title: Automatic generation of data-oriented exploits

abstract: As defense solutions against control-flow hijacking attacks gain wide deployment, control-oriented exploits from memory errors become difficult. As an alternative, attacks targeting non-control data do not require diverting the application's control flow during an attack. Although it is known that such data-oriented attacks can mount significant damage, no systematic methods to automatically construct them from memory errors have been developed. In this work, we develop a new technique called data-flow stitching, which systematically finds ways to join data flows in the program to generate data-oriented exploits. We build a prototype embodying our technique in a tool called FLOWSTITCH that works directly on Windows and Linux binaries. In our experiments, we find that FLOWSTITCH automatically constructs 16 previously unknown and three known data-oriented attacks from eight real-world vulnerable programs. All the automatically-crafted exploits respect fine-grained CFI and DEP constraints, and 10 out of the 19 exploits work with standard ASLR defenses enabled. The constructed exploits can cause significant damage, such as disclosure of sensitive information (e.g., passwords and encryption keys) and escalation of privilege.


title: Pathophysiology, Relevance and Natural History of Oropharyngeal Dysphagia among Older People

abstract: Oropharyngeal dysphagia (OD) is a very frequent condition among older people with a prevalence ranging from mild symptoms in 25% of the independently living to severe symptoms in more than 50% living in nursing homes. There are several validated methods of screening, and clinical assessment and videofluoroscopy are the gold standard for the study of the mechanisms of OD in the elderly. Oropharyngeal residue is mainly caused by weak bolus propulsion forces due to tongue sarcopenia. The neural elements of swallow response are also impaired in older persons, with prolonged and delayed laryngeal vestibule closure and slow hyoid movement causing oropharyngeal aspirations. OD causes malnutrition, dehydration, impaired quality of life, lower respiratory tract infections, aspiration pneumonia, and poor prognosis including prolonged hospital stay and enhanced morbidity and mortality in several phenotypes of older patients ranging from independently living older people, hospitalized older patients and nursing home residents. Enhancing bolus viscosity of fluids greatly improves safety of swallow in all these patients. We believe OD should be recognized as a major geriatric syndrome, and we recommend a policy of systematic and universal screening and assessment of OD among older people to prevent its severe complications.


Boon and Bane of IT Consumerization: The Burnout-Engagement-Continuum


title: Simultaneous Discovery of Common and Discriminative Topics via Joint Nonnegative Matrix Factorization

abstract: Understanding large-scale document collections in an efficient manner is an important problem. Usually, document data are associated with other information (e.g., an author's gender, age, and location) and their links to other entities (e.g., co-authorship and citation networks). For the analysis of such data, we often have to reveal common as well as discriminative characteristics of documents with respect to their associated information, e.g., male- vs. female-authored documents, old vs. new documents, etc. To address such needs, this paper presents a novel topic modeling method based on joint nonnegative matrix factorization, which simultaneously discovers common as well as discriminative topics given multiple document sets. Our approach is based on a block-coordinate descent framework and is capable of utilizing only the most representative, thus meaningful, keywords in each topic through a novel pseudo-deflation approach. We perform both quantitative and qualitative evaluations using synthetic as well as real-world document data sets such as research paper collections and nonprofit micro-finance data. We show our method has a great potential for providing in-depth analyses by clearly identifying common and discriminative topics among multiple document sets.


title: Airport Gate Assignment A Hybrid Model and Implementation

abstract: With the rapid development of airlines, airports today become much busier and more complicated than previous days. During airlines daily operations, assigning the available gates to the arriving aircrafts based on the fixed schedule is a very important issue, which motivates researchers to study and solve Airport Gate Assignment Problems (AGAP) with all kinds of state-of-the-art combinatorial optimization techniques. In this paper, we study the AGAP and propose a novel hybrid mathematical model based on the method of constraint programming and 0 - 1 mixed-integer programming. With the objective to minimize the number of gate conflicts of any two adjacent aircrafts assigned to the same gate, we build a mathematical model with logical constraints and the binary constraints. For practical considerations, the potential objective of the model is also to minimize the number of gates that airlines must lease or purchase in order to run their business smoothly. We implement the model in the Optimization Programming Language (OPL) and carry out empirical studies with the data obtained from online timetable of Continental Airlines, Houston Gorge Bush Intercontinental Airport IAH, which demonstrate that our model can provide an efficient evaluation criteria for the airline companies to estimate the efficiency of their current gate assignments.


title: Non-interactive zero-knowledge arguments for voting

abstract: In voting based on homomorphic threshold encryption, the voter encrypts his vote and sends it in to the authorities that tally the votes. If voters can send in arbitrary plaintexts then they can cheat. It is therefore important that they attach an argument of knowledge of the plaintext being a correctly formed vote. Typically, these arguments are honest verifier zero-knowledge arguments that are made non-interactive using the Fiat-Shamir heuristic. Security is argued in the random oracle model. ::: ::: The simplest case is where each voter has a single vote to cast. Practical solutions have already been suggested for the single vote case. However, as we shall see homomorphic threshold encryption can be used for a variety of elections, in particular there are many cases where voters can cast multiple votes at once. In these cases, it remains important to bring down the cost of the NIZK argument. ::: ::: We improve on state of the art in the case of limited votes, where each voter can vote a small number of times. We also improve on the state of the art in shareholder elections, where each voter may have a large number of votes to spend. Moreover, we improve on the state of the art in Borda voting. Finally, we suggest a NIZK argument for correctness of an approval vote. To the best of our knowledge, approval voting has not been considered before in the cryptographic literature.


title: Comparing Different Moving Target Defense Techniques

abstract: Moving Target Defense techniques have been proposed to increase uncertainty and apparent complexity for attackers. When more than one Moving Target Defense techniques are effective to limit opportunities of an attack, it is required to compare these techniques and select the best defense choice. In this paper, we propose a three-layer model to evaluate and compare effectiveness of different Moving Target Defenses. This model is designed as an attempt to fill a gap among existing evaluation methods and works as a systematic framework for Moving Target Defense comparison.


title: We Built a Fake News&Click-bait Filter: What Happened Next Will Blow Your Mind!

abstract: It is completely amazing! Fake news and click-baits have totally invaded the cyber space. Let us face it: everybody hates them for three simple reasons. Reason #2 will absolutely amaze you. What these can achieve at the time of election will completely blow your mind! Now, we all agree, this cannot go on, you know, somebody has to stop it. So, we did this research on fake news/click-bait detection and trust us, it is totally great research, it really is! Make no mistake. This is the best research ever! Seriously, come have a look, we have it all: neural networks, attention mechanism, sentiment lexicons, author profiling, you name it. Lexical features, semantic features, we absolutely have it all. And we have totally tested it, trust us! We have results, and numbers, really big numbers. The best numbers ever! Oh, and analysis, absolutely top notch analysis. Interested? Come read the shocking truth about fake news and click-bait in the Bulgarian cyber space. You won't believe what we have found!


Graph Sparsification Approaches for Laplacian Smoothing


Racial IQ Differences among Transracial Adoptees: Fact or Artifact?


title: Open-Ended Visual Question-Answering

abstract: This thesis report studies methods to solve Visual Question-Answering (VQA) tasks with a Deep Learning framework. As a preliminary step, we explore Long Short-Term Memory (LSTM) networks used in Natural Language Processing (NLP) to tackle Question-Answering (text based). We then modify the previous model to accept an image as an input in addition to the question. For this purpose, we explore the VGG-16 and K-CNN convolutional neural networks to extract visual features from the image. These are merged with the word embedding or with a sentence embedding of the question to predict the answer. This work was successfully submitted to the Visual Question Answering Challenge 2016, where it achieved a 53,62% of accuracy in the test dataset. The developed software has followed the best programming practices and Python code style, providing a consistent baseline in Keras for different configurations.


Dialog Generation Using Multi-Turn Reasoning Neural Networks


title: Efficient Training and Evaluation of Recurrent Neural Network Language Models for Automatic Speech Recognition

abstract: Recurrent neural network language models RNNLMs are becoming increasingly popular for a range of applications including automatic speech recognition. An important issue that limits their possible application areas is the computational cost incurred in training and evaluation. This paper describes a series of new efficiency improving approaches that allows RNNLMs to be more efficiently trained on graphics processing units GPUs and evaluated on CPUs. First, a modified RNNLM architecture with a nonclass-based, full output layer structure F-RNNLM is proposed. This modified architecture facilitates a novel spliced sentence bunch mode parallelization of F-RNNLM training using large quantities of data on a GPU. Second, two efficient RNNLM training criteria based on variance regularization and noise contrastive estimation are explored to specifically reduce the computation associated with the RNNLM output layer softmax normalisation term. Finally, a pipelined training algorithm utilizing multiple GPUs is also used to further improve the training speed. Initially, RNNLMs were trained on a moderate dataset with 20M words from a large vocabulary conversational telephone speech recognition task. The training time of RNNLM is reduced by up to a factor of 53 on a single GPU over the standard CPU-based RNNLM toolkit. A 56 times speed up in test time evaluation on a CPU was obtained over the baseline F-RNNLMs. Consistent improvements in both recognition accuracy and perplexity were also obtained over C-RNNLMs. Experiments on Google's one billion corpus also reveals that the training of RNNLM scales well.


title: Soft wearable motion sensing suit for lower limb biomechanics measurements

abstract: Motion sensing has played an important role in the study of human biomechanics as well as the entertainment industry. Although existing technologies, such as optical or inertial based motion capture systems, have relatively high accuracy in detecting body motions, they still have inherent limitations with regards to mobility and wearability. In this paper, we present a soft motion sensing suit for measuring lower extremity joint motion. The sensing suit prototype includes a pair of elastic tights and three hyperelastic strain sensors. The strain sensors are made of silicone elastomer with embedded microchannels filled with conductive liquid. To form a sensing suit, these sensors are attached at the hip, knee, and ankle areas to measure the joint angles in the sagittal plane. The prototype motion sensing suit has significant potential as an autonomous system that can be worn by individuals during many activities outside the laboratory, from running to rock climbing. In this study we characterize the hyperelastic sensors in isolation to determine their mechanical and electrical responses to strain, and then demonstrate the sensing capability of the integrated suit in comparison with a ground truth optical motion capture system. Using simple calibration techniques, we can accurately track joint angles and gait phase. Our efforts result in a calculated trade off: with a maximum error less than 8%, the sensing suit does not track joints as accurately as optical motion capture, but its wearability means that it is not constrained to use only in a lab.


title: A Biologically Inspired Appearance Model for Robust Visual Tracking

abstract: In this paper, we propose a biologically inspired appearance model for robust visual tracking. Motivated in part by the success of the hierarchical organization of the primary visual cortex (area V1), we establish an architecture consisting of five layers: whitening, rectification, normalization, coding, and pooling. The first three layers stem from the models developed for object recognition. In this paper, our attention focuses on the coding and pooling layers. In particular, we use a discriminative sparse coding method in the coding layer along with spatial pyramid representation in the pooling layer, which makes it easier to distinguish the target to be tracked from its background in the presence of appearance variations. An extensive experimental study shows that the proposed method has higher tracking accuracy than several state-of-the-art trackers.


title: The Significant Fictivity of Maxine Hong Kingston's The Woman Warrior

abstract: The speech act theory of discourse expounded by Mary Louise Pratt describes Maxine Hong Kingston's use of fictional technique in The Woman Warrior . It shows that the language of the autobiography is a creation resulting from the author's effort to extricate her voice from the discourse of her community.


title: Deep-Space Communications and Coding: A Marriage Made in Heaven

abstract: It is almost a quarter of a century since the launch in 1968 of NASA's Pioneer 9 spacecraft on the first mission into deep-space that relied on coding to enhance communications on the critical downlink channel. [The channel code used was a binary convolutional code that was decoded with sequential decoding--we will have much to say about this code in the sequel.] The success of this channel coding system had repercussions that extended far beyond NASA's space program. It is no exaggeration to say that the Pioneer 9 mission provided communications engineers with the first incontrovertible demonstration of the practical utility of channel coding techniques and thereby paved the way for the successful application of coding to many other channels.


An Analysis of Mobile Banking User Behavior Using Customer Segmentation


title: TinyECCK: Efficient Elliptic Curve Cryptography Implementation over GF (2 m) on

abstract: In this paper, we revisit a generally accepted opinion: implementing Elliptic Curve Cryptosystem (ECC) over GF(2m) on sensor motes using small word size is not appropriate because XOR multiplication over GF(2m) is not efficiently supported by current low-powered microprocessors. Although there are some implementations over GF(2m) on sensor motes, their performances are not satisfactory enough to be used for wireless sensor networks (WSNs). We have found that a field multiplication over GF(2m) are involved in a number of redundant memory accesses and its inefficiency is originated from this problem. Moreover, the field reduction process also requires many redundant memory accesses. Therefore, we propose some techniques for reducing unnecessary memory accesses. With the proposed strategies, the running time of field multiplication and reduction over GF(2163) can be decreased by 21.1% and 24.7%, respectively. These savings noticeably decrease execution times spent in Elliptic Curve Digital Signature Algorithm (ECDSA) operations (signing and verification) by around 15–19%. We present TinyECCK (Tiny Elliptic Curve Cryptosystem with Koblitz curve – a kind of TinyOS package supporting elliptic curve operations) which is the first implementation of Koblitz curve on sensor motes as far as we know. Through comparisons with existing software implementations of ECC built in C or hybrid of C and inline assembly on sensor motes, we show that TinyECCK outperforms them in terms of running time, code size and supporting services. Furthermore, we show that a field multiplication over GF(2m) can be faster than that over GF(p) on 8-bit Atmega128 processor by comparing TinyECCK with TinyECC, a well-known ECC implementation over GF(p). TinyECCK with sect163k1 can generate a signature and verify it in 1.37 and 2.32 secs on a Micaz mote with 13,748-byte of ROM and 1,004-byte of RAM.


Deep Neural Network Embeddings for Text-Independent Speaker Verification


RABIT : A Reliable Allreduce and Broadcast Interface


title: A New Softmax Operator for Reinforcement Learning

abstract: A softmax operator applied to a set of values acts somewhat like the maximization function and somewhat like an average. In sequential decision making, softmax is often used in settings where it is necessary to maximize utility but also to hedge against problems that arise from putting all of one's weight behind a single maximum utility decision. The Boltzmann softmax operator is the most commonly used softmax operator in this setting, but we show that this operator is prone to misbehavior. In this work, we study an alternative softmax operator that, among other properties, is both a non-expansion (ensuring convergent behavior in learning and planning) and differentiable (making it possible to improve decisions via gradient descent methods). We provide proofs of these properties and present empirical comparisons between various softmax operators.


title: Automatically Constructing a Dictionary for Information Extraction Tasks

abstract: Knowledge-based natural language processing systems have achieved good success with certain tasks but they are often criticized because they depend on a domain-specific dictionary that requires a great deal of manual knowledge engineering. This knowledge engineering bottleneck makes knowledge-based NLP systems impractical for real-world applications because they cannot be easily scaled up or ported to new domains. In response to this problem, we developed a system called AutoSlog that automatically builds a domain-specific dictionary of concepts for extracting information from text. Using AutoSlog, we constructed a dictionary for the domain of terrorist event descriptions in only 5 person-hours. We then compared the AutoSlog dictionary with a hand-crafted dictionary that was built by two highly skilled graduate students and required approximately 1500 person-hours of effort. We evaluated the two dictionaries using two blind test sets of 100 texts each. Overall, the AutoSlog dictionary achieved 98% of the performance of the hand-crafted dictionary. On the first test set, the AutoSlog dictionary obtained 96.3% of the performance of the hand-crafted dictionary. On the second test set, the overall scores were virtually indistinguishable with the AutoSlog dictionary achieving 99.7% of the performance of the handcrafted dictionary.


A Review of Contemporary E-voting : Requirements , Technology , Systems and Usability


title: Orthogonal Coaxial Cavity Filters With Distributed Cross-Coupling

abstract: In this letter, six-pole filters are presented, in which the coaxial cavity resonators are arranged with 90 degree rotated spatial orientation. Cross-coupling between nonadjacent resonators will appear. In the new proposed filter topologies the cross-coupling elements are not arranged in separated triplet or quadruplet blocks. They interlace to form distributed cross-coupling. Synthesis of the distributed coupling matrix based on the symmetric canonical prototype is discussed. Two different bandpass filter topologies are presented. The first one has an inline arrangement of six orthogonal coaxial cavity resonators. Its distributed cross-coupling consists of four interlaced triplets, which generate two transmission zeros above the passband. The second filter design has a folded arrangement of two inline triplets with strong cross-coupling. An additional interlaced quadruplet pushes the resulting transmission zeros to the complex frequency domain. Measurement results of two corresponding filter prototypes confirm the predicted performance.


title: Sociable robot improves toddler vocabulary skills

abstract: We report results of a study in which a low cost sociable robot was immersed at an Early Childhood Education Center for a period of 2 weeks. The study was designed to investigate whether the robot, which operated fully autonomously during the intervention period, could improve target vocabulary skills of 18-24 month of age toddlers. The results showed a 27 % improvement in knowledge of the target words taught by the robot when compared to a matched set of control words. The results suggest that sociable robots may be an effective and low cost technology to enrich Early Childhood Education environments.


title: Real-time failure prediction in online services

abstract: Current data mining techniques used to create failure predictors for online services require massive amounts of data to build, train, and test the predictors. These operations are tedious, time consuming, and are not done in real-time. Also, the accuracy of the resulting predictor is highly compromised by changes that affect the environment and working conditions of the predictor. We propose a new approach to creating a dynamic failure predictor for online services in real-time and keeping its accuracy high during the services run-time changes. We use synthetic transactions during the run-time lifecycle to generate current data about the service. This data is used in its ephemeral state to build, train, test, and maintain an up-to-date failure predictor. We implemented the proposed approach in a large-scale online ad service that processes billions of requests each month in six data centers distributed in three continents. We show that the proposed predictor is able to maintain failure prediction accuracy as high as 86% during online service changes, whereas the accuracy of the state-of-the-art predictors may drop to less than 10%.


title: A Type-2 Fuzzy Ontology and Its Application to Personal Diabetic-Diet Recommendation

abstract: It has been widely pointed out that classical ontology is not sufficient to deal with imprecise and vague knowledge for some real-world applications like personal diabetic-diet recommendation. On the other hand, fuzzy ontology can effectively help to handle and process uncertain data and knowledge. This paper proposes a novel ontology model, which is based on interval type-2 fuzzy sets (T2FSs), called type-2 fuzzy ontology (T2FO), with applications to knowledge representation in the field of personal diabetic-diet recommendation. The T2FO is composed of 1) a type-2 fuzzy personal profile ontology ( type-2 FPPO); 2) a type-2 fuzzy food ontology ( type-2 FFO); and 3) a type-2 fuzzy-personal food ontology (type-2 FPFO). In addition, the paper also presents a T2FS-based intelligent diet-recommendation agent ( IDRA), including 1) T2FS construction; 2) a T2FS-based personal ontology filter; 3) a T2FS-based fuzzy inference mechanism; 4) a T2FS-based diet-planning mechanism; 5) a T2FS-based menu-recommendation mechanism; and 6) a T2FS-based semantic-description mechanism. In the proposed approach, first, the domain experts plan the diet goal for the involved diabetes and create the nutrition facts of common Taiwanese food. Second, the involved diabetics are requested to routinely input eaten items. Third, the ontology-creating mechanism constructs a T2FO, including a type-2 FPPO, a type-2 FFO, and a set of type-2 FPFOs. Finally, the T2FS-based IDRA retrieves the built T2FO to recommend a personal diabetic meal plan. The experimental results show that the proposed approach can work effectively and that the menu can be provided as a reference for the involved diabetes after diet validation by domain experts.


title: Dryad: distributed data-parallel programs from sequential building blocks

abstract: Dryad is a general-purpose distributed execution engine for coarse-grain data-parallel applications. A Dryad application combines computational "vertices" with communication "channels" to form a dataflow graph. Dryad runs the application by executing the vertices of this graph on a set of available computers, communicating as appropriate through flies, TCP pipes, and shared-memory FIFOs. The vertices provided by the application developer are quite simple and are usually written as sequential programs with no thread creation or locking. Concurrency arises from Dryad scheduling vertices to run simultaneously on multiple computers, or on multiple CPU cores within a computer. The application can discover the size and placement of data at run time, and modify the graph as the computation progresses to make efficient use of the available resources. Dryad is designed to scale from powerful multi-core single computers, through small clusters of computers, to data centers with thousands of computers. The Dryad execution engine handles all the difficult problems of creating a large distributed, concurrent application: scheduling the use of computers and their CPUs, recovering from communication or computer failures, and transporting data between vertices.


title: Utility function security in artificially intelligent agents

abstract: The notion of ‘wireheading’, or direct reward centre stimulation of the brain, is a well-known concept in neuroscience. In this paper, we examine the corresponding issue of reward (utility) function integrity in artificially intelligent machines. We survey the relevant literature and propose a number of potential solutions to ensure the integrity of our artificial assistants. Overall, we conclude that wireheading in rational self-improving optimisers above a certain capacity remains an unsolved problem despite opinion of many that such machines will choose not to wirehead. A relevant issue of literalness in goal setting also remains largely unsolved and we suggest that the development of a non-ambiguous knowledge transfer language might be a step in the right direction.


title: Adverse childhood experiences, chronic diseases, and risky health behaviors in Saudi Arabian adults: a pilot study.

abstract: Adverse childhood experiences (ACEs) have been linked with risky health behaviors and the development of chronic diseases in adulthood. This study examined associations between ACEs, chronic diseases, and risky behaviors in adults living in Riyadh, Saudi Arabia in 2012 using the ACE International Questionnaire (ACE-IQ). A cross-sectional design was used, and adults who were at least 18 years of age were eligible to participate. ACEs event scores were measured for neglect, household dysfunction, abuse (physical, sexual, and emotional), and peer and community violence. The ACE-IQ was supplemented with questions on risky health behaviors, chronic diseases, and mood. A total of 931 subjects completed the questionnaire (a completion rate of 88%); 57% of the sample was female, 90% was younger than 45 years, 86% had at least a college education, 80% were Saudi nationals, and 58% were married. One-third of the participants (32%) had been exposed to 4 or more ACEs, and 10%, 17%, and 23% had been exposed to 3, 2, or 1 ACEs respectively. Only 18% did not have an ACE. The prevalence of risky health behaviors ranged between 4% and 22%. The prevalence of self-reported chronic diseases ranged between 6% and 17%. Being exposed to 4 or more ACEs increased the risk of having chronic diseases by 2-11 fold, and increased risky health behaviors by 8-21 fold. The findings of this study will contribute to the planning and development of programs to prevent child maltreatment and to alleviate the burden of chronic diseases in adults.


title: $PROST$ : Privacy-Preserving and Truthful Online Double Auction for Spectrum Allocation

abstract: Spectrum auction is an effective way to redistribute scarce spectrum resources. However, most spectrum auction designs only target at economic robustness, while neglecting the inherent privacy leakage problem. Existing secure spectrum auction mechanisms fail to provide adequate security, and they all neglect the online fashion of spectrum request arrival. In this paper, for the first time, we propose a Privacy-pReserving and truthful Online double auction mechanism for Spectrum allocaTion in wireless networks, PROST . Compared with the state-of-the-art solutions, PROST provides a comprehensive and strong protection for users’ sensitive information, especially for location privacy and time dynamics. PROST is constructed based on our carefully designed security building blocks, which support various arithmetics over encrypted real numbers, and they are also well applicable in other spectrum auctions. Besides, we improve on the existing online spectrum auction mechanisms by designing a novel privacy-preserving buyer grouping protocol for spectrum reuse. We not only theoretically prove that PROST can realize an all-round security against semi-honest adversaries but also extensively evaluate its performance. Experimental results validate that PROST achieves nice spectrum allocation efficiency with light computation and communication costs.


title: Brief Papers_______________________________________________________________________________ Dimensionality Reduction Using Genetic Algorithms

abstract: Pattern recognition generally requires that objects be described in terms of a set of measurable features. The selection and quality of the features representing each pattern affect the success of subsequent classification. Feature extraction is the process of deriving new features from original features to reduce the cost of feature measurement, increase classifier efficiency, and allow higher accuracy. Many feature extraction techniques involve linear transformations of the original pattern vectors to new vectors of lower dimensionality. While this is useful for data visualization and classification efficiency, it does not necessarily reduce the number of features to be measured since each new feature may be a linear combination of all of the features in the original pattern vector. Here, we present a new approach to feature extraction in which feature selection and extraction and classifier training are performed simultaneously using a genetic algorithm. The genetic algorithm optimizes a feature weight vector used to scale the individual features in the original pattern vectors. A masking vector is also employed for simultaneous selection of a feature subset. We employ this technique in combination with the k nearest neighbor classification rule, and compare the results with classical feature selection and extraction techniques, including sequential floating forward feature selection, and linear discriminant analysis. We also present results for the identification of favorable water-binding sites on protein surfaces.


title: Microplastic ingestion by riverine macroinvertebrates

abstract: Abstract Although microplastics are a recognised pollutant in marine environments, less attention has been directed towards freshwater ecosystems despite their greater proximity to possible plastic sources. Here, we quantify the presence of microplastic particles (MPs) in river organisms upstream and downstream of five UK Wastewater Treatment Works (WwTWs). MPs were identified in approximately 50% of macroinvertebrate samples collected (Baetidae, Heptageniidae and Hydropsychidae) at concentrations up to 0.14 MP mg tissue−1 and they occurred at all sites. MP abundance was associated with macroinvertebrate biomass and taxonomic family, but MPs occurred independently of feeding guild and biological traits such as habitat affinity and ecological niche. There was no increase in plastic ingestion downstream of WwTW discharges averaged across sites, but MP abundance in macroinvertebrates marginally increased where effluent discharges contributed more to total runoff and declined with increasing river discharge. The ubiquity of microplastics within macroinvertebrates in this case study reveals a potential risk from MPs entering riverine food webs through at least two pathways, involving detritivory and filter-feeding, and we recommend closer attention to freshwater ecosystems in future research.


title: Miniaturized Wilkinson power dividers utilizing capacitive loading

abstract: The authors report the miniaturization of a planar Wilkinson power divider by capacitive loading of the quarter wave transmission lines employed in conventional Wilkinson power dividers. Reduction of the transmission line segments from /spl lambda//4 to between /spl lambda//5 and /spl lambda//12 are reported here. The input and output lines at the three ports and the lines comprising the divider itself are coplanar waveguide (CPW) and asymmetric coplanar stripline (ACPS), respectively. The 10 GHz power dividers are fabricated on high resistivity silicon (HRS) and alumina wafers. These miniaturized dividers are 74% smaller than conventional Wilkinson power dividers, and have a return loss better than +30 dB and an insertion loss less than 0.55 dB. Design equations and a discussion about the effect of parasitic reactance on the isolation are presented for the first time.


A Provably Efficient Algorithm for Training Deep Networks


title: The interproximal height of bone: a guidepost to predictable aesthetic strategies and soft tissue contours in anterior tooth replacement.

abstract: Enhanced aesthetic objectives can be achieved with precision and predictability due to recent advances in restorative materials and procedures. Although these developments have expanded the therapeutic options available to practitioners and their patients, anterior hard and soft tissue deformities in the aesthetic zone continue to represent a significant technical challenge to the reconstructive team. The objective of this article is to present diagnostic and prognostic criteria that emphasize the osseous-gingival relationship as a means to achieve predictable aesthetic results in the anterior segment with conventional or implant-supported restorations.


title: Analyzing Forged SSL Certificates in the Wild

abstract: The SSL man-in-the-middle attack uses forged SSL certificates to intercept encrypted connections between clients and servers. However, due to a lack of reliable indicators, it is still unclear how commonplace these attacks occur in the wild. In this work, we have designed and implemented a method to detect the occurrence of SSL man-in-the-middle attack on a top global website, Facebook. Over 3 million real-world SSL connections to this website were analyzed. Our results indicate that 0.2% of the SSL connections analyzed were tampered with forged SSL certificates, most of them related to antivirus software and corporate-scale content filters. We have also identified some SSL connections intercepted by malware. Limitations of the method and possible defenses to such attacks are also discussed.


title: The resilience of the intestinal microbiota influences health and disease

abstract: The composition of the intestinal microbiota varies among individuals and throughout development, and is dependent on host and environmental factors. However, although the microbiota is constantly exposed to environmental challenges, its composition and function in an individual are stable against perturbations, as microbial communities are resilient and resistant to change. The maintenance of a beneficial microbiota requires a homeostatic equilibrium within microbial communities, and also between the microorganisms and the intestinal interface of the host. The resilience of the healthy microbiota protects us from dysbiosis-related diseases, such as inflammatory bowel disease (IBD) or metabolic disorder. By contrast, a resilient dysbiotic microbiota may cause disease. In this Opinion article, we propose that microbial resilience has a key role in health and disease. We will discuss the concepts and mechanisms of microbial resilience against dietary, antibiotic or bacteriotherapy-induced perturbations and the implications for human health.


title: Feeling cybervictims' pain-The effect of empathy training on cyberbullying.

abstract: As the world's population increasingly relies on the use of modern technology, cyberbullying becomes an omnipresent risk for children and adolescents and demands counteraction to prevent negative (online) experiences. The classroom-based German preventive intervention “Medienhelden” (engl.: “Media Heroes”) builds on previous knowledge about links between cognitive empathy, affective empathy, and cyberbullying, among others. For an evaluation study, longitudinal data were available from 722 high school students aged 11–17 years (M = 13.36, SD = 1.00, 51.8% female) before and six months after the implementation of the program. A 10-week version and a 1-day version were conducted and compared with a control group (controlled pre-long-term-follow-up study). Schools were asked to randomly assign their participating classes to the intervention conditions. Multi-group structural equation modeling (SEM) showed a significant effect of the short intervention on cognitive empathy and significant effects of the long intervention on affective empathy and cyberbullying reduction. The results suggest the long-term intervention to be more effective in reducing cyberbullying and promoting affective empathy. Without any intervention, cyberbullying increased and affective empathy decreased across the study period. Empathy change was not generally directly linked to change in cyberbullying behavior. “Media Heroes” provides effective teaching materials and empowers schools to address the important topic of cyberbullying in classroom settings without costly support from the outside. Aggr. Behav. 9999:1–10, 2015. © 2015 Wiley Periodicals, Inc.


title: Detecting hot topics from Twitter: A multiview approach

abstract: Twitter is widely used all over the world, and a huge number of hot topics are generated by Twitter users in real time. These topics are able to reflect almost every aspect of people's daily lives. Therefore, the detection of topics in Twitter can be used in many real applications, such as monitoring public opinion, hot product recommendation and incidence detection. However, the performance of traditional topic detection methods is still far from perfect largely owing to the tweets' features, such as their limited length and arbitrary abbreviations. To address these problems, we propose a novel framework (MVTD) for Twitter topic detection using multiview clustering, which can integrate multirelations among tweets, such as semantic relations, social tag relations and temporal relations. We also propose some methods for measuring relations among tweets. In particular, to better measure the semantic similarity of tweets, we propose a new document similarity measure based on a suffix tree (STVSM). In addition, a new keyword extraction method based on a suffix tree is proposed. Experiments on real datasets show that the performance of MVTD is much better than that of a single view, and it is useful for detecting topics from Twitter.


title: Characterization and Design of Logic Circuits in the Presence of Carbon Nanotube Density Variations

abstract: Variations in the spatial density of carbon nanotubes (CNTs), resulting from the lack of precise control over CNT positioning during chemical synthesis, is a major hurdle to the scalability of carbon nanotube field effect transistor (CNFET) circuits. Such CNT density variations can lead to non-functional CNFET circuits. This paper presents a probabilistic framework for modeling the CNT count distribution contained in a CNFET of given width, and establishes the accuracy of the model using experimental data obtained from CNT growth. Using this model, we estimate the impact of CNT density variations on the yield of CNFET very large-scale integrated circuits. Our estimation results demonstrate that CNT density variations can significantly degrade the yield of CNFETs, and can be a major concern for scaled CNFET circuits. Finally, we analyze the impact of CNT correlation (i.e., correlation of CNT count between CNFETs) that exists in CNT growth, and demonstrate how the yield of a CNFET storage circuit (primarily limited by its noise immunity) can be significantly improved by taking advantage of such correlation.


title: SeGAN: Segmenting and Generating the Invisible

abstract: Objects often occlude each other in scenes; Inferring their appearance beyond their visible parts plays an important role in scene understanding, depth estimation, object interaction and manipulation. In this paper, we study the challenging problem of completing the appearance of occluded objects. Doing so requires knowing which pixels to paint (segmenting the invisible parts of objects) and what color to paint them (generating the invisible parts). Our proposed novel solution, SeGAN, jointly optimizes for both segmentation and generation of the invisible parts of objects. Our experimental results show that: (a) SeGAN can learn to generate the appearance of the occluded parts of objects; (b) SeGAN outperforms state-of-the-art segmentation baselines for the invisible parts of objects; (c) trained on synthetic photo realistic images, SeGAN can reliably segment natural images; (d) by reasoning about occluder-occludee relations, our method can infer depth layering.


title: A semantic-enhanced trust based recommender system using ant colony optimization

abstract: Collaborative Filtering (CF) is the most popular recommendation technique that uses preferences of users in a community to make personal recommendations for other users. Despite its popularity and success, CF suffers from the data sparsity and cold-start problems. To alleviate these issues, in recent years, there has been an upsurge of interest in exploiting trust information to improve the performance of CF. In general, trust has a number of distinct properties such as asymmetry, transitivity, dynamicity and context-dependency. However, conventional trust-based CF systems do not address trust computation by considering all the properties of trust. Particularly, the context-dependency property has received less attention in the existing approaches. The consideration of all these properties leads to more accurate recommendations since the quality of the inferred is improved. In this paper, we propose a novel trust-based approach, called Semantic-enhanced Trust based Ant Recommender System (STARS), which satisfies all the properties mentioned above. Using ant colony optimization, the proposed system performs a depth- first search for the optimal trust paths in the trust network and selects the best neighbors of an active user to provide better recommendations. To consider the context-dependency property, trust inference in STARS depends on the semantic descriptions of items. Incorporation of both global and local trust in CF-based recommender systems in addition to the trust computation based on the semantic features of items allows STARS to alleviate the data sparsity, cold-start and "multiple-interests and multiple-content" problems of CF. Experimental results on real-world data sets show that STARS outperforms its counterparts in terms of prediction accuracy and recommendation quality and can overcome the above problems.


title: Level Shifters and DCVSL for a Low-Voltage CMOS 4.2-V Buck Converter

abstract: In this paper, high-voltage (HV)-tolerant level shifters with combinational functionality are proposed based on differential cascode voltage switch logic (DCVSL). These level shifters are tolerant to supply voltages higher than the process limit for individual CMOS transistors. The proposed HV DCVSL level shifters are particularly useful when it is mandatory to constrain the output using a logic function during out of the normal mode periods (power-up, power-down, reset, etc.). These HV-tolerant logic circuits were used in the power block of a buck converter designed in a standard 3.3-V 0.13-mum CMOS process, powered by an input voltage range from 2.7 to 4.2 V. Simulation and experimental results of the buck are analyzed, and the topology is evaluated.


title: Ransomware at X-Rays

abstract: Ransomware is a new form of malware that is plaguing an impressive number of victims, by applying a very effective paradigm of attack: to block data or computers and require a ransom to the victim for restoring the functionality of the infected machine. Different families and variants of this malware arose in a few years, rapidly diffusing in the wild and quickly increasing the robustness of the attack procedures. In this paper we discuss the most peculiar characteristics of this kind of malware, that we extracted from 76 samples.


title: FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence

abstract: We present a descriptor, called fully convolutional self-similarity (FCSS), for dense semantic correspondence. Unlike traditional dense correspondence approaches for estimating depth or optical flow, semantic correspondence estimation poses additional challenges due to intra-class appearance and shape variations among different instances within the same object or scene category. To robustly match points across semantically similar images, we formulate FCSS using local self-similarity (LSS), which is inherently insensitive to intra-class appearance variations. LSS is incorporated through a proposed convolutional self-similarity (CSS) layer, where the sampling patterns and the self-similarity measure are jointly learned in an end-to-end and multi-scale manner. Furthermore, to address shape variations among different object instances, we propose a convolutional affine transformer (CAT) layer that estimates explicit affine transformation fields at each pixel to transform the sampling patterns and corresponding receptive fields. As training data for semantic correspondence is rather limited, we propose to leverage object candidate priors provided in most existing datasets and also correspondence consistency between object pairs to enable weakly-supervised learning. Experiments demonstrate that FCSS significantly outperforms conventional handcrafted descriptors and CNN-based descriptors on various benchmarks.


title: Autocalibration and Uncalibrated Reconstruction of Shape from Defocus

abstract: Most algorithms for reconstructing shape from defocus assume that the images are obtained with a camera that has been previously calibrated so that the aperture, focal plane, and focal length are known. In this manuscript we characterize the set of scenes that can be reconstructed from defocused images regardless of calibration parameters. In lack of knowledge about the camera or about the scene, reconstruction is possible only up to an equivalence class that is described analytically. When weak knowledge about the scene is available, however, we show how it can be exploited in order to auto-calibrate the imaging device. This includes imaging a slanted plane or generic assumptions on the restoration of the deblurred images.


title: Extensible effects: an alternative to monad transformers

abstract: We design and implement a library that solves the long-standing problem of combining effects without imposing restrictions on their interactions (such as static ordering). Effects arise from interactions between a client and an effect handler (interpreter); interactions may vary throughout the program and dynamically adapt to execution conditions. Existing code that relies on monad transformers may be used with our library with minor changes, gaining efficiency over long monad stacks. In addition, our library has greater expressiveness, allowing for practical idioms that are inefficient, cumbersome, or outright impossible with monad transformers. Our alternative to a monad transformer stack is a single monad, for the coroutine-like communication of a client with its handler. Its type reflects possible requests, i.e., possible effects of a computation. To support arbitrary effects and their combinations, requests are values of an extensible union type, which allows adding and, notably, subtracting summands. Extending and, upon handling, shrinking of the union of possible requests is reflected in its type, yielding a type-and-effect system for Haskell. The library is lightweight, generalizing the extensible exception handling to other effects and accurately tracking them in types.


title: Cloud radio access network (C-RAN): a primer

abstract: In the era of mobile Internet, mobile operators are facing pressure on ever-increasing capital expenditures and operating expenses with much less growth of income. Cloud Radio Access Network (C-RAN) is expected to be a candidate of next generation access network techniques that can solve operators' puzzle. In this article, on the basis of a general survey of C-RAN, we present a novel logical structure of C-RAN that consists of a physical plane, a control plane, and a service plane. Compared to traditional architecture, the proposed C-RAN architecture emphasizes the notion of service cloud, service-oriented resource scheduling and management, thus it facilitates the utilization of new communication and computer techniques. With the extensive computation resource offered by the cloud platform, a coordinated user scheduling algorithm and parallel optimum precoding scheme are proposed, which can achieve better performance. The proposed scheme opens another door to design new algorithms matching well with C-RAN architecture, instead of only migrating existing algorithms from traditional architecture to C-RAN.


title: Linear Hamilton Jacobi Bellman Equations in high dimensions

abstract: The Hamilton Jacobi Bellman Equation (HJB) provides the globally optimal solution to large classes of control problems. Unfortunately, this generality comes at a price, the calculation of such solutions is typically intractible for systems with more than moderate state space size due to the curse of dimensionality. This work combines recent results in the structure of the HJB, and its reduction to a linear Partial Differential Equation (PDE), with methods based on low rank tensor representations, known as a separated representations, to address the curse of dimensionality. The result is an algorithm to solve optimal control problems which scales linearly with the number of states in a system, and is applicable to systems that are nonlinear with stochastic forcing in finite-horizon, average cost, and first-exit settings. The method is demonstrated on inverted pendulum, VTOL aircraft, and quadcopter models, with system dimension two, six, and twelve respectively.


title: Avoiding the streetlight effect: I/O workload analysis with SSDs in mind

abstract: Storage systems are designed and optimized relying on wisdom derived from analysis studies of file-system and block-level workloads. However, while SSDs are becoming a dominant building block in many storage systems, their design continues to build on knowledge derived from analysis targeted at hard disk optimization. Though still valuable, it does not cover important aspects relevant for SSD performance. In a sense, we are "searching under the streetlight", possibly missing important opportunities for optimizing storage system design. ::: ::: We present the first I/O workload analysis designed with SSDs in mind. We characterize traces from four repositories and examine their 'temperature' ranges, sensitivity to page size, and 'logical locality'. Our initial results reveal nontrivial aspects that can significantly influence the design and performance of SSD-based systems.


title: Deep Spectral-Based Shape Features for Alzheimer’s Disease Classification

abstract: Alzheimer’s disease (AD) and mild cognitive impairment (MCI) are the most prevalent neurodegenerative brain diseases in elderly population. Recent studies on medical imaging and biological data have shown morphological alterations of subcortical structures in patients with these pathologies. In this work, we take advantage of these structural deformations for classification purposes. First, triangulated surface meshes are extracted from segmented hippocampus structures in MRI and point-to-point correspondences are established among population of surfaces using a spectral matching method. Then, a deep learning variational auto-encoder is applied on the vertex coordinates of the mesh models to learn the low dimensional feature representation. A multi-layer perceptrons using softmax activation is trained simultaneously to classify Alzheimer’s patients from normal subjects. Experiments on ADNI dataset demonstrate the potential of the proposed method in classification of normal individuals from early MCI (EMCI), late MCI (LMCI), and AD subjects with classification rates outperforming standard SVM based approach.


title: An Improved Scene Text Extraction Method Using Conditional Random Field and Optical Character Recognition

abstract: Over the past few years, research on scene text extraction has developed rapidly. Recently, condition random field (CRF) has been used to give connected components (CCs) 'text' or 'non-text' labels. However, a burning issue in CRF model comes from multiple text lines extraction. In this paper, we propose a two-step iterative CRF algorithm with a Belief Propagation inference and an OCR filtering stage. Two kinds of neighborhood relationship graph are used in the respective iterations for extracting multiple text lines. Furthermore, OCR confidence is used as an indicator for identifying the text regions, while a traditional OCR filter module only considered the recognition results. The first CRF iteration aims at finding certain text CCs, especially in multiple text lines, and sending uncertain CCs to the second iteration. The second iteration gives second chance for the uncertain CCs and filter false alarm CCs with the help of OCR. Experiments based on the public dataset of ICDAR 2005 prove that the proposed method is comparative with the existing algorithms.


title: Extracting information from the text of electronic medical records to improve case detection: a systematic review

abstract: Background: Electronic medical records (EMRs) are revolutionizing health-related research. One key issue for study quality is the accurate identification of patients with the condition of interest. Information in EMRs can be entered as structured codes or unstructured free text. The majority of research studies have used only coded parts of EMRs for case-detection, which may bias findings, miss cases, and reduce study quality. This review examines whether incorporating information from text into case-detection algorithms can improve research quality. ::: ::: Methods: A systematic search returned 9659 papers, 67 of which reported on the extraction of information from free text of EMRs with the stated purpose of detecting cases of a named clinical condition. Methods for extracting information from text and the technical accuracy of case-detection algorithms were reviewed. ::: Results: Studies mainly used US hospital-based EMRs, and extracted information from text for 41 conditions using keyword searches, rule-based algorithms, and machine learning methods. There was no clear difference in case-detection algorithm accuracy between rule-based and machine learning methods of extraction. Inclusion of information from text resulted in a significant improvement in algorithm sensitivity and area under the receiver operating characteristic in comparison to codes alone (median sensitivity 78% (codes + text) vs 62% (codes), P = .03; median area under the receiver operating characteristic 95% (codes + text) vs 88% (codes), P = .025). ::: ::: Conclusions: Text in EMRs is accessible, especially with open source information extraction algorithms, and significantly improves case detection when combined with codes. More harmonization of reporting within EMR studies is needed, particularly standardized reporting of algorithm accuracy metrics like positive predictive value (precision) and sensitivity (recall).


title: QAT: Evaluation of a dedicated hardware accelerator for high performance web service

abstract: Security is increasingly becoming a vital necessity for web service. Secure Sockets Layer (SSL) and Transport Layer Security (TLS) are cryptographic protocols that are used to provide confidentiality and data integrity between two applications. SSL/TLS has been widely integrated into web service applications. However, the heavy overhead that is brought by frequent encryption and decryption operations in SSL/TLS has terribly burden the CPU. To address this issue, various vendors and researchers focused on developing new alternatives, such as GPU-based and FPGA-based accelerators. Unfortunately, expensive development cost and time cost seriously restrict their deployment in web servers. In this paper, we present and evaluate an emerging dedicated hardware accelerator named Quick Assist Technology(QAT). We analyse and explain the QAT's asynchronous working mode and how it cooperates with OpenSSL and Nginx server. The experimental results show that the dedicated hardware accelerator can speed up the number of connections by 10x comparing to the traditional software ways.


title: Generation of Panoramic View from 360 Degree Fisheye Images Based on Angular Fisheye Projection

abstract: this paper proposes a algorithm to generate the panoramic view from 360 iafisheye images based on angular fisheye projection model. Firstly, the image plane will be mapped to the view plane, the sphere plane, to obtain the coordinates of spherical points. Then use the angular fisheye projection model to get fisheye images' radius. Secondly, calculate the coordinates of fisheye images' points with the radius of fisheye images and the coordinates of spherical points. Finally, map the points of fisheye images to the target plane by the way of backward mapping. Experiments show that this algorithm is simple and fast, and can obtain the ideal images.


title: Auxin and H+-Excretion: The State of Our Knowledge

abstract: For more than 50 years scientists have been attempting to explain how auxin induces cell elongation in stem and coleoptile cells. Many mechanisms have been proposed only to be discarded with amazing rapidity. The theory receiving the most attention at present is the acid-growth theory which states (1) that auxin causes cells to excrete protons and that the resulting lowered pH of the wall solution activates some enzyme capable of cleaving load-bearing bonds in the cell wall. Since this theory was first formulated in 1970–71 (2,3), a large body of papers have appeared, both attacking and supporting this theory, with the result that considerable confusion exists. The purpose of this article is to summarize some of the conflicting information, and attempt to show why problems have arisen and how they can be overcome.


title: An analytical framework for data stream mining techniques based on challenges and requirements

abstract: A growing number of applications that generate massive streams of data need intelligent data processing and online analysis. Real-time surveillance systems, telecommunication systems, sensor networks and other dynamic environments are such examples. The imminent need for turning such data into useful information and knowledge augments the development of systems, algorithms and frameworks that address streaming challenges. The storage, querying and mining of such data sets are highly computationally challenging tasks. Mining data streams is concerned with extracting knowledge structures represented in models and patterns in non stopping streams of information. Generally, two main challenges are designing fast mining methods for data streams and need to promptly detect changing concepts and data distribution because of highly dynamic nature of data streams. The goal of this article is to analyze and classify the application of diverse data mining techniques in different challenges of data stream mining. In this paper, we present the theoretical foundations of data stream analysis and propose an analytical framework for data stream mining techniques.


title: Chatbot: A Chatbot for Business Card Management

abstract: This article presents an application which combines chatbot and OCR techniques. The core value of CardBot is helping people manage business card cleverly and intuitively, and providing personalized services as a virtual assistant.


title: Data-Driven Methods for Advancing Precision Oncology

abstract: Purpose of Review ::: This article discusses the advances, methods, challenges, and future directions of data-driven methods in advancing precision oncology for biomedical research, drug discovery, clinical research, and practice.


title: Neural Sentence Ordering

abstract: Sentence ordering is a general and critical task for natural language generation applications. Previous works have focused on improving its performance in an external, downstream task, such as multi-document summarization. Given its importance, we propose to study it as an isolated task. We collect a large corpus of academic texts, and derive a data driven approach to learn pairwise ordering of sentences, and validate the efficacy with extensive experiments. Source codes and dataset of this paper will be made publicly available.


title: Multi-Agent Systems for Urban Planning

abstract: Cities provide homes for over half of the world’s population, and this proportion is expected to increase throughout the next century. The growth of cities raises many questions and challenges for urban planning including which cities and regions are most likely to grow, what the pattern of urban growth will be, and how the existing infrastructure will cope with such growth. One way to explore these types of questions is through the use of multi-agent systems (MAS) that are capable of modeling how individuals interact and how structures emerge through such interactions, in terms of both the social and physical environment of cities. Within this chapter, the authors focus on how MAS can lead to insights into urban problems and aid urban planning from the bottom up. They review MAS models that explore the growth of cities and regions, models that explore land-use patterns resulting from such growth along with the rise of slums. Furthermore, the authors demonstrate how MAS models can be used to model transportation and the changing demographics of cities. Through these examples the authors also demonstrate how this style of modeling can give insights into such issues that cannot be gleamed from other modeling methodologies. The chapter concludes with challenges and future research directions of MAS models with respect to capturing the dynamics of human behavior in urban planning.


Evaluating Program Representation in a Visual Shell


title: A Localisation-Segmentation Approach for Multi-label Annotation of Lumbar Vertebrae using Deep Nets

abstract: Multi-class segmentation of vertebrae is a non-trivial task mainly due to the high correlation in the appearance of adjacent vertebrae. Hence, such a task calls for the consideration of both global and local context. Based on this motivation, we propose a two-staged approach that, given a computed tomography dataset of the spine, segments the five lumbar vertebrae and simultaneously labels them. The first stage employs a multi-layered perceptron performing non-linear regression for locating the lumbar region using the global context. The second stage, comprised of a fully-convolutional deep network, exploits the local context in the localised lumbar region to segment and label the lumbar vertebrae in one go. Aided with practical data augmentation for training, our approach is highly generalisable, capable of successfully segmenting both healthy and abnormal vertebrae (fractured and scoliotic spines). We consistently achieve an average Dice coefficient of over 90 percent on a publicly available dataset of the xVertSeg segmentation challenge of MICCAI 2016. This is particularly noteworthy because the xVertSeg dataset is beset with severe deformities in the form of vertebral fractures and scoliosis.


title: Choice Without Awareness: Ethical and Policy Implications of Defaults

abstract: Defaults have such powerful and pervasive effects on consumer behavior that they could be considered “hidden persuaders” in some settings. Ignoring defaults is not a sound option for marketers or consumer policy makers. The authors identify three theoretical causes of default effects—implied endorsement, cognitive biases, and effort—to guide thought on the appropriate marketer and policy maker responses to the issues posed for consumer welfare and consumer autonomy, including proposals for benign “nudges” of behavior. Defaults can be a preferred form of decision architecture; that is, other nonconscious influences on choice and an absence of established preferences can mean that active choice is not always the better alternative. The authors propose “smart defaults” as welfare-enhancing and market-oriented alternatives to the current practice of generally ignoring default effects. Their analysis highlights the importance of considering the process as well as the outcomes of consumer decision making and ta...


title: Authenticated storage using small trusted hardware

abstract: A major security concern with outsourcing data storage to third-party providers is authenticating the integrity and freshness of data. State-of-the-art software-based approaches require clients to maintain state and cannot immediately detect forking attacks, while approaches that introduce limited trusted hardware (e.g., a monotonic counter) at the storage server achieve low throughput. This paper proposes a new design for authenticating data storage using a small piece of high-performance trusted hardware attached to an untrusted server. The proposed design achieves significantly higher throughput than previous designs. The server-side trusted hardware allows clients to authenticate data integrity and freshness without keeping any mutable client-side state. Our design achieves high performance by parallelizing server-side authentication operations and permitting the untrusted server to maintain caches and schedule disk writes, while enforcing precise crash recovery and write access control.


COLLABORATIVE NETWORKS AND THE CENTRAL CONNECTIVITY EFFECT Student Paper


title: The organized/disorganized typology of serial murder: myth or model?

abstract: Despite weaknesses in the organized/disorganizedclassification of serial killers, it is drawn on for “offender profiles,” theories of offending, and in murder trials. This dichotomy was therefore tested by the multidimensional scaling of the co-occurrence of 39 aspects of serial killings derived 100 murders committed by 100 U.S. serial killers. Results revealed no distinct subsets of offense characteristics reflecting the dichotomy. They showed a subset of organized features typical of most serial killings. Disorganized features are much rarer and do not form a distinct type. These results have implications for testing typologies supporting expert opinion or to help understand variations in criminal acts, as well as the development of a science of investigative psychology that goes beyond offender profiling. The organized/disorganized dichotomy is one of the most widely cited classifications of violent, serial offenders. Although first introduced by the special agents of the U.S. Federal Bureau of Investigation (FBI) Training Academy at Quantico in an examination of lust and sexual sadistic murders (Ressler, Burgess, Douglas, Hartman, & D’Agostino, 1986) the distinction has since been put forward to differentiate all sexual homicides and also types of arson in Douglas, Burgess, Burgess, and Ressler’s (1992) Crime Classification Manual. These authors have made the distinction between organized and disorganized offenders on criteria that they claim can be drawn from an examination of the crime scene, the victim, and forensic reports. Ressler et al. (1986) claimed that “. . . facets of the criminal’s personality are evident in his offense. Like a fingerprint, the crime scene can be used to aid in identifying the murderer” (p. 291). They proposed that offenders’ behavioral and personality characteristics can be determined from evidence at a crime scene (Ressler et al., 1986). This “fingerprint” is proposed to take one of two distinct forms, either organized or disorganized. The organized offender is described as leading an orderly life that is also reflected in the way he commits his crimes. Highlighting some proposed characteristics, he is claimed to be of average to high intelligence, socially competent, and more likely than the disorganized offender to have skilled employment. It is also claimed that he is apt to plan his offenses, use restraints on his victim, and to bring a weapon with him to commit the murder and to take the weapon away with him from the crime scene. In contrast, the crime scene of the disorganized


title: Automated Trajectory Planner of Industrial Robot for Pick-and-Place Task

abstract: Industrial robots, due to their great speed, precision and cost-effectiveness in repetitive tasks, now tend to be used in place of human workers in automated manufacturing systems. In particular, they perform the pick-and-place operation, a non-value-added activity which at the same time cannot be eliminated. Hence, minimum time is an important consideration for economic reasons in the trajectory planning system of the manipulator. The trajectory should also be smooth to handle parts precisely in applications such as semiconductor manufacturing, processing and handling of chemicals and medicines, and fluid and aerosol deposition. In this paper, an automated trajectory planner is proposed to determine a smooth, minimum-time and collision-free trajectory for the pick-and-place operations of a 6-DOF robotic manipulator in the presence of an obstacle. Subsequently, it also proposes an algorithm for the jerk- bounded Synchronized Trigonometric S-curve Trajectory (STST) and the 'forbidden-sphere' technique to avoid the obstacle. The proposed planner is demonstrated with suitable examples and comparisons. The experiments show that the proposed planner is capable of providing a smoother trajectory than the cubic spline based trajectory.


title: Houston, we have a problem...: a survey of actual problems in computer games development

abstract: This paper presents a survey of problems found in the development process of electronic games. These problems were collected mainly from game postmortems and specialized litterature on game development, allowing a comparison with respect to well-known problems in the traditional software industry.


title: A New Bit-Serial Architecture for Field Multiplication Using Polynomial Bases

abstract: Multiplication is the main finite field arithmetic operation in elliptic curve cryptography and its bit-serial hardware implementation is attractive in resource constrained environments such as smart cards, where the chip area is limited. In this paper, a new serial-output bit-serial multiplier using polynomial bases over binary extension fields is proposed. It generates a bit of the multiplication in each clock cycle with the latency of one cycle. To the best of our knowledge, this is the first time that such a serial-output bit-serial multiplier architecture using polynomial bases for general irreducible polynomials is proposed.


title: Non-local dual image denoising

abstract: The current state-of-the-art non-local algorithms for image denoising have the tendency to remove many low contrast details. Frequency-based algorithms keep these details, but on the other hand many artifacts are introduced. Recently, the Dual Domain Image Denoising (DDID) method has been proposed to address this issue. While beating the state-of-the-art, this algorithm still causes strong frequency domain artifacts. This paper reviews DDID under a different light, allowing to understand their origin. The analysis leads to the development of NLDD, a new denoising algorithm that outperforms DDID, BM3D and other state-of-the-art algorithms. NLDD is also three times faster than DDID and easily parallelizable.


title: Dynamic Resource Management for Efficient Utilization of Multitasking GPUs

abstract: As graphics processing units (GPUs) are broadly adopted, running multiple applications on a GPU at the same time is beginning to attract wide attention. Recent proposals on multitasking GPUs have focused on either spatial multitasking, which partitions GPU resource at a streaming multiprocessor (SM) granularity, or simultaneous multikernel (SMK), which runs multiple kernels on the same SM. However, multitasking performance varies heavily depending on the resource partitions within each scheme, and the application mixes. In this paper, we propose GPU Maestro that performs dynamic resource management for efficient utilization of multitasking GPUs. GPU Maestro can discover the best performing GPU resource partition exploiting both spatial multitasking and SMK. Furthermore, dynamism within a kernel and interference between the kernels are automatically considered because GPU Maestro finds the best performing partition through direct measurements. Evaluations show that GPU Maestro can improve average system throughput by 20.2% and 13.9% over the baseline spatial multitasking and SMK, respectively.


title: Investigation of financial market prediction by recurrent neural network

abstract: Recurrent neural networks as fundamentally different neural network from feed-forward architec- tures was investigated for modelling of non linear behaviour of financial markets. Recurrent neural networks could be configured with the correct choice of parameters such as the number of neurons, the number of epochs, the amount of data and their relationship with the training data for predictions of financial markets. By exploring of learning and forecasting of the recurrent neural networks is observed the same effect: better learning, which often is described by the root mean square error does not guarantee a better prediction. There are such a recurrent neural networks settings where the best results of non linear time series fore- casting could be obtained. New method of orthogonal input data was proposed, which improve process of EVOLINO RNN learning and forecasting.


title: Team assembly mechanisms determine collaboration network structure and team performance

abstract: Agents in creative enterprises are embedded in networks that inspire, support, and evaluate their work. Here, we investigate how the mechanisms by which creative teams self-assemble determine the structure of these collaboration networks. We propose a model for the self-assembly of creative teams that has its basis in three parameters: team size, the fraction of newcomers in new productions, and the tendency of incumbents to repeat previous collaborations. The model suggests that the emergence of a large connected community of practitioners can be described as a phase transition. We find that team assembly mechanisms determine both the structure of the collaboration network and team performance for teams derived from both artistic and scientific fields.


title: Science in the city AR: using mobile augmented reality for science inquiry activities

abstract: The Exploratorium, a museum of science, art, and human perception in San Francisco, California, is developing science activities for informal learners to use with augmented reality (AR) browsers on mobile devices. We're experimenting with this technique to learn about engaging museum visitors, as well as other informal learners in our community, in personal inquiry. Using the Layar AR platform, we've developed a set of interactive science activity tools that smartphone users can use to explore phenomena via augmented exhibits at the museum and at locations in the San Francisco Bay Area. The tools, which are freely available, are part of our Science in the City video series and provide location-contextualized activities for different programs. This series introduces online visitors to stories about locations, people, and themes relating to natural phenomena and the built environment. The mobile AR science activity tools became available in the spring of 2011 in a Science in the City "layer" within the Layar mobile browser and via a link at the Exploratorium's website, www.exploratorium.edu/scienceinthecity.


title: CEC: Constraints based Explanation for Classifications

abstract: Explaining the results of data-intensive computation via provenance has been extensively studied in the literature. We focus here on explaining the output of Machine Learning Classifiers, which are main components of many contemporary Data Science applications. We have developed a simple generic approach for explaining classification results, by looking for constrained perturbations to parts of the input that would have the most significant effect on the classification. Our solution requires white-box access to the model internals and a specification of constraints that define which perturbations are "reasonable" in the application domain; both are typically available to the data scientist. We propose to demonstrate CEC, a system prototype that is based on these foundations to provide generic explanations for Neural Networks and Random Forests. We will demonstrate the system usefulness in the context of two application domains: bank marketing campaigns, and visually clear explanations for image classifications. We will highlight the benefit that such explanations could yield to the data scientist and interactively engage the audience in computing and viewing explanations for different cases and different sets of constraints.


title: Sentiment Analysis of Movie Reviews using POS tags and Term Frequencies

abstract: Sentiment analysis and opinion mining play an important role in judging and predicting people's views. Recently, sentiment analysis has focused on assigning positive and negative polarities to opinions. More methods are being devised to find the weightage of a particular expression in a sentence, whether the particular expression gives the sentence a positive, negative or a neutral meaning. Most of the work on sentiment analysis in the past has been carried out by determining the strength of a subjective expression within a sentence using the parts of speech. Sentiment analysis tries to classify opinion sentences in a document on the basis of their polarity as positive or negative, which can be used in various ways and in many applications for example, marketing and contextual advertising, suggestion systems based on the user likes and ratings, recommendation systems etc. This paper presents a novel approach for classification of online movie reviews using parts of speech and machine learning


title: A Multi-Agent Advanced Traveler Information System for Optimal Trip Planning in a Co-Modal Framework

abstract: We present an advanced traveler information system (ATIS) for public and private transportation, including vehicle sharing and pooling services. The ATIS uses an agent-based architecture and multi-objective optimization to answer trip planning requests from multiple users in a co-modal setting, considering vehicle preferences and conflicting criteria. At each set of users’ requests, the transportation network is represented by a co-modal graph that allows decomposing the trip planning problem into smaller tasks: the shortest routes between the network nodes are determined and then combined to obtain possible itineraries. Using multi-objective optimization, the set of user-vehicle-route combinations according to the users’ preferences is determined, ranking all possible route agents’ coalitions. The ATIS is tested for the real case study of the Lille metropolitan area (Nord Pas de Calais, France).


title: A framework for evaluating quality-driven self-adaptive software systems

abstract: Over the past decade the dynamic capabilities of self-adaptive software-intensive systems have proliferated and improved significantly. To advance the field of self-adaptive and self-managing systems further and to leverage the benefits of self-adaptation, we need to develop methods and tools to assess and possibly certify adaptation properties of self-adaptive systems, not only at design time but also, and especially, at run-time. In this paper we propose a framework for evaluating quality-driven self-adaptive software systems. Our framework is based on a survey of self-adaptive system papers and a set of adaptation properties derived from control theory properties. We also establish a mapping between these properties and software quality attributes. Thus, corresponding software quality metrics can then be used to assess adaptation properties.


title: Automatic Construction of Object Oriented Design Models [UML Diagrams] from Natural Language Requirements Specification

abstract: Application of natural language understanding to requirements gathering remains a field that has only limited explorations so far. This paper presents an approach to extract the object oriented elements of the required system. This approach starts with assigning the parts of speech tags to each word in the given input document. Further, to resolve the ambiguity posed by the pronouns, the pronoun resolutions are performed before normalizing the text. Finally the elements of the object-oriented system namely the classes, the attributes, methods and relationships between the classes, sequence of actions, the use-cases and actors are identified by mapping the 'parts of speech- tagged' words onto the Object Oriented Modeling Language elements using mapping rules which is the key to a successful implementation of user requirements.


title: Low Power CMOS Dynamic Latch Comparator using 0.18?m Technology

abstract: The design and analysis of low power, high speed CMOS dynamic latch comparator is presented. The comparator combines the features of both, the resistive dividing network and differential current sensing comparator. The proposed design will improve the comparator performance by reducing the propagation delay, power dissipation. Simulation results are obtained in 0.18um with supply voltages of 1.8v respectively. The schematic of comparator is captured using Cadence Virtuoso schematic editor and simulated using the Cadence Spectre simulator.


title: Lightweight authentication of freshness in outsourced key-value stores

abstract: Data outsourcing offers cost-effective computing power to manage massive data streams and reliable access to data. Data owners can forward their data to clouds, and the clouds provide data mirroring, backup, and online access services to end users. However, outsourcing data to untrusted clouds requires data authenticity and query integrity to remain in the control of the data owners and users. In this paper, we address the authenticated data-outsourcing problem specifically for multi-version key-value data that is subject to continuous updates under the constraints of data integrity, data authenticity, and "freshness" (i.e., ensuring that the value returned for a key is the latest version). We detail this problem and propose IncBM-Tree, a novel construct delivering freshness and authenticity. Compared to existing work, we provide a solution that offers (i) lightweight signing and verification on massive data update streams for data owners and users (e.g., allowing for small memory footprint and CPU usage for a low-budget IT department), (ii) immediate authentication of data freshness, (iii) support of authentication in the presence of both real-time and historical data accesses. Extensive benchmark evaluations demonstrate that IncBM-Tree achieves higher throughput (in an order of magnitude) for data stream authentication than existing work. For data owners and end users that have limited computing power, IncBM-Tree can be a practical solution to authenticate the freshness of outsourced data while reaping the benefits of broadly available cloud services.


title: Hierarchical RNN with Static Sentence-Level Attention for Text-Based Speaker Change Detection

abstract: Speaker change detection (SCD) is an important task in dialog modeling. Our paper addresses the problem of text-based SCD, which differs from existing audio-based studies and is useful in various scenarios, for example, processing dialog transcripts where speaker identities are missing (e.g., OpenSubtitle), and enhancing audio SCD with textual information. We formulate text-based SCD as a matching problem of utterances before and after a certain decision point; we propose a hierarchical recurrent neural network (RNN) with static sentence-level attention. Experimental results show that neural networks consistently achieve better performance than feature-based approaches, and that our attention-based model significantly outperforms non-attention neural networks.


title: Parallel Data, Tools and Interfaces in OPUS

abstract: This paper presents the current status of OPUS, a growing language resource of parallel corpora and related tools. The focus in OPUS is to provide freely available data sets in various formats together with basic annotation to be useful for applications in computational linguistics, translation studies and cross-linguistic corpus studies. In this paper, we report about new data sets and their features, additional annotation tools and models provided from the website and essential interfaces and on-line services included in the project.


title: An AES Smart Card Implementation Resistant to Power Analysis Attacks

abstract: In this article we describe an efficient AES software implementation that is well suited for 8-bit smart cards and resistant against power analysis attacks. Our implementation masks the intermediate results and randomizes the sequence of operations at the beginning and the end of the AES execution. Because of the masking, it is secure against simple power analysis attacks, template attacks and first-order DPA attacks. Due to the combination of masking and randomization, it is resistant against higher-order DPA attacks. Resistant means that a large number of measurements is required for a successful attack. This expected number of measurements is tunable. The designer can choose the amount of randomization and thereby increase the number of measurements. This article also includes a practical evaluation of the countermeasures. The results prove the theoretical assessment of the countermeasures to be correct.


title: Automated segmentation of math-zones from document images

abstract: With an aim to high-level understanding of the mathematicalcontents in a document image the requirement ofmath-zone extraction and recognition technique is obvious.In this paper we present fully auotmatic segmentation ofdisplayed-math zones from the document image, using onlythe spatial layout information of math-formulas and equations,so as to help commercial OCR systems which cannotdiscern math-zones and also for the identification and arrangementof math symbols by others.


title: Secure routing for internet of things

abstract: The Internet of Things (IoT) could be described as the pervasive and global network which aids and provides a system for the monitoring and control of the physical world through the collection, processing and analysis of generated data by IoT sensor devices. It is projected that by 2020 the number of connected devices is estimated to grow exponentially to 50 billion. The main drivers for this growth are our everyday devices such as cars, refrigerators, fans, lights, mobile phones and other operational technologies including the manufacturing infrastructures which are now becoming connected systems across the world. It is apparent that security will pose a fundamental enabling factor for the successful deployment and use of most IoT applications and in particular secure routing among IoT sensor nodes thus, mechanisms need to be designed to provide secure routing communications for devices enabled by the IoT technology. This survey analyzes existing routing protocols and mechanisms to secure routing communications in IoT, as well as the open research issues. We further analyze how existing approaches ensure secure routing in IoT, their weaknesses, threats to secure routing in IoT and the open challenges and strategies for future research work for a better secure IoT routing.


title: Clone Detection Using Abstract Syntax Suffix Trees

abstract: Reusing software through copying and pasting is a continuous plague in software development despite the fact that it creates serious maintenance problems. Various techniques have been proposed to find duplicated redundant code (also known as software clones). A recent study has compared these techniques and shown that token-based clone detection based on suffix trees is extremely fast but yields clone candidates that are often no syntactic units. Current techniques based on abstract syntax trees--on the other hand--find syntactic clones but are considerably less efficient. This paper describes how we can make use of suffix trees to find clones in abstract syntax trees. This new approach is able to find syntactic clones in linear time and space. The paper reports the results of several large case studies in which we empirically compare the new technique to other techniques using the Bellon benchmark for clone detectors.


title: An information integration theory of consciousness

abstract: BackgroundConsciousness poses two main problems. The first is understanding the conditions that determine to what extent a system has conscious experience. For instance, why is our consciousness generated by certain parts of our brain, such as the thalamocortical system, and not by other parts, such as the cerebellum? And why are we conscious during wakefulness and much less so during dreamless sleep? The second problem is understanding the conditions that determine what kind of consciousness a system has. For example, why do specific parts of the brain contribute specific qualities to our conscious experience, such as vision and audition?Presentation of the hypothesisThis paper presents a theory about what consciousness is and how it can be measured. According to the theory, consciousness corresponds to the capacity of a system to integrate information. This claim is motivated by two key phenomenological properties of consciousness: differentiation – the availability of a very large number of conscious experiences; and integration – the unity of each such experience. The theory states that the quantity of consciousness available to a system can be measured as the Φ value of a complex of elements. Φ is the amount of causally effective information that can be integrated across the informational weakest link of a subset of elements. A complex is a subset of elements with Φ>0 that is not part of a subset of higher Φ. The theory also claims that the quality of consciousness is determined by the informational relationships among the elements of a complex, which are specified by the values of effective information among them. Finally, each particular conscious experience is specified by the value, at any given time, of the variables mediating informational interactions among the elements of a complex.Testing the hypothesisThe information integration theory accounts, in a principled manner, for several neurobiological observations concerning consciousness. As shown here, these include the association of consciousness with certain neural systems rather than with others; the fact that neural processes underlying consciousness can influence or be influenced by neural processes that remain unconscious; the reduction of consciousness during dreamless sleep and generalized seizures; and the time requirements on neural interactions that support consciousness.Implications of the hypothesisThe theory entails that consciousness is a fundamental quantity, that it is graded, that it is present in infants and animals, and that it should be possible to build conscious artifacts.


title: Face spoofing detection from single images using texture and local shape analysis

abstract: Current face biometric systems are vulnerable to spoofing attacks. A spoofing attack occurs when a person tries to masquerade as someone else by falsifying data and thereby gaining illegitimate access. Inspired by image quality assessment, characterisation of printing artefacts and differences in light reflection, the authors propose to approach the problem of spoofing detection from texture analysis point of view. Indeed, face prints usually contain printing quality defects that can be well detected using texture and local shape features. Hence, the authors present a novel approach based on analysing facial image for detecting whether there is a live person in front of the camera or a face print. The proposed approach analyses the texture and gradient structures of the facial images using a set of low-level feature descriptors, fast linear classification scheme and score level fusion. Compared to many previous works, the authors proposed approach is robust and does not require user-cooperation. In addition, the texture features that are used for spoofing detection can also be used for face recognition. This provides a unique feature space for coupling spoofing detection and face recognition. Extensive experimental analysis on three publicly available databases showed excellent results compared to existing works.


title: Anomaly detection of web-based attacks

abstract: Web-based vulnerabilities represent a substantial portion of the security exposures of computer networks. In order to detect known web-based attacks, misuse detection systems are equipped with a large number of signatures. Unfortunately, it is difficult to keep up with the daily disclosure of web-related vulnerabilities, and, in addition, vulnerabilities may be introduced by installation-specific web-based applications. Therefore, misuse detection systems should be complemented with anomaly detection systems. This paper presents an intrusion detection system that uses a number of different anomaly detection techniques to detect attacks against web servers and web-based applications. The system correlates the server-side programs referenced by client queries with the parameters contained in these queries. The application-specific characteristics of the parameters allow the system to perform focused analysis and produce a reduced number of false positives. The system derives automatically the parameter profiles associated with web applications (e.g., length and structure of parameters) from the analyzed data. Therefore, it can be deployed in very different application environments without having to perform time-consuming tuning and configuration.


title: Cross-Coupled Substrate Integrated Waveguide Filters With Improved Stopband Performance

abstract: In this letter, two $K$ -band cross-coupled substrate integrated waveguide (SIW) filters with improved stopband performance are proposed. To implement the negative coupling, ${\rm TE}_{201}$ -mode response is used in the first cavity, which is also used to implement cross-coupling with ${\rm TE}_{101}$ mode for generating additional transmission zeros (TZs) in the stopband of the filters. For each filter, two additional TZs are introduced and the stopband performance is then improved. The TZ provided by the first cavity can be introduced either below or above the passband, while the TZ provided by the fourth cavity can only be introduced above the passband. The two novel SIW filters are designed at the same center frequency of 20 GHz with the same bandwidth of 600 MHz. The measured data agree well with the simulated results.


title: An investigation on local wrinkle-based extractor of age estimation

abstract: Research related to age estimation using face images has become increasingly important due to its potential use in various applications such as age group estimation in advertising and age estimation in access control. In contrast to other facial variations, age variation has several unique characteristics which make it a challenging task. As we age, the most pronounced facial changes are the appearance of wrinkles (skin creases), which is the focus of ageing research in cosmetic and nutrition studies. This paper investigates an algorithm for wrinkle detection and the use of wrinkle data as an age predictor. A novel method in detecting and classifying facial age groups based on a local wrinkle-based extractor (LOWEX) is introduced. First, each face image is divided into several convex regions representing wrinkle distribution areas. Secondly, these areas are analysed using a Canny filter and then concatenated into an enhanced feature vector. Finally, the face is classified into an age group using a supervised learning algorithm. The experimental results show that the accuracy of the proposed method is 80% when using FG-NET dataset. This investigation shows that local wrinkle-based features have great potential in age estimation. We conclude that wrinkles can produce a prominent ageing descriptor and identify some future research challenges.