our algorithm by doing thorough experiment on a large real dataset. The results show that our algorithm can do a timely and adaptive prediction even in the rush hour when the traffic conditions change rapidly. At the same time, the prediction results are more accurate compared to four baseline methods.


title: A Modified MDP Algorithm for Multi-Pedestrians Tracking

abstract: The multi-pedestrians tracking(MPT) are crucial for the majority of computer vision applications. We propose a modified MDP framework so that it effectively solves the sharp task of data association in the tracking process. We formulate online crowd tracking as decision making in modified Markov Decision Processes(MDPs)., where we added a new state to address frequent and long-term occlusions based on the original period policy. Taking into account the characteristics of pedestrian tracking, we added color features to the original twelve features. Our modified tracker performs state-of-the-art on the MOT15 benchmark and the result of experiments show it helps to recover heavily occluded persons and to reduce false positives., showing superior performance than the original algorithm.


title: Low Loss Compact Meander Stripline Delay Lines Using LTCC

abstract: Ultra-low loss compact delay lines with grid ground strip line structure are proposed. The loss is less than full metal ground strip line. These delay lines are half loss(0.4dB/ns) and 59.5% size(214mm3/ns) compared to commercial product D20 series of EMC Tech. The self-interference canceling circuits of 4G(the 4th generation mobile communication system) base station running at co-frequency, real-time full duplex mode need ultra-low loss and compact delay lines. The ultra-low loss is especially desired. Otherwise, too much energy are injected from transmitter power amplifier(PA) to self-interference canceling circuits, and the efficiency of PA will decrease obviously. Both performance requests are satisfied by the delay lines this paper proposed.


title: RACOG and wRACOG: Two Probabilistic Oversampling Techniques

abstract: As machine learning techniques mature and are used to tackle complex scientific problems, challenges arise such as the imbalanced class distribution problem, where one of the target class labels is under-represented in comparison with other classes. Existing oversampling approaches for addressing this problem typically do not consider the probability distribution of the minority class while synthetically generating new samples. As a result, the minority class is not represented well which leads to high misclassification error. We introduce two probabilistic oversampling approaches, namely RACOG and wRACOG, to synthetically generating and strategically selecting new minority class samples. The proposed approaches use the joint probability distribution of data attributes and Gibbs sampling to generate new minority class samples. While RACOG selects samples produced by the Gibbs sampler based on a predefined lag, wRACOG selects those samples that have the highest probability of being misclassified by the existing learning model. We validate our approach using nine UCI data sets that were carefully modified to exhibit class imbalance and one new application domain data set with inherent extreme class imbalance. In addition, we compare the classification performance of the proposed methods with three other existing resampling techniques.


title: Deconstructing arousal into wakeful, autonomic and affective varieties

abstract: Arousal plays a central role in a wide variety of phenomena, including wakefulness, autonomic function, affect and emotion. Despite its importance, it remains unclear as to how the neural mechanisms for arousal are organized across them. In this article, we review neuroscience findings for three of the most common origins of arousal: wakeful arousal, autonomic arousal, and affective arousal. Our review makes two overarching points. First, research conducted primarily in non-human animals underscores the importance of several subcortical nuclei that contribute to various sources of arousal, motivating the need for an integrative framework. Thus, we outline an integrative neural reference space as a key first step in developing a more systematic understanding of central nervous system contributions to arousal. Second, there is a translational gap between research on non-human animals, which emphasizes subcortical nuclei, and research on humans using non-invasive neuroimaging techniques, which focuses more on gross anatomical characterizations of cortical (e.g. network architectures including the default mode network) and subcortical structures. We forecast the importance of high-field neuroimaging in bridging this gap to examine how the various networks within the neural reference space for arousal operate across varieties of arousal-related phenomena.


title: Reading Hidden Emotions: Spontaneous Micro-expression Spotting and Recognition

abstract: Micro-expressions (MEs) are rapid, involuntary facial expressions which reveal emotions that people do not intend to show. Studying MEs is valuable as recognizing them has many important applications, particularly in forensic science and psychotherapy. However, analyzing spontaneous MEs is very challenging due to their short duration and low intensity. Automatic ME analysis includes two tasks: ME spotting and ME recognition.For ME spotting, previous studies have focused on posed rather than spontaneous videos. For ME recognition, the performance of previous studies is low. To address these challenges, we make the following contributions: (i) We propose the first method for spotting spontaneous MEs in long videos (by exploiting feature difference contrast). This method is training free and works on arbitrary unseen videos. (ii) We present an advanced ME recognition framework, which outperforms previous work by a large margin on two challenging spontaneous ME databases (SMIC and CASMEII). (iii) We propose the first automatic ME analysis system (MESR), which can spot and recognize MEs from spontaneous video data. Finally, we show that our method achieves comparable performance to humans at this very challenging task, and outperforms humans in the ME recognition task by a large margin.