title: A Multi-Batch L-BFGS Method for Machine Learning

abstract: The question of how to parallelize the stochastic gradient descent (SGD) method has received much attention in the literature. In this paper, we focus instead on batch methods that use a sizeable fraction of the training set at each iteration to facilitate parallelism, and that employ second-order information. In order to improve the learning process, we follow a multi-batch approach in which the batch changes at each iteration. This can cause difficulties because L-BFGS employs gradient differences to update the Hessian approximations, and when these gradients are computed using different data points the process can be unstable. This paper shows how to perform stable quasi-Newton updating in the multi-batch setting, illustrates the behavior of the algorithm in a distributed computing platform, and studies its convergence properties for both the convex and nonconvex cases.


title: Genome-Wide Association Study in Bipolar Patients Stratified by Co-Morbidity

abstract: BACKGROUND ::: Bipolar disorder is a severe psychiatric disorder with high heritability. Co-morbid conditions are common and might define latent subgroups of patients that are more homogeneous with respect to genetic risk factors. ::: ::: ::: METHODOLOGY ::: In the Caucasian GAIN bipolar disorder sample of 1000 cases and 1034 controls, we tested the association of single nucleotide polymorphisms with patient subgroups defined by co-morbidity. ::: ::: ::: RESULTS ::: Bipolar disorder with psychosis and/or substance abuse in the absence of alcohol dependence was associated with the rare variant rs1039002 in the vicinity of the gene phosphodiesterase 10A (PDE10A) on chromosome 6q27 (p = 1.7×10⁻⁸). PDE10A has been implicated in the pathophysiology of psychosis. Antagonists to the encoded protein are currently in clinical testing. Another rare variant, rs12563333 (p = 5.9×10⁻⁸) on chromosome 1q41 close to the MAP/microtubule affinity-regulating kinase 1 (MARK1) gene, approached the genome-wide level of significance in this subgroup. Homozygotes for the minor allele were present in cases and absent in controls. Bipolar disorder with alcohol dependence and other co-morbidities was associated with SNP rs2727943 (p = 3.3×10⁻⁸) on chromosome 3p26.3 located between the genes contactin-4 precursor (BIG-2) and contactin 6 (CNTN6). All three associations were found under the recessive genetic model. Bipolar disorder with low probability of co-morbid conditions did not show significant associations. ::: ::: ::: CONCLUSION ::: Conceptualizing bipolar disorder as a heterogeneous disorder with regard to co-morbid conditions might facilitate the identification of genetic risk alleles. Rare variants might contribute to the susceptibility to bipolar disorder.


title: Recognition of curiosity using eye movement analysis

abstract: Among the different personality traits that guide our behaviour, curiosity is particularly interesting for context-aware assistive systems as it is closely linked to our well-being and the way we learn. This work proposes eye movement analysis for automatic recognition of different levels of curiosity. We present a 26-participant gaze dataset recorded during a real-world shopping task with empirically validated curiosity questionnaires as ground truth. Using a support vector machine classiffer and a leave-one-person-out evaluation scheme we can discriminate between two to four classes of standard curiosity scales well above chance. These results are promising and point towards a new class of context-aware systems that take the user's curiosity into account, thereby enabling new types of interaction and user adaptation.


title: The role of social media in reducing stigma and discrimination

abstract: This editorial explores the implications of social media practices whereby people with mental health problems share their experiences in online public spaces and challenge mental health stigma. Social media enable individuals to bring personal experience into the public domain with the potential to affect public attitudes and mainstream media. We draw tentative conclusions regarding the use of social media by campaigning organisations.


title: Wearable Electronics and Smart Textiles: A Critical Review

abstract: Electronic Textiles (e-textiles) are fabrics that feature electronics and interconnections woven into them, presenting physical flexibility and typical size that cannot be achieved with other existing electronic manufacturing techniques. Components and interconnections are intrinsic to the fabric and thus are less visible and not susceptible of becoming tangled or snagged by surrounding objects. E-textiles can also more easily adapt to fast changes in the computational and sensing requirements of any specific application, this one representing a useful feature for power management and context awareness. The vision behind wearable computing foresees future electronic systems to be an integral part of our everyday outfits. Such electronic devices have to meet special requirements concerning wearability. Wearable systems will be characterized by their ability to automatically recognize the activity and the behavioral status of their own user as well as of the situation around her/him, and to use this information to adjust the systems' configuration and functionality. This review focuses on recent advances in the field of Smart Textiles and pays particular attention to the materials and their manufacturing process. Each technique shows advantages and disadvantages and our aim is to highlight a possible trade-off between flexibility, ergonomics, low power consumption, integration and eventually autonomy.


title: Microwave Near-Field Focusing Properties of Width-Tapered Microstrip Leaky-Wave Antenna

abstract: The possibility of microwave near-field focusing using a simple width-tapered microstrip leaky-wave antenna is theoretically and experimentally proven in this paper. The design procedure to obtain the modulation of the microstrip width along the antenna length is described, illustrating the conditions and limitations for optimum focusing at a certain focal point and using a given substrate. A 7.5 λ0 -long microstrip lens is designed to operate at 15 GHz, and its focusing properties are studied. An array of two in-phase microstrip lenses located front-to-front is proposed to obtain a symmetric focusing pattern with higher spatial resolution. Theoretical and experimental results demonstrate the optimum synthesis of a focusing region with focal length of 3 λ0, presenting focal width of only 0.33 λ0 , and focal depth of 1.25 λ0. Finally, it is reported the frequency focus-scanning performance in the range 14-17 GHz. All these features make the proposed microstrip lens a simpler alternative solution to focused planar phased-arrays.


title: Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising

abstract: Patch based image modeling has achieved a great success in low level vision such as image denoising. In particular, the use of image nonlocal self-similarity (NSS) prior, which refers to the fact that a local patch often has many nonlocal similar patches to it across the image, has significantly enhanced the denoising performance. However, in most existing methods only the NSS of input degraded image is exploited, while how to utilize the NSS of clean natural images is still an open problem. In this paper, we propose a patch group (PG) based NSS prior learning scheme to learn explicit NSS models from natural images for high performance denoising. PGs are extracted from training images by putting nonlocal similar patches into groups, and a PG based Gaussian Mixture Model (PG-GMM) learning algorithm is developed to learn the NSS prior. We demonstrate that, owe to the learned PG-GMM, a simple weighted sparse coding model, which has a closed-form solution, can be used to perform image denoising effectively, resulting in high PSNR measure, fast speed, and particularly the best visual quality among all competing methods.


title: Joint allocation of computation and communication resources in multiuser mobile cloud computing

abstract: Mobile cloud computing is offering a very powerful storage and computational facility to enhance the capabilities of resource-constrained mobile handsets. However, full exploitation of the cloud computing capabilities can be achieved only if the allocation of radio and computational capabilities is performed jointly. In this paper, we propose a method to jointly optimize the transmit power, the number of bits per symbol and the CPU cycles assigned to each application in order to minimize the power consumption at the mobile side, under an average latency constraint dictated by the application requirements. We consider the case of a set of mobile handsets served by a single cloud and we show that the optimization leads to a one-to-one relationship between the transmit power and the percentage of CPU cycles assigned to each user. Based on our optimization, we propose then a computation scheduling technique and verify the stability of the computations queue. Then we show how these queues are affected by the degrees of freedom of the channels between mobile handsets and server.


title: Deep Rewiring: Training very sparse deep networks

abstract: Neuromorphic hardware tends to pose limits on the connectivity of deep networks that one can run on them. But also generic hardware and software implementations of deep learning run more efficiently for sparse networks. Several methods exist for pruning connections of a neural network after it was trained without connectivity constraints. We present an algorithm, DEEP R, that enables us to train directly a sparsely connected neural network. DEEP R automatically rewires the network during supervised training so that connections are there where they are most needed for the task, while its total number is all the time strictly bounded. We demonstrate that DEEP R can be used to train very sparse feedforward and recurrent neural networks on standard benchmark tasks with just a minor loss in performance. DEEP R is based on a rigorous theoretical foundation that views rewiring as stochastic sampling of network configurations from a posterior.


title: Principles of survey research: part 1: turning lemons into lemonade

abstract: Surveys are probably the most commonly-used research method world-wide. Survey work is visible not only because we see many examples of it in software engineering research, but also because we are often asked to participate in surveys in our private capacity, as electors, consumers, or service users. This widespread use of surveys may give us the impression that surveybased research is straightforward, an easy option for researchers to gather important information about products, context, processes, workers and more. In our personal experience with applying and evaluating research methods and their results, we certainly did not expect to encounter major problems with a survey that we planned, to investigate issues associated with technology adoption. This article and subsequent ones in this series describe how wrong we were. We do not want to give the impression that there is any way of turning a bad survey into a good one; if a survey is a lemon, it stays a lemon. However, we believe that learning from our mistakes is the way to make lemonade from lemons. So this series of articles shares with you our lessons learned, in the hope of improving survey research in software engineering.


title: Using LSTM recurrent neural networks for detecting anomalous behavior of LHC superconducting magnets

abstract: The superconducting LHC magnets are coupled with an electronic monitoring system which records and analyses voltage time series reflecting their performance. A currently used system is based on a range of preprogrammed triggers which launches protection procedures when a misbehavior of the magnets is detected. All the procedures used in the protection equipment were designed and implemented according to known working scenarios of the system and are updated and monitored by human operators. This paper proposes a novel approach to monitoring and fault protection of the Large Hadron Collider (LHC) superconducting magnets which employs state-of-the-art Deep Learning algorithms. Consequently, the authors of the paper decided to examine the performance of LSTM recurrent neural networks for anomaly detection in voltage time series of the magnets. In order to address this challenging task different network architectures and hyper-parameters were used to achieve the best possible performance of the solution. The regression results were measured in terms of RMSE for different number of future steps and history length taken into account for the prediction. The best result of RMSE=0.00104 was obtained for a network of 128 LSTM cells within the internal layer and 16 steps history buffer.


title: A REVIEW OF GAME-TREE PRUNING †

abstract: Chess programs have three major components: move generation, search, and evaluation. All components are important, although evaluation with its quiescence analysis is the part which makes each program’s play unique. The speed of a chess program is a function of its m ove generation cost, the complexity of the position under study and the brevity of its evaluation. More important, however, is the quality of the mechanisms used to discontinue (prune) search of unprofitable continuations. The most reliable pruning method in popular use is the robust alpha-beta algorithm, and its many supporting aids. These essential parts of game-tree searching and pruning are reviewed here, and the performance of refinements, such as aspiration and principal variation search, and aids like transposition and history tables are compared. † Much of this article is a revision of material condensed from an entry entitled ‘‘Computer Chess Methods,’’ prepared for the Encyclopedia of Artificial Intelligence , S. Shapiro (editor), to be published by John Wiley & Sons in 1987. The transposition table pseudo code of Figure 7 is similar to that in another paper: ‘‘Parallel Search of Strongly Ordered Game Trees,’’ T.A. Marsland and M. Campbell, ACM Computing Surveys, Vol 14, No. 4, copyright 1982, Association for Computing Machinery Inc., and is reprinted by permission.


title: Graphical passwords: Learning from the first twelve years

abstract: Starting around 1999, a great many graphical password schemes have been proposed as alternatives to text-based password authentication. We provide a comprehensive overview of published research in the area, covering both usability and security aspects as well as system evaluation. The article first catalogues existing approaches, highlighting novel features of selected schemes and identifying key usability or security advantages. We then review usability requirements for knowledge-based authentication as they apply to graphical passwords, identify security threats that such systems must address and review known attacks, discuss methodological issues related to empirical evaluation, and identify areas for further research and improved methodology.


title: Words Matter: Scene Text for Image Classification and Retrieval

abstract: Text in natural images typically adds meaning to an object or scene. In particular, text specifies which business places serve drinks (e.g., cafe, teahouse) or food (e.g., restaurant, pizzeria), and what kind of service is provided (e.g., massage, repair). The mere presence of text, its words, and meaning are closely related to the semantics of the object or scene. This paper exploits textual contents in images for fine-grained business place classification and logo retrieval. There are four main contributions. First, we show that the textual cues extracted by the proposed method are effective for the two tasks. Combining the proposed textual and visual cues outperforms visual only classification and retrieval by a large margin. Second, to extract the textual cues, a generic and fully unsupervised word box proposal method is introduced. The method reaches state-of-the-art word detection recall with a limited number of proposals. Third, contrary to what is widely acknowledged in text detection literature, we demonstrate that high recall in word detection is more important than high f-score at least for both tasks considered in this work. Last, this paper provides a large annotated text detection dataset with 10 K images and 27 601 word boxes.


title: Modeling Trolling in Social Media Conversations

abstract: Social media websites, electronic newspapers and Internet forums allow visitors to leave comments for others to read and interact. This exchange is not free from participants with malicious intentions, who troll others by positing messages that are intended to be provocative, offensive, or menacing. With the goal of facilitating the computational modeling of trolling, we propose a trolling categorization that is novel in the sense that it allows comment-based analysis from both the trolls' and the responders' perspectives, characterizing these two perspectives using four aspects, namely, the troll's intention and his intention disclosure, as well as the responder's interpretation of the troll's intention and her response strategy. Using this categorization, we annotate and release a dataset containing excerpts of Reddit conversations involving suspected trolls and their interactions with other users. Finally, we identify the difficult-to-classify cases in our corpus and suggest potential solutions for them.


title: Crawling the Hidden Web

abstract: Current-day crawlers retrieve content only from the publicly indexable Web, i.e., the set of Web pages reachable purely by following hypertext links, ignoring search forms and pages that require authorization or prior registration. In particular, they ignore the tremendous amount of high quality content “hidden” behind search forms, in large searchable electronic databases. In this paper, we address the problem of designing a crawler capable of extracting content from this hidden Web. We introduce a generic operational model of a hidden Web crawler and describe how this model is realized in HiWE (Hidden Web Exposer), a prototype crawler built at Stanford. We introduce a new Layout-based Information Extraction Technique (LITE) and demonstrate its use in automatically extracting semantic information from search forms and response pages. We also present results from experiments conducted to test and validate our techniques.


title: Special transformers arrangement for AC railway systems

abstract: This paper deals with special transformer connections for mitigating the current and voltage unbalances generated by single-phase AC traction loads, as typically occurs for electric high speed railway loads supplied by the three phase power system. In fact they represent one of the main sources of power quality perturbation of the grid, with consequent denigration of its performance. The current unbalance reduces efficiency, productivity and profits at the generation, transmission and distribution of electric energy. Voltage asymmetry reduces efficiency, productivity and profits at the consumption and utilization level. Transformer connections for mitigating these perturbations are summarized and compared each other. These mainly consist on different types of connections of single-phase transformers of the substation to the three-phase primary section with phase-transformation methods. Finally, an advanced scheme for current balancing and power factor improving of two unbalanced loads is performed.


title: Crowdfunding genomics and bioinformatics

abstract: Online crowdfunding websites such as RocketHub, Indiegogo and Kickstarter have financed an increasingly eclectic variety of initiatives: multimillion dollar movie projects attached to big Hollywood names, music and book publishing, gadget development, a hoodie that lasts ten years. Controversially, one crowdfunding campaign aimed to raise funds for a drug dealer, who it was thought might be persuaded to hand over compromising video footage of a prominent politician in exchange for the cash. Recently, a number of scientists have sought to use crowdfunding as a means to bypass traditional funding routes when budgeting for new projects. In doing so, they are expanding on the concept of citizen science from crowd participation to crowdfunding. As is the traditional crowdfunding method, these scientists hope to attract funding using two main incentives: (1) the desire by the funder to see the project get off the ground, and (2) the acknowledgement of donations with a range of rewards – some of significant monetary value. Donors also have the security of knowing that payment will only be taken once a project is fully funded, thereby reducing the risk of wasting money on a dud. To start crowdfunding your own science, or to discover projects to donate to, you can now even use a dedicated crowdfunding website for scientific research, Microryza (https://www.microryza.com/), alongside the more general crowdfunding websites. Rather than receive physical rewards, donors on Microryza gain exclusive access to updates on the progress of their funded research. Here, three sets of scientists describe their experience of crowdfunding projects in the fields of genomics and bioinformatics: PathoMap (http://www.indiegogo.com/ projects/pathomap-mapping-nyc-s-microscopic-residents),


title: 3D printed 77 GHz Planar Yagi-Uda Antenna

abstract: A proof of concept 3D printed Yagi-Uda antenna for automotive radar at 77 GHz and 3D printed fabrication plan are presented. The initial design on Connex VeroWhite yields a return loss of 12.7 dB at 73.2 GHz, and 8.7 dBi phi-pol realized gain in the endfire direction, with cross-pol 20 dB down. Two 3D printed fabrication plans are discussed. The fabricated antenna and measured results will be presented at the conference.


title: Efficient OFDMA for LiFi Downlink

abstract: Light-fidelity (LiFi) relies on visible light communication to provide multiuser access in a small cell. For higher throughput, this paper studies the resource allocation problem for an orthogonal frequency-division multiplexing access scheme for LiFi downlink transmissions and presents a joint design of the bias level, power and subcarrier allocation. Decomposing the original optimization into subproblems, we first develop an efficient algorithm for the bias optimization, along with its performance analysis in terms of convergence properties and the global optimality. We then propose several power and subcarrier allocation strategies, including an optimal algorithm and a near-optimal simplified method, to satisfy different complexity and accuracy requirements. Finally, we present two algorithms to jointly optimize the bias, power, and subcarrier, and analyze the convergence properties in terms of both the objective and the solution. Comprehensive simulation results under practical LiFi system setups illustrate the effectiveness of the proposed algorithms.


title: Design and evaluation of Feedforward Active ripple filters

abstract: An active ripple filter is an electronic circuit that cancels or suppresses the ripple current and electromagnetic interference generated by the power stage of a power converter, thus reducing the passive filtration requirements. This paper explores the design of feedforward active ripple filters for current ripple cancellation, including the design tradeoffs, advantages, and limitations of different implementation methods. The design and performance of an active filter using a novel Rogowski-coil current sensor is discussed in detail. Experimental results from a prototype converter system using this approach are presented, and quantitative comparisons are made between a hybrid passive/active filter and a purely passive filter. It is demonstrated that substantial improvements in filter mass and converter transient performance are achievable using this active ripple filtering method.


title: IP traffic classification in NFV: A benchmarking of supervised Machine Learning algorithms

abstract: The Network Functions Virtualization (NFV) is an emerging solution that improves the flexibility, efficiency, and manageability of networks by leveraging virtualization and cloud computing technologies to run network appliances in software. The implementation of NFV presents issues such as the introduction of new software components, bottleneck performance, and monitoring hidden traffic. A considerable amount of traffic in NFV is invisible using traditional monitoring strategies because it does not hit a physical link. The implementation of autonomic management and supervised algorithms from Machine Learning (ML) become a key strategy to manage this hidden traffic. In this work, we focus on analyzing the traffic features of an NFV-based network while performing a benchmarking of the behavior of supervised ML algorithms in the IP traffic classification regarding their efficiency; considering that the efficiency of an algorithm depends on the trade-off between the response time and the precision. Our results demonstrate the NaiveBayes algorithm as the best traffic classifier. NaiveBayes reaches values of 99.9% with precision in 1.1sec.


title: ALCOVE: design and implementation of an object-centric virtual environment

abstract: We present a new interaction metaphor for object-centric tasks in the form of a prototype VR system, ALCOVE. Through analytic calculations, we quantitatively demonstrate the benefits of restructuring the interaction volume offered by current systems. Our metrics show that many applications' interaction volume increases by 1.5 to 2.6 times when using the ALCOVE system. We also offer an informal user task analysis and evaluations of previous VR systems that qualitatively support this improved interaction volume as well as demonstrate the need for a shift from room and desk-sized systems to desktop units. We present some testbed applications which benefit from this object-centric design and discuss some of the advantages and shortcomings of our system.


title: Rajala, Rossi and Tuunainen A Framework for Analyzing Software Business Models A Framework for Analyzing Software Business Models

abstract: In this study we explore the concept of business model and its essential elements in software business. There are no rigorous previous definitions or descriptions of the term business model in the context of software businesses in existing academic literature. Hence, a conceptual definition was found essential. Furthermore, there is a clear managerial need for constructs that help understanding and managing the bounded variations of different aspects of software businesses. We believe that decomposition of the business model concept is of help on that score, too. Based on cases representing different businesses in software industry, we explore business models for creating of a holistic view of business options based on schemes of things that the managers of our case companies found essential when describing their businesses. As a result of our study, we combine product development, marketing, sales, revenue logic, services and implementation into a cohesive framework describing the generic elements of business models in the software industry.


title: Public perceptions of biotechnology

abstract: The very term ‘Biotechnology’ elicits a range of emotions, from wonder and awe to downright fear and hostility. This is especially true among non-scientists, particularly in respect of agricultural and food biotechnology. These emotions indicate just how poorly understood agricultural biotechnology is and the need for accurate, dispassionate information in the public sphere to allow a rational public debate on the actual, as opposed to the perceived, risks and benefits of agricultural biotechnology. This review considers first the current state of public knowledge on agricultural biotechnology, and then explores some of the popular misperceptions and logical inconsistencies in both Europe and North America. I then consider the problem of widespread scientific illiteracy, and the role of the popular media in instilling and perpetuating misperceptions. The impact of inappropriate efforts to provide ‘balance’ in a news story, and of belief systems and faith also impinges on public scientific illiteracy. Getting away from the abstract, we explore a more concrete example of the contrasting approach to agricultural biotechnology adoption between Europe and North America, in considering divergent approaches to enabling coexistence in farming practices. I then question who benefits from agricultural biotechnology. Is it only the big companies, or is it society at large – and the environment-also deriving some benefit? Finally, a crucial aspect in such a technologically complex issue, ordinary and intelligent non-scientifically trained consumers cannot be expected to learn the intricacies of the technology to enable a personal choice to support or reject biotechnology products. The only reasonable and pragmatic alternative is to place trust in someone to provide honest advice. But who, working in the public interest, is best suited to provide informed and accessible, but objective, advice to wary consumers?


title: A New Planar Dual-Band GPS Antenna Designed for Reduced Susceptibility to Low-Angle Multipath

abstract: A new Global Positioning System (GPS) microstrip patch antenna designed for dual-band (LI /L2) operation is introduced. The antenna design is based on the reduced-surface-wave (RSW) concept and, as a result, is much less susceptible to low-angle multipath interference effects than some of the more commonly-used high-precision GPS antennas. In this paper, the radiation characteristics of this new design was compared to a dual- band choke-ring and a dual-band pinwheel antenna. In addition to having the advantages typically associated with microstrip patch antennas, this planar dual-band antenna lacks the design complications associated with the frequently-used stacked-patch method for realizing dual-band microstrip antenna performance. Thus, the simplicity of the design, together with the reduced horizon and backside radiation levels and excellent circular polarization characteristics indicate that this new antenna design is a promising candidate for dual-band, high-precision applications.


title: Statistical Metaphor Processing

abstract: Metaphor is highly frequent in language, which makes its computational processing indispensable for real-world NLP applications addressing semantic tasks. Previous approaches to metaphor modeling rely on task-specific hand-coded knowledge and operate on a limited domain or a subset of phenomena. We present the first integrated open-domain statistical model of metaphor processing in unrestricted text. Our method first identifies metaphorical expressions in running text and then paraphrases them with their literal paraphrases. Such a text-to-text model of metaphor interpretation is compatible with other NLP applications that can benefit from metaphor resolution. Our approach is minimally supervised, relies on the state-of-the-art parsing and lexical acquisition technologies distributional clustering and selectional preference induction, and operates with a high accuracy.


title: Robustness and generalization

abstract: We derive generalization bounds for learning algorithms based on their robustness: the property that if a testing sample is"similar"to a training sample, then the testing error is close to the training error. This provides a novel approach, different from the complexity or stability arguments, to study generalization of learning algorithms. We further show that a weak notion of robustness is both sufficient and necessary for generalizability, which implies that robustness is a fundamental property for learning algorithms to work.


title: A 160mV 670nW 8-bit SAR ADC in 0.13μm CMOS

abstract: An 8-bit SAR ADC capable of working under 160mV supply voltage is presented. To facilitate the ultra-low voltage comparator, a novel inverter-based amplifier is proposed and a dynamic latch with both gate and bulk driven input is exploited. An improved switching technique utilizing clock boosting and device stacking is employed for the ultra-low voltage sampling network. Implemented in a 0.13μm CMOS, the fabricated ADC works from 40kS/s to 400kS/s sampling rate under 160mV to 300mV supply voltage, respectively. Drawing 670nW from a single 160mV supply, the ADC achieves 0.5LSB DNL, 0.62LSB INL, 61.1dB SFDR and 7.3bit ENOB at a near-Nyquist input frequency of 19.7 kHz. To the best of authors' knowledge, this is one of the lowest reported supply voltages in analog design.


title: Servicescapes: The impact of physical surroundings on customers and employees.

abstract: A typology of service organizations is presented and a conceptual framework is advanced for exploring the impact of physical surroundings on the behaviors of both customers and employees. The abili...


title: Leveraging Cloud Computing in Geodatabase Management

abstract: In this work, we leverage Cloud computing technologies in scaling out data management in geographical databases. In particular, we tackle the issue of data indexing in parallel. First, spatial data is partitioned and indexed in a Hadoop MapReduce cluster. Two main partitioning strategies are evaluated: a) A linear-complexity method based on Zorder values, and b) An iterative algorithm based on X-means clustering. The advantages and drawbacks of each method are weighted in with relation to query performance. Second, interactive queries are processed from a local site using the index data structures built in the Cloud. We perform an experimental study on a real dataset of 110 million spatial objects representing property parcels in the United States. Our results support Cloud computing as an effective technology to cope up with huge datasets and, in particular, MapReduce parallel programming model in easing parallel processing implementations.


PLANAR COMPACT MULTI-BAND C-SHAPE MONOPOLE ANTENNA WITH INVERTED L-SHAPE PARASITIC STRIP FOR WIMAX APPLICATIONS


title: Uncovering cabdrivers' behavior patterns from their digital traces

abstract: Recognizing high-level human behavior and decisions from their digital traces are critical issues in pervasive computing systems. In this paper, we develop a novel methodology to reveal cabdrivers’ operation patterns by analyzing their continuous digital traces. For the first time, we systematically study large scale cabdrivers’ behavior in a real and complex city context through their daily digital traces. We identify a set of valuable features, which are simple and effective to classify cabdrivers, delineate cabdrivers’ operation patterns and compare the different cabdrivers’ behavior. The methodology and steps could spatially and temporally quantify, visualize, and examine different cabdrivers’ operation patterns. Drivers were categorized into top drivers and ordinary drivers by their daily income. We use the daily operations of 3000 cabdrivers in over 48 million of trips and 240 million kilometers to uncover: (1) spatial selection behavior, (2) context-aware spatio-temporal operation behavior, (3) route choice behavior, and (4) operation tactics. Though we focused on cabdriver operation patterns analysis from their digital traces, the methodology is a general empirical and analytical methodology for any GPS-like trace analysis. Our work demonstrates the great potential to utilize the massive pervasive data sets to understand human behavior and high-level intelligence.


title: An adaptive parameter space-filling algorithm for highly interactive cluster exploration

abstract: For a user to perceive continuous interactive response time in a visualization tool, the rule of thumb is that it must process, deliver, and display rendered results for any given interaction in under 100 milliseconds. In many visualization systems, successive interactions trigger independent queries and caching of results. Consequently, computationally expensive queries like multidimensional clustering cannot keep up with rapid sequences of interactions, precluding visual benefits such as motion parallax. In this paper, we describe a heuristic prefetching technique to improve the interactive response time of KMeans clustering in dynamic query visualizations of multidimensional data. We address the tradeoff between high interaction and intense query computation by observing how related interactions on overlapping data subsets produce similar clustering results, and characterizing these similarities within a parameter space of interaction. We focus on the two-dimensional parameter space defined by the minimum and maximum values of a time range manipulated by dragging and stretching a one-dimensional filtering lens over a plot of time series data. Using calculation of nearest neighbors of interaction points in parameter space, we reuse partial query results from prior interaction sequences to calculate both an immediate best-effort clustering result and to schedule calculation of an exact result. The method adapts to user interaction patterns in the parameter space by reprioritizing the interaction neighbors of visited points in the parameter space. A performance study on Mesonet meteorological data demonstrates that the method is a significant improvement over the baseline scheme in which interaction triggers on-demand, exact-range clustering with LRU caching. We also present initial evidence that approximate, temporary clustering results are sufficiently accurate (compared to exact results) to convey useful cluster structure during rapid and protracted interaction.


title: Dermal Fillers: Types, Indications, and Complications

abstract: There are many types of dermal fillers currently used for cosmetic and medical indications in routine clinical practice. Fillers can be classified as temporary, semipermanent, or permanent depending on the length of time the substance remains in tissue. They can also be classified by the composition of the product. Materials can be based on collagen (bovine, porcine, and human), hyaluronic acid, poly-L-lactic acid, calcium hydroxylapatite, polymethyl methacrylates, and polyacrylamide gels, among others. Temporary fillers are the products most often used for cosmetic purposes, in particular hyaluronic acid. This is due to the ease of application of fillers based on this substance, the good results obtained, and their safety profile. This review presents an overview of the techniques used for the correct placement of dermal fillers and the most common clinical indications for these procedures. It also covers the nature, properties, and mechanisms of action of the principal temporary, semipermanent, and permanent dermal fillers as well as the indications for each type of material. Finally, we describe the most common complications encountered and their treatment.


title: Towards a Unified Theory of State Abstraction for MDPs

abstract: State abstraction (or state aggregation) has been extensively studied in the fields of artificial intelligence and operations research. Instead of working in the ground state space, the decision maker usually finds solutions in the abstract state space much faster by treating groups of states as a unit by ignoring irrelevant state information. A number of abstractions have been proposed and studied in the reinforcement-learning and planning literatures, and positive and negative results are known. We provide a unified treatment of state abstraction for Markov decision processes. We study five particular abstraction schemes, some of which have been proposed in the past in different forms, and analyze their usability for planning and learning.


title: Interpretive Impacts of Text Visualization: Mitigating Political Framing Effects

abstract: Information visualizations are often evaluated as a tool in terms of their ability to support performance of a specific task. This article argues that value can be gained by instead evaluating visualizations from a communicative perspective. Specifically, it explores how text visualization can influence the impacts that framing has on the perception of political issues. Using data from a controlled laboratory study, the results presented here demonstrate that exposure to a text visualization can mitigate framing effects. Furthermore, it also shows a transfer effect, where participants who saw the visualization remained uninfluenced by framing in subsequent texts, even when the visualization was absent. These results carry implications for the methods used to evaluate information visualization systems, for understanding the cognitive and interpretive mechanisms by which framing effects occur, and for exploring the design space of interactive text visualization.


title: Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection

abstract: Machine learning-based solutions have been successfully employed for the automatic detection of malware on Android. However, machine learning models lack robustness to adversarial examples, which are crafted by adding carefully chosen perturbations to the normal inputs. So far, the adversarial examples can only deceive detectors that rely on syntactic features ( e.g. , requested permissions, API calls, etc. ), and the perturbations can only be implemented by simply modifying application’s manifest. While recent Android malware detectors rely more on semantic features from Dalvik bytecode rather than manifest, existing attacking/defending methods are no longer effective. In this paper, we introduce a new attacking method that generates adversarial examples of Android malware and evades being detected by the current models. To this end, we propose a method of applying optimal perturbations onto Android APK that can successfully deceive the machine learning detectors. We develop an automated tool to generate the adversarial examples without human intervention. In contrast to existing works, the adversarial examples crafted by our method can also deceive recent machine learning-based detectors that rely on semantic features such as control-flow-graph. The perturbations can also be implemented directly onto APK’s Dalvik bytecode rather than Android manifest to evade from recent detectors. We demonstrate our attack on two state-of-the-art Android malware detection schemes, MaMaDroid and Drebin. Our results show that the malware detection rates decreased from 96% to 0% in MaMaDroid, and from 97% to 0% in Drebin, with just a small number of codes to be inserted into the APK.


title: A Generative Model for Dynamic Networks with Applications

abstract: Networks observed in real world like social networks, collaboration networks etc., exhibit temporal dynamics, i.e. nodes and edges appear and/or disappear over time. In this paper, we propose a generative, latent space based, statistical model for such networks (called dynamic networks). We consider the case where the number of nodes is fixed, but the presence of edges can vary over time. Our model allows the number of communities in the network to be different at different time steps. We use a neural network based methodology to perform approximate inference in the proposed model and its simplified version. Experiments done on synthetic and real world networks for the task of community detection and link prediction demonstrate the utility and effectiveness of our model as compared to other similar existing approaches.


title: A median cut algorithm for light probe sampling

abstract: We present a technique for approximating a light probe image as a constellation of light sources based on a median cut algorithm. The algorithm is efficient, simple to implement, and can realistically represent a complex lighting environment with as few as 64 point light sources.


title: Millisecond-scale molecular dynamics simulations on Anton

abstract: Anton is a recently completed special-purpose supercomputer designed for molecular dynamics (MD) simulations of biomolecular systems. The machine's specialized hardware dramatically increases the speed of MD calculations, making possible for the first time the simulation of biologicl molecules at an atomic level of detail for periods on the order of a millisecond---about two orders of magnitude beyond the previous state of the art. Anton is now running simulations on a timescale at which many critically important, but poorly understood phenomena are known to occur, allowing the observation of aspects of protein dynamics that were previously inaccessible to both computational and experimental study. Here, we report Anton's performance when executing actual MD simulations whose accuracy has been validated against both existing MD software and experimental observations. We also discuss the manner in which novel algorithms have been coordinated with Anton's co-designed, application-specific hardware to achieve these results.


CUNI Submissions in WMT18


title: Minimum Control Latency of SDN Controller Placement

abstract: SDN is known as a decoupled architecture separatingthe control and data planes. The controller is in charge ofnetwork nodes through a southbound interface. However, it iscostly to impose all controllers to control each node separately. Meanwhile, the layout of controllers affects the network's abilityto respond to network events. Therefore, the controller placementis an important problem in SDN community. When latencyand reliability metrics are both considered, there is usuallyno single best controller placement solution, but a trade-offinstead. Most of deployment schemes based on latency are mainlyfocused on transmission delay (TD) or Propagation delay (PD) atpresent. However, to our best knowledge, no existing works haveconsidered the influence of control latency on SDN controllerplacement problem while control latency makes great sense inquick response to network events. Thus, in this paper, a minimum-control-latency optimized algorithmbased on greedy controlling pattern design is proposed. Theideas and mechanisms are illustrated using the Internet2 OS3Etopology compared to average-latency-optimized placement andworst-case-optimized placement. What's more, the accuracy oflatency measurement is also a main concern in SDN research. Itis not precise enough when measuring only based on Ping results. In this paper, a novel method based on active latency measurementis also presented to obtain more accurate control latency. Extensive experiments have shown that our minimum-controllatency-optimized can improve the imbalance when partitioningSDN domains and achieve the maximum number of nodes percontroller controlled.


title: Genetics of emotional regulation: the role of the serotonin transporter in neural function

abstract: Identifying biological mechanisms through which genes lead to individual differences in emotional behavior is paramount to our understanding of how such differences confer risk for neuropsychiatric illness. The emergence of techniques such as in vivo imaging of brain function in humans and genetic engineering in rodents has provided important new insights into the impact of serotonin (5-HT), a key modulator of emotional behavior, on neural systems subserving anxiety and depression. A major finding has been the discovery of genetic variation in a crucial regulatory molecule within the 5-HT system, the 5HT transporter (5-HTT), and its influence on emotional traits. The study of the 5-HTT provides a new foundation for understanding the neurobiological and genetic basis of emotional regulation and affective illness.


Detecting Most Frequent Sense using Word Embeddings and BabelNet


title: Analysis and Design of a 45 $^{\circ}$ Slant-Polarized Omnidirectional Antenna

abstract: A 45° slant-polarized omnidirectional antenna is proposed for mobile communication base stations. The proposed antenna consists of four printed crossed-dipole elements which are rolled up into a cylinder for omnidirectional radiation. Each crossed-dipole element is composed of a horizontal dipole and a vertical dipole. The 45° slant polarization is achieved by adjusting the lengths of the horizontal and vertical dipoles. A broadband feeding network consisting of four broadband baluns and an impedance matching circuit is introduced to feed the four crossed-dipole elements. Experimental results show that the omnidirectionality or gain variation in the horizontal plane is less than 1 dB while the cross-polarization level is below -15 dB over a bandwidth of 15% (1.9-2.2 GHz). The 45° slant-polarized omnidirectional antenna has a bandwidth of 22% (1.75-2.18 GHz) for 15-dB return loss. Theoretical analysis and design procedure are presented.


"If You Don't Have It, You Can't Find It." A Close Look at Students' Perceptions of Using Technology


title: TristouNet: Triplet loss for speaker turn embedding

abstract: TristouNet is a neural network architecture based on Long Short-Term Memory recurrent networks, meant to project speech sequences into a fixed-dimensional euclidean space. Thanks to the triplet loss paradigm used for training, the resulting sequence embeddings can be compared directly with the euclidean distance, for speaker comparison purposes. Experiments on short (between 500ms and 5s) speech turn comparison and speaker change detection show that TristouNet brings significant improvements over the current state-of-the-art techniques for both tasks.


title: Classification of Battlefield Ground Vehicles Using Acoustic Features and Fuzzy Logic Rule-Based Classifiers

abstract: In this paper, we demonstrate, through the multicategory classification of battlefield ground vehicles using acoustic features, how it is straightforward to directly exploit the information inherent in a problem to determine the number of rules, and subsequently the architecture, of fuzzy logic rule-based classifiers (FLRBC). We propose three FLRBC architectures, one non-hierarchical and two hierarchical (HFLRBC), conduct experiments to evaluate the performances of these architectures, and compare them to a Bayesian classifier. Our experimental results show that: 1) for each classifier the performance in the adaptive mode that uses simple majority voting is much better than in the non-adaptive mode; 2) all FLRBCs perform substantially better than the Bayesian classifier; 3) interval type-2 (T2) FLRBCs perform better than their competing type-1 (T1) FLRBCs, although sometimes not by much; 4) the interval T2 nonhierarchical and HFLRBC-series architectures perform the best; and 5) all FLRBCs achieve higher than the acceptable 80% classification accuracy


title: Design of a Wearable Device for Reading Positive Expressions from Facial EMG Signals

abstract: In this paper we present the design of a wearable device that reads positive facial expressions using physiological signals. We first analyze facial morphology in 3 dimensions and facial electromyographic signals on different facial locations and show that we can detect electromyographic signals with high amplitude on areas of low facial mobility on the side of the face, which are correlated to ones obtained from electrodes on traditional surface electromyographic capturing positions on top of facial muscles on the front of the face. We use a multi-attribute decision-making method to find adequate electrode positions on the side of face to capture these signals. Based on this analysis, we design and implement an ergonomic wearable device with high reliability. Because the signals are recorded distally, the proposed device uses independent component analysis and an artificial neural network to analyze them and achieve a high facial expression recognition rate on the side of the face. The recognized emotional facial expressions through the wearable interface device can be recorded during therapeutic interventions and for long-term facial expression recognition to quantify and infer the user’s affective state in order to support medical professionals.


title: Time–Frequency Matrix Feature Extraction and Classification of Environmental Audio Signals

abstract: Audio feature extraction and classification are important tools for audio signal analysis in many applications, such as multimedia indexing and retrieval, and auditory scene analysis. However, due to the nonstationarities and discontinuities exist in these signals, their quantification and classification remains a formidable challenge. In this paper, we develop a new approach for audio feature extraction to effectively quantify these nonstationarities in an attempt to achieve high classification accuracy for environmental audio signals. Our approach consists of three stages: first we propose to construct the time-frequency matrix (TFM) of audio signals using matching-pursuit time-frequency distribution (MP-TFD) technique, and then apply the non-negative matrix decomposition (NMF) technique to decompose the TFM into its significant components. Finally, we propose seven novel features from the spectral and temporal structures of the decomposed vectors in a way that they successfully represent joint TF structure of the audio signal, and combine them with the Mel-frequency cepstral coefficients (MFCCs) features. These features are examined using a database of 192 environmental audio signals which includes 20 aircraft, 17 helicopter, 20 drum, 15 flute, 20 piano, 20 animal, 20 bird, and 20 insect sounds, and the speech of 20 males and 20 females. The results of the numerical simulation support the effectiveness of the proposed approach for environmental audio classification with over 10% accuracy-rate improvement compared to the MFCC features.


title: Prototyping vs. specifying: A multi-project experiment

abstract: In this experiment, seven software teams developed versions of the same small-size (2000-4000 source instruction) application software product. Four teams used the Specifying approach. Three teams used the Prototyping approach. The main results of the experiment were: Prototyping yielded products with roughly equivalent performance, but with about 40% less code and 45% less effort. The prototyped products rated somewhat lower on functionality and robustness, but higher on ease of use and ease of learning. Specifying produced more coherent designs and software that was easier to integrate. The paper presents the experimental data supporting these and a number of additional conclusions.


title: Investigation of Advanced Dicing Technologies for Ultra Low-k and 3D Integration

abstract: The introduction and use of ultra low-k dielectric materials in an attempt to achieve better die performance brought new challenges to the wafer singulation / dicing process. Ultra low-k dielectric materials can easily get damaged during conventional blade dicing due to their brittleness. Thus, there is a need to investigate alternative or more advanced dicing techniques that can deliver successful dicing processes without inducing stress or damage. Apart from looking at the compatibility of the different dicing techniques with (ultra) low-k dielectric materials, the integration of these different dicing techniques into the different 3D-technology configurations was also investigated. This paper reports on the post dicing results and the challenges for 3D technology for the following different dicing technologies: blade dicing, laser grooving + blade dicing, laser grooving + plasma dicing, stealth dicing, plasma dicing, and laser full cut dicing.


title: User Elicitation on Single-hand Microgestures

abstract: Gestural interaction has become increasingly popular, as enabling technologies continue to transition from research to retail. The mobility of miniaturized (and invisible) technologies introduces new uses for gesture recognition. This paper investigates single-hand microgestures (SHMGs), detailed gestures in a small interaction space. SHMGs are suitable for the mobile and discrete nature of interactions for ubiquitous computing. However, there has been a lack of end-user input in the design of such gestures. We performed a user-elicitation study with 16 participants to determine their preferred gestures for a set of referents. We contribute an analysis of 1,632 gestures, the resulting gesture set, and prevalent conceptual themes amongst the elicited gestures. These themes provide a set of guidelines for gesture designers, while informing the designs of future studies. With the increase in hand-tracking and electronic devices in our surroundings, we see this as a starting point for designing gestures suitable to portable ubiquitous computing.


title: Short sleep mediates the association between long work hours and increased body mass index

abstract: This study examined whether short sleep duration, physical activity and time spent sitting each day mediated the association between long work hours and body mass index (BMI). Participants included 16,951 middle aged Australian adults who were employed in full time work (i.e. ≥35 h a week). Data on BMI, sleep duration, work hours and other health and demographic variables were obtained through a self-report questionnaire. A multiple mediation model was tested whereby sleep duration, physical activity and amount of time spent sitting were entered as potential mediators between work hours and BMI. The results demonstrated that short sleep partially mediated the association between long work hours and increased BMI in males. In females, long work hours were indirectly related to higher BMI through short sleep. The results provide some support for the hypothesis that long work hours could contribute to obesity via a reduction in sleep duration; this warrants further investigation in prospective studies.


title: Generalizable Adversarial Examples Detection Based on Bi-model Decision Mismatch

abstract: Modern applications of artificial neural networks have yielded remarkable performance gains in a wide range of tasks. However, recent studies have discovered that such modelling strategy is vulnerable to Adversarial Examples, i.e. examples with subtle perturbations often too small and imperceptible to humans, but that can easily fool neural networks. Defense techniques against adversarial examples have been proposed, but ensuring robust performance against varying or novel types of attacks remains an open problem. In this work, we focus on the detection setting, in which case attackers become identifiable while models remain vulnerable. Particularly, we employ the decision layer of independently trained models as features for posterior detection. The proposed framework does not require any prior knowledge of adversarial examples generation techniques, and can be directly employed along with unmodified off-the-shelf models. Experiments on the standard MNIST and CIFAR10 datasets deliver empirical evidence that such detection approach generalizes well across not only different adversarial examples generation methods but also quality degradation attacks. Non-linear binary classifiers trained on top of our proposed features can achieve a high detection rate $(\gt 90$%) in a set of white-box attacks and maintain such performance when tested against unseen attacks.


title: A literature survey on older adults' use of social network services and social applications

abstract: With aging, adults which once worked and were active become isolated from the world because with retirement comes a whole range of problems which span from the physical to the social scope. One of the most concerning is social isolation, which can only be fought by satisfying social needs. In terms of technology, the challenges are in designing social technologies that encourage older people to actively engage with each other and with the people around them. This paper presents a survey reviewing research surrounding the emergent field of Social Network Services (SNSs), along with other meaningful social applications, and its use by the older segment of the population. A total of thirteen domains are identified related with how these services can be improved to consider older adults characteristics: from the most important related with the family role and privacy control, to issues related with the design of the user interface, the importance of multimodal interaction and adaptive solutions to compensate age-related declines, to several other focusing on the importance of groups, photos, cultural and health information. Main contributions to the field of SNS and older adults are given in a set of recommendations which result from discussions on each domain and which aim at the design of a more inclusive SNS solution. We review literature surrounding Social Network Services and applications.Identified the 13 most relevant domains concerning older adults.Compiled a list of recommendations concerning the development of inclusive SNS.Family, interface complexity and privacy issues are the most important domains.Photos, multimodal interaction and adaptation play an important role on adoption.


title: Stressed state corresponding to the formation of large-scale brittle failure of rocks

abstract: Issue of relations between brittle failure and localized plastic flow, which are realized in the fault zones of the Earth’s crust, is one of the most important problems in geomechanics. New data on values of natural stresses in seismoactive regions allowed us to distinguish the regularities of their distribution, which correspond to the possibility of the appearance of large-scale brittle failure. It was found that areas of medium and low levels of confining pressure are the most hazardous regions in terms of a strong earthquake. Here, a small part of the released energy is consumed for negotiating friction forces. Therefore, brittle failure is more efficient. In the regions of high rock pressure, the dissipation of elastic energy is realized as creep along the fault or cataclastic flow due to a large number of weak earthquakes.


title: A Dynamic Oracle for Arc-Eager Dependency Parsing

abstract: The standard training regime for transition-based dependency parsers makes use of an oracle, which predicts an optimal transition sequence for a sentence and its gold tree. We present an improved oracle for the arc-eager transition system, which provides a set of optimal transitions for every valid parser configuration, including configurations from which the gold tree is not reachable. In such cases, the oracle provides transitions that will lead to the best reachable tree from the given configuration. The oracle is efficient to implement and provably correct. We use the oracle to train a deterministic left-to-right dependency parser that is less sensitive to error propagation, using an online training procedure that also explores parser configurations resulting from non-optimal sequences of transitions. This new parser outperforms greedy parsers trained using conventional oracles on a range of data sets, with an average improvement of over 1.2 LAS points and up to almost 3 LAS points on some data sets.


title: Development of an upper limb rehabilitation system using inertial movement units and kinect device

abstract: It has become an increasingly important issue with respect to improve the efficacy of rehabilitation and increase patient willingness to attend rehabilitation treatment in recent years. Virtual Reality (VR) has been used in combination with medical treatment and technology. More specifically, its skeletal tracking system is being widely used in the field of medicine and rehabilitation. Although the Microsoft Kinect device with low cost and easy development it may encounter the problem of misdetection when it comes to extremity angles or overlapping extremity. In this study, we proposed a new Rehabilitation Gaming System (RGS) which focuses on the upper part of the body with wireless Inertial Measurement Units (IMUs) and Kinect device. The Kinect is being used as the base tracking system by the operation of gaming system. Meanwhile, multiple sets of IMU were integrated into the extremity of the subject to calculate the angles through algorithms. Wireless IMUs could compensate the calculating error of angles in Kinect device. In view of the evaluation of angle difference between the IMUs and Kinect device in different upper extremity gestures when playing the rehabilitation game, the results showed that Kinect has a greater error ratio 23% on extremity angles with regard to depth or overlapping extremities, but calculation by IMUs has higher accuracy and stability. The proposed system has incredible potential to enhance the reliability and validity of the efficacy of rehabilitation.


Leaf-like morphology and behaviour of juvenile horsefish (Congiopodus peruvianus) (Scorpaeniformes: Congiopodidae) from Chilean Patagonia


title: Interpersonal connectedness: conceptualization and directions for a measurement instrument

abstract: Interpersonal connectedness is the sense of belonging based on the appraisal of having sufficient close social contacts. This feeling is regarded as one of the major outcomes of successful (mediated) social interaction and as such an important construct for HCI. However, the exact nature of this feeling, how to achieve it, and how to assess it remain unexplored to date. In the current paper we start the theoretical conceptualization of this phenomenon by exploring its basic origins in psychological literature and simultaneously formulate requirements for a measurement instrument to be developed in the service of exploring and testing CMC applications, in particular awareness technologies.


title: Safety++: Designing IoT and Wearable Systems for Industrial Safety through a User Centered Design Approach

abstract: In this paper, we describe Safety++, an IoT ecosystem of connected wearable elements aimed at improving safety in the workplace in the energy industry. Safety is a major concern for energy companies and, despite the large availability of protective equipment and the adoption of strict safety procedures, it still remains a problem. Unsafe behavior is the main cause of incidents and is not addressed by current solutions. We exploit advancements in IoT and wearable computing to design, build and test a platform aimed at improving awareness, peer supervision and emergency fast response in the workplace. We do this by following a user centered design approach, which takes into consideration the user needs and actual experience, in order to increase also the solution's acceptability and adoption. Results demonstrate that focusing on real time feedback, awareness and peer communication in IoT systems can reinforce safe practices and attitudes and lead to a safer environment in energy companies.


title: Recurrent Scale Approximation for Object Detection in CNN

abstract: Since convolutional neural network (CNN) lacks an inherent mechanism to handle large scale variations, we always need to compute feature maps multiple times for multi-scale object detection, which has the bottleneck of computational cost in practice. To address this, we devise a recurrent scale approximation (RSA) to compute feature map once only, and only through this map can we approximate the rest maps on other levels. At the core of RSA is the recursive rolling out mechanism: given an initial map at a particular scale, it generates the prediction at a smaller scale that is half the size of input. To further increase efficiency and accuracy, we (a): design a scale-forecast network to globally predict potential scales in the image since there is no need to compute maps on all levels of the pyramid. (b): propose a landmark retracing network (LRN) to trace back locations of the regressed landmarks and generate a confidence score for each landmark; LRN can effectively alleviate false positives caused by the accumulated error in RSA. The whole system can be trained end-to-end in a unified CNN framework. Experiments demonstrate that our proposed algorithm is superior against state-of-the-art methods on face detection benchmarks and achieves comparable results for generic proposal generation. The source code of RSA is available at github.com/sciencefans/RSA-for-object-detection.


A Security Analysis of Honeywords


title: Grounding words in perception and action: computational insights

abstract: We use words to communicate about things and kinds of things, their properties, relations and actions. Researchers are now creating robotic and simulated systems that ground language in machine perception and action, mirroring human abilities. A new kind of computational model is emerging from this work that bridges the symbolic realm of language with the physical realm of real-world referents. It explains aspects of context-dependent shifts of word meaning that cannot easily be explained by purely symbolic models. An exciting implication for cognitive modeling is the use of grounded systems to ‘step into the shoes’ of humans by directly processing first-personperspective sensory data, providing a new methodology for testing various hypotheses of situated communication and learning.


title: Static prediction games for adversarial learning problems

abstract: The standard assumption of identically distributed training and test data is violated when the test data are generated in response to the presence of a predictive model. This becomes apparent, for example, in the context of email spam filtering. Here, email service providers employ spam filters, and spam senders engineer campaign templates to achieve a high rate of successful deliveries despite the filters. We model the interaction between the learner and the data generator as a static game in which the cost functions of the learner and the data generator are not necessarily antagonistic. We identify conditions under which this prediction game has a unique Nash equilibrium and derive algorithms that find the equilibrial prediction model. We derive two instances, the Nash logistic regression and the Nash support vector machine, and empirically explore their properties in a case study on email spam filtering.


title: The phone rings but the user doesn't answer: unavailability in mobile communication

abstract: We know that phone calls and mobile text messages are not always promptly answered and responded to, yet we know little about the reasons for unavailability, its effects on a user's image, the ways in which users explain the reasons for it, and actions when users cannot reach someone. Usage logs (2,983 phone use events), Web-based diaries, and interviews (N = 20) were used to investigate occasional unavailability in a mobile communication context. We identified four categories of unavailability and found that 31.1% of the phone calls consisted of unsuccessful communication attempts and reciprocal calls back from people who were unavailable earlier. Interestingly, while participants paid attention to the need to give reasons for unavailability, they did not require the explanations to be truthful. These findings have implications for design of systems that better support the needs to manage and explain unavailability and manage pending communication requests.


title: A Practical Differentially Private Random Decision Tree Classifier

abstract: In this paper, we study the problem of constructing private classifiers using decision trees, within the framework of differential privacy. We first present experimental evidence that creating a differentially private ID3 tree using differentially private low-level queries does not simultaneously provide good privacy and good accuracy, particularly for small datasets. ::: ::: In search of better privacy and accuracy, we then present a differentially private decision tree ensemble algorithm based on random decision trees. We demonstrate experimentally that this approach yields good prediction while maintaining good privacy, even for small datasets. We also present differentially private extensions of our algorithm to two settings: (1) new data is periodically appended to an existing database and (2) the database is horizontally or vertically partitioned between multiple users.


Effective advertising and its influence on consumer buying behavior


Closures and Cavities in the Human Connectome


Attention-Based Convolutional Neural Network for Semantic Relation Extraction


title: Large-scale learning of word relatedness with constraints

abstract: Prior work on computing semantic relatedness of words focused on representing their meaning in isolation, effectively disregarding inter-word affinities. We propose a large-scale data mining approach to learning word-word relatedness, where known pairs of related words impose constraints on the learning process. We learn for each word a low-dimensional representation, which strives to maximize the likelihood of a word given the contexts in which it appears. Our method, called CLEAR, is shown to significantly outperform previously published approaches. The proposed method is based on first principles, and is generic enough to exploit diverse types of text corpora, while having the flexibility to impose constraints on the derived word similarities. We also make publicly available a new labeled dataset for evaluating word relatedness algorithms, which we believe to be the largest such dataset to date.


title: Crayon: saving power through shape and color approximation on next-generation displays

abstract: We present Crayon, a library and runtime system that reduces display power dissipation by acceptably approximating displayed images via shape and color transforms. Crayon can be inserted between an application and the display to optimize dynamically generated images before they appear on the screen. It can also be applied offline to optimize stored images before they are retrieved and displayed. Crayon exploits three fundamental properties: the acceptability of small changes in shape and color, the fact that the power dissipation of OLED displays and DLP pico-projectors is different for different colors, and the relatively small energy cost of computation in comparison to display energy usage. We implement and evaluate Crayon in three contexts: a hardware platform with detailed power measurement facilities and an OLED display, an Android tablet, and a set of cross-platform tools. Our results show that Crayon's color transforms can reduce display power dissipation by over 66% while producing images that remain visually acceptable to users. The measured whole-system power reduction is approximately 50%. We quantify the acceptability of Crayon's shape and color transforms with a user study involving over 400 participants and over 21,000 image evaluations.


title: Playing Repeated Security Games with No Prior Knowledge

abstract: This paper investigates repeated security games with unknown (to the defender) game payoffs and attacker behaviors. As existing work assumes prior knowledge about either the game payoffs or the attacker's behaviors, they are not suitable for tackling our problem. Given this, we propose the first efficient defender strategy, based on an adversarial online learning framework, that can provably achieve good performance guarantees without any prior knowledge. In particular, we prove that our algorithm can achieve low performance loss against the best fixed strategy on hindsight (i.e., having full knowledge of the attacker's moves). In addition, we prove that our algorithm can achieve an efficient competitive ratio against the optimal adaptive defender strategy. We also show that for zero-sum security games, our algorithm achieves efficient results in approximating a number of solution concepts, such as algorithmic equilibria and the minimax value. Finally, our extensive numerical results demonstrate that, without having any prior information, our algorithm still achieves good performance, compared to state-of-the-art algorithms from the literature on security games, such as SUQR, which require significant amount of prior knowledge.


title: E-books versus print books: Readers' choices and preferences across contexts

abstract: With electronic book (e-book) sales and readership rising, are e-books positioned to replace print books? This study examines the preference for e-books and print books in the contexts of reading purpose, reading situation, and contextual variables such as age, gender, education level, race/ethnicity, income, community type, and Internet use. In addition, this study aims to identify factors that contribute to e-book adoption. Participants were a nationally representative sample of 2,986 people in the United States from the Reading Habits Survey, conducted by the Pew Research Center's Internet & American Life Project (http://pewinternet.org/Shared-Content/Data-Sets/2011/December-2011--Reading-Habits.aspx). While the results of this study support the notion that e-books have firmly established a place in people's lives, due to their convenience of access, e-books are not yet positioned to replace print books. Both print books and e-books have unique attributes and serve irreplaceable functions to meet people's reading needs, which may vary by individual demographic, contextual, and situational factors. At this point, the leading significant predictors of e-book adoption are the number of books read, the individual's income, the occurrence and frequency of reading for research topics of interest, and the individual's Internet use, followed by other variables such as race/ethnicity, reading for work/school, age, and education.


title: Health on Impulse : When Low Self-Control Promotes Healthy Food Choices

abstract: OBJECTIVE: Food choices are often made mindlessly, when individuals are not able or willing to exert self-control. Under low self-control, individuals have difficulties to resist palatable but unhealthy food products. In contrast to previous research aiming to foster healthy choices by promoting high self-control, this study exploits situations of low self-control, by strategically using the tendency under these conditions to rely on heuristics (simple decision rules) as quick guides to action. More specifically, the authors associated healthy food products with the social proof heuristic (i.e., normative cues that convey majority endorsement for those products). METHOD: One hundred seventy-seven students (119 men), with an average age of 20.47 years (SD = 2.25) participated in the experiment. This study used a 2 (low vs. high self-control) × 2 (social proof vs. no heuristic) × 2 (trade-off vs. control choice) design, with the latter as within-subjects factor. The dependent variable was the number of healthy food choices in a food-choice task. RESULTS: In line with previous studies, people made fewer healthy food choices under low self-control. However, this negative effect of low self-control on food choice was reversed when the healthy option was associated with the social proof heuristic. In that case, people made more healthy choices under conditions of low self-control. CONCLUSION: Low self-control may be even more beneficial for healthy food choices than high self-control in the presence of a heuristic. Exploiting situations of low self-control is a new and promising method to promote health on impulse.


title: Chatbot for admissions

abstract: The communication of potential students with a university department is performed manually and it is a very time consuming procedure. The opportunity to communicate with on a one-to-one basis is highly valued. However with many hundreds of applications each year, one-to-one conversations are not feasible in most cases. The communication will require a member of academic staff to expend several hours to find suitable answers and contact each student. It would be useful to reduce his costs and time. ::: The project aims to reduce the burden on the head of admissions, and potentially other users, by developing a convincing chatbot. A suitable algorithm must be devised to search through the set of data and find a potential answer. The program then replies to the user and provides a relevant web link if the user is not satisfied by the answer. Furthermore a web interface is provided for both users and an administrator. ::: The achievements of the project can be summarised as follows. To prepare the background of the project a literature review was undertaken, together with an investigation of existing tools, and consultation with the head of admissions. The requirements of the system were established and a range of algorithms and tools were investigated, including keyword and template matching. An algorithm that combines keyword matching with string similarity has been developed. A usable system using the proposed algorithm has been implemented. The system was evaluated by keeping logs of questions and answers and by feedback received by potential students that used it.


title: Realizing the Internet of Nano Things: Challenges, Solutions, and Applications

abstract: Embedding nanosensors in the environment would add a new dimension to the Internet of Things, but realizing the IoNT vision will require developing new communication paradigms and overcoming various technical obstacles.


title: Toward a Social Psychology of Race and Race Relations for the Twenty-First Century.

abstract: The United States, like many nations, continues to experience rapid growth in its racial minority population and is projected to attain so-called majority-minority status by 2050. Along with these demographic changes, staggering racial disparities persist in health, wealth, and overall well-being. In this article, we review the social psychological literature on race and race relations, beginning with the seemingly simple question: What is race? Drawing on research from different fields, we forward a model of race as dynamic, malleable, and socially constructed, shifting across time, place, perceiver, and target. We then use classic theoretical perspectives on intergroup relations to frame and then consider new questions regarding contemporary racial dynamics. We next consider research on racial diversity, focusing on its effects during interpersonal encounters and for groups. We close by highlighting emerging topics that should top the research agenda for the social psychology of race and race relations ...


title: Human activity recognition using multisensor data fusion based on Reservoir Computing

abstract: Activity recognition plays a key role in providing activity assistance and care for users in smart homes. In this work, we present an activity recognition system that classifies in the near real-time a set of common daily activities exploiting both the data sampled by sensors embedded in a smartphone carried out by the user and the reciprocal Received Signal Strength (RSS) values coming from worn wireless sensor devices and from sensors deployed in the environment. In order to achieve an effective and responsive classification, a decision tree based on multisensor data-stream is applied fusing data coming from embedded sensors on the smartphone and environmental sensors before processing the RSS stream. To this end, we model the RSS stream, obtained from a Wireless Sensor Network (WSN), using Recurrent Neural Networks (RNNs) implemented as efficient Echo State Networks (ESNs), within the Reservoir Computing (RC) paradigm. We targeted the system for the EvAAL scenario, an international competition that aims at establishing benchmarks and evaluation metrics for comparing Ambient Assisted Living (AAL) solutions. In this paper, the performance of the proposed activity recognition system is assessed on a purposely collected real-world dataset, taking also into account a competitive neural network approach for performance comparison. Our results show that, with an appropriate configuration of the information fusion chain, the proposed system reaches a very good accuracy with a low deployment cost.


title: Europe’s High-Speed Rail Network: Maturation and Opportunities

abstract: High speed rail is a good example of a complex social-technical system, it combines high technology with social institutions to provide transport service. However, in spite of advances in HSR technology, HSR service is operated in much the same manner as traditional passenger rail service – an unsuccessful long-term business model. This research argues that HSR needs disruptive innovation to create attractive and efficient new services tailored for today’s transport demands. Since HSR technology is already well developed, the key to creating these new services is overcoming institutional barriers to innovation in service design, pricing strategies and integration with other transport networks. European HSR operators have spent many years developing technology and building infrastructure thus creating a nascent international HSR network. The question is, how can this network be operated to create a truly innovative new transport service? The paper summarizes development of Europe’s HSR network. Next, it describes innovation theory in the railway context. Third it describes ideas for transforming HSR into an innovative new service. Implementing these ideas will require overcoming institutional barriers. Finally, the paper presents conclusions.


title: Modeling Coverage for Neural Machine Translation

abstract: Attention mechanism has enhanced stateof-the-art Neural Machine Translation (NMT) by jointly learning to align and translate. It tends to ignore past alignment information, however, which often leads to over-translation and under-translation. To address this problem, we propose coverage-based NMT in this paper. We maintain a coverage vector to keep track of the attention history. The coverage vector is fed to the attention model to help adjust future attention, which lets NMT system to consider more about untranslated source words. Experiments show that the proposed approach significantly improves both translation quality and alignment quality over standard attention-based NMT. 1


title: Personality and leadership: A qualitative and quantitative review

abstract: This article provides a qualitative review of the trait perspective in leadership research, followed by a meta-analysis. The authors used the five-factor model as an organizing framework and meta-analyzed 222 correlations from 73 samples. Overall, the correlations with leadership were Neuroticism .24, Extraversion .31, Openness to Experience .24, Agreeableness .08, and Conscientiousness .28. Results indicated that the relations of Neuroticism, Extraversion, Openness to Experience, and Conscientiousness with leadership generalized in that more than 90% of the individual correlations were greater than 0. Extraversion was the most consistent correlate of leadership across study settings and leadership criteria (leader emergence and leadership effectiveness). Overall, the five-factor model had a multiple correlation of .48 with leadership, indicating strong support for the leader trait perspective when traits are organized according to the five-factor model.


title: IEEE 802.11 ac based client centered indoor positioning system

abstract: As the number of indoor location based services increase in the mobile market, service providers demanding more accurate indoor positioning information. To improve the location estimation accuracy, this paper presents an enhanced client centered location prediction scheme and filtering algorithm based on IEEE 802.11 ac standard for indoor positioning system. We proposed error minimization filtering algorithm for accurate location prediction and also introduce IEEE 802.11 ac mobile client centered location correction scheme without modification of preinstalled infrastructure Access Point. Performance evaluation based on MATLAB simulation highlight the enhanced location prediction performance. We observe a significant error reduction of angle estimation.


title: Rural sector responses to economic crisis in Uganda

abstract: This paper, based on a survey of farm households in Uganda's Masaka district, analyses the impact of the country's economic decline on the rural sector. The economic crisis has reduced the flow of resources from the urban areas, in terms of both goods and services and remittances from urban-based relatives. To preserve their livelihoods, rural dwellers have had to diversify their sources of income. Ability to do this is constrained not only by household characteristics, such as size, age and education of head and time endowment, but also by economic variables such as the existence of markets, size of land holding and credit availability. It is argued that to reincorporate peasants into the modern sector it is necessary to remove the barriers which prevent them from realizing a fair return on their production factors and assets.


title: Combining Singular-Spectrum Analysis and neural networks for time series forecasting

abstract: In this paper, we propose a combination of an adaptive noise-reduction algorithm based on Singular-Spectrum Analysis (SSA) and a standard feedforward neural prediction model. We test the forecast skill of our method on some short real-world and computergenerated time series with different amounts of additive noise. The results show that our combined technique has better performances than those offered by the same network directly applied to raw data, and therefore is well suited to forecast short and noisy time series with an underlying deterministic data generating process (DGP).


title: Algorithms for Infinitely Many-Armed Bandits

abstract: We consider multi-armed bandit problems where the number of arms is larger than the possible number of experiments. We make a stochastic assumption on the mean-reward of a new selected arm which characterizes its probability of being a near-optimal arm. Our assumption is weaker than in previous works. We describe algorithms based on upper-confidence-bounds applied to a restricted set of randomly selected arms and provide upper-bounds on the resulting expected regret. We also derive a lower-bound which matches (up to a logarithmic factor) the upper-bound in some cases.


title: Role of childhood traumatic experience in personality disorders in China

abstract: Abstract Background There has been no large-scale examination of the association between types of childhood abuse and personality disorders (PDs) in China using standardized assessment tools and the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition ( DSM-IV ) criteria. Hence, this study aimed to explore the relationship between retrospective reports of various types of childhood maltreatments and current DSM-IV PDs in a clinical population in China, Shanghai. Method One thousand four hundred two subjects were randomly sampled from the Shanghai Psychological Counselling Centre. PDs were assessed using the Personality Diagnostic Questionnaire, Fourth Edition Plus. Participants were also interviewed using the Structured Clinical Interview for DSM-IV axis II. The Child Trauma Questionnaire (CTQ) was used to assess childhood maltreatment in 5 domains (emotional abuse, physical abuse, sexual abuse, emotional neglect, and physical neglect). Results According to Pearson correlations, childhood maltreatment had a strong association with most PDs. Subsequently, using partial correlations, significant relationships were also demonstrated between cluster B PDs and all the traumatic factors except physical neglect. A strongest positive correlation was found between cluster B PD and CTQ total scores ( r = .312, P χ 2 = 34.864, P χ 2 = 14.996, P χ 2 = 9.211, P χ 2 = 17.987, P Conclusion Early traumatic experiences are strongly related to the development of PDs. The effects of childhood maltreatment in the 3 clusters of PDs are different. Childhood trauma has the most significant impact on cluster B PD.


title: Synthesis and characterization of band gap-reduced ZnO:N and ZnO:(Al,N) films for photoelectrochemical water splitting

abstract: ZnO thin films with significantly reduced band gaps were synthesized by dopingN and codoping Al and N at 100 C. All the films were synthesized by radiofrequencymagnetron sputtering on F-doped tin-oxide-coated glass. We found that codoped ZnO:(Al,N) thin films exhibited significantly enhanced crystallinity compared with ZnO dopedsolely with N, ZnO:N, at the same growth conditions. Furthermore, annealed ZnO:(Al,N)thin films exhibited enhanced N incorporation over ZnO:N films. As a result, ZnO:(Al,N)films exhibited better photocurrents than ZnO:N films grown with pure N doping,suggesting that charge-compensated donor–acceptor codoping could be a potentialmethod for band gap reduction of wide-band gap oxide materials to improve theirphotoelectrochemical performance.I. INTRODUCTIONTransition-metal oxides are potential candidates forphotoelectrochemical (PEC) H


title: Touching the void: direct-touch interaction for intangible displays

abstract: In this paper, we explore the challenges in applying and investigate methodologies to improve direct-touch interaction on intangible displays. Direct-touch interaction simplifies object manipulation, because it combines the input and display into a single integrated interface. While traditional tangible display-based direct-touch technology is commonplace, similar direct-touch interaction within an intangible display paradigm presents many challenges. Given the lack of tactile feedback, direct-touch interaction on an intangible display may show poor performance even on the simplest of target acquisition tasks. In order to study this problem, we have created a prototype of an intangible display. In the initial study, we collected user discrepancy data corresponding to the interpretation of 3D location of targets shown on our intangible display. The result showed that participants performed poorly in determining the z-coordinate of the targets and were imprecise in their execution of screen touches within the system. Thirty percent of positioning operations showed errors larger than 30mm from the actual surface. This finding triggered our interest to design a second study, in which we quantified task time in the presence of visual and audio feedback. The pseudo-shadow visual feedback was shown to be helpful both in improving user performance and satisfaction.


title: The evolving role of natural products in drug discovery

abstract: Natural products and their derivatives have historically been invaluable as a source of therapeutic agents. However, in the past decade, research into natural products in the pharmaceutical industry has declined, owing to issues such as the lack of compatibility of traditional natural-product extract libraries with high-throughput screening. However, as discussed in this review, recent technological advances that help to address these issues, coupled with unrealized expectations from current lead-generation strategies, have led to a renewed interest in natural products in drug discovery.


Real-Time Fire Detection for Video Surveillance Applications Using a Combination of Experts Based On Color , Shape and Motion