title: Smart wearable systems: Current status and future challenges

abstract: Objective: Extensive efforts have been made in both academia and industry in the research and development of smart wearable systems (SWS) for health monitoring (HM). Primarily influenced by skyrocketing healthcare costs and supported by recent technological advances in micro- and nanotechnologies, miniaturisation of sensors, and smart fabrics, the continuous advances in SWS will progressively change the landscape of healthcare by allowing individual management and continuous monitoring of a patient's health status. Consisting of various components and devices, ranging from sensors and actuators to multimedia devices, these systems support complex healthcare applications and enable low-cost wearable, non-invasive alternatives for continuous 24-h monitoring of health, activity, mobility, and mental status, both indoors and outdoors. Our objective has been to examine the current research in wearable to serve as references for researchers and provide perspectives for future research. Methods: Herein, we review the current research and development of and the challenges facing SWS for HM, focusing on multi-parameter physiological sensor systems and activity and mobility measurement system designs that reliably measure mobility or vital signs and integrate real-time decision support processing for disease prevention, symptom detection, and diagnosis. For this literature review, we have chosen specific selection criteria to include papers in which wearable systems or devices are covered. Results: We describe the state of the art in SWS and provide a survey of recent implementations of wearable health-care systems. We describe current issues, challenges, and prospects of SWS. Conclusion: We conclude by identifying the future challenges facing SWS for HM.


title: Thoughts, behaviour, and brain dynamics during navigation in the real world.

abstract: How does the human brain allow us to interact with and navigate through a constantly changing world? Whilst controlled experiments using functional brain imaging can give insightful snapshots of neuronal responses to relatively simplified stimuli, they cannot hope to mirror the challenges faced by the brain in the real world. However, trying to study the brain mechanisms supporting daily living represents a huge challenge. By combining functional neuroimaging, an accurate interactive virtual simulation of a bustling central London (UK), and a novel means of ‘reading’ participants' thoughts whilst they moved around the city, we ascertained the online neural correlates underpinning navigation in this real-world context. A complex choreography of neural dynamics was revealed comprising focal and distributed, transient and sustained brain activity. Our results provide new insights into the specific roles of individual brain areas, in particular the hippocampus, retrosplenial, and frontal cortices, as well as offering clues about how functional specialisations operate within dynamic brain systems.


title: Predictive Power Management for Internet of Battery-Less Things

abstract: Energy harvesting technology provides a promising solution to enable internet of battery-less things (IoBT), as the lifetime and size of batteries become major limiting factors in the design and effective operation of internet of things (IoT). However, with constrained energy buffer size, the variation of ambient energy availability and wireless communication cast adverse effect on the operation of IoBT. There is a pressing demand for developing IoBT-specialized power management. In this paper, we propose a novel predictive power management (PPM) framework combining optimal working point, deviation aware predictive energy allocation, and energy efficient transmission power control. The optimal working point guarantees minimum power loss of IoBT systems. By predictively budgeting the available energy and using the optimal working point as a set-point, PPM mitigates the prediction error so that both power failure time and system power loss is minimized. The transmission power control module of PPM improves energy efficiency by dynamically selecting optimal transmission power level with minimum energy consumption. Real-world harvesting profiles are tested to validate the effectiveness of PPM. The results indicate that compared with the previous predictive power managers, PPM incurs up to $17.49\%$ reduction in system power loss and $93.88\%$ less power failure time while maintaining a high energy utilization rate. PPM also achieves $9.4\%$ to $23.22\%$ of maximum improvement of transmission energy efficiency compared with the state-of-the-art transmission power control schemes.


title: An integrated portable Raman sensor with nanofabricated gold bowtie array substrates for energetics detection

abstract: An integrated field-portable surface enhanced Raman scattering (SERS) sensing system has been developed and evaluated for quantitative analysis of energetics such as perchlorate (ClO4−) and trinitrotoluene (TNT) at environmentally relevant concentrations and conditions. The detection system consists of a portable Raman spectrometer equipped with an optical fiber probe that is coupled with novel elevated gold bowtie nanostructural arrays as a sensitive and reproducible SERS substrate. Using the standard addition technique, we show that ClO4− and TNT can be quantified at concentrations as low as 0.66 mg L−1 (or ∼6.6 µM) and 0.20 mg L−1 (∼0.9 µM), respectively, in groundwater samples collected from selected military sites. This research represents the first step toward the development of a field SERS sensor which may permit rapid, in situ screening and analysis for various applications including national security, chemical, biological and environmental detection.


title: Developing a Web-Based Application using OWL and SWRL.

abstract: Data integration is central in Web application development because these applications typically deal with a variety of information formats. Ontology-driven applications face the additional challenge of integrating these multiple formats with the information stored in ontologies. A number of mappings are required to reconcile the variety of formats to produce a coherent overall system. To address these mappings we have developed a number of open source tools that support transformations between some of the common formats encountered when developing an ontology-driven Web application. The Semantic Web Rule Language (SWRL) is a central building block in these tools. We describe these tools and illustrate their use in the development of a prototype Web-based application.


title: III-nitride micro-emitter arrays: development and applications

abstract: III-nitride micro-emitter array technology was developed in the authors' laboratory around 1999. Since its inception, much progress has been made by several groups and the technology has led to the invention of several novel devices. This paper provides an overview on recent progress in single-chip ac-micro-size light emitting diodes (μLEDs) that can be plugged directly into standard high ac voltage power outlets, self-emissive microdisplays and interconnected μLEDs for boosting light emitting diodes's wall-plug efficiency, all of which were evolved from III-nitride micro-emitter array technology. Finally, potential applications of III-nitride visible micro-emitter arrays as a light source for DNA microarrays and future prospects of III-nitride deep ultraviolet micro-emitter arrays for label-free protein analysis in microarray format by taking advantage of the direct excitation of intrinsic protein fluorescence are discussed.


title: Learning to Paraphrase for Question Answering

abstract: Question answering (QA) systems are sensitive to the many different ways natural language expresses the same information need. In this paper we turn to paraphrases as a means of capturing this knowledge and present a general framework which learns felicitous paraphrases for various QA tasks. Our method is trained end-to-end using question-answer pairs as a supervision signal. A question and its paraphrases serve as input to a neural scoring model which assigns higher weights to linguistic expressions most likely to yield correct answers. We evaluate our approach on QA over Freebase and answer sentence selection. Experimental results on three datasets show that our framework consistently improves performance, achieving competitive results despite the use of simple QA models.


title: BoostHand : Distance-free Object Manipulation System with Switchable Non-linear Mapping for Augmented Reality Classrooms

abstract: In this paper, we propose BoostHand, a freehand, distance-free object-manipulation system that supports simple trigger gestures using Leap Motion. In AR classrooms, it is necessary to allow both lecturers and students to utilize virtual teaching materials without any spatial restrictions, while handling virtual objects easily, regardless of distance. To provide efficient and accurate methods of handling AR classroom objects, our system requires only simple intuitive freehand gestures to control the users virtual hands in an enlarged, shared control space of users. We modified the GoGo interaction technique [5] by adding simple trigger gestures, and we evaluated performance against gaze-assisted selection (GaS) capabilities. Our proposed system enables both lecturers and students to utilize virtual teaching materials easily from their remote positions.


title: Eight-minute self-regulation intervention raises educational attainment at scale in individualist but not collectivist cultures

abstract: Academic credentials open up a wealth of opportunities. However, many people drop out of educational programs, such as community college and online courses. Prior research found that a brief self-regulation strategy can improve self-discipline and academic outcomes. Could this strategy support learners at large scale? Mental contrasting with implementation intentions (MCII) involves writing about positive outcomes associated with a goal, the obstacles to achieving it, and concrete if–then plans to overcome them. The strategy was developed in Western countries (United States, Germany) and appeals to individualist tendencies, which may reduce its efficacy in collectivist cultures such as India or China. We tested this hypothesis in two randomized controlled experiments in online courses ( n = 17,963). Learners in individualist cultures were 32% (first experiment) and 15% (second experiment) more likely to complete the course following the MCII intervention than a control activity. In contrast, learners in collectivist cultures were unaffected by MCII. Natural language processing of written responses revealed that MCII was effective when a learner’s primary obstacle was predictable and surmountable, such as everyday work or family obligations but not a practical constraint (e.g., Internet access) or a lack of time. By revealing heterogeneity in MCII’s effectiveness, this research advances theory on self-regulation and illuminates how even highly efficacious interventions may be culturally bounded in their effects.


title: Open-loop stochastic optimal control of a passive noise-rejection variable stiffness actuator: Application to unstable tasks

abstract: In this paper we propose a methodology to control a novel class of actuators that we called passive noise rejection variable stiffness actuators (pnrVSA). Differently from nowadays classical VSA designs, this novel class of actuators mimics the human musculoskeletal ability to increase noise rejection without relying on feedback. To fully highlight the potentialities behind these actuators we consider movement planning under two constraints: (1) absence of feedback, i.e. purely open-loop planning1; (2) uncertain dynamic model. Under these constraints, movement planning can be formalized as an open-loop stochastic optimal control. Due to the lack of classical methods forcing the open-loop nature of the computed solution, we used here a slight modification of available methodologies based on importance sampling of trajectories using forward diffusion processes. Simulations show that the proposed algorithm can be effectively used to plan open-loop movements with pnrVSA. In particular, two different scenarios are considered: the control of a single joint pnrVSA and the control of a two degrees of freedom planar arm equipped with antagonist pnrVSAs at each joint. In both cases, movement has to be planned in presence of uncertain dynamics for unstable tasks. It is shown that open-loop stochastic optimal control can modulate the intrinsic stiffness of the system to cope with both instability and noise.


title: Arm/trunk motion generation for humanoid robot

abstract: This paper develops two motion generation methods for the upper body of humanoid robots based on compensating for the yaw moment of whole body during motion. These upper body motions can effectively solve the stability problem of feet spin for robot walk. We analyze the ground reactive torque, separate the yaw moment as the compensating object and discuss the effect of arms swinging on whole body locomotion. By taking the ZMP as the reference point, trunk spin motion and arms swinging motion are generated to improve the biped motion stability, based on compensating for the yaw moment. The methods are further compared from the energy consumption point of view. Simulated experimental results validate the performance and the feasibility of the proposed methods.


Design of Vivaldi Antenna at 10 % Bandwidth


title: Frequent itemset mining of uncertain data streams using the damped window model

abstract: With advances in technology, large amounts of streaming data can be generated continuously by sensors in applications like environment surveillance. Due to the inherited limitation of sensors, these continuous data can be uncertain. This calls for stream mining of uncertain data. In recent years, tree-based algorithms have been proposed to use the sliding window model for mining frequent itemsets from streams of uncertain data. Besides the sliding window model, there are other window models for processing data streams. In this paper, we propose tree-based algorithms that use the damped window model to mine frequent itemsets from streams of uncertain data.


title: Self-Calibration from Multiple Views with a Rotating Camera

abstract: A new practical method is given for the self-calibration of a camera. In this method, at least three images are taken from the same point in space with different orientations of the camera and calibration is computed from an analysis of point matches between the images. The method requires no knowledge of the orientations of the camera. Calibration is based on the image correspondences only. This method differs fundamentally from previous results by Maybank and Faugeras on selfcalibration using the epipolar structure of image pairs. In the method of this paper, there is no epipolar structure since all images are taken from the same point in space. Since the images are all taken from the same point in space, determination of point matches is considerably easier than for images taken with a moving camera, since problems of occlusion or change of aspect or illumination do not occur. The calibration method is evaluated on several sets of synthetic and real image data.


title: Connect: an effective community-based youth suicide prevention program

abstract: Youth suicide prevention is an important public health issue. However, few prevention programs are theory driven or systematically evaluated. This study evaluated Connect, a community-based youth suicide prevention program. Analysis of pre and posttraining questionnaires from 648 adults and 204 high school students revealed significant changes in knowledge and attitudes about suicide, increased belief in the usefulness of mental health care, and reduction of stigma associated with seeking help. Adults’ preparedness to help also increased significantly as did the likelihood that youth participants would seek adult assistance if they were concerned about a peer. Implications of findings are discussed.


title: The Danger of USB Drives

abstract: This study suggests that average users don't recognize the danger of connecting unknown peripherals to a computer, underscoring the continued risk posed by USB drives. Steps organizations can take to safeguard against USB-based attacks are discussed.


title: Matrix Factorisation for Predicting Student Performance

abstract: Predicting student performance in tertiary institutions has potential to improve curriculum advice given to students, the planning of interventions for academic support and monitoring and curriculum design. The student performance prediction problem, as defined in this study, is the prediction of a student's mark for a module, given the student's performance in previously attempted modules. The prediction problem is amenable to machine learning techniques, provided that sufficient data is available for analysis. This work reports on a study undertaken at the College of Agriculture, Engineering and Science at University of KwaZulu- Natal that investigates the efficacy of Matrix Factorization as a technique for solving the prediction problem. The study uses Singular Value Decomposition (SVD), a Matrix Factorization technique that has been successfully used in recommender systems. The performance of the technique was benchmarked against the use of student and course average marks as predictors of performance. The results obtained suggests that Matrix Factorization performs better than both benchmarks.


title: Collision Avoidance using Intuitive Physics

abstract: One strategy for intelligent agents in order to reach their goals is to plan their actions in advance. This can be done by simulating how the agent’s actions affect the environment and how it evolves independently of the agent. For this simulation, a model of the environment is needed. However, the creation of this model might be labor-intensive and it might be computational complex to evaluate during simulation. That is why, we suggest to equip an intelligent agent with a learned intuition about the dynamics of its environment by utilizing the concept of intuitive physics. To demonstrate our approach, we used an agent that can freely move in a two dimensional floor plan. It has to collect moving targets while avoiding the collision with static and dynamic obstacles. In order to do so, the agent plans its actions up to a defined planning horizon. The performance of our agent, which intuitively estimates the dynamics of its surrounding objects based on artificial neural networks, is compared to an agent which has a physically exact model of the world and one that acts randomly. The evaluation shows comparatively good results for the intuition based agent considering it uses only a quarter of the computation time in comparison to the agent with a physically exact model.


title: General Purpose Operating System Support for Multiple Page Sizes

abstract: Many commercial microprocessor architectures support translation lookaside buffer (TLB) entries with multiple page sizes. This support can be used to substantially reduce the overhead introduced by TLB misses incurred when the processor runs an application with a large working set. Applications are currently not able to take advantage of this hardware feature because most commercial operating systems support only one page size. In this paper we present a design that provides general purpose operating system support that allows applications to use multiple page sizes. The paper describes why providing this support is not as simple as it first seems. If not designed carefully, adding this support will require significant modifications and add a lot of overhead to the operating system. The paper shows how our approach simplifies the design without sacrificing functionality and performance. In addition, applications need not be modified to make use of this feature. The design has been implemented on IRIX 6.4 operating system running on the SGI Origin platform. We include some performance results at the end to show how our design allows applications to take advantage of large pages to gain performance improvements.


title: Explore Efficient Data Organization for Large Scale Graph Analytics and Storage

abstract: Many Big Data analytics essentially explore the relationship among interconnected entities, which are naturally represented as graphs. However, due to the irregular data access patterns in the graph computations, it remains a fundamental challenge to deliver highly efficient solutions for large scale graph analytics. Such inefficiency restricts the utilization of many graph algorithms in Big Data scenarios. To address the performance issues in large scale graph analytics, we develop a graph processing system called System G, which explores efficient graph data organization for parallel computing architectures. We discuss various graph data organizations and their impact on data locality during graph traversals, which results in various cache performance behavior on processor side. In addition, we analyze data parallelism from architecture's perspective and experimentally show the efficiency for System G based graph analytics. We present experimental results for commodity multicore clusters and IBM PERCS supercomputers to illustrate the performance of System G for large scale graph analytics.


title: Automatic Classification Of Citation Function

abstract: Citation function is defined as the author's reason for citing a given paper (e.g. acknowledgement of the use of the cited method). The automatic recognition of the rhetorical function of citations in scientific text has many applications, from improvement of impact factor calculations to text summarisation and more informative citation indexers. We show that our annotation scheme for citation function is reliable, and present a supervised machine learning framework to automatically classify citation function, using both shallow and linguistically-inspired features. We find, amongst other things, a strong relationship between citation function and sentiment classification.


title: Improvised Comedy as a Turing Test

abstract: The best improvisational theatre actors can make any scene partner, of any skill level or ability, appear talented and proficient in the art form, and thus "make them shine". To challenge this improvisational paradigm, we built an artificial intelligence (AI) trained to perform live shows alongside human actors for human audiences. Over the course of 30 performances to a combined audience of almost 3000 people, we have refined theatrical games which involve combinations of human and (at times, adversarial) AI actors. We have developed specific scene structures to include audience participants in interesting ways. Finally, we developed a complete show structure that submitted the audience to a Turing test and observed their suspension of disbelief, which we believe is key for human/non-human theatre co-creation.


title: A MATLAB-Simulink-Based PV Module Model and Its Application Under Conditions of Nonuniform Irradiance

abstract: The performance of a photovoltaic (PV) module is mostly affected by array configuration, irradiance, and module temperature. It is important to understand the relationship between these effects and the output power of the PV array. This paper presents a MATLAB-Simulink-based PV module model which includes a controlled current source and an S-Function builder. The modeling scheme in S-Function builder is deduced by some predigested functions. Under the conditions of nonuniform irradiance, the model is practically validated by using different array configurations in testing platform. The comparison experiments indicate that I-V and P-V characteristic curves of simulation match the measurements from outdoor experiment well. Under the conditions of nonuniform irradiance, both simulation and experiment show that the output power of a PV array gets more complicated due to multiple peaks. Moreover, the proposed model can also simulate electric circuit and its maximum power point tracking (MPPT) in the environment of MATLAB-Simulink. The experiments show that the proposed model has good predictability in the general behaviors of MPPT under the conditions of both nonuniform and uniform irradiance.


title: Entity Set Expansion with Meta Path in Knowledge Graph

abstract: Entity set expansion (ESE) is the problem that expands a small set of seed entities into a more complete set, entities of which have common traits. As a popular data mining task, ESE has been widely used in many applications, such as dictionary construction and query suggestion. Contemporary ESE mainly utilizes text and Web information. That is, the intrinsic relation among entities is inferred from their occurrences in text or Web. With the surge of knowledge graph in recent years, it is possible to extend entities according to their occurrences in knowledge graph. In this paper, we consider the knowledge graph as a heterogeneous information network (HIN) that contains different types of objects and links, and propose a novel method, called MP_ESE, to extend entities in the HIN. The MP_ESE employs meta paths, a relation sequence connecting entities, in HIN to capture the implicit common traits of seed entities, and an automatic meta path generation method, called SMPG, is provided to exploit the potential relations among entities. With these generated and weighted meta paths, the MP_ESE can effectively extend entities. Experiments on real datasets validate the effectiveness of MP_ESE.


title: WRPN: Wide Reduced-Precision Networks

abstract: For computer vision applications, prior works have shown the efficacy of reducing numeric precision of model parameters (network weights) in deep neural networks. Activation maps, however, occupy a large memory footprint during both the training and inference step when using mini-batches of inputs. One way to reduce this large memory footprint is to reduce the precision of activations. However, past works have shown that reducing the precision of activations hurts model accuracy. We study schemes to train networks from scratch using reduced-precision activations without hurting accuracy. We reduce the precision of activation maps (along with model parameters) and increase the number of filter maps in a layer, and find that this scheme matches or surpasses the accuracy of the baseline full-precision network. As a result, one can significantly improve the execution efficiency (e.g. reduce dynamic memory footprint, memory band- width and computational energy) and speed up the training and inference process with appropriate hardware support. We call our scheme WRPN -- wide reduced-precision networks. We report results and show that WRPN scheme is better than previously reported accuracies on ILSVRC-12 dataset while being computationally less expensive compared to previously reported reduced-precision networks.


title: Conditioning Deep Generative Raw Audio Models for Structured Automatic Music

abstract: Existing automatic music generation approaches that feature deep learning can be broadly classified into two types: raw audio models and symbolic models. Symbolic models, which train and generate at the note level, are currently the more prevalent approach; these models can capture long-range dependencies of melodic structure, but fail to grasp the nuances and richness of raw audio generations. Raw audio models, such as DeepMind's WaveNet, train directly on sampled audio waveforms, allowing them to produce realistic-sounding, albeit unstructured music. In this paper, we propose an automatic music generation methodology combining both of these approaches to create structured, realistic-sounding compositions. We consider a Long Short Term Memory network to learn the melodic structure of different styles of music, and then use the unique symbolic generations from this model as a conditioning input to a WaveNet-based raw audio generator, creating a model for automatic, novel music. We then evaluate this approach by showcasing results of this work.


Mapping and Control with Telepresence Robots


title: An Isolated Bidirectional DC-DC Soft Switching Converter for Super Capacitor Based Energy Storage Systems

abstract: In recent years, power electronic energy storage systems using super capacitor bank have been widely studied and developed for the electronic vehicles. In this paper, a full-bridge/centertapped push-pull circuit with active clamp-based soft switching bidirectional DC-DC converter and its control method are presented and discussed. From the results of basic experimental demonstration, the proposed system is able to perform adequate charging and discharging operation between low- voltage high-current super capacitor side and high-voltage low-current side with drive train and main battery. In addition, RCDi snubber losses appeared in the basic circuit topology are drastically reduced by ZCS/ZVS operation with the assistance of newly added active clump circuit, as well as ZVS operation with lossless snubber capacitor in high-voltage primary side.


title: A Cyclic Weighted Median Method for L1 Low-Rank Matrix Factorization with Missing Entries ∗

abstract: A challenging problem in machine learning, information retrieval and computer vision research is how to recover a low-rank representation of the given data in the presence of outliers and missing entries. The L1-norm low-rank matrix factorization (LRMF) has been a popular approach to solving this problem. However, L1-norm LRMF is difficult to achieve due to its nonconvexity and non-smoothness, and existing methods are often inefficient and fail to converge to a desired solution. In this paper we propose a novel cyclic weighted median (CWM) method, which is intrinsically a coordinate decent algorithm, for L1-norm LRMF. The CWM method minimizes the objective by solving a sequence of scalar minimization sub-problems, each of which is convex and can be easily solved by the weighted median filter. The extensive experimental results validate that the CWM method outperforms state-of-the-arts in terms of both accuracy and computational efficiency.


title: An operational semantics for javascript

abstract: We define a small-step operational semantics for the ECMAScript standard language corresponding to JavaScript, as a basis for analyzing security properties of web applications and mashups. The semantics is based on the language standard and a number of experiments with different implementations and browsers. Some basic properties of the semantics are proved, including a soundness theorem and a characterization of the reachable portion of the heap.


title: Chronotype, cognitive abilities, and academic achievement: A meta-analytic investigation☆

abstract: Abstract Four meta-analyses examined relationships between morningness and cognitive ability (total N = 2177), eveningness and cognitive ability (total N = 1519), morningness and academic achievement (total N = 3220), and eveningness and academic achievement (total N = 700). The analyses focused on the population effect size (to reveal the effect across studies) and the homogeneity (to determine if the results of the several experiments are sufficiently similar to warrant their combination into an overall result). In all four cases, the aggregated correlations between chronotype and cognitive ability, as well as chronotype and academic achievement were found to be significant. Eveningness was found to be positively related to individuals' cognitive ability ( r = .08), yet negatively related to indicators of academic achievement ( r = −.14). Conversely, morningness had a negative relationship with cognitive ability ( r = −.04) and a positive correlation with academic indicators ( r = .16). Practical implications, including those pertaining to educational policy, are discussed.


title: Learning image-to-image translation using paired and unpaired training samples

abstract: Image-to-image translation is a general name for a task where an image from one domain is converted to a corresponding image in another domain, given sufficient training data. Traditionally different approaches have been proposed depending on whether aligned image pairs or two sets of (unaligned) examples from both domains are available for training. While paired training samples might be difficult to obtain, the unpaired setup leads to a highly under-constrained problem and inferior results. In this paper, we propose a new general purpose image-to-image translation model that is able to utilize both paired and unpaired training data simultaneously. We compare our method with two strong baselines and obtain both qualitatively and quantitatively improved results. Our model outperforms the baselines also in the case of purely paired and unpaired training data. To our knowledge, this is the first work to consider such hybrid setup in image-to-image translation.


title: Leveraging existing sensor networks as IoT devices for smart buildings

abstract: As the Internet-of-Things (IoT) grows and data analytics mature, their application to new buildings is proved a significant opportunity for improved building performance at reduced energy costs. In existing buildings, however, the cost to replace existing Building Management Systems (BMS) with IoT-compatible devices poses a barrier to adoption. Additionally, the limited data storage on these existing systems further precludes data analytics for optimization. This research responds to this need by presenting a novel approach to pre-process and stream the BMS data to a cloud-based database on a private network. Afterwards, in the centralized data warehouse, larger scale and more complex analytics can be performed. This paper presents the development of both the new database architecture and supporting infrastructure to support the streaming of BMS, as well as the pre-processing to optimize big data analytics and visualization. A proof-of-concept visualization for a 14,000m2 student learning center is presented to demonstrate the application of this architecture.


title: State-Space Reduction Techniques in Agent Verification

abstract: We have developed a set of tools to allow the use of model-checking techniques for the verification of systems directly implemented in an agent-oriented programming language. The success of model checking as a verification technique for large systems is dependent partly on its use in combination with various state-space reduction techniques. An important example of such techniques is property-based slicing. This paper introduces an algorithm for property-based slicing of AgentSpeak multi-agent systems. We apply our approach to the AgentSpeak code for a scenario inspired by routine tasks of autonomous Mars rovers, and explain how slicing reduces the search space in theory. We consider experiments on such scenarios, and initial results show a significant reduction in the state space, thus indicating that this approach can have an important impact on the practicality of agent verification.


title: Enriching Complex Networks with Word Embeddings for Detecting Mild Cognitive Impairment from Speech Transcripts

abstract: Mild Cognitive Impairment (MCI) is a mental disorder difficult to diagnose. Linguistic features, mainly from parsers, have been used to detect MCI, but this is not suitable for large-scale assessments. MCI disfluencies produce non-grammatical speech that requires manual or high precision automatic correction of transcripts. In this paper, we modeled transcripts into complex networks and enriched them with word embedding (CNE) to better represent short texts produced in neuropsychological assessments. The network measurements were applied with well-known classifiers to automatically identify MCI in transcripts, in a binary classification task. A comparison was made with the performance of traditional approaches using Bag of Words (BoW) and linguistic features for three datasets: DementiaBank in English, and Cinderella and Arizona-Battery in Portuguese. Overall, CNE provided higher accuracy than using only complex networks, while Support Vector Machine was superior to other classifiers. CNE provided the highest accuracies for DementiaBank and Cinderella, but BoW was more efficient for the Arizona-Battery dataset probably owing to its short narratives. The approach using linguistic features yielded higher accuracy if the transcriptions of the Cinderella dataset were manually revised. Taken together, the results indicate that complex networks enriched with embedding is promising for detecting MCI in large-scale assessments


title: Serious Games in language learning and teaching - a theoretical perspective

abstract: The paper focuses on a part of a new project Serious Games on a Global Market which focuses on language learning and teaching. Serious Games are digital games and equipment with an agenda of educational design. The paper makes a theoretical argument for a Serious Games product based on theories of educational design and learning in relation to games. Furthermore technology based perspectives on language teaching and learning are described and in addition to this, the paper draws on data from two recent research projects that have studied the role of game based activity in children’s digitally based practices in off school contexts. This theoretical construction will be employed in the developing of the prototype of the digitally based educational platform ”Mingoville”. Author


title: Adaptive Markov random fields for example-based super-resolution of faces

abstract: Image enhancement of low-resolution images can be done through methods such as interpolation, super-resolution using multiple video frames, and example-based super-resolution. Example-based super-resolution, in particular, is suited to images that have a strong prior (for those frameworks that work on only a single image, it is more like image restoration than traditional, multiframe super-resolution). For example, hallucination and Markov random field (MRF) methods use examples drawn from the same domain as the image being enhanced to determine what the missing high-frequency information is likely to be. We propose to use even stronger prior information by extending MRF-based super-resolution to use adaptive observation and transition functions, that is, to make these functions region-dependent. We show with face images how we can adapt the modeling for each image patch so as to improve the resolution.


title: Deep learning for sentiment analysis: successful approaches and future challenges

abstract: Sentiment analysis also known as opinion mining is an active research area in natural language processing. It aims at identifying, extracting and organizing sentiments from user generated texts in social networks, blogs or product reviews. A lot of studies in literature exploit machine learning approaches to solve sentiment analysis tasks from different perspectives in the past 15 years. Since the performance of a machine learner heavily depends on the choices of data representation, many studies devote to building powerful feature extractor with domain expert and careful engineering. Recently, deep learning approaches emerge as powerful computational models that discover intricate semantic representations of texts automatically from data without feature engineering. These approaches have improved the state-of-the-art in many sentiment analysis tasks including sentiment classification of sentences/documents, sentiment extraction and sentiment lexicon learning. In this paper, we provide an overview of the successful deep learning approaches for sentiment analysis tasks, lay out the remaining challenges and provide some suggestions to address these challenges. WIREs Data Mining Knowl Discov 2015, 5:292-303. doi: 10.1002/widm.1171


A Matched-Filter Method For Motion-Compensated Spotlight-Mode Sar Processing


title: From simulation to experimentable digital twins: Simulation-based development and operation of complex technical systems

abstract: Way beyond its industrial roots, robotics evolved to be a highly interdisciplinary field with a variety of applications in a smart world. The eRobotics methodology addresses this evolution by providing platforms where roboticist can exchange ideas and collaborate with experts from other disciplines for developing complex technical systems and automated solutions. Virtual Testbeds are the central method in eRobotics, where complex technical systems and their interaction with prospective working environments are first designed, programmed, controlled and optimized in simulation before commissioning the real system. On the other hand, Industry 4.0 concepts promote the notion of “Digital Twins”, virtual substitutes of real world objects consisting of virtual representations and communication capabilities making up smart objects acting as intelligent nodes inside the internet of things and services. Combining these two approaches, Virtual Testbeds and Digital Twins, leads to a new kind of “Experimentable Digital Twins” breaking new ground in the simulation-based development and operation of complex technical systems. In this contribution, we describe how such “Experimentable Digital Twins” can act as the very core of simulation-based development processes streamlining the development process, enabling detailed simulations at system level and realizing intelligent systems. Besides this, the multiple use of models and simulations in various scenarios significantly reduces the effort for the use of simulation technology throughout the life cycle of complex technical systems.


title: Diagnosing network-wide traffic anomalies

abstract: Anomalies are unusual and significant changes in a network's traffic levels, which can often span multiple links. Diagnosing anomalies is critical for both network operators and end users. It is a difficult problem because one must extract and interpret anomalous patterns from large amounts of high-dimensional, noisy data.In this paper we propose a general method to diagnose anomalies. This method is based on a separation of the high-dimensional space occupied by a set of network traffic measurements into disjoint subspaces corresponding to normal and anomalous network conditions. We show that this separation can be performed effectively by Principal Component Analysis.Using only simple traffic measurements from links, we study volume anomalies and show that the method can: (1) accurately detect when a volume anomaly is occurring; (2) correctly identify the underlying origin-destination (OD) flow which is the source of the anomaly; and (3) accurately estimate the amount of traffic involved in the anomalous OD flow.We evaluate the method's ability to diagnose (i.e., detect, identify, and quantify) both existing and synthetically injected volume anomalies in real traffic from two backbone networks. Our method consistently diagnoses the largest volume anomalies, and does so with a very low false alarm rate.


title: A survey of evaluation techniques used in augmented reality studies

abstract: ABSTRACT In this paper we report on an initial survey of user evaluation techniques used in Augmented Reality (AR) research. To identify all papers which include AR evaluations we reviewed research publications between the years 1993 and 2007 from online databases of selected scientific publishers. Starting with a total of 6071 publications we filtered the articles in several steps which resulted in 165 AR related publications with user evaluations. These publications were classified in two different ways: according to the evaluation type used following an earlier literature survey classification scheme; and according to the evaluation methods or approach used. We present the results of out literature survey, provide a comprehensive list of references of the selected publications, and discuss some possible research opportunities for future work. Keywords: Augmented Reality, user evaluation, evaluation methods. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for non-profit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of


title: An Algorithm for image stitching and blending

abstract: In many clinical studies, including those of cancer, it is highly desirable to acquire images of whole tumour sections whilst retaining a microscopic resolution. A usual approach to this is to create a composite image by appropriately overlapping individual images acquired at high magnification under a microscope. A mosaic of these images can be accurately formed by applying image registration, overlap removal and blending techniques. We describe an optimised, automated, fast and reliable method for both image joining and blending. These algorithms can be applied to most types of light microscopy imaging. Examples from histology, from in vivo vascular imaging and from fluorescence applications are shown, both in 2D and 3D. The algorithms are robust to the varying image overlap of a manually moved stage, though examples of composite images acquired both with manually-driven and computer-controlled stages are presented. The overlap-removal algorithm is based on the cross-correlation method; this is used to determine and select the best correlation point between any new image and the previous composite image. A complementary image blending algorithm, based on a gradient method, is used to eliminate sharp intensity changes at the image joins, thus gradually blending one image onto the adjacent 'composite'. The details of the algorithm to overcome both intensity discrepancies and geometric misalignments between the stitched images will be presented and illustrated with several examples.


title: A Solvable Tensor Field Theory

abstract: We solve the closed Schwinger–Dyson equation for the 2-point function of a tensor field theory with a quartic melonic interaction, in terms of Lambert’s W function, using a perturbative expansion and Lagrange–Burmann resummation. Higher-point functions are then obtained recursively.


The Kinetics of the Reactions of Phenyl Isocyanate with Certain Thiols1


title: Memes as genre: A structurational analysis of the memescape

abstract: A tenable genre development of Internet memes is introduced in three categories to describe memetic transformation: spreadable media, emergent meme, and meme. We argue that memes are remixed, iterated messages which are rapidly spread by members of participatory digital culture for the purpose of continuing a conversation. We understand that memes develop from emergent memes, which we define as altered or remixed spreadable media. We have adapted and modified Jenkins’ term “spreadable media” to refer to original or non-parodied messages. Our analysis benefits from the inclusion of Anthony Giddens’ structuration theory to aid in understanding how memes as artifacts of participatory digital culture are created. Our genre development of memes demonstrates the generative capacity for continued memetic transformation and for participation among members of digital culture. We use structuration to position these dynamic components as the core of a duality of structure for Internet memes.


title: A Component-Labeling Algorithm Using Contour Tracing Technique

abstract: A new linear-time algorithm is presented in this paper that simultaneously labels connected components (to be referred to merely as components in this paper) and their contours in binary images. The main step of this algorithm is to use a contour tracing technique to detect the external contour and possible internal contours of each component, and also to identify and label the interior area of each component. Labeling is done in a single pass over the image, while contour points are revisited more than once, but no more than a constant number of times. Moreover, no re-labeling is required throughout the entire process, as it is required by other algorithms. Experimentation on various types of images (characters, half-tone pictures, photographs, newspaper, etc.) shows that our method outperforms methods that use the equivalence technique. Our algorithm not only labels components but also extracts component contours and sequential orders of contour points, which can be useful for many applications.


title: A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks

abstract: Biological intelligence processes information using impulses or spikes, which makes those living creatures able to perceive and act in the real world exceptionally well and outperform state-of-the-art robots in almost every aspect of life. To make up the deficit, emerging hardware technologies and software knowledge in the fields of neuroscience, electronics, and computer science have made it possible to design biologically realistic robots controlled by spiking neural networks (SNNs), inspired by the mechanism of brains. However, a comprehensive review on controlling robots based on SNNs is still missing. In this paper, we survey the developments of the past decade in the field of spiking neural networks for control tasks, with particular focus on the fast emerging robotics-related applications. We first highlight the primary impetuses of SNN-based robotics tasks in terms of speed, energy efficiency, and computation capabilities. We then classify those SNN-based robotic applications according to different learning rules and explicate those learning rules with their corresponding robotic applications. We also briefly present some existing platforms that offer an interaction between SNNs and robotics simulations for exploration and exploitation. Finally, we conclude our survey with a forecast of future challenges and some associated potential research topics in terms of controlling robots based on SNNs.


title: Robin problems with indefinite linear part and competition phenomena

abstract: We consider a parametric semilinear Robin problem driven by the Laplacian plus an indefinite potential. The reaction term involves competing nonlinearities. More precisely, it is the sum of a parametric sublinear (concave) term and a superlinear (convex) term. The superlinearity is not expressed via the Ambrosetti-Rabinowitz condition. Instead, a more general hypothesis is used. We prove a bifurcation-type theorem describing the set of positive solutions as the parameter $\lambda > 0$ varies. We also show the existence of a minimal positive solution $\tilde{u}_\lambda$ and determine the monotonicity and continuity properties of the map $\lambda \mapsto \tilde{u}_\lambda$.


title: ORIGINAL ARTICLE Deficient Fear Conditioning in Psychopathy A Functional Magnetic Resonance Imaging Study

abstract: Context Psychopaths belong to a larger group of persons with antisocial personality disorder and are characterized by an inability to have emotional involvement and by the repeated violation of the rights of others. It was hypothesized that this behavior might be the consequence of deficient fear conditioning. Objective To study the cerebral, peripheral, and subjective correlates of fear conditioning in criminal psychopaths and healthy control subjects. Design An aversive differential pavlovian delay conditioning paradigm with slides of neutral faces serving as conditioned and painful pressure as unconditioned stimuli. Setting The Department of Medical Psychology at the University of Tubingen, Tubingen, Germany. Participants Ten male psychopaths as defined by the Hare Psychopathy Checklist–Revised and 10 age- and education-matched healthy male controls. The psychopaths were criminal offenders on bail and waiting for their trial or were on parole. The healthy controls were recruited from the community. Main Outcome Measures Brain activation based on functional magnetic resonance imaging, electrodermal responses, emotional valence, arousal, and contingency ratings. Results The healthy controls showed enhanced differential activation in the limbic-prefrontal circuit (amygdala, orbitofrontal cortex, insula, and anterior cingulate) during the acquisition of fear and successful verbal and autonomic conditioning. The psychopaths displayed no significant activity in this circuit and failed to show conditioned skin conductance and emotional valence ratings, although contingency and arousal ratings were normal. Conclusion This dissociation of emotional and cognitive processing may be the neural basis of the lack of anticipation of aversive events in criminal psychopaths.


title: Fast depth estimation from single image using structured forest

abstract: Depth estimation from single image is an important component of many vision systems, including robot navigation, motion capture and video surveillance. In this paper, we propose to apply a structure forest framework to infer depth information from single RGB image. The core idea of our approach is to exploit the structure properties exhibit in local patches of depth map to learn the depth level for each pixel. We formulate the problem of depth estimation in a structured learning framework based on random decision forests. Each trained forest infers a patch of structured labels that are accumulated across the image to obtain the final depth map. Moreover, we systematically investigate a variety of depth-relevant features and the regression forest framework automatically determines the best feature combination and uses as input of structure forest. Our approach achieves quasi real-time performance that is orders of magnitude faster than state-of-the-art approaches, while also achieving state-of-the-art depth estimation results on the Make3D dataset.


title: Multiple causes of difficulty in insight: the case of the nine-dot problem.

abstract: Theories of insight problems are often tested by formulating hypotheses about the particular difficulties of individual insight problems. Such evaluations often implicitly assume that there is a single difficulty. We argue that the quantitatively small effects of many studies arise because the difficulty of many insight problems is determined by multiple factors, so the removal of 1 factor has limited effect on the solution rate. Difficulties can reside either in problem perception, in prior knowledge, or in the processing of the problem information. We support this multiple factors perspective through 3 experiments on the 9-dot problem (N.R.F. Maier, 1930). Our results lead to a significant reformulation of the classical hypothesis as to why this problem is difficult. The results have general implications for our understanding of insight problem solving and for the interpretation of data from studies that aim to evaluate hypotheses about the sources of difficulty of particular insight problems.


title: Gibbs Sampling Methods for Stick-Breaking Priors

abstract: A rich and flexible class of random probability measures, which we call stick-breaking priors, can be constructed using a sequence of independent beta random variables. Examples of random measures that have this characterization include the Dirichlet process, its two-parameter extension, the two-parameter Poisson–Dirichlet process, finite dimensional Dirichlet priors, and beta two-parameter processes. The rich nature of stick-breaking priors offers Bayesians a useful class of priors for nonparametric problems, while the similar construction used in each prior can be exploited to develop a general computational procedure for fitting them. In this article we present two general types of Gibbs samplers that can be used to fit posteriors of Bayesian hierarchical models based on stick-breaking priors. The first type of Gibbs sampler, referred to as a Polya urn Gibbs sampler, is a generalized version of a widely used Gibbs sampling method currently employed for Dirichlet process computing. This method applies t...


title: Interfacial enhancement of carbon fiber composites by poly(amido amine) functionalization

abstract: Abstract Carbon fiber reinforced composites have stimulated increasing attention due to their excellent mechanical properties. However, the interface tends to be weak because carbon fiber does not naturally have a good wettability and adsorption with most polymers matrix, which could not effectively transfer the load from the matrix to the fibers. Here we proposed to chemically functionalize carbon fiber (CF) by poly (amido amine) (PAMAM), which has more concentration of amino groups than traditional coupling agents. The chemical bond formation between acid-treated CF and PAMAM was confirmed. The PAMAM functionalization did not change the surface morphology while significantly increased the wettability of CF surface. The interfacial shear strength has an increase of 85% compared to that without functionalization. The interfacial enhancement mechanism was also explored in details.


title: Electric field Monte Carlo simulation of polarized light propagation in turbid media

abstract: A Monte Carlo method based on tracing the multiply scattered electric field is presented to simulate the propagation of polarized light in turbid media. Multiple scattering of light comprises a series of updates of the parallel and perpendicular components of the complex electric field with respect to the scattering plane by the amplitude scattering matrix and rotations of the local coordinate system spanned by the unit vectors in the directions of the parallel and perpendicular electric field components and the propagation direction of light. The backscattering speckle pattern and the backscattering Mueller matrix of an aqueous suspension of polystyrene spheres in a slab geometry are computed using this Electric Field Monte Carlo (EMC) method. An efficient algorithm computing the Mueller matrix in the pure backscattering direction is detailed in the paper.


title: General Survey on Security Issues on Internet of Things

abstract: technologies. The Internet of Things incorporates transparently and impeccably large number of assorted end systems, providing open access to selected data for digital services. Internet of things is a promising research in commerce, industry, and education applications. The abundance of sensors and actuators motivates sensing and actuate devices in communication scenarios thus enabling sharing of information in Internet of Things. Advances in sensor data collection technology and Radio Frequency Identification technology has led large number of smart devices connected to the Internet, continuously transmitting data over time.In the context of security, due to different communication overloads and standards conventional security services are not applicable on Internet of Things as a result of which the technological loopholes leads to the generation of malicious data, devices are compromised and so on. Hence a flexible mechanism can deal with the security threats in the dynamic environment of Internet of Things and continuous researches and new ideas needs to be regulated periodically for various upcoming challenges. This paper basically tries to cover up the security issues and challenges of Internet of Things along with a brief introduction on Internet of Things, its elements and components such as Radio Frequency Identification, Wireless Sensor Network and Near Field Communication.


title: Wnt-signalling pathways and microRNAs network in carcinogenesis: experimental and bioinformatics approaches

abstract: Over the past few years, microRNAs (miRNAs) have not only emerged as integral regulators of gene expression at the post-transcriptional level but also respond to signalling molecules to affect cell function(s). miRNAs crosstalk with a variety of the key cellular signalling networks such as Wnt, transforming growth factor-β and Notch, control stem cell activity in maintaining tissue homeostasis, while if dysregulated contributes to the initiation and progression of cancer. Herein, we overview the molecular mechanism(s) underlying the crosstalk between Wnt-signalling components (canonical and non-canonical) and miRNAs, as well as changes in the miRNA/Wnt-signalling components observed in the different forms of cancer. Furthermore, the fundamental understanding of miRNA-mediated regulation of Wnt-signalling pathway and vice versa has been significantly improved by high-throughput genomics and bioinformatics technologies. Whilst, these approaches have identified a number of specific miRNA(s) that function as oncogenes or tumour suppressors, additional analyses will be necessary to fully unravel the links among conserved cellular signalling pathways and miRNAs and their potential associated components in cancer, thereby creating therapeutic avenues against tumours. Hence, we also discuss the current challenges associated with Wnt-signalling/miRNAs complex and the analysis using the biomedical experimental and bioinformatics approaches.


title: From perception to pleasure: Music and its neural substrates

abstract: Music has existed in human societies since prehistory, perhaps because it allows expression and regulation of emotion and evokes pleasure. In this review, we present findings from cognitive neuroscience that bear on the question of how we get from perception of sound patterns to pleasurable responses. First, we identify some of the auditory cortical circuits that are responsible for encoding and storing tonal patterns and discuss evidence that cortical loops between auditory and frontal cortices are important for maintaining musical information in working memory and for the recognition of structural regularities in musical patterns, which then lead to expectancies. Second, we review evidence concerning the mesolimbic striatal system and its involvement in reward, motivation, and pleasure in other domains. Recent data indicate that this dopaminergic system mediates pleasure associated with music; specifically, reward value for music can be coded by activity levels in the nucleus accumbens, whose functional connectivity with auditory and frontal areas increases as a function of increasing musical reward. We propose that pleasure in music arises from interactions between cortical loops that enable predictions and expectancies to emerge from sound patterns and subcortical systems responsible for reward and valuation.


title: Value-based software engineering: reinventing

abstract: The Value-Based Software Engineering (VBSE) agenda described in the preceding article has the objectives of integrating value considerations into current and emerging software engineering principles and practices, and of developing an overall framework in which they compatibly reinforce each other. In this paper, we provide a case study illustrating some of the key VBSE practices, and focusing on a particular anomaly in the monitoring and control area: the "Earned Value Management System." This is a most useful technique for monitoring and controlling the cost, schedule, and progress of a complex project. But it has absolutely nothing to say about the stakeholder value of the system being developed. The paper introduces an example order-processing software project, and shows how the use of Benefits Realization Analysis, stake-holder value proposition elicitation and reconciliation, and business case analysis provides a framework for stakeholder-earned-value monitoring and control.


Fingerprint Based Student Attendance System Using GSM


title: MEBN: A Logic for Open-World Probabilistic Reasoning

abstract: Partial Support: ::: DARPA & AFRL contract F33615-98-C-1314 ::: Alphatech subcontract 98036-7488 ::: Additional Support: ::: Advanced Research and Development Activity (ARDA) contract NBCH030059 issued by the Department of the Interior


title: Dimensional synthesis of a lightweight shoulder exoskeleton

abstract: Powered exoskeletons can provide motion enhancement for both healthy and physically challenged people. Compared with lower limb exoskeletons, upper limb exoskeletons are required to have multiple degrees-of-freedom and can still produce sufficient augmentation force. Designs using serial mechanisms usually result in complicated and bulky exoskeletons that prevent themselves from being wearable. This paper presents a new exoskeleton aimed to achieve compactness and wearability. We consider a shoulder exoskeleton that consists of two spherical mechanisms with two slider crank mechanisms. The actuators can be made stationary and attached side-by-side, close to a human body. Thus better inertia properties can be obtained while maintaining lightweight. The dimensions of the exoskeleton are synthesized to achieve maximum output force. Through illustrations of a prototype, the exoskeleton is shown to be wearable and can provide adequate motion enhancement of a human's upper limb.


title: Replisom: Disciplined Tiny Memory Replication for Massive IoT Devices in LTE Edge Cloud

abstract: Augmenting the long-term evolution (LTE)-evolved NodeB (eNB) with cloud resources offers a low-latency, resilient, and LTE-aware environment for offloading the Internet of Things (IoT) services and applications. By means of devices memory replication, the IoT applications deployed at an LTE-integrated edge cloud can scale its computing and storage requirements to support different resource-intensive service offerings. Despite this potential, the massive number of IoT devices limits the LTE edge cloud responsiveness as the LTE radio interface becomes the major bottleneck given the unscalability of its uplink access and data transfer procedures to support a large number of devices that simultaneously replicate their memory objects with the LTE edge cloud. We propose Replisom ; an LTE-aware edge cloud architecture and an LTE-optimized memory replication protocol which relaxes the LTE bottlenecks by a delay and radio resource-efficient memory replication protocol based on the device-to-device communication technology and the sparse recovery in the theory of compressed sampling. Replisom effectively schedules the memory replication occasions to resolve contentions for the radio resources as a large number of devices simultaneously transmit their memory replicas. Our analysis and numerical evaluation suggest that this system has significant potential in reducing the delay, energy consumption, and cost for cloud offloading of IoT applications given the massive number of devices with tiny memory sizes.


title: Non-Volatile Non-Shadow flip-flop using Spin Orbit Torque for efficient normally-off computing

abstract: With technology scaling, conventional CMOS-based flipflops can no longer efficiently cope with the increasing leakage power challenge. Therefore, various non-volatile flip-flop designs were recently introduced to reduce the static power consumption. However, these flipflop architectures employ non-volatile Magnetic Tunnel Junction (MTJ) storing devices only for backup, i.e. to save and restore the content before and after power gating. This limits their efficiency for aggressive power gating for effective power reduction. To overcome this limitation, we propose a novel Non-Volatile Non-Shadow flip-flop (NVNS-FF) using Spin Orbit Torque (SOT) based MTJ cells. In this design, we exploit the high speed, low energy and high-reliability features of SOT devices to employ them as active components of the flip-flop. This enables efficient normallyoff computing by allowing very aggressive power gating for both short and long standby periods. Experimental results show that the NVNS-FF has similar energy and timing characteristics as conventional CMOSbased flip-flops in active mode, and at the same time it allows to reduce the static power by 5X compared to backup flip-flops.


title: An improved Opposition-Based Sine Cosine Algorithm for global optimization

abstract: A new method to solve global optimization and engineering problems called OBSCA.The proposed method improves the SCA by using opposite-based learning.We apply the OBSCA over mathematical benchmark functions.We test OBSCA in engineering optimization problems.Comparisons support the improvement on the performance of OBCSA. Real life optimization problems require techniques that properly explore the search spaces to obtain the best solutions. In this sense, it is common that traditional optimization algorithms fail in local optimal values. The Sine Cosine Algorithms (SCA) has been recently proposed; it is a global optimization approach based on two trigonometric functions. SCA uses the sine and cosine functions to modify a set of candidate solutions; such operators create a balance between exploration and exploitation of the search space. However, like other similar approaches, SCA tends to be stuck into sub-optimal regions that it is reflected in the computational effort required to find the best values. This situation occurs due that the operators used for exploration do not work well to analyze the search space. This paper presents an improved version of SCA that considers the opposition based learning (OBL) as a mechanism for a better exploration of the search space generating more accurate solutions. OBL is a machine learning strategy commonly used to increase the performance of metaheuristic algorithms. OBL considers the opposite position of a solution in the search space. Based on the objective function value, the OBL selects the best element between the original solution and its opposite position; this task increases the accuracy of the optimization process. The hybridization of concepts from different fields is crucial in intelligent and expert systems; it helps to combine the advantages of algorithms to generate more efficient approaches. The proposed method is an example of this combination; it has been tested over several benchmark functions and engineering problems. Such results support the efficacy of the proposed approach to find the optimal solutions in complex search spaces.


title: MAC layer-based evaluation of IoT technologies: LoRa, SigFox and NB-IoT

abstract: In this paper, we explore functionalities of MAC layer for three Low Power Wide Area Networks (LPWAN) technologies (LoRa, Sigfox and NB-IoT) dedicated for Smart Cities applications and Internet of Things (IoT). These networks are dedicated to long-range (up to dozens of kilometers) and low rate communication to ensure a good autonomy up to 10 years. The technical differences at the level of MAC layer between Sigfox and LoRa and NB-IoT are explained and evaluated. We apply our simulator to evaluate the performances of these technologies in terms of Packet Error Rate.


title: Usability measurement and metrics: A consolidated model

abstract: Usability is increasingly recognized as an important quality factor for interactive software systems, including traditional GUIs-style applications, Web sites, and the large variety of mobile and PDA interactive services. Unusable user interfaces are probably the single largest reasons why encompassing interactive systems --- computers plus people, fail in actual use. The design of this diversity of applications so that they actually achieve their intended purposes in term of ease of use is not an easy task. Although there are many individual methods for evaluating usability; they are not well integrated into a single conceptual framework that facilitate their usage by developers who are not trained in the filed of HCI. This is true in part because there are now several different standards (e.g., ISO 9241, ISO/IEC 9126, IEEE Std.610.12) or conceptual models (e.g., Metrics for Usability Standards in Computing [MUSiC]) for usability, and not all of these standards or models describe the same operational definitions and measures. This paper first reviews existing usability standards and models while highlighted the limitations and complementarities of the various standards. It then explains how these various models can be unified into a single consolidated, hierarchical model of usability measurement. This consolidated model is called Quality in Use Integrated Measurement (QUIM). Included in the QUIM model are 10 factors each of which corresponds to a specific facet of usability that is identified in an existing standard or model. These 10 factors are decomposed into a total of 26 sub-factors or measurable criteria that are furtherdecomposed into 127 specific metrics. The paper explains also how a consolidated model, such as QUIM, can help in developing a usability measurement theory.


title: TensorFlow: learning functions at scale

abstract: TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. Its computational model is based on dataflow graphs with mutable state. Graph nodes may be mapped to different machines in a cluster, and within each machine to CPUs, GPUs, and other devices. TensorFlow supports a variety of applications, but it particularly targets training and inference with deep neural networks. It serves as a platform for research and for deploying machine learning systems across many areas, such as speech recognition, computer vision, robotics, information retrieval, and natural language processing. In this talk, we describe TensorFlow and outline some of its applications. We also discuss the question of what TensorFlow and deep learning may have to do with functional programming. Although TensorFlow is not purely functional, many of its uses are concerned with optimizing functions (during training), then with applying those functions (during inference). These functions are defined as compositions of simple primitives (as is common in functional programming), with internal data representations that are learned rather than manually designed. TensorFlow is joint work with many other people in the Google Brain team and elsewhere. More information is available at tensorflow.org.


title: A novel broadband circularly polarized printed monopole antenna

abstract: This paper presents a broadband printed circularly polarized monopole antenna. The base antenna consists of a falcate-shaped monopole antenna which provides 43% axial ratio bandwidth. By inserting a falcate-shaped slot in the base patch, 56% axial ratio is obtained. Moreover, inserting a slot in the base patch has no effect on the impedance bandwidth of the original structure. The proposed antenna dimension is 40 × 46 mm2 and is fabricated on 1 mm FR4 substrate. The simulated return loss and axial ratio bandwidth of the antenna structure with and without the falcate shaped slot are presented and compared. Moreover, circular polarization radiation patterns of the proposed antenna are presented.


title: Fusion of Smartphone Motion Sensors for Physical Activity Recognition

abstract: For physical activity recognition, smartphone sensors, such as an accelerometer and a gyroscope, are being utilized in many research studies. So far, particularly, the accelerometer has been extensively studied. In a few recent studies, a combination of a gyroscope, a magnetometer (in a supporting role) and an accelerometer (in a lead role) has been used with the aim to improve the recognition performance. How and when are various motion sensors, which are available on a smartphone, best used for better recognition performance, either individually or in combination? This is yet to be explored. In order to investigate this question, in this paper, we explore how these various motion sensors behave in different situations in the activity recognition process. For this purpose, we designed a data collection experiment where ten participants performed seven different activities carrying smart phones at different positions. Based on the analysis of this data set, we show that these sensors, except the magnetometer, are each capable of taking the lead roles individually, depending on the type of activity being recognized, the body position, the used data features and the classification method employed (personalized or generalized). We also show that their combination only improves the overall recognition performance when their individual performances are not very high, so that there is room for performance improvement. We have made our data set and our data collection application publicly available, thereby making our experiments reproducible.


title: Development of an Adaptive Learning System with Multiple Perspectives based on Students' Learning Styles and Cognitive Styles.

abstract: In this study, an adaptive learning system is developed by taking multiple dimensions of personalized features into account. A personalized presentation module is proposed for developing adaptive learning systems based on the field dependent/independent cognitive style model and the eight dimensions of Felder-Silverman's learning style. An experiment has been conducted to evaluate the performance of the proposed approach in a computer science course. Fifty-four participants were randomly assigned to an experimental group which learned with an adaptive learning system developed based on the personalized presentation module, and a control group which learned with the conventional learning system without personalized presentation. The experimental results showed that the experimental group students revealed significantly better learning achievements than the control group students, implying that the proposed approach is able to assist the students in improving their learning performance.


title: Membrane lipid fluidity as rate limiting in the concanavalin A-mediated agglutination of pyBHK cells.

abstract: The initial rate of concanavalin A-mediated agglutination of polyoma transformed Baby Hamster Kidney (pyBHK) cells follows Arrhenius kinetics. There is a smooth decrease in the agglutination rate from 37 degrees C to 22 degrees C with an activation energy of 11.8 +/- 0.2 kcal/mol in this region. There is a sharp decrease in agglutination rate below 22 degrees C. The addition of 0.1 mM 1,3-di-tert-2-hydroxyl-5-methylbenzene, a lipid perturber, increases the agglutination rate by a factor of two and increases the membrane lipid fluidity as determined by the spin label method. The rotational correlation time of the spin label 2N14 (2,2-dimethyl-5-dodecyl-5-methyloxazolidine-N-oxide) was measured. The sum of the enthalpy of activation of rotational diffusion and the enthalpy of activation of translational diffusion is very nearly equal to the enthalpy of activation of agglutination. This is consistent with the rate limiting step of agglutination being receptor diffusion, which is probably limited in pyBHK cells by membrane lipid fluidity.


Terrain modeling


title: Throughput Optimization, Spectrum Allocation, and Access Control in Two-Tier Femtocell Networks

abstract: The deployment of femtocells in a macrocell network is an economical and effective way to increase network capacity and coverage. Nevertheless, such deployment is challenging due to the presence of inter-tier and intra-tier interference, and the ad hoc operation of femtocells. Motivated by the flexible subchannel allocation capability of OFDMA, we investigate the effect of spectrum allocation in two-tier networks, where the macrocells employ closed access policy and the femtocells can operate in either open or closed access. By introducing a tractable model, we derive the success probability for each tier under different spectrum allocation and femtocell access policies. In particular, we consider joint subchannel allocation, in which the whole spectrum is shared by both tiers, as well as disjoint subchannel allocation, whereby disjoint sets of subchannels are assigned to both tiers. We formulate the throughput maximization problem subject to quality of service constraints in terms of success probabilities and per-tier minimum rates, and provide insights into the optimal spectrum allocation. Our results indicate that with closed access femtocells, the optimized joint and disjoint subchannel allocations provide the highest throughput among all schemes in sparse and dense femtocell networks, respectively. With open access femtocells, the optimized joint subchannel allocation provides the highest possible throughput for all femtocell densities.


Meeting Spiritual Needs through Dance/Movement Therapy when the Mind Dissipates in Dementia: Development of a Method


title: Advanced Driver Assistance Systems - Past, present and future

abstract: Every year, automobile manufactures spend millions in funding development of cutting edge technologies to keep drivers safe and accident free while operating their vehicles. These technologies are known in the industry as Advanced Driver Assistance Systems and each one is controlled by complex real-time embedded systems. This paper will present the state of the art for Advanced Driver Assistance Systems and will give detailed overviews of past, present and future designs of each variant. Each overview will focus on the design of the actual real-time embedded system involved.


title: Using a model to compute the optimal schedule of practice

abstract: By balancing the spacing effect against the effects of recency and frequency, this paper explains how practice may be scheduled to maximize learning and retention. In an experiment, an optimized condition using an algorithm determined with this method was compared with other conditions. The optimized condition showed significant benefits with large effect sizes for both improved recall and recall latency. The optimization method achieved these benefits by using a modeling approach to develop a quantitative algorithm, which dynamically maximizes learning by determining for each item when the balance between increasing temporal spacing (that causes better long-term recall) and decreasing temporal spacing (that reduces the failure related time cost of each practice) means that the item is at the spacing interval where long-term gain per unit of practice time is maximal. As practice repetitions accumulate for each item, items become stable in memory and this optimal interval increases.


Emotion Detection using EPOC EEG Device


title: Ambulance redeployment: an approximate dynamic programming approach

abstract: Emergency medical service (EMS) providers are charged with the task of managing ambulances so that the time required to respond to emergency calls is minimized. One approach that may assist in reducing response times is ambulance redeployment, i.e., repositioning idle ambulances in real time. We formulate a simulation model of EMS operations to evaluate the performance of a given allocation policy and use this model in an approximate dynamic programming (ADP) context to compute high-quality redeployment policies. We find that the resulting ADP policies perform much better than sub-optimal static policies and marginally better than near-optimal static policies. Representative computational results for Edmonton, Alberta are included.


title: Production Potential of a Natural Pasture Compared to a Wheat Pasture, Both Grazed by Lactating Goats under Mediterranean Climate Conditions

abstract: The aim of this study was to extend the grazing period using a wheat pasture that produced high quality forage, and to compare the productivity of natural and wheat pastures for grazing goats during a 3-year period (2003-2005) in a Mediterranean zone of Turkey. Twelve 3-4-year-old Saanen goats were allocated to each pasture (10 goats ha-1). Pastures were grazed for 2 months (May and June) in 2003 and for 3 months (April, May, and June) in 2004 and 2005. In the study, pasture yield and hay intake, crude protein (CP), neutral detergent fiber (NDF), and acid detergent fiber (ADF) content of the hay, and milk yield and milk fat percentage were determined. Pasture yield was significantly affected by pasture type (P < 0.001) for each year and the wheat pasture (5.34-6.51 t ha-1) yielded approximately 3.5-fold more hay than the natural pasture (1.48-1.88 t ha-1). Goats in the wheat pasture consumed significantly more forage in 2003 (P = 0.015), 2004 (P < 0.001), and 2005 (P < 0.001). Average hay intake in the wheat pasture ranged between 2.32 ± 0.27 and 2.53 ± 0.27 kg d-1 in 2003, 1.42 ± 0.26 and 3.35 ± 0.26 kg d-1 in 2004, and 2.69 ± 0.25 and 3.33 ± 0.25 kg d-1 in 2005, whereas it ranged between 1.54 ± 0.27 and 1.95 ± 0.27 kg d-1 in 2003, 0.79 ± 0.26 and 1.79 ± 0.26 kg d-1 in 2004, and 0.81 ± 0.25 and 1.60 ± 0.25 kg d-1 in 2005 in the natural pasture. Most of the interactions between pasture type and grazing month were statistically significant in terms of the CP, NDF, and ADF content of the pasture samples. In the first months CP content of the hay in the wheat pasture was higher than in the natural pasture, whereas this was reversed, especially in June. In addition, NDF and ADF content were not similar to CP content. In general, daily milk yield of the goats grazed on the wheat pasture was higher than that of those grazed on the natural pasture throughout the study. Milk yield was consistent with hay intake and milk fat percentage was inversely related to milk yield during the study. It was concluded that the wheat pasture had better potential for supplying nutrients to lactating dairy goats than did the natural pasture in spring.


title: Geo-Spatial Multimedia Sentiment Analysis in Disasters

abstract: Sentiment analysis of disaster-related posts in social media can contribute to the situation awareness and better understanding of the dynamics of disaster events by identifying the polarity of sentiments expressed by the public. However, Even though many sentiment analysis techniques have been developed and available, there are still limitations in reliably using sentiment analysis since there is no dominantly accepted technique in disasters. Taking advantage of existing state-of-the-art sentiment classifiers, this paper proposes a novel framework for geo-spatial sentiment analysis of disaster-related social media data objects. Our framework addresses three types of challenges: the inaccuracy and discrepancy associated with various text and image sentiment classifiers, the geo-sentiment discrepancy among data objects in a local geographical area, and observing diverse sentiments from multimedia data objects (i.e., text and image). The extracted sentiments are aggregated geographically for the purpose of extracting more accurate local regional insights. For the evaluation of the framework, we explored Twitter and Flickr datasets at the time of Hurricane Sandy and Napa Earthquake and showed how our approach can provide a better understanding of disaster events.


title: Pro-Eating Disorder Communities on Social Networking Sites: A Content Analysis

abstract: The purpose of this study was to assess the number of pro-ana groups on social networking sites and to analyze their content. A general inductive approach was used to analyze the content. Two main themes emerged from the content analysis: social support and eating disorder specific content. Themes were similar across all groups; however, a linguistic analysis indicated differences between groups on the two different networking sites. There was an absence of content typically found on Internet sites. Pro-ana groups on social networking sites are focused on social interactions, and lack eating disorder specific content found on Internet sites.


title: When the High School Coach Is a Bully

abstract: Though it is known that a minority of bully coaches exist, statistics regarding the actual prevalence of bully coaches in high schools are not well researched. School nurses may be placed in a consultative role with student athletes and their family members. Resource availability and suggestions for a plan of action are important for the school nurse. Support and encouragement for student athletes is critical for the development of positive mental health for young adults; therefore, families, peers, school personnel, and school nurses must all work together to produce positive sports experiences for high school age students.


title: Beyond the point cloud: from transductive to semi-supervised learning

abstract: Due to its occurrence in engineering domains and implications for natural learning, the problem of utilizing unlabeled data is attracting increasing attention in machine learning. A large body of recent literature has focussed on the transductive setting where labels of unlabeled examples are estimated by learning a function defined only over the point cloud data. In a truly semi-supervised setting however, a learning machine has access to labeled and unlabeled examples and must make predictions on data points never encountered before. In this paper, we show how to turn transductive and standard supervised learning algorithms into semi-supervised learners. We construct a family of data-dependent norms on Reproducing Kernel Hilbert Spaces (RKHS). These norms allow us to warp the structure of the RKHS to reflect the underlying geometry of the data. We derive explicit formulas for the corresponding new kernels. Our approach demonstrates state of the art performance on a variety of classification tasks.


title: Soft Wearable Augmented Walking Suit With Pneumatic Gel Muscles and Stance Phase Detection System to Assist Gait

abstract: The lower limbs of the human body are responsible for human locomotion and maintaining a good quality of life. However, there are many instances of muscle fatigue or injuries caused by stressful work environments, aging, and work that involves walking a long distance. Therefore, there is a need for an assistive suit for walking that can unload muscle efforts during walking and reduce the chances of lower-limb muscle fatigue. In this letter, we discuss the development of a lightweight and wearable augmented walking suit (AWS) using pneumatic gel muscle and its actuation control the using lower limb pose detection mechanism considering the human gait cycle. The objective of this assistive suit is to reduce the required muscle effort of the posterior and anterior muscles during the swing phase of the gait cycle, thereby making it easier to move forward. To evaluate the effects of the AWS, an experiment was conducted to record the surface EMG (sEMG) of eight primary lower limb muscles of seven subjects for two levels of assistive air pressure. The evaluation was done based on the sEMG signal envelope and the statistical difference in the average percentage of maximum voluntary contraction of the measured muscles. In our result, we found that all muscles showed a statistically significant reduction or no change in muscle activity while wearing the AWS as compared with when the AWS was not worn.


title: IoT Security and Privacy Issues

abstract: Internet of Things (IoT) represents the future of technology where objects will be able to communicate with each other and connect to the internet to create self-configuration, intelligent system. Beside IoT development benefits, there is still great doubt about security and privacy which are considered major challenges facing the adaption and development of IoT architecture. Security and privacy issues are the most critical challenge that needs to be addressed in each IoT layer and it is not fully addressed yet. There have been many studies propose solutions to address security issues. Protecting IoT require appropriate security framework that covers all IoT layer-security issues. This paper presents a review of security and privacy issues and challenges in IoT. It also shed a light on security requirements relevant to IoT environment and some proposed solutions.


title: An interpretable LSTM neural network for autoregressive exogenous model

abstract: In this paper, we propose an interpretable LSTM recurrent neural network, i.e., multi-variable LSTM for time series with exogenous variables. Currently, widely used attention mechanism in recurrent neural networks mostly focuses on the temporal aspect of data and falls short of characterizing variable importance. To this end, our multi-variable LSTM equipped with tensorized hidden states is developed to learn variable specific representations, which give rise to both temporal and variable level attention. Preliminary experiments demonstrate comparable prediction performance of multi-variable LSTM w.r.t. encoder-decoder based baselines. More interestingly, variable importance in real datasets characterized by the variable attention is highly in line with that determined by statistical Granger causality test, which exhibits the prospect of multi-variable LSTM as a simple and uniform end-to-end framework for both forecasting and knowledge discovery.


title: A Survey on Uncertainty Management in Data Integration

abstract: In the last few years, uncertainty management has come to be recognized as a fundamental aspect of data integration. It is now accepted that it may not be possible to remove uncertainty generated during data integration processes and that uncertainty in itself may represent a source of relevant information. Several issues, such as the aggregation of uncertain mappings and the querying of uncertain mediated schemata, have been addressed by applying well-known uncertainty management theories. However, several problems lie unresolved. This article sketches an initial picture of this highly active research area; it details existing works in the light of a homogeneous framework, and identifies and discusses the leading issues awaiting solutions.


title: Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization

abstract: We address the problem of maximizing an unknown submodular function that can only be accessed via noisy evaluations. Our work is motivated by the task of summarizing content, e.g., image collections, by leveraging users' feedback in form of clicks or ratings. For summarization tasks with the goal of maximizing coverage and diversity, submodular set functions are a natural choice. When the underlying submodular function is unknown, users' feedback can provide noisy evaluations of the function that we seek to maximize. We provide a generic algorithm -- \submM{} -- for maximizing an unknown submodular function under cardinality constraints. This algorithm makes use of a novel exploration module -- \blbox{} -- that proposes good elements based on adaptively sampling noisy function evaluations. \blbox{} is able to accommodate different kinds of observation models such as value queries and pairwise comparisons. We provide PAC-style guarantees on the quality and sampling cost of the solution obtained by \submM{}. We demonstrate the effectiveness of our approach in an interactive, crowdsourced image collection summarization application.


title: Hardware system synthesis from Domain-Specific Languages

abstract: Field Programmable Gate Arrays (FPGAs) are very versatile devices, but their complicated programming model has stymied their widespread usage. While modern High-Level Synthesis (HLS) tools provide better programming models, the interface they offer is still too low-level. In order to produce good quality hardware designs with these tools, the users are forced to manually perform optimizations that demand detailed knowledge of both the application and the implementation platform. Additionally, many HLS tools only generate isolated hardware modules that the user still needs to integrate into a system design before generating the FPGA bitstream. These problems make HLS tools difficult to use for application developers who have little hardware design knowledge. To address these problems, we propose an automated methodology to generate FPGA bitstreams from high-level programs written in Domain-Specific Languages (DSLs). We leverage the domain-knowledge conveyed by the DSL and its domain-specific semantics to extract application parallelism, perform optimizations and also identify a suitable system-architecture for the implementation, thereby, relieving the user from most of the hardware-level details. We demonstrate the high productivity and high design quality this approach offers by automatically generating hardware systems from applications written in OptiML, a machine-learning DSL. To evaluate our methodology, we use four OptiML applications and show that we can easily generate different solutions which achieve different trade-offs between performance and area. More importantly, the results reveal that our generated hardware achieves much better performance compared to the one obtained from using the HLS tool without platform-specific optimizations.


title: A Voltage Efficient PMOS Charge Pump Architecture

abstract: In this paper a charge pump circuit is presented: it is based on PMOS pass transistors with dynamic control of the gate and body voltages. By controlling the gate and the bulk of each PMOS pass transistor, the voltage loss due to the device threshold is removed and the charge is pumped from one stage to the other with negligible voltage drop. Furthermore, the overdrive voltage of the pass transistors grows progressively from the first to the last stage. Hence, a larger output voltage can be achieved compared to conventional charge pumps, still retaining a simple two-phase clocking scheme, along with the Dickson basic architecture. The prototype was fabricated using the STMicroelectronics T10 90nm CMOS process. Measurements results are also presented confirming the validity of the design.


IntentNet: Learning to Predict Intention from Raw Sensor Data


title: Classification of heuristic techniques for performance comparisons

abstract: In this paper we proposed an enhanced classification of heuristics and developed a unified heuristic classifying scheme and templates for future researchers to conduct a fair heuristic performance. The process involved a thorough diagnostic of the proposed heuristic classification schemes in order to find unique characteristics that can assist us to distinguish each heuristic. We started by carrying out a survey of the heuristic classification schemes and templates previously proposed in the literature. We discovered that most of heuristic classifying schemes and templates proposed in the literature looked at individual heuristic class without integrating together all the classes of heuristics, thus justifying the need for a unified heuristic classifying scheme and templates. We also discovered some common interlinked and interrelated templates of classifying heuristics within which sub-templates were identified. The major challenge in classifying heuristics is basically on discovering unique features (of these heuristics and their variants) that are capable to unlock the element of heuristics belonging to the classifying templates.