title: Seemo: A Computational Approach to See Emotions

abstract: Successful human interactions are based on becoming aware of other's emotion and making adaptations accordingly. However, understanding emotion is a complex task that has generated countless debates among researchers over the past decades. The abstract nature of human emotion highlights the need for a new data-driven approach that can better describe and compare across fine-grained emotional states. In this study, we propose Seemo, a novel neural embedding framework, which allows us to map human emotions into vector space representations. Seemo is trained using Twitter data and is evaluated on two fundamental use cases in traditional emotion research: determining the underlying dimensions of emotions and identifying the set of basic emotions. The evaluation reveals that on both tasks Seemo can generate results consistent with the mainstream theories. Results also show that the vector space representation of Seemo can effectively decode the important relationships between emotions that were usually not explicitly presented.


title: A framework for consciousness

abstract: Here we summarize our present approach to the problem of consciousness. After an introduction outlining our general strategy, we describe what is meant by the term 'framework' and set it out under ten headings. This framework offers a coherent scheme for explaining the neural correlates of (visual) consciousness in terms of competing cellular assemblies. Most of the ideas we favor have been suggested before, but their combination is original. We also outline some general experimental approaches to the problem and, finally, acknowledge some relevant aspects of the brain that have been left out of the proposed framework.


title: Real-time nonintrusive monitoring and detection of eye blinking in view of accident prevention due to drowsiness

abstract: Driver fatigue is the major cause of accidents in the world. Detecting the drowsiness of the driver is the surest ways of measuring the driver fatigue. The purpose of this paper is to develop a drowsiness detection system. This system works by analyzing the eye movement of the driver and alerting the driver by activating the buzzer when he/she is drowsy. The system so implemented is a nonintrusive real-time monitoring system for eye detection. During monitoring, the system is able to decide whether the eyes were opened or closed. When the eyes were detected closed for too long, a signal was issued to warn the driver. In addition, the system also have an option for making vibration when drowsiness was detected. The aim is on improving the safety of the driver without being obtrusive. Visual cues were obtained through eye blink rate by using a camera, which typically characterize the level of alertness of a person. These were extracted in real-time and systematically joined to check the fatigue level of the driver. The system can monitor the driver's eyes to detect short periods of sleep lasting 3 to 4 seconds. The system implemented in this approach runs at 8–15 frames per second. The application was implemented using Open CV in Raspberry Pi environment with a single camera view. This system was used to detect the drowsiness of the driver and thereby reducing the road accidents.


title: Virtual Reality-Induced Symptoms and Effects (VRISE)

abstract: An experimental program of research was carried out to assess the potential health and safety effects of participating in virtual environments (VEs) via head-mounted displays (HMDs). This paper presents the results obtained from nine experiments examining the effects experienced during and after participation in a variety of VR systems, VE designs, and task requirements, for a total participant sample of 148 individuals. A combination of methods including self-report scales, performance measures, physiological indicators, observation, interview, and user attitude/opinion questionnaires were used to measure simulator (VE) sickness, postural instability, psychomotor control, perceptual judgment, concentration, stress, and ergonomics effects. Greatest effects across the different systems, VEs, and exposure times were found for sickness symptoms and physiological measures, with some concern over postural instability and physical ergonomics, also. Although many of the effects were relatively minor and short lived, they were serious for five percent of participants and irritating for a considerable percentage more. The aetiology of the effects is sufficiently different to that for simulators or transport systems to justify us using a new term, virtual reality-induced symptoms and effects (VRISE). Implications are drawn for VR system design, VE specification, and the ways in which industrial use of VR/VE should be planned and supported.


title: The Autism-Spectrum Quotient (AQ)—Adolescent Version

abstract: This tool was developed for use with children and young people under 16 who have autistic spectrum syndrome. It comprises a set of 50 short statements about behaviour and feelings in different situations. The adult version has been adapted slightly for adolescents and is available online. The adolescent version is for age 11-16. The adult version can be used from age 16.


title: A Case Study in Robot Exploration

abstract: This paper reports on a case study in autonomous robot exploration. In particular, we describe a working mobile manipulator robot that explores our laboratory to identify and collect cups. This system is a first step toward our research goal of developing an architecture for robust robot planning, control, adaptation, error monitoring, error recovery, and interaction with users. We describe the current system, lessons learned from our earlier failures, organizing principles employed in the current system, and limits to the current approach. We also discuss the implications of this work for a more robust robot control architecture which is presently under development.


title: SpiderGL: a JavaScript 3D graphics library for next-generation WWW

abstract: Thanks to the WebGL graphics API specification for the JavaScript programming language, the possibility of using the GPU capabilities in a web browser without the need for an ad-hoc plug-in is now coming true. This paper introduces SpiderGL, a JavaScript library for developing 3D graphics web applications. SpiderGL provides data structures and algorithms to ease the use of WebGL, to define and manipulate shapes, to import 3D models in various formats, to handle asynchronous data loading. We show the potential of this novel library with a number of demo applications. Furthermore, we introduce MeShade, a SpiderGL-based web application for shader material editing from within the web browser, which produces all the code needed for embedding interactive 3D model visualization capabilities inside web pages and online repositories.


title: Approximation-aware Multi-Level Cells STT-RAM cache architecture

abstract: Current manycore processors exhibit large on-chip last-level caches that may reach sizes of 32MB -- 128MB and incur high power/energy consumption. The emerging Multi-Level Cells (MLC) STT-RAM memory technology improves the capacity and energy efficiency issues of large-sized memory banks. However, MLC STT-RAM incurs non-negligible protection overhead to ensure reliable operations when compared to the Single-Level Cells (SLC) STT-RAM. In this paper, we propose an approximation-aware MLC STT-RAM cache architecture, which is partially-protected to restrict the reliability overhead and in turn leverages variable resilience characteristics of different applications for adaptively curtailing the protection overhead under a given error tolerance level. It thereby improves the energy-efficiency of the cache while meeting the reliability requirements. Our cache architecture is equipped with a latency-aware hardware module for double-error correction. To achieve high energy efficiency, approximation-aware read and write policies are proposed that perform approximate storage management while tolerating some errors bounded within the user-provided tolerance level. The architecture also facilitates runtime control on the quality of applications' results. We perform a case study on the next-generation advanced video encoding that exhibit memory-intensive functional blocks with varying resilience properties and support for parallelism. Experimental results demonstrate that our approximation-aware MLC STT-RAM based cache architecture can improve the energy efficiency compared to state-of-the-art fully-protected caches (7%--19%, on average), while incurring minimal quality penalties in the output (-0.219% to -0.426%, on average). Furthermore, our architecture supports complete error protection coverage for all cache data when processing non-resilient application. The hardware overhead to implement our approximation-aware management negligibly affects the energy efficiency (0.15%--1.3% of overhead) and the access latency (only 0.02%--1.56% of overhead).


title: Multimodal imaging in Alzheimer's disease: validity and usefulness for early detection

abstract: Summary Alzheimer's disease is a progressive neurodegenerative disease that typically manifests clinically as an isolated amnestic deficit that progresses to a characteristic dementia syndrome. Advances in neuroimaging research have enabled mapping of diverse molecular, functional, and structural aspects of Alzheimer's disease pathology in ever increasing temporal and regional detail. Accumulating evidence suggests that distinct types of imaging abnormalities related to Alzheimer's disease follow a consistent trajectory during pathogenesis of the disease, and that the first changes can be detected years before the disease manifests clinically. These findings have fuelled clinical interest in the use of specific imaging markers for Alzheimer's disease to predict future development of dementia in patients who are at risk. The potential clinical usefulness of single or multimodal imaging markers is being investigated in selected patient samples from clinical expert centres, but additional research is needed before these promising imaging markers can be successfully translated from research into clinical practice in routine care.


title: Feature Selection Methods in Persian Sentiment Analysis

abstract: With the enormous growth of digital content in internet, various types of online reviews such as product and movie reviews present a wealth of subjective information that can be very helpful for potential users. Sentiment analysis aims to use automated tools to detect subjective information from reviews. Up to now as there are few researches conducted on feature selection in sentiment analysis, there are very rare works for Persian sentiment analysis. This paper considers the problem of sentiment classification using different feature selection methods for online customer reviews in Persian language. Three of the challenges of Persian text are using of a wide variety of declensional suffixes, different word spacing and many informal or colloquial words. In this paper we study these challenges by proposing a model for sentiment classification of Persian review documents. The proposed model is based on stemming and feature selection and is employed Naive Bayes algorithm for classification. We evaluate the performance of the model on a collection of cellphone reviews, where the results show the effectiveness of the proposed approaches.


title: Classification of the estrous cycle through texture and shape features

abstract: We show, for the first time, an autonomous classification of the estrous cycle (the reproductive cycle in rats), This cycle consists of 4 stages: Proestrus, Estrus, Metestrus and Diestrus. The short duration of the cycle in rats makes them an ideal model for research about changes that occur during the reproductive cycle. Classification is based on the cytology shown by vaginal smear. For this reason, we used texture and shape features on the gray level color space and CIELAB color space on channels A and B, which were classified using support vector machines (SVM) and the artificial neural network multilayer perceptron (MLP). As dataset of 412 images of estrous cycle was used. It was divided into two sets. The first contains all four stages, the second contains two classes. The first class is formed by the stages Proestrus and Estrous and the second class is formed by the stages Metestrus and Diestrus. The two sets were formed to solve the main problems, the research of the reproductive cycle and the reproduction control of rodents. For the first set, we obtained an 87% of validation accuracy and 100% of validation accuracy for the second set using the multilayer perceptron. The results were validated through cross validation using 5 sets and F 1 metric.


WIDM at the NTCIR-13 STC-2 Task


title: A 6.3 µW 20 bit Incremental Zoom-ADC with 6 ppm INL and 1 µV Offset

abstract: A 20-bit incremental ADC for battery-powered sensor applications is presented. It is based on an energy-efficient zoom ADC architecture, which employs a coarse 6-bit SAR conversion followed by a fine 15-bit ΔΣ conversion. To further improve its energy efficiency, the ADC employs integrators based on cascoded dynamic inverters for extra gain and PVT tolerance. Dynamic error correction techniques such as auto-zeroing, chopping and dynamic element matching are used to achieve both low offset and high linearity. Measurements show that the ADC achieves 20-bit resolution, 6 ppm INL and 1 μV offset in a conversion time of 40 ms, while drawing only 3.5 μA current from a 1.8 V supply. This corresponds to a state-of-the-art figure-of-merit (FoM) of 182.7 dB. The 0.35 mm2 chip was fabricated in a standard 0.16 μm CMOS process.


title: Augmented reality in education: a meta-review and cross-media analysis

abstract: Augmented reality (AR) is an educational medium increasingly accessible to young users such as elementary school and high school students. Although previous research has shown that AR systems have the potential to improve student learning, the educational community remains unclear regarding the educational usefulness of AR and regarding contexts in which this technology is more effective than other educational mediums. This paper addresses these topics by analyzing 26 publications that have previously compared student learning in AR versus non-AR applications. It identifies a list of positive and negative impacts of AR experiences on student learning and highlights factors that are potentially underlying these effects. This set of factors is argued to cause differences in educational effectiveness between AR and other media. Furthermore, based on the analysis, the paper presents a heuristic questionnaire generated for judging the educational potential of AR experiences.


title: Infants perceiving and acting on the eyes: Tests of an evolutionary hypothesis

abstract: It has been hypothesized that an evolutionarily ancient mechanism underlies the ability of human infants to detect and act upon the direction of eye gaze of another human face. However, the evidence from behavioral studies with infants is also consistent with a more domain-general system responsive to the lateral motion of stimuli regardless of whether or not eyes are involved. To address this issue three experiments with 4-month-old infants are reported that utilize a standard face-cueing paradigm. In the first experiment an inverted face was used to investigate whether the motion of the pupils elicits the cueing effect regardless of the surrounding face context. In the second experiment pupil motion and eye gaze direction were opposed, allowing us to assess their relative importance. In a third experiment, a more complex gaze shift sequence allowed us to analyse the importance of beginning with a period of mutual gaze. Overall, the results were consistent with the importance of the perceived direction of motion of pupils. However, to be effective in cueing spatial locations this motion needs to be preceded by a period of direct mutual gaze (eye contact). We suggest that evolution results in information-processing biases that shape and constrain the outcome of individual development to eventually result in adult adaptive specializations.


title: Automatically Extracting High-Quality Negative Examples for Answer Selection in Question Answering

abstract: We propose a heuristic called "one answer per document" for automatically extracting high-quality negative examples for answer selection in question answering. Starting with a collection of question-answer pairs from the popular TrecQA dataset, we identify the original documents from which the answers were drawn. Sentences from these source documents that contain query terms (aside from the answers) are selected as negative examples. Training on the original data plus these negative examples yields improvements in effectiveness by a margin that is comparable to successive recent publications on this dataset. Our technique is completely unsupervised, which means that the gains come essentially for free. We confirm that the improvements can be directly attributed to our heuristic, as other approaches to extracting comparable amounts of training data are not effective. Beyond the empirical validation of this heuristic, we also share our improved TrecQA dataset with the community to support further work in answer selection.


title: Role-based EAM Using X.509 Attribute Certificate

abstract: In this paper, we describe an experiment of designing and implementing a role-based extranet access management (EAM) by leveraging rolebased access control (RBAC) and X.509 attribute certificate for scalable and interoperable authorization. Compared with previous works in this area, we show that our approach can overcome the problems of previous solutions and broaden RBAC’s applicability into large-scale networks. The components for role administration are defined and a security architecture is discussed. We also demonstrate the feasibility of our approach through a proof-of-concept implementation. Several issues from our experiment are briefly discussed as well.


title: Simple sequence repeat markers that identify Claviceps species and strains

abstract: Claviceps purpurea is a pathogen that infects most members of Pooideae, a subfamily of Poaceae, and causes ergot, a floral disease in which the ovary is replaced with a sclerotium. When the ergot body is accidently consumed by either man or animal in high enough quantities, there is extreme pain, limb loss and sometimes death. This study was initiated to develop simple sequence repeat (SSRs) markers for rapid identification of C. purpurea. SSRs were designed from sequence data stored at the National Center for Biotechnology Information database. The study consisted of 74 ergot isolates, from four different host species, Lolium perenne, Poa pratensis, Bromus inermis, and Secale cereale plus three additional Claviceps species, C. pusilla, C. paspali and C. fusiformis. Samples were collected from six different counties in Oregon and Washington over a 5-year period. Thirty-four SSR markers were selected, which enabled the differentiation of each isolate from one another based solely on their molecular fingerprints. Discriminant analysis of principle components was used to identify four isolate groups, CA Group 1, 2, 3, and 4, for subsequent cluster and molecular variance analyses. CA Group 1 consisting of eight isolates from the host species P. pratensis, was separated on the cluster analysis plot from the remaining three groups and this group was later identified as C. humidiphila. The other three groups were distinct from one another, but closely related. These three groups contained samples from all four of the host species. These SSRs are simple to use, reliable and allowed clear differentiation of C. humidiphila from C. purpurea. Isolates from the three separate species, C. pusilla, C. paspali and C. fusiformis, also amplified with these markers. The SSR markers developed in this study will be helpful in defining the population structure and genetics of Claviceps strains. They will also provide valuable tools for plant breeders needing to identify resistance in crops or for researchers examining fungal movements across environments.


title: Keyword Generation for Search Engine Advertising

abstract: Keyword3 generation for search engine advertising is an important problem for sponsored search or paidplacement advertising. A recent strategy in this area is bidding on nonobvious yet relevant words, which are economically more viable. Targeting many such nonobvious words lowers the advertising cost, while delivering the same click volume as expensive words. Generating the right nonobvious yet relevant keywords is a challenging task. The challenge lies in not only finding relevant words, but also in finding many such words. In this paper, we present TermsNet, a novel approach to this problem. This approach leverages search engines to determine relevance between terms and captures their semantic relationships as a directed graph. By observing the neighbors of a term in such a graph, we generate the common as well as the nonobvious keywords related to a term.


title: Users' Preference Prediction of Real Estates Featuring Floor Plan Analysis using FloorNet

abstract: In recent years, with the progress of e-commerce, recommendation for not only mass-produced daily items, such as books, but also special items that are not mass-produced has become an important task. In this study, we present an algorithm for real estate recommendation. There are no identical properties in the world, properties already occupied by someone else cannot be recommended, and users rent or buy properties only a few times in their lives. Therefore, automatic property recommendation is one of the most difficult tasks. In this study, we predict users' preference for properties, which is the first step of property recommendation, by combining content-based filtering and multilayer perceptron (MLP). In the MLP, we used not only attribute data of users and properties but also the deep features extracted from floor plan images of properties. As a result, we succeeded in predicting users' preference with an accuracy of 60.7%.


title: Spatiotemporal Multiplier Networks for Video Action Recognition

abstract: This paper presents a general ConvNet architecture for video action recognition based on multiplicative interactions of spacetime features. Our model combines the appearance and motion pathways of a two-stream architecture by motion gating and is trained end-to-end. We theoretically motivate multiplicative gating functions for residual networks and empirically study their effect on classification accuracy. To capture long-term dependencies we inject identity mapping kernels for learning temporal relationships. Our architecture is fully convolutional in spacetime and able to evaluate a video in a single forward pass. Empirical investigation reveals that our model produces state-of-the-art results on two standard action recognition datasets.


title: New Mothers and Media Use: Associations Between Blogging, Social Networking, and Maternal Well-Being

abstract: Drawing on Bronfenbrenner’s ecological theory and prior empirical research, the current study examines the way that blogging and social networking may impact feelings of connection and social support, which in turn could impact maternal well-being (e.g., marital functioning, parenting stress, and depression). One hundred and fifty-seven new mothers reported on their media use and various well-being variables. On average, mothers were 27 years old (SD = 5.15) and infants were 7.90 months old (SD = 5.21). All mothers had access to the Internet in their home. New mothers spent approximately 3 hours on the computer each day, with most of this time spent on the Internet. Findings suggested that frequency of blogging predicted feelings of connection to extended family and friends which then predicted perceptions of social support. This in turn predicted maternal well-being, as measured by marital satisfaction, couple conflict, parenting stress, and depression. In sum, blogging may improve new mothers’ well-being, as they feel more connected to the world outside their home through the Internet.


title: Synthesis and Characterization of Carboxymethylcellulose-Methacrylate Hydrogel Cell Scaffolds

abstract: Many carbohydrates pose advantages for tissue engineering applications due to their hydrophilicity, degradability, and availability of chemical groups for modification. For example, carboxymethylcellulose (CMC) is a water-soluble cellulose derivative that is degradable by cellulase. Though this enzyme is not synthesized by mammalian cells, cellulase and the fragments derived from CMC degradation are biocompatible. With this in mind, we created biocompatible, selectively degradable CMC-based hydrogels that are stable in routine culture, but degrade when exposed to exogenous cellulase. Solutions of CMC-methacrylate and polyethylene glycol dimethacrylate (PEG-DM) were co-crosslinked to form stable hydrogels; we found that greater CMC-methacrylate content resulted in increased gel swelling, protein diffusion and rates of degradation by cellulase, as well as decreased gel shear modulus. CMC-methacrylate/PEG-DM gels modified with the adhesive peptide RGD supported fibroblast adhesion and viability. We conclude that hydrogels based on CMC-methacrylate are suitable for bioengineering applications where selective degradability may be favorable, such as cell scaffolds or controlled release devices.


title: Safe human-robot-cooperation: image-based collision detection for industrial robots

abstract: This paper analyzes the problem of sensor-based collision detection for an industrial robotic manipulator. A method to perform collision tests based on images taken from several stationary cameras in the work cell is presented. The collision test works entirely based on the images, and does not construct a representation of the Cartesian space. It is shown how to perform a collision test for all possible robot configurations using only a single set of images taken simultaneously.


title: Compact three-phase AC inductor network for PFC converter using magnetic coupled technique

abstract: Three-phase power factor correction (PFC) converters have been widely utilized in industrial power applications to improve the power factor and reduce current harmonics in three-phase power grid. The PFC converters commonly incorporate large-sized inductors as a noise filter to suppress the noise emission to the grid. However, as many other power converters, the PFC converters are intensely required to achieve high power density by reducing the size of the inductors. The purpose of this paper is to propose a magnetic structure to miniaturize the inductors. The proposed magnetic structure consists of an integrated magnetic component and a three-phase common-mode choke. By integrating the inductors, the proposed magnetic structure can miniaturize the magnetic core volume, while keeping the saturation current level and the noise filtering capability unchanged. Experiment was carried out to evaluate the appropriateness of the proposed magnetic structure as well as possible volume reduction. As a result, 32.2% volume reduction was found in the proposed magnetic structure without deterioration of the filtering capability.


title: CATalyst: Defeating last-level cache side channel attacks in cloud computing

abstract: Cache side channel attacks are serious threats to multi-tenant public cloud platforms. Past work showed how secret information in one virtual machine (VM) can be extracted by another co-resident VM using such attacks. Recent research demonstrated the feasibility of high-bandwidth, low-noise side channel attacks on the last-level cache (LLC), which is shared by all the cores in the processor package, enabling attacks even when VMs are scheduled on different cores. This paper shows how such LLC side channel attacks can be defeated using a performance optimization feature recently introduced in commodity processors. Since most cloud servers use Intel processors, we show how the Intel Cache Allocation Technology (CAT) can be used to provide a system-level protection mechanism to defend from side channel attacks on the shared LLC. CAT is a way-based hardware cache-partitioning mechanism for enforcing quality-of-service with respect to LLC occupancy. However, it cannot be directly used to defeat cache side channel attacks due to the very limited number of partitions it provides. We present CATalyst, a pseudo-locking mechanism which uses CAT to partition the LLC into a hybrid hardware-software managed cache. We implement a proof-of-concept system using Xen and Linux running on a server with Intel processors, and show that LLC side channel attacks can be defeated. Furthermore, CATalyst only causes very small performance overhead when used for security, and has negligible impact on legacy applications.


title: Spoken content retrieval: beyond cascading speech recognition with text retrieval

abstract: Spoken content retrieval refers to directly indexing and retrieving spoken content based on the audio rather than text descriptions. This potentially eliminates the requirement of producing text descriptions for multimedia content for indexing and retrieval purposes, and is able to precisely locate the exact time the desired information appears in the multimedia. Spoken content retrieval has been very successfully achieved with the basic approach of cascading automatic speech recognition (ASR) with text information retrieval: after the spoken content is transcribed into text or lattice format, a text retrieval engine searches over the ASR output to find desired information. This framework works well when the ASR accuracy is relatively high, but becomes less adequate when more challenging real-world scenarios are considered, since retrieval performance depends heavily on ASR accuracy. This challenge leads to the emergence of another approach to spoken content retrieval: to go beyond the basic framework of cascading ASR with text retrieval in order to have retrieval performances that are less dependent on ASR accuracy. This overview article is intended to provide a thorough overview of the concepts, principles, approaches, and achievements of major technical contributions along this line of investigation. This includes five major directions: 1) Modified ASR for Retrieval Purposes: cascading ASR with text retrieval, but the ASR is modified or optimized for spoken content retrieval purposes; 2) Exploiting the Information not present in ASR outputs: to try to utilize the information in speech signals inevitably lost when transcribed into phonemes and words; 3) Directly Matching at the Acoustic Level without ASR: for spoken queries, the signals can be directly matched at the acoustic level, rather than at the phoneme or word levels, bypassing all ASR issues; 4) Semantic Retrieval of Spoken Content: trying to retrieve spoken content that is semantically related to the query, but not necessarily including the query terms themselves; 5) Interactive Retrieval and Efficient Presentation of the Retrieved Objects: with efficient presentation of the retrieved objects, an interactive retrieval process incorporating user actions may produce better retrieval results and user experiences.


title: Training Ternary Neural Networks with Exact Proximal Operator

abstract: In this paper, we propose a stochastic proximal gradient method to train ternary weight neural networks (TNN). The proposed method features weight ternarization via an exact formula of proximal operator. Our experiments show that our trained TNN are able to preserve the state-of-the-art performance on MNIST and CIFAR10 benchmark datesets.


title: TraCI: an interface for coupling road traffic and network simulators

abstract: Vehicular Ad-Hoc Networks (VANETs) enable communication among vehicles as well as between vehicles and roadside infrastructures. Currently available software tools for VANET research still lack the ability to asses the usability of vehicular applications. In this article, we present Tra ffic C ontrol I nterface (TraCI) a technique for interlinking road traffic and network simulators. It permits us to control the behavior of vehicles during simulation runtime, and consequently to better understand the influence of VANET applications on traffic patterns. In contrast to the existing approaches, i.e., generating mobility traces that are fed to a network simulator as static input files, the online coupling allows the adaptation of drivers' behavior during simulation runtime. This technique is not limited to a special traffic simulator or to a special network simulator. We introduce a general framework for controlling the mobility which is adaptable towards other research areas. We describe the basic concept, design decisions and the message format of this open-source architecture. Additionally, we provide implementations for non-commercial traffic and network simulators namely SUMO and ns2, respectively. This coupling enables for the first time systematic evaluations of VANET applications in realistic settings.


title: Importance Weighted Inductive Transfer Learning for Regression

abstract: We consider inductive transfer learning for dataset shift, a situation in which the distributions of two sampled, but closely related, datasets differ. When the target data to be predicted is scarce, one would like to improve its prediction by employing data from the other, secondary, dataset. Transfer learning tries to address this task by suitably compensating such a dataset shift. In this work we assume that the distributions of the covariates and the dependent variables can differ arbitrarily between the datasets. We propose two methods for regression based on importance weighting. Here to each instance of the secondary data a weight is assigned such that the data contributes positively to the prediction of the target data. Experiments show that our method yields good results on benchmark and real world datasets.


title: Neural networks for automatic target recognition

abstract: Abstract Many applications reported in artificial neural networks are associated with military problems. This paper reviews concepts associated with the processing of military data to find and recognize targets—automatic target recognition (ATR). A general-purpose automatic target recognition system does not exist. The work presented here is demonstrated on military data, but it can only be consideredproof of principle until systems are fielded andproven “under-fire”. ATR data can be in the form of non-imaging one-dimensional sensor returns, such as ultra-high range-resolution radar returns for air-to-air automatic target recognition and vibration signatures from a laser radar for recognition of ground targets. The ATR data can be two-dimensional images. The most common ATR images are infrared, but current systems must also deal with synthetic aperture radar images. Finally, the data can be three-dimensional, such as sequences of multiple exposures taken over time from a nonstationary world. Targets move, as do sensors, and that movement can be exploited by the ATR. Hyperspectral data, which are views of the same piece of the world looking at different spectral bands, is another example of multiple image data; the third dimension is now wavelength and not time. ATR system design usually consists of four stages. The first stage is to select the sensor or sensors to produce the target measurements. The next stage is the preprocessing of the data and the location of regions of interest within the data (segmentation). The human retina is a ruthless preprocessor. Physiology motivated preprocessing and segmentation is demonstrated along with supervised and unsupervised artificial neural segmentation techniques. The third design step is feature extraction and selection: the extraction of a set of numbers which characterize regions of the data. The last step is the processing of the features for decision making (classification). The area of classification is where most ATR related neural network research has been accomplished. The relation of neural classifiers to Bayesian techniques is emphasized along with the more recent use of feature sequences to enhance classification. The principal theme of this paper is that artificial neural networks have proven to be an interesting and useful alternate processing strategy. Artificial neural techniques, however, are not magical solutions with mystical abilities that work without good engineering. Good understanding of the capabilities and limitations of neural techniques is required to apply them productively to ATR problems.


title: Heart rate monitoring from wrist-type PPG based on singular spectrum analysis with motion decision

abstract: Heart rate (HR) monitoring is necessary for daily healthcare. Wrist-type photoplethsmography (PPG) is a convenient and non-invasive technique for HR monitoring. However, motion artifacts (MA) caused by subjects' movements can extremely interfere the results of HR monitoring. In this paper, we propose a high accuracy method using motion decision, singular spectrum analysis (SSA) and spectral peak searching for daily HR estimation. The proposed approach was evaluated on 8 subjects under a series of different motion states. Compared with electrocardiogram (ECG) recorded simultaneously, the experimental results indicated that the averaged absolute estimation error was 2.33 beats per minute (BPM).


title: Flexible Spacing Adaptive Cruise Control Using Stochastic Model Predictive Control

abstract: This paper proposes a stochastic model predictive control (MPC) approach to optimize the fuel consumption in a vehicle following context. The practical solution of that problem requires solving a constrained moving horizon optimal control problem using a short-term prediction of the preceding vehicle’s velocity. In a deterministic framework, the prediction errors lead to constraint violations and to harsh control reactions. Instead, the suggested method considers errors, and limits the probability of a constraint violation. A conditional linear Gauss model is developed and trained with real measurements to estimate the probability distribution of the future velocity of the preceding vehicle. The prediction model is used to evaluate two different stochastic MPC approaches. On the one hand, an MPC with individual chance constraints is applied. On the other hand, samples are drawn from the conditional Gaussian model and used for a scenario-based optimization approach. Finally, both developed control strategies are evaluated and compared against a standard deterministic MPC. The evaluation of the controllers shows a significant reduction of the fuel consumption compared with standard adaptive cruise control algorithms.


User Acceptance of Voice Recognition Technology: An Empirical Extension of the Technology Acceptance Model


title: The Kuramoto model : A simple paradigm for synchronization phenomena

abstract: Synchronization phenomena in large populations of interacting elements are the subject of intense research efforts in physical, biological, chemical, and social systems. A successful approach to the problem of synchronization consists of modeling each member of the population as a phase oscillator. In this review, synchronization is analyzed in one of the most representative models of coupled phase oscillators, the Kuramoto model. A rigorous mathematical treatment, specific numerical methods, and many variations and extensions of the original model that have appeared in the last few years are presented. Relevant applications of the model in different contexts are also included.


title: doi:10.1155/2010/740130 Research Article Moving Target Indication via RADARSAT-2 Multichannel Synthetic Aperture Radar Processing

abstract: With the recent launches of the German TerraSAR-X and the Canadian RADARSAT-2, both equipped with phased array antennas and multiple receiver channels, synthetic aperture radar, ground moving target indication (SAR-GMTI) data are now routinely being acquired from space. Defence R&D Canada has been conducting SAR-GMTI trials to assess the performance and limitations of the RADARSAT-2 GMTI system. Several SAR-GMTI modes developed for RADARSAT-2 are described and preliminary test results of these modes are presented. Detailed equations of motion of a moving target for multiaperture spaceborne SAR geometry are derived and a moving target parameter estimation algorithm developed for RADARSAT-2 (called the Fractrum Estimator) is presented. Limitations of the simple dual-aperture SAR-GMTI mode are analysed as a function of the signal-to-noise ratio and target speed. Recently acquired RADARSAT-2 GMTI data are used to demonstrate the capability of different system modes and to validate the signal model and the algorithm.


title: Dropout Improves Recurrent Neural Networks for Handwriting Recognition

abstract: Recurrent neural networks (RNNs) with Long Short-Term memory cells currently hold the best known results in unconstrained handwriting recognition. We show that their performance can be greatly improved using dropout - a recently proposed regularization method for deep architectures. While previous works showed that dropout gave superior performance in the context of convolutional networks, it had never been applied to RNNs. In our approach, dropout is carefully used in the network so that it does not affect the recurrent connections, hence the power of RNNs in modeling sequence is preserved. Extensive experiments on a broad range of handwritten databases confirm the effectiveness of dropout on deep architectures even when the network mainly consists of recurrent and shared connections.


title: Rice Classification Using Spatio-Spectral Deep Convolutional Neural Network

abstract: Rice has been one of the staple foods that contribute significantly to human food supplies. Numerous rice varieties have been cultivated, imported, and exported worldwide. Different rice varieties could be mixed during rice production and trading. Rice impurities could damage the trust between rice importers and exporters, calling for the need to develop a rice variety inspection system. In this work, we develop a non-destructive rice variety classification system that benefits from the synergy between hyperspectral imaging and deep convolutional neural network (CNN). The proposed method uses a hyperspectral imaging system to simultaneously acquire complementary spatial and spectral information of rice seeds. The rice varieties are then determined from the acquired spatio-spectral data using a deep CNN. As opposed to several existing rice variety classification methods that require hand-engineered features, the proposed method automatically extracts features from the raw sensor data. As demonstrated using two types of rice datasets, the proposed method achieved up to 8% relative improvement in the classification accuracy compared to the commonly used classification methods based on support vector machines.


title: Adaptive Computation Time for Recurrent Neural Networks

abstract: This paper introduces Adaptive Computation Time (ACT), an algorithm that allows recurrent neural networks to learn how many computational steps to take between receiving an input and emitting an output. ACT requires minimal changes to the network architecture, is deterministic and differentiable, and does not add any noise to the parameter gradients. Experimental results are provided for four synthetic problems: determining the parity of binary vectors, applying binary logic operations, adding integers, and sorting real numbers. Overall, performance is dramatically improved by the use of ACT, which successfully adapts the number of computational steps to the requirements of the problem. We also present character-level language modelling results on the Hutter prize Wikipedia dataset. In this case ACT does not yield large gains in performance; however it does provide intriguing insight into the structure of the data, with more computation allocated to harder-to-predict transitions, such as spaces between words and ends of sentences. This suggests that ACT or other adaptive computation methods could provide a generic method for inferring segment boundaries in sequence data.


title: Rank Measures for Ordering

abstract: Many data mining applications require a ranking, rather than a mere classification, of cases. Examples of these applications are widespread, including Internet search engines (ranking of pages returned) and customer relationship management (ranking of profitable customers). However, little theoretical foundation and practical guideline have been established to assess the merits of different rank measures for ordering. In this paper, we first review several general criteria to judge the merits of different single-number measures. Then we propose a novel rank measure, and compare the commonly used rank measures and our new one according to the criteria. This leads to a preference order for these rank measures. We conduct experiments on real-world datasets to confirm the preference order. The results of the paper will be very useful in evaluating and comparing rank algorithms.


title: CSS-Palm 2.0: an updated software for palmitoylation sites prediction.

abstract: Protein palmitoylation is an essential post-translational lipid modification of proteins, and reversibly orchestrates a variety of cellular processes. Identification of palmitoylated proteins with their sites is the foundation for understanding molecular mechanisms and regulatory roles of palmitoylation. Contrasting to the labor-intensive and time-consuming experimental approaches, in silico prediction of palmitoylation sites has attracted much attention as a popular strategy. In this work, we updated our previous CSS-Palm into version 2.0. An updated clustering and scoring strategy (CSS) algorithm was employed with great improvement. The leave-one-out validation and 4-, 6-, 8- and 10-fold cross-validations were adopted to evaluate the prediction performance of CSS-Palm 2.0. Also, an additional new data set not included in training was used to test the robustness of CSS-Palm 2.0. By comparison, the performance of CSS-Palm was much better than previous tools. As an application, we performed a small-scale annotation of palmitoylated proteins in budding yeast. The online service and local packages of CSS-Palm 2.0 were freely available at: http://bioinformatics.lcd-ustc.org/css_palm.


title: PET-CT image registration in the chest using free-form deformations

abstract: We have implemented and validated an algorithm for three-dimensional positron emission tomography transmission-to-computed tomography registration in the chest, using mutual information as a similarity criterion. Inherent differences in the two imaging protocols produce significant nonrigid motion between the two acquisitions. A rigid body deformation combined with localized cubic B-splines is used to capture this motion. The deformation is defined on a regular grid and is parameterized by potentially several thousand coefficients. Together with a spline-based continuous representation of images and Parzen histogram estimates, our deformation model allows closed-form expressions for the criterion and its gradient. A limited-memory quasi-Newton optimization algorithm is used in a hierarchical multiresolution framework to automatically align the images. To characterize the performance of the method, 27 scans from patients involved in routine lung cancer staging were used in a validation study. The registrations were assessed visually by two expert observers in specific anatomic locations using a split window validation technique. The visually reported errors are in the 0- to 6-mm range and the average computation time is 100 min on a moderate-performance workstation.


title: Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis

abstract: Previous work on action recognition has focused on adapting hand-designed local features, such as SIFT or HOG, from static images to the video domain. In this paper, we propose using unsupervised feature learning as a way to learn features directly from video data. More specifically, we present an extension of the Independent Subspace Analysis algorithm to learn invariant spatio-temporal features from unlabeled video data. We discovered that, despite its simplicity, this method performs surprisingly well when combined with deep learning techniques such as stacking and convolution to learn hierarchical representations. By replacing hand-designed features with our learned features, we achieve classification results superior to all previous published results on the Hollywood2, UCF, KTH and YouTube action recognition datasets. On the challenging Hollywood2 and YouTube action datasets we obtain 53.3% and 75.8% respectively, which are approximately 5% better than the current best published results. Further benefits of this method, such as the ease of training and the efficiency of training and prediction, will also be discussed. You can download our code and learned spatio-temporal features here: http://ai.stanford.edu/∼wzou/


title: IEEE 802.11ax: How to Build High Efficiency WLANs

abstract: Wi-Fi was designed just to replace wired Internet connection, but it has changed the Internet as a whole. Wireless devices generate the largest percentage of the Internet traffic and the Wi-Fi market is continuously raising, which brings new challenges connected with more devices, more data, and higher Quality of Experience expectations. Since todays Wi-Fi is inefficient in terms of these challenges, in 2013, IEEE LAN/MAN Standard Committee launched a new task group, which aims to improve Wi-Fi by 2019. In a short time, the group has attracted much attention from leading telecommunication manufacturers. In the paper, we observe activities of the group, paying much attention to the challenges and to those solutions, which have been proposed by November 2015.


title: Probabilistic Inference of Twitter Users' Age based on What They Follow

abstract: Twitter provides an open and rich source of data for studying human behaviour at scale and is widely used in social and network sciences. However, a major criticism of Twitter data is that demographic information is largely absent. Enhancing Twitter data with user ages would advance our ability to study social network structures, information flows and the spread of contagions. Approaches toward age detection of Twitter users typically focus on specific properties of tweets, e.g., linguistic features, which are language dependent. In this paper, we devise a language-independent methodology for determining the age of Twitter users from data that is native to the Twitter ecosystem. The key idea is to use a Bayesian framework to generalise ground-truth age information from a few Twitter users to the entire network based on what/whom they follow. Our approach scales to inferring the age of 700 million Twitter accounts with high accuracy.


title: A Calibration Technique for Bang-Bang ADPLLs Using Jitter Distribution Monitoring

abstract: This brief presents a built-in self-calibration (BISC) technique for minimization of the total jitter in bang-bang all-digital phase-locked loops (ADPLLs). It is based on the addition of a monitoring phase-frequency detector (PFD) with tunable delay cells for the reference clock and the divider clock and a counter for this PFD output signal. This allows for on-chip binary comparison of the jitter distribution widths at the ADPLL PFD input, when ADPLL filter parameters are altered. Since only a relative comparison is performed, no accurate delay calibration is required. The statistical properties of this comparison of two random distributions are analyzed theoretically, and guidelines for circuit dimensioning are derived. The proposed method is used for BISC by adaption of the ADPLL filter coefficients. This allows for jitter minimization under process, voltage and temperature variations as well as gain and period jitter of the digitally controlled oscillator. The proposed calibration technique is verified by system simulations and measurements of a silicon prototype implementation in 28-nm CMOS technology.


title: FAROS: Illuminating In-memory Injection Attacks via Provenance-Based Whole-System Dynamic Information Flow Tracking

abstract: In-memory injection attacks are extremely challenging to reverse engineer because they operate stealthily without leaving artifacts in the system or in any easily observable events from outside of a virtual machine. Because these attacks perform their actions in memory only, current malware analysis solutions cannot expose their behavior. This paper introduces FAROS^1 a reverse engineering tool for Windows malware analysis based on dynamic information flow tracking (DIFT), which can flag stealthy in-memory-only malware injection attacks by leveraging the synergy of: (i) whole-system taint analysis; (ii) per security policy-based handling of the challenge of indirect flows via the application of tags of different types, and (iii) the use of tags with fine-grained provenance information. We evaluated FAROS with six advanced in-memory-injecting malware and it flagged the attacks for all samples. We also analyzed FAROS' false positive rate with 90 non-injecting malware samples and 14 benign software from various categories. FAROS presented a very low false positive rate of 2%, which shows its potential towards practical solutions against advanced in-memory-only anti-reverse-engineering attacks.


title: Impact of claw-pole geometry variations on the performance of machine used in automotive application

abstract: This paper presents the influence of variation in various geometric parameters of claw-pole topology on the performance of machine used in automotive application. Due to three-dimensional flux distribution in the claw-pole topology, variation in geometric parameters of the claw-poles results into change in performance of the machine. The geometric parameters which have been varied are claw core outer diameter, claw inside radius, claw side plate thickness and claw undercut angle, and the performance of the machine has been evaluated for the complete operational speed range from low speed of 600 rpm to high speed of 18,000 rpm. By carrying out the aforesaid variations, it has been observed that there is a significant change in torque during low speed operation.


title: Left-handed People in a Right-handed World: A Phenomenological Study

abstract: The purpose of this research was to explore the experiences of left-handed adults. Four semi-structured interviews were conducted with left-handed adults (2 men and 2 women) about their experiences. After transcribing the data, interpretative phenomenological analysis (IPA), which is a qualitative approach, was utilized to analyze the data. The analysis highlighted some major themes which were organized to make a model of life experiences of left-handers. The highlighted themes included Left-handers' Development: Interplay of Heredity Basis and Environmental Influences, Suppression of Left-hand, Support and Consideration, Feeling It Is Okay, Left-handers as Being Particular, Physical and Psychological Health Challenges, Agonized Life, Struggle for Maintaining Identity and Transforming Attitude, Attitudinal Barriers to Equality and Acceptance. Implications of the research for parents, teachers, and psychologists are discussed.Keywords: Left-handedness, suppression, discrimination, IPARight-handedness or dexterity (from Latin word "dextra", meaning right) is an estimable trait very different from being left-handed or sinister (Latin "sinistra", left). Left-handedness is a tendency to use the left-hand more proficiently than the right-hand. A person can be thought to be a left-hander when he/she attains better results with the left hand, as well as when he/she gives preference to the left hand in activities which need strength, good co-ordination and accuracy (Meyer, 1998). As universally known, generally people use right hand overwhelmingly than the left and the population which is more agile with the left hand is comparatively small, i.e., about 10-15%.For centuries, individuals' have been in search for the answers that why people have a preference for left-hand over the right and why the left hand is chosen in such a minority (Franklin, 2008). The evidence for the determination of left-handedness has been attributed to heredity, environment, or to the brain functioning and data of various sorts have been used in attempts to establish one fact or another.Left-handedness in Different Cultures and ReligionsIn determination of hand preference, culture, and religion also play their role. The difference between left and right-handers in different cultures and belief systems focuses on the likelihood that left-handedness may be a sign or an indicator of some pathology, problem or evil. Supporters of this perspective have suggested that these problems have been noticeable enough to work their way into folk psychology. The traditions from numerous disparate cultures reflect the underlying assumption that right-handedness is allied with normality and left-handedness stands for abnormality or pathology.Stan Gooch (as cited in Crabtree, 2002a) highlights the fact that in vast majority of cultures, from every continent like Africa, Asia, Australasia, Europe, and America, the "left" is normally associated with femininity and the "right" with masculinity.Left-handedness is extremely disapproved in most cultures. Ancient Greeks and Romans considered the left side as imperfect and blasphemous. In Nigeria, people are inclined to view left-handedness as worst as a sign of evil or at best an expectantly curable adversity. About half a century ago in Japan, left-handedness in a wife was thought to be more than enough for divorce. Only a decade ago in Taiwan, left-handed people were highly supported to swap to being right-handed or at least to write with the right-hand (Dada, 2000; Handedness, 2009; Kalafiit, 2008).In South America, the right is regarded as good, life and divine but the left as bad, evil and morose. Similarly, among North American Indians the right stands for bravery and virility but the left indicates death and interment (Crabtree, 2002b).Mandal and Dutta (2001) establish in a series of studies that about 10% of humans are left-handed, though the prevalence rate varies due to sex, age, and cultural geographical locations. …


title: A Study on Big Data Thinking of the Internet of Things-Based Smart-Connected Car in Conjunction with Controller Area Network Bus and 4G-Long Term Evolution

abstract: A smart connected car in conjunction with the Internet of Things (IoT) is an emerging topic. The fundamental concept of the smart connected car is connectivity, and such connectivity can be provided by three aspects, such as Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Everything (V2X). To meet the aspects of V2V and V2I connectivity, we developed modules in accordance with international standards with respect to On-Board Diagnostics II (OBDII) and 4G Long Term Evolution (4G-LTE) to obtain and transmit vehicle information. We also developed software to visually check information provided by our modules. Information related to a user’s driving, which is transmitted to a cloud-based Distributed File System (DFS), was then analyzed for the purpose of big data analysis to provide information on driving habits to users. Yet, since this work is an ongoing research project, we focus on proposing an idea of system architecture and design in terms of big data analysis. Therefore, our contributions through this work are as follows: (1) Develop modules based on Controller Area Network (CAN) bus, OBDII, and 4G-LTE; (2) Develop software to check vehicle information on a PC; (3) Implement a database related to vehicle diagnostic codes; (4) Propose system architecture and design for big data analysis.


title: Visual-lidar odometry and mapping: low-drift, robust, and fast

abstract: Here, we present a general framework for combining visual odometry and lidar odometry in a fundamental and first principle method. The method shows improvements in performance over the state of the art, particularly in robustness to aggressive motion and temporary lack of visual features. The proposed on-line method starts with visual odometry to estimate the ego-motion and to register point clouds from a scanning lidar at a high frequency but low fidelity. Then, scan matching based lidar odometry refines the motion estimation and point cloud registration simultaneously.We show results with datasets collected in our own experiments as well as using the KITTI odometry benchmark. Our proposed method is ranked #1 on the benchmark in terms of average translation and rotation errors, with a 0.75% of relative position drift. In addition to comparison of the motion estimation accuracy, we evaluate robustness of the method when the sensor suite moves at a high speed and is subject to significant ambient lighting changes.


MIandT Lab at SemEval-2017 task 4: An Integrated Training Method of Word Vector for Sentiment Classification


title: The five factor model of personality and job performance in the European Community.

abstract: In 3 prior meta-analyses, the relationship between the Big Five factors of personality and job criteria was investigated. However, these meta-analyses showed different findings. Furthermore, these reviews included studies carried out only in the United States and Canada. This study reports meta-analytic research on the same topic but with studies conducted in the European Community, which were not included in the prior reviews. The results indicate that Conscientiousness and Emotional Stability are valid predictors across job criteria and occupational groups. The remaining factors are valid only for some criteria and for some occupational groups. Extraversion was a predictor for 2 occupations, and Openness and Agreeableness were valid predictors of training proficiency. These findings are consistent with M. R. Barrick and M. K. Mount (1991) and L. M. Hough, N. K. Eaton, M. D. Dunnette, J. D. Kamp, and R. A. McCloy (1990). Implications of the results for future research and the practice of personnel selection are suggested.


title: Men’s Behavior Toward Women After Viewing Sexually-Explicit Films: Degradation Makes a Difference

abstract: Past research indicates that short-term exposure to nonviolent sexual media stimuli can produce cognitive changes in men which, in turn, can affect their behavior toward women. This study explored differences among these behavioral effects based upon sexual degradation in film content and male viewers' gender schematicity, as an extension of the study of cognitive effects reported in Jansma, Linz, Mulac, and Imrich (1997). Seventy-one men viewed one of three films: (a) sexually-explicit and degrading to women, (b) sexually-explicit but non-degrading, or (c) non-sexual. The men then interacted with women in problem-solving dyads that were recorded on videotape. Men who viewed either sexually-explicit film displayed more dominance and anxiety than did the men who viewed the non-sexual film. This was true, however, for only the two-thirds who were non sex-typed (BSRI, Bem, 1974). Sex-typed men's behavior was consistent across sexual versus non-sexual film conditions. In addition, men who viewed the degrading...


title: Motifs in Temporal Networks

abstract: Networks are a fundamental tool for modeling complex systems in a variety of domains including social and communication networks as well as biology and neuroscience. The counts of small subgraph patterns in networks, called network motifs, are crucial to understanding the structure and function of these systems. However, the role of network motifs for temporal networks, which contain many timestamped links between nodes, is not well understood. Here we develop a notion of a temporal network motif as an elementary unit of temporal networks and provide a general methodology for counting such motifs. We define temporal network motifs as induced subgraphs on sequences of edges, design several fast algorithms for counting temporal network motifs, and prove their runtime complexity. We also show that our fast algorithms achieve 1.3x to 56.5x speedups compared to a baseline method. We use our algorithms to count temporal network motifs in a variety of real-world datasets. Results show that networks from different domains have significantly different motif frequencies, whereas networks from the same domain tend to have similar motif frequencies. We also find that measuring motif counts at various time scales reveals different behavior.


title: Broadband horizontally polarized HF antenna with extremely low profile above conducting ground

abstract: A broadband bow-tie shaped HF antenna placed above a conducting ground plane with a very low height (around 1 meter) is presented in this paper. Through a simple analysis of a horizontal antenna above a ground plane by the image theory, ferrite materials are utilized to eliminate the destructive impact of the conductive ground such that the broadband characteristic of the bow-tie antenna is maintained. Simulated VSWR of the HF antenna from 6 MHz to 30 MHz is less than 2.5. Radiation patterns of the proposed antenna are suitable for HF applications. Simulated results have been provided to demonstrate the radiation performance of the proposed wideband bow-tie antenna operating in the HF (6MHz - 30MHz) band.


title: On-line consistent ranking on e-recruitment: seeking the truth behind a well-formed CV

abstract: In this work we present a novel approach for evaluating job applicants in online recruitment systems, using machine learning algorithms to solve the candidate ranking problem and performing semantic matching techniques. An application of our approach is implemented in the form of a prototype system, whose functionality is showcased and evaluated in a real-world recruitment scenario. The proposed system extracts a set of objective criteria from the applicants' LinkedIn profile, and compares them semantically to the job's prerequisites. It also infers their personality characteristics using linguistic analysis on their blog posts. Our system was found to perform consistently compared to human recruiters, thus it can be trusted for the automation of applicant ranking and personality mining.


title: Robust Non-Rigid Motion Tracking and Surface Reconstruction Using $L_0$ Regularization

abstract: We present a new motion tracking technique to robustly reconstruct non-rigid geometries and motions from a single view depth input recorded by a consumer depth sensor. The idea is based on the observation that most non-rigid motions (especially human-related motions) are intrinsically involved in articulate motion subspace. To take this advantage, we propose a novel $L_0$ based motion regularizer with an iterative solver that implicitly constrains local deformations with articulate structures, leading to reduced solution space and physical plausible deformations. The $L_0$ strategy is integrated into the available non-rigid motion tracking pipeline, and gradually extracts articulate joints information online with the tracking, which corrects the tracking errors in the results. The information of the articulate joints is used in the following tracking procedure to further improve the tracking accuracy and prevent tracking failures. Extensive experiments over complex human body motions with occlusions, facial and hand motions demonstrate that our approach substantially improves the robustness and accuracy in motion tracking.


title: Enriching textbooks through data mining

abstract: Textbooks play an important role in any educational system. Unfortunately, many textbooks produced in developing countries are not written well and they often lack adequate coverage of important concepts. We propose a technological solution to address this problem based on enriching textbooks with authoritative web content. We augment textbooks at the section level for key concepts discussed in the section. We use ideas from data mining for identifying the concepts that need augmentation as well as to determine the links to the authoritative content that should be used for augmentation. Our evaluation, employing textbooks from India, shows that we are able to enrich textbooks on different subjects and across different grades with high quality augmentations using automated techniques.


title: Learning of Social Signatures Through Imitation Game Between a Robot and a Human Partner

abstract: In this paper, a robot learns different postures by imitating several partners. We assessed the effect of the type of partners, i.e., adults, typically developing (TD) children and children with autism spectrum disorder (ASD), on robot learning during an imitation game. The experimental protocol was divided into two phases: (1) a learning phase, during which the robot produced a random posture and the partner imitated the robot; and (2) a phase in which the roles were reversed and the robot had to imitate the posture of the human partner. Robot learning was based on a sensory-motor architecture whereby neural networks (N.N.) enabled the robot to associate what it did with what it saw. Several metrics (i.e., annotation, the number of neurons needed to learn, and normalized mutual information) were used to show that the partners affected robot learning. The first result obtained was that learning was easier with adults than with both groups of children, indicating a developmental effect. Second, learning was more complex with children with ASD compared to both adults and TD children. Third, learning with the more complex partner first (i.e., children with ASD) enabled learning to be more easily generalized.


title: Factors Influencing Audit Technology Acceptance by Audit Firms: A New I-TOE Adoption Framework

abstract: Many businesses are now moving to e-business and implementing computerized accounting information systems. This phenomenon has given impact to audit profession in performing IT audit, financial reports audit and tracing electronic source documents. Computer-Assisted-Auditing Techniques and Tools (CAATTs) are audit technologies that allow IT audit work to be performed efficiently, effectively and reduce audit time. However, little is known about CAATTs adoption by public audit firms. This paper presents a new paradigm of Individual-Technology-OrganizationEnvironment (I-TOE) to investigate the acceptance of CAATTs in audit firms. There are gaps that exist in prior literatures which studied CAATTs acceptance from only individual auditor views and did not deliberate on issues from both organizational and individual perspectives. Consequently, this paper contributes to extend the literature by providing a better understanding on relationship of both organizational and individual factors in foreseeing CAATTs adoption and investment. A combination of Unified Theory of Acceptance and Use of Technology 2, and TechnologyOrganization-Environment framework are used as the underlying theories. In addition to that, this paper complements the framework with new variables of technology risk, technology task fit, organization readiness and top management commitment. I-TOE framework contributes to professional audit firms that need to measure CAATTs acceptance for the advancement of audit profession. Future experimental studies may be done to provide evidence and empirically validate I-TOE framework in other domain.


title: On the security evaluation of the ARM TrustZone extension in a heterogeneous SoC

abstract: As the complexity of System-on-Chip (SoC) and the reuse of third party IP continues to grow, the security of a heterogeneous SoC has become a critical issue. In order to increase the software security of such SoC, the TrustZone technology has been proposed by ARM to enforce software security. Nevertheless, many SoC embed non-trusted third party Intellectual Property (IP) trying to take the benefits of this technology. In such case, is the security guaranteed by the ARM TrustZone technology reduced by the heterogeneity of SoC? In order to answer to this question, this paper presents relevant attack scenarios based on third party IP to exploit some security failures of the TrustZone extension through the all SoC. At the end, this article proposes to SoC designers to consider some design solutions to limit the impact of a malicious IP.


title: A Multiscale and Hierarchical Feature Extraction Method for Terrestrial Laser Scanning Point Cloud Classification

abstract: The effective extraction of shape features is an important requirement for the accurate and efficient classification of terrestrial laser scanning (TLS) point clouds. However, the challenge of how to obtain robust and discriminative features from noisy and varying density TLS point clouds remains. This paper introduces a novel multiscale and hierarchical framework, which describes the classification of TLS point clouds of cluttered urban scenes. In this framework, we propose multiscale and hierarchical point clusters (MHPCs). In MHPCs, point clouds are first resampled into different scales. Then, the resampled data set of each scale is aggregated into several hierarchical point clusters, where the point cloud of all scales in each level is termed a point-cluster set. This representation not only accounts for the multiscale properties of point clouds but also well captures their hierarchical structures. Based on the MHPCs, novel features of point clusters are constructed by employing the latent Dirichlet allocation (LDA). An LDA model is trained according to a training set. The LDA model then extracts a set of latent topics, i.e., a feature of topics, for a point cluster. Finally, to apply the introduced features for point-cluster classification, we train an AdaBoost classifier in each point-cluster set and obtain the corresponding classifiers to separate the TLS point clouds with varying point density and data missing into semantic regions. Compared with other methods, our features achieve the best classification results for buildings, trees, people, and cars from TLS point clouds, particularly for small and moving objects, such as people and cars.


On-line trajectory planning of robot manipulator ’ s end effector in Cartesian Space using quaternions


title: Improving the Performance of OpenCL-based FPGA Accelerator for Convolutional Neural Network

abstract: OpenCL FPGA has recently gained great popularity with emerging needs for workload acceleration such as Convolutional Neural Network (CNN), which is the most popular deep learning architecture in the domain of computer vision. While OpenCL enhances the code portability and programmability of FPGA, it comes at the expense of performance. The key challenge is to optimize the OpenCL kernels to efficiently utilize the flexible hardware resources in FPGA. Simply optimizing the OpenCL kernel code through various compiler options turns out insufficient to achieve desirable performance for both compute-intensive and data-intensive workloads such as convolutional neural networks. In this paper, we first propose an analytical performance model and apply it to perform an in-depth analysis on the resource requirement of CNN classifier kernels and available resources on modern FPGAs. We identify that the key performance bottleneck is the on-chip memory bandwidth. We propose a new kernel design to effectively address such bandwidth limitation and to provide an optimal balance between computation, on-chip, and off-chip memory access. As a case study, we further apply these techniques to design a CNN accelerator based on the VGG model. Finally, we evaluate the performance of our CNN accelerator using an Altera Arria 10 GX1150 board. We achieve 866 Gop/s floating point performance at 370MHz working frequency and 1.79 Top/s 16-bit fixed-point performance at 385MHz. To the best of our knowledge, our implementation achieves the best power efficiency and performance density compared to existing work.


title: Game mechanics telling stories? An experiment

abstract: We investigate the question of how game mechanics influence or produce narrative by way of empirical experiment. The experimental setup involves a game with reconfigurable game mechanics and minimal representational elements. 69 players played games with randomized game mechanics, and 16 players formed a control group playing with a fixed set of mechanics. All players were asked to describe the story of the game with minimal prompting, and these free-text answers were tagged. Associations between tags and mechanics were analyzed using correlation analysis and frequent pattern mining. It was found that the choice of mechanics significantly impact what stories players tell about the game, but that most of the associations are relatively simple and straightforward.


title: G . 729 Error Recovery for Internet Telephony

abstract: This memorandum discusses the use of the ITU G.729 CS-ACELP speech coder on the Internet for telephony applications. In particular, the memo explores issues of error resiliency and recovery. In particular, the paper considers two questions. First, given N consecutive frame erasures (due to packet losses), how long does the decoder take to resynchronize its state with the encoder? What is the strength of the resulting error signal, both in terms of objective and subjective measures? The second issue explores which particular factors contribute to the strength of the error signal: the distortion of the speech due to the incorrect concealment of the erasures, or the subsequent distortion due to the loss of state synchronization, even though correct packets are being received. Both objective and subjective measures are used to characterize the importance of each of these two factors.


Experience Factory


Microbiota and Mucosal Immunity in Amphibians


title: Compression and Explanation Using Hierarchical Grammars

abstract: This paper describes an algorithm, called SEQUITUR, that identi"es hierarchical structure in sequences of discrete symbols and uses that information for compression. On many practical sequences it performs well at both compression and structural inference, producing comprehensible descriptions of sequence structure in the form of grammar rules. The algorithm can be stated concisely in the form of two constraints on a context-free grammar. Inference is performed incrementally, the structure faithfully representing the input at all times. It can be implemented ef"ciently and operates in a time that is approximately linear in sequence length. Despite its simplicity and ef"ciency, SEQUITUR succeeds in inferring a range of interesting hierarchical structures from naturally occurring sequences.


title: Configuration and performance analysis of a compact planetary geared Elastic Actuator

abstract: Safety, compliance and human-friendliness are trending keywords in robotics now. The most significant reason for the growth in the use of these keywords is the progress of the service robot interacting with environments, including humans. Following these trends, Series Elastic Actuator (SEA) has received a large attention as a promising actuator in this field. Consequently, various SEA mechanisms have been developed by a lot of researchers ever since it was firstly introduced in 1990s. In spite of the development and applications at various SEAs, there still are challenging problems: backlash of gear reducer, encoder resolution problem, spring non-linearity, limited working range, etc.


title: Vaginal calculi secondary to urethrovaginal fistula with vaginal stenosis in a 14-year-old girl

abstract: The author reports a case of vaginal calculi secondary to urethrovaginal fistula with vaginal stenosis in a 14-year-old girl. The initial urethrovaginal fistula and vaginal stenosis resulted from pelvic trauma after a traffic accident, with subsequent surgical urethral realignment and anterior colporrhaphy without success. The patient had continuous urinary incontinence for a duration of 11 years after surgery, and was finally diagnosed with urethrovaginal fistula with primary multiple vaginal calculi in our hospital. Surgeries were performed to remove the stones and repair the urethrovaginal fistula. Colpoplasty was also given by an obstetrician-gynecologist to solve the vaginal stenosis. After the procedure, the girl urinated normally. At her 3-month follow-up, the patient had no sign of urine leakage.


title: A Fish-Like Magnetically Propelled Microswimmer Fabricated by 3D Laser Lithography

abstract: This paper presents the development of a fish-like magnetically propelled microswimmer fabricated by 3D laser lithography. The microswimmer consists of a head and a caudal fin, just like a natural fish. There is a joint between the head and the fin so that the caudal fin can oscillate around the head to generate thrust, and the oscillation of the fin hardly transfers to the head, which benefits the stable motion of the microswimmer. The caudal fin of the microswimmer is deposited with a layer of 50 nm nickel (Ni) for magnetic actuation. Through applying an oscillating uniform magnetic field, the microswimmer can move along with the direction guided by the external magnetic field. A magnetic control system with permanent magnets is designed to provide such an oscillating uniform magnetic field, where the oscillating frequency and amplitude are controllable. A micro probe operation platform is used to detach the fabricated microswimmers from glass substrate in manufacturing. The proposed magnetically propelled microswimmer can be potentially used as powerful detoxification and biosensing tools for medical diagnosis and treatment in precision medicine.


title: Complementary Set Variational Autoencoder for Supervised Anomaly Detection

abstract: Anomalies have broad patterns corresponding to their causes. In industry, anomalies are typically observed as equipment failures. Anomaly detection aims to detect such failures as anomalies. Although this is usually a binary classification task, the potential existence of unseen (unknown) failures makes this task difficult. Conventional supervised approaches are suitable for detecting seen anomalies but not for unseen anomalies. Although, unsupervised neural networks for anomaly detection now detect unseen anomalies well, they cannot utilize anomalous data for detecting seen anomalies even if some data have been made available. Thus, providing an anomaly detector that finds both seen and unseen anomalies well is still a tough problem. In this paper, we introduce a novel probabilistic representation of anomalies to solve this problem. The proposed model defines the normal and anomaly distributions using the analogy between a set and the complementary set. We applied these distributions to an unsupervised variational autoencoder (VAE)-based method and turned it into a supervised VAE-based method. We tested the proposed method with well-known data and real industrial data to show that the proposed method detects seen anomalies better than the conventional unsupervised method without degrading the detection performance for unseen anomalies.


title: Deep Learning for Super-Resolution Channel Estimation and DOA Estimation Based Massive MIMO System

abstract: The recent concept of massive multiple-input multiple-output (MIMO) can significantly improve the capacity of the communication network, and it has been regarded as a promising technology for the next-generation wireless communications. However, the fundamental challenge of existing massive MIMO systems is that high computational complexity and complicated spatial structures bring great difficulties to exploit the characteristics of the channel and sparsity of these multi-antennas systems. To address this problem, in this paper, we focus on channel estimation and direction-of-arrival (DOA) estimation, and a novel framework that integrates the massive MIMO into deep learning is proposed. To realize end-to-end performance, a deep neural network (DNN) is employed to conduct offline learning and online learning procedures, which is effective to learn the statistics of the wireless channel and the spatial structures in the angle domain. Concretely, the DNN is first trained by simulated data in different channel conditions with the aids of the offline learning, and then corresponding output data can be obtained based on current input data during online learning process. In order to realize super-resolution channel estimation and DOA estimation, two algorithms based on the deep learning are developed, in which the DOA can be estimated in the angle domain without additional complexity directly. Furthermore, simulation results corroborate that the proposed deep learning based scheme can achieve better performance in terms of the DOA estimation and the channel estimation compared with conventional methods, and the proposed scheme is well investigated by extensive simulation in various cases for testing its robustness.


title: Traffic engineering in software-defined networking: Measurement and management

abstract: As the next generation network architecture, software-defined networking (SDN) has exciting application prospects. Its core idea is to separate the forwarding layer and control layer of network system, where network operators can program packet forwarding behavior to significantly improve the innovation capability of network applications. Traffic engineering (TE) is an important network application, which studies measurement and management of network traffic, and designs reasonable routing mechanisms to guide network traffic to improve utilization of network resources, and better meet requirements of the network quality of service (QoS). Compared with the traditional networks, the SDN has many advantages to support TE due to its distinguish characteristics, such as isolation of control and forwarding, global centralized control, and programmability of network behavior. This paper focuses on the traffic engineering technology based on the SDN. First, we propose a reference framework for TE in the SDN, which consists of two parts, traffic measurement and traffic management. Traffic measurement is responsible for monitoring and analyzing real-time network traffic, as a prerequisite for traffic management. In the proposed framework, technologies related to traffic measurement include network parameters measurement, a general measurement framework, and traffic analysis and prediction; technologies related to traffic management include traffic load balancing, QoS-guarantee scheduling, energy-saving scheduling, and traffic management for the hybrid IP/SDN. Current existing technologies are discussed in detail, and our insights into future development of TE in the SDN are offered.


title: Sequential Optimization for Efficient High-Quality Object Proposal Generation

abstract: We are motivated by the need for a generic object proposal generation algorithm which achieves good balance between object detection recall, proposal localization quality and computational efficiency. We propose a novel object proposal algorithm, BING++ , which inherits the virtue of good computational efficiency of BING [1] but significantly improves its proposal localization quality. At high level we formulate the problem of object proposal generation from a novel probabilistic perspective, based on which our BING++ manages to improve the localization quality by employing edges and segments to estimate object boundaries and update the proposals sequentially. We propose learning the parameters efficiently by searching for approximate solutions in a quantized parameter space for complexity reduction. We demonstrate the generalization of BING++ with the same fixed parameters across different object classes and datasets. Empirically our BING++ can run at half speed of BING on CPU, but significantly improve the localization quality by 18.5 and 16.7 percent on both VOC2007 and Microhsoft COCO datasets, respectively. Compared with other state-of-the-art approaches, BING++ can achieve comparable performance, but run significantly faster.


title: Cloud resource provisioning: survey, status and future research directions

abstract: Cloud resource provisioning is a challenging job that may be compromised due to unavailability of the expected resources. Quality of Service (QoS) requirements of workloads derives the provisioning of appropriate resources to cloud workloads. Discovery of best workload---resource pair based on application requirements of cloud users is an optimization problem. Acceptable QoS cannot be provided to the cloud users until provisioning of resources is offered as a crucial ability. QoS parameters-based resource provisioning technique is therefore required for efficient provisioning of resources. This research depicts a broad methodical literature analysis of cloud resource provisioning in general and cloud resource identification in specific. The existing research is categorized generally into various groups in the area of cloud resource provisioning. In this paper, a methodical analysis of resource provisioning in cloud computing is presented, in which resource management, resource provisioning, resource provisioning evolution, different types of resource provisioning mechanisms and their comparisons, benefits and open issues are described. This research work also highlights the previous research, current status and future directions of resource provisioning and management in cloud computing.


title: Blended learning effectiveness: the relationship between student characteristics, design features and outcomes

abstract: This paper investigates the effectiveness of a blended learning environment through analyzing the relationship between student characteristics/background, design features and learning outcomes. It is aimed at determining the significant predictors of blended learning effectiveness taking student characteristics/background and design features as independent variables and learning outcomes as dependent variables. A survey was administered to 238 respondents to gather data on student characteristics/background, design features and learning outcomes. The final semester evaluation results were used as a measure for performance as an outcome. We applied the online self regulatory learning questionnaire for data on learner self regulation, the intrinsic motivation inventory for data on intrinsic motivation and other self-developed instruments for measuring the other constructs. Multiple regression analysis results showed that blended learning design features (technology quality, online tools and face-to-face support) and student characteristics (attitudes and self-regulation) predicted student satisfaction as an outcome. The results indicate that some of the student characteristics/backgrounds and design features are significant predictors for student learning outcomes in blended learning.


Profitable restaurant management


title: Development of pseudorandom binary arrays for calibration of surface profile metrology tools

abstract: Optical metrology tools, especially for short wavelengths (extreme ultraviolet and x-ray), must cover a wide range of spatial frequencies from the very low, which affects figure, to the important mid-spatial frequencies and the high spatial frequency range, which produces undesirable scattering. A major difficulty in using surface profilometers arises due to the unknown point-spread function (PSF) of the instruments [G. D. Boreman, Modulation Transfer Function in Optical and Electro-Optical Systems (SPIE, Bellingham, WA, 2001)] that is responsible for distortion of the measured surface profile. Generally, the distortion due to the PSF is difficult to account for because the PSF is a complex function that comes to the measurement via the convolution operation, while the measured profile is described with a real function. Accounting for instrumental PSF becomes significantly simpler if the result of measurement of a profile is presented in the spatial frequency domain as a power spectral density (PSD) distr...


title: Learning to Merge Word Senses

abstract: It has been widely observed that different NLP applications require different sense granularities in order to best exploit word sense distinctions, and that for many applications WordNet senses are too fine-grained. In contrast to previously proposed automatic methods for sense clustering, we formulate sense merging as a supervised learning problem, exploiting human-labeled sense clusterings as training data. We train a discriminative classifier over a wide variety of features derived from WordNet structure, corpus-based evidence, and evidence from other lexical resources. Our learned similarity measure outperforms previously proposed automatic methods for sense clustering on the task of predicting human sense merging judgments, yielding an absolute F-score improvement of 4.1% on nouns, 13.6% on verbs, and 4.0% on adjectives. Finally, we propose a model for clustering sense taxonomies using the outputs of our classifier, and we make available several automatically sense-clustered WordNets of various sense granularities.


title: Mobile phone name extraction from internet forums: a semi-supervised approach

abstract: Collecting users' feedback on products from Internet forums is challenging because users often mention a product with informal abbreviations or nicknames. In this paper, we propose a method named Gren to recognize and normalize mobile phone names from domain-specific Internet forums. Instead of directly recognizing phone names from sentences as in most named entity recognition tasks, we propose an approach to generating candidate names as the first step. The candidate names capture short forms, spelling variations, and nicknames of products, but are not noise free. To predict whether a candidate name mention in a sentence indeed refers to a specific phone model, a Conditional Random Field (CRF)-based name recognizer is developed. The CRF model is trained by using a large set of sentences obtained in a semi-automatic manner with minimal manual labeling effort. Lastly, a rule-based name normalization component maps a recognized name to its formal form. Evaluated on more than 4000 manually labeled sentences with about 1000 phone name mentions, Gren outperforms all baseline methods. Specifically, it achieves precision and recall of 0.918 and 0.875 respectively, with the best feature setting. We also provide detailed analysis of the intermediate results obtained by each of the three components in Gren.


title: Curing regular expressions matching algorithms from insomnia, amnesia, and acalculia

abstract: The importance of network security has grown tremendously and a collection of devices have been introduced, which can improve the security of a network. Network intrusion detection systems (NIDS) are among the most widely deployed such system; popular NIDS use a collection of signatures of known security threats and viruses, which are used to scan each packet's payload. Today, signatures are often specified as regular expressions; thus the core of the NIDS comprises of a regular expressions parser; such parsers are traditionally implemented as finite automata. Deterministic Finite Automata (DFA) are fast, therefore they are often desirable at high network link rates. DFA for the signatures, which are used in the current security devices, however require prohibitive amounts of memory, which limits their practical use. In this paper, we argue that the traditional DFA based NIDS has three main limitations: first they fail to exploit the fact that normal data streams rarely match any virus signature; second, DFAs are extremely inefficient in following multiple partially matching signatures and explodes in size, and third, finite automaton are incapable of efficiently keeping track of counts. We propose mechanisms to solve each of these drawbacks and demonstrate that our solutions can implement a NIDS much more securely and economically, and at the same time substantially improve the packet throughput.


title: Probability of Regaining Dexterity in the Flaccid Upper Limb: Impact of Severity of Paresis and Time Since Onset in Acute Stroke

abstract: Background and Purpose— To improve the accuracy of early postonset prediction of motor recovery in the flaccid hemiplegic arm, the effects of change in motor function over time on the accuracy of prediction were evaluated, and a prediction model for the probability of regaining dexterity at 6 months was developed. Methods— In 102 stroke patients, dexterity and paresis were measured with the Action Research Arm Test, Motricity Index, and Fugl-Meyer motor evaluation. For model development, 23 candidate determinants were selected. Logistic regression analysis was used for prognostic factors and model development. Results— At 6 months, some dexterity in the paretic arm was found in 38%, and complete functional recovery was seen in 11.6% of the patients. Total anterior circulation infarcts, right hemisphere strokes, homonymous hemianopia, visual gaze deficit, visual inattention, and paresis were statistically significant related to a poor arm function. Motricity Index leg scores of at least 25 points in the fi...


title: Point Cloud Attribute Compression Using 3-D Intra Prediction and Shape-Adaptive Transforms

abstract: With the increased proliferation of applications using 3-D capture technologies for applications such as virtual reality, mobile mapping, scanning of historical artifacts, and 3-D printing, representing these kinds of data as 3-Dpoint clouds has become a popular method for storing and conveying the data independently of how it was captured. A point cloud consists of a set of coordinates indicating the location of each point, along with one or more attributes such as color associated with each point. Because the size of point cloud data can be quite large, compression is needed to efficiently store or transmit this data. This paper, motivated by techniques currently being used for image and video coding, proposes methods using 3-D block-based prediction and transform coding to compress point cloud attributes. Experimental results using a modified shape-adaptive DCT tailored for use in 3-D point clouds and a benchmark using 3-D graph transforms are shown.