title: Discrepancies between dimensions of interoception in autism: Implications for emotion and anxiety

abstract: Emotions and affective feelings are influenced by one's internal state of bodily arousal via interoception. Autism Spectrum Conditions (ASC) are associated with difficulties in recognising others' emotions, and in regulating own emotions. We tested the hypothesis that, in people with ASC, such affective differences may arise from abnormalities in interoceptive processing. We demonstrated that individuals with ASC have reduced interoceptive accuracy (quantified using heartbeat detection tests) and exaggerated interoceptive sensibility (subjective sensitivity to internal sensations on self-report questionnaires), reflecting an impaired ability to objectively detect bodily signals alongside an over-inflated subjective perception of bodily sensations. The divergence of these two interoceptive axes can be computed as a trait prediction error. This error correlated with deficits in emotion sensitivity and occurrence of anxiety symptoms. Our results indicate an origin of emotion deficits and affective symptoms in ASC at the interface between body and mind, specifically in expectancy-driven interpretation of interoceptive information.


Multi-Contrast Imaging and Digital Refocusing on a Mobile Microscope with a Domed LED Array


abstract: Social media, e.g. Twitter, has become a widely used medium for the exchange of information, but it has also become a valuable tool for hackers to spread misinformation through compromised accounts. Hence, detecting compromised accounts is a necessary step toward a safe and secure social media environment. Nevertheless, detecting compromised accounts faces several challenges. First, social media activities of users are temporally correlated which plays an important role in compromised account detection. Second, data associated with social media accounts is inherently sparse. Finally, social contagions where multiple accounts become compromised, take advantage of the user connectivity to propagate their attack. Thus how to represent each user's network features for compromised account detection is an additional challenge. To address these challenges, we propose an End-to-End Compromised Account Detection framework (E2ECAD). E2ECAD effectively captures temporal correlations via an LSTM (Long Short-Term Memory) network. Further, it addresses the sparsity problem by defining and employing a user context representation. Meanwhile, informative network-related features are modeled efficiently. To verify the working of the framework, we construct a real-world dataset of compromised accounts on Twitter and conduct extensive experiments. The results of experiments show that E2ECAD outperforms the state of the art compromised account detection algorithms.


title: A structural model for FOXRED1, an FAD-dependent oxidoreductase necessary for NADH: Ubiquinone oxidoreductase (complex I) assembly.

abstract: The biogenesis of mitochondrial respiratory chain components is complex. Mammalian complex I (NADH:ubiquinone oxidoreductase) contains 44 different subunits, an FMN and seven iron-sulfur centers. Its assembly involves at least twelve additional proteins, called assembly factors. One of these is FOXRED1, a 486-amino acid FAD-dependent oxidoreductase. FOXRED1 is a member of the d-amino acid oxidase (DAO) family. A structural model of FOXRED1 reveals a large substrate-binding cavity and a putative oxygen-binding site. These features strongly suggest that FOXRED1 is catalytically active as an oxidoreductase. A metabolic role for FOXRED1 in the biogenesis of complex I should be considered.


title: xCLiMF: optimizing expected reciprocal rank for data with multiple levels of relevance

abstract: Extended Collaborative Less-is-More Filtering xCLiMF is a learning to rank model for collaborative filtering that is specifically designed for use with data where information on the level of relevance of the recommendations exists, e.g. through ratings. xCLiMF can be seen as a generalization of the Collaborative Less-is-More Filtering (CLiMF) method that was proposed for top-N recommendations using binary relevance (implicit feedback) data. The key contribution of the xCLiMF algorithm is that it builds a recommendation model by optimizing Expected Reciprocal Rank, an evaluation metric that generalizes reciprocal rank in order to incorporate user feedback with multiple levels of relevance. Experimental results on real-world datasets show the effectiveness of xCLiMF, and also demonstrate its advantage over CLiMF when more than two levels of relevance exist in the data.


title: Plug-and-Play priors for model based reconstruction

abstract: Model-based reconstruction is a powerful framework for solving a variety of inverse problems in imaging. In recent years, enormous progress has been made in the problem of denoising, a special case of an inverse problem where the forward model is an identity operator. Similarly, great progress has been made in improving model-based inversion when the forward model corresponds to complex physical measurements in applications such as X-ray CT, electron-microscopy, MRI, and ultrasound, to name just a few. However, combining state-of-the-art denoising algorithms (i.e., prior models) with state-of-the-art inversion methods (i.e., forward models) has been a challenge for many reasons. In this paper, we propose a flexible framework that allows state-of-the-art forward models of imaging systems to be matched with state-of-the-art priors or denoising models. This framework, which we term as Plug-and-Play priors, has the advantage that it dramatically simplifies software integration, and moreover, it allows state-of-the-art denoising methods that have no known formulation as an optimization problem to be used. We demonstrate with some simple examples how Plug-and-Play priors can be used to mix and match a wide variety of existing denoising models with a tomographic forward model, thus greatly expanding the range of possible problem solutions.


title: Comparative Study of CNN and RNN for Natural Language Processing

abstract: Deep neural networks (DNN) have revolutionized the field of natural language processing (NLP). Convolutional neural network (CNN) and recurrent neural network (RNN), the two main types of DNN architectures, are widely explored to handle various NLP tasks. CNN is supposed to be good at extracting position-invariant features and RNN at modeling units in sequence. The state of the art on many NLP tasks often switches due to the battle between CNNs and RNNs. This work is the first systematic comparison of CNN and RNN on a wide range of representative NLP tasks, aiming to give basic guidance for DNN selection.


title: Coarse-grained Dynamic Taint Analysis for Defeating Control and Non-control Data Attacks

abstract: Memory corruption attacks remain the primary threat for computer security. Information flow tracking or taint analysis has been proven to be effective against most memory corruption attacks. However, there are two shortcomings with current taint analysis based techniques. First, these techniques cause application slowdown by about 76% thereby limiting their practicality. Second, these techniques cannot handle non-control data attacks i.e., attacks that do not overwrite control data such as return address, but instead overwrite critical application configuration data or user identity data. In this work, to address these problems, we describe a coarse-grained taint analysis technique that uses information flow tracking at the level of application data objects. We propagate a one-bit taint over each application object that is modified by untrusted data thereby reducing the taint management overhead considerably. We performed extensive experimental evaluation of our approach and show that it can detect all critical attacks such as buffer overflows, and format string attacks, including non-control data attacks. Unlike the currently known approaches that can detect such a wide range of attacks, our approach does not require the source code or any hardware extensions. Run-time performance overhead evaluation shows that, on an average, our approach causes application slowdown by only 37% which is an order of magnitude improvement over existing approaches. Finally, since our approach performs run-time binary instrumentation, it is easier to integrate it with existing applications and systems.


title: Supporting risks in software project management

abstract: Complex software development is a risky job. The number of unsuccessful projects surpasses the number of successful developments, particularly when large projects are analyzed. This paper describes an approach to develop, retrieve, and reuse management knowledge and experience concerned with software development risks. Scenarios are used to model risk impact and resolution strategies efficacy within risk archetypes. A risk archetype is an information structure that holds knowledge about software development risks. A risk management process organizes the use of risk archetypes within an application development effort. The process resembles a reuse process framework, where two sub-processes are respectively responsible for identifying and reusing risk information. Simulating the impact of the expected risks can support some of the decisions throughout the software development process. The contribution of this paper is to show how risk archetypes and scenario models can represent reusable project management knowledge. An observational analysis of applying such an approach in an industrial environment and a feasibility study are also described.


title: VineSens: An Eco-Smart Decision-Support Viticulture System

abstract: This article presents VineSens, a hardware and software platform for supporting the decision-making of the vine grower. VineSens is based on a wireless sensor network system composed by autonomous and self-powered nodes that are deployed throughout a vineyard. Such nodes include sensors that allow us to obtain detailed knowledge on different viticulture processes. Thanks to the use of epidemiological models, VineSens is able to propose a custom control plan to prevent diseases like one of the most feared by vine growers: downy mildew. VineSens generates alerts that warn farmers about the measures that have to be taken and stores the historical weather data collected from different spots of the vineyard. Such data can then be accessed through a user-friendly web-based interface that can be accessed through the Internet by using desktop or mobile devices. VineSens was deployed at the beginning in 2016 in a vineyard in the Ribeira Sacra area (Galicia, Spain) and, since then, its hardware and software have been tested to prevent the development of downy mildew, showing during its first season that the system can led to substantial savings, to decrease the amount of phytosanitary products applied, and, as a consequence, to obtain a more ecologically sustainable and healthy wine.


title: Automated Wolf Motor Function Test (WMFT) for Upper Extremities Rehabilitation

abstract: The Wolf Motor Function Test (WMFT), which involves real time timing measurements, is used to define the functional ability of the paretic limb of post stroke patients. The accuracy and consistency of the timing results are crucial for scoring, while manual stopwatch measuring introduces mistakes and uncertainty. A technique which uses camera and sensors to automate the test and achieve accurate results is proposed here. This system measures time for seven tasks with a monitor camera and a Xilinx Virtex II Pro Field Programmable Gate Array (FPGA). The FPGA was mainly chosen for its better performance compared to general purpose System on Chip (SoC) for computational intensive tasks, such as image processing in this system. The accuracy of the system has been validated in a user study on selected WMFT tasks with ground truth time measurement obtained from pressure sensors. Suggestions are also given on how the framework can be adapted to the remaining tasks.


title: Production of Materials with Spatially-Controlled Cross-Link Density via Vat Photopolymerization

abstract: We describe an efficient method to produce objects comprising spatially controlled and graded cross-link densities using vat photopolymerization additive manufacturing (AM). Using a commercially available diacrylate-based photoresin, 3D printer, and digital light processing (DLP) projector, we projected grayscale images to print objects in which the varied light intensity was correlated to controlled cross-link densities and associated mechanical properties. Cylinder and bar test specimens were used to establish correlations between light intensities used for printing and cross-link density in the resulting specimens. Mechanical testing of octet truss unit cells in which the properties of the crossbars and vertices were independently modified revealed unique mechanical responses from the different compositions. From the various test geometries, we measured changes in mechanical properties such as increased strain-to-break in inhomogeneous structures in comparison with homogeneous variants.


title: Energy comparison of MPPT techniques for PV Systems

abstract: Many maximum power point tracking techniques for photovoltaic systems have been developed to maximize the produced energy and a lot of these are well established in the literature. These techniques vary in many aspects as: simplicity, convergence speed, digital or analogical implementation, sensors required, cost, range of effectiveness, and in other aspects. This paper presents a comparative study of ten widely-adopted MPPT algorithms; their performance is evaluated on the energy point of view, by using the simulation tool Simulink®, considering different solar irradiance variations. Key-Words: - Maximum power point (MPP), maximum power point tracking (MPPT), photovoltaic (PV), comparative study, PV Converter.


title: Vehicle Detection Based on Laser Radar

abstract: This paper describes the detection of moving obstacles using laser radar in road environments. This application is designed to be implemented in further research on data fusion technologies. The developed application uses only a laser radar which provides information to sort objects according to their shape and movement. The subsequent detection and classification provide higher level tracking.


title: Sleep Quality Prediction From Wearable Data Using Deep Learning

abstract: Background: The importance of sleep is paramount to health. Insufficient sleep can reduce physical, emotional, and mental well-being and can lead to a multitude of health complications among people with chronic conditions. Physical activity and sleep are highly interrelated health behaviors. Our physical activity during the day (ie, awake time) influences our quality of sleep, and vice versa. The current popularity of wearables for tracking physical activity and sleep, including actigraphy devices, can foster the development of new advanced data analytics. This can help to develop new electronic health (eHealth) applications and provide more insights into sleep science. Objective: The objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality. Methods: Actigraphy sensors are wearable medical devices used to study sleep and physical activity patterns. The dataset used in our experiments contained the complete actigraphy data from a subset of 92 adolescents over 1 full week. Physical activity data during awake time was used to create predictive models for sleep quality, in particular, poor or good sleep efficiency. The physical activity data from sleep time was used for the evaluation. We compared the predictive performance of traditional logistic regression with more advanced deep learning methods: multilayer perceptron (MLP), convolutional neural network (CNN), simple Elman-type recurrent neural network (RNN), long short-term memory (LSTM-RNN), and a time-batched version of LSTM-RNN (TB-LSTM). Results: Deep learning models were able to predict the quality of sleep (ie, poor or good sleep efficiency) based on wearable data from awake periods. More specifically, the deep learning methods performed better than traditional logistic regression. “CNN had the highest specificity and sensitivity, and an overall area under the receiver operating characteristic (ROC) curve (AUC) of 0.9449, which was 46% better as compared with traditional logistic regression (0.6463). Conclusions: Deep learning methods can predict the quality of sleep based on actigraphy data from awake periods. These predictive models can be an important tool for sleep research and to improve eHealth solutions for sleep. [JMIR Mhealth Uhealth 2016;4(4):e125]


6 Wire Robots Part I Kinematics , Analysis & Design


title: FocusMusicRecommender: A System for Recommending Music to Listen to While Working

abstract: This paper proposes FocusMusicRecommender, an automated system recommending background music to listen to while working. Recommendation systems matching user preferences have been widely researched even though research has shown that music that listeners strongly like is not suitable background music because it interferes with their concentration. FocusMusicRecommender plays songs that users may "neither like nor dislike" instead of "like very much." It is designed to by default summarize a song automatically so that users can give "like very much" feedback by pressing a "keep listening" button or "dislike very much" feedback by pressing a "skip" button. It uses this feedback, along with users» concentration levels estimated from their behavior history, to distinguish between the preference levels "like" and "like very much." It then estimates the preference levels of unplayed songs and selects the most suitable song by considering the user»s current concentration level. The effectiveness of the proposed feedback method and suitability of the recommendation results were verified experimentally and in user studies. Furthermore, it is confirmed that the proposed method can estimate the user»s concentration level more accurately than the previous methods.


title: BoxCars: Improving Fine-Grained Recognition of Vehicles using 3D Bounding Boxes in Traffic Surveillance

abstract: In this paper, we focus on fine-grained recognition of vehicles mainly in traffic surveillance applications. We propose an approach that is orthogonal to recent advancements in fine-grained recognition (automatic part discovery and bilinear pooling). In addition, in contrast to other methods focused on fine-grained recognition of vehicles, we do not limit ourselves to a frontal/rear viewpoint, but allow the vehicles to be seen from any viewpoint. Our approach is based on 3-D bounding boxes built around the vehicles. The bounding box can be automatically constructed from traffic surveillance data. For scenarios where it is not possible to use precise construction, we propose a method for an estimation of the 3-D bounding box. The 3-D bounding box is used to normalize the image viewpoint by “unpacking” the image into a plane. We also propose to randomly alter the color of the image and add a rectangle with random noise to a random position in the image during the training of convolutional neural networks (CNNs). We have collected a large fine-grained vehicle data set BoxCars116k, with 116k images of vehicles from various viewpoints taken by numerous surveillance cameras. We performed a number of experiments, which show that our proposed method significantly improves CNN classification accuracy (the accuracy is increased by up to 12% points and the error is reduced by up to 50% compared with CNNs without the proposed modifications). We also show that our method outperforms the state-of-the-art methods for fine-grained recognition.


Test anxiety and direction of attention.


title: Optimal Discrete Power Control in Poisson-Clustered Ad Hoc Networks

abstract: Power control in a digital handset is practically implemented in a discrete fashion and usually such a discrete power control (DPC) scheme is suboptimal. In this paper, we first show that in a Poison-distributed ad hoc network, if DPC is properly designed with a certain condition satisfied, it can strictly work better than constant power control (i.e. no power control) in terms of average signal-to-interference ratio, outage probability and spatial reuse. This motivates us to propose an $N$-layer DPC scheme in a wireless clustered ad hoc network, where transmitters and their intended receivers in circular clusters are characterized by a Poisson cluster process (PCP) on the plane $\mathbb{R}^2$. The cluster of each transmitter is tessellated into $N$-layer annuli with transmit power $P_i$ adopted if the intended receiver is located at the $i$-th layer. Two performance metrics of transmission capacity (TC) and outage-free spatial reuse factor are redefined based on the $N$-layer DPC. The outage probability of each layer in a cluster is characterized and used to derive the optimal power scaling law $P_i=\Theta\left(\eta_i^{-\frac{\alpha}{2}}\right)$, with $\eta_i$ the probability of selecting power $P_i$ and $\alpha$ the path loss exponent. Moreover, the specific design approaches to optimize $P_i$ and $N$ based on $\eta_i$ are also discussed. Simulation results indicate that the proposed optimal $N$-layer DPC significantly outperforms other existing power control schemes in terms of TC and spatial reuse.


title: Diffusing Policies : Towards Wasserstein Policy Gradient Flows

abstract: Policy gradients methods often achieve better performance when the change in policy is limited to a small Kullback-Leibler divergence. We derive policy gradients where the change in policy is limited to a small Wasserstein distance (or trust region). This is done in the discrete and continuous multi-armed bandit settings with entropy regularisation. We show that in the small steps limit with respect to the Wasserstein distance $W_2$, policy dynamics are governed by the heat equation, following the Jordan-Kinderlehrer-Otto result. This means that policies undergo diffusion and advection, concentrating near actions with high reward. This helps elucidate the nature of convergence in the probability matching setup, and provides justification for empirical practices such as Gaussian policy priors and additive gradient noise.


title: Dynamic categorization of clinical research eligibility criteria by hierarchical clustering

abstract: Objective: To semi-automatically induce semantic categories of eligibility criteria from text and to automatically classify eligibility criteria based on their semantic similarity. Design: The UMLS semantic types and a set of previously developed semantic preference rules were utilized to create an unambiguous semantic feature representation to induce eligibility criteria categories through hierarchical clustering and to train supervised classifiers. Measurements: We induced 27 categories and measured the prevalence of the categories in 27,278 eligibility criteria from 1578 clinical trials and compared the classification performance (i.e., precision, recall, and F1-score) between the UMLS-based feature representation and the ''bag of words'' feature representation among five common classifiers in Weka, including J48, Bayesian Network, Naive Bayesian, Nearest Neighbor, and instance-based learning classifier. Results: The UMLS semantic feature representation outperforms the ''bag of words'' feature representation in 89% of the criteria categories. Using the semantically induced categories, machine-learning classifiers required only 2000 instances to stabilize classification performance. The J48 classifier yielded the best F1-score and the Bayesian Network classifier achieved the best learning efficiency. Conclusion: The UMLS is an effective knowledge source and can enable an efficient feature representation for semi-automated semantic category induction and automatic categorization for clinical research eligibility criteria and possibly other clinical text.


title: Modeling, Metamodeling, and Taxonomy of System Failures

abstract: A methodology, called the metasystem approach, is used to discuss modeling and metamodeling of systems failures. The methodology is based on a hierarchy of control where decisional and behavioral processes are differentiated on the basis of domain and logic levels.


title: A flexible dual-band metamaterial based on hairpin split-ring resonators for wireless communication application

abstract: In this paper, a hairpin split ring resonator (SRR) is used to design a miniaturized dual-band metamaterial (MM). The unit cell of this MM structure is constructed by four hairpin SRRs and all of them are mutually rotated by 90°. Furthermore, a flexible substrate is adopted to improve the practicability of the MM. The current distribution of the MM has also been investigated to explain the principle of the dual-band resonances.


Optimizing Surface Electromyography Acquisition without Right Leg Drive Circuit


title: Comparative study of polyphenols and caffeine in different coffee varieties affected by the degree of roasting

abstract: The bioactive composition of coffee, as one of the most popular beverages in the world, has attracted interest as a potential source of beneficial bioactive compounds, especially polyphenols and caffeine. Since the content of these compounds is affected by the processing conditions, the objective of this study was to determine the content of polyphenolic compounds and caffeine in four different coffee varieties: Minas and Cioccolatato (Coffea arabica), and Cherry and Vietnam (Coffea canephora syn. Coffea robusta), roasted by three varying degrees (light, medium and dark). The content of the polyphenolic compounds and the antioxidant capacity of coffees were determined using UV/Vis spectrophotometric methods, while the content of chlorogenic acid derivatives was determined using HPLC analysis. The caffeine content was determined by means of two spectrophotometric methods, as well as HPLC analysis. Additionally, raw caffeine was also obtained by an isolation procedure with chloroform. Cherry coffee, a variety of C. canephora exhibited the highest overall content of total phenols (42.37 mg GAE/g), followed by Minas coffee, while Cioccolatato contained the lowest TPC (33.12 mg GAE/g). Cherry coffee also exhibited the highest content of individual classes of polyphenols (flavan-3-ols, procyanidins and tannins), while the highest content of chlorogenic acid (CQA) derivatives was determined in Minas and Cioccolatato coffees (C. arabica). The highest content of total and individual polyphenolic compounds was determined in coffees roasted in both light and medium roasting conditions, which was also observed for the content of CQA derivatives and antioxidant capacity of roasted coffees. The highest caffeine content in the coffee samples was determined by employing the HPLC analysis (0.06–2.55%). Light roasted Cherry coffee contained the highest overall content of caffeine among all coffees, which exhibited a decrease with intensified roasting.


Social and Information Network Analysis Final Report REVIEWER EXPERTISE RANKING USING LINK ANALYSIS


title: High Efficiency Flyback Converter Technology

abstract: Technologies are discussed to realize a DC/DC Flyback converter with high power conversion efficiency. The focus is on efficiency because of its high value in the application to process electricity generated by photovoltaic modules. Experiments with a 200 W active-clamped bidirectional Flyback converter prototype demonstrate power train efficiencies above 90% between 10% and 100% of the rated power and peak value of 96%.


title: Domain Adaptation for Sequence Labeling Tasks with a Probabilistic Language Adaptation Model

abstract: In this paper, we propose to address the problem of domain adaptation for sequence labeling tasks via distributed representation learning by using a log-bilinear language adaptation model. The proposed neural probabilistic language model simultaneously models two different but related data distributions in the source and target domains based on induced distributed representations, which encode both generalizable and domain-specific latent features. We then use the learned dense real-valued representation as augmenting features for natural language processing systems. We empirically evaluate the proposed learning technique on WSJ and MEDLINE domains with POS tagging systems, and on WSJ and Brown corpora with syntactic chunking and named entity recognition systems. Our primary results show that the proposed domain adaptation method outperforms a number of comparison methods for cross domain sequence labeling tasks.


title: Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images

abstract: Staining and scanning of tissue samples for microscopic examination is fraught with undesirable color variations arising from differences in raw materials and manufacturing techniques of stain vendors, staining protocols of labs, and color responses of digital scanners. When comparing tissue samples, color normalization and stain separation of the tissue images can be helpful for both pathologists and software. Techniques that are used for natural images fail to utilize structural properties of stained tissue samples and produce undesirable color distortions. The stain concentration cannot be negative. Tissue samples are stained with only a few stains and most tissue regions are characterized by at most one effective stain. We model these physical phenomena that define the tissue structure by first decomposing images in an unsupervised manner into stain density maps that are sparse and non-negative. For a given image, we combine its stain density maps with stain color basis of a pathologist-preferred target image, thus altering only its color while preserving its structure described by the maps. Stain density correlation with ground truth and preference by pathologists were higher for images normalized using our method when compared to other alternatives. We also propose a computationally faster extension of this technique for large whole-slide images that selects an appropriate patch sample instead of using the entire image to compute the stain color basis.


title: Location Privacy Protection Through Obfuscation-Based Techniques

abstract: The widespread adoption of mobile communication devices combined with technical improvements of location technologies are fostering the development of a new wave of applications that manage physical positions of individuals to offer location-based services for business, social or informational purposes. As an effect of such innovative services, however, privacy concerns are increasing, calling for more sophisticated solutions for providing users with different and manageable levels of privacy. In this work, we propose a way to express users privacy preferences on location information in a straightforward and intuitive way. Then, based on such location privacy preferences, we discuss a new solution, based on obfuscation techniques, which permits us to achieve, and quantitatively estimate through a metric, different degrees of location privacy.


title: The Behavioral Immune System

abstract: Human psychology is characterized by a motivational system (the “behavioral immune system”) that appears to have evolved as a means of providing prophylactic protection against pathogen infection. This chapter provides an overview of research on the behavioral immune system, describing why it evolved, how it operates, and its many implications—some obvious and some not—for human cognition and human behavior. This overview is presented in the form of 12 “things you need to know” that, collectively, summarize the scope of contemporary research on the behavioral immune system, and provide a foundation for thinking critically about it. ::: ::: ::: Keywords: ::: ::: behavioral immune system; ::: disease; ::: disgust; ::: evolution; ::: social cognition; ::: social interaction


title: Coordinated Multiple Points Transmission for LTE-Advanced Systems

abstract: LTE-Advanced systems which based on orthogonal frequency division multiplexing (OFDM) can eliminate intra-cell interference but still can not mitigate inter-cell interference (ICI). Coordinated Multi-Point transmission/reception (CoMP) is one of the candidate techniques for LTE-Advanced systems to increase the average cell throughput and cell edge user throughput in the both uplink and downlink. Although CoMP naturally increases system complexity, it has potentially significant capacity and coverage benefits, making it worth a more detailed consideration. In this paper, we focus on the downlink CoMP-MU-MIMO transmission scheme and present some system level performance evaluation which shows that the CoMP-MU-MIMO transmission scheme can bring significant gains to both the average cell throughput and the cell edge user throughput.


RCFA For Recurring Impeller Failures In A 4.7 Mtpa LNG Train Propane Compressor


title: Evaluating the impact of social media in detecting health-violating restaurants

abstract: Nowadays, detecting health-violating restaurants is a serious problem due to the limited number of health inspectors in a city as compared to the number of restaurants. Rarely inspectors are helped by formal complains, but many complaints are reported as reviews on social media such as Yelp. In this paper we propose new predictors to detect health-violating restaurants based on restaurant sub-area location, previous inspections history, Yelp reviews content, and Yelp users behavior. The resulting method outperforms past work, with a percentage of improvement in Cohen's kappa and Matthews correlation coefficient of at least 16%. In addition, we define a new method that directly evaluates the benefit of a classifier on the ability of an inspector in detecting health-violating restaurants. We show that our classification method really improves the ability of the inspector and outperforms previous solutions.


title: Detecting botnet by anomalous traffic

abstract: Botnets can cause significant security threat and huge loss to organizations, and are difficult to discover their existence. Therefore they have become one of the most severe threats on the Internet. The core component of botnets is their command and control channel. Botnets often use IRC (Internet Relay Chat) as a communication channel through which the botmaster can control the bots to launch attacks or propagate more infections. In this paper, anomaly score based botnet detection is proposed to identify the botnet activities by using the similarity measurement and the periodic characteristics of botnets. To improve the detection rate, the proposed system employs two-level correlation relating the set of hosts with same anomaly behaviors. The proposed method can differentiate the malicious network traffic generated by infected hosts (bots) from that by normal IRC clients, even in a network with only a very small number of bots. The experiment results show that, regardless the size of the botnet in a network, the proposed approach efficiently detects abnormal IRC traffic and identifies botnet activities.


A Survey and Comparison of Artificial Intelligence Techniques for Image Classification and Their Applications


title: Teacher–Student Interaction in EFL Reading Comprehension Contexts at University Level: A Critical Thinking Perspective

abstract: 1. M. Reza Talebinejad[1][1] ::: 2. Zahra Matou[1][1] ::: ::: ::: ::: ::: 1. 1Islamic Azad University, Isfahan, Iran ::: ::: ::: ::: ::: 1. M. Reza Talebinejad, Shahreza Branch, Islamic Azad University, Isfahan, Iran Email: rtalebinezhad{at}yahoo.com ::: ::: In line with previous studies in English as a Foreign Language/English as a Second Language (EFL/ESL) contexts confirming the positive correlation between critical thinking and reading comprehension, this study was conducted to determine how frequently critical thinking is used in EFL reading comprehension contexts at the tertiary level in one Iranian university. To this end, all question types associated with general and Critical Reading Questions (CRQs), Vocabulary in Context (VIC), Literal Comprehension (LC), and Extended Reasoning (ER) were identified. The principal focus of the study was on ER questions leading to critical thinking. To classify the questions formulated by teachers and students for CRQs, Peterson’s model was used. To specify critical thinking question types, a framework proposed by Academic Skills Unit was used as another model. To collect the data, the researchers observed all reading comprehension courses in one of the universities in Isfahan Province. They recorded 30% of the total number of sessions using two mini-size MP4 wireless recorders during the second semester of the 2010-2011 academic year. The findings seem to suggest that teachers’ focus on each CRQ type strongly influences students’ attention when reading different passages. It was noted that students had serious problems with textually implicit information included in reading passages. Given this finding and the fact that the observations illustrated most teachers devote the bulk of their attention to other CRQs, this study highlights the need for raising teachers’ awareness of ER-based reading comprehension questions. Moreover, the findings have implications for researchers and teachers in EFL settings. ::: ::: [1]: #aff-1


title: Robust image corner detection through curvature scale space

abstract: This paper describes a novel method for image corner detection based on the curvature scale-space (CSS) representation. The first step is to extract edges from the original image using a Canny detector (1986). The corner points of an image are defined as points where image edges have their maxima of absolute curvature. The corner points are detected at a high scale of the CSS and tracked through multiple lower scales to improve localization. This method is very robust to noise, and we believe that it performs better than the existing corner detectors An improvement to Canny edge detector's response to 45/spl deg/ and 135/spl deg/ edges is also proposed. Furthermore, the CSS detector can provide additional point features (curvature zero-crossings of image edge contours) in addition to the traditional corners.


title: Investigating Open Source Software Benefits in Public Sector

abstract: This paper investigates the benefits of OSS in public sector organizations in order to understand the trends and patterns in different regions over time. Although open source software is used widely, in this study the authors examine the adoption of open source software in the public sector. As such, the paper uses content analysis to review published articles on open source software in the public sector or government organizations between 2003 and 2012 across the regions (Africa, America, Asia, and Europe). The results suggest that that there is no-one-size-fit-all to open source software adoption benefits to the public sector in different regions. The results also show that technical benefits, vendor independence and customization are considered to be important for open source software adoption in public sector organizations. While this suggests that public sector organizations perceive open source software as one step towards vendor independence, customization is considered a very important benefit of open source software adoption in Asia than is the case in America.


title: A comparative analysis of tightly-coupled monocular, binocular, and stereo VINS

abstract: In this paper, a sliding-window two-camera vision-aided inertial navigation system (VINS) is presented in the square-root inverse domain. The performance of the system is assessed for the cases where feature matches across the two-camera images are processed with or without any stereo constraints (i.e., stereo vs. binocular). To support the comparison results, a theoretical analysis on the information gain when transitioning from binocular to stereo is also presented. Additionally, the advantage of using a two-camera (both stereo and binocular) system over a monocular VINS is assessed. Furthermore, the impact on the achieved accuracy of different image-processing frontends and estimator design choices is quantified. Finally, a thorough evaluation of the algorithm's processing requirements, which runs in real-time on a mobile processor, as well as its achieved accuracy as compared to alternative approaches is provided, for various scenes and motion profiles.


title: An open architecture for enabling CBM/PHM capabilities in ground vehicles

abstract: This paper describes a comprehensive, open and extensible architecture for a Ground Vehicle Health and Usage Monitoring System (GV-HUMS) that enables both Condition Based Maintenance (CBM) and Prognostics and Health Management (PHM) in vehicles. The on-board software provides near real time diagnostic and prognostic analysis in addition to gathering sensor data for off-board data mining. The CBM and PHM capabilities are provided as services and can be incrementally refined and expanded over time as the system collects more data without requiring an on-board software refresh in the system. The off-board software component allows for efficient data collection, data management, enables data mining, development and refinement of PHM and CBM capabilities and efficient collection of maintenance and fault data. The GV-HUMS system is modular and can be easily adapted to provide diagnostic and prognostic analysis for other vehicles on ground, water or air. This paper will provide a high level overview of the architecture, the incremental design approach, and current status of the system.


title: Interactive and Iterative Annotation for Biomedical Entity Recognition

abstract: In this paper, we demonstrate the impact of interactive machine learning for the development of a biomedical entity recognition dataset using a human-into-the-loop approach: during annotation, a machine learning model is built on previous annotations and used to propose labels for subsequent annotation. To demonstrate that such interactive and iterative annotation speeds up the development of quality dataset annotation, we conduct two experiments. In the first experiment, we carry out an iterative annotation experimental simulation and show that only a handful of medical abstracts need to be annotated to produce suggestions that increase annotation speed. In the second experiment, clinical doctors have conducted a case study in annotating medical terms documents relevant for their research. The experiments validate our method qualitatively and quantitatively, and give rise to a more personalized, responsive information extraction technology.


Risky calculations: financial mathematics and securitization since the 1970s


title: Self-regulated learning: where are we today

abstract: Abstract Self-regulated learning has emerged as an important new construct in education. Our understanding of self-regulated learning has been informed by three schools of thought: (1) research on learning styles, (2) research on metacognition and regulation styles, and (3) theories of the self, including goal-directed behavior. Based on these schools of thought, a three-layer model is presented. The innermost layer pertains to regulation of the processing modes. The middle layer represents regulation of the learning process. The outermost layer concerns regulation of the self. Educators and researchers would benefit from an integration of these three frames of reference into a comprehensive model of self-regulated learning.


title: Model predictive path-following for constrained nonlinear systems

abstract: We propose a model predictive control approach to path-following problems of constrained nonlinear systems. We directly consider input and state constraints. Furthermore, we introduce an extended corridor path-following problem, which allows to add spatial degrees of freedom to the path formulation.We give sufficient stability conditions for predictive solutions to 1d and corridor path-following problems. To illustrate the performance of our approach we discuss the example of an autonomous vehicle subject to input constraints.


title: Empirical Evaluation and Combination of Advanced Language Modeling Techniques

abstract: We present results obtained with several advanced language modeling techniques, including class based model, cache model, maximum entropy model, structured language model, random forest language model and several types of neural network based language models. We show results obtained after combining all these models by using linear interpolation. We conclude that for both small and moderately sized tasks, we obtain new state of the art results with combination of models, that is significantly better than performance of any individual model. Obtained perplexity reductions against Good-Turing trigram baseline are over 50% and against modified Kneser-Ney smoothed 5-gram over 40%. Index Terms: language modeling, neural networks, model combination, speech recognition


title: Hex-Layer: Layered All-Hex Mesh Generation on Thin Section Solids via Chordal Surface Transformation

abstract: This paper proposes chordal surface transform for representation and discretization of thin section solids, such as automobile bodies, plastic injection mold components and sheet metal parts. A multiple-layered all-hex mesh with a high aspect ratio is a typical requirement for mold flow simulation of thin section objects. The chordal surface transform reduces the problem of 3D hex meshing to 2D quad meshing on the chordal surface. The chordal surface is generated by cutting a tet mesh of the input CAD model at its mid plane. Radius function and curvature of the chordal surface are used to provide sizing function for quad meshing. Two-way mapping between the chordal surface and the boundary is used to sweep the quad elements from the chordal surface onto the boundary, resulting in a layered all-hex mesh. The algorithm has been tested on industrial models, whose chordal surface is 2-manifold. The graphical results of the chordal surface and the multiple-layered all-hex mesh are presented along with the quality measures. The results show geometrically adaptive high aspect ratio all-hex mesh, whose average scaled Jacobean, is close to 1.0.


AProbabilisticModel for SemanticWord Vectors


title: Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons

abstract: Evolutionary techniques for multi-objective (MO) optimization are currently gaining significant attention from researchers in various fields due to their effectiveness and robustness in searching for a set of trade-off solutions. Unlike conventional methods that aggregate multiple attributes to form a composite scalar objective function, evolutionary algorithms with modified reproduction schemes for MO optimization are capable of treating each objective component separately and lead the search in discovering the global Pareto-optimal front. The rapid advances of multi-objective evolutionary algorithms, however, poses the difficulty of keeping track of the developments in this field as well as selecting an existing approach that best suits the optimization problem in-hand. This paper thus provides a survey on various evolutionary methods for MO optimization. Many well-known multi-objective evolutionary algorithms have been experimented with and compared extensively on four benchmark problems with different MO optimization difficulties. Besides considering the usual performance measures in MO optimization, e.g., the spread across the Pareto-optimal front and the ability to attain the global trade-offs, the paper also presents a few metrics to examine the strength and weakness of each evolutionary approach both quantitatively and qualitatively. Simulation results for the comparisons are analyzed, summarized and commented.


title: Behind closed doors: measurement and analysis of CryptoLocker ransoms in Bitcoin

abstract: Bitcoin, a decentralized cryptographic currency that has experienced proliferating popularity over the past few years, is the common denominator in a wide variety of cybercrime. We perform a measurement analysis of CryptoLocker, a family of ransomware that encrypts a victim's files until a ransom is paid, within the Bitcoin ecosystem from September 5, 2013 through January 31, 2014. Using information collected from online fora, such as reddit and BitcoinTalk, as an initial starting point, we generate a cluster of 968 Bitcoin addresses belonging to CryptoLocker. We provide a lower bound for CryptoLocker's economy in Bitcoin and identify 795 ransom payments totalling 1,128.40 BTC ($310,472.38), but show that the proceeds could have been worth upwards of $1.1 million at peak valuation. By analyzing ransom payment timestamps both longitudinally across CryptoLocker's operating period and transversely across times of day, we detect changes in distributions and form conjectures on CryptoLocker that corroborate information from previous efforts. Additionally, we construct a network topology to detail CryptoLocker's financial infrastructure and obtain auxiliary information on the CryptoLocker operation. Most notably, we find evidence that suggests connections to popular Bitcoin services, such as Bitcoin Fog and BTC-e, and subtle links to other cybercrimes surrounding Bitcoin, such as the Sheep Marketplace scam of 2013. We use our study to underscore the value of measurement analyses and threat intelligence in understanding the erratic cybercrime landscape.


title: Verifiable smart packaging with passive RFID

abstract: Smart packaging adds sensing abilities to traditional packages. This paper investigates the possibility of using RF signals to test the internal status of packages and detect abnormal internal changes. Towards this goal, we design and implement a nondestructive package testing and verification system using commodity passive RFID systems, called Echoscope. Echoscope extracts unique features from the backscatter signals penetrating the internal space of a package and compares them with the previously collected features during the check-in phase. The use of backscatter signals guarantees that there is no difference in RF sources and the features reflecting the internal status will not be affected. Compared to other nondestructive testing methods such as X-ray and ultrasound, Echoscope is much cheaper and provides ubiquitous usage. Our experiments in practical environments show that Echoscope can achieve very high accuracy and is very sensitive to various types abnormal changes.


title: Fast surfing, broad scanning and deep diving

abstract: Purpose – To explore information behavior from a psychological perspective by relating information seeking to personality traits and study approaches.Design/methodology/approach – The research design was quantitative and consisted of statistical analysis of three questionnaires, i.e. the NEO Five‐Factor Inventory measuring personality, the ASSIST test measuring approaches to studying, and a questionnaire regarding information behavior. A total of 305 university students who were in the process of writing a Master's thesis responded to the questionnaires.Findings – Three information‐seeking patterns – fast surfing, broad scanning and deep diving – emerged from the statistical analyses. Fast surfing could be related to a surface study approach and emotionality, as well as to low openness to experience and low conscientiousness. Broad scanning was linked to extraversion, openness, and competitiveness, whereas deep diving was a search pattern typical of analytical students with a deep and strategic study appr...


title: Hg2+ sensing in aqueous solutions: an intramolecular charge transfer emission quenching fluorescent chemosensors

abstract: Abstract Compounds 4a and 4b , comprising an anthracene moiety as the fluorophore and a pair of dithiocarbamate functionalities as ligating groups, were designed as fluorescent chemosensors for Hg(II). In aqueous solvent systems, upon excitation, in addition to the normal emission bands of locally excited (LE) state of anthracene, both compounds show a prominent pH-independent intramolecular charge transfer (ICT) emissive band, which can be modulated by Hg 2+ binding. The systems can be exploited to develop a fluorescent sensitive probe for Hg 2+ .


On the Need of Cross Validation for Discourse Relation Classification


A portable binaural beat device for learning stimulation


title: Learning a Multi-View Stereo Machine

abstract: We present a learnt system for multi-view stereopsis. In contrast to recent learning based methods for 3D reconstruction, we leverage the underlying 3D geometry of the problem through feature projection and unprojection along viewing rays. By formulating these operations in a differentiable manner, we are able to learn the system end-to-end for the task of metric 3D reconstruction. End-to-end learning allows us to jointly reason about shape priors while conforming to geometric constraints, enabling reconstruction from much fewer images (even a single image) than required by classical approaches as well as completion of unseen surfaces. We thoroughly evaluate our approach on the ShapeNet dataset and demonstrate the benefits over classical approaches and recent learning based methods.


Freepal: A Large Collection of Deep Lexico-Syntactic Patterns for Relation Extraction


title: Privacy preservation and public auditing for cloud data using ASS in Multi-cloud

abstract: Cloud computing is the new business paradigm introduced in the IT market in the current decade providing the customers with the option to pay for the amount of service utilized. The use of cloud computing has increased rapidly in many organizations. Ensuring security of data in a cloud is a major factor in the cloud computing environment, as users often store fragile information in cloud storage. Public auditing on shared data stored in the cloud is done by exploiting Aggregate Signature Scheme. With this mechanism, the identity of the signer of various blocks of shared data is kept private from public verifiers, who are able to verify efficiently the integrity of shared data without retrieving the entire file. However traceability is a major issue. A Multi-clouds Database Model (MCDB) which is based on Multi-clouds service providers instead of using single cloud service provider such as in Amazon cloud service is used to implement traceability. Also, dealing with single cloud providers is predicted to become less popular with customers due to risks of service availability failure and the possibility of malicious insiders in the single cloud. A movement towards multi-clouds is highly efficient and this system would also check for the data's which are attacked by malicious users, with short time and cost requirement.


title: Structure Preserving Video Prediction

abstract: Despite recent emergence of adversarial based methods for video prediction, existing algorithms often produce unsatisfied results in image regions with rich structural information (i.e., object boundary) and detailed motion (i.e., articulated body movement). To this end, we present a structure preserving video prediction framework to explicitly address above issues and enhance video prediction quality. On one hand, our framework contains a two-stream generation architecture which deals with high frequency video content (i.e., detailed object or articulated motion structure) and low frequency video content (i.e., location or moving directions) in two separate streams. On the other hand, we propose a RNN structure for video prediction, which employs temporal-adaptive convolutional kernels to capture time-varying motion patterns as well as tiny objects within a scene. Extensive experiments on diverse scenes, ranging from human motion to semantic layout prediction, demonstrate the effectiveness of the proposed video prediction approach.


title: A Review of the Artificial Reproduction of ASP, Aspius Aspius (L.), and Nase, Chondrostoma Nasus (L.)

abstract: This paper presents the results of studies of the reproductive biotechnology of asp, Aspius aspius (L.), and nase, Chondrostoma nasus (L.), supplemented with unpublished materials of the authors. Reproducing these species under controlled conditions without hormonal preparations is not possible. The most frequently used preparations are carp pituitary homogenate (CPH) and gonadotropin releasing hormone analogue (GnRH) combined with a dopamine receptor antagonist. In both asp and nase, the highest percentage of ovulating females, the highest fecundity, and the highest embryo survival were attained after stimulation with Ovopel and Ovaprim. However, the results obtained with CPH were not much worse. The semen of both species was cryopreserved, which permits the long-term safeguarding of sperm genetic material.


title: Walking Assistance Using Artificial Primitives: A Novel Bioinspired Framework Using Motor Primitives for Locomotion Assistance Through a Wearable Cooperative Exoskeleton

abstract: Bioinspiration in robotics deals with applying biological principles to the design of better performing devices. In this article, we propose a novel bioinspired framework using motor primitives for locomotion assistance through a wearable cooperative exoskeleton. In particular, the use of motor primitives for assisting different locomotion modes (i.e., ground-level walking at several cadences and ascending and descending stairs) is explored by means of two different strategies. In the first strategy, identified motor primitives are combined through weights to directly produce the desired assistive torque profiles. In the second strategy, identified motor primitives are combined to serve as neural stimulations to a virtual model of the musculoskeletal system, which, in turn, produces the desired assistive torque profiles.


title: Demand forecasting in smart grids

abstract: Data analytics in smart grids can be leveraged to channel the data downpour from individual meters into knowledge valuable to electric power utilities and end-consumers. Short-term load forecasting (STLF) can address issues vital to a utility but it has traditionally been done mostly at system (city or country) level. In this case study, we exploit rich, multi-year, and high-frequency annotated data collected via a metering infrastructure to perform STLF on aggregates of power meters in a mid-sized city. For smart meter aggregates complemented with geo-specific weather data, we benchmark several state-of-the-art forecasting algorithms, including kernel methods for nonlinear regression, seasonal and temperature-adjusted auto-regressive models, exponential smoothing and state-space models. We show how STLF accuracy improves at larger meter aggregation (at feeder, substation, and system-wide level). We provide an overview of our algorithms for load prediction and discuss system performance issues that impact real time STLF. ® 2014 Alcatel-Lucent.


title: Towards a Smart Community Centre: SEIDET Digital Village

abstract: South African communities are constantly being developed through new ICT projects which are initiated by individuals, government and private organisations. The problem with these developments is that they are implemented in isolation. This isolation causes limited sharing of resources, duplication, poor-governance of the resources and in worse-case scenarios, failure of project initiatives. This paper proposes a model that could be used to address these problems by focusing on the SEIDET community centre using it as an example. The model follows a descriptive analysis of ICT related work spanning over two decades performed within the SEIDET context, including the ongoing SEIDET Digital Village. The benefits of the proposed smart community centre model include community and rural development through sharing of scarce ICT resources. It could further provide support for entrepreneurs through training interventions, action-based research for policy development as well as spawn local innovation and free-sharing of resources and services.


title: Large-vocabulary audio-visual speech recognition by machines and humans

abstract: We compare automatic recognition with human perception of audio-visual speech, in the large-vocabulary, continuous speech recognition (LVCSR) domain. Specifically, we study the benefit of the visual modality for both machines and humans, when combined with audio degraded by speech-babble noise at various signal-to-noise ratios (SNRs). We first consider an automatic speechreading system with a pixel based visual front end that uses feature fusion for bimodal integration, and we compare its performance with an audio-only LVCSR system. We then describe results of human speech perception experiments, where subjects are asked to transcribe audio-only and audiovisual utterances at various SNRs. For both machines and humans, we observe approximately a 6 dB effective SNR gain compared to the audio-only performance at 10 dB, however such gains significantly diverge at other SNRs. Furthermore, automatic audio-visual recognition outperforms human audioonly speech perception at low SNRs.


title: Fast Vehicle Detection in Aerial Imagery

abstract: In recent years, several real-time or near real-time object detectors have been developed. However these object detectors are typically designed for first-person view images where the subject is large in the image and do not directly apply well to detecting vehicles in aerial imagery. Though some detectors have been developed for aerial imagery, these are either slow or do not handle multi-scale imagery very well. Here the popular YOLOv2 detector is modified to vastly improve it's performance on aerial data. The modified detector is compared to Faster RCNN on several aerial imagery datasets. The proposed detector gives near state of the art performance at more than 4x the speed.


title: Many regression algorithms, one unified model: A review

abstract: Regression is the process of learning relationships between inputs and continuous outputs from example data, which enables predictions for novel inputs. The history of regression is closely related to the history of artificial neural networks since the seminal work of Rosenblatt (1958). The aims of this paper are to provide an overview of many regression algorithms, and to demonstrate how the function representation whose parameters they regress fall into two classes: a weighted sum of basis functions, or a mixture of linear models. Furthermore, we show that the former is a special case of the latter. Our ambition is thus to provide a deep understanding of the relationship between these algorithms, that, despite being derived from very different principles, use a function representation that can be captured within one unified model. Finally, step-by-step derivations of the algorithms from first principles and visualizations of their inner workings allow this article to be used as a tutorial for those new to regression.


title: 2002: Knowing food and growing food: beyond the productionconsumption debate in the sociology of agriculture

abstract: What does the rise of organic food as a social phenomenon mean politically? What political impact does this form of consumption have on society? Is it a small and unimportant “blip” in the overall march of globalized, industrialized food and rationalized consumption systems. Is it even, perhaps, complicit in this process? Or is it a radical break ? The array of answers to these questions forms the “production–consumption debate” currently taking place in the examination of agro–food systems. If agro–food networks are conceptualized as interactive, socio–ecological metabolic circuits linking agricultural nature, social labor, the corporeal and the symbolic, then this paper argues that analytical concern in agrofood studies has focused overwhelmingly on the production ‘moment’ in these circuits. Despite the lessons of numerous ‘food scares’, anti–GMO movements and the mad cow disease pandemic, an asymmetry now holds sway in agro–food studies between production and consumption even though in other fields, as Jackson (1999) notes, consumption has been “duly acknowledged” (p. 95). As this asymmetry is addressed, a contemporary reformulation of the ‘agrarian question’ might investigate the potential for new forms of progressive food politics, ranging from ‘weak’ struggles over the modes of social orderings, such as knowledge systems, to more formal alliances between producers and consumers


title: Image and video upscaling from local self-examples

abstract: We propose a new high-quality and efficient single-image upscaling technique that extends existing example-based super-resolution frameworks. In our approach we do not rely on an external example database or use the whole input image as a source for example patches. Instead, we follow a local self-similarity assumption on natural images and extract patches from extremely localized regions in the input image. This allows us to reduce considerably the nearest-patch search time without compromising quality in most images. Tests, that we perform and report, show that the local self-similarity assumption holds better for small scaling factors where there are more example patches of greater relevance. We implement these small scalings using dedicated novel nondyadic filter banks, that we derive based on principles that model the upscaling process. Moreover, the new filters are nearly biorthogonal and hence produce high-resolution images that are highly consistent with the input image without solving implicit back-projection equations. The local and explicit nature of our algorithm makes it simple, efficient, and allows a trivial parallel implementation on a GPU. We demonstrate the new method ability to produce high-quality resolution enhancement, its application to video sequences with no algorithmic modification, and its efficiency to perform real-time enhancement of low-resolution video standard into recent high-definition formats.


title: File system workload analysis for large scale scientific computing applications

abstract: Parallel scientific applications require high-performanc e I/O support from underlying file systems. A comprehensive understanding of the expected workload is t herefore essential for the design of high-performance parallel file systems. We re-examine the w orkload characteristics in parallel computing environments in the light of recent technology ad vances and new applications.


title: A Parallel Optimization of the Fast Algorithm of Convolution Neural Network on CPU

abstract: The application of convolutional neural network (CNN) is mainly based on the training over the big data, and it is necessary to obtain the relatively accurate values in real time. Although the GPU has several advantages over CPU, most of the clusters in mainstream parallel environments are composed of servers based on CPUs. So it is more useful to speed up the algorithms over the CPU-based clusters by parallelization. In this paper, an improved CNN algorithm based on Winogard algorithm was proposed. Firstly, we analyzed Winogard algorithm and found it has the best advantage in speed to small convolution kernel, such as 3×3. Then we increase the convolution process speed by reducing the complexity of convolution calculation. We also discussed the parallel implementation and optimization to the improved Winogard algorithm on CPU-based cluster. Finally, we compared the computing efficiency of different numbers of computing cores in the Intel Xeon Phi CPU platform and the results show that the best parallelism is about 6 TFLOPS.


title: A Text Detection System for Natural Scenes with Convolutional Feature Learning and Cascaded Classification

abstract: We propose a system that finds text in natural scenes using a variety of cues. Our novel data-driven method incorporates coarse-to-fine detection of character pixels using convolutional features (Text-Conv), followed by extracting connected components (CCs) from characters using edge and color features, and finally performing a graph-based segmentation of CCs into words (Word-Graph). For Text-Conv, the initial detection is based on convolutional feature maps similar to those used in Convolutional Neural Networks (CNNs), but learned using Convolutional k-means. Convolution masks defined by local and neighboring patch features are used to improve detection accuracy. The Word-Graph algorithm uses contextual information to both improve word segmentation and prune false character/word detections. Different definitions for foreground (text) regions are used to train the detection stages, some based on bounding box intersection, and others on bounding box and pixel intersection. Our system obtains pixel, character, and word detection f-measures of 93.14%, 90.26%, and 86.77% respectively for the ICDAR 2015 Robust Reading Focused Scene Text dataset, out-performing state-of-the-art systems. This approach may work for other detection targets with homogenous color in natural scenes.


title: SEEOF: Smart energy efficient objective function: Adapting RPL objective function to enable an IPv6 meshed topology solution for battery operated smart meters

abstract: In this research, a new Smart Energy Efficient Objective Function (SEEOF) is designed to develop an IPv6 meshed topology for IoT based smart metering applications. IoT protocols offer horizontal scalability, interoperability and multi-service environment versus isolated, proprietary and service based environment of traditional smart metering network solutions. Smart metering is not limited to electricity meters; it comprises gas and/or water meters. In other words, it encompasses battery powered devices with limited computing functionalities and energy resources along with full functional devices powered by main line. The battery consumption of end-points is a major challenge and should be addressed in IoT smart-metering solutions. Among the various techniques to reduce the energy consumption, routing is a key element affecting the network performance and lifetime. IETF in its ROLL working group has considered RPL as the routing protocol for IoT applications in low power and lossy networks (LLN). SEEOF, based on RPL, is designed to use energy efficiently and extend network lifetime. It takes into account both the link quality and node energy. We have implemented SEEOF in ContikiOS and compared the energy consumption in RPL using SEEOF with existing objective function. Simulation results show that up to 27% improvement is attained in the network life time when using the proposed objective function. More importantly, the results show that SEEOF balances the energy consumption more uniformly among the battery powered devices keeping them alive for longer duration.


title: A protective mechanism using variable-instruction-sets encryption for Java applications in mobile terminal platforms

abstract: In some mobile terminal platforms, such as Android, OMS (Open Mobile System), etc, the compiled Java application files could be easily decompiled, so the security of source codes in Java application is a problem. In this paper, we analyze traditional protective methods for Java application files. Then a class-loading mechanism using variable-instruction-sets encryption is raised. Variable-instruction-sets encryption is a highly reliable and efficient algorithm which is applied on instruction level. Test results on prototype machine show that this mechanism is effective.


[Hematologic effects of dapsone (Disulone)].


Sense of Community : A Definition and Theory


title: Ant Colony System Algorithm for the Vehicle Routing Problem with Simultaneous Delivery and Pick-Up

abstract: Due to the increased demands for distribution/redistribution, recycling and repacking, there has been a significant change in the logistics process. This article presented an Ant Colony System (ACS) algorithm to solve a Vehicle Routing Problem with Simultaneous Delivery and Pick-up (VRPSDP). The objective of this problem is to determine the optimal set of routes to totally satisfy both the delivery and pick-up demand of the customer population. To solve the VRPSDP model, ACS approach is based in the pheromone strategy, inspired in the natural Ants behavior. This behavior is assigned to vehicles, which move along the route on which the pheromone intensity is strong. The ACS algorithm provides good solutions and shows that it is a competitive method related with other heuristics or meta-heuristics that have been proposed in literature.


title: Grounding the Ungrounded: Estimating Locations of Unknown Place Names from Linguistic Associations and Grounded Representations

abstract: Abstract Spatial locations can be extracted from language statistics, based on the idea that nearby locations are mentioned in similar linguistic contexts, akin to Tobler's first law of geography. However, the performance of language-based estimates is inferior to human estimates, raising questions about whether human spatial representations can actually be informed by such (inferior) statistics. We show that alternative methods of computing co-occurrence statistics improve language-based estimates, illustrating that simple linguistic


ON ATTRIBUTE BASED ACCESS CONTROL SCHEME IN CLOUD ENVIRONMENT


title: Low-power FPGA using pre-defined dual-Vdd/dual-Vt fabrics

abstract: Traditional FPGAs use uniform supply voltage Vdd and uniform threshold voltage Vt. We propose to use pre-defined dual-Vdd and dual-Vt fabrics to reduce FPGA power. We design FPGA circuits with dual-Vdd/dual-Vt to effectively reduce both dynamic power and leakage power, and define dual-Vdd/dual-Vt FPGA fabrics based on the profiling of benchmark circuits. We further develop CAD algorithms including power-sensitivity based voltage assignment and simulated-annealing based placement to leverage such fabrics. Compared to the conventional fabric using uniform Vdd/Vt at the same target clock frequency, our new fabric using dual Vt achieves 9% to 20% power reduction. However, the pre-defined FPGA fabric using both dual Vdd and dual Vt only achieves on average 2% extra power reduction. It is because that the pre-designed dual-Vdd layout pattern introduces non-negligible performance penalty. Therefore, programmability of supply voltage is needed to achieve significant power saving for dual-Vdd FPGAs. To our best knowledge, it is the first in-depth study on applying both dual-Vdd and dual-Vt to FPGA considering circuits, fabrics and CAD algorithms.


Error detection in content word combinations


title: Transformerless capacitive coupling of gate signals for series operation of power MOS devices

abstract: A reliable configuration for triggering a series string of power MOS devices without the use of transformer coupling is presented. A capacitor is inserted between the gate and ground of each MOSFET, except for the bottom MOSFET in the stack. Using a single input voltage signal to trigger the bottom MOSFET, a voltage division across the network of device capacitance and inserted capacitances triggers the entire series stack reliably. Design formulae are presented and simple circuit protection is discussed. Simulation shows reliable operation and experimental verification is presented. Application of the method is applied to series IGBTs.


title: Evaluating and understanding text-based stock price prediction models

abstract: Despite the fact that both the Efficient Market Hypothesis and Random Walk Theory postulate that it is impossible to predict future stock prices based on currently available information, recent advances in empirical research have been proving the opposite by achieving what seems to be better than random prediction performance. We discuss some of the (dis)advantages of the most widely used performance metrics and conclude that is difficult to assess the external validity of performance using some of these measures. Moreover, there remain many questions as to the real-world applicability of these empirical models. In the first part of this study we design novel stock price prediction models, based on state-of-the-art text-mining techniques to assert whether we can predict the movement of stock prices more accurately by including indicators of irrationality. Along with this, we discuss which metrics are most appropriate for which scenarios in order to evaluate the models. Finally, we discuss how to gain insight into text-mining-based stock price prediction models in order to evaluate, validate and refine the models.


title: The immune response in atherosclerosis: a double-edged sword

abstract: Immune responses participate in every phase of atherosclerosis. There is increasing evidence that both adaptive and innate immunity tightly regulate atherogenesis. Although improved treatment of hyperlipidaemia reduces the risk for cardiac and cerebral complications of atherosclerosis, these remain among the most prevalent of diseases and will probably become the most common cause of death globally within 15 years. This Review focuses on the role of immune mechanisms in the formation and activation of atherosclerotic plaques, and also includes a discussion of the use of inflammatory markers for predicting cardiovascular events. We also outline possible future targets for prevention, diagnosis and treatment of atherosclerosis.


title: The Relative Importance of Perceived Ease of Use in IS Adoption: A Study of E-Commerce Adoption

abstract: The technology acceptance model (Davis 1989) is one of the most widely used models of IT adoption. According to TAM, IT adoption is influenced by two perceptions: usefulness and ease-of-use. Research has shown that perceived usefulness (PU) affects intended adoption of IT, but has mostly failed to do so regarding perceived ease of use (PEOU). The basic proposition of this study is that this varying importance of PEOU may be related to the nature of the task. PEOU relates to assessments of the intrinsic characteristics of IT, such as the ease of use, ease of learning, flexibility, and clarity of its interface. PU, on the other hand, is a response to user assessment of its extrinsic, i.e., task-oriented, outcomes: how IT


title: Characterizing and profiling scientific workflows

abstract: Researchers working on the planning, scheduling, and execution of scientific workflows need access to a wide variety of scientific workflows to evaluate the performance of their implementations. This paper provides a characterization of workflows from six diverse scientific applications, including astronomy, bioinformatics, earthquake science, and gravitational-wave physics. The characterization is based on novel workflow profiling tools that provide detailed information about the various computational tasks that are present in the workflow. This information includes I/O, memory and computational characteristics. Although the workflows are diverse, there is evidence that each workflow has a job type that consumes the most amount of runtime. The study also uncovered inefficiency in a workflow component implementation, where the component was re-reading the same data multiple times.


title: Two types of dominant male cichlid fish: behavioral and hormonal characteristics

abstract: ABSTRACT Male African cichlid fish, Astatotilapia burtoni , have been classified as dominant or subordinate, each with unique behavioral and endocrine profiles. Here we characterize two distinct subclasses of dominant males based on types of aggressive behavior: (1) males that display escalating levels of aggression and court females while they establish a territory, and (2) males that display a stable level of aggression and delay courting females until they have established a territory. To profile differences in their approach to a challenge, we used an intruder assay. In every case, there was a male-male confrontation between the resident dominant male and the intruder, with the intruder quickly taking a subordinate role. However, we found that dominant males with escalating aggression spent measurably more time attacking subordinates than did dominant males with stable aggression that instead increased their attention toward the females in their tank. There was no difference in the behavior of intruders exposed to either type of dominant male, suggesting that escalating aggression is an intrinsic characteristic of some dominant males and is not elicited by the behavior of their challengers. Male behavior during the first 15 min of establishing a territory predicts their aggressive class. These two types of dominant males also showed distinctive physiological characteristics. After the intruder assay, males with escalating aggression had elevated levels of 11-ketotestosterone (11-KT), testosterone, estradiol, and cortisol, while those with stable aggression did not. These observations show that the same stimulus can elicit different behavioral and endocrine responses among A. burtoni dominant males that characterize them as either escalating or stable aggressive types. Our ability to identify which individuals within a population have escalating levels of aggressive responses versus those which have stable levels of aggressive responses when exposed to the same stimulus, offers a potentially powerful model for investigating the underlying molecular mechanisms that modulate aggressive behavior.


title: SPECIAL ISSUE ON DESIGNING FOR AESTHETICS OF INTERACTION Designing Behavior in Interaction: Using Aesthetic Experience as a Mechanism for Design

abstract: As design moves into the realm of intelligent products and systems, interactive product behavior becomes an ever more prominent aspect of design, raising the question of how to design the aesthetics of such interactive behavior. To address this challenge, we developed a conception of aesthetics based on Pragmatist philosophy and translated it into a design approach. Our notion of Aesthetic Interaction consists of four principles: Aesthetic Interaction (1) has practical use next to intrinsic value, (2) has social and ethical dimensions, (3) has satisfying dynamic form, and (4) actively involves people’s bodily, cognitive, emotional and social skills. Our design approach based on this notion is called ‘designing for Aesthetic Interaction through Aesthetic Interaction’, referring to the use of aesthetic experience as a design mechanism. We explore our design approach through a case study that involves the design of intelligent lamps and outlines the utilized design techniques. The paper concludes with a set of practical recommendations for designing the aesthetics of interactive product behavior.


title: Unsupervised Clickstream Clustering for User Behavior Analysis

abstract: Online services are increasingly dependent on user participation. Whether it's online social networks or crowdsourcing services, understanding user behavior is important yet challenging. In this paper, we build an unsupervised system to capture dominating user behaviors from clickstream data (traces of users' click events), and visualize the detected behaviors in an intuitive manner. Our system identifies "clusters" of similar users by partitioning a similarity graph (nodes are users; edges are weighted by clickstream similarity). The partitioning process leverages iterative feature pruning to capture the natural hierarchy within user clusters and produce intuitive features for visualizing and understanding captured user behaviors. For evaluation, we present case studies on two large-scale clickstream traces (142 million events) from real social networks. Our system effectively identifies previously unknown behaviors, e.g., dormant users, hostile chatters. Also, our user study shows people can easily interpret identified behaviors using our visualization tool.


title: Optimal pilot beam pattern design for massive MIMO systems

abstract: In this paper, channel estimation for massive multiple-input multiple-output (MIMO) systems with a large number of transmit antennas at the base station is considered, and a new algorithm for pilot beam pattern design for optimal channel estimation under the assumption of Gauss-Markov channel processes is proposed. The proposed algorithm designs the optimal pilot beam pattern sequentially by exploiting the statistics of the channel, antenna correlation, and temporal correlation. The algorithm provides a sequentially optimal sequence of pilot beam patterns for a given set of system parameters. Numerical results show the effectiveness of the proposed algorithm.


title: Multiple Kernel Learning with Data Augmentation

abstract: © 2016 K. Nguyen, T. Le, V. Nguyen, T.D. Nguyen & D. Phung. The motivations of multiple kernel learning (MKL) approach are to increase kernel expressiveness capacity and to avoid the expensive grid search over a wide spectrum of kernels. A large amount of work has been proposed to improve the MKL in terms of the computational cost and the sparsity of the solution. However, these studies still either require an expensive grid search on the model parameters or scale unsatisfactorily with the numbers of kernels and training samples. In this paper, we address these issues by conjoining MKL, Stochastic Gradient Descent (SGD) framework, and data augmentation technique. The pathway of our proposed method is developed as follows. We first develop a maximum-aposteriori (MAP) view for MKL under a probabilistic setting and described in a graphical model. This view allows us to develop data augmentation technique to make the inference for finding the optimal parameters feasible, as opposed to traditional approach of training MKL via convex optimization techniques. As a result, we can use the standard SGD framework to learn weight matrix and extend the model to support online learning. We validate our method on several benchmark datasets in both batch and online settings. The experimental results show that our proposed method can learn the parameters in a principled way to eliminate the expensive grid search while gaining a significant computational speedup comparing with the state-of-the-art baselines.


title: Developing competency models to promote integrated human resource practices

abstract: Today, competencies are used in many facets of human resource management, ranging from individual selection, development, and performance management to organizational strategic planning. By incorporating competencies into job analysis methodologies, the Office of Personnel Management (OPM) has developed robust competency models that can form the foundation for each of these initiatives. OPM has placed these models into automated systems to ensure access for employees, human resources professionals, and managers. Shared access to the data creates a shared frame of reference and a common language of competencies that have provided the basis for competency applications in public sector agencies. © 2002 Wiley Periodicals, Inc.


title: A compact printed log-periodic antenna with loaded stub

abstract: A compact printed log-periodic dipole antenna (LPDA) with distributed inductive load has been presented in this paper. By adding a stub on the top of each element, the dimension of the LPAD can be reduced by 60%. The antenna has obtained an impedance bandwidth of 10GHz (8GHz-18GHz). According to the simulation results, the designed structure achieves stable radiation patterns throughout the operating frequency band.


title: An effective hybrid transactional memory system with strong isolation guarantees

abstract: We propose signature-accelerated transactional memory (SigTM), ahybrid TM system that reduces the overhead of software transactions. SigTM uses hardware signatures to track the read-set and write-set forpending transactions and perform conflict detection between concurrent threads. All other transactional functionality, including dataversioning, is implemented in software. Unlike previously proposed hybrid TM systems, SigTM requires no modifications to the hardware caches, which reduces hardware cost and simplifies support for nested transactions and multithreaded processor cores. SigTM is also the first hybrid TM system to provide strong isolation guarantees between transactional blocks and non-transactional accesses without additional read and write barriers in non-transactional code. Using a set of parallel programs that make frequent use of coarse-grain transactions, we show that SigTM accelerates software transactions by 30% to 280%. For certain workloads, SigTM can match the performance of a full-featured hardware TM system, while for workloads with large read-sets it can be up to two times slower. Overall, we show that SigTM combines the performance characteristics and strong isolation guarantees of hardware TM implementations with the low cost and flexibility of software TM systems.


title: Arabic Morphological Analyzer with Agglutinative Affix Morphemes and Fusional Concatenation Rules

abstract: Current concatenative morphological analyzers consider prefix, suffix and stem morphemes based on lexicons of morphemes, and morpheme concatenation rules that determine whether prefix-stem, stem-suffix, and prefix-suffix concatenations are allowed. Existing affix lexicons contain extensive redundancy, suffer from inconsistencies, and require significant manual work to augment with clitics and partial affixes if needed. Unlike traditional work, our method considers Arabic affixes as fusional and agglutinative, i.e. composed of one or more morphemes, introduces new compatibility rules for affix-affix concatenations, and refines the lexicons of the SAMA and BAMA analyzers to be smaller, less redundant, and more consistent. It also automatically and perfectly solves the correspondence problem between the segments of a word and the corresponding tags, e.g. part of speech and gloss tags. Title and Abstract in another language, L2 (optional, and on same page) BAMA SAMA


Evaluating the cost of software quality


title: CinemaScreen recommender agent: combining collaborative and content-based filtering

abstract: A film recommender agent expands and fine-tunes collaborative-filtering results according to filtered content elements - namely, actors, directors, and genres. This approach supports recommendations for newly released, previously unrated titles. Directing users to relevant content is increasingly important in today's society with its ever-growing information mass. To this end, recommender systems have become a significant component of e-commerce systems and an interesting application domain for intelligent agent technology.


title: Efficient Deep Learning Model for Text Classification Based on Recurrent and Convolutional Layers

abstract: Natural Language Processing (NLP) systems conventionally treat words as distinct atomic symbols. The model can leverage small amounts of information regarding the relationship between the individual symbols. Today when it comes to texts; one common technique to extract fixed-length features is bag-of-words. Despite its popularity the bag-of-words feature has two major weaknesses: it ignores semantics of the words and the order of words. In this paper, we propose a neural language model that relies on Convolutional Neural Network (CNN) and Bidirectional Recurrent Neural Network (BRNN) over pre-trained word vectors. We utilize bidirectional layers as a substitute of pooling layers in CNN in order to reduce the loss of detailed local information, and to capture long-term dependencies across input sequences. We validate the proposed model on two benchmark sentiment analysis datasets, Stanford Large Movie Review (IMDB), and Stanford Sentiment Treebank (SSTb). Our model achieves a competitive advantage compared with neural language models on the sentiment analysis datasets.


title: Perception of short tactile pulses generated by a vibration motor in a mobile phone

abstract: This paper describes an experimental setup and results of user tests focusing on the perception of temporal characteristics of vibration of a mobile device. The experiment consisted of six vibration stimuli of different length. We asked the subjects to score the subjective perception level in a five point Lickert scale. The results suggest that the optimal duration of the control signal should be between 50 and 200 ms in this specific case. Longer durations were perceived as being irritating.


title: Confidence and Risk Estimation Plugins for Multi-Modal Authentication on Mobile Devices using CORMORANT

abstract: Mobile devices, ubiquitous in modern lifestyle, embody and provide convenient access to our digital lives. Being small and mobile, they are easily lost or stole, therefore require strong authentication to mitigate the risk of unauthorized access. Common knowledge-based mechanism like PIN or pattern, however, fail to scale with the high frequency but short duration of device interactions and ever increasing number of mobile devices carried simultaneously. To overcome these limitations, we present CORMORANT, an extensible framework for risk-aware multi-modal biometric authentication across multiple mobile devices that offers increased security and requires less user interaction.


title: A Theory of Goal-Oriented MDPs with Dead Ends

abstract: Stochastic Shortest Path (SSP) MDPs is a problem class widely studied in AI, especially in probabilistic planning. They describe a wide range of scenarios but make the restrictive assumption that the goal is reachable from any state, i.e., that dead-end states do not exist. Because of this, SSPs are unable to model various scenarios that may have catastrophic events (e.g., an airplane possibly crashing if it flies into a storm). Even though MDP algorithms have been used for solving problems with dead ends, a principled theory of SSP extensions that would allow dead ends, including theoretically sound algorithms for solving such MDPs, has been lacking. In this paper, we propose three new MDP classes that admit dead ends under increasingly weaker assumptions. We present Value Iteration-based as well as the more efficient heuristic search algorithms for optimally solving each class, and explore theoretical relationships between these classes. We also conduct a preliminary empirical study comparing the performance of our algorithms on different MDP classes, especially on scenarios with unavoidable dead ends.


title: Non-uniform Deblurring for Shaken Images

abstract: Photographs taken in low-light conditions are often blurry as a result of camera shake, i.e. a motion of the camera while its shutter is open. Most existing deblurring methods model the observed blurry image as the convolution of a sharp image with a uniform blur kernel. However, we show that blur from camera shake is in general mostly due to the 3D rotation of the camera, resulting in a blur that can be significantly non-uniform across the image. We propose a new parametrized geometric model of the blurring process in terms of the rotational motion of the camera during exposure. This model is able to capture non-uniform blur in an image due to camera shake using a single global descriptor, and can be substituted into existing deblurring algorithms with only small modifications. To demonstrate its effectiveness, we apply this model to two deblurring problems; first, the case where a single blurry image is available, for which we examine both an approximate marginalization approach and a maximum a posteriori approach, and second, the case where a sharp but noisy image of the scene is available in addition to the blurry image. We show that our approach makes it possible to model and remove a wider class of blurs than previous approaches, including uniform blur as a special case, and demonstrate its effectiveness with experiments on synthetic and real images.


title: Deep Learning-based Image Super-Resolution Considering Quantitative and Perceptual Quality

abstract: Abstract Recently, it has been shown that in super-resolution, there exists a tradeoff relationship between the quantitative and perceptual quality of super-resolved images, which correspond to the similarity to the ground-truth images and the naturalness, respectively. In this paper, we propose a novel super-resolution method that can improve the perceptual quality of the upscaled images while preserving the conventional quantitative performance. The proposed method employs a deep network for multi-pass upscaling in company with a discriminator network and two qualitative score predictor networks. Experimental results demonstrate that the proposed method achieves a good balance of the quantitative and perceptual quality, showing more satisfactory results than existing methods.


Reconstruction of Three-Dimensional (3D) Indoor Interiors with Multiple Stories via Comprehensive Segmentation


title: On the mathematics of beauty: beautiful music

abstract: In this paper, we will study the simplest kind of beauty that can be found in a simple piece of music and can be appreciated universally. The proposed model suggest that there is a link between beautiful music and the Dyson distribution which seems to be the result of a deeper optimisation process between randomness and regularity. Then we show that beautiful music need to satisfy a more fundamental law that seeks to deliver the highest amount of information using the least amount of energy. The proposed model is tested on a set of beautiful music pieces.


title: A smart sensor to detect the falls of the elderly

abstract: Falls are a major health hazard for the elderly and a major obstacle to independent living. The estimated incidence of falls for both institutionalized and independent persons aged over 75 is at least 30 percent per year. In the SIMBAD (Smart Inactivity Monitor using Array-Based Detectors) project, we've developed an intelligent fall detector based on a low-cost array of infrared detectors. A field trial and user research indicate that SIMBAD could significantly enhance the functionality and effectiveness of existing monitoring systems and community alarm systems.