title: Regulation of abdominal adiposity by probiotics (Lactobacillus gasseri SBT2055) in adults with obese tendencies in a randomized controlled trial

abstract: BACKGROUND/OBJECTIVES ::: In spite of the much evidence for the beneficial effects of probiotics, their anti-obesity effects have not been well examined. We evaluated the effects of the probiotic Lactobacillus gasseri SBT2055 (LG2055) on abdominal adiposity, body weight and other body measures in adults with obese tendencies. ::: ::: ::: SUBJECTS/METHODS ::: We conducted a multicenter, double-blind, randomized, placebo-controlled intervention trial. Subjects (n=87) with higher body mass index (BMI) (24.2-30.7 kg/m(2)) and abdominal visceral fat area (81.2-178.5 cm(2)) were randomly assigned to receive either fermented milk (FM) containing LG2055 (active FM; n=43) or FM without LG2055 (control FM; n=44), and were asked to consume 200 g/day of FM for 12 weeks. Abdominal fat area was determined by computed tomography. ::: ::: ::: RESULTS ::: In the active FM group, abdominal visceral and subcutaneous fat areas significantly (P<0.01) decreased from baseline by an average of 4.6% (mean (confidence interval): -5.8 (-10.0, -1.7) cm(2)) and 3.3% (-7.4 (-11.6, -3.1) cm(2)), respectively. Body weight and other measures also decreased significantly (P<0.001) as follows: body weight, 1.4% (-1.1 (-1.5, -0.7) kg); BMI, 1.5% (-0.4 (-0.5, -0.2) kg/m(2)); waist, 1.8% (-1.7 (-2.1, -1.4) cm); hip, 1.5% (-1.5 (-1.8, -1.1) cm). In the control group, by contrast, none of these parameters decreased significantly. High-molecular weight adiponectin in serum increased significantly (P<0.01) in the active and control groups by 12.7% (0.17 (0.07, 0.26) microg/ml) and 13.6% (0.23 (0.07, 0.38) microg/ml), respectively. ::: ::: ::: CONCLUSION ::: The probiotic LG2055 showed lowering effects on abdominal adiposity, body weight and other measures, suggesting its beneficial influence on metabolic disorders.


title: An EFL Flipped Classroom Teaching Model: Effects on English Language Higher-order Thinking Skills, Student Engagement and Satisfaction

abstract: This study aimed at investigating the effect of a suggested EFL Flipped Classroom Teaching Model (EFL-FCTM) on graduate students' English higher-order thinking skills (HOTS), engagement and satisfaction. Also, it investigated the relationship between higher-order thinking skills, engagement and satisfaction. The sample comprised (67) graduate female students; an experimental group (N=33) and a control group (N=34), studying an English course at Taif University, KSA. The study used mixed method design; a pre-post HOTS test was carried out and two 5-Likert scale questionnaires had been designed and distributed; an engagement scale and a satisfaction scale. The findings of the study revealed statistically significant differences between the two group in HOTS in favor of the experimental group. Also, there was significant difference between the pre and post administration of the engagement scale in favor of the post administration. Moreover, students satisfaction on the (EFL-FCTM) was high. Finally, there were high significant relationships between HOTS and student engagement, HOTS and satisfaction and between student engagement and satisfaction. Key Words: flipped Classroom; English Language; HOTS; Engagement; Satisfaction.


title: Effects of Switching Network Topologies on Stealthy False Data Injection Attacks Against State Estimation in Power Networks

abstract: The stealthy attack, as a strategically designed false data injection attack against the power system state estimation mechanism, is able to let the corrupted measurements bypass residue-based bad data detectors with the same probability as that of uncorrupted measurements and is able to fool the system operator with the deviated estimates. While most of the existing articles assume the network topology to be fixed, the effects of switching topologies on such an attack are shown in this paper. A new mechanism is proposed to eliminate the possibility of such an attack via strategically shutting down some preselected transmission lines by turns and therefore switching the network topologies. The necessary and sufficient condition to achieve such elimination and the general form of possible attacks when the elimination is impossible are both formulated. The case where the attack is only stealthy in a subset of the preselected topologies is also studied. The general form of the possible estimate deviations caused by this “partially” stealthy attack is derived. Simulations and case studies are provided using different IEEE bus systems to show the efficiency of the proposed strategy, to discuss the countermeasures in the case when there always exist possible stealthy attacks, and to show how the possible deviations introduced by a “partially” stealthy attack could be affected by the decisions made by both the attacker and the system operator.


title: Low-Shot Visual Recognition by Shrinking and Hallucinating Features

abstract: Low-shot visual learning---the ability to recognize novel object categories from very few examples---is a hallmark of human visual intelligence. Existing machine learning approaches fail to generalize in the same way. To make progress on this foundational problem, we present a low-shot learning benchmark on complex images that mimics challenges faced by recognition systems in the wild. We then propose a) representation regularization techniques, and b) techniques to hallucinate additional training examples for data-starved classes. Together, our methods improve the effectiveness of convolutional networks in low-shot learning, improving the one-shot accuracy on novel classes by 2.3x on the challenging ImageNet dataset.


title: Video salient object detection via cross-frame cellular automata

abstract: Salient object detection aims to detect the attractive objects on images and videos. In this paper, we propose a novel salient object detection method for videos based on cross-frame cellular automata. Given a video, we first represent the video frames with super-pixels, and construct a saliency propagation network among super-pixels within a frame and between adjacent frames based on their appearance similarities and temporal coherency. Second, we initialize the saliency map of each frame with the fusion of two saliency maps generated by appearance and motion features independently. Finally, we utilize cellular automata updating to propagate saliency among super-pixels iteratively and generate the coherent saliency maps with complete objects. The experimental results show that our method outperforms the state-of-the-art methods on different types of videos.


title: Introducing the Robot Security Framework (RSF), a standardized methodology to perform security assessments in robotics

abstract: Robots have gained relevance in society, increasingly performing critical tasks. Nonetheless, robot security is being underestimated. Robotics security is a complex landscape, which often requires a cross-disciplinar perspective to which classical security lags behind. To address this issue, we present the Robot Security Framework (RSF), a methodology to perform systematic security assessments in robots. We propose, adapt and develop specific terminology and provide guidelines to enable a holistic security assessment following four main layers (Physical, Network, Firmware and Application). We argue that modern robotics should regard as equally relevant internal and external communication security. Finally, we advocate against "security by obscurity". We conclude that the field of security in robotics deserves further research efforts.


title: Taxonomy-Regularized Semantic Deep Convolutional Neural Networks

abstract: We propose a novel convolutional network architecture that abstracts and differentiates the categories based on a given class hierarchy. We exploit grouped and discriminative information provided by the taxonomy, by focusing on the general and specific components that comprise each category, through the min- and difference-pooling operations. Without using any additional parameters or substantial increase in time complexity, our model is able to learn the features that are discriminative for classifying often confused sub-classes belonging to the same superclass, and thus improve the overall classification performance. We validate our method on CIFAR-100, Places-205, and ImageNet Animal datasets, on which our model obtains significant improvements over the base convolutional networks.


title: Inverse kinematics of a NAO humanoid robot using kinect to track and imitate human motion

abstract: In this paper, three methods to imitate human upper body motion are implemented on a NAO humanoid robot: (i) direct angle mapping method (ii) inverse kinematics using fuzzy logic and (iii) inverse kinematics using iterative Jacobian. In the first method, the Kinect sensor is used to obtain coordinates of the shoulder, elbow and wrist joints of the operator. The four angles that are required to completely describe the position of the robot's wrist- shoulder roll, shoulder pitch, elbow roll and elbow yaw- are then calculated using vector algebra. In a unique approach, the human arm lengths instead of the robot's link lengths are used to find the inverse kinematics model for the robot arms. The model is then used to train adaptive neural network and the inverse kinematics problem is solved using the trained ANFIS in the second method. In the third approach, the Jacobian matrices of the arms are first found by differentiating the position components of the transformation matrix with respect to the joint variables. The Moore-Penrose pseudo-inverse of the Jacobian is then used to iteratively solve the inverse kinematics problem. Performance of the three methods is then compared.


title: Effect of equestrian therapy and onotherapy in physical and psycho-social performances of adults with intellectual disability: a preliminary study of evaluation tools based on the ICF classification

abstract: Purpose: To assess the effects of equestrian rehabilitation (ER) and onotherapy (Ono) on physical and psycho-social performances of subjects affected by intellectual disability (ID), and to develop a measurement tool based on the International Classification of Functioning Disability and Health-Children and Youth (ICF-CY). Method: A tool based on the ICF-CY classification was designed to evaluate subjects undergoing equine rehabilitation within a bio-psychosocial approach. A simplified version of this evaluation form was developed for the equestrian instructors. The agreement between the two tools was evaluated with the Cohen’s κ coefficient. Treatment benefits were evaluated in the different areas covered by the evaluation tool. Results: A general improvement in the autonomy and social integration of subjects with ID undergoing horse and donkey therapy was observed. ER and Ono produced maximum benefits respectively at six and 3 months in the large majority of patients, and benefits persisted over time. Although the agreement between the two tools proposed was rather slight, both evaluation groups measured similar improvements in subjects undergoing equine rehabilitation. Conclusions: There is an improvement in autonomy and social integration for subjects with ID, undergoing horse and donkey therapy. Ono emerged as a suitable and effective alternative to equestrian therapy. The simplified measurement tool proved to be sensitive and easy to apply, even if further improvements will be necessary.


title: EEG-vigilance and BOLD effect during simultaneous EEG/fMRI measurement.

abstract: Abstract Different EEG-vigilance stages from full alertness to sleep onset can be separated during rest. Also fMRI research recently focused on the resting condition and identified several resting state networks. In order to deepen the understanding of different levels of global brain function from relaxed wakefulness to sleep onset the association between EEG-vigilance stages and BOLD signals was analysed. EEG-vigilance stages were attributed to consecutive 3-sec-EEG-segments by an algorithm using topographic and spectral information. Results of the classification were validated by analysing the heart rates during the different brain states. Vigilance stages served as regressors for the analysis of the simultaneously acquired fMRI data. Additionally resting state networks were derived from the fMRI data using independent component analysis (ICA). Also vigilance associated brain activity revealed by EEG-based standardized low resolution tomography (sLORETA) was compared to the results of the fMRI analysis. Results showed increased BOLD signal in the occipital cortex, the anterior cingulate cortex, the frontal cortex, the parietal cortices and the temporal cortices and decreasing BOLD signals in the thalamus and the frontal cortex for declining vigilance stages (A2, A3, B1, B2/B3) in comparison to the high vigilance stage A1. Resting state networks revealed a spatial overlap with the vigilance stage associated BOLD maps in conjunction analyses. sLORETA showed increased neuroelectric alpha activity at the occipital cortex comparable to occipital BOLD signal decreases when comparing stage A with stage B. Different EEG-vigilance stages during rest are associated with pronounced differences of BOLD signals in several brain areas which partly correspond to the resting state networks. For cognitive fMRI-research it therefore seems important to pay attention to vigilance switches in order to separate vigilance associated BOLD signal changes from those specifically related to cognition.


title: The AgreementMakerLight Ontology Matching System

abstract: AgreementMaker is one of the leading ontology matching systems, thanks to its combination of a flexible and extensible framework with a comprehensive user interface. In many domains, such as the biomedical, ontologies are becoming increasingly large thus presenting new challenges. We have developed a new core framework, AgreementMakerLight, focused on computational efficiency and designed to handle very large ontologies, while preserving most of the flexibility and extensibility of the original AgreementMaker framework. We evaluated the efficiency of AgreementMakerLight in two OAEI tracks: Anatomy and Large Biomedical Ontologies, obtaining excellent run time results. In addition, for the Anatomy track, AgreementMakerLight is now the best system as measured in terms of F-measure. Also in terms of F-measure, AgreementMakerLight is competitive with the best OAEI performers in two of the three tasks of the Large Biomedical Ontologies track that match whole ontologies.


title: Paper-based versus computer-based assessment: key factors associated with the test mode effect

abstract: This investigation seeks to confirm several key factors in computer-based versus paper-based assessment. Based on earlier research, the factors considered here include content familiarity, computer familiarity, competitiveness, and gender. Following classroom instruction, freshman business undergraduates (N = 105) were randomly assigned to either a computer-based or identical paper-based test. ANOVA of test data showed that the computer-based test group outperformed the paper-based test group. Gender, competitiveness, and computer familiarity were NOT related to this performance difference, though content familiarity was. Higher-attaining students benefited most from computer-based assessment relative to higher-attaining students under paper-based testing. With the current increase in computer-based assessment, instructors and institutions must be aware of and plan for possible test mode effects.


title: Area-Efficient ESD-Transient Detection Circuit With Smaller Capacitance for On-Chip Power-Rail ESD Protection in CMOS ICs

abstract: The RC-based power-rail electrostatic-discharge (ESD) clamp circuit with big field-effect transistor (BigFET) layout style in the main ESD clamp n-channel metal-oxide-semiconductor (NMOS) transistor was widely used to enhance the ESD robustness of a CMOS IC fabricated in advanced CMOS processes. To further reduce the occupied layout area of the RC in the power-rail ESD clamp circuit, a new ESD-transient detection circuit realized with smaller capacitance has been proposed and verified in a 0.13-mum CMOS process. From the experimental results, the power-rail ESD clamp circuit with the new proposed ESD-transient detection circuit can achieve a long-enough turn-on duration and higher ESD robustness under ESD stress condition, as well as better immunity against mis-trigger and latch-on event under the fast-power-on condition.


title: Cosmetic surgery attitudes among midlife women: Appearance esteem, weight esteem, and fear of negative appearance evaluation

abstract: Previous research has examined factors thought to influence individuals’ interest in cosmetic surgery, yet few studies have examined these issues among midlife women. This study examines predictors of cosmetic surgery attitudes among midlife women (N = 114; age = 45–65 years; Mage = 53.7) and considers a previously unexplored variable: fear of negative appearance evaluation. Results indicated that lower weight and appearance esteem were associated with more positive cosmetic surgery attitudes and greater fear of negative appearance evaluation. Furthermore, fear of negative appearance evaluation mediated the relationship between appearance and weight esteem and cosmetic surgery attitudes. We conclude that fear of negative appearance evaluation is an important factor to consider in examining cosmetic surgery attitudes.


title: Gamifying learning experiences: Practical implications and outcomes

abstract: Gamification is the use of game design elements and game mechanics in non-game contexts. This idea has been used successfully in many web based businesses to increase user engagement. Some researchers suggest that it could also be used in web based education as a tool to increase student motivation and engagement. In an attempt to verify those theories, we have designed and built a gamification plugin for a well-known e-learning platform. We have made an experiment using this plugin in a university course, collecting quantitative and qualitative data in the process. Our findings suggest that some common beliefs about the benefits obtained when using games in education can be challenged. Students who completed the gamified experience got better scores in practical assignments and in overall score, but our findings also suggest that these students performed poorly on written assignments and participated less on class activities, although their initial motivation was higher.


title: A unified approach for domain-specific tweet sentiment analysis

abstract: Twitter is an online social networking (OSN) service that enables users to send and read short messages called “tweets”. As of December 2014, Twitter has more than 500 million users, out of which more than 284 million are active users and about 500 million tweets are posted every day. Tweet sentiment analysis (TSA) identifies a valuable platform for the OSN study which provides insights into the opinion of the public about culture, products and political agendas and thereby is able to predict the trends in specific domains. In order to execute efficient TSA on a particular topic or domain, a TSA approach with unified tool, UnB TSA, is proposed consisting of four steps: tweets collection, refinement (excluding noisy tweets), sentiment lexicon creation and sentiment analysis. As a key part, the lexicon is domain-specific that incorporates expressions whose sentiment varies from one domain to another. Four algorithms including expanding limited hashtags into a larger and more complete set to collect tweets have been implemented. Experiments on the ‘iPhone 6’ domain which obtains convincing results in all of the four phases, showing the superiority of the domain-specific TSA approach over a generic one.


title: Distributional Semantics Beyond Words: Supervised Learning of Analogy and Paraphrase

abstract: There have been several efforts to extend distributional semantics beyond individual words, to measure the similarity of word pairs, phrases, and sentences (briefly, tuples; ordered sets of words, contiguous or noncontiguous). One way to extend beyond words is to compare two tuples using a function that combines pairwise similarities between the component words in the tuples. A strength of this approach is that it works with both relational similarity (analogy) and compositional similarity (paraphrase). However, past work required hand-coding the combination function for different tasks. The main contribution of this paper is that combination functions are generated by supervised learning. We achieve state-of-the-art results in measuring relational similarity between word pairs (SAT analogies and SemEval~2012 Task 2) and measuring compositional similarity between noun-modifier phrases and unigrams (multiple-choice paraphrase questions).


title: Performance Comparison and Detection Analysis in Snort and Suricata Environment

abstract: Recently, crimes are cause in the internet by hacking to target one's and the companies financial. Due to the massive crimes that are caused by digital convergence and ubiquitous IT system, it is clear that the amount of network packet which need to be processed are rising. The digital convergence and ubiquitous IT system caused the IDS (Intrusion Detection System) to process packets more than the past. Snort (version 2.x) is a leading open source IDS which has a long history but since it was built a long time ago, it has several limitations which are not fit for today's requirements. Such as, it's processing unit is in single threading. On the other hand, Suricara was built to cover Snorts these disadvantages. To cover massive amount of packets which are caused by digital convergence and ubiquitous IT system Suricata's have the availability to process packets in multi-threading environment. In this paper we have analyzed and compared Snort and Suricata's processing and detection rate to decide which is better in single threading or multi-threading environment.


title: Real-time canny edge detection parallel implementation for FPGAs

abstract: Edge detection is one of the most fundamental algorithms in digital image processing. The Canny edge detector is the most implemented edge detection algorithm because of its ability to detect edges even in images that are intensely contaminated by noise. However, this is a time consuming algorithm and therefore its implementations are difficult to reach real time response speeds. Especially nowadays where the demand for high resolution image processing is constantly increasing, the need for fast and efficient edge detector implementations is ever so present. A new parallel Canny edge detector FPGA implementation is proposed in this paper to answer this demand. This design takes advantage of 4-pixel parallel computations to achieve high throughput without increasing the on-chip memory demands. Synthesis and simulation results are presented to prove the design's efficiency and high frames per second rate.


title: SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks

abstract: Skin lesion segmentation (SLS) in dermoscopic images is a crucial task for automated diagnosis of melanoma. In this paper, we present a robust deep learning SLS model, so-called SLSDeep, which is represented as an encoder-decoder network. The encoder network is constructed by dilated residual layers, in turn, a pyramid pooling network followed by three convolution layers is used for the decoder. Unlike the traditional methods employing a cross-entropy loss, we investigated a loss function by combining both Negative Log Likelihood (NLL) and End Point Error (EPE) to accurately segment the melanoma regions with sharp boundaries. The robustness of the proposed model was evaluated on two public databases: ISBI 2016 and 2017 for skin lesion analysis towards melanoma detection challenge. The proposed model outperforms the state-of-the-art methods in terms of segmentation accuracy. Moreover, it is capable to segment more than $100$ images of size 384x384 per second on a recent GPU.


title: Reflection from layered surfaces due to subsurface scattering

abstract: The reflection of light from most materials consists of two major terms: the specular and the diffuse. Specular reflection may be modeled from first principles by considering a rough surface consisting of perfect reflectors, or micro-facets. Diffuse reflection is generally considered to result from multiple scattering either from a rough surface or from within a layer near the surface. Accounting for diffuse reflection by Lambert’s Cosine Law, as is universally done in computer graphics, is not a physical theory based on first principles. This paper presents a model for subsurface scattering in layered surfaces in terms of one-dimensional linear transport theory. We derive explicit formulas for backscattering and transmission that can be directly incorporated in most rendering systems, and a general Monte Carlo method that is easily added to a ray tracer. This model is particularly appropriate for common layered materials appearing in nature, such as biological tissues (e.g. skin, leaves, etc.) or inorganic materials (e.g. snow, sand, paint, varnished or dusty surfaces). As an application of the model, we simulate the appearance of a face and a cluster of leaves from experimental data describing their layer properties. CR Categories and Subject Descriptors: I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism.


title: Alterations in electrical activity and membrane currents induced by intracellular oxygen-derived free radical stress in guinea pig ventricular myocytes.

abstract: Oxygen-derived free radicals (O-Rs) are thought to induce alterations in cardiac electrical activity; however, the underlying membrane ionic currents affected by O-Rs and the mechanisms by which O-Rs induce their effects on ion channels in the heart are not well defined. In this study, we investigated the time-dependent changes in resting membrane potential and action potential configuration and changes in steady-state membrane currents in guinea pig ventricular myocytes after intracellular application of an O-R-generating system. O-Rs were generated from the combination of dihydroxyfumaric acid (3 mM) and FeCl3:ADP (0.05:0.5 mM) added to the pipette solution that was used to record membrane potential and currents via the whole-cell variant of the patch-clamp technique. Intracellular exposure of myocytes to the O-R-generating solution induced three stages of changes: 1) an early depolarization (5-10 mV) and an increase in action potential duration accompanied by a decrease in resting inward rectifying K+ current conductance, 2) delayed afterdepolarizations and triggered activity caused by the activation of transient inward current mediated by Na(+)-Ca2+ exchange, with failure to repolarize and sustained depolarization between -35 and -20 mV, reflecting the stimulation of nonselective cation current, and 3) a late stage of marked decline in action potential duration, hyperpolarization, and loss of excitability accompanied by activation of the outward current through ATP-sensitive K+ channels. These alterations in electrical activity and membrane currents could be prevented by pretreatment with N-(2-mercaptopropionyl)glycine (500 microM), a scavenger of hydroxyl free radicals. The alterations associated with stages 1 and 2 but not stage 3 were completely abolished on intracellular Ca2+ chelation (5 mM EGTA in the pipette solution) or disruption of sarcoplasmic reticulum Ca2+ handling with ryanodine (10 microM). This study shows that intracellular O-R stress causes specific alterations in membrane ionic currents, leading to changes in resting membrane potential and action potential configuration. Moreover, the data indicate that an elevation in intracellular Ca2+ due to abnormal Ca2+ handling by the sarcoplasmic reticulum is a cause of some of the alterations in membrane currents during O-R stress.


title: Defining and Setting National Goals for Cardiovascular Health Promotion and Disease Reduction: The American Heart Association's Strategic Impact Goal Through 2020 and Beyond

abstract: This document details the procedures and recommendations of the Goals and Metrics Committee of the Strategic Planning Task Force of the American Heart Association, which developed the 2020 Impact Goals for the organization. The committee was charged with defining a new concept, cardiovascular health, and determining the metrics needed to monitor it over time. Ideal cardiovascular health, a concept well supported in the literature, is defined by the presence of both ideal health behaviors (nonsmoking, body mass index <25 kg/m(2), physical activity at goal levels, and pursuit of a diet consistent with current guideline recommendations) and ideal health factors (untreated total cholesterol <200 mg/dL, untreated blood pressure <120/<80 mm Hg, and fasting blood glucose <100 mg/dL). Appropriate levels for children are also provided. With the use of levels that span the entire range of the same metrics, cardiovascular health status for the whole population is defined as poor, intermediate, or ideal. These metrics will be monitored to determine the changing prevalence of cardiovascular health status and define achievement of the Impact Goal. In addition, the committee recommends goals for further reductions in cardiovascular disease and stroke mortality. Thus, the committee recommends the following Impact Goals: "By 2020, to improve the cardiovascular health of all Americans by 20% while reducing deaths from cardiovascular diseases and stroke by 20%." These goals will require new strategic directions for the American Heart Association in its research, clinical, public health, and advocacy programs for cardiovascular health promotion and disease prevention in the next decade and beyond.


title: No Free Lunch Theorems: Limitations and Perspectives of Metaheuristics

abstract: The No Free Lunch (NFL) theorems for search and optimization are reviewed and their implications for the design of metaheuristics are discussed. The theorems state that any two search or optimization algorithms are equivalent when their performance is averaged across all possible problems and even over subsets of problems fulfilling certain constraints. The NFL results show that if there is no assumption regarding the relation between visited and unseen search points, efficient search and optimization is impossible. There is no well-performing universal metaheuristic, but the heuristics must be tailored to the problem class at hand using prior knowledge. In practice, it is not likely that the preconditions of the NFL theorems are fulfilled for a problem class and thus differences between algorithms exist. Therefore, tailored algorithms can exploit structure underlying the optimization problem. Given full knowledge about the problem class, it is, in theory, possible to construct an optimal algorithm.


title: A scalable neural chip with synaptic electronics using CMOS integrated memristors

abstract: The design and simulation of a scalable neural chip with synaptic electronics using nanoscale memristors fully integrated with complementary metal?oxide?semiconductor (CMOS) is presented. The circuit consists of integrate-and-fire neurons and synapses with spike-timing dependent plasticity (STDP). The synaptic conductance values can be stored in memristors with eight levels, and the topology of connections between neurons is reconfigurable. The circuit has been designed using a 90 nm CMOS process with via connections to on-chip post-processed memristor arrays. The design has about 16 million CMOS transistors and 73?728 integrated memristors. We provide circuit level simulations of the entire chip performing neuronal and synaptic computations that result in biologically realistic functional behavior.


title: Noise adaptive soft-switching median filter

abstract: Existing state-of-the-art switching-based median filters are commonly found to be nonadaptive to noise density variations and prone to misclassifying pixel characteristics at high noise density interference. This reveals the critical need of having a sophisticated switching scheme and an adaptive weighted median filter. We propose a novel switching-based median filter with incorporation of fuzzy-set concept, called the noise adaptive soft-switching median (NASM) filter, to achieve much improved filtering performance in terms of effectiveness in removing impulse noise while preserving signal details and robustness in combating noise density variations. The proposed NASM filter consists of two stages. A soft-switching noise-detection scheme is developed to classify each pixel to be uncorrupted pixel, isolated impulse noise, nonisolated impulse noise or image object's edge pixel. "No filtering" (or identity filter), standard median (SM) filter or our developed fuzzy weighted median (FWM) filter will then be employed according to the respective characteristic type identified. Experimental results show that our NASM filter impressively outperforms other techniques by achieving fairly close performance to that of ideal-switching median filter across a wide range of noise densities, ranging from 10% to 70%


title: User-Driven Design Principles for Gesture Representations

abstract: Many recent studies have explored user-defined interactions for touch and gesture-based systems through end-user elicitation. While these studies have facilitated the user-end of the human-computer dialogue, the subsequent design of gesture representations to communicate gestures to the user vary in style and consistency. Our study explores how users interpret, enact, and refine gesture representations adapting techniques from recent elicitation studies. To inform our study design, we analyzed gesture representations from 30 elicitation papers and developed a taxonomy of design elements. We then conducted a partnered elicitation study with 30 participants producing 657 gesture representations accompanied by think-aloud data. We discuss design patterns and themes that emerged from our analysis, and supplement these findings with an in-depth look at users' mental models when perceiving and enacting gesture representations. Finally, based on the results, we provide recommendations for practitioners in need of "visual language" guidelines to communicate possible user actions.


title: Impact of the needle winding technology on the operational behavior of an asynchronous machine

abstract: Distributed round-wire windings are usually manufactured using the insertion technology. If the needle winding technology is applied instead the end windings have to be conducted in a three-layer axial arrangement. This leads to differing coil lengths and thus to a phase asymmetry which is much more distinct than the one resulting from the insertion technology. In addition, it is possible that the first phase exhibits a higher end winding leakage inductance than the other phases if the distance between the first phase and the front end side of the stator core is too short. In this case the magnetic flux lines of the end windings partially close across the stator core producing an increase of the end winding leakage inductance. Therefore, in this paper the impact of the needle winding technology on the operational behavior of an asynchronous machine is investigated. For this purpose a needle wound electrical machine with three-layer end windings is compared to an electrical machine with very symmetric windings built up using manual insertion. By the use of the no load and blocked rotor test as well as a static stator measurement the machine parameters are determined and the impact of the phase asymmetry is investigated. In addition, load measurements are conducted in order to quantify the impact of the production related differences.


title: Competing Values in the Era of Digitalization

abstract: This case study examines three different digital innovation projects within Auto Inc -- a large European automaker. By using the competing values framework as a theoretical lens we explore how dynamic capabilities occur in a firm trying to meet increasing demands in originating and innovating from digitalization. In this digitalization process, our study indicates that established socio-technical congruences are being challenged. More so, we pinpoint the need for organizations to find ways to embrace new experimental learning processes in the era of digitalization. While such a change requires long-term commitment and vision, this study presents three informal enablers for such experimental processes these enablers are timing, persistence, and contacts.


Data Augmentation for Visual Question Answering


title: Towards Automatic Wild Animal Monitoring: Identification of Animal Species in Camera-trap Images using Very Deep Convolutional Neural Networks

abstract: Non intrusive monitoring of animals in the wild is possible using camera trapping framework, which uses cameras triggered by sensors to take a burst of images of animals in their habitat. However camera trapping framework produces a high volume of data (in the order on thousands or millions of images), which must be analyzed by a human expert. In this work, a method for animal species identification in the wild using very deep convolutional neural networks is presented. Multiple versions of the Snapshot Serengeti dataset were used in order to probe the ability of the method to cope with different challenges that camera-trap images demand. The method reached 88.9% of accuracy in Top-1 and 98.1% in Top-5 in the evaluation set using a residual network topology. Also, the results show that the proposed method outperforms previous approximations and proves that recognition in camera-trap images can be automated.


title: Using quantitative analysis to implement autonomic IT systems

abstract: The software underpinning today's IT systems needs to adapt dynamically and predictably to rapid changes in system workload, environment and objectives. We describe a software framework that achieves such adaptiveness for IT systems whose components can be modelled as Markov chains. The framework comprises (i) an autonomic architecture that uses Markov-chain quantitative analysis to dynamically adjust the parameters of an IT system in line with its state, environment and objectives; and (ii) a method for developing instances of this architecture for real-world systems. Two case studies are presented that use the framework successfully for the dynamic power management of disk drives, and for the adaptive management of cluster availability within data centres, respectively.


Business model development in IT startups – the role of scarcity and personalization in generating user feedback


title: A Novel Uplink Multiple Access Scheme Based on TDS-FDMA

abstract: This contribution proposes a novel time-domain synchronous frequency division multiple access (TDS-FDMA) scheme to support multi-user uplink application. A unified frame structure for both single-carrier and multi-carrier transmissions and the corresponding low-complexity receiver design are derived. Compared with standard cyclic prefix based orthogonal frequency division multiple access systems, the proposed TDS-FDMA scheme improves the spectral efficiency by about 5% to 10% as well as imposes a similarly low computational complexity, while obtaining a slightly better bit error rate performance over Rayleigh fading channels.


title: Modern Operating Systems

abstract: For software development professionals and computer science students, Modern Operating Systems gives a solid conceptual overview of operating system design, including detailed case studies of Unix/Linux and Windows 2000. What makes an operating system modern? According to author Andrew Tanenbaum, it is the awareness of high-demand computer applications--primarily in the areas of multimedia, parallel and distributed computing, and security. The development of faster and more advanced hardware has driven progress in software, including enhancements to the operating system. It is one thing to run an old operating system on current hardware, and another to effectively leverage current hardware to best serve modern software applications. If you don't believe it, install Windows 3.0 on a modern PC and try surfing the Internet or burning a CD. Readers familiar with Tanenbaum's previous text, Operating Systems, know the author is a great proponent of simple design and hands-on experimentation. His earlier book came bundled with the source code for an operating system called Minux, a simple variant of Unix and the platform used by Linus Torvalds to develop Linux. Although this book does not come with any source code, he illustrates many of his points with code fragments (C, usually with Unix system calls). The first half of Modern Operating Systems focuses on traditional operating systems concepts: processes, deadlocks, memory management, I/O, and file systems. There is nothing groundbreaking in these early chapters, but all topics are well covered, each including sections on current research and a set of student problems. It is enlightening to read Tanenbaum's explanations of the design decisions made by past operating systems gurus, including his view that additional research on the problem of deadlocks is impractical except for "keeping otherwise unemployed graph theorists off the streets." It is the second half of the book that differentiates itself from older operating systems texts. Here, each chapter describes an element of what constitutes a modern operating system--awareness of multimedia applications, multiple processors, computer networks, and a high level of security. The chapter on multimedia functionality focuses on such features as handling massive files and providing video-on-demand. Included in the discussion on multiprocessor platforms are clustered computers and distributed computing. Finally, the importance of security is discussed--a lively enumeration of the scores of ways operating systems can be vulnerable to attack, from password security to computer viruses and Internet worms. Included at the end of the book are case studies of two popular operating systems: Unix/Linux and Windows 2000. There is a bias toward the Unix/Linux approach, not surprising given the author's experience and academic bent, but this bias does not detract from Tanenbaum's analysis. Both operating systems are dissected, describing how each implements processes, file systems, memory management, and other operating system fundamentals. Tanenbaum's mantra is simple, accessible operating system design. Given that modern operating systems have extensive features, he is forced to reconcile physical size with simplicity. Toward this end, he makes frequent references to the Frederick Brooks classic The Mythical Man-Month for wisdom on managing large, complex software development projects. He finds both Windows 2000 and Unix/Linux guilty of being too complicated--with a particular skewering of Windows 2000 and its "mammoth Win32 API." A primary culprit is the attempt to make operating systems more "user-friendly," which Tanenbaum views as an excuse for bloated code. The solution is to have smart people, the smallest possible team, and well-defined interactions between various operating systems components. Future operating system design will benefit if the advice in this book is taken to heart. --Pete Ostenson


title: Surface drawing: creating organic 3D shapes with the hand and tangible tools

abstract: Surface Drawing is a system for creating organic 3D shapes in a manner which supports the needs and interests of artists. This medium facilitates the early stages of creative design which many 3D modeling programs neglect. Much like traditional media such as line drawing and painting, Surface Drawing lets users construct shapes through repeated marking. In our case, the hand is used to mark 3D space in a semi-immersive virtual environment. The interface is completed with tangible tools to edit and manipulate models. We introduce the use of tongs to move and scale 3D shapes and demonstrate a magnet tool which is comfortably held without restricting hand motion. We evaluated our system through collaboration with artists and designers, exhibition before hundreds of users, our own extensive exploration of the medium, and an informal user study. Response was especially positive from users with an artistic background.


title: Internet gaming disorder in children and adolescents: a systematic review

abstract: AIM ::: Internet gaming disorder (IGD) is a serious disorder leading to and maintaining pertinent personal and social impairment. IGD has to be considered in view of heterogeneous and incomplete concepts. We therefore reviewed the scientific literature on IGD to provide an overview focusing on definitions, symptoms, prevalence, and aetiology. ::: ::: ::: METHOD ::: We systematically reviewed the databases ERIC, PsyARTICLES, PsycINFO, PSYNDEX, and PubMed for the period January 1991 to August 2016, and additionally identified secondary references. ::: ::: ::: RESULTS ::: The proposed definition in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition provides a good starting point for diagnosing IGD but entails some disadvantages. Developing IGD requires several interacting internal factors such as deficient self, mood and reward regulation, problems of decision-making, and external factors such as deficient family background and social skills. In addition, specific game-related factors may promote IGD. Summarizing aetiological knowledge, we suggest an integrated model of IGD elucidating the interplay of internal and external factors. ::: ::: ::: INTERPRETATION ::: So far, the concept of IGD and the pathways leading to it are not entirely clear. In particular, long-term follow-up studies are missing. IGD should be understood as an endangering disorder with a complex psychosocial background. ::: ::: ::: WHAT THIS PAPER ADDS ::: In representative samples of children and adolescents, on average, 2% are affected by Internet gaming disorder (IGD). The mean prevalences (overall, clinical samples included) reach 5.5%. Definitions are heterogeneous and the relationship with substance-related addictions is inconsistent. Many aetiological factors are related to the development and maintenance of IGD. This review presents an integrated model of IGD, delineating the interplay of these factors.


[The G-spot and lack of female sexual medicine].


Extracts Sentiment and Sentiment-Derived Plot Arcs from Text


The Relationship of Teachers' Professional Competence toward Students' Learning Motivation at SMAN 1 North Siberut Mentawai Islands


Vaccine hesitancy: understanding better to address better


title: Evaluating Ontologies with Competency Questions

abstract: Competency Questions(CQs) play an important role in the ontology development lifecycle, as they represent the ontology requirements. Although the main methodologies describe and use CQs, the current practice of ontology engineering makes a superficial use of CQs. One of the main problems that hamper their proper use lies on the lack of tools that assist users to check if CQs are being fulfilled by the ontology being defined, particularly when these ontologies are defined in OWL (Ontology Web Language), under the Description Logic formalism. We propose a mechanism to support evaluating whether the ontology follows their correspondent CQs.


title: Compressing Neural Networks with the Hashing Trick

abstract: As deep nets are increasingly used in applications suited for mobile devices, a fundamental dilemma becomes apparent: the trend in deep learning is to grow models to absorb ever-increasing data set sizes; however mobile devices are designed with very little memory and cannot store such large models. We present a novel network architecture, HashedNets, that exploits inherent redundancy in neural networks to achieve drastic reductions in model sizes. HashedNets uses a low-cost hash function to randomly group connection weights into hash buckets, and all connections within the same hash bucket share a single parameter value. These parameters are tuned to adjust to the HashedNets weight sharing architecture with standard backprop during training. Our hashing procedure introduces no additional memory overhead, and we demonstrate on several benchmark data sets that HashedNets shrink the storage requirements of neural networks substantially while mostly preserving generalization performance.


title: A Compact Directional Slot Antenna and Its Application in MIMO Array

abstract: In this communication, a new design for the microstrip-fed slot antenna with a directive radiation pattern for WLAN/WiMAX application is presented. By utilizing a two-element array topology, a unidirectional radiation pattern is obtained. Without any directors and reflectors, the proposed slot antenna shows advantages in terms of compact size and high front-to-back gain ratio in the azimuthal plane. Four directional slot antennas are then placed in a square loop array to obtain nearly orthogonal patterns. Thus, a compact multiple input multiple output array with high isolation among those four elements is achieved. Simulated and measured results are presented to validate the practicality of the proposed antenna structures.


title: Adaptive ADMM with Spectral Penalty Parameter Selection

abstract: The alternating direction method of multipliers (ADMM) is a versatile tool for solving a wide range of constrained optimization problems, with differentiable or non-differentiable objective functions. Unfortunately, its performance is highly sensitive to a penalty parameter, which makes ADMM often unreliable and hard to automate for a non-expert user. We tackle this weakness of ADMM by proposing a method to adaptively tune the penalty parameters to achieve fast convergence. The resulting adaptive ADMM (AADMM) algorithm, inspired by the successful Barzilai-Borwein spectral method for gradient descent, yields fast convergence and relative insensitivity to the initial stepsize and problem scaling.


title: Thinning of concentric circular arrays using galaxy based search algorithm

abstract: In this work, the thinning of concentric circular arrays using Galaxy Based Search Algorithm (GBSA). The purpose is to obtain reduce the power taken by the array while preserving acceptable properties as compared to the conventional arrays. Three arrays are considered: a three ring array, an array of five rings and another one of seven rings were used. The aim is to see the impact and effectiveness of thinning as the number of elements of the array increases. The varied parameter is amplitude-only and the variation is not continuous from 0 to 1 but is constrained to take binary values i.e. either 0 or 1. This work exploits the GBSA adaptability to this problem where the variables can only take binary values, in addition to the good performance it demonstrated with the continuous problems.


title: Thermal Therapy, Part 1: An Introduction to Thermal Therapy

abstract: Thermal therapy is widely known and electromagnetic (EM) energy, ultrasonic waves, and other thermal-conduction-based devices have been used as heating sources. In particular, advances in EM technology have paved the way for promising trends in thermotherapeutical applications such as oncology, physiotherapy, urology, cardiology, ophthalmology, and in other areas of medicine as well. This series of articles is generally written for oncologists, cancer researchers, medical students, biomedical researchers, clinicians, and others who have an interest in this topic. This article reviews key processes and developments in thermal therapy with emphasis on two techniques, namely, hyperthermia [including long-term low-temperature hyperthermia (40-41 degrees C for 6-72 hr), moderate-temperature hyperthermia (42-45 degrees C for 15-60 min), and thermal ablation, or high-temperature hyperthermia (> 50 degrees C for > 4-6 min)]. The article will also provide an overview of a wide range of possible mechanisms and biological effects of heat. This information will be discussed in light of what is known about the degree of temperature rise that is expected from various sources of energy. The review concludes with an evaluation of human exposure risk to EM energy or the corresponding heat, trends in equipment development, and future research directions.


title: Cover Song Identification Using Song-to-Song Cross-Similarity Matrix with Convolutional Neural Network

abstract: In this paper, we propose a cover song identification algorithm using a convolutional neural network (CNN). We first train the CNN model to classify any non-/cover relationship, by feeding a cross-similarity matrix that is generated from a pair of songs as an input. Our main idea is to use the CNN output–the cover-probabilities of one song to all other candidate songs–as a new representation vector for measuring the distance between songs. Based on this, the present algorithm searches cover songs by applying several ranking methods: 1. sorting without using the representation vectors; 2. the cosine distance between the representation vectors; and 3. the correlation between the vectors. In our experiment, the proposed algorithm significantly outperformed the algorithms used in recent studies, by achieving a mean average precision (MAP) of 93.18% in a dataset consisting of 3,300 cover-pairs and 496,200 non-cover-pairs.


title: Understanding the Long-Term Use of Smart Speaker Assistants

abstract: Over the past two years the Ubicomp vision of ambient voice assistants, in the form of smart speakers such as the Amazon Echo and Google Home, has been integrated into tens of millions of homes. However, the use of these systems over time in the home has not been studied in depth. We set out to understand exactly what users are doing with these devices over time through analyzing voice history logs of 65,499 interactions with existing Google Home devices from 88 diverse homes over an average of 110 days. We found that specific types of commands were made more often at particular times of day and that commands in some domains increased in length over time as participants tried out new ways to interact with their devices, yet exploration of new topics was low. Four distinct user groups also emerged based on using the device more or less during the day vs. in the evening or using particular categories. We conclude by comparing smart speaker use to a similar study of smartphone use and offer implications for the design of new smart speaker assistants and skills, highlighting specific areas where both manufacturers and skill providers can focus in this domain.


title: Augmented reality in the psychomotor phase of a procedural task

abstract: Procedural tasks are common to many domains, ranging from maintenance and repair, to medicine, to the arts. We describe and evaluate a prototype augmented reality (AR) user interface designed to assist users in the relatively under-explored psychomotor phase of procedural tasks. In this phase, the user begins physical manipulations, and thus alters aspects of the underlying task environment. Our prototype tracks the user and multiple components in a typical maintenance assembly task, and provides dynamic, prescriptive, overlaid instructions on a see-through head-worn display in response to the user's ongoing activity. A user study shows participants were able to complete psychomotor aspects of the assembly task significantly faster and with significantly greater accuracy than when using 3D-graphics-based assistance presented on a stationary LCD. Qualitative questionnaire results indicate that participants overwhelmingly preferred the AR condition, and ranked it as more intuitive than the LCD condition.


title: Using Discourse Signals for Robust Instructor Intervention Prediction

abstract: We tackle the prediction of instructor intervention in student posts from discussion forums in Massive Open Online Courses (MOOCs). Our key finding is that using automatically obtained discourse relations improves the prediction of when instructors intervene in student discussions, when compared with a state-of-the-art, feature-rich baseline. Our supervised classifier makes use of an automatic discourse parser which outputs Penn Discourse Treebank (PDTB) tags that represent in-post discourse features. We show PDTB relation-based features increase the robustness of the classifier and complement baseline features in recalling more diverse instructor intervention patterns. In comprehensive experiments over 14 MOOC offerings from several disciplines, the PDTB discourse features improve performance on average. The resultant models are less dependent on domain-specific vocabulary, allowing them to better generalize to new courses.


Computational Thinking: What and Why?


title: You Will Succeed or Not? Matching Prediction in a Marriage Consulting Service

abstract: On online dating websites, a match is accomplished only when two users have mutual interests. Directed contact is a sign of users' interest and our research intends to provide recommendations by predicting whether two users will have reciprocal contact. We learn matching patterns by supervised machine learning and evaluate our framework on a large dataset extracted from a popular dating website. We propose a novel computational pipeline to extract features from both users' profiles and dating social networks to know users' basic information and their preferences. Our results show that both profile-based features and graph-based features can be used to effectively predict whether two users are likely to make reciprocal contact. Best results are achieved when using all of the features as the input to a Support Vector Machine (SVM) and its Matthews correlation coefficient is 0.372. We also prove that users' facial features provide effective information about them and can be used to predict whether two users match each other.


title: 3D LIDAR-Camera Extrinsic Calibration Using an Arbitrary Trihedron

abstract: This paper presents a novel way to address the extrinsic calibration problem for a system composed of a 3D LIDAR and a camera. The relative transformation between the two sensors is calibrated via a nonlinear least squares (NLS) problem, which is formulated in terms of the geometric constraints associated with a trihedral object. Precise initial estimates of NLS are obtained by dividing it into two sub-problems that are solved individually. With the precise initializations, the calibration parameters are further refined by iteratively optimizing the NLS problem. The algorithm is validated on both simulated and real data, as well as a 3D reconstruction application. Moreover, since the trihedral target used for calibration can be either orthogonal or not, it is very often present in structured environments, making the calibration convenient.


title: Determining embryonic connection timeout in stateful inspection

abstract: Purging embryonic connection states after an appropriate time interval is essential for connection-level monitoring devices such as stateful firewalls in order to minimize security holes and improve state lookup performance. This paper investigates what timeout intervals are adequate, based on the analysis of real-life Internet traces. It reveals that (R+T) seconds are useful timeout periods where R=0,3,9 and 1/spl les/T/spl les/2, and that wide implementation of RFC 2988 is behind the phenomenon.


Multi-Scale Fully Convolutional Network for Fast Face Detection


title: Prediction of chaotic time series using computational intelligence

abstract: In this paper, two CI techniques, namely, single multiplicative neuron (SMN) model and adaptive neuro-fuzzy inference system (ANFIS), have been proposed for time series prediction. A variation of particle swarm optimization (PSO) with co-operative sub-swarms, called COPSO, has been used for estimation of SMN model parameters leading to COPSO-SMN. The prediction effectiveness of COPSO-SMN and ANFIS has been illustrated using commonly used nonlinear, non-stationary and chaotic benchmark datasets of Mackey-Glass, Box-Jenkins and biomedical signals of electroencephalogram (EEG). The training and test performances of both hybrid CI techniques have been compared for these datasets.


title: Alexa, My Love: Analyzing Reviews of Amazon Echo

abstract: The phenomenal success of Amazon Echo as a voice-activated wireless speaker has intrigued us to understand how consumers use the device with an embodied conversational agent in real-world situations. In addition, we are interested in learning what kind of relationship people develop with the device, which has a humanized name. Thus, we performed a large-scale analysis of the user reviews of Amazon Echo, with the dataset consisting of 55,502 reviews spanning over 2 years from May 2015 to May 2017. We first conducted a qualitative study using content analysis of 144 representative reviews to discover what features people like and dislike about Echo. Two case studies were then performed, one for the hands-free feature that customers raved about and the other for speech recognition and understanding which is one key feature of the Echo. To automate the analyzing on a large scale of reviews, we used natural language processing techniques for sentiment analysis and compared Echo reviews with reviews of two traditional electronic devices, Fire Tablet and the popular Bluetooth speaker Dknight MagicBox II. Interestingly we found a significant number of reviewers personify Echo as an assistant, a friend, or a family member. Some reviewers even compare Echo with their wives or girlfriends. Using emotion analysis, we found the reviews personifying Echo showed more positive emotions than those simply treated Echo as a device. In the future work, we plan to conduct field studies to understand interesting use cases of Echo and the novel applications enabled by such powerful ubicomp devices.


title: Recognition and classification of animals based on texture features through parallel computing

abstract: In this work, we proposed an efficient system for animal recognition and classification based on texture features which are obtained from the local appearance and texture of animals. The classification of animals are done by training and subsequently testing two different machine learning techniques, namely k-Nearest Neighbors (k-NN) and Support Vector Machines (SVM). Computer-assisted technique when applied through parallel computing makes the work efficient by reducing the time taken for the task of animal recognition and classification. Here we propose a parallel algorithm for the same. Experimentation is done for about 30 different classes of animals containing more than 3000 images. Among the different classifiers, k-Nearest Neighbor classifiers have achieved a better accuracy.


title: Online event recognition from moving vessel trajectories

abstract: We present a system for online monitoring of maritime activity over streaming positions from numerous vessels sailing at sea. It employs an online tracking module for detecting important changes in the evolving trajectory of each vessel across time, and thus can incrementally retain concise, yet reliable summaries of its recent movement. In addition, thanks to its complex event recognition module, this system can also offer instant notification to marine authorities regarding emergency situations, such as risk of collisions, suspicious moves in protected zones, or package picking at open sea. Not only did our extensive tests validate the performance, efficiency, and robustness of the system against scalable volumes of real-world and synthetically enlarged datasets, but its deployment against online feeds from vessels has also confirmed its capabilities for effective, real-time maritime surveillance.


title: A Novel SDASS Descriptor for Fully Encoding the Information of 3D Local Surface

abstract: Local feature description is a fundamental yet challenging task in 3D computer vision. This paper proposes a novel descriptor, named Statistic of Deviation Angles on Subdivided Space (SDASS), for comprehensive encoding geometrical and spatial in-formation of local surface on Local Reference Axis (LRA). The SDASS descriptor is generated by one geometrical feature and two spatial features. Considering that surface normals, which are usually used for encoding geometrical information of local surface, are vulnerable to various nuisances, we propose a robust geometrical attribute, called Local Principal Axis (LPA), to replace the normals for generating the geometrical feature of our SDASS descriptor. For accurately encoding spatial information, we use two spatial features for fully encoding the spatial information of a local surface based on LRA. Besides, an improved LRA is proposed for increasing the robustness of our SDASS to noise and varying mesh resolutions. The performance of the SDASS descriptor is rigorously tested on several popular datasets. Results show that our descriptor has a high descriptiveness and strong robustness, and its performance outperform existing algorithms by a large margin. Finally, the proposed descriptor is applied to 3D registration. The accurate result further confirms the effectiveness of the SDASS method.


title: A Chair–Based Unobtrusive Cuffless Blood Pressure Monitoring System Based on Pulse Arrival Time

abstract: In this paper, we present an unobtrusive cuffless blood pressure (BP) monitoring system based on pulse arrival time (PAT) for facilitating long-term home BP monitoring. The proposed system consists of an electrocardiograph (ECG), a photoplethysmograph (PPG), and a control circuit with a Bluetooth module, all of which are mounted on a common armchair to measure ECG and PPG signals from users while sitting on the armchair in order to calculate continuous PAT. Considering the good linear correlation of systolic BP (SBP) and the nonlinear correlation of diastolic BP (DBP) with PAT, a new BP estimation method was proposed. Ten subjects underwent BP monitoring experiments involving stationary sitting on a chair, lying on a bed, and pedaling using an ergometer in order to assess the accuracy of the estimated BP. A cuff-type BP monitor was used as reference in the experiments. Results showed that the mean difference of the estimated SBP and DBP was within 0.2 ± 5.8 mmHg ( $p$ $p$ < 0.00001), respectively, and the mean absolute difference of the estimated SBP and DBP were 4.4 and 4.6 mmHg, respectively, compared to references. Additionally, five subjects participated in data collections consisting of sitting on a chair twice a day for one month. Compared to the reference, the difference did not obviously increase along with time, even though individualized calibration was executed only once at the beginning. These results suggest that the proposed system has quite the potential for long-term home BP monitoring.


title: $\mathsf {pSCAN}$ : Fast and Exact Structural Graph Clustering

abstract: We study the problem of structural graph clustering, a fundamental problem in managing and analyzing graph data. Given an undirected unweighted graph, structural graph clustering is to assign vertices to clusters, and to identify the sets of hub vertices and outlier vertices as well, such that vertices in the same cluster are densely connected to each other while vertices in different clusters are loosely connected. In this paper, we develop a new two-step paradigm for scalable structural graph clustering based on our three observations. Then, we present a $\mathsf {pSCAN}$ approach, within the paradigm, aiming to reduce the number of structural similarity computations, and propose optimization techniques to speed up checking whether two vertices are structure-similar. $\mathsf {pSCAN}$ outputs exactly the same clusters as the existing approaches $\mathsf {SCAN}$ and $\mathsf {SCAN\text{++}}$ , and we prove that $\mathsf {pSCAN}$ is worst-case optimal. Moreover, we propose efficient techniques for updating the clusters when the input graph dynamically changes, and we also extend our techniques to other similarity measures, e.g., Jaccard similarity. Performance studies on large real and synthetic graphs demonstrate the efficiency of our new approach and our dynamic cluster maintenance techniques. Noticeably, for the twitter graph with 1 billion edges, our approach takes 25 minutes while the state-of-the-art approach cannot finish even after 24 hours.


title: ReLiDSS: Novel lie detection system from speech signal

abstract: Lying is among the most common wrong human acts that merits spending time thinking about it. The lie detection is until now posing a problem in recent research which aims to develop a non-contact application in order to estimate physiological changes. In this paper, we have proposed a preliminary investigation on which relevant acoustic parameter can be useful to classify lie or truth from speech signal. Our proposed system in is based on the Mel Frequency Cepstral Coefficient (MFCC) commonly used in automatic speech processing on our own constructed database ReLiDDB (ReGIM-Lab Lie Detection DataBase) for both cases lie detection and person voice recognition. We have performed on this database the Support Vector Machines (SVM) classifier using Linear kernel and we have obtained an accuracy of Lie and Truth detection of speech audio respectively 88.23% and 84.52%.


title: Economy-Focused PHEV Battery Lifetime Management through Optimal Fuel Cell Load Sharing

abstract: This paper demonstrates an optimal plug-in hybrid electric vehicle (PHEV) energy management strategy aimed at actively controlling the degradation of a given vehicle''s main energy carriers: in the proposed scenario, these consist of a lithium-ion battery pack and a polymer electrolyte membrane fuel cell (PEMFC). This is achieved by optimizing said vehicle''s operating cost during an active driving cycle while including each energy carrier''s usage-sensitive lifetime in addition to fuel and grid recharge expenses. The proposed scenario will lead to detailed solutions for the optimal process''s response and performance, which will be exploited to evaluate its performance versus alternative strategies. Our results demonstrate the economic gains of our solution versus common rule-based charge depleting PHEV energy management strategies as well as insights into the mechanisms at play during the process.


title: Role of the gut microbiota in immunity and inflammatory disease

abstract: The mammalian intestine is colonized by trillions of microorganisms, most of which are bacteria that have co-evolved with the host in a symbiotic relationship. The collection of microbial populations that reside on and in the host is commonly referred to as the microbiota. A principal function of the microbiota is to protect the intestine against colonization by exogenous pathogens and potentially harmful indigenous microorganisms via several mechanisms, which include direct competition for limited nutrients and the modulation of host immune responses. Conversely, pathogens have developed strategies to promote their replication in the presence of competing microbiota. Breakdown of the normal microbial community increases the risk of pathogen infection, the overgrowth of harmful pathobionts and inflammatory disease. Understanding the interaction of the microbiota with pathogens and the host might provide new insights into the pathogenesis of disease, as well as novel avenues for preventing and treating intestinal and systemic disorders.


title: Parkinson's disease assessment based on gait analysis using an innovative RGB-D camera system

abstract: Movement-related diseases, such as Parkinson's disease (PD), progressively affect the motor function, many times leading to severe motor impairment and dramatic loss of the patients' quality of life. Human motion analysis techniques can be very useful to support clinical assessment of this type of diseases. In this contribution, we present a RGB-D camera (Microsoft Kinect) system and its evaluation for PD assessment. Based on skeleton data extracted from the gait of three PD patients treated with deep brain stimulation and three control subjects, several gait parameters were computed and analyzed, with the aim of discriminating between non-PD and PD subjects, as well as between two PD states (stimulator ON and OFF). We verified that among the several quantitative gait parameters, the variance of the center shoulder velocity presented the highest discriminative power to distinguish between non-PD, PD ON and PD OFF states (p = 0.004). Furthermore, we have shown that our low-cost portable system can be easily mounted in any hospital environment for evaluating patients' gait. These results demonstrate the potential of using a RGB-D camera as a PD assessment tool.


title: Representation Learning by Rotating Your Faces

abstract: The large pose discrepancy between two face images is one of the fundamental challenges in automatic face recognition. Conventional approaches to pose-invariant face recognition either perform face frontalization on, or learn a pose-invariant representation from, a non-frontal face image. We argue that it is more desirable to perform both tasks jointly to allow them to leverage each other. To this end, this paper proposes a Disentangled Representation learning-Generative Adversarial Network (DR-GAN) with three distinct novelties. First, the encoder-decoder structure of the generator enables DR-GAN to learn a representation that is both generative and discriminative, which can be used for face image synthesis and pose-invariant face recognition. Second, this representation is explicitly disentangled from other face variations such as pose, through the pose code provided to the decoder and pose estimation in the discriminator. Third, DR-GAN can take one or multiple images as the input, and generate one unified identity representation along with an arbitrary number of synthetic face images. Extensive quantitative and qualitative evaluation on a number of controlled and in-the-wild databases demonstrate the superiority of DR-GAN over the state of the art in both learning representations and rotating large-pose face images.


title: Recent development on time and wavelength-division multiplexed passive optical network (TWDM-PON) for next-generation passive optical network stage 2 (NG-PON2)

abstract: The second stage of next-generation passive optical network (NG-PON2) based on time and wavelength division multiplexed passive optical network (TWDM-PON) was proposed by a telecommunication group research to enhance the performance of broadband access networks. TWDM-PON was selected as the best candidate for NG-PON2 solution because of its ability to support the NG-PON2 requirements, such as enhanced bandwidth capacity, 40Gb/s, and coexistence with previously existing generations without any change to optical distribution network (ODN). This paper reviewed the recent progress carried out on a TWDM-PON system configuration, with emphasis on tunable transmitter and receiver optical network unit (ONU) in terms of the amount of tuning range reported in exploiting the wavelength plan provided with cost efficiency. The speed of data rate transmitted on the downstream and upstream links between optical line terminal (OLT) and ONU with the way of stacking approach in NG-PON2 is reviewed. In addition, the power system budget is reviewed to determine the number of users allocated with the system with each transmission allowed.


title: Natural language based financial forecasting: a survey

abstract: Natural language processing (NLP), or the pragmatic research perspective of computational linguistics, has become increasingly powerful due to data availability and various techniques developed in the past decade. This increasing capability makes it possible to capture sentiments more accurately and semantics in a more nuanced way. Naturally, many applications are starting to seek improvements by adopting cutting-edge NLP techniques. Financial forecasting is no exception. As a result, articles that leverage NLP techniques to predict financial markets are fast accumulating, gradually establishing the research field of natural language based financial forecasting (NLFF), or from the application perspective, stock market prediction. This review article clarifies the scope of NLFF research by ordering and structuring techniques and applications from related work. The survey also aims to increase the understanding of progress and hotspots in NLFF, and bring about discussions across many different disciplines.


title: Strip-fed rectangular dielectric resonator antennas with/without a parasitic patch

abstract: A rectangular dielectric resonator antenna (DRA) was studied theoretically and experimentally. The rectangular DRA is excited by a strip, which is compatible with a coaxial probe. Both linearly polarized (LP) and circularly polarized (CP) fields of the antenna are considered. In previous studies of the LP rectangular DRA, only the fundamental TE/sub 111/ mode has received much attention. In this paper, it is found that the fundamental TE/sub 111/ mode, together with the higher-order TE/sub 113/ mode, can be used to design a wide-band LP DRA. The bandwidth of the dual-mode DRA can be over 40% for a conventional rectangular DRA with a simple feed. For the CP mode, a parasitic patch is attached on a side wall of the DRA to excite a degenerate mode. In both the LP and CP cases, the finite-difference time-domain (FDTD) method is used to analyze the problems. The results agree reasonably with measurements.


title: ARTICLE IN PRESS Neural correlates of humor detection and appreciation

abstract: Humor is a uniquely human quality whose neural substrates remain enigmatic. The present report combined dynamic, real-life content and event-related functional magnetic resonance imaging (fMRI) to dissociate humor detection (“getting the joke”) from humor appreciation (the affective experience of mirth). During scanning, subjects viewed full-length episodes of the television sitcoms Seinfeld or The Simpsons. Brain activity time-locked to humor detection moments revealed increases in left inferior frontal and posterior temporal cortices, whereas brain activity time-locked to moments of humor appreciation revealed increases in bilateral regions of insular cortex and the amygdala. These findings provide evidence that humor depends critically upon extant neural systems important for resolving incongruities (humor detection) and for the expression of affect (humor appreciation).


Petri Lankoski Character Design Fundamentals for


title: Automatic Image Annotation Using Auxiliary Text Information

abstract: The availability of databases of images labeled with keywords is necessary for developing and evaluating image annotation models. Dataset collection is however a costly and time consuming task. In this paper we exploit the vast resource of images available on the web. We create a database of pictures that are naturally embedded into news articles and propose to use their captions as a proxy for annotation keywords. Experimental results show that an image annotation model can be developed on this dataset alone without the overhead of manual annotation. We also demonstrate that the news article associated with the picture can be used to boost image annotation performance.


title: A DCT-based robust video steganographic method using BCH error correcting codes

abstract: Due to the significant growth of video data over the Internet, it has become a popular choice for data hiding field. The performance of any steganographic algorithm relies on the embedding efficiency, embedding payload, and robustness against attackers. Low hidden ratio, less security, and low quality of stego videos are the major issues of many existing steganographic methods. In this paper, we propose a DCT-based robust video steganographic method using BCH codes. To improve the security of the proposed algorithm, a secret message is first encrypted and encoded by using BCH codes. Then, it is embedded into the discrete cosine transform (DCT) coefficients of video frames. The hidden message is embedded into DCT coefficients of each Y, U, and V planes excluding DC coefficients. The proposed algorithm is tested under two types of videos that contain slow and fast moving objects. The results of the proposed algorithm are compared with three existing methods. The results demonstrate better performance for the proposed algorithm than for the others. The hidden ratio of the proposed algorithm is approximately 27.53%, which is evaluated as a high hiding capacity with a minimal tradeoff of the visual quality. The robustness of the proposed algorithm was tested under different attacks.


title: Regularizing CNNs with Locally Constrained Decorrelations

abstract: Regularization is key for deep learning since it allows training more complex models while keeping lower levels of overfitting. However, the most prevalent regularizations do not leverage all the capacity of the models since they rely on reducing the effective number of parameters. Feature decorrelation is an alternative for using the full capacity of the models but the overfitting reduction margins are too narrow given the overhead it introduces. In this paper, we show that regularizing negatively correlated features is an obstacle for effective decorrelation and present OrthoReg, a novel regularization technique that locally enforces feature orthogonality. As a result, imposing locality constraints in feature decorrelation removes interferences between negatively correlated feature weights, allowing the regularizer to reach higher decorrelation bounds, and reducing the overfitting more effectively. In particular, we show that the models regularized with OrthoReg have higher accuracy bounds even when batch normalization and dropout are present. Moreover, since our regularization is directly performed on the weights, it is especially suitable for fully convolutional neural networks, where the weight space is constant compared to the feature map space. As a result, we are able to reduce the overfitting of state-of-the-art CNNs on CIFAR-10, CIFAR-100, and SVHN.


title: Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets

abstract: Bayesian optimization has become a successful tool for hyperparameter optimization of machine learning algorithms, such as support vector machines or deep neural networks. Despite its success, for large datasets, training and validating a single configuration often takes hours, days, or even weeks, which limits the achievable performance. To accelerate hyperparameter optimization, we propose a generative model for the validation error as a function of training set size, which is learned during the optimization process and allows exploration of preliminary configurations on small subsets, by extrapolating to the full dataset. We construct a Bayesian optimization procedure, dubbed Fabolas, which models loss and training time as a function of dataset size and automatically trades off high information gain about the global optimum against computational cost. Experiments optimizing support vector machines and deep neural networks show that Fabolas often finds high-quality solutions 10 to 100 times faster than other state-of-the-art Bayesian optimization methods or the recently proposed bandit strategy Hyperband.


title: An efficient botnet detection system for P2P botnet

abstract: Peer-to-Peer (P2P) botnets are exploited by the botmasters for their resiliency against the take down efforts. As the modern botnets are stealthier, the traditional botnet detection approaches are not suitable for the botnet detection. In this paper, an efficient botnet detection system is proposed for detecting the P2P botnet. The proposed botnet detection system estimates the flow export using NetFlow protocol. The packet flow is analyzed using three main components namely, Exporter, Collector, and Analyzer. The exporter captures the packet and monitors the contents of the packet. The collector captures the flow traffic and the analyzer component initiates an automated analysis of traffic with the captured packet information. The packet flow information is collected by virtual interface and physical probe. The virtual interface is used for collecting the malicious traffic information between the Virtual Machines (VMs) and the physical probe gathers malicious traffic information between the network bridges connecting VMs. The information collected from these techniques are analyzed for detecting the botnets in inter VM and intra VM. Compared to the existing Dendritic Cell Algorithm (DCA), the proposed VM based botnet detection system has minimal time consumption, increased detection speed, and higher attack prevention ratio.


ERRoR ANAlysIs AND RETENTIoN-AwARE ERRoR MANAgEMENT FoR NAND FlAsh MEMoRy


title: Comprehensible Credit Scoring Models Using Rule Extraction From Support Vector Machines

abstract: In recent years, support vector machines (SVMs) were successfully applied to a wide range of applications. However, since the classifier is described as a complex mathematical function, it is rather incomprehensible for humans. This opacity property prevents them from being used in many real-life applications where both accuracy and comprehensibility are required, such as medical diagnosis and credit risk evaluation. To overcome this limitation, rules can be extracted from the trained SVM that are interpretable by humans and keep as much of the accuracy of the SVM as possible. In this paper, we will provide an overview of the recently proposed rule extraction techniques for SVMs and introduce two others taken from the artificial neural networks domain, being Trepan and G-REX. The described techniques are compared using publicly available datasets, such as Ripley’s synthetic dataset and the multi-class iris dataset. We will also look at medical diagnosis and credit scoring where comprehensibility is a key requirement and even a regulatory recommendation. Our experiments show that the SVM rule extraction techniques lose only a small percentage in performance compared to SVMs and therefore rank at the top of comprehensible classification techniques.


title: Notice of RetractionB to C E-Commerce Platform Software Development Cost and Schedule Estimation Method

abstract: The exactly software cost and progress estimates is the necessary method to ensure the development and application of e-commerce platform to operate, while in this article we take use of combining the Function Point Analysis and COCOMO II model to estimate. In the procedure of estimation, the first thing is to estimate the function points, which is according to the type and basis for classification, B to C e-commerce platform, functions of the complexity level and other factors point to estimate the unadjusted function points, the adjustment factor calculated by the adjusted function points; then based e-commerce platform development tool in the general level of the developer's estimated development costs of labor productivity; and finally take the use of COCOMO II model to estimate the progress of the development.


title: Bayesian Nonparametric Poisson Factorization for Recommendation Systems

abstract: We develop a Bayesian nonparametric Poisson factorization model for recommendation systems. Poisson factorization implicitly models each user’s limited budget of attention (or money) that allows consumption of only a small subset of the available items. In our Bayesian nonparametric variant, the number of latent components is theoretically unbounded and eectively estimated when computing a posterior with observed user behavior data. To approximate the posterior, we develop an ecient variational inference algorithm. It adapts the dimensionality of the latent components to the data, only requires iteration over the user/item pairs that have been rated, and has computational complexity on the same order as for a parametric model with xed dimensionality. We studied our model and algorithm with large realworld data sets of user-movie preferences. Our model eases the computational burden of searching for the number of latent components and gives better predictive performance than its parametric counterpart.


title: Spinal 5-HT3 Receptor Activation Induces Behavioral Hypersensitivity via a Neuronal-Glial-Neuronal Signaling Cascade

abstract: Recent studies indicate that the descending serotonin (5-HT) system from the rostral ventromedial medulla (RVM) in the brainstem and the 5-HT 3 receptor subtype in the spinal dorsal horn are involved in enhanced descending pain facilitation after tissue and nerve injury. However, the mechanisms underlying the activation of the 5-HT 3 receptor and its contribution to facilitation of pain remain unclear. In the present study, activation of spinal 5-HT 3 receptor by intrathecal injection of a selective 5-HT 3 receptor agonist, SR57227, induced spinal glial hyperactivity, neuronal hyperexcitability, and pain hypersensitivity in rats. We found that there was neuron-to-microglia signaling via chemokine fractalkine, microglia to astrocyte signaling via the cytokine IL-18, astrocyte to neuronal signaling by IL-1β, and enhanced activation of GluN (NMDA) receptors in the spinal dorsal horn. In addition, exogenous brain-derived neurotrophic factor-induced descending pain facilitation was accompanied by upregulation of CD11b and GFAP expression in the spinal dorsal horn after microinjection in the RVM, and these events were significantly prevented by functional blockade of spinal 5-HT 3 receptors. Enhanced expression of spinal CD11b and GFAP after hindpaw inflammation was also attenuated by molecular depletion of the descending 5-HT system by intra-RVM Tph-2 shRNA interference. Thus, these findings offer new insights into the cellular and molecular mechanisms at the spinal level responsible for descending 5-HT-mediated pain facilitation during the development of persistent pain after tissue and nerve injury. New pain therapies should focus on prime targets of descending facilitation-induced glial involvement, and in particular the blocking of intercellular signaling transduction between neuron and glia.


DECOR + HOP : A Scalable Blockchain Protocol


title: Clustering and Summarizing Protein-Protein Interaction Networks: A Survey

abstract: The increasing availability and significance of large-scale protein-protein interaction ( ppi ) data has resulted in a flurry of research activity to comprehend the organization, processes, and functioning of cells by analyzing these data at network level. Network clustering , that analyzes the topological and functional properties of a ppi network to identify clusters of interacting proteins, has gained significant popularity in the bioinformatics as well as data mining research communities. Many studies since the last decade have shown that clustering ppi networks is an effective approach for identifying functional modules , revealing functions of unknown proteins, etc. In this paper, we examine this issue by classifying, discussing, and comparing a wide ranging approaches proposed by the bioinformatics community to cluster ppi networks. A pervasive desire of this review is to emphasize the uniqueness of the network clustering problem in the context of ppi networks and highlight why generic network clustering algorithms proposed by the data mining community cannot be directly adopted to address this problem effectively. We also review a closely related problem to ppi network clustering, network summarization , which can enable us to make sense out of the information contained in large ppi networks by generating multi-level functional summaries .


title: A new bioinformatics analysis tools framework at EMBL–EBI

abstract: The EMBL-EBI provides access to various mainstream sequence analysis applications. These include sequence similarity search services such as BLAST, FASTA, InterProScan and multiple sequence alignment tools such as ClustalW, T-Coffee and MUSCLE. Through the sequence similarity search services, the users can search mainstream sequence databases such as EMBL-Bank and UniProt, and more than 2000 completed genomes and proteomes. We present here a new framework aimed at both novice as well as expert users that exposes novel methods of obtaining annotations and visualizing sequence analysis results through one uniform and consistent interface. These services are available over the web and via Web Services interfaces for users who require systematic access or want to interface with customized pipe-lines and workflows using common programming languages. The framework features novel result visualizations and integration of domain and functional predictions for protein database searches. It is available at http://www.ebi.ac.uk/Tools/sss for sequence similarity searches and at http://www.ebi.ac.uk/Tools/msa for multiple sequence alignments.


title: LIGHTNETs: Smart LIGHTing and Mobile Optical Wireless NETworks — A Survey

abstract: Recently, rapid increase of mobile devices pushed the radio frequency (RF)-based wireless technologies to their limits. Free-space-optical (FSO), a.k.a. optical wireless, communication has been considered as one of the viable solutions to respond to the ever-increasing wireless capacity demand. Particularly, Visible Light Communication (VLC) which uses light emitting diode (LED) based smart lighting technology provides an opportunity and infrastructure for the high-speed low-cost wireless communication. Though stemming from the same core technology, the smart lighting and FSO communication have inherent tradeoffs amongst each other. In this paper, we present a tutorial and survey of advances in these two technologies and explore the potential for integration of the two as a single field of study: LIGHTNETs. We focus our survey to the context of mobile communications given the recent pressing needs in mobile wireless networking. We deliberate on key challenges involved in designing technologies jointly performing the two functions simultaneously: LIGHTing and NETworking.


title: Loss Aversion in Riskless Choice: A Reference-Dependent Model

abstract: Much experimental evidence indicates that choice depends on the status quo or reference level: changes of reference point often lead to reversals of preference. We present a reference-dependent theory of consumer choice, which explains such effects by a deformation of indifference curves about the reference point. The central assumption of the theory is that losses and disadvantages have greater impact on preferences than gains and advantages. Implications of loss aversion for economic behavior are considered.


title: Network Coding: Connections Between Information Theory And Estimation Theory

abstract: In this paper, we prove the existence of fundamental relations between information theory and estimation theory for network-coded flows. When the network is represented by a directed graph g = (V, e) and under the assumption of uncorrelated noise over information flows between the directed links connecting transmitters, switches (relays), and receivers. We unveil that there yet exist closed-form relations for the gradient of the mutual information with respect to different components of the system matrix M. On the one hand, this result opens a new class of problems casting further insights into effects of the network topology, topological changes when nodes are mobile, and the impact of errors and delays in certain links into the network capacity which can be further studied in scenarios where one source multi-sinks multicasts and multi-source multicast where the invertibility and the rank of matrix M plays a significant role in the decoding process and therefore, on the network capacity. On the other hand, it opens further research questions of finding precoding solutions adapted to the network level.


title: Uplink Achievable Rate for Massive MIMO with Low-Resolution ADC

abstract: In this letter, we derive an approximate analytical expression for the uplink achievable rate of a massive multiinput multioutput (MIMO) antenna system when finite precision analog-digital converters (ADCs) and the common maximal-ratio combining technique are used at the receivers. To obtain this expression, we treat quantization noise as an additive quantization noise model. Considering the obtained expression, we show that low-resolution ADCs lead to a decrease in the achievable rate but the performance loss can be compensated by increasing the number of receiving antennas. In addition, we investigate the relation between the number of antennas and the ADC resolution, as well as the power-scaling law. These discussions support the feasibility of equipping highly economical ADCs with low resolution in practical massive MIMO systems.


Cluster Analysis


title: Trotting, pacing and bounding by a quadruped robot

abstract: Abstract This paper explores the quadruped running gaits that use the legs in pairs: the trot (diagonal pairs), the pace (lateral pairs), and the bound (front and rear pairs). Rather than study these gaits in quadruped animals, we studied them in a quadruped robot. We found that each of the gaits that use the legs in pairs can be transformed into a common underlying gait, a virtual biped gait . Once transformed, a single set of control algorithms produce all three gaits, with modest parameter variations between them. The control algorithms manipulated rebound height, running speed, and body attitude, while a low-level mechanism coordinated the behavior of the legs in each pair. The approach was tested with laboratory experiments on a four-legged robot. Data are presented that show the details of the running motion for the three gaits and for transitions from one gait to another.


title: A MRF-based approach for real-time subway monitoring

abstract: There has been an increase in the use of video surveillance and monitoring in public areas to improve safety and security. Change detection and crowding/congestion density estimation are two sub-tasks in a subway monitoring system. We propose a method that decomposes this problem into two steps. The first step consists of a change detection algorithm that distinguishes the background from the foreground. This is done using a discontinuity preserving MRF-based approach where the information from different sources (background subtraction, intensity modeling) is combined with spatial constraints to provide a smooth motion detection map. Then, the obtained change detection map is combined with a geometry module that performs a soft auto-calibration to estimate a measure of congestion of the observed area (platform). Extensive experimental results in a metro station of a metropolitan city demonstrates the performance and the potential of our method.