title: User-interface for teleoperation with mixed-signal haptic feedback

abstract: Haptic feedback for minimally invasive surgery is a widely debated topic, especially force sensors for this kind of application are thoroughly researched. The current state-of-the-art includes several realization options, but none of them are suitable to be integrated in small diameter instruments. In this work, we present a new approach for the generation of haptic feedback by using two separate feedback mechanisms for spatial kinesthetic and unidirectional tactile feedback in a teleoperation system with a parallel kinematic slave robot and the design of the corresponding user interfaces. Kinesthetic forces are measured with proximal sensors in the robot kinematic and displayed with a delta-type kinematic mechanism. Tactile signals are acquired with an acceleration sensor placed closely at the tip of the robot manipulator and conveyed open-loop by a vibrating cylinder to the user's palm. Kinesthetic forces up to 15 N can be displayed up to a frequency of 50 Hz. The tactile interface displays accelerations up to 15m/s for frequencies from 50 Hz to 1000 Hz. The combination of the two interfaces allows the display of high-fidelity haptic feedback with greatly reduced requirements on the interface components.


title: A survey on player tracking in soccer videos

abstract: Abstract There is a growth of demand for automatically analyzing soccer matches and tactics. Since players are the focus of attentions in soccer matches, player tracking is a fundamental element in most soccer video analysis. The aim of player tracking is to extract the trajectories of players, and its input is provided through some preprocessing steps including playfield detection, player detection, player labeling, occlusion handling and player appearance modeling. Soccer player tracking is a complex and challenging task due to difficulties such as blur, illumination change and heavy occlusions. This paper presents the state-of-the-art in preprocessing and processing methods for soccer player tracking. We categorize different approaches, analyze their strengths and weaknesses, review evaluation criteria and conclude future research directions.


abstract: Unsupervised learning with generative adversarial networks (GANs) has proven hugely successful. Regular GANs hypothesize the discriminator as a classifier with the sigmoid cross entropy loss function. However, we found that this loss function may lead to the vanishing gradients problem during the learning process. To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss function for the discriminator. We show that minimizing the objective function of LSGAN yields minimizing the Pearson X2 divergence. There are two benefits of LSGANs over regular GANs. First, LSGANs are able to generate higher quality images than regular GANs. Second, LSGANs perform more stable during the learning process. We evaluate LSGANs on LSUN and CIFAR-10 datasets and the experimental results show that the images generated by LSGANs are of better quality than the ones generated by regular GANs. We also conduct two comparison experiments between LSGANs and regular GANs to illustrate the stability of LSGANs.


title: Understanding motor events: a neurophysiological study

abstract: Neurons of the rostral part of inferior premotor cortex of the monkey discharge during goal-directed hand movements such as grasping, holding, and tearing. We report here that many of these neurons become active also when the monkey observes specific, meaningful hand movements performed by the experimenters. The effective experimenters' movements include among others placing or retrieving a piece of food from a table, grasping food from another experimenter's hand, and manipulating objects. There is always a clear link between the effective observed movement and that executed by the monkey and, often, only movements of the experimenter identical to those controlled by a given neuron are able to activate it. These findings indicate that premotor neurons can retrieve movements not only on the basis of stimulus characteristics, as previously described, but also on the basis of the meaning of the observed actions.


PROCESSING-EE 5359 A UNIVERSAL IMAGE QUALITY INDEX and SSIM comparison


title: Violent video game effects on salivary cortisol, arousal, and aggressive thoughts in children

abstract: An experiment investigated the effects of violent content in video games on two physiological indicators of the fight-or-flight response (cortisol and cardiovascular changes) and on accessibility of aggressive thoughts in children. Participants played a randomly assigned violent or nonviolent video game, rated the game on several dimensions, and did a word completion task. Results showed that the violent video game increased cortisol and (for boys) cardiovascular arousal (relative to baseline) more than did the equally exciting nonviolent game. The violent game also increased the accessibility of aggressive thoughts. The cortisol findings in particular suggest that playing a violent video game may activate the sympathetic nervous system and elicit a fight-or-flight type response in children. Theoretical implications and future research are discussed.


title: Online Social Networking and Mental Health

abstract: Abstract During the past decade, online social networking has caused profound changes in the way people communicate and interact. It is unclear, however, whether some of these changes may affect certain normal aspects of human behavior and cause psychiatric disorders. Several studies have indicated that the prolonged use of social networking sites (SNS), such as Facebook, may be related to signs and symptoms of depression. In addition, some authors have indicated that certain SNS activities might be associated with low self-esteem, especially in children and adolescents. Other studies have presented opposite results in terms of positive impact of social networking on self-esteem. The relationship between SNS use and mental problems to this day remains controversial, and research on this issue is faced with numerous challenges. This concise review focuses on the recent findings regarding the suggested connection between SNS and mental health issues such as depressive symptoms, changes in self-esteem, and I...


title: Coexistence in millimeter-wave WBAN: A game theoretic approach

abstract: This paper examines and provides the theoretical evidence of the feasibility of 60 GHz mmWave in wireless body area networks (WBANs), by analyzing its properties. It has been shown that 60 GHz based communication could better fit WBANs compared to traditional 2.4 GHz based communication because of its compact network coverage, miniaturized devices, superior frequency reuse, multi-gigabyte transmission rate and the therapeutic merits for human health. Since allowing coexistence among the WBANs can enhance the efficiency of the mmWave based WBANs, we formulated the coexistence problem as a non-cooperative distributed power control game. This paper proves the existence of Nash equilibrium (NE) and derives the best response move as a solution. The efficiency of the NE is also improved by modifying the utility function and introducing a pair of pricing factors. Our simulation results indicate that the proposed pricing policy significantly improves the efficiency in terms of Pareto optimality and social optimality.


title: Intelligent system for vehicular accident detection and notification

abstract: Road accidents are a human tragedy. They involve high human suffering and monetary costs in terms of untimely deaths, injuries and loss of potential income. There are so many new techniques such as Antilock Breaking System (ABS), Adaptive Cruise Control (ACC), Anti Collision System (ACS) to avoid accidents and in spite of all this, such large number of accidents takes place. Hence this paper presents a system which gives an idea about what can be done to provide medical help and other facilities after accident as soon as possible. Accident can be detected using flex sensor and accelerometer, while location of accident will be informed to desired persons such as nearest hospital, police and owner of vehicle through sms sent using GSM modem containing co-ordinates obtained from GPS along with time of accident and vehicle number. Camera located inside vehicle will transmit real time video to see current situation of passengers inside vehicle. Thus this paper emphasizes on post-accident system for detecting and informing about it. Simulation result on hyper terminal is also presented in this paper.


title: A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives

abstract: Human fMRI signals exhibit a spatial patterning that contains detailed information about a person's mental states. Using classifiers it is possible to access this information and study brain processes at the level of individual mental representations. The precise link between fMRI signals and neural population signals still needs to be unraveled. Also, the interpretation of classification studies needs to be handled with care. Nonetheless, pattern-based analyses make it possible to investigate human representational spaces in unprecedented ways, especially when combined with computational modeling.


title: Uni-directional trusted path: Transaction confirmation on just one device

abstract: Commodity computer systems today do not include a full trusted path capability. Consequently, malware can control the user's input and output in order to reveal sensitive information to malicious parties or to generate manipulated transaction requests to service providers. Recent hardware offers compelling features for remote attestation and isolated code execution, however, these mechanisms are not widely used in deployed systems to date. We show how to leverage these mechanisms to establish a “one-way” trusted path allowing service providers to gain assurance that users' transactions were indeed submitted by a human operating the computer, instead of by malware such as transaction generators. We design, implement, and evaluate our solution, and argue that it is practical and offers immediate value in e-commerce, as a replacement for captchas, and in other Internet scenarios.


Hindi Character Recognition Using Neural Network and Various Optimizing Techniques : A Survey


title: Automated Genome Annotation and Pathway Identification Using the KEGG Orthology ( KO ) As a Controlled Vocabulary

abstract: Motivation: High-throughput technologies such as DNA sequencing and microarrays have created the need for automated annotation of large sets of genes, including whole genomes, and automated identification of pathways. Ontologies, such as the popular Gene Ontology (GO), provide a common controlled vocabulary for these types of automated analysis. Yet, while GO offers tremendous value, it also has certain limitations such as the lack of direct association with pathways. ::: ::: Results: We demonstrated the use of the KEGG Orthology (KO), part of the KEGG suite of resources, as an alternative controlled vocabulary for automated annotation and pathway identification. We developed a KO-Based Annotation System (KOBAS) that can automatically annotate a set of sequences with KO terms and identify both the most frequent and the statistically significantly enriched pathways. Results from both whole genome and microarray gene cluster annotations with KOBAS are comparable and complementary to known annotations. KOBAS is a freely available standalone Python program that can contribute significantly to genome annotation and microarray analysis. ::: ::: Availability: Supplementary data and the KOBAS system are available at http://genome.cbi.pku.edu.cn/download.html ::: ::: Contact: weilp@mail.cbi.pku.edu.cn


title: Where are you from?: confusing location distinction using virtual multipath camouflage

abstract: In wireless networks, location distinction aims to detect location changes or facilitate authentication of wireless users. To achieve location distinction, recent research has been focused on investigating the spatial uncorrelation property of wireless channels. Specifically, the differences of wireless channel characteristics are used to distinguish locations or identify location changes. However, we discover a new attack against all existing location distinction approaches that are built on the spatial uncorrelation property of wireless channels. In such an attack, the adversary can easily hide her location changes or impersonate movements by injecting fake wireless channel characteristics into a target receiver. Experimental results on our USRP-based prototype show that the discovered attack can craft any desired channel characteristic with a successful probability of 95.0% to defeat spatial uncorrelation based location distinction schemes. To defend against this attack, we propose a detection technique that utilizes an auxiliary receiver or antenna to identify these fake channel characteristics. Experiments demonstrate that our novel detection method achieves a detection rate higher than 91.2% while maintaining a very low false alarm rate.


title: A 66dB continuous gain adjusting CMOS AGC amplifier with both feedforward and feedback loop

abstract: A CMOS continuous gain adjusting AGC amplifier with 66dB maximum gain and 30MHz bandwidth is proposed in this paper. Such AGC amplifier consumes a power of 1.1mW under the supply of 1.2V. It includes a fast responding feedforward loop and a high linearity feedback loop. Hysteresis comparator is adopted to overcome the ripples of successive detection RSSI, generating stable control word to complete the coarse gain adjustment in less than 2μs, which simplifies the design of VGA. The design is completed in TSMC 130nm CMOS technology and simulation results are given at last.


Virtual petz (video session): a hybrid approach to creating autonomous, lifelike dogz and catz


title: A hot-spot mutation in CDC42 (p.Tyr64Cys) and novel phenotypes in the third patient with Takenouchi-Kosaki syndrome

abstract: Takenouchi-Kosaki syndrome (TKS) is a congenital malformation syndrome characterized by severe developmental delay, macrothrombocytopenia, camptodactyly, sensorineural hearing loss, and dysmorphic facial features. Recently, a heterozygous de novo mutation (p.Tyr64Cys) in the CDC42 gene, which encodes a key small GTP-binding protein of the Rho-subfamily, was identified in two unrelated patients with TKS. We herein report a third patient with TKS who had the same heterozygous CDC42 mutation. The phenotype of the patient was very similar to those of the two previously reported patients with TKS; however, she also demonstrated novel clinical manifestations, such as congenital hypothyroidism and immunological disturbance. Thus, despite the heterozygous mutation of CDC42 (p.Tyr64Cys) likely being a hot-spot mutation for TKS, its phenotype may be variable. Further studies and the accumulation of patients with CDC42 mutations are needed to clarify the phenotype in patients with TKS and the pathophysiological roles of the CDC42 mutation.


title: Timeline Generation: Tracking individuals on Twitter

abstract: In this paper, we preliminarily learn the problem of reconstructing users' life history based on the their Twitter stream and proposed an unsupervised framework that create a chronological list for personal important events (PIE) of individuals. By analyzing individ- ual tweet collections, we find that what are suitable for inclusion in the personal timeline should be tweets talking about personal (as opposed to public) and time-specific (as opposed to time-general) topics. To further extract these types of topics, we introduce a non-parametric multi-level Dirichlet Process model to recognize four types of tweets: personal time-specific (PersonTS), personal time-general (PersonTG), public time-specific (PublicTS) and pub- lic time-general (PublicTG) topics, which, in turn, are used for fur- ther personal event extraction and timeline generation. To the best of our knowledge, this is the first work focused on the generation of timeline for individuals from Twitter data. For evaluation, we have built gold standard timelines that contain PIE related events from 20 ordinary twitter users and 20 celebrities. Experimental results demonstrate that it is feasible to automatically extract chronologi- cal timelines for Twitter users from their tweet collection


title: Automated Pre–processing Method for Dermoscopic Images and its Application to Pigmented Skin Lesion Segmentation

abstract: In this paper, we put forward a new pre–processing scheme for automatic analysis of dermoscopic images. Our contributions are two-fold. First, we present a procedure, an extension of previous approaches, which succeeds in removing confounding factors from dermoscopic images: these include shading induced by imaging non-flat skin surfaces and the effect of light-intensity falloff toward the edges of the dermoscopic image. This procedure is shown to facilitate the detection and removal of artifacts such as hairs as well. Second, we present a novel simple yet effective greyscale conversion approach that is based on physics and biology of human skin. Our proposed greyscale image provides high separability between a pigmented lesion and normal skin surrounding it. Finally, using our pre–processing scheme, we perform segmentation based on simple grey-level thresholding, with results outperforming the state of the art.


title: Half Field Offense in RoboCup Soccer : A Multiagent Reinforcement Learning Case Study

abstract: We present half field offense, a novel subtask of RoboCup simulated soccer, and pose it as a problem for reinforcement learning. In this task, an offense team attempts to outplay a defense team in order to shoot goals. Half field offense extends keepaway [11], a simpler subtask of RoboCup soccer in which one team must try to keep possession of the ball within a small rectangular region, and away from the opposing team. Both keepaway and half field offense have to cope with the usual problems of RoboCup soccer, such as a continuous state space, noisy actions, and multiple agents, but the latter is a significantly harder multiagent reinforcement learning problem because of sparse rewards, a larger state space, a richer action set, and the sheer complexity of the policy to be learned. We demonstrate that the algorithm that has been successful for keepaway is inadequate to scale to the more complex half field offense task, and present a new algorithm to address the aforementioned problems in multiagent reinforcement learning. The main feature of our algorithm is the use of inter-agent communication, which allows for more frequent and reliable learning updates. We show empirical results verifying that our algorithm registers significantly higher performance and faster learning than the earlier approach. We also assess the contribution of inter-agent communication by considering several variations of the basic learning method. This work is a step further in the ongoing challenge to learn complete team behavior for the RoboCup simulated soccer task.


title: Wideband Tri-Polarization Reconfigurable Magneto-Electric Dipole Antenna

abstract: A wideband polarization reconfigurable magneto-electric (ME) dipole antenna is presented. The proposed ME dipole is able to work in three different polarization states including one linear polarization and two orthogonal circular polarizations. The linearly polarized operation mode is based on a $\Gamma $ -probe-fed ME dipole of four-sectional structure. And perturbation is introduced by connecting the +45° (or −45°) diagonal parts of the four sections with a diode embedded line to realize circularly polarized (CP) radiation. A simple biasing network driven by one 1.5 V dc source is designed to manipulate the state of four p-i-n diodes and hence switch the polarization. The presented reconfigurable ME dipole demonstrates distinctive features such as stable gain, high front-to-back ratio, and symmetric radiation pattern. Furthermore, a wide effective bandwidth (BW) in terms of the overlapped frequency region within which the impedance BW ( $S_{11} dB) for all polarization states and the axial ratio (AR) BW(AR < 3) for both CP states are sustained simultaneously is obtained. An antenna prototype based on the proposed design is fabricated and tested for verification. Measured results show an effective BW of 7.9% (with an overlapped impedance BW of 31% and overlapped AR BW of 7.9%), comparatively high gain of 9 ± 0.8 dBi, and high efficiency of 80%–90% for all polarizations across the operating band.


title: Reliable Power Gating With NBTI Aging Benefits

abstract: In this paper, we show that negative bias temperature instability (NBTI) aging of sleep transistors (STs), together with its detrimental effect for circuit performance and lifetime (LT), presents considerable benefits for power-gated circuits. Indeed, it reduces static power due to leakage current, and increases ST switch efficiency, making power gating more efficient and effective over time. The magnitude of these aging benefits depends on operating and environmental conditions. By means of HSPICE simulations, considering a 32-nm CMOS technology, we demonstrate that static power may reduce by more than 80% in 10 years of operation. Static power decrease over time due to NBTI aging is also proven experimentally, using a test chip manufactured with a 65-nm technology. We propose an ST design strategy for reliable power gating, in order to harvest the benefits offered by NBTI aging. It relies on the design of STs with a proper lower $V_{\textrm {th}}$ compared with the standard STs. This can be achieved by either redesigning the STs with the identified $V_{\textrm {th}}$ value or applying a proper forward body bias to the available power switching fabrics. Through the HSPICE simulations, we show LT extension up to $21.4\times $ and average static power reduction up to 16.3% compared with the standard ST design approach, without additional area overhead. Finally, we show LT extension and several performance-cost tradeoffs when a target maximum LT is considered.


title: An integrated GPU power and performance model

abstract: GPU architectures are increasingly important in the multi-core era due to their high number of parallel processors. Performance optimization for multi-core processors has been a challenge for programmers. Furthermore, optimizing for power consumption is even more difficult. Unfortunately, as a result of the high number of processors, the power consumption of many-core processors such as GPUs has increased significantly. Hence, in this paper, we propose an integrated power and performance (IPP) prediction model for a GPU architecture to predict the optimal number of active processors for a given application. The basic intuition is that when an application reaches the peak memory bandwidth, using more cores does not result in performance improvement. We develop an empirical power model for the GPU. Unlike most previous models, which require measured execution times, hardware performance counters, or architectural simulations, IPP predicts execution times to calculate dynamic power events. We then use the outcome of IPP to control the number of running cores. We also model the increases in power consumption that resulted from the increases in temperature. With the predicted optimal number of active cores, we show that we can save up to 22.09%of runtime GPU energy consumption and on average 10.99% of that for the five memory bandwidth-limited benchmarks.


title: Random Forest variable importance with missing data

abstract: Random Forests are commonly applied for data prediction and interpretation. The latter purpose is supported by variable importance measures that rate the relevance of predictors. Yet existing measures can not be computed when data contains missing values. Possible solutions are given by imputation methods, complete case analysis and a newly suggested importance measure. However, it is unknown to what extend these approaches are able to provide a reliable estimate of a variables relevance. An extensive simulation study was performed to investigate this property for a variety of missing data generating processes. Findings and recommendations: Complete case analysis should not be applied as it inappropriately penalized variables that were completely observed. The new importance measure is much more capable to reflect decreased information exclusively for variables with missing values and should therefore be used to evaluate actual data situations. By contrast, multiple imputation allows for an estimation of importances one would potentially observe in complete data situations.


title: Distributed Real-Time Software for Cyber–Physical Systems

abstract: Real-time embedded software today is commonly built using programming abstractions with little or no temporal semantics. This paper addresses this problem by presenting a programming model called programming temporally integrated distributed embedded systems (PTIDES) that serves as a coordination language for model-based design of distributed real-time embedded systems. Specifically, the paper describes the principles of PTIDES, which leverages network time synchronization to provide a determinate distributed real-time semantics. We show how PTIDES can function as a coordination language, orchestrating components that may be designed and specified using different formalisms. We show the use of this environment in the design of interesting and practical cyber-physical systems, such as a power plant control system.


title: Dynamic Test Generation to Find Integer Bugs in x86 Binary Linux Programs

abstract: Recently, integer bugs, including integer overflow, width conversion, and signed/unsigned conversion errors, have risen to become a common root cause for serious security vulnerabilities. We introduce new methods for discovering integer bugs using dynamic test generation on x86 binaries, and we describe key design choices in efficient symbolic execution of such programs. We implemented our methods in a prototype tool SmartFuzz, which we use to analyze Linux x86 binary executables. We also created a reporting service, metafuzz.com, to aid in triaging and reporting bugs found by SmartFuzz and the black-box fuzz testing tool zzuf. We report on experiments applying these tools to a range of software applications, including the mplayer media player, the exiv2 image metadata library, and ImageMagick convert. We also report on our experience using SmartFuzz, zzuf, and metafuzz.com to perform testing at scale with the Amazon Elastic Compute Cloud (EC2). To date, the metafuzz.com site has recorded more than 2; 614 test runs, comprising 2; 361; 595 test cases. Our experiments found approximately 77 total distinct bugs in 864 compute hours, costing us an average of $2:24 per bug at current EC2 rates. We quantify the overlap in bugs found by the two tools, and we show that SmartFuzz finds bugs missed by zzuf, including one program where Smart-Fuzz finds bugs but zzuf does not.


title: Local Rademacher Complexity for Multi-Label Learning

abstract: We analyze the local Rademacher complexity of empirical risk minimization (ERM)-based multi-label learning algorithms, and in doing so propose a new algorithm for multi-label learning. Rather than using the trace norm to regularize the multi-label predictor, we instead minimize the tail sum of the singular values of the predictor in multi-label learning. Benefiting from the use of the local Rademacher complexity, our algorithm, therefore, has a sharper generalization error bound and a faster convergence rate. Compared to methods that minimize over all singular values, concentrating on the tail singular values results in better recovery of the low-rank structure of the multi-label predictor, which plays an import role in exploiting label correlations. We propose a new conditional singular value thresholding algorithm to solve the resulting objective function. Empirical studies on real-world datasets validate our theoretical results and demonstrate the effectiveness of the proposed algorithm.


Elevated Lipase during Initial Presentation of Ulcerative Colitis in a Pediatric Patient: Do We Check for It


title: An improved frequency compensation technique for CMOS operational amplifiers

abstract: The commonly used two-stage CMOS operational amplifier suffers from two basic performance limitations due to the RC compensation network around the second gain stage. First, it provides stable operation for only a limited range of capacitive loads, and second, the power supply rejection shows severe degradation above the open-loop pole frequency. The technique described provides stable operation for a much larger range of capacitive loads, as well as much improved V/SUB BB/ power supply rejection over very wide bandwidths for the same basic operational amplifier circuit. The author presents a mathematical analysis of this new technique in terms of its frequency and noise characteristics followed by its implementation in all n-well CMOS process. Experimental results show 70-dB negative power supply rejection at 100 kHz and an input noise density of 58 nV/(Hz)/SUP 1/2/ at 1 kHz.


title: Design of a Snort-Based Hybrid Intrusion Detection System

abstract: Computer security has become a major problem in our society. In particular, computer network security is concerned with preventing the intrusion of an unauthorized person into a network of computers. An intrusion detection system (IDS) is a tool to monitor the network traffic and users' activity with the aim of distinguishing between hostile and non-hostile traffic. Snort is an IDS available under GPL, which allows pattern search. This paper presents a new anomaly pre-processor that extends the functionality of Snort IDS, making it a hybrid IDS.


title: Quantising Opinions for Political Tweets Analysis

abstract: There have been increasing interests in recent years in analyzing tweet messages relevant to political events so as to understand public opinions towards certain political issues. We analyzed tweet messages crawled during the eight weeks leading to the UK General Election in May 2010 and found that activities at Twitter is not necessarily a good predictor of popularity of political parties. We then proceed to propose a statistical model for sentiment detection with side information such as emoticons and hash tags implying tweet polarities being incorporated. Our results show that sentiment analysis based on a simple keyword matching against a sentiment lexicon or a supervised classifier trained with distant supervision does not correlate well with the actual election results. However, using our proposed statistical model for sentiment analysis, we were able to map the public opinion in Twitter with the actual offline sentiment in real world.


title: SDUMLA-HMT: a multimodal biometric database

abstract: In this paper, the acquisition and content of a new homologous multimodal biometric database are presented. The SDUMLA-HMT database consists of face images from 7 view angles, finger vein images of 6 fingers, gait videos from 6 view angles, iris images from an iris sensor, and fingerprint images acquired with 5 different sensors. The database includes real multimodal data from 106 individuals. In addition to database description, we also present possible use of the database. The database is available to research community through http://mla.sdu.edu.cn/sdumla-hmt.html.


title: On Semantic Algebra: A Denotational Mathematics for Natural Language Comprehension and Cognitive Computing

abstract: Semantics is composed meaning of language expressions and perceptions at the levels of words, phrase, sentence, paragraph, and essay. The basic unit of formal semantics is a concept as a formal model of words in a natural language. Cognitive linguistics focuses on cognitive semantics and its interaction with underlying syntactic structures. A denotational mathematics known as semantic algebra is presented for rigorous semantics manipulations in cognitive linguistics. Semantic algebra deals with formal semantics of linguistics by a general mathematical model of semantics and a set of algebraic operators. On the basis of semantic algebra, language semantics may not only be deductively analyzed based on syntactic structures from the top down, but also be synthetically composed by the algebraic semantic operators from the bottom up at different levels of language units. Semantic algebra enables a wide range of applications in cognitive informatics, cognitive linguistics, computational linguistics, semantic computing, cognitive computing, machine learning, computing with words, as well as natural language analysis, synthesis, and comprehension.


title: A Binning Scheme for Fast Hard Drive Based Image Search

abstract: In this paper we investigate how to scale a content based image retrieval approach beyond the RAM limits of a single computer and to make use of its hard drive to store the feature database. The feature vectors describing the images in the database are binned in multiple independent ways. Each bin contains images similar to a representative prototype. Each binning is considered through two stages of processing. First, the prototype closest to the query is found. Second, the bin corresponding to the closest prototype is fetched from disk and searched completely. The query process is repeatedly performing these two stages, each time with a binning independent of the previous ones. The scheme cuts down the hard drive access significantly and results in a major speed up. An experimental comparison between the binning scheme and a raw search shows competitive retrieval quality.


title: Chained Predictions Using Convolutional Neural Networks

abstract: In this work, we present an adaptation of the sequence-to-sequence model for structured vision tasks. In this model, the output variables for a given input are predicted sequentially using neural networks. The prediction for each output variable depends not only on the input but also on the previously predicted output variables. The model is applied to spatial localization tasks and uses convolutional neural networks (CNNs) for processing input images and a multi-scale deconvolutional architecture for making spatial predictions at each step. We explore the impact of weight sharing with a recurrent connection matrix between consecutive predictions, and compare it to a formulation where these weights are not tied. Untied weights are particularly suited for problems with a fixed sized structure, where different classes of output are predicted at different steps. We show that chain models achieve top performing results on human pose estimation from images and videos.


title: Contributions from research on anger and cognitive dissonance to understanding the motivational functions of asymmetrical frontal brain activity

abstract: Research has suggested that approach-related positive emotions are associated with greater left frontal brain activity and that withdrawal-related negative emotions are associated with greater right frontal brain activity. Different explanations have been proposed. One posits that frontal asymmetry is due to emotional valence (positivity/negativity), one posits that frontal asymmetry is due to motivational direction (approach/withdrawal), and one posits that frontal asymmetry is due to a combination of emotional valence and motivational direction (positive-approach/negative-withdrawal). Because research had confounded emotional valence and motivational direction, the theoretical explanation was muddled. Solely supporting the motivational direction model, recent research has revealed that anger and cognitive dissonance, emotions with negative valence and approach motivational tendencies, are related to relatively greater left frontal activity.


title: DAMTP-R/96/7 SYMMETRIC VARIATIONS OF THE METRIC AND EXTREMA OF THE ACTION FOR PURE GRAVITY

abstract: Symmetries of generalized gravitational actions, yielding field equations which typically involve at most second-order derivatives of the metric, are considered. The field equations for several different higher-derivative theories in the first-order formalism are derived, and variations of a generic set of higher-order curvature terms appearing in string effective actions are studied. It is shown that there often exists a particular set of solutions to the field equations of pure gravity theories, consisting of different combinations of curvature tensors, which satisfies the vacuum equations with cosmological constant. Implications of generalized symmetries of the field equations derived from the superstring effective action for the cosmological constant problem are discussed.


title: Multi-robot manipulation with no communication using only local measurements

abstract: This paper presents a novel approach to coordinate the manipulation forces of a group of robots without explicit communication during a cooperative manipulation task. Robots use the measurements of the motion of the object as the only information to reach a consensus on their forces. It is proven that the consensus can be reached even if all the robots have different velocity and acceleration measurements since they take measurements at different attachment points around the object while the object is rotating and translating. The convergence of the leader-following process where a leader robot actively steers the forces of all follower robots to navigate the object along a desired trajectory is also proven with Lyapunov stability arguments. We verify our method in both numerical simulations and a physics simulator, where we transport a grand piano with 1001 robots.


title: Image segmentation using automatic seeded region growing and instance-based learning

abstract: Segmentation through seeded region growing is widely used because it is fast, robust and free of tuning parameters. However, the seeded region growing algorithm requires an automatic seed generator, and has problems to label unconnected pixels (the unconnected pixel problem). This paper introduces a new automatic seeded region growing algorithm called ASRG-IB1 that performs the segmentation of color (RGB) and multispectral images. The seeds are automatically generated via histogram analysis; the histogram of each band is analyzed to obtain intervals of representative pixel values. An image pixel is considered a seed if its gray values for each band fall in some representative interval. After that, our new seeded region growing algorithm is applied to segment the image. This algorithm uses instance-based learning as distance criteria. Finally, according to the user needs, the regions are merged using ownership tables. The algorithm was tested on several leukemia medical images showing good results.


title: Bulk optical properties of healthy female breast tissue

abstract: We have measured the bulk optical properties of healthy female breast tissues in vivo in the parallel plate, transmission geometry. Fifty-two volunteers were measured. Blood volume and blood oxygen saturation were derived from the optical property data using a novel method that employed a priori spectral information to overcome limitations associated with simple homogeneous tissue models. The measurements provide an estimate of the variation of normal breast tissue optical properties in a fairly large population. The mean blood volume was 34 +/- 9 microM and the mean blood oxygen saturation was 68 +/- 8%. We also investigated the correlation of these optical properties with demographic factors such as body mass index (BMI) and age. We observed a weak correlation of blood volume and reduced scattering coefficient with BMI: correlation with age, however, was not evident within the statistical error of these experiments. The new information on healthy breast tissue provides insight about the potential contrasts available for diffuse optical tomography of breast tumours.


title: Bayesian optimization of hyper-parameters in reservoir computing

abstract: We describe a method for searching the optimal hyper-parameters in reservoir computing, which consists of a Gaussian process with Bayesian optimization. It provides an alternative to other frequently used optimization methods such as grid, random, or manual search. In addition to a set of optimal hyper-parameters, the method also provides a probability distribution of the cost function as a function of the hyper-parameters. We apply this method to two types of reservoirs: nonlinear delay nodes and echo state networks. It shows excellent performance on all considered benchmarks, either matching or significantly surpassing results found in the literature. In general, the algorithm achieves optimal results in fewer iterations when compared to other optimization methods. We have optimized up to six hyper-parameters simultaneously, which would have been infeasible using, e.g., grid search. Due to its automated nature, this method significantly reduces the need for expert knowledge when optimizing the hyper-parameters in reservoir computing. Existing software libraries for Bayesian optimization, such as Spearmint, make the implementation of the algorithm straightforward. A fork of the Spearmint framework along with a tutorial on how to use it in practice is available at this https URL


title: The Quadroller: Modeling of a UAV/UGV hybrid quadrotor

abstract: Energetic efficiency is a key limiting factor of hovering UAVs. Equipping a quadrotor with low-friction wheels allows it to exploit efficient rolling locomotion to travel long distances on smooth surfaces - common in human environments. A novel feature of this approach is the use of skateboard steering trucks that use lateral tilt to affect steering. This allows existing quadrotor flight controls for driving without modification to the avionics. In this paper we present turning mechanics for driving along the ground and performance curves for the vehicle rolling over different surfaces. We show experimentally that the rolling range of a commercial off-the-shelf quadrotor greatly exceeds its flying range, despite the small added mass of rolling wheels.


title: Classification and Information Extraction for Complex and Nested Tabular Structures in Images

abstract: Understanding of technical documents, like manuals, is one of the most important steps in automatic reporting and/or troubleshooting of defects. The majority of the relevant information exists in tabular structure. There are some solutions for extracting tabular structures from text. However, it is still a big issue to extract tabular information from images and, on top of that, from complex and nested tables. This paper aims to propose classification and information extraction methods for complex tabular structures in document images. These are hybrid approaches using both image layout and OCRed text. The proposed methods outperform on a real-world technical documents dataset from a German railway company (Deutsche Bahn AG) as compared to other state-of-the-art approaches. As a result, the proposed approaches won the competition held by Deutsche Bahn AG in 2016 against other participating research groups and companies.


title: A 6.8-W purely-resistive AC light-emitting diode driver circuit with 95% power factor

abstract: A light-emitting diode (LED) driver circuit that can operate off of an AC power supply without any reactive elements is described. The proposed circuit consists of resistive components and switches only, making it suitable for a single-chip integrated-circuit (IC) driver solution to realize a low-cost LED replacement for incandescent light bulbs. The driver circuit employs multiple strings of LEDs and sequences their conduction to shape the off-line current in proportion to the offline voltage, improving the power factors compared to the existing resistive drivers. A prototype circuit made with discrete components demonstrates the peak power factor of 95% and efficacy of 78-lm/W, when delivering 6.8-W to the load at a 220-V AC supply. An equivalent IC chip that integrates all components but the rectifying diodes would occupy only 2.76 mm2 when fabricated in a 1.0-μ m BCDMOS process.


title: Taking the Human Out of the Loop : A Review of Bayesian Optimization The paper introduces the reader to Bayesian optimization , highlighting its methodical aspects and showcasing its applications

abstract: Big Data applications are typically associated with systems involving large numbers of users, massive complex software systems, and large-scale heterogeneous computing and storage architectures. The construction of such systems involves many distributed design choices. The end products (e.g., recommendation systems, medical analysis tools, real-time game engines, speech recognizers) thus involve many tunable configuration parameters. These parameters are often specified and hard-coded into the software by various developers or teams. If optimized jointly, these parameters can result in significant improvements. Bayesian optimization is a powerful tool for the joint optimization of design choices that is gaining great popularity in recent years. It promises greater automation so as to increase both product quality and human productivity. This review paper introduces Bayesian optimization, highlights some of its methodological aspects, and showcases a wide range of applications.


title: Why People Continue to Play Online Games: In Search of Critical Design Factors to Increase Customer Loyalty to Online Contents

abstract: As people increasingly play online games, numerous new features have been proposed to increase players' log-on time at online gaming sites. However, few studies have investigated why people continue to play certain online games or which design features are most closely related to the amount of time spent by players at particular online gaming sites. This study proposes a theoretical model using the concepts of customer loyalty, flow, personal interaction, and social interaction to explain why people continue to play online network games. The study then conducts a large-scale survey to validate the model. Finally, it analyzes current online games to identify design features that are closely related to the theoretical concepts. The results indicate that people continue to play online games if they have optimal experiences while playing the games. This optimal experience can be attained if the player has effective personal interaction with the system or pleasant social interactions with other people connecte...


title: Uniform Random Number Generators

abstract: Abstract : This paper discusses the testing of methods for generating uniform numbers in a computer--the commonly used multiplicative and mixed congruential generators as well as two methods. Tests proposed here are more stringent than those usually applied, because the usual tests for randomness have passed several of the commonly used pprocedures which subsequently gave poor results in actual Monte Carlo calculations. The principal difficulty seems to be that certain simple functions of n-tuples of uniform random numbers do not have the distribution that probability theory predicts. Two alternative generating methods are described, one of them using a table of uniform numbers, the other one combining two congruential generators. Both of these methods passed the tests, whereas the conventional multiplicative and mixed congruential methods did not.


title: Financial Frictions and the Persistence of History: A Quantitative Exploration

abstract: We quantitatively analyze the role of financial frictions and resource misallocation in explaining development dynamics. Our model economy with financial frictions converges to the new steady state slowly after a reform triggers efficient reallocation of resources; the transition speed is half that of the conventional neoclassical model. Furthermore, in the model economy, investment rates and total factor productivity are initially low and increase over time. We present data from the so-called miracle economies on the evolution of macro aggregates, factor reallocation, and establishment size distribution that support the aggregate and micro-level implications of our theory.


title: Further improvement of direct back EMF detection for sensorless brushless DC (BLDC) motor drives

abstract: The direct back EMF detection method previously described (J. Shao, et al., 2002) synchronously samples the motor back EMF during PWM "off" time without the need to sense or reconstruct the motor neutral in a sensorless BLDC motor drive system. Since this direct back EMF sensing scheme requires minimum PWM "off" time to sample the back EMF signal, the duty cycle can't reach 100%. Also in some applications, e.g. high inductance motors, the long settling time of a parasitic resonant between the motor inductance and the parasitic capacitance of power devices can cause false zero crossing detection of back EMF. In this paper, an improved direct back EMF detection scheme which samples the motor back EMF synchronously during PWM "on" time is proposed to overcome the problems. In the final system, the motor back EMF can be detected during PWM "off' time at start-up and low speed, and it is detected during PWM "on" time at high speed. With the combination of the original and the improved back EMF sensing scheme, the duty cycle can be extended to 100% duty cycle, and the impact of the parasitic resonant between motor inductance and the power device capacitance can be minimized. Experimental results verify the analysis


title: Observational Learning by Reinforcement Learning

abstract: Observational learning is a type of learning that occurs as a function of observing, retaining and possibly imitating the behaviour of another agent. It is a core mechanism appearing in various instances of social learning and has been found to be employed in several intelligent species, including humans. In this paper, we investigate to what extent the explicit modelling of other agents is necessary to achieve observational learning through machine learning. Especially, we argue that observational learning can emerge from pure Reinforcement Learning (RL), potentially coupled with memory. Through simple scenarios, we demonstrate that an RL agent can leverage the information provided by the observations of an other agent performing a task in a shared environment. The other agent is only observed through the effect of its actions on the environment and never explicitly modeled. Two key aspects are borrowed from observational learning: i) the observer behaviour needs to change as a result of viewing a 'teacher' (another agent) and ii) the observer needs to be motivated somehow to engage in making use of the other agent's behaviour. The later is naturally modeled by RL, by correlating the learning agent's reward with the teacher agent's behaviour.


title: FE Analysis and Computer-Aided Design of a Sandwiched Axial-Flux Permanent Magnet Brushless DC Motor

abstract: In this paper, a comprehensive computer-aided design (CAD) procedure for an axial-flux permanent magnet brushless dc (PM BLDC) motor having a stator sandwiched between two permanent magnet rotors is presented. The basic output equations are derived and used for the design algorithm. The developed CAD program gives the design data and the calculated performances of the motor. The optimized ratio of outer diameter to the inner diameter is used to derive the sizing equation. The design variables such as airgap flux density, slot electric loading, winding factor, stacking factor, stator current density, slot space factor, magnet fraction, slot fraction, flux density in the stator back iron, etc., are assumed. Three different motors or rating 70 W, 2.2 kW, and 20 kW are designed using the developed program, and then three dimensional finite-element analyses are carried out to validate the designs


On leveraging conversational data for building a text dependent speaker verification system.


title: Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System

abstract: Precision Farming (PF) management strategies are commonly based on estimations of within-field yield potential, often derived from remotely-sensed products, e.g., Vegetation Index (VI) maps. These well-established means, however, lack important information, like crop height. Combinations of VI-maps and detailed 3D Crop Surface Models (CSMs) enable advanced methods for crop yield prediction. This work utilizes an Unmanned Aircraft System (UAS) to capture standard RGB imagery datasets for corn grain yield prediction at three early- to mid-season growth stages. The imagery is processed into simple VI-orthoimages for crop/non-crop classification and 3D CSMs for crop height determination at different spatial resolutions. Three linear regression models are tested on their prediction ability using site-specific (i) unclassified mean heights, (ii) crop-classified mean heights and (iii) a combination of crop-classified mean heights with according crop coverages. The models show determination coefficients \({R}^{2}\) of up to 0.74, whereas model (iii) performs best with imagery captured at the end of stem elongation and intermediate spatial resolution (0.04m\(\cdot\)px\(^{-1}\)).Following these results, combined spectral and spatial modeling, based on aerial images and CSMs, proves to be a suitable method for mid-season corn yield prediction.


title: Applications of artificial intelligence in intelligent manufacturing: a review

abstract: Based on research into the applications of artificial intelligence (AI) technology in the manufacturing industry in recent years, we analyze the rapid development of core technologies in the new era of ‘Internet plus AI’, which is triggering a great change in the models, means, and ecosystems of the manufacturing industry, as well as in the development of AI. We then propose new models, means, and forms of intelligent manufacturing, intelligent manufacturing system architecture, and intelligent manufacturing technology system, based on the integration of AI technology with information communications, manufacturing, and related product technology. Moreover, from the perspectives of intelligent manufacturing application technology, industry, and application demonstration, the current development in intelligent manufacturing is discussed. Finally, suggestions for the application of AI in intelligent manufacturing in China are presented.


title: Designing for Flexible Interaction Between Humans and Automation: Delegation Interfaces for Supervisory Control

abstract: OBJECTIVE ::: To develop a method enabling human-like, flexible supervisory control via delegation to automation. ::: ::: ::: BACKGROUND ::: Real-time supervisory relationships with automation are rarely as flexible as human task delegation to other humans. Flexibility in human-adaptable automation can provide important benefits, including improved situation awareness, more accurate automation usage, more balanced mental workload, increased user acceptance, and improved overall performance. ::: ::: ::: METHOD ::: We review problems with static and adaptive (as opposed to "adaptable") automation; contrast these approaches with human-human task delegation, which can mitigate many of the problems; and revise the concept of a "level of automation" as a pattern of task-based roles and authorizations. We argue that delegation requires a shared hierarchical task model between supervisor and subordinates, used to delegate tasks at various levels, and offer instruction on performing them. A prototype implementation called Playbook is described. ::: ::: ::: RESULTS ::: On the basis of these analyses, we propose methods for supporting human-machine delegation interactions that parallel human-human delegation in important respects. We develop an architecture for machine-based delegation systems based on the metaphor of a sports team's "playbook." Finally, we describe a prototype implementation of this architecture, with an accompanying user interface and usage scenario, for mission planning for uninhabited air vehicles. ::: ::: ::: CONCLUSION ::: Delegation offers a viable method for flexible, multilevel human-automation interaction to enhance system performance while maintaining user workload at a manageable level. ::: ::: ::: APPLICATION ::: Most applications of adaptive automation (aviation, air traffic control, robotics, process control, etc.) are potential avenues for the adaptable, delegation approach we advocate. We present an extended example for uninhabited air vehicle mission planning.


title: Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting

abstract: The paper presents a spatio-temporal wind speed forecasting algorithm using Deep Learning (DL)and in particular, Recurrent Neural Networks(RNNs). Motivated by recent advances in renewable energy integration and smart grids, we apply our proposed algorithm for wind speed forecasting. Renewable energy resources (wind and solar)are random in nature and, thus, their integration is facilitated with accurate short-term forecasts. In our proposed framework, we model the spatiotemporal information by a graph whose nodes are data generating entities and its edges basically model how these nodes are interacting with each other. One of the main contributions of our work is the fact that we obtain forecasts of all nodes of the graph at the same time based on one framework. Results of a case study on recorded time series data from a collection of wind mills in the north-east of the U.S. show that the proposed DL-based forecasting algorithm significantly improves the short-term forecasts compared to a set of widely-used benchmarks models.


title: DSP-Based Control of Grid-Connected Power Converters Operating Under Grid Distortions

abstract: Power electronic Grid-Connected Converters (GCCs) are widely applied as grid interface in renewable energy sources. This paper proposes an extended Direct Power Control with Space Vector Modulation (DPC-SVM) scheme with improved operation performance under grid distortions. The real-time operated DPC-SVM scheme has to execute several important tasks as: space vector pulse width modulation, active and reactive power feedback control, grid current harmonics and voltage dips compensation. Thus, development and implementation of the DPC-SVM algorithm using single chip floating-point microcontroller TMS320F28335 is described. It combines large peripheral equipment, typical for microcontrollers, with high computation capacity characteristic for Digital Signal Processors (DSPs). The novelty of the proposed system lies in extension of the generic DPC-SVM scheme by additional higher harmonic and voltage dips compensation modules and implementation of the whole algorithm in a single chip floating point microcontroller. Overview of the laboratory setup, description of basic algorithm subtasks sequence, software optimization as well as execution time of specific program modules on fixed-point and floating-point processors are discussed. Selected oscillograms illustrating operation and robustness of the developed algorithm used in 5 kVA laboratory model of the GCC are presented.


title: Teaching social influence: Demonstrations and exercises from the discipline of social psychology

abstract: Education is enhanced when students are able to be active, rather than passive, learners (McKeachie, 2002). Fortunately, social psychologists have a rich history of creating and publishing classroom demonstrations that allow for such active learning. Unfortunately, these demonstrations have been published in diverse journals, teaching manuals, and edited volumes that are not always readily available. The purpose of this article is to review demonstrations and exercises that have been developed for teaching students about social influence. Using an annotated bibliography format, we review more than five dozen techniques that assist instructors in demonstrating such social influence principles as cognitive dissonance, conformity, obedience, deindividuation, propaganda, framing, persuasion, advertising, social norms, and the self‐fulfilling prophecy.


title: Understanding hybrid cdn-p2p: Why limelight needs its own red swoosh

abstract: In this paper, we quantify the potential gains of hybrid CDN-P2P for two of the leading CDN companies, Akamai and Limelight. We first develop novel measurement methodology for mapping the topologies of CDN networks. We then consider ISP-friendly P2P distribution schemes which work in conjunction with the CDNs to localize traffic within regions of ISPs. To evaluate these schemes, we use two recent, real-world traces: a video-on-demand trace and a large-scale software update trace. We find that hybrid CDN-P2P can significantly reduce the cost of content distribution, even when peer sharing is localized within ISPs and further localized within regions of ISPs. We conclude that hybrid CDN-P2P distribution can economically satisfy the exponential growth of Internet video content without placing an unacceptable burden on regional ISPs.


title: Boosting Algorithms as Gradient Descent

abstract: We provide an abstract characterization of boosting algorithms as gradient decsent on cost-functionals in an inner-product function space. We prove convergence of these functional-gradient-descent algorithms under quite weak conditions. Following previous theoretical results bounding the generalization performance of convex combinations of classifiers in terms of general cost functions of the margin, we present a new algorithm (DOOM II) for performing a gradient descent optimization of such cost functions. Experiments on several data sets from the UC Irvine repository demonstrate that DOOM II generally outperforms AdaBoost, especially in high noise situations, and that the overfitting behaviour of AdaBoost is predicted by our cost functions.


title: A network security architecture to reduce the risk of data leakage for health care organizations

abstract: Health care is a highly regulated industry in which much value is placed upon privacy and confidentiality. The business of health care, particularly in certain academic environments, requires access to data of varying sensitivities, including information from the public Internet. This paper proposes a VLAN-based architecture for segregating data of varying sensitivities, a list of components that facilitate access to and distillation of data, and a method for one-way promotion of individual nodes from areas of lower security to areas of higher security. The proposed solution is an implementable and pragmatic approach to reducing the risk of data leakage. Quality of experience (QoE) measures of two methods for access (node promotion and porthole-based access) are compared. The node promotion method improves the user-perceived responsiveness of applications over the porthole-based method while reducing flexibility.


title: Foot contact force of walk gait for a quadruped robot

abstract: Central Pattern Generator (CPG) can be used to generate rhythmic control signals for quadruped locomotion, such as walk, trot, and gallop gait. The walk gait is a statically stable quadruped gait, and suitable for enhancing the robustness of robots. However, as a result of lacking consideration of dynamics characteristics of robots, the swing phase of control signals generated by CPG may be not consistent with the actual phase of the quadruped limb. For example, there is a fact in walk gait that the control signal generated by CPG is in swing phase, while the quadruped limb is still in contact with the ground. So we attach the force sensor on the quadruped foot to detect the real-time foot contact force when a quadruped robot walks on the ground substrate, and the actual contact-lift state of the limb. Using the foot contact force information, we accurately obtain the difference between the theoretical-actual swing phase. Moreover, we discuss that foot-substrate contact force can be used to distinguish the ground substrate with different properties such as friction coefficient. Experiment results show the possibility to utilize force sensor information for ground substrate recognition and classification.


title: Jensen-Shannon divergence and Hilbert space embedding

abstract: This paper describes the Jensen-Shannon divergence (JSD) and Hilbert space embedding. With natural definitions making these considerations precise, one finds that the general Jensen-Shannon divergence related to the mixture is the minimum redundancy, which can be achieved by the observer. The set of distributions with the metric /spl radic/JSD can even be embedded isometrically into Hilbert space and the embedding can be identified.


title: Word Meaning in Context: A Simple and Effective Vector Model

abstract: We present a model that represents word meaning in context by vectors which are modified according to the words in the target’s syntactic context. Contextualization of a vector is realized by reweighting its components, based on distributional information about the context words. Evaluation on a paraphrase ranking task derived from the SemEval 2007 Lexical Substitution Task shows that our model outperforms all previous models on this task. We show that our model supports a wider range of applications by evaluating it on a word sense disambiguation task. Results show that our model achieves state-of-the-art performance.


title: PIN tuned phase-gradient-metasurface transmitarray for beam steering application

abstract: This work presents a study of a PIN tuned active phase gradient metasurface to be used for beam-steering application. The unit cell of the active metasurface consists of square-loop slot loaded with PIN diodes. The phase difference between the unit cells of the metasurface is generated by different position of the PIN diodes welded in the unit cell. And the refractive beam angle of the metasurface can be tuned by changing the state of the PIN diodes. The active metasurface is designed at S band and the effectiveness of the design is validated by numerical simulation.


title: Federated Optimization: Distributed Machine Learning for On-Device Intelligence

abstract: We introduce a new and increasingly relevant setting for distributed optimization in machine learning, where the data defining the optimization are unevenly distributed over an extremely large number of nodes. The goal is to train a high-quality centralized model. We refer to this setting as Federated Optimization. In this setting, communication efficiency is of the utmost importance and minimizing the number of rounds of communication is the principal goal. ::: A motivating example arises when we keep the training data locally on users' mobile devices instead of logging it to a data center for training. In federated optimziation, the devices are used as compute nodes performing computation on their local data in order to update a global model. We suppose that we have extremely large number of devices in the network --- as many as the number of users of a given service, each of which has only a tiny fraction of the total data available. In particular, we expect the number of data points available locally to be much smaller than the number of devices. Additionally, since different users generate data with different patterns, it is reasonable to assume that no device has a representative sample of the overall distribution. ::: We show that existing algorithms are not suitable for this setting, and propose a new algorithm which shows encouraging experimental results for sparse convex problems. This work also sets a path for future research needed in the context of \federated optimization.


title: Swarm Chemistry Evolving

abstract: 1. There are now two categories of particles, active (moving and interacting kinetically) and passive (remaining still and inactive). An active particle holds a recipe of the swarm (i.e., a list of kinetic parameter sets) in it (Fig. 1(a)). 2. A recipe is transmitted from an active particle to a passive particle when they collide, making the latter active (Fig. 1(b)). 3. The activated particle differentiates randomly into a type specified by one of the kinetic parameter sets in the recipe given to it (Fig. 1(c)). 4. Active particles randomly re-differentiate with small probability.


title: Liquid Crystal Polymer (LCP): The ultimate solution for low-cost RF flexible electronics and antennas

abstract: In this paper, solutions for developing low cost electronics for antenna transceivers that take advantage of the stable electrical properties of the organic substrate liquid crystal polymer (LCP) has been presented. Three important ingredients in RF wireless transceivers namely embedded passives, a dual band filter and a RFid antenna have been designed and fabricated on LCP. Test results of all 3 of the structures show good agreement between the simulated and measured results over their respective bandwidths, demonstrating stable performance of the LCP substrate.


title: Deployment of Customized Deep Learning based Video Analytics On Surveillance Cameras

abstract: This paper demonstrates the effectiveness of our customized deep learning based video analytics system in various applications focused on security, safety, customer analytics and process compliance. We describe our video analytics system comprising of Search, Summarize, Statistics and real-time alerting, and outline its building blocks. These building blocks include object detection, tracking, face detection and recognition, human and face sub-attribute analytics. In each case, we demonstrate how custom models trained using data from the deployment scenarios provide considerably superior accuracies than off-the-shelf models. Towards this end, we describe our data processing and model training pipeline, which can train and fine-tune models from videos with a quick turnaround time. Finally, since most of these models are deployed on-site, it is important to have resource constrained models which do not require GPUs. We demonstrate how we custom train resource constrained models and deploy them on embedded devices without significant loss in accuracy. To our knowledge, this is the first work which provides a comprehensive evaluation of different deep learning models on various real-world customer deployment scenarios of surveillance video analytics. By sharing our implementation details and the experiences learned from deploying customized deep learning models for various customers, we hope that customized deep learning based video analytics is widely incorporated in commercial products around the world.


title: A Kinect-Based Segmentation of Touching-Pigs for Real-Time Monitoring

abstract: Segmenting touching-pigs in real-time is an important issue for surveillance cameras intended for the 24-h tracking of individual pigs. However, methods to do so have not yet been reported. We particularly focus on the segmentation of touching-pigs in a crowded pig room with low-contrast images obtained using a Kinect depth sensor. We reduce the execution time by combining object detection techniques based on a convolutional neural network (CNN) with image processing techniques instead of applying time-consuming operations, such as optimization-based segmentation. We first apply the fastest CNN-based object detection technique (i.e., You Only Look Once, YOLO) to solve the separation problem for touching-pigs. If the quality of the YOLO output is not satisfied, then we try to find the possible boundary line between the touching-pigs by analyzing the shape. Our experimental results show that this method is effective to separate touching-pigs in terms of both accuracy (i.e., 91.96%) and execution time (i.e., real-time execution), even with low-contrast images obtained using a Kinect depth sensor.


title: Analysis and Application of Potential Energy Smoothing for Global Optimization

abstract: Global energy optimization of a molecular system is difficult due to the well-known “multiple minimum” problem. The rugged potential energy surface (PES) characteristic of multidimensional systems can be transformed reversibly using potential smoothing to generate a new surface that is easier to search for favorable configurations. The diffusion equation method (DEM) is one example of a potential smoothing algorithm. Potential smoothing as implemented in DEM is intuitively appealing and has certain appropriate statistical mechanical properties, but often fails to identify the global minimum even for relatively small problems. In the present paper, extensions to DEM capable of correcting its empirical behavior are systematically investigated. Two types of local search (LS) procedures are applied during the reversing schedule from the smooth deformed PES to the undeformed surface. Changes needed to generate smoothable versions of standard molecular mechanics force fields such as AMBER/OPLS and MM2 are also described. The resulting methods are applied in an attempt to determine the global energy minimum for a variety of systems in different coordinate representations. The problems studied include argon clusters, cycloheptadecane, capped polyalanine, and the docking of R-helices. Depending on the specific problem, potential smoothing and search (PSS) is performed in Cartesian, torsional, or rigid body space. For example, PSS finds a very low energy structure for cycloheptadecane with much greater efficiency than a search restricted to the undeformed potential surface. It is shown that potential smoothing is characterized by three salient features. As the level of smoothing is increased, unique minima merge into a common basin, crossings can occur in the relative energies of a pair of minima, and the spatial locations of minima are shifted due to the averaging effects of smoothing. Local search procedures improve the ability of smoothing methods to locate global minima because they facilitate the post facto correction of errors due to energy crossings that may have occurred at higher levels of smoothing. PSS methods should serve as useful tools for global energy optimization on a variety of difficult problems of practical interest.


title: Acoustical Quality Assessment of the Classroom Environment

abstract: Teaching is one of the most important factors affecting any education system. Many research efforts have been conducted to facilitate the presentation modes used by instructors in classrooms as well as provide means for students to review lectures through web browsers. Other studies have been made to provide acoustical design recommendations for classrooms like room size and reverberation times. However, using acoustical features of classrooms as a way to provide education systems with feedback about the learning process was not thoroughly investigated in any of these studies. We propose a system that extracts different sound features of students and instructors, and then uses machine learning techniques to evaluate the acoustical quality of any learning environment. We infer conclusions about the students' satisfaction with the quality of lectures. Using classifiers instead of surveys and other subjective ways of measures can facilitate and speed such experiments which enables us to perform them continuously. We believe our system enables education systems to continuously review and improve their teaching strategies and acoustical quality of classrooms.


title: Real-time hand gesture detection and recognition for human computer interaction

abstract: This thesis focuses on bare hand gesture recognition by proposing a new architecture to solve the problem of real-time vision-based hand detection, tracking, and gesture recognition for interaction with an application via hand gestures. The first stage of our system allows detecting and tracking a bare hand in a cluttered background using face subtraction, skin detection and contour comparison. The second stage allows recognizing hand gestures using bag-of-features and multi-class Support Vector Machine (SVM) algorithms. Finally, a grammar has been developed to generate gesture commands for application control. ::: Our hand gesture recognition system consists of two steps: offline training and online testing. In the training stage, after extracting the keypoints for every training image using the Scale Invariance Feature Transform (SIFT), a vector quantization technique will map keypoints from every training image into a unified dimensional histogram vector (bag-of-words) after K-means clustering. This histogram is treated as an input vector for a multi-class SVM to build the classifier. In the testing stage, for every frame captured from a webcam, the hand is detected using my algorithm. Then, the keypoints are extracted for every small image that contains the detected hand posture and fed into the cluster model to map them into a bag-of-words vector, which is fed into the multi-class SVM classifier to recognize the hand gesture. ::: Another hand gesture recognition system was proposed using Principle Components Analysis (PCA). The most eigenvectors and weights of training images are determined. In the testing stage, the hand posture is detected for every frame using my algorithm. Then, the small image that contains the detected hand is projected onto the most eigenvectors of training images to form its test weights. Finally, the minimum Euclidean distance is determined among the test weights and the training weights of each training image to recognize the hand gesture. ::: Two application of gesture-based interaction with a 3D gaming virtual environment were implemented. The exertion videogame makes use of a stationary bicycle as one of the main inputs for game playing. The user can control and direct left-right movement and shooting actions in the game by a set of hand gesture commands, while in the second game, the user can control and direct a helicopter over the city by a set of hand gesture commands.


title: Why Anger and Disappointment Affect Other’s Bargaining Behavior Differently The Moderating Role of Power and the Mediating Role of Reciprocal and Complementary Emotions

abstract: In two experiments, the authors investigated the interpersonal effects of anger and disappointment in negotiations. Whereas previous research focused on the informational inferences that bargainers make based on others’ emotions, this article emphasizes the importance of affective reactions. The findings of this study show that anger evoked a complementary emotion (fear) in targets when reported by a high-power bargainer but evoked a reciprocal emotion (anger) when reported by a low-power bargainer. This reciprocal anger led participants to offer less to low-power counterparts who reported anger. Disappointed bargainers, however, evoked a complementary emotion (guilt) in participants and increased offers, regardless of the bargainer’s power position.


DOTA 2 Win Prediction


title: 2 Early Development of Entrepreneurial Qualities: the Role of Initial Education

abstract: This study attempts to create a better understanding of the role of entrepreneurship education in developing entrepreneurial qualities. Theory and practice are confronted. The paper discusses the extent to which entrepreneurship can be taught, and the way in which it should be taught. The focus is on the phase of initial education.


title: Machine Learning Techniques Applied to Wireless Ad-Hoc Networks : Guide and Survey

abstract: Team-partitioned, opaque-transition reinforcement learning (TPOT-RL) is a distributed reinforcement learning technique that allows a team of independent agents to learn a collaborative task. TPOT-RL was first successfully applied to simulated robotic soccer (Stone & Veloso, 1999). This paper demonstrates that TPOT-RL is general enough to apply to a completely different domain, namely network packet routing. Empirical results in an abstract network routing simulator indicate that agents situated at individual nodes can learn to efficiently route packets through a network that exhibits changing traffic patterns, based on locally observable sensations.


Compressive sampling


title: Negative consequences from heavy social networking in adolescents: The mediating role of fear of missing out

abstract: Abstract Social networking sites (SNS) are especially attractive for adolescents, but it has also been shown that these users can suffer from negative psychological consequences when using these sites excessively. We analyze the role of fear of missing out (FOMO) and intensity of SNS use for explaining the link between psychopathological symptoms and negative consequences of SNS use via mobile devices. In an online survey, 1468 Spanish-speaking Latin-American social media users between 16 and 18 years old completed the Hospital Anxiety and Depression Scale (HADS), the Social Networking Intensity scale (SNI), the FOMO scale (FOMOs), and a questionnaire on negative consequences of using SNS via mobile device (CERM). Using structural equation modeling, it was found that both FOMO and SNI mediate the link between psychopathology and CERM, but by different mechanisms. Additionally, for girls, feeling depressed seems to trigger higher SNS involvement. For boys, anxiety triggers higher SNS involvement.


title: Bootstrapping for HElib

abstract: Gentry’s bootstrapping technique is still the only known method of obtaining fully homomorphic encryption where the system’s parameters do not depend on the complexity of the evaluated functions. Bootstrapping involves a recryption procedure where the scheme’s decryption algorithm is evaluated homomorphically. So far, there have been precious few implementations of recryption, and fewer still that can handle “packed ciphertexts” that encrypt vectors of elements.


title: An information retrieval approach to concept location in source code

abstract: Concept location identifies parts of a software system that implement a specific concept that originates from the problem or the solution domain. Concept location is a very common software engineering activity that directly supports software maintenance and evolution tasks such as incremental change and reverse engineering. This work addresses the problem of concept location using an advanced information retrieval method, Latent Semantic Indexing (LSI). LSI is used to map concepts expressed in natural language by the programmer to the relevant parts of the source code. Results of a case study on NCSA Mosaic are presented and compared with previously published results of other static methods for concept location.


title: A Low-Profile Wideband Circularly Polarized Crossed-Dipole Antenna With Wide Axial-Ratio and Gain Beamwidths

abstract: A low-profile wideband circularly polarized (CP) crossed-dipole antenna that has both wide axial-ratio beamwidth (ARBW) and half-power beamwidth (HPBW) is presented in this paper. The crossed dipoles are composed of four trapezoidal patch arms, and they are fed by a pair of vacant-quarter printed rings to generate CP radiation. Four identical parasitic elements that consist of a horizontal triangle patch and a vertical metallic plate are symmetrically intervened between the crossed dipoles and the ground plane. It has been found that the parasitic elements can effectively decrease the antenna profile, increase the operating bandwidth, and simultaneously enhance the ARBW as well as the HPBW. A prototype was fabricated and measured to verify the design. The measured results show that the prototype has a low profile of $0.1\lambda _{0}$ , a −10 dB impedance bandwidth of 78.3% and a 3 dB AR bandwidth of 63.4%. Moreover, a 3 dB ARBW of more than 120° and an HPBW of more than 110° are achieved simultaneously within a wide passband of 50.7%.


title: Clinical performance of porcelain laminate veneers: outcomes of the aesthetic pre-evaluative temporary (APT) technique.

abstract: This article evaluates the long-term clinical performance of porcelain laminate veneers bonded to teeth prepared with the use of an additive mock-up and aesthetic pre-evaluative temporary (APT) technique over a 12-year period. Sixty-six patients were restored with 580 porcelain laminate veneers. The technique, used for diagnosis, esthetic design, tooth preparation, and provisional restoration fabrication, was based on the APT protocol. The influence of several factors on the durability of veneers was analyzed according to pre- and postoperative parameters. With utilization of the APT restoration, over 80% of tooth preparations were confined to the dental enamel. Over 12 years, 42 laminate veneers failed, but when the preparations were limited to the enamel, the failure rate resulting from debonding and microleakage decreased to 0%. Porcelain laminate veneers presented a successful clinical performance in terms of marginal adaptation, discoloration, gingival recession, secondary caries, postoperative sensitivity, and satisfaction with restoration shade at the end of 12 years. The APT technique facilitated diagnosis, communication, and preparation, providing predictability for the restorative treatment. Limiting the preparation depth to the enamel surface significantly increases the performance of porcelain laminate veneers.


title: Identifying individuality and variability in team tactics by means of statistical shape analysis and multilayer perceptrons.

abstract: Abstract Offensive and defensive systems of play represent important aspects of team sports. They include the players’ positions at certain situations during a match, i.e., when players have to be on specific positions on the court. Patterns of play emerge based on the formations of the players on the court. Recognition of these patterns is important to react adequately and to adjust own strategies to the opponent. Furthermore, the ability to apply variable patterns of play seems to be promising since they make it harder for the opponent to adjust. The purpose of this study is to identify different team tactical patterns in volleyball and to analyze differences in variability. Overall 120 standard situations of six national teams in women’s volleyball are analyzed during a world championship tournament. Twenty situations from each national team are chosen, including the base defence position (start configuration) and the two players block with middle back deep (end configuration). The shapes of the defence formations at the start and end configurations during the defence of each national team as well as the variability of these defence formations are statistically analyzed. Furthermore these shapes data are used to train multilayer perceptrons in order to test whether artificial neural networks can recognize the teams by their tactical patterns. Results show significant differences between the national teams in both the base defence position at the start and the two players block with middle back deep at the end of the standard defence situation. Furthermore, the national teams show significant differences in variability of the defence systems and start-positions are more variable than the end-positions. Multilayer perceptrons are able to recognize the teams at an average of 98.5%. It is concluded that defence systems in team sports are highly individual at a competitive level and variable even in standard situations. Artificial neural networks can be used to recognize teams by the shapes of the players’ configurations. These findings support the concept that tactics and strategy have to be adapted for the team and need to be flexible in order to be successful.


title: A hybrid BCI based on EEG and fNIRS signals improves the performance of decoding motor imagery of both force and speed of hand clenching

abstract: Objective. In order to increase the number of states classified by a brain–computer interface (BCI), we utilized a motor imagery task where subjects imagined both force and speed of hand clenching. Approach. The BCI utilized simultaneously recorded electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) signals. The time-phase-frequency feature was extracted from EEG, whereas the HbD [the difference of oxy-hemoglobin (HbO) and deoxy-hemoglobin (Hb)] feature was used to improve the classification accuracy of fNIRS. The EEG and fNIRS features were combined and optimized using the joint mutual information (JMI) feature selection criterion; then the extracted features were classified with the extreme learning machines (ELMs). Main results. In this study, the averaged classification accuracy of EEG signals achieved by the time-phase-frequency feature improved by 7%, to 18%, more than the single-type feature, and improved by 15% more than common spatial pattern (CSP) feature. The HbD feature of fNIRS signals improved the accuracy by 1%, to 4%, more than Hb, HbO, or HbT (total hemoglobin). The EEG–fNIRS feature for decoding motor imagery of both force and speed of hand clenching achieved an accuracy of 89% ± 2%, and improved the accuracy by 1% to 5% more than the sole EEG or fNIRS feature. Significance. Our novel motor imagery paradigm improves BCI performance by increasing the number of extracted commands. Both the time-phase-frequency and the HbD feature improve the classification accuracy of EEG and fNIRS signals, respectively, and the hybrid EEG–fNIRS technique achieves a higher decoding accuracy for two-class motor imagery, which may provide the framework for future multi-modal online BCI systems.


title: Evolutionary under-sampling based bagging ensemble method for imbalanced data classification

abstract: In the class imbalanced learning scenario, traditional machine learning algorithms focusing on optimizing the overall accuracy tend to achieve poor classification performance especially for the minority class in which we are most interested. To solve this problem, many effective approaches have been proposed. Among them, the bagging ensemble methods with integration of the under-sampling techniques have demonstrated better performance than some other ones including the bagging ensemble methods integrated with the over-sampling techniques, the cost-sensitive methods, etc. Although these under-sampling techniques promote the diversity among the generated base classifiers with the help of random partition or sampling for the majority class, they do not take any measure to ensure the individual classification performance, consequently affecting the achievability of better ensemble performance. On the other hand, evolutionary under-sampling EUS as a novel undersampling technique has been successfully applied in searching for the best majority class subset for training a good-performance nearest neighbor classifier. Inspired by EUS, in this paper, we try to introduce it into the under-sampling bagging framework and propose an EUS based bagging ensemble method EUS-Bag by designing a new fitness function considering three factors to make EUS better suited to the framework. With our fitness function, EUS-Bag could generate a set of accurate and diverse base classifiers. To verify the effectiveness of EUS-Bag, we conduct a series of comparison experiments on 22 two-class imbalanced classification problems. Experimental results measured using recall, geometric mean and AUC all demonstrate its superior performance.


title: Direct-touch vs. mouse input for tabletop displays

abstract: We investigate the differences -- in terms of bothquantitative performance and subjective preference -- between direct-touch and mouse input for unimanual andbimanual tasks on tabletop displays. The results of twoexperiments show that for bimanual tasks performed ontabletops, users benefit from direct-touch input. However,our results also indicate that mouse input may be moreappropriate for a single user working on tabletop tasksrequiring only single-point interaction.


title: Evaluation of initial cryptoasset offerings: the state of the practice

abstract: Initial Cryptoasset Offering (ICO), also often called Initial Coin Offering or Initial Token Offering (ITO) is a new means of fundraising through blockchain technology, which allows startups to raise large amounts of funds from the crowd in an unprecedented speed. However it is not easy for ordinary investors to distinguish genuine fundraising activities through ICOs from scams. Different websites that gather and evaluate ICOs at different stages have emerged as a solution to this issue. What remains unclear is how these websites are evaluating ICOs, and consequently how reliable and credible their evaluations are. In this paper we present the first findings of an analysis of a set of 28 ICO evaluation websites, aiming at revealing the state of the practice in terms of ICO evaluation. Key information about ICOs collected by these websites are categorised, and key factors that differentiate the evaluation mechanisms employed by these evaluation websites are identified. The findings of our study could help a better understanding of what entails to properly evaluate ICOs. It is also a first step towards discovering the key success factors of ICOs.


title: The quality of the physical environment and its association with activities and well-being among older people in residential care facilities

abstract: The physical environment can influence older people’s health and well-being, and is often mentioned as being an important factor for person-centred care. Due to high levels of frail health, many ol ...


A machine learning approach to textual entailment recognition


title: Structure and evolution of online social networks

abstract: In this paper, we consider the evolution of structure within large online social networks. We present a series of measurements of two such networks, together comprising in excess of five million people and ten million friendship links, annotated with metadata capturing the time of every event in the life of the network. Our measurements expose a surprising segmentation of these networks into three regions: singletons who do not participate in the network; isolated communities which overwhelmingly display star structure; and a giant component anchored by a well-connected core region which persists even in the absence of stars.We present a simple model of network growth which captures these aspects of component structure. The model follows our experimental results, characterizing users as either passive members of the network; inviters who encourage offline friends and acquaintances to migrate online; and linkers who fully participate in the social evolution of the network.


title: Evaluation of clustering algorithms for financial risk analysis using MCDM methods

abstract: The evaluation of clustering algorithms is intrinsically difficult because of the lack of objective measures. Since the evaluation of clustering algorithms normally involves multiple criteria, it can be modeled as a multiple criteria decision making (MCDM) problem. This paper presents an MCDM-based approach to rank a selection of popular clustering algorithms in the domain of financial risk analysis. An experimental study is designed to validate the proposed approach using three MCDM methods, six clustering algorithms, and eleven cluster validity indices over three real-life credit risk and bankruptcy risk data sets. The results demonstrate the effectiveness of MCDM methods in evaluating clustering algorithms and indicate that the repeated-bisection method leads to good 2-way clustering solutions on the selected financial risk data sets.


title: Media refusal and conspicuous non-consumption: The performative and political dimensions of Facebook abstention:

abstract: This paper is a study of consumer resistance among active abstainers of the Facebook social network site. I analyze the discourses invoked by individuals who consciously choose to abstain from participation on the ubiquitous Facebook platform. This discourse analysis draws from approximately 100 web and print publications from 2006 to early 2012, as well as personal interviews conducted with 20 Facebook abstainers. I conceptualize Facebook abstention as a performative mode of resistance, which must be understood within the context of a neoliberal consumer culture, in which subjects are empowered to act through consumption choices - or in this case non-consumption choices - and through the public display of those choices. I argue that such public displays are always at risk of misinterpretation due to the dominant discursive frameworks through which abstention is given meaning. This paper gives particular attention to the ways in which connotations of taste and distinction are invoked by refusers through their conspicuous displays of non-consumption. This has the effect of framing refusal as a performance of elitism, which may work against observers interpreting conscientious refusal as a persuasive and emulable practice of critique. The implication of this is that refusal is a limited tactic of political engagement where media platforms are concerned.


title: Detection of phishing websites using C4.5 data mining algorithm

abstract: Phishing sites are fake sites that are made by deceptive persons which are copy of genuine sites. These websites look like an official website of any company such as bank, institute, etc. The main aim of phishing is that to steal sensitive information of user such as password, username, pin number, etc. Victims of phishing attacks may uncover their money related delicate data to the attackers who may utilize this data for budgetary and criminal exercises. Different technical and non-technical approaches have been proposed to identify phishing sites. Non-Technical approach has no solution against the fast disappearance feature of phishing websites. Data mining technique, one of the classifications of technical approach, has shown promising results in detection of phishing websites. As compared to non-technical approaches, data mining techniques can generate classification models which can make prediction on phishing websites in real-time. In this paper analysis of C4.5 (J48) data mining algorithm has been done using WEKA tool. C4.5 is a benchmark data mining technique which can accurately identify phishing websites. A training dataset of 750 URLs has been made to train the algorithm J48, which is an implementation of C4.5 algorithm in WEKA. Testing dataset of 300 URLs is used to make prediction using the classifier generated after the training of J48. True positive rate, True negative rate, False positive rate, False negative rate, Success rate, Error rate and Accuracy are calculated after testing process. Result shows C4.5 has an accuracy of 82.6%.