title: Model predictive control for dynamic footstep adjustment using the divergent component of motion

abstract: This paper presents an extension of previous model predictive control (MPC) schemes to the stabilization of the time-varying divergent component of motion (DCM). To address the control authority limitations caused by fixed footholds, the step positions and rotations are treated as control inputs, allowing the generation and execution of stable walking motions, both at high speeds and in the face of disturbances. Rotation approximations are handled by applying a mixed-integer program, which, when combined with the use of the time-varying DCM to account for the effects of height changes, improve the versatility of MPC. Simulation results of fast walking and step recovery with the ESCHER humanoid demonstrate the effectiveness of this approach.


title: Thighrim and Calf-Life: A Study of the Conversion of Off-the-Shelf Video Games into Exergames

abstract: Exergames are a fun and engaging way to participate in physical activity. Exergame users consistently require new content to maintain interest in the activity. One way to provide users with high quality content with minimal development work is to convert existing off-the-shelf digital games into exergames by using the game's "modding" interface. To explore the potential of converted exergames for inspiring high exertion levels we performed a conversion on two popular games: Half-Life 2 and The Elder Scrolls V: Skyrim. The conversions were performed in two stages. The first stage mimics existing conversion techniques and a second stage provides added incentive for players to reach higher exertion levels. A study of 18 participants found that the resulting games support anti-sedentary levels of exertion while falling slightly below national recommendations for cardiovascular exercise. Adding exercise to the games did not affect players' enjoyment.


title: Adapting recommender systems to the requirements of personal health record systems

abstract: In the future many people in industrialized countries will manage their personal health data electronically in centralized, reliable and trusted repositories - so-called personal health record systems (PHR). At this stage PHR systems still fail to satisfy the individual medical information needs of their users. Personalized recommendations could solve this problem. A first approach of integrating recommender system (RS) methodology into personal health records - termed health recommender system (HRS) - is presented. By exploitation of existing semantic networks like Wikipedia a health graph data structure is obtained. The data kept within such a graph represent health related concepts and are used to compute semantic distances among pairs of such concepts. A ranking procedure based on the health graph is outlined which enables a match between entries of a PHR system and health information artifacts. This way a PHR user will obtain individualized health information he might be interested in.


title: Surface-acoustic-wave devices for communications

abstract: Surface-wave components have demonstrated performance capabilities in the VHF/UHF range which are available with no other filtering technique. The major advantages of the filters are their small size, high reliability, low cost, high Q, good reproducibility, temperature stability, wide dynamic range, and linear phase response, as well as special characteristics relevant to specific applications. Surface-acousticwave (SAW) devices are used for bandpass filtering [1], matched filtering for radar pulse compression [2], [3] or spread-spectrum signal processing [4], delay lines [5], and frequency control elements [3], [6]. Within the context of communication systems' applications, this paper reviews the state of the art for SAW components from high-performance fixed-tuned devices to tunable or programmable filters. Major technological advances discussed include unidirectional SAW filters [7] which eliminate bidirectionality loss and simultaneously suppress in-band spurious responses to achieve an insertion loss below 1 dB with a 0.04-dB peak-to-peak ripple. A novel surface-wave sub-system [8] which offers revolutionary signal processing functions through transform processing is also described.


title: Dental-gingival remodeling with BOPT no-prep veneers

abstract: Recent years have seen increasing demand for treatments aimed at improving dental esthetics. In this context, both patients and dentists prefer to preserve dental structures as far as possible; thanks to technological advances, especially in adhesive dentistry, new materials and minimally invasive techniques such as “no-prep” (no preparation) veneers have made this possible. Nevertheless, no-prep veneers have specific indications and suffer certain disadvantages. ::: Objectives: This clinical case describes the rehabilitation of the upper anterior region by means of no-prep veneers, with BOPT (Biologically Oriented Preparation Technique) cervical margins. The patient had requested an aesthetic treatment to improve irregularities of the gingival margins associated with the presence of diastemata resulting from microdontia.


title: Assessing the relationships between nurse work hours/overtime and nurse and patient outcomes: Systematic literature review

abstract: Abstract Background The effects of work hours/overtime on nurse and patient outcomes and specific components of work hours (per shift and per week) and overtime on these effects have not been systematically examined. Purpose The purpose of this review was to systematically evaluate the effect of nurse overtime and long work hours on nurse and patient outcomes. Methods An online search of six electronic bibliographic databases was conducted for research published from 2000 to 2013. Discussion Twenty-one nurse outcome measures and 19 patient outcome measures were found in relationships with work hours and overtime. A total of 67 relationships to nurse outcomes and 41 relationships to patient outcomes were examined. Conclusions The findings of this review suggested that evidence supporting positive relationships between working long hours and adverse outcomes to the nurses is strong. However, to make a conclusion of the positive relationship between long work hours and adverse patient outcomes, more evidence is needed.


A Survey on Hate Speech Detection using Natural Language Processing


title: Sikolohiyang Pilipino (Filipino psychology): A legacy of Virgilio G. Enriquez

abstract: Sikolohiyang Pilipino(Filipino psychology) refers to the psychology born out of the experience, thought and orientation of the Filipinos, based on the full use of Filipino culture and language. The approach is one of ''indigenization from within'' whereby the theoretical framework and methodology emerge from the experiences of the people from the indigenous culture. It is based on assessing historical and socio-cultural realities, understanding the local language, unraveling Filipino characteristics, and explaining them through the eyes of the native Filipino. Among the outcomes are: a body of knowledge including indigenous concepts, development of indigenous research methods and indigenous personality testing, new directions in teaching psychology, and an active participation in organisations among Filipino psychologists and social scientists, both in the Philippines and overseas.


title: Developing a blueprint for cultural competence education at Penn.

abstract: This article describes the structure, process, and outcomes of developing a blueprint for integration of cultural competence education into the curriculum at the University of Pennsylvania, School of Nursing. The overarching framework of Kotter (1995) on leading change and organizational transformation was used as a guide for evaluation of faculty efforts. Within the setting of a research-intensive university, the process consisted of implementing a series of action steps which included appointment of a Director of Diversity Affairs, selection of a Master Teachers Taskforce on Cultural Diversity as catalysts for change; conduction of intensive faculty development programs, dissemination of information about cultural competence education, and use of innovative teaching approaches and student participation in curriculum activities. In addition, a Blueprint for Integration of Cultural Competence in the Curriculum (BICCC) was developed and used as the instrument for faculty surveys for 2 consecutive academic years. Faculty survey findings showed a substantial increase in the number of courses integrating cultural competence content in the programs of study. Successful outcomes of the Penn initiative were due to administrative and faculty support, utilization of a Director of Diversity Affairs, innovative work of the Master Teachers Taskforce on Cultural Diversity, faculty development initiatives, and development of the BICCC as a guiding framework for identifying areas of needed curricular change.


title: Transient behavior of heat transport in a thermal switch

abstract: We study the time-dependent transport of heat in a nanoscale thermal switch. The switch consists of left and right leads that are initially uncoupled. During switch-on the coupling between the leads is abruptly turned on. We use the nonequilibrium Green's function formalism and numerically solve the constructed Dyson equation to determine the nonperturbative heat current. At the transient regime we find that the current initially flows simultaneously into both of the leads and then afterwards oscillates between flowing into and out of the leads. At later times the oscillations decay away and the current settles into flowing from the hotter to the colder lead. We find the transient behavior to be influenced by the extra energy added during switch-on. Such a transient behavior also exists even when there is no temperature difference between the leads. The current at the long-time limit approaches the steady-state value independently calculated from the Landauer formula.


title: Extensive and mutilating craniofacial trauma involving defleshing and decapitation: unusual features of fatal dog attacks in the young.

abstract: Four cases of fatal dog attacks are reported in 3 children aged 6, 10, and 11 years and in an infant aged 3 weeks. The cases were all characterized by extensive and mutilative stripping of soft tissues from the face and scalp, progressing to decapitation in the infant. The attacks were highly focused, involving 2 dogs in all but 1 case, with the area of trauma localized to the craniofacial region. The injuries resembled those found after postmortem animal depredation. The involvement of more than 1 dog may account for the severity of the injuries due to "pack" behavior. Deaths were due to exsanguination, air embolism, and decapitation. Necropsy examination of the attacking dogs revealed tissues from the victims in 2 of the animals' stomachs. These cases demonstrate the vulnerability of infants and young children to fatal dog attacks, with an unusual concentration of severe injuries to the head regions. Necropsy of the canine assailant, with collaboration between pathologists and veterinarians, is an important part of such investigations as it may provide information helping to establish the identity and ownership of the animal, along with trace evidence confirming that the dog was involved in the attack.


title: Autonomous indoor object tracking with the Parrot AR.Drone

abstract: This article presents an image-based visual servoing system for indoor visual tracking of 3D moving objects by an Unmanned Aerial Vehicle. This system autonomously follows a 3D moving target object, maintaining it with a fixed distance and centered on its image plane. The initial setup is tested in a detailed simulation environment. The system is then validated on flights in indoor scenarios using the Parrot AR.Drone and the CMT tracker, demonstrating the robustness of the system to differences in object features, environmental clutter, and target trajectory. The obtained results indicate that the proposed system is suitable for complex controls task, such object surveillance and pursuit.


title: Reconfigurable Pipelined 2D Convolvers for Fast Digital Signal Processing

abstract: In order to make software applications simpler to write and easier to maintain, a software digital signal-processing library that performs essential signal- and image-processing functions is an important part of every digital signal processor (DSP) developer's toolset. In general, such a library provides high-level interface and mechanisms, therefore, developers only need to know how to use algorithms, not the details of how they work. Complex signal transformations then become function calls, e.g., C-callable functions. Considering the two-dimensional (2-D) convolver function as an example of great significance for DSP's, this paper proposes to replace this software function by an emulation on a field-programmable gate array (FPGA) initially configured by software programming. Therefore, the exploration of the 2-D convolver's design space will provide guidelines for the development of a library of DSP-oriented hardware configurations intended to significantly speed up the performance of general DSP processors. Based on the specific convolver, and considering operators supported in the library as hardware accelerators, a series of tradeoffs for efficiently exploiting the bandwidth between the general-purpose DSP and accelerators are proposed. In terms of implementation, this paper explores the performance and architectural tradeoffs involved in the design of an FPGA-based 2-D convolution coprocessor for the TMS320C40 DSP microprocessor available from Texas Instruments Incorporated. However, the proposed concept is not limited to a particular processor.


Male and female cells: same stress, different response


title: A minimax algorithm better than alpha-beta?

abstract: Abstract An algorithm based on state space search is introduced for computing the minimax value of game trees. The new algorithm SSS∗ is shown to be more efficient than α-s in the sense that SSS∗ never evaluates a node that α-s can ignore. Moreover, for practical distributions of tip node values, SSS∗ can expect to do strictly better than α-s in terms of average number of nodes explored. In order to be more informed than α-s, SSS∗ sinks paths in parallel across the full breadth of the game tree. The penalty for maintaining these alternate search paths is a large increase in storage requirement relative to α-s. Some execution time data is given which indicates that in some cases the tradeoff of storage for execution time may be favorable to SSS∗.


Spatialized Normal Cone Hierarchies


title: Web Accessibility Evaluation

abstract: Web accessibility evaluation is a broad field that combines different disciplines and skills. It encompasses technical aspects such as the assessment of conformance to standards and guidelines, as well as non-technical aspects such as the involvement of end-users during the evaluation process. Since Web accessibility is a qualitative and experiential measure rather than a quantitative and concrete property, the evaluation approaches need to include different techniques and maintain flexibility and adaptability toward different situations. At the same time, evaluation approaches need to be robust and reliable so that they can be effective. This chapter explores some of the techniques and strategies to evaluate the accessibility of Web content for people with disabilities. It highlights some of the common approaches to carry out and manage evaluation processes rather than list out individual steps for evaluating Web content. This chapter also provides an outlook to some of the future directions in which the field seems to be heading, and outlines some opportunities for research and development.


title: Aggressive 3-D collision avoidance for high-speed navigation

abstract: Autonomous robot navigation through unknown, cluttered environments at high-speeds is still an open problem. Quadrotor platforms with this capability have only begun to emerge with the advancements in light-weight, small form factor sensing and computing. Many of the existing platforms, however, require excessive computation time to perform collision avoidance, which ultimately limits the vehicle's top speed. This work presents an efficient perception and planning approach that significantly reduces the computation time by using instantaneous perception data for collision avoidance. Minimum-time, state and input constrained motion primitives are generated by sampling terminal states until a collision-free path is found. The worst case performance of the Triple Integrator Planner (TIP) is nearly an order of magnitude faster than the state-of-the-art. Experimental results demonstrate the algorithm's ability to plan and execute aggressive collision avoidance maneuvers in highly cluttered environments.


title: Research on question classification for Automatic Question Answering

abstract: Automatic Question Answering (QA) is a hot topic in both Natural Language Processing (NLP) and Information Retrieval (IR). And question classification is the key step of a successful automatic QA system. In this paper, an SVM-based approach is firstly proposed as our baseline system. Then two additional features, i.e., top-words and dependency relations, are introduced to improve the performance of our baseline system. Experiments on the UIUC corpus show that the introduced features can improve our baseline system significantly. In comparison with the state-of-the-art system, our proposed approach also achieves better performance.


title: DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents

abstract: We introduce a Deep Stochastic IOC RNN Encoder-decoder framework, DESIRE, for the task of future predictions of multiple interacting agents in dynamic scenes. DESIRE effectively predicts future locations of objects in multiple scenes by 1) accounting for the multi-modal nature of the future prediction (i.e., given the same context, future may vary), 2) foreseeing the potential future outcomes and make a strategic prediction based on that, and 3) reasoning not only from the past motion history, but also from the scene context as well as the interactions among the agents. DESIRE achieves these in a single end-to-end trainable neural network model, while being computationally efficient. The model first obtains a diverse set of hypothetical future prediction samples employing a conditional variational auto-encoder, which are ranked and refined by the following RNN scoring-regression module. Samples are scored by accounting for accumulated future rewards, which enables better long-term strategic decisions similar to IOC frameworks. An RNN scene context fusion module jointly captures past motion histories, the semantic scene context and interactions among multiple agents. A feedback mechanism iterates over the ranking and refinement to further boost the prediction accuracy. We evaluate our model on two publicly available datasets: KITTI and Stanford Drone Dataset. Our experiments show that the proposed model significantly improves the prediction accuracy compared to other baseline methods.


title: Semiconducting black phosphorus: synthesis, transport properties and electronic applications

abstract: Phosphorus is one of the most abundant elements preserved in earth, and it comprises a fraction of ∼0.1% of the earth crust. In general, phosphorus has several allotropes, and the two most commonly seen allotropes, i.e. white and red phosphorus, are widely used in explosives and safety matches. In addition, black phosphorus, though rarely mentioned, is a layered semiconductor and has great potential in optical and electronic applications. Remarkably, this layered material can be reduced to one single atomic layer in the vertical direction owing to the van der Waals structure, and is known as phosphorene, in which the physical properties can be tremendously different from its bulk counterpart. In this review article, we trace back to the research history on black phosphorus of over 100 years from the synthesis to material properties, and extend the topic from black phosphorus to phosphorene. The physical and transport properties are highlighted for further applications in electronic and optoelectronics devices.


Fundamentals of Parameterized Complexity


title: Stabilizing Training of Generative Adversarial Networks through Regularization

abstract: Deep generative models based on Generative Adversarial Networks (GANs) have demonstrated impressive sample quality but in order to work they require a careful choice of architecture, parameter initialization, and selection of hyper-parameters. This fragility is in part due to a dimensional mismatch or non-overlapping support between the model distribution and the data distribution, causing their density ratio and the associated f -divergence to be undefined. We overcome this fundamental limitation and propose a new regularization approach with low computational cost that yields a stable GAN training procedure. We demonstrate the effectiveness of this regularizer accross several architectures trained on common benchmark image generation tasks. Our regularization turns GAN models into reliable building blocks for deep learning.


title: Word similarity computation based on HowNet

abstract: Word similarity computation has important applications in many areas, such as natural language processing, intelligence information retrieval, document clustering, automatic answering, word sense disambiguation, machine translation etc. This article makes an intensive study of word similarity computation based on HowNet and the word similarity is computed in three steps: (1) computes sememes similarity, (2) computes concepts similarity using the weight sum method of sememes similarity, (3) takes the maximum similarity of concepts as words similarity. This article mainly introduces numbering the sememes and presents a more accurate method of decomposing the concept description type. The experiment shows that the algorithm of word similarity presented in this article is simple and feasible, and the computed results are relatively consistent with awareness.


title: DPNE: Differentially Private Network Embedding

abstract: Learning the low-dimensional representations of the vertices in a network can help users understand the network structure and perform other data mining tasks efficiently. Various network embedding approaches such as DeepWalk and LINE have been developed recently. However, how to protect the individual privacy in network embedding has not been exploited. It is challenging to achieve high utility as the sensitivity of stochastic gradients in random walks and that of edge sampling are very high, thus incurring high utility loss when applying Laplace mechanism and exponential mechanism to achieve differential privacy. In this paper, we develop a differentially private network embedding method (DPNE). In this method, we leverage the recent theoretical findings that network embedding methods such as DeepWalk and LINE are equivalent to factorization of some matrices derived from the adjacency matrix of the original network and apply objective perturbation on the objective function of matrix factorization. We evaluate the learned representations by our DPNE from three different real world datasets on two data mining tasks: vertex classification and link prediction. Experiment results show the effectiveness of DPNE. To our best knowledge, this is the first work on how to preserve differential privacy in network embedding.


title: An optimized analog layout for a Low Power 3-bit flash type ADC modified with the CMOS inverter based comparator designs

abstract: This paper introduces a Low Power 3-bit flash type ADC (Analog-to-Digital Converter) where the conventional comparators have been replaced with the CMOS inverter based comparator designs. The reported structure of the ADC is designed using 180nm technology and it consumes 130.9 µWatt of average power while operating with an input frequency (f IN ) of 30MHz, and a supply voltage of 1.8 Volt. Moreover, with the aim of increasing the design efficiency, an optimized analog layout, which occupies an area of 0.068mm2, has been presented.


title: Automatic Question Generation system

abstract: The process of automating the question generation consists of many tasks. Selecting the target content (what to ask), question type (who, why, how) and actual question generation are the major issue of Automatic Question Generation. Certain definitions retrieved is available in Wikipedia either directly or is the outcome of executing set of sub queries for each key phrase categories The problem in the existing system is that some of the definition sentences which are taken out from Wikipedia were implicit. The proposed system overcomes the problems by using Supervised Learning Approach, Naive Bayes method. It also extends its work to use Summarization, Noun Filtering and Question Generation in the aim of generating semantically correct questions.


title: A Screening Questionnaire for Asperger Syndrome and Other High-Functioning Autism Spectrum Disorders in School Age Children

abstract: The high-functioning Autism Spectrum Screening Questionnaire (ASSQ) is a 27-item checklist for completion by lay informants when assessing symptoms characteristic of Asperger syndrome and other high-functioning autism spectrum disorders in children and adolescents with normal intelligence or mild mental retardation. Data for parent and teacher ratings in a clinical sample are presented along with various measures of reliability and validity. Optimal cutoff scores were estimated, using Receiver Operating Characteristic analysis. Findings indicate that the ASSQ is a useful brief screening device for the identification of autism spectrum disorders in clinical settings.


title: Vocsyl: visualizing syllable production for children with ASD and speech delays

abstract: Communication disorders occur across the lifespan and encompass a wide range of conditions that interfere with individuals' abilities to hear (e.g., hearing loss), speak (e.g., voice disorders; motor speech disorders), and/or use language (e.g., specific language impairment; aphasia) to meet their communication needs. Such disorders often compromise the social, recreational, emotional, educational, and vocational aspects of an individual's life. This research examines the development and implementation of new software that facilitates multi-syllabic speech production in children with autism and speech delays. The VocSyl software package utilizes a suite of audio visualizations that represent a myriad of audio features in abstract representations. The goal of these visualizations is to provide children with language impairments a new persistent modality in which to experience and practice speech-language skills.


A Survey of Partitional and Hierarchical Clustering Algorithms


title: Layout Decomposition Approaches for Double Patterning Lithography

abstract: In double patterning lithography (DPL) layout decomposition for 45 nm and below process nodes, two features must be assigned opposite colors (corresponding to different exposures) if their spacing is less than the minimum coloring spacing. However, there exist pattern configurations for which pattern features separated by less than the minimum coloring spacing cannot be assigned different colors. In such cases, DPL requires that a layout feature be split into two parts. We address this problem using two layout decomposition approaches based on a conflict graph. First, node splitting is performed at all feasible dividing points. Then, one approach detects conflict cycles in the graph which are unresolvable for DPL coloring, and determines the coloring solution for the remaining nodes using integer linear programming (ILP). The other approach, based on a different ILP problem formulation, deletes some edges in the graph to make it two-colorable, then finds the coloring solution in the new graph. We evaluate our methods on both real and artificial 45 nm test-cases. Experimental results show that our proposed layout decomposition approaches effectively decompose given layouts to satisfy the key goals of minimized line-ends and maximized overlap margin. There are no design rule violations in the final decomposed layout.


title: Internet of Things — Propagation modelling for precision agriculture applications

abstract: The ubiquitous deployment of IEEE 802.11 based Wireless Mesh Networks (WMNs) has facilitated non-real-time applications effectively to date. However the advancement in IEEE 802.15.4 Wireless Sensor Network (WSN) technology has dramatically increased the volume and range of applications available under the umbrella of the Internet of Things (IoTs). Such applications cross the divide of simple environmental data collectors to highly specific multimedia real-time applications demanding high Quality of Service (QoS) guarantees. However delivering QoS guarantees is difficult to achieve when deployed in a hostile environment such as that found in the Precision Agriculture (PA) industry. Difficulties in wireless propagation attenuation delivers an uncertainty to the network design phase. Therefore the motivation of this research is a requirement for real-time characterisation and derivation of suitable propagation models to support network design and deployment planning. In this work, empirical results were collected from a heterogeneous wireless deployment (the real-world ECOMESH test-bed) in a native woodland environment. We derive two empirical models which can be utilised in predicting attenuation network performance in two different scenarios. These models deliver the first phase of a Dynamic Network Management System for maintaining QoS guarantees in heterogeneous wireless networks.


title: Quality in use: Meeting user needs for quality

abstract: There is an increasing demand for software that matches real user needs in a working environment. The paper describes the new framework for software product quality developed for ISO/IEC 9126-1: internal quality (static properties of the code), external quality (behaviour of the software when it is executed) and quality in use (the extent to which the software meets the needs of the user). Quality in use is a broader view of the ergonomic concept of usability in ISO 9241-11. Achieving quality in use requires a user-centred design process which has cultural, strategic and technical implications.


title: A low-fat vegan diet and a conventional diabetes diet in the treatment of type 2 diabetes: a randomized, controlled, 74-wk clinical trial

abstract: BACKGROUND ::: Low-fat vegetarian and vegan diets are associated with weight loss, increased insulin sensitivity, and improved cardiovascular health. ::: ::: ::: OBJECTIVE ::: We compared the effects of a low-fat vegan diet and conventional diabetes diet recommendations on glycemia, weight, and plasma lipids. ::: ::: ::: DESIGN ::: Free-living individuals with type 2 diabetes were randomly assigned to a low-fat vegan diet (n = 49) or a diet following 2003 American Diabetes Association guidelines (conventional, n = 50) for 74 wk. Glycated hemoglobin (Hb A(1c)) and plasma lipids were assessed at weeks 0, 11, 22, 35, 48, 61, and 74. Weight was measured at weeks 0, 22, and 74. ::: ::: ::: RESULTS ::: Weight loss was significant within each diet group but not significantly different between groups (-4.4 kg in the vegan group and -3.0 kg in the conventional diet group, P = 0.25) and related significantly to Hb A(1c) changes (r = 0.50, P = 0.001). Hb A(1c) changes from baseline to 74 wk or last available values were -0.34 and -0.14 for vegan and conventional diets, respectively (P = 0.43). Hb A(1c) changes from baseline to last available value or last value before any medication adjustment were -0.40 and 0.01 for vegan and conventional diets, respectively (P = 0.03). In analyses before alterations in lipid-lowering medications, total cholesterol decreased by 20.4 and 6.8 mg/dL in the vegan and conventional diet groups, respectively (P = 0.01); LDL cholesterol decreased by 13.5 and 3.4 mg/dL in the vegan and conventional groups, respectively (P = 0.03). ::: ::: ::: CONCLUSIONS ::: Both diets were associated with sustained reductions in weight and plasma lipid concentrations. In an analysis controlling for medication changes, a low-fat vegan diet appeared to improve glycemia and plasma lipids more than did conventional diabetes diet recommendations. Whether the observed differences provide clinical benefit for the macro- or microvascular complications of diabetes remains to be established. This trial was registered at clinicaltrials.gov as NCT00276939.


title: Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization

abstract: In this paper, we propose a new clustering model, called DEeP Embedded Regularized ClusTering (DEPICT), which efficiently maps data into a discriminative embedding subspace and precisely predicts cluster assignments. DEPICT generally consists of a multinomial logistic regression function stacked on top of a multi-layer convolutional autoencoder. We define a clustering objective function using relative entropy (KL divergence) minimization, regularized by a prior for the frequency of cluster assignments. An alternating strategy is then derived to optimize the objective by updating parameters and estimating cluster assignments. Furthermore, we employ the reconstruction loss functions in our autoencoder, as a data-dependent regularization term, to prevent the deep embedding function from overfitting. In order to benefit from end-to-end optimization and eliminate the necessity for layer-wise pre-training, we introduce a joint learning framework to minimize the unified clustering and reconstruction loss functions together and train all network layers simultaneously. Experimental results indicate the superiority and faster running time of DEPICT in real-world clustering tasks, where no labeled data is available for hyper-parameter tuning.


title: Strategic roadmap for an early diagnosis of Alzheimer's disease based on biomarkers

abstract: The diagnosis of Alzheimer's disease can be improved by the use of biological measures. Biomarkers of functional impairment, neuronal loss, and protein deposition that can be assessed by neuroimaging (ie, MRI and PET) or CSF analysis are increasingly being used to diagnose Alzheimer's disease in research studies and specialist clinical settings. However, the validation of the clinical usefulness of these biomarkers is incomplete, and that is hampering reimbursement for these tests by health insurance providers, their widespread clinical implementation, and improvements in quality of health care. We have developed a strategic five-phase roadmap to foster the clinical validation of biomarkers in Alzheimer's disease, adapted from the approach for cancer biomarkers. Sufficient evidence of analytical validity (phase 1 of a structured framework adapted from oncology) is available for all biomarkers, but their clinical validity (phases 2 and 3) and clinical utility (phases 4 and 5) are incomplete. To complete these phases, research priorities include the standardisation of the readout of these assays and thresholds for normality, the evaluation of their performance in detecting early disease, the development of diagnostic algorithms comprising combinations of biomarkers, and the development of clinical guidelines for the use of biomarkers in qualified memory clinics.


title: PowerPlay: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problem

abstract: Most of computer science focuses on automatically solving given computational problems. I focus on automatically inventing or discovering problems in a way inspired by the playful behavior of animals and humans, to train a more and more general problem solver from scratch in an unsupervised fashion. Consider the infinite set of all computable descriptions of tasks with possibly computable solutions. The novel algorithmic framework POWERPLAY (2011) continually searches the space of possible pairs of new tasks and modifications of the current problem solver, until it finds a more powerful problem solver that provably solves all previously learned tasks plus the new one, while the unmodified predecessor does not. Wow-effects are achieved by continually making previously learned skills more efficient such that they require less time and space. New skills may (partially) re-use previously learned skills. POWERPLAY's search orders candidate pairs of tasks and solver modifications by their conditional computational (time & space) complexity, given the stored experience so far. The new task and its corresponding task-solving skill are those first found and validated. The computational costs of validating new tasks need not grow with task repertoire size. POWERPLAY's ongoing search for novelty keeps breaking the generalization abilities of its present solver. This is related to Goedel's sequence of increasingly powerful formal theories based on adding formerly unprovable statements to the axioms without affecting previously provable theorems. The continually increasing repertoire of problem solving procedures can be exploited by a parallel search for solutions to additional externally posed tasks. POWERPLAY may be viewed as a greedy but practical implementation of basic principles of creativity. A first experimental analysis can be found in separate papers [53,54].


title: Momentum Effect, Value Effect, Risk Premium and Predictability of Stock Returns - A Study on Indian Market

abstract: Efficient market hypothesis (EMH), one of the central pillars of modern financial theories, often fails to explain the ?financial anomalies?. One fatal challenge of EMH probably comes from the theoretical assumption of ?rational man?. According to EMH, the fully rational investor may change his demand for financial assets on the basis of available information. According to EMH, at any given point of time, the stock price should reflect all the available information, and predictability of stock returns should be impossible. However, the literature shows ample evidence of abnormal returns related to firm and market specific attributes. In financial literature, these variations are often termed as ?financial anomalies?. Within the framework of behavioural finance, there are research results that contain evidence on predictability of future stock market returns based on financial anomalies (Stanivuk et al., 2012). Value effect and momentum effect are the two prominent financial anomalies (Ho, 2012). This paper explores the predictability of Indian stock market returns using multiple discriminant analysis. Our result shows that the risk premium, momentum and value effect may have significant power for predicting the Indian stock market returns. The validity test of the model also corroborates the impact of financial anomalies over predictability of stock returns.


title: Datum: Managing Data Purchasing and Data Placement in a Geo-Distributed Data Market

abstract: This paper studies two design tasks faced by a geo-distributed cloud data market: which data to purchase data purchasing and where to place/replicate the data for delivery data placement. We show that the joint problem of data purchasing and data placement within a cloud data market can be viewed as a facility location problem and is thus NP-hard. However, we give a provably optimal algorithm for the case of a data market made up of a single data center and then generalize the structure from the single data center setting in order to develop a near-optimal, polynomial-time algorithm for a geo-distributed data market. The resulting design, $\mathsf {Datum}$ , decomposes the joint purchasing and placement problem into two subproblems, one for data purchasing and one for data placement, using a transformation of the underlying bandwidth costs. We show, via a case study, that $\mathsf {Datum}$ is near optimal within 1.6% in practical settings.


title: The Effects of Myofascial Release With Foam Rolling on Performance

abstract: AbstractHealey, KC, Hatfield, DL, Blanpied, P, Dorfman, LR, and Riebe, D. The effects of myofascial release with foam rolling on performance. J Strength Cond Res 28(1): 61–68, 2014—In the last decade, self-myofascial release has become an increasingly common modality to supplement traditional method


title: Research on Zero-Knowledge Proof Protocol

abstract: Zero-knowledge proof protocol is a critical component of cryptography, which in recent years has raised increasing concern of many scholars.Its application field is very extensive,and it has made breakthrough progress in many aspects,including mathematics and network safety and so on. This article launches the elaboration from the concept, nature, mathematics theory, general proof process of the zeroknowledge proof , focusing on the application research of polynomial function root, graph isomorphism, cloud storage service, RFID, proxy digital signature and identity authentication etc.Finally, the direction for further research is summed up.The systematic introduction to zero-knowledge proof protocol has important theoretical guidance and practical significance on attracting more scholars involved in the research as well as expanding application fields.


title: Hardware resource estimation for heterogeneous FPGA-based SoCs

abstract: The increasing complexity of recent System-on-Chip (SoC) designs introduces new challenges for design space exploration tools. In addition to the time-to-market challenge, designers need to estimate rapidly and accurately both performance and area occupation of complex and diverse applications. High-Level Synthesis (HLS) has been emerged as an attractive solution for designers to address these challenges in order to explore a large number of SoC configurations. In this paper, we target hybrid CPU-FPGA based SoCs. We propose a high-level area estimation tool based on an analytic model without requiring register-transfer level (RTL) implementations. This technique allows to estimate the required FPGA resources at the source code level to map an application to a hybrid CPU-FPGA system. The proposed model also enables a fast design exploration with different trade-offs through HLS optimization pragmas. Experimental results show that the proposed area analytic model provides an accurate estimation with a negligible error (less than 5+) compared to RTL implementations.


title: Taxonomies of workflow scheduling problem and techniques in the cloud

abstract: Scientific workflows, like other applications, benefit from the cloud computing, which offers access to virtually unlimited resources provisioned elastically on demand. In order to efficiently execute a workflow in the cloud, scheduling is required to address many new aspects introduced by cloud resource provisioning. In the last few years, many techniques have been proposed to tackle different cloud environments enabled by the flexible nature of the cloud, leading to the techniques of different designs. In this paper, taxonomies of cloud workflow scheduling problem and techniques are proposed based on analytical review. We identify and explain the aspects and classifications unique to workflow scheduling in the cloud environment in three categories, namely, scheduling process, task and resource. Lastly, review of several scheduling techniques are included and classified onto the proposed taxonomies. We hope that our taxonomies serve as a stepping stone for those entering this research area and for further development of scheduling technique. We propose taxonomies of cloud workflow scheduling problem and techniques.Aspects and classifications unique to cloud workflow scheduling are identified.Several techniques are reviewed and classified based on the proposed taxonomies.Some issues of future concern in cloud workflow scheduling are discussed.


title: Single document extractive text summarization using Genetic Algorithms

abstract: This paper presents an extraction based single document text summarization technique using Genetic Algorithms. A given document is represented as a weighted Directed Acyclic Graph. A fitness function is defined to mathematically express the quality of a summary in terms of some desired properties of a summary, such as, topic relation, cohesion and readability. Genetic Algorithm is designed to maximize this fitness function, and get the corresponding summary by extracting the most important sentences. Results are compared with a couple of other existing text summarization methods keeping the DUC2002 data as benchmark, and using the precision-recall evaluation technique. The initial results obtained seem promising and encouraging for future work in this area.


title: Ensembles of nested dichotomies for multi-class problems

abstract: Nested dichotomies are a standard statistical technique for tackling certain polytomous classification problems with logistic regression. They can be represented as binary trees that recursively split a multi-class classification task into a system of dichotomies and provide a statistically sound way of applying two-class learning algorithms to multi-class problems (assuming these algorithms generate class probability estimates). However, there are usually many candidate trees for a given problem and in the standard approach the choice of a particular tree is based on domain knowledge that may not be available in practice. An alternative is to treat every system of nested dichotomies as equally likely and to form an ensemble classifier based on this assumption. We show that this approach produces more accurate classifications than applying C4.5 and logistic regression directly to multi-class problems. Our results also show that ensembles of nested dichotomies produce more accurate classifiers than pairwise classification if both techniques are used with C4.5, and comparable results for logistic regression. Compared to error-correcting output codes, they are preferable if logistic regression is used, and comparable in the case of C4.5. An additional benefit is that they generate class probability estimates. Consequently they appear to be a good general-purpose method for applying binary classifiers to multi-class problems.


title: Corpus-Guided Sentence Generation of Natural Images

abstract: We propose a sentence generation strategy that describes images by predicting the most likely nouns, verbs, scenes and prepositions that make up the core sentence structure. The input are initial noisy estimates of the objects and scenes detected in the image using state of the art trained detectors. As predicting actions from still images directly is unreliable, we use a language model trained from the English Gigaword corpus to obtain their estimates; together with probabilities of co-located nouns, scenes and prepositions. We use these estimates as parameters on a HMM that models the sentence generation process, with hidden nodes as sentence components and image detections as the emissions. Experimental results show that our strategy of combining vision and language produces readable and descriptive sentences compared to naive strategies that use vision alone.


title: The algebra of connectors: structuring interaction in BIP

abstract: We provide an algebraic formalisation of connectors in BIP. These are used to structure interactions in a component-based system. A connector relates a set of typed ports. Types are used to describe different modes of synchronisation: rendezvous and broadcast, in particular. Connectors on a set of ports P are modelled as terms of the algebra AC(P), generated from P by using a binary fusion operator and a unary typing operator. Typing associates with terms (ports or connectors) synchronisation types - trigger or synchron - , which determine modes of synchronisation. Broadcast interactions are initiated by triggers. Rendezvous is a maximal interaction of a connector including only synchrons. The semantics of AC(P) associates with a connector the set of its interactions. It induces on connectors an equivalence relation which is not a congruence as it is not stable for fusion. We provide a number of properties of AC(P) used to symbolically simplify and handle connectors. We provide examples illustrating applications of AC(P), including a general component model encompassing synchrony, methods for incremental model decomposition, and efficient implementation by using symbolic techniques.


title: Conflict-Probability-Estimation-Based Overtaking for Intelligent Vehicles

abstract: Overtaking is a complex and hazardous driving maneuver for intelligent vehicles. When to initiate overtaking and how to complete overtaking are critical issues for an overtaking intelligent vehicle. We propose an overtaking control method based on the estimation of the conflict probability. This method uses the conflict probability as the safety indicator and completes overtaking by tracking a safe conflict probability. The conflict probability is estimated by the future relative position of intelligent vehicles, and the future relative position is estimated by using the dynamics models of the intelligent vehicles. The proposed method uses model predictive control to track a desired safe conflict probability and synthesizes decision making and control of the overtaking maneuver. The effectiveness of this method has been validated in different experimental configurations, and the effects of some parameters in this control method have also been investigated.


title: Transient Induced Gamma-Band Response in EEG as a Manifestation of Miniature Saccades

abstract: Summary The induced gamma-band EEG response (iGBR) recorded on the scalp is widely assumed to reflect synchronous neural oscillation associated with object representation, attention, memory, and consciousness. The most commonly reported EEG iGBR is a broadband transient increase in power at the gamma range ∼200–300 ms following stimulus onset. A conspicuous feature of this iGBR is the trial-to-trial poststimulus latency variability, which has been insufficiently addressed. Here, we show, using single-trial analysis of concomitant EEG and eye tracking, that this iGBR is tightly time locked to the onset of involuntary miniature eye movements and reflects a saccadic "spike potential." The time course of the iGBR is related to an increase in the rate of saccades following a period of poststimulus saccadic inhibition. Thus, whereas neuronal gamma-band oscillations were shown conclusively with other methods, the broadband transient iGBR recorded by scalp EEG reflects properties of miniature saccade dynamics rather than neuronal oscillations.


A Survey on Arabic Character Recognition


title: App Store, Marketplace, Play! An Analysis of Multi-Homing in Mobile Software Ecosystems

abstract: Multi-homing, a strategy where a developer is publishing products for multiple platforms, is studied in this paper using descriptive analysis. The data of over 850,000 applications and their developers were collected from Apple App Store, Google Play, and Microsoft's Windows Phone Marketplace. The developers publishing for several software ecosystems were then identified. The findings show that multi-homing is utilized by only a small set of developers. In addition, the applications available at several platforms do not seem to differ by the type or the popularity from single-homing applications.


title: Humor Modulates the Mesolimbic Reward Centers

abstract: Humor plays an essential role in many facets of human life including psychological, social, and somatic functioning. Recently, neuroimaging has been applied to this critical human attribute, shedding light on the affective, cognitive, and motor networks involved in humor processing. To date, however, researchers have failed to demonstrate the subcortical correlates of the most fundamental feature of humor-reward. In an effort to elucidate the neurobiological substrate that subserves the reward components of humor, we undertook a high-field (3 Tesla) event-related functional MRI study. Here we demonstrate that humor modulates activity in several cortical regions, and we present new evidence that humor engages a network of subcortical regions including the nucleus accumbens, a key component of the mesolimbic dopaminergic reward system. Further, the degree of humor intensity was positively correlated with BOLD signal intensity in these regions. Together, these findings offer new insight into the neural basis of salutary aspects of humor.


title: WristWhirl: One-handed Continuous Smartwatch Input using Wrist Gestures

abstract: We propose and study a new input modality, WristWhirl, that uses the wrist as an always-available joystick to perform one-handed continuous input on smartwatches. We explore the influence of the wrist's bio-mechanical properties for performing gestures to interact with a smartwatch, both while standing still and walking. Through a user study, we examine the impact of performing 8 distinct gestures (4 directional marks, and 4 free-form shapes) on the stability of the watch surface. Participants were able to perform directional marks using the wrist as a joystick at an average rate of half a second and free-form shapes at an average rate of approximately 1.5secs. The free-form shapes could be recognized by a $1 gesture recognizer with an accuracy of 93.8% and by three human inspectors with an accuracy of 85%. From these results, we designed and implemented a proof-of-concept device by augmenting the watchband using an array of proximity sensors, which can be used to draw gestures with high quality. Finally, we demonstrate a number of scenarios that benefit from one-handed continuous input on smartwatches using WristWhirl.


title: CAD of Waveguide Low-Pass Filters for Satellite Applications

abstract: Corrugated-waveguide low-pass filters are analysed by successive reflection coefficient transformations. The discontinuities are modelled by waveguide equivalent circuits, including interaction of higher order modes. The filter response is determined by TEm0-transfer functions (m=1,2...) each of which can be calculated independently. Implementing this method in an optimization routine allows complete computer-aided design according to given specifications.


title: Text comprehension, memory, and learning.

abstract: People are often able to reproduce a text quite well but are unable to use the information in the text for other purposes. Factors that help people to reproduce a text have been studied for some time. This article explores ways that enable people to learn from texts. Content overlap between a text and the reader's prior knowledge is identified as one factor, and methods are proposed to identify whether a text is suitable for readers with given background knowledge. For readers with low background knowledge, a text should be as coherent and explicit as possible to facilitate learning. However, data are presented to show that for readers with adequate background knowledge, texts with coherence gaps that stimulate constructive activities are in fact better for learning.


title: Boosting and Differential Privacy

abstract: Boosting is a general method for improving the accuracy of learning algorithms. We use boosting to construct improved {\em privacy-preserving synopses} of an input database. These are data structures that yield, for a given set $\Q$ of queries over an input database, reasonably accurate estimates of the responses to every query in~$\Q$, even when the number of queries is much larger than the number of rows in the database. Given a {\em base synopsis generator} that takes a distribution on $\Q$ and produces a ``weak'' synopsis that yields ``good'' answers for a majority of the weight in $\Q$, our {\em Boosting for Queries} algorithm obtains a synopsis that is good for all of~$\Q$. We ensure privacy for the rows of the database, but the boosting is performed on the {\em queries}. We also provide the first synopsis generators for arbitrary sets of arbitrary low-sensitivity queries, {\it i.e.}, queries whose answers do not vary much under the addition or deletion of a single row. In the execution of our algorithm certain tasks, each incurring some privacy loss, are performed many times. To analyze the cumulative privacy loss, we obtain an $O(\eps^2)$ bound on the {\em expected} privacy loss from a single $\eps$-\dfp{} mechanism. Combining this with evolution of confidence arguments from the literature, we get stronger bounds on the expected cumulative privacy loss due to multiple mechanisms, each of which provides $\eps$-differential privacy or one of its relaxations, and each of which operates on (potentially) different, adaptively chosen, databases.


Green infrastructure and health


title: Deep Reinforcement Learning for Dexterous Manipulation with Concept Networks

abstract: Deep reinforcement learning yields great results for a large array of problems, but models are generally retrained anew for each new problem to be solved. Prior learning and knowledge are difficult to incorporate when training new models, requiring increasingly longer training as problems become more complex. This is especially problematic for problems with sparse rewards. We provide a solution to these problems by introducing Concept Network Reinforcement Learning (CNRL), a framework which allows us to decompose problems using a multi-level hierarchy. Concepts in a concept network are reusable, and flexible enough to encapsulate feature extractors, skills, or other concept networks. With this hierarchical learning approach, deep reinforcement learning can be used to solve complex tasks in a modular way, through problem decomposition. We demonstrate the strength of CNRL by training a model to grasp a rectangular prism and precisely stack it on top of a cube using a gripper on a Kinova JACO arm, simulated in MuJoCo. Our experiments show that our use of hierarchy results in a 45x reduction in environment interactions compared to the state-of-the-art on this task.


title: Indian Face Age Database: A Database for Face Recognition with Age Variation

abstract: recognition with age variation is a challenging task for researchers. In this research paper a new face database is proposed. The proposed name of this database is Indian Face Age Database (IFAD). It consists of images of Indian celebrities. To enhance the unconstrained age invariant face recognition research, a more challenging Indian Face Age Database (IFAD) is proposed and introduced that has much more variability compared to any other age variation face database. The database consists of 3296 faces of 55 known Indian celebrities collected from online sources. In IFAD face detection is done by Viola Jones face detection algorithm. This database has different phases of life of celebrities from childhood to old age. IFAD is a huge, real-world face database has 55 subjects and each has more than 50 images per subject. All images have large variation in age parameter and also have variation in lighting, expression and pose variation. The main purpose of IFAD is to show age variation. In proposed IFAD images are manually selected from online sources resulted in high degree of variation in scale, pose, expression, illumination, age, occlusion, makeup which one could ever see in natural world. IFAD is the first face database that provides a detailed annotation in terms of age, pose, gender, expression, amount of occlusion, for each face which may help other face related applications. The script of IFAD is written in MATLAB. Keywordsprocessing, Feature extraction, Face recognition, Machine vision


title: Intense acts of violence during video game play make daily life aggression appear innocuous: A new mechanism why violent video games increase aggression.

abstract: Abstract Computer players often deny that playing violent video games makes them aggressive, which is in contrast to the findings of a recent comprehensive meta-analysis. The present research examines whether comparison processes between the players' intense acts of violence in a video game and their comparatively harmless aggressive behavior in daily life not only account for this apparent discrepancy but also underlie the effect of playing violent video games on aggressive behavior. In fact, two experiments reveal that playing a violent video game leads to a bias in the perception of what counts as aggressive, which in turn evokes aggressive behavior.


title: Spatially supervised recurrent convolutional neural networks for visual object tracking

abstract: In this paper, we develop a new approach of spatially supervised recurrent convolutional neural networks for visual object tracking. Our recurrent convolutional network exploits the history of locations as well as the distinctive visual features learned by the deep neural networks. Inspired by recent bounding box regression methods for object detection, we study the regression capability of Long Short-Term Memory (LSTM) in the temporal domain, and propose to concatenate high-level visual features produced by convolutional networks with region information. In contrast to existing deep learning based trackers that use binary classification for region candidates, we use regression for direct prediction of the tracking locations both at the convolutional layer and at the recurrent unit. Our extensive experimental results and performance comparison with state-of-the-art tracking methods on challenging benchmark video tracking datasets shows that our tracker is more accurate and robust while maintaining low computational cost. For most test video sequences, our method achieves the best tracking performance, often outperforms the second best by a large margin.


title: Demand Prediction and Placement Optimization for Electric Vehicle Charging Stations

abstract: Due to the environmental impact of fossil fuels and high variability in their prices, there is rising interest in adopting electric vehicles (EVs) by both individuals and governments. Despite the advances in vehicle efficiency and battery capacity, a key hurdle is the inherent interdependence between EV adoption and charging station deployment–EV adoption (and hence, charging demand) increases with the availability of charging stations (operated by service providers) and vice versa. Thus, effective placement of charging stations plays a key role in EV adoption. In the placement problem, given a set of candidate sites, an optimal subset needs to be selected with respect to the concerns of both (a) the charging station service provider, such as the demand at the candidate sites and the budget for deployment, and (b) the EV user, such as charging station reachability and short waiting times at the station. This work addresses these concerns, making the following three novel contributions: (i) a supervised multi-view learning framework using Canonical Correlation Analysis (CCA) for demand prediction at candidate sites, using multiple datasets such as points of interest information, traffic density, and the historical usage at existing charging stations; (ii) a “mixed-packing-and-covering” optimization framework that models competing concerns of the service provider and EV users, which is also extended to optimize government grant allocation to multiple service providers; (iii) an iterative heuristic to solve these problems by alternately invoking knapsack and set cover algorithms. The performance of the demand prediction model and the placement optimization heuristic are evaluated using real world data. In most cases, and especially when budget is scarce, our heuristic achieves an improvement of 10-20% over a naive heuristic, both in finding feasible solutions and maximizing demand.


title: Distributed e-voting using the Smart Card Web Server

abstract: Voting in elections is the basis of democracy, but citizens may not be able or willing to go to polling stations to vote on election days. Remote e-voting via the Internet provides the convenience of voting on the voter's own computer or mobile device, but Internet voting systems are vulnerable to many common attacks, affecting the integrity of an election. Distributing the processing of votes over many web servers installed in tamper-resistant, secure environments can improve security: this is possible by using the Smart Card Web Server (SCWS) on a mobile phone Subscriber Identity Module (SIM). This paper proposes a generic model for a voting application installed in the SIM/SCWS, which uses standardised Mobile Network Operator (MNO) management procedures to communicate (via HTTPs) with a voting authority to vote. The generic SCWS voting model is then used with the e-voting system Pret a Voter. A preliminary security analysis of the proposal is carried out, and further research areas are identified. As the SCWS voting application is used in a distributed processing architecture, e-voting security is enhanced because to compromise an election, an attacker must target many individual mobile devices rather than a centralised web server.


title: HERB: a home exploring robotic butler

abstract: We describe the architecture, algorithms, and experiments with HERB, an autonomous mobile manipulator that performs useful manipulation tasks in the home. We present new algorithms for searching for objects, learning to navigate in cluttered dynamic indoor scenes, recognizing and registering objects accurately in high clutter using vision, manipulating doors and other constrained objects using caging grasps, grasp planning and execution in clutter, and manipulation on pose and torque constraint manifolds. We also present numerous severe real-world test results from the integration of these algorithms into a single mobile manipulator.


title: A Multirelational Social Network Analysis of an Online Health Community for Smoking Cessation

abstract: Background: Online health communities (OHCs) provide a convenient and commonly used way for people to connect around shared health experiences, exchange information, and receive social support. Users often interact with peers via multiple communication methods, forming a multirelational social network. Use of OHCs is common among smokers, but to date, there have been no studies on users’ online interactions via different means of online communications and how such interactions are related to smoking cessation. Such information can be retrieved in multirelational social networks and could be useful in the design and management of OHCs. Objective: To examine the social network structure of an OHC for smoking cessation using a multirelational approach, and to explore links between subnetwork position (ie, centrality) and smoking abstinence. Methods: We used NetworkX to construct 4 subnetworks based on users’ interactions via blogs, group discussions, message boards, and private messages. We illustrated topological properties of each subnetwork, including its degree distribution, density, and connectedness, and compared similarities among these subnetworks by correlating node centrality and measuring edge overlap. We also investigated coevolution dynamics of this multirelational network by analyzing tie formation sequences across subnetworks. In a subset of users who participated in a randomized, smoking cessation treatment trial, we conducted user profiling based on users’ centralities in the 4 subnetworks and identified user groups using clustering techniques. We further examined 30-day smoking abstinence at 3 months postenrollment in relation to users’ centralities in the 4 subnetworks. Results: The 4 subnetworks have different topological characteristics, with message board having the most nodes (36,536) and group discussion having the highest network density (4.35×10−3). Blog and message board subnetworks had the most similar structures with an in-degree correlation of .45, out-degree correlation of .55, and Jaccard coefficient of .23 for edge overlap. A new tie in the group discussion subnetwork had the lowest probability of triggering subsequent ties among the same two users in other subnetworks: 6.33% (54,142/855,893) for 2-tie sequences and 2.13% (18,207/855,893) for 3-tie sequences. Users’ centralities varied across the 4 subnetworks. Among a subset of users enrolled in a randomized trial, those with higher centralities across subnetworks generally had higher abstinence rates, although high centrality in the group discussion subnetwork was not associated with higher abstinence rates. Conclusions: A multirelational approach revealed insights that could not be obtained by analyzing the aggregated network alone, such as the ineffectiveness of group discussions in triggering social ties of other types, the advantage of blogs, message boards, and private messages in leading to subsequent social ties of other types, and the weak connection between one’s centrality in the group discussion subnetwork and smoking abstinence. These insights have implications for the design and management of online social networks for smoking cessation. [J Med Internet Res 2016;18(8):e233]


title: Artificial Intelligence in Medicine and Cardiac Imaging: Harnessing Big Data and Advanced Computing to Provide Personalized Medical Diagnosis and Treatment

abstract: Although advances in information technology in the past decade have come in quantum leaps in nearly every aspect of our lives, they seem to be coming at a slower pace in the field of medicine. However, the implementation of electronic health records (EHR) in hospitals is increasing rapidly, accelerated by the meaningful use initiatives associated with the Center for Medicare & Medicaid Services EHR Incentive Programs. The transition to electronic medical records and availability of patient data has been associated with increases in the volume and complexity of patient information, as well as an increase in medical alerts, with resulting "alert fatigue" and increased expectations for rapid and accurate diagnosis and treatment. Unfortunately, these increased demands on health care providers create greater risk for diagnostic and therapeutic errors. In the near future, artificial intelligence (AI)/machine learning will likely assist physicians with differential diagnosis of disease, treatment options suggestions, and recommendations, and, in the case of medical imaging, with cues in image interpretation. Mining and advanced analysis of "big data" in health care provide the potential not only to perform "in silico" research but also to provide "real time" diagnostic and (potentially) therapeutic recommendations based on empirical data. "On demand" access to high-performance computing and large health care databases will support and sustain our ability to achieve personalized medicine. The IBM Jeopardy! Challenge, which pitted the best all-time human players against the Watson computer, captured the imagination of millions of people across the world and demonstrated the potential to apply AI approaches to a wide variety of subject matter, including medicine. The combination of AI, big data, and massively parallel computing offers the potential to create a revolutionary way of practicing evidence-based, personalized medicine.


title: A lightweight scripting engine for the Slocum Glider

abstract: Underwater Vehicle (AUV) capable of deployments lasting several weeks or months. The layered control architecture used by the vehicle is difficult to program and restrictive. As part of previous work we have developed a more flexible programming framework capable of performing dynamic feature tracking. However, the gliders new and more computationally capable Linux Single Board Computer (SBC) results in additional energy demands.


title: Nonlinear residual acoustic echo suppression for high levels of harmonic distortion

abstract: Linear adaptive filters are often used for acoustic echo cancellation (AEC) but sometimes fail to perform well in notebook computers and inexpensive telephony devices. Low-quality speakers and poorly-designed enclosures that produce vibrations often generate harmonic distortion, and this nonlinear effect degrades the performance of linear AEC algorithms considerably. In this work, we present a new AEC architecture that consists of a linear, subband adaptive AEC filter followed a nonlinear residual echo suppression (RES) stage specifically designed to address harmonic distortion. In addition to suppressing the residual echo in the primary subband, the proposed model also suppresses the residual echo in a window of bands surrounding the higher order harmonics. Results show considerable improvement over other proposed algorithms, and the new algorithm has much lower implementation costs compared to nonlinear AEC models based on Volterra filters and a previously proposed, nonlinear residual echo suppression algorithm.


title: Support Vector Ordinal Regression

abstract: In this letter, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution. The size of these optimization problems is linear in the number of training samples. The sequential minimal optimization algorithm is adapted for the resulting optimization problems; it is extremely easy to implement and scales efficiently as a quadratic function of the number of examples. The results of numerical experiments on some benchmark and real-world data sets, including applications of ordinal regression to information retrieval, verify the usefulness of these approaches.


title: Hearing Your Voice is Not Enough: An Articulatory Gesture Based Liveness Detection for Voice Authentication

abstract: Voice biometrics is drawing increasing attention as it is a promising alternative to legacy passwords for mobile authentication. Recently, a growing body of work shows that voice biometrics is vulnerable to spoofing through replay attacks, where an adversary tries to spoof voice authentication systems by using a pre-recorded voice sample collected from a genuine user. In this work, we propose VoiceGesture, a liveness detection system for replay attack detection on smartphones. It detects a live user by leveraging both the unique articulatory gesture of the user when speaking a passphrase and the mobile audio hardware advances. Specifically, our system re-uses the smartphone as a Doppler radar, which transmits a high frequency acoustic sound from the built-in speaker and listens to the reflections at the microphone when a user speaks a passphrase. The signal reflections due to user's articulatory gesture result in Doppler shifts, which are then analyzed for live user detection. VoiceGesture is practical as it requires neither cumbersome operations nor additional hardware but a speaker and a microphone that are commonly available on smartphones. Our experimental evaluation with 21 participants and different types of phones shows that it achieves over 99% detection accuracy at around 1% Equal Error Rate (EER). Results also show that it is robust to different phone placements and is able to work with different sampling frequencies.


title: Self-Driving Cars and the Urban Challenge

abstract: Self-driving cars have been a dream as long automobiles have existed. The automobile is ubiquitous in the developed world and is becoming so in the developing world. In 2007, the world's two largest automakers sold over 18 million vehicles worldwide. As we consider domains to which we can apply intelligent systems, the automotive industry stands out as having the most potential for impact.


title: Stable Nash Equilibria in the Gale-Shapley Matching Game

abstract: In this article we study the stable marriage game induced by the men-proposing Gale-Shapley algorithm. Our setting is standard: all the lists are complete and the matching mechanism is the men-proposing Gale-Shapley algorithm. It is well known that in this setting, men cannot cheat, but women can. In fact, Teo, Sethuraman and Tan \cite{TST01}, show that there is a polynomial time algorithm to obtain, for a given strategy (the set of all lists) $Q$ and a woman $w$, the best partner attainable by changing her list. However, what if the resulting matching is not stable with respect to $Q$? Obviously, such a matching would be vulnerable to further manipulation, but is not mentioned in \cite{TST01}. In this paper, we consider (safe) manipulation that implies a stable matching in a most general setting. Specifically, our goal is to decide for a given $Q$, if w can manipulate her list to obtain a strictly better partner with respect to the true strategy $P$ (which may be different from $Q$), and also the outcome is a stable matching for $P$.


title: Probabilistic Latent-Factor Database Models

abstract: We describe a general framework for modelling probabilistic databases using factorization approaches. The framework includes tensor-based approaches which have been very successful in modelling triple-oriented databases and also includes recently developed neural network models. We consider the case that the target variable models the existence of a tuple, a continuous quantity associated with a tuple, multi-class variables or count variables. We discuss appropriate cost functions with different parameterizations and optimization approaches. We argue that, in general, some combination of models leads to best predictive results. We present experimental results on the modelling of existential variables and count variables.


Temporal JSON Schema Versioning in the τJSchema Framework


title: Higher order MRF for foreground-background separation in multi-spectral images of historical manuscripts

abstract: Multi-spectral imaging for the analysis and preservation of ancient documents has gained high attention in recent years. While readability enhancement is based on the multi-spectral image corpus, foreground-background separation still relies mainly on gray level or color images. In this paper we propose a foreground-background separation algorithm designed for multi-spectral images. The main contribution is the simultaneously utilization of spectral and spatial features. While spectral features incorporate the spectral components of the multi-spectral images, the spatial features are based on stroke properties. Higher order Markov Random Fields enables an efficient way to combine both features. To solve higher order energy functions, we introduce a new message update rule in the well known belief propagation algorithm based on a higher order potential function.


title: UKP: Computing Semantic Textual Similarity by Combining Multiple Content Similarity Measures

abstract: We present the UKP system which performed best in the Semantic Textual Similarity (STS) task at SemEval-2012 in two out of three metrics. It uses a simple log-linear regression model, trained on the training data, to combine multiple text similarity measures of varying complexity. These range from simple character and word n-grams and common subsequences to complex features such as Explicit Semantic Analysis vector comparisons and aggregation of word similarity based on lexical-semantic resources. Further, we employ a lexical substitution system and statistical machine translation to add additional lexemes, which alleviates lexical gaps. Our final models, one per dataset, consist of a log-linear combination of about 20 features, out of the possible 300+ features implemented.


title: An alternative for Wang tiles: colored edges versus colored corners

abstract: In this article we revisit the concept of Wang tiles and introduce corner tiles, square tiles with colored corners. During past years, Wang tiles have become a valuable tool in computer graphics. Important applications of Wang tiles include texture synthesis, tile-based texture mapping, and generating Poisson disk distributions. Through their colored edges, Wang tiles enforce continuity with their direct neighbors. However, Wang tiles do not directly constrain their diagonal neighbors. This leads to continuity problems near tile corners, a problem commonly known as the corner problem. Corner tiles, on the other hand, do impose restrictions on their diagonal neighbors, and thus are not subject to the corner problem. In this article we show that previous applications of Wang tiles can also be done using corner tiles, but that corner tiles have distinct advantages for each of these applications. Compared to Wang tiles, corner tiles are easier to tile, textures synthesized with corner tiles contain more samples from the original texture, corner tiles reduce the required texture memory by a factor of two for tile-based texture mapping, and Poisson disk distributions generated with corner tiles have better spectral properties. Corner tiles result in cleaner, simpler, and more efficient applications.


title: Narcissism and Childhood Recollections: A Quantitative Test of Psychoanalytic Predictions

abstract: Different psychotherapeutic theories provide contradictory accounts of adult narcissism as the product of either parental coldness or excessive parental admiration during childhood. Yet, none of these theories has been tested systematically in a nonclinical sample. The authors compared four structural equation models predicting overt and covert narcissism among 120 United Kingdom adults. Both forms of narcissism were predicted by both recollections of parental coldness and recollections of excessive parental admiration. Moreover, a suppression relationship was detected between these predictors: The effects of each were stronger when modeled together than separately. These effects were found after controlling for working models of attachment; covert narcissism was predicted also by attachment anxiety. This combination of childhood experiences may help to explain the paradoxical combination of grandiosity and fragility in adult narcissism.


title: THE SCOPE AND TREATMENT OF THREATS IN ENDANGERED SPECIES RECOVERY PLANS

abstract: The recovery of threatened and endangered species is complicated by the number, severity, and tractability of the threats facing each species. We investigated the nature and the treatment of threats in recovery plans for 181 threatened and endangered species. We examined the types of threats facing species, as well as the degree to which threats were understood and addressed. We found that >85% of all species faced at least four out of nine distinct types of threats. The most common threats were those related to resource use, exotic species, construction, and the alteration of habitat dynamics. Recovery plans lacked basic information about the magnitude, timing, frequency, or severity of 39% of all threats facing the 181 species. Likewise, 37% of all threats were not directly addressed with recovery tasks. Threats from pollution were more poorly understood than other threats, and threats from exotics were better addressed than other types of threats. Finally, we found that threats that were better understood were assigned recovery tasks more often than threats that were more poorly understood. Thus our results suggest that a lack of basic understanding of the nature of the threats facing threatened and endangered species may, in part, be undermining our recovery efforts.


title: Serious social media: On the use of social media for improving students' adjustment to college

abstract: Abstract A considerable body of research indicates that social support plays an integral role in determining students' successful adjustment to college. Unlike previous research that has evaluated face-to-face support interventions that occur during students' first semester at college, the current study reports on a student-centered social media site designed to enhance students' perceptions of social support prior to their arrival on campus. Results indicated that site usage increased students' perceptions that they would have a diverse social support network during their first semester at college, even when controlling for other potent predictors. The importance of social support perceptions for college adjustment is detailed and the ramifications of the social media intervention are discussed.


title: Biodiesel Production from Azolla filiculoides (Water Fern)

abstract: Purpose: To assess the potential of Azolla filiculoides, total body collected from a rice farm in northern Iran as source for biodiesel production. Methods: Solvent extraction using Soxhlet apparatus with chloroform-methanol (2:1 v/v) solvent blend was used to obtain crude oil from freeze-dried the Azolla plant. Acid-catalyzed transesterification was used to convert fatty acids (FA), monoglycerides (MG), diglycerides (DG) and triglycerides (TG) in the extracts to fatty acid methyl esters (FAMEs) by acid-catalyzed methylation. Gas chromatography–mass spectrometry (GC–MS) was employed to analyze the FAMEs in the macroalgae biodiesel. Results: The presence of myristic acid (C14:0), palmitic acid (C16:0), palmitoleic acid (C16:1), myristic acid (C14:0), stearic acid (C18:3), oleic acid (C18:1) and linoleic acid 9C18:2), eicosenoic acid (C20:1), eicosapentaenoic acid (C20:5), erucic acid (C22:1) and docosahexaenoic acid (C22:6) in the macroalgae biodiesel was confirmed. Conclusion: The results indicate that biodiesel can be produced from macroalgae and that water fern is potentially an economical source of biodiesel due its ready availability and probable low cost.


title: Sub-50 nm p-channel FinFET

abstract: High-performance PMOSFETs with sub-50-nm gate-length are reported. A self-aligned double-gate MOSFET structure (FinFET) is used to suppress the short-channel effects. This vertical double-gate SOI MOSFET features: 1) a transistor channel which is formed on the vertical surfaces of an ultrathin Si fin and controlled by gate electrodes formed on both sides of the fin; 2) two gates which are self-aligned to each other and to the source/drain (S/D) regions; 3) raised S/D regions; and 4) a short (50 nm) Si fin to maintain quasi-planar topology for ease of fabrication. The 45-nm gate-length p-channel FinFET showed an I/sub dsat/ of 820 /spl mu/A//spl mu/m at V/sub ds/=V/sub gs/=1.2 V and T/sub ox/=2.5 mm. Devices showed good performance down to a gate-length of 18 nm. Excellent short-channel behavior was observed. The fin thickness (corresponding to twice the body thickness) is found to be critical for suppressing the short-channel effects. Simulations indicate that the FinFET structure can work down to 10 nm gate length. Thus, the FinFET is a very promising structure for scaling CMOS beyond 50 nm.


title: A low-power low-noise CMOS voltage reference with improved PSR for wearable sensor systems

abstract: This paper describes a novel sub-threshold voltage reference generator suitable for very low power and very low voltage wearable systems. Indeed, the circuit, designed and extensively simulated in a standard 0.18-μm CMOS technology, generates a 51.3-mV output voltage with a nominal supply voltage of 0.5 V and consumes as low as 185 nW. Design optimisation and a first order temperature compensation allow achieving a temperature coefficient of 25.4 ppm/ C in the temperature range from −40 to 125° C. The proposed architecture optimizes the output noise performance by minimizing the current mirrors flicker and thermal noise contributions which, in conventional schemes, severely affect the output node. In addition, the same circuital arrangement allows obtaining high power supply rejection (PSR). The simulated root mean square output noise is 1.6 μV in the frequency range from 0.1 Hz to 10 Hz, while the PSR is −76 dB at 100 Hz, a remarkable value.


title: General Multi-Level Linear Modelling for Group Analysis in FMRI

abstract: This article discusses general modeling of multisubject and/or multisession FMRI data. In particular, we show that a two-level mixed-effects model (where parameters of interest at the group level are estimated from parameter and variance estimates from the single-session level) can be made equivalent to a single complete mixed-effects model (where parameters of interest at the group level are estimated directly from all of the original single sessions' time series data) if the (co-)variance at the second level is set equal to the sum of the (co-)variances in the single-level form, using the BLUE with known covariances. This result has significant implications for group studies in FMRI, since it shows that the group analysis requires only values of the parameter estimates and their (co-)variance from the first level, generalizing the well-established “summary statistics” approach in FMRI. The simple and generalized framework allows different prewhitening and different first-level regressors to be used for each subject. The framework incorporates multiple levels and cases such as repeated measures, paired or unpaired t tests and F tests at the group level; explicit examples of such models are given in the article. Using numerical simulations based on typical first-level covariance structures from real FMRI data we demonstrate that by taking into account lower-level covariances and heterogeneity a substantial increase in higher-level Z score is possible.


title: Named data networking

abstract: Named Data Networking (NDN) is one of five projects funded by the U.S. National Science Foundation under its Future Internet Architecture Program. NDN has its roots in an earlier project, Content-Centric Networking (CCN), which Van Jacobson first publicly presented in 2006. The NDN project investigates Jacobson's proposed evolution from today's host-centric network architecture (IP) to a data-centric network architecture (NDN). This conceptually simple shift has far-reaching implications for how we design, develop, deploy, and use networks and applications. We describe the motivation and vision of this new architecture, and its basic components and operations. We also provide a snapshot of its current design, development status, and research challenges. More information about the project, including prototype implementations, publications, and annual reports, is available on named-data.net.


title: Assembly Process driven Component Data Model in Cyber-Physical Production Systems

abstract: Abstract — In times of global markets, the cost pressure on manufacturing companies in high-wage countries is increasing. One way to deal with globalized markets is to develop new production systems. The German Federal Government supports an initiative for the research on highly adaptable, cyber-physical production systems called Industrie 4.0 . In cyber-physical production systems components communicate with assembly stations and other components to guarantee the ideal manufacturing process. As part of this initiative the description of communication and decision processes in the assembly systems is researched. Therefore a UML based modeling method is chosen to model the communications and material flows in such a system. The component data model is one central element in the process. In the model knowledge about the individual components can be stored and used to make decisions in the assembly process. During the communication process components exchange


title: Towards Conversational Recommender Systems

abstract: People often ask others for restaurant recommendations as a way to discover new dining experiences. This makes restaurant recommendation an exciting scenario for recommender systems and has led to substantial research in this area. However, most such systems behave very differently from a human when asked for a recommendation. The goal of this paper is to begin to reduce this gap. In particular, humans can quickly establish preferences when asked to make a recommendation for someone they do not know. We address this cold-start recommendation problem in an online learning setting. We develop a preference elicitation framework to identify which questions to ask a new user to quickly learn their preferences. Taking advantage of latent structure in the recommendation space using a probabilistic latent factor model, our experiments with both synthetic and real world data compare different types of feedback and question selection strategies. We find that our framework can make very effective use of online user feedback, improving personalized recommendations over a static model by 25% after asking only 2 questions. Our results demonstrate dramatic benefits of starting from offline embeddings, and highlight the benefit of bandit-based explore-exploit strategies in this setting.


title: An algebraic approach to affine registration of point sets

abstract: This paper proposes a new affine registration algorithm for matching two point sets in ℝ2 or ℝ3. The input point sets are represented as probability density functions, using either Gaussian mixture models or discrete density models, and the problem of registering the point sets is treated as aligning the two distributions. Since polynomials transform as symmetric tensors under an affine transformation, the distributions' moments, which are the expected values of polynomials, also transform accordingly. Therefore, instead of solving the harder problem of aligning the two distributions directly, we solve the softer problem of matching the distributions' moments. By formulating a least-squares problem for matching moments of the two distributions up to degree three, the resulting cost function is a polynomial that can be efficiently optimized using techniques originated from algebraic geometry: the global minimum of this polynomial can be determined by solving a system of polynomial equations. The algorithm is robust in the presence of noises and outliers, and we validate the proposed algorithm on a variety of point sets with varying degrees of deformation and noise.


Robustness of Federated Averaging for Non-IID Data


The image of India as a Travel Destination and the attitude of viewers towards Indian TV Dramas


What is the domain of causal perception? Investigating causal perception of motion and non-motion state change events in infancy


title: A Generic Digital Forensic Investigation Framework for Internet of Things (IoT)

abstract: Although numerous researches have been carried on Internet of Things (IoT), little focus has been employed on how Digital Forensics (DF) techniques can be used to conduct Digital Forensic Investigations (DFIs) in IoT-based infrastructures. Up to this point, IoT has not fully adapted to DF techniques owing to the fact that the current DF tools and procedures are not able to meet the heterogeneity and distributed nature of the IoT infrastructures. As a result, gathering, examining and analysing potential evidence from IoT environments that may be used as admissible evidence in a court of law poses a challenge to DF investigators and Law Enforcement Agencies (LEA). Therefore, the problem addressed is that, at the time of writing this paper, there currently exist no accepted DF frameworks that can help to conduct DFIs in an IoT-based environment. Based on this premise, the authors have proposed a generic Digital Forensic Investigation Framework for IoT (DFIF-IoT) that is able to support future IoT investigative capabilities with a degree of certainty. The proposed framework includes the following advantage: It complies with the ISO/IEC 27043: 2015 which is an international standard for information technology, security techniques, incident investigation principles, and process. It is, therefore, the authors' opinion that if the proposed framework is successfully incorporated in future DF tool development, it will facilitate effective digital forensic crime investigation for IoT infrastructures.


title: Dengue fever: Mathematical modelling and computer simulation

abstract: Abstract Dengue fever is a re-emergent disease affecting people in more than 100 countries. Its incidence has increased fourfold since 1970 and nearly half the world’s population is now at risk. In the present paper, a mathematical model is proposed to simulate the succession of two epidemics with variable human populations. Stability analysis of the equilibrium points is carried out and a simulation is given for different parameter settings.


title: The MOS social support survey.

abstract: This paper describes the development and evaluation of a brief, multidimensional, self-administered, social support survey that was developed for patients in the Medical Outcomes Study (MOS), a two-year study of patients with chronic conditions. This survey was designed to be comprehensive in terms of recent thinking about the various dimensions of social support. In addition, it was designed to be distinct from other related measures. We present a summary of the major conceptual issues considered when choosing items for the social support battery, describe the items, and present findings based on data from 2987 patients (ages 18 and older). Multitrait scaling analyses supported the dimensionality of four functional support scales (emotional/informational, tangible, affectionate, and positive social interaction) and the construction of an overall functional social support index. These support measures are distinct from structural measures of social support and from related health measures. They are reliable (all Alphas greater than 0.91), and are fairly stable over time. Selected construct validity hypotheses were supported.