title: Charging architecture for M2M communications

abstract: Machine to Machine (M2M) communications or the Internet of Thing (IoT) is emerging as a new form of communications in our society. With such proliferation of the IoT/M2M, we have started to experience various smart services such as smart home, connected car, e-health, smart grid, etc. These IoT/M2M services are set to improve our life significantly. However, in IoT/M2M many things only exchange a small amount of data while generate a large amount of requests such as network connectivity requests, notification requests and storage requests. The traditional charging models such as those based on amount of data transfer are no longer adequate for M2M communications. As new charging factors and charging models are identified for M2M communications, a new charging architecture needs to be designed for M2M communications. This paper proposes such an M2M charging architecture that is designed to collect charging information for M2M communications based on the notions of charging factors and charging models.


title: Differentiable Programs with Neural Libraries

abstract: We develop a framework for combining differentiable programming languages with neural networks. Using this framework we create end-to-end trainable systems that learn to write interpretable algorithms with perceptual components. We explore the benefits of inductive biases for strong generalization and modularity that come from the program-like structure of our models. In particular, modularity allows us to learn a library of (neural) functions which grows and improves as more tasks are solved. Empirically, we show that this leads to lifelong learning systems that transfer knowledge to new tasks more effectively than baselines.


title: Biological tissues imaging with time domain topological energy

abstract: Abstract Time Domain Topological Energy (TDTE) is a new imaging method that comes from the field of shape optimisation under constraints. The adjoint problem in time domain shows a link with the refocusing properties of time reversal, giving thus an efficient solution of the inverse problem of acoustical imaging. It has been first employed for Non Destructive Testing (NDT) where the performances have proved to be higher than classical tools. This method has given good results for the detection of high acoustic impedance defects and has been successfully adapted to the imaging of low impedance variations such as biological tissues.


title: Differential resistance to paralytic poliomyelitis controlled by histocompatibility leukocyte antigens.

abstract: To investigate whether genetic factors encoded by the histocompatibility leukocyte antigen (HLA) complex influence resistance to poliomyelitis, the pattern of inheritance of HLA haplotypes within affected families was studied. Seventeen informative families were available from a genetically homogeneous, unvaccinated community in The Netherlands after a single epidemic of poliomyelitis. Whereas none of 17 other polymorphic markers deviated from the expected Mendelian pattern of inheritance, the inheritance pattern of HLA haplotypes did. In sibpairs consisting of a patient with paralytic poliomyelitis and a nonparalytic control sib, significantly less sharing of HLA haplotypes was observed than would be expected by chance alone (P = 0.014). The sharing of HLA haplotypes between patients with nonparalytic poliomyelitis and healthy controls did not deviate significantly from the expected value. These data suggest that HLA-encoded genetic factors control resistance to the paralytic form of poliomyelitis.


title: Development of a 4-joint 3-DOF robotic arm with anti-reaction force mechanism for a multicopter

abstract: In this paper, we propose a design method for the mechanism of a robotic arm suitable for a multicopter. The motion of an arm attached to a multicopter can possibly disturb the multicopter attitude. Our aim is to suppress this attitude disturbance by mechanical methods. In the proposed design method, the arm has the following features for suppressing the disturbance of the multicopter attitude. 1. The robotic arm can adjust its center of gravity (COG). 2. In the simplified model, the sum of the angular momentum of the rotating parts constituting the robot arm is equal to zero. These features can compensate for both the displacement of the COG of the arm and the counter torque generated from rotating parts, which cause the disturbance to the multicopter attitude. Since the disturbance of the multicopter attitude can be suppressed mechanically, the robotic arm does not require a special flight control law. In other words, this robotic arm has the advantage of being attached to a multicopter using a common, off-the-shelf, multipurpose flight controller. Furthermore, we discuss the mass distribution, power transmission method, and the moment of inertia of rotating parts, such as the speed reducer, motor, and arm. Additionally, we fabricate a prototype robotic arm with four joints and three degrees of freedom (DOFs), based on the proposed design method. Finally, the experiments showed that the robotic arm suppressed the disturbance of the multicopter attitude caused by the arm motion.


A Hybrid Gaussian-HMM-Deep Learning Approach for Automatic Chord Estimation with Very Large Vocabulary


Intrinsic PUFs from Flip-flops on Reconfigurable Devices


title: Is mobile payment still relevant in the fintech era?

abstract: Abstract Mobile phones have provided such an enormous opportunity for financial development, and are anticipated to become a common tool for carrying out various financial transactions. Hundreds of mobile payment services have been introduced across the globe. Strikingly, many of these efforts have failed. This study analyzes whether mobile payment is still relevant in the fintech era. I compare three mobile payment projects – Oi Paggo in Brazil, TCASH in Indonesia, and M-PESA in Kenya – as case studies. I argue that mobile payment systems currently operate in such complex and multidimensional networks with shared common infrastructures and compete for producing and providing value to the customers on the basis of these infrastructures. Three attributes of industry and network structure are immediately of interest: (1) cutting the vertical supply chain more finely may encourage large players to extend their domination, (2) the basic platform continues to be governed by multinational operators (MNOs) – telecom firms, and (3) openness may be decreasing or increasing even as the number of independent parties in the supply chain drops.


title: Broadband Circularly Polarized Slot Antenna Array Using Sequentially Rotated Technique for $C$-Band Applications

abstract: This letter presents the investigation results on a novel circularly polarized square-slot antenna (CPSSA) array designed to operate at a frequency of 5.5 GHz. In order to realize the proposed antenna array, four miniature circular polarized square-slot antennas are used with L-shape grounded strips located at the slots' opposite corners to reduce cross polarization. The antenna is fed by symmetric coplanar waveguide. The CPSSA element achieves a bandwidth of 16.6% for an axial ratio $\leq 3~$ dB. The CPSSA's performance is further enhanced with the construction of a novel 2 $\,\times\,$ 2 antenna array that is designed using sequentially rotated feed technique. The 3-dB axial ratio of the array extends to approximately 2 GHz with an impedance bandwidth of 31%. The CPSSA array was designed to operate over the frequency range between 4 and 6.825 GHz corresponding to an impedance bandwidth of 52% for ${\rm VSWR} (1.71:1). Acceptable agreement between the simulation and measured results validates the proposed design.


Exposing Congestion Attack on Emerging Connected Vehicle based Traffic Signal Control


title: Deep Voice: Real-time Neural Text-to-Speech

abstract: We present Deep Voice, a production-quality text-to-speech system constructed entirely from deep neural networks. Deep Voice lays the groundwork for truly end-to-end neural speech synthesis. The system comprises five major building blocks: a segmentation model for locating phoneme boundaries, a grapheme-to-phoneme conversion model, a phoneme duration prediction model, a fundamental frequency prediction model, and an audio synthesis model. For the segmentation model, we propose a novel way of performing phoneme boundary detection with deep neural networks using connectionist temporal classification (CTC) loss. For the audio synthesis model, we implement a variant of WaveNet that requires fewer parameters and trains faster than the original. By using a neural network for each component, our system is simpler and more flexible than traditional text-to-speech systems, where each component requires laborious feature engineering and extensive domain expertise. Finally, we show that inference with our system can be performed faster than real time and describe optimized WaveNet inference kernels on both CPU and GPU that achieve up to 400x speedups over existing implementations.


title: Inferring Perceived Demographics from User Emotional Tone and User-Environment Emotional Contrast

abstract: We examine communications in a social network to study user emotional contrast – the propensity of users to express different emotions than those expressed by their neighbors. Our analysis is based on a large Twitter dataset, consisting of the tweets of 123,513 users from the USA and Canada. Focusing on Ekman’s basic emotions, we analyze differences between the emotional tone expressed by these users and their neighbors of different types, and correlate these differences with perceived user demographics. We demonstrate that many perceived demographic traits correlate with the emotional contrast between users and their neighbors. Unlike other approaches on inferring user attributes that rely solely on user communications, we explore the network structure and show that it is possible to accurately predict a range of perceived demographic traits based solely on the emotions emanating from users and their neighbors.


title: A content analysis of multinationals’ Web communication strategies: cross‐cultural research framework and pre‐testing

abstract: Despite the growing trend toward the interactive medium, there would appear to be a lack of comprehensive research methodology for evaluating the degree of standardisation in multinational corporations’ (MNCs’) online communication strategies across differing cultures. The objective of this exploratory study is to construct a research framework for cross‐cultural comparison of corporate Web pages, applying traditional advertising content study techniques. A series of pre‐tests were conducted to examine three explanatory variables, i.e. information content, cultural values and creative strategies on Japanese MNCs’ product‐based home pages in Japan, Spain and the USA. The results revealed that Japanese firms tended to localise their online communication strategies in their target markets. In closing, future research directions are discussed and content analysis research designs are summarised.


title: Development of a Texture Sensor Emulating the Tissue Structure and Perceptual Mechanism of Human Fingers

abstract: This paper discusses a novel approach in developing a texture sensor emulating the major features of a human finger. The aim of this study is to realize precise and quantitative texture sensing. Three physical properties, roughness, softness, and friction are known to constitute texture perception of humans. The sensor is designed to measure the three specific types of information by adopting the mechanism of human texture perception. First, four features of the human finger that were focused on in designing the novel sensor are introduced. Each feature is considered to play an important role in texture perception; the existence of nails and bone, the multiple layered structure of soft tissue, the distribution of mechanoreceptors, and the deployment of epidermal ridges. Next, detailed design of the texture sensor based on the design concept is explained, followed by evaluating experiments and analysis of the results. Finally, we conducted texture perceptive experiments of actual material using the developed sensor, thus achieving the information expected. Results show the potential of our approach.


title: A service oriented reflective wireless middleware

abstract: The role of middleware has become increasingly important in mobile computing, where the integration of different applications and services from different wired and wireless businesses and service providers exist. The requirements and functionalities of the wireless middleware can be achieved by Service Oriented Computing which can be an ideal paradigm for mobile services. Reflective middleware responses are optimized to changing environments and requirements. In this paper a Service Oriented Reflective Wireless Middleware (SORWiM) is proposed. It provides basic (Event, Messaging, Location, and Redirection) and composite services for efficient and reliable information discovery and dissemination in ad hoc mobile environments. One of the primary goals of this research is to investigate how the construction of mobile services can benefit from the Service-Oriented paradigm.


title: The costs and benefits of replication studies

abstract: The debate about whether replication studies should become mainstream is essentially driven by disagreements about their costs and benefits and the best ways to allocate limited resources. Determining when replications are worthwhile requires quantifying their expected utility. We argue that a formalized framework for such evaluations can be useful for both individual decision-making and collective discussions about replication.


title: Sequential Dual Deep Learning with Shape and Texture Features for Sketch Recognition

abstract: Recognizing freehand sketches with high arbitrariness is greatly challenging. Most existing methods either ignore the geometric characteristics or treat sketches as handwritten characters with fixed structural ordering. Consequently, they can hardly yield high recognition performance even though sophisticated learning techniques are employed. In this paper, we propose a sequential deep learning strategy that combines both shape and texture features. A coded shape descriptor is exploited to characterize the geometry of sketch strokes with high flexibility, while the outputs of constitutional neural networks (CNN) are taken as the abstract texture feature. We develop dual deep networks with memorable gated recurrent units (GRUs), and sequentially feed these two types of features into the dual networks, respectively. These dual networks enable the feature fusion by another gated recurrent unit (GRU), and thus accurately recognize sketches invariant to stroke ordering. The experiments on the TU-Berlin data set show that our method outperforms the average of human and state-of-the-art algorithms even when significant shape and appearance variations occur.


Provable Low-Rank Tensor Recovery


title: Mining positive and negative patterns for relevance feature discovery

abstract: It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of the large number of terms, patterns, and noise. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern-based methods should perform better than term-based ones in describing user preferences, but many experiments do not support this hypothesis. The innovative technique presented in paper makes a breakthrough for this difficulty. This technique discovers both positive and negative patterns in text documents as higher level features in order to accurately weight low-level features (terms) based on their specificity and their distributions in the higher level features. Substantial experiments using this technique on Reuters Corpus Volume 1 and TREC topics show that the proposed approach significantly outperforms both the state-of-the-art term-based methods underpinned by Okapi BM25, Rocchio or Support Vector Machine and pattern based methods on precision, recall and F measures.


title: Effect of The Dry Aqueous Leaf Extract Of Cnidoscolus Aconitifolius On Blood Alcohol Clearance In Rabbits

abstract: Herbal therapy or plant based drugs might be useful in the management of alcohol-related complications, hence the influence of various substances on alcohol “affects” are being investigated in a search for a substance which could scavenge radicals produced during ethanol metabolism and accelerate ethanol clearance to reduce blood ethanol levels. Oral administration of Cnidoscolus aconitifolius leaf extract increased blood ethanol clearance rate in a dose dependent manner. The ingestion of 0.55 g ethanol/kg body weight produced a peak blood alcohol level (BAL) of 0.121% and 0.156%. The administration of 0.5 g/kg of the extract produced a peak blood alcohol level ( P BAL) of 0.112% as against 0.121% by ethanol alone. Also, the administration of 1 g/kg of the extract produced a peak blood alcohol level ( P BAL) of 0.127% as against 0.156% by ethanol only. The 0.5 g/kg and 1 g/kg of the extract also reduced the intoxication time (i.e. time to zero BAL) from146min to 137min and 160min to 128min respectively. Results from this study revealed that Cnidoscolus aconitifolius leaf extract might have amethystic property as locally claimed but further investigations is required to show the mechanism on how the extract accelerates ethanol clearance. Keyword: Cnidoscolus aconitifolius , alcohol, and Blood alcohol level (BAL)


Linguistic diversity in mainland Southeast Asia


title: Convolutional Neural Network implementation for image-based Salak sortation

abstract: Salak is one of potential export commodities from Indonesia. However, the main obstacle to perform Salak export is transportation which requires rigorous selection of the fruit. In the current packaging process, this sortation is done manually. In this study, convolution neural network (CNN) is applied to automatically distinguish quality of Salak. Input used in this study is the image of salak. The process involved in this study is data collection, preprocessing, classification and testing. Preprocessing is done by cutting a region of interest (ROI) containing only salak image. Classification is done by CNN, for which to get the best accuracy of the model, existing parameters should be tested and evaluated. Testing is done for two types of model, 2-class models and 4-class models. The experiments result showed that the best accuracy obtained for 2-class model is 81.45% by using learning rate of 0.0001, a single layer convolution with fifteen filters and 100 neurons in the hidden layer. The filters' size is 3×3×3. While 4-class model obtained best accuracy of 70.71% with two convolutional layers. The numbers of filter in each layer are 6 filters with the size of 3×5×5 in the first layer and 18 filters with the size of 6×3×3 in the second layer.


title: Opinion mining and sentiment analysis on a Twitter data stream

abstract: Opinion mining and sentiment analysis is a fast growing topic with various world applications, from polls to advertisement placement. Traditionally individuals gather feedback from their friends or relatives beforepurchasing an item, but today the trend is to identify the opinions of a variety ofindividuals around the globe using microbloggingdata. This paper discusses an approach where a publicised stream of tweets from the Twitter microblogging site are preprocessed and classified based on their emotional content as positive, negative and irrelevant; and analyses the performance of various classifying algorithms based on their precision and recall in such cases. Further, the paper exemplifies the applications of this research and its limitations.


title: Identifying Functions of Citations with CiTalO

abstract: Bibliographic citation is one of the most important activities of an author in the production of any scientific work. The reasons that an author cites other publications are varied: to gain assistance of some sort, to review, critique or refute previous works, etc. In this paper we propose a tool, called CiTalO, to infer automatically the nature of citations by means of Semantic Web technologies and NLP techniques. Such a characterisation makes citations more effective for linking, disseminating, exploring and evaluating research.


title: Day-ahead optimal charging/discharging scheduling for electric vehicles in microgrids

abstract: Microgrid as an important part of smart grid comprises distributed generators (DGs), adjustable loads, energy storage systems (ESSs) and control units. It can be operated either connected with the external system or islanded with the support of ESSs. While the daily output of DGs strongly depends on the temporal distribution of natural resources such as wind and solar, unregulated electric vehicle (EV) charging demand will deteriorate the unbalance between the daily load curve and generation curve. In this paper, a statistic model is presented to describe daily EV charging/discharging behaviors considering the randomness of the initial state of charge (SOC) of EV batteries. The optimization problem is proposed to obtain the economic operation for the microgrid based on this model. In day-ahead scheduling, with the estimated power generation and load demand, the optimal charging/discharging scheduling of EVs during 24 h is achieved by serial quadratic programming. With the optimal charging/discharging scheduling of EVs, the daily load curve can better track the generation curve. The network loss in grid-connected operation mode and required ESS capacity in islanded operation mode are both decreased.


title: Usability evaluation with the cognitive walkthrough

abstract: The cognitive walkthrough is a technique for evaluating the design of a user interface, with speciaJ attention to how well the interface supports "exploratory learning," i.e., first-time use without formal training. The evaluation can be performed by the system's designers in the e,arly stages of design, before empirical user testing is possible. Early versions of the walkthrough method relied on a detailed series of questions, to be answered on paper or electronic forms. This tutorial presents a simpler method, founded in an understanding of the cognitive theory that describes a user's interactions with a system. The tutorial refines the method on the basis of recent empirical and theoretical studies of exploratory learning with display-based interfaces. The strengths and limitations of the walkthrough method are considered, and it is placed into the context of a more complete design approach.


title: Fog computing and its role in the internet of things

abstract: Fog Computing extends the Cloud Computing paradigm to the edge of the network, thus enabling a new breed of applications and services. Defining characteristics of the Fog are: a) Low latency and location awareness; b) Wide-spread geographical distribution; c) Mobility; d) Very large number of nodes, e) Predominant role of wireless access, f) Strong presence of streaming and real time applications, g) Heterogeneity. In this paper we argue that the above characteristics make the Fog the appropriate platform for a number of critical Internet of Things (IoT) services and applications, namely, Connected Vehicle, Smart Grid, Smart Cities, and, in general, Wireless Sensors and Actuators Networks (WSANs).


title: The future is in the past: designing for exploratory search

abstract: Exploratory search activities tend to span multiple sessions and involve finding, analyzing and evaluating information found through many queries. Typical search systems, on the other hand, are designed to support single query, precision-oriented search tasks. We describe a search interface and system design of a multi-session exploratory search system, discuss design challenges encountered, and chronicle the evolution of our design. Our design describes novel displays for visualizing retrieval history information, and introduces ambient displays and persuasive elements to interactive information retrieval.


Compact local Gabor directional number pattern for facial expression recognition


title: A dual-band printed dipole antenna with integrated balun covering UMTS and WiMAX bands

abstract: In this paper, a uniplanar dual-band dipole antenna merged with an asymmetrical broadside-coupled balun that offers wideband impedance matching is presented. The balun has compact size and simple layout profile; on the other hand, its shape can be arbitrarily changed as long as the determined centerline length is maintained. By properly increasing the spacing between the long-arm and short-arm dipoles, the effect of the mutual coupling is improved such that dual-band frequency characteristics can be controlled and optimized, respectively. To validate the transition capability of the proposed balun, the balun has been employed to feed the dual-band antenna. Measurements show that the peak gains in the lower and upper bands are 3 dBi@ 2 GHz and 4 dBi@ 3.5 GHz, respectively, and the radiation patterns are almost omnidirectional at H-plane.


From TreeBank to PropBank


title: Dominator Tree Certification and Independent Spanning Trees

abstract: How does one verify that the output of a complicated program is correct? One can formally prove that the program is correct, but this may be beyond the power of existing methods. Alternatively one can check that the output produced for a particular input satisfies the desired input-output relation, by running a checker on the input-output pair. Then one only needs to prove the correctness of the checker. But for some problems even such a checker may be too complicated to formally verify. There is a third alternative: augment the original program to produce not only an output but also a correctness certificate, with the property that a very simple program (whose correctness is easy to prove) can use the certificate to verify that the input-output pair satisfies the desired input-output relation. We consider the following important instance of this general question: How does one verify that the dominator tree of a flow graph is correct? Existing fast algorithms for finding dominators are complicated, and even verifying the correctness of a dominator tree in the absence of additional information seems complicated. We define a correctness certificate for a dominator tree, show how to use it to easily verify the correctness of the tree, and show how to augment fast dominator-finding algorithms so that they produce a correctness certificate. We also relate the dominator certificate problem to the problem of finding independent spanning trees in a flow graph, and we develop algorithms to find such trees. All our algorithms run in linear time. Previous algorithms apply just to the special case of only trivial dominators, and they take at least quadratic time.


A Comparative Study of Stemming Algorithms Ms .


title: Social Cognition: Thinking Categorically about Others

abstract: In attempting to make sense of other people, perceivers regularly construct and use categorical representations to simplify and streamline the person perception process. Noting the importance of categorical thinking in everyday life, our emphasis in this chapter is on the cognitive dynamics of categorical social perception. In reviewing current research on this topic, three specific issues are addressed: (a) When are social categories activated by perceivers, (b) what are the typical consequences of category activation, and (c) can perceivers control the influence and expression of categorical thinking? Throughout the chapter, we consider how integrative models of cognitive functioning may inform our understanding of categorical social perception.


title: An Array of Half-Width Microstrip Leaky-Wave Antennas Radiating on Boresight

abstract: An array of four uniform half-width microstrip leaky-wave antennas (MLWAs) was designed and tested to obtain maximum radiation in the boresight direction. To achieve this, uniform MLWAs are placed at 90 $ ^\circ $ and fed by a single probe at the center. Four beams from four individual branches combine to form the resultant directive beam. The measured matched bandwidth of the array is 300 MHz (3.8–4.1 GHz). Its beam toward boresight occurs over a relatively wide 6.4% (3.8–4.05 GHz) band. The peak measured boresight gain of the array is 10.1 dBi, and its variation within the 250-MHz boresight radiation band is only 1.7 dB.


title: Measuring user confidence in smartphone security and privacy

abstract: In order to direct and build an effective, secure mobile ecosystem, we must first understand user attitudes toward security and privacy for smartphones and how they may differ from attitudes toward more traditional computing systems. What are users' comfort levels in performing different tasks? How do users select applications? What are their overall perceptions of the platform? This understanding will help inform the design of more secure smartphones that will enable users to safely and confidently benefit from the potential and convenience offered by mobile platforms. To gain insight into user perceptions of smartphone security and installation habits, we conduct a user study involving 60 smartphone users. First, we interview users about their willingness to perform certain tasks on their smartphones to test the hypothesis that people currently avoid using their phones due to privacy and security concerns. Second, we analyze why and how they select applications, which provides information about how users decide to trust applications. Based on our findings, we present recommendations and opportunities for services that will help users safely and confidently use mobile applications and platforms.


title: Folk music classification using hidden Markov models

abstract: Automatic music classification is essential for implementing efficient music information retrieval systems; meanwhile, it may shed light on the process of human’s music perception. This paper describes our work on the classification of folk music from different countries based on their monophonic melodies using hidden Markov models. Music corpora of Irish, German and Austrian folk music in various symbolic formats were used as the data set. Different representations and HMM structures were tested and compared. The classification performances achieved 75%, 77% and 66% for 2-way classifications and 63% for 3-way classification using 6-state left-right HMM with the interval representation in the experiment. This shows that the melodies of folk music do carry some statistical features to distinguish them. We expect that the result will improve if we use a more discriminable data set and the approach should be applicable to other music classification tasks and acoustic musical signals. Furthermore, the results suggest to us a new way to think about musical style similarity.


title: Task assignment in spatial crowdsourcing: challenges and approaches

abstract: Spatial crowdsourcing (a.k.a mobile crowdsourcing) is a new paradigm of data collection, which has been emerged in the last few years to enable workers to perform tasks in the physical world. The objective of spatial crowdsourcing is to outsource a set of location-specific tasks to a set of workers, in which the workers are required to physically be at the task locations to complete them, i.e., taking pictures or collecting air quality information at specified locations of interest. In this paper, we discuss the unique challenges of spatial crowdsourcing: task assignment, incentive mechanism, worker's location privacy and the absence of real-world datasets. Thereafter, we present our current approaches to those issues.


title: Momentum Control with Hierarchical Inverse Dynamics on a Torque-Controlled Humanoid

abstract: Hierarchical inverse dynamics based on cascades of quadratic programs have been proposed for the control of legged robots. They have important benefits but to the best of our knowledge have never been implemented on a torque controlled humanoid where model inaccuracies, sensor noise and real-time computation requirements can be problematic. Using a reformulation of existing algorithms, we propose a simplification of the problem that allows to achieve real-time control. Momentum-based control is integrated in the task hierarchy and a LQR design approach is used to compute the desired associated closed-loop behavior and improve performance. Extensive experiments on various balancing and tracking tasks show very robust performance in the face of unknown disturbances, even when the humanoid is standing on one foot. Our results demonstrate that hierarchical inverse dynamics together with momentum control can be efficiently used for feedback control under real robot conditions.


title: Using twitter to recommend real-time topical news

abstract: Recommending news stories to users, based on their preferences, has long been a favourite domain for recommender systems research. In this paper, we describe a novel approach to news recommendation that harnesses real-time micro-blogging activity, from a service such as Twitter, as the basis for promoting news stories from a user's favourite RSS feeds. A preliminary evaluation is carried out on an implementation of this technique that shows promising results.


title: Conceptual modeling for ETL processes

abstract: Extraction-Transformation-Loading (ETL) tools are pieces of software responsible for the extraction of data from several sources, their cleansing, customization and insertion into a data warehouse. In this paper, we focus on the problem of the definition of ETL activities and provide formal foundations for their conceptual representation. The proposed conceptual model is (a) customized for the tracing of inter-attribute relationships and the respective ETL activities in the early stages of a data warehouse project; (b) enriched with a 'palette' of a set of frequently used ETL activities, like the assignment of surrogate keys, the check for null values, etc; and (c) constructed in a customizable and extensible manner, so that the designer can enrich it with his own re-occurring patterns for ETL activities.


I-Vector / PLDA Variants for Text-Dependent Speaker Recognition


title: A 200Mbps random number generator with jitter-amplified oscillator

abstract: This paper presents a hardware efficient oscillator-based true random number generator with a very high throughput. A new jitter-amplified oscillator has been proposed to produce high frequency oscillated signal with greater jitter, and the distribution of jitter is relatively homogeneous. Theoretical discussion about the scheme of sampling model is presented, and the circuit detail is also given. The prototype has been designed in SMIC 0.13μm technology and has a 200Mbps throughput. Binary numbers generated from post-simulate platform has been verified with NIST 800-22 standard and passed the randomness tests.


title: Advances in active-feedback frequency compensation with power optimization and transient improvement

abstract: This paper presents a low-power stability strategy to significantly reduce the power consumption of a three-stage amplifier using active-feedback frequency compensation (AFFC). The bandwidth of the amplifier can also be enhanced. Simulation results verify that the power dissipation of the AFFC amplifier is reduced by 43% and the bandwidth is improved by 32.5% by using the proposed stability strategy. In addition, a dynamic feedforward stage (DFS), which can be embedded into the AFFC amplifier to improve the transient responses without consuming extra power, is proposed. Implemented in a 0.6-/spl mu/m CMOS process, experimental results show that both AFFC amplifiers with and without DFS achieve almost the same small-signal performances while the amplifier with DFS improves both the negative slew rate and negative 1% settling time by two times.


title: Hybrid Analogies in Conceptual Innovation in Science

abstract: Analogies are ubiquitous in science, both in theory and experiments. Based on an ethnographic study of a research lab in neural engineering, we focus on a case of conceptual innovation where the cross-breeding of two types of analogies led to a breakthrough. In vivo phenomena were recreated in two analogical forms: one, as an in vitro physical model, and the other, as a computational model of the first physical model. The computational model also embodied constraints drawn from the neuroscience and engineering literature. Cross connections and linkages were then made between these two analogical models, over time, to solve problems. We describe how the development of the intermediary, hybrid computational model led to a conceptual innovation, and subsequent engineering innovations. Using this case study, we highlight some of the peculiar features of such hybrid analogies that are now used widely in the sciences and engineering sciences, and the significant questions they raise for current theories of analogy.


title: Deep unfolding for multichannel source separation

abstract: Deep unfolding has recently been proposed to derive novel deep network architectures from model-based approaches. In this paper, we consider its application to multichannel source separation. We unfold a multichannel Gaussian mixture model (MCGMM), resulting in a deep MCGMM computational network that directly processes complex-valued frequency-domain multichannel audio and has an architecture defined explicitly by a generative model, thus combining the advantages of deep networks and model-based approaches. We further extend the deep MCGMM by modeling the GMM states using an MRF, whose unfolded mean-field inference updates add dynamics across layers. Experiments on source separation for multichannel mixtures of two simultaneous speakers shows that the deep MCGMM leads to improved performance with respect to the original MCGMM model.


title: Architectural-space exploration of approximate multipliers

abstract: This paper presents an architectural-space exploration methodology for designing approximate multipliers. Unlike state-of-the-art, our methodology generates various design points by adapting three key parameters: (1) different types of elementary approximate multiply modules, (2) different types of elementary adder modules for summing the partial products, and (3) selection of bits for approximation in a wide-bit multiplier design. Generation and exploration of such a design space enables a wide-range of multipliers with varying approximation levels, each exhibiting distinct area, power, and output quality, and thereby facilitates approximate computing at higher abstraction levels. We synthesized our designs using Synopsys Design Compiler with a TSMC 45nm technology library and verified using ModelSim gate-level simulations. Power and quality evaluations for various designs are performed using PrimeTime and behavioral models, respectively. The selected designs are then deployed in a JPEG application. For reproducibility and to facilitate further research and development at higher abstraction layers, we have released the RTL and behavioral models of these approximate multipliers and adders as an open-source library at https://sourceforge.net/projects/lpaclib/.


Human Action Recognition by Semi-Latent Topic Models


Categorical Equational Systems: Algebraic Models and Equational Reasoning


title: Semantic relation computing theory and its application

abstract: Semantic relations among objects are primary semantic factors, which play the most important role for human and smart systems /machines to understand and control the situation in the context of connected systems. However, few existing works focus on the study of semantic relations from the mathematical view, though it would be the basis for further research on semantics. Existing research works focus on the representation of semantic relations and reasoning with relations, but seldom concentrate on the semantic relation computing including accurate reasoning, integrity checking and redundancy checking. In this paper, we aim at exploring algebraic computing approach of semantic relations. A mathematical computing theory for establishing semantic relations - semantic relation space theory - is proposed for the first time, including conceptions of semantic relation basis, orthogonal basis, and some basic operations for semantic relations. The proposed theory can be used to represent the semantic relations among objects in an accurate way and to deduce implicit relations in the connected smart systems, especially for automatic reasoning and autonomous computing. Furthermore, integrity and consistency issues among semantic relations are also discussed based on the theory. A case study in scientific research domain has shown the feasibility and effectiveness of the proposed theory.


title: Classification and regression trees

abstract: Classification and regression trees are machine-learning methods for constructing prediction models from data. The models are obtained by recursively partitioning the data space and fitting a simple prediction model within each partition. As a result, the partitioning can be represented graphically as a decision tree. Classification trees are designed for dependent variables that take a finite number of unordered values, with prediction error measured in terms of misclassification cost. Regression trees are for dependent variables that take continuous or ordered discrete values, with prediction error typically measured by the squared difference between the observed and predicted values. This article gives an introduction to the subject by reviewing some widely available algorithms and comparing their capabilities, strengths, and weakness in two examples. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 14-23 DOI: 10.1002/widm.8 ::: ::: This article is categorized under: ::: ::: Technologies > Classification ::: Technologies > Machine Learning ::: Technologies > Prediction ::: Technologies > Statistical Fundamentals


title: Flip-Invariant SIFT for Copy and Object Detection

abstract: Scale-invariant feature transform (SIFT) feature has been widely accepted as an effective local keypoint descriptor for its invariance to rotation, scale, and lighting changes in images. However, it is also well known that SIFT, which is derived from directionally sensitive gradient fields, is not flip invariant. In real-world applications, flip or flip-like transformations are commonly observed in images due to artificial flipping, opposite capturing viewpoint, or symmetric patterns of objects. This paper proposes a new descriptor, named flip-invariant SIFT (or F-SIFT), that preserves the original properties of SIFT while being tolerant to flips. F-SIFT starts by estimating the dominant curl of a local patch and then geometrically normalizes the patch by flipping before the computation of SIFT. We demonstrate the power of F-SIFT on three tasks: large-scale video copy detection, object recognition, and detection. In copy detection, a framework, which smartly indices the flip properties of F-SIFT for rapid filtering and weak geometric checking, is proposed. F-SIFT not only significantly improves the detection accuracy of SIFT, but also leads to a more than 50% savings in computational cost. In object recognition, we demonstrate the superiority of F-SIFT in dealing with flip transformation by comparing it to seven other descriptors. In object detection, we further show the ability of F-SIFT in describing symmetric objects. Consistent improvement across different kinds of keypoint detectors is observed for F-SIFT over the original SIFT.


title: Vision-based detection and tracking of aerial targets for UAV collision avoidance

abstract: Machine vision represents a particularly attractive solution for sensing and detecting potential collision-course targets due to the relatively low cost, size, weight, and power requirements of the sensors involved (as opposed to radar). This paper describes the development and evaluation of a vision-based collision detection algorithm suitable for fixed-wing aerial robotics. The system was evaluated using highly realistic vision data of the moments leading up to a collision. Based on the collected data, our detection approaches were able to detect targets at distances ranging from 400m to about 900m. These distances (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning of between 8–10 seconds ahead of impact, which approaches the 12.5 second response time recommended for human pilots. We make use of the enormous potential of graphic processing units to achieve processing rates of 30Hz (for images of size 1024-by-768). Currently, integration in the final platform is under way.


title: Distributed Storage Allocation for High Reliability

abstract: We consider the problem of optimally allocating a given total storage budget in a distributed storage system. A source has a data object which it can code and store over a set of storage nodes; it is allowed to store any amount of data in each storage node, subject to a given total storage budget constraint. A data collector subsequently attempts to recover the original data object by accessing a random fixed-size subset of these storage nodes. Successful recovery of the data object occurs when the total amount of coded data in this subset of storage nodes is at least the size of the original data object. The goal is to determine the amount of data to store in each storage node so that the probability of successful recovery is maximized. We solve this problem in the high recovery probability regime. Our results can be applied to a variety of distributed storage systems, including delay tolerant networks (DTNs), content delivery networks (CDNs), and sensor networks.


title: New horizons in cardiac CT.

abstract: Until recently, cardiovascular computed tomography angiography (CCTA) was associated with considerable radiation doses. The introduction of tube current modulation and automatic tube potential selection as well as high-pitch prospective ECG-triggering and iterative reconstruction offer the ability to decrease dose with approximately one order of magnitude, often to sub-millisievert dose levels. In parallel, advancements in computational technology have enabled the measurement of fractional flow reserve (FFR) from CCTA data (FFRCT). This technique shows potential to replace invasively measured FFR to select patients in need of coronary intervention. Furthermore, developments in scanner hardware have led to the introduction of dual-energy and photon-counting CT, which offer the possibility of material decomposition imaging. Dual-energy CT reduces beam hardening, which enables CCTA in patients with a high calcium burden and more robust myocardial CT perfusion imaging. Future-generation CT systems will be capable of counting individual X-ray photons. Photon-counting CT is promising and may result in a substantial further radiation dose reduction, vastly increased spatial resolution, and the introduction of a whole new class of contrast agents.


title: Overcoming partial shading issue of PV modules by using a resonant switched capacitor converter

abstract: Partial shading effect is one of the most serious challenges in photovoltaic (PV) structures. In this paper a new switched-capacitor convertor has been proposed to reduce the partial shading problems. The proposed converter is a combination of dual-phase and half bridge converters that reduce the partial shading effect base on Differential Power Processing (DPP) technique, the proposed converter can increase the output voltage, as well. Also, soft switching is realized by the converter which leads to high efficiency value. The resonant power converter principle operation mode has been analyzed, mathematically. To verify the given analyses two 60 W prototype converters have been implemented.


Association Rule Mining : A Survey Gurneet


title: Social trust: impacts on social influential diffusion

abstract: Purpose ::: ::: ::: ::: ::: This paper aims to present social trust as a variable of influence by demonstrating the possibilities of trusted social nodes to improve influential capability and rate of successfully influenced social nodes within a social networking environment. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: This research will be conducted using simulated experiments. The base algorithm in research uses genetics algorithm diffusion model (GADM) where it carries out social influence calculations within a social networking environment. The GADM algorithm will be enhanced by integrating trust values into its influential calculations. The experiment simulates a virtual social network based on a social networking site architecture from the data set used to conduct experiments on the enhanced GADM and observe their influence capabilities. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The presence of social trust can effectively increase the rate of successfully influenced social nodes by factorizing trust value of one source node and acceptance rate of another recipient node into its probabilistic equation, hence increasing the final acceptance probability. ::: ::: ::: ::: ::: Research limitations/implications ::: ::: ::: ::: ::: This research focused exclusively on conceptual mathematical models and technical aspects so far; comprehensive user study, extensive performance and scalability testing is left for future work. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: Two key contributions of this paper are the calculation of social trust via content integrity and the application of social trust in social influential diffusion algorithms. Two models will be designed, implemented and evaluated on the application of social trust via trusted social nodes and domain-specified (of specific interest groups) trusted social nodes.


title: An improved analytical IGBT model for loss calculation including junction temperature and stray inductance

abstract: An improved analytical model suitable for IGBT modules is proposed in this paper to calculate the power losses with high accuracy and short calculation time. In this model, the parameters varying with junction temperature of the modules, such as di/dt in the turn-on period and dv/dt in the turn-off period, are discussed and derived according to several equivalent models. In addition, the parasitic inductance in the circuit including the emitter and collector inductance in the power circuits and the gate inductance in the driving loop are considered in this model. Based on this proposed model, the simulation switching waveforms of collector currents and collector-emitter voltages are provided to verify the model. Meanwhile, the calculated power losses are confirmed to be precise by comparing with measurement results.


title: SafeBand: A Wearable Device for the Safety of Women in Bangladesh

abstract: Even in the twenty-first century, while people are debating over various reasons of assault, women from all walks of life are continuously being harassed and violated in the streets, public transportation and public spaces. The safety of women has become a progressively emphasized concern in Bangladesh also. Though a number of initiatives have been introduced by the government to keep the women safe, there is no or little Information and Communication Technology (ICT) based solution exist to ensure the woman's safety in the context of Bangladesh. Therefore, the objective of this paper is to design and develop a safety device called 'SafeBand' for women to fight against physical harassment. The 'SafeBand' system is comprised of a wearable band to be used by the victim, and two mobile applications to be used by the victim and by the police. Women can wear the device as a wristband or locket which will comprise of a button and a light. When the user (victim) presses the button, it identifies the location of the user through Global Positioning System (GPS) and sends a message incorporating the location to the nearest police station and previously saved contacts (number of relatives). An empirical study was carried out to evaluate the 'SafeBand' system, which was replicated with 15 participants. The study results show that the developed system is effective, efficient, useful and acceptable to all users.


title: Dual Monitors Support Group Awareness in Multiplayer Computer Games 1 + Centre for Internet Computing and E-Commerce (CICEC)

abstract: This paper reports our exploratory study that investigates the potential of dual monitors in facilitating multiplayer computer games (MCG's). We conducted a laboratory experiment of MCG's played with single and with dual monitors. Dual monitors were used to display awareness information about team players. Participants showed overwhelming interest in playing games with dual monitors. Overall, they found dual ::: monitors useful in supporting their games as dual monitors facilitate team communication and stimulate informal communication.


title: Network Theory of Organization: A Multilevel Approach

abstract: Management research regularly considers social networks and their effects on a wide range of organizational phenomena. Scholars employing the social network perspective have generated a considerable body of organizational research, with much of this scholarship single-level in its focus: exploring how networks of individuals, groups, or firms relate to organizational outcomes at the same level of analysis. However, given that organizations are multilevel systems, a network theory of the organization should, by definition, be multilevel in its scope, considering how networks at one level of the organizational system influence networks at higher and/or lower levels. In this article, the authors overlay canonical multilevel theory on the social network perspective to derive postulates defining the broad theoretical domain of a multilevel network theory of organization. The link between these two theoretical perspectives is the graph theoretical notion of systems of nested networks, allowing the authors to ex...


title: A dithering algorithm for local composition control with three-dimensional printing

abstract: A dithering algorithm is presented for application to local composition control (LCC) with three-dimensional printing (3D printing) to convert continuous-tone representation of objects with LCC into discrete (pointwise) version of machine instructions. The algorithm presented effectively reduces undesirable low frequency textures of composition for individual 3D layers and also for 3D volumes. Peculiarities of the 3D printing machine, including anisotropic geometry of its picture elements (PELs) and uncertainties in droplet placement, are addressed by adapting a standard digital halftoning algorithm. Without loss of generality, our algorithm also accounts for technical limitations in the printing device, only generating lattices that can be represented within the finite memory limits of the hardware.


title: Design and implementation of high efficiency vedic binary multiplier circuit based on squaring circuits

abstract: Electronics, and in particular the integrated circuits has been made possible the design of powerful and flexible processors. Having this vision in mind, a dedicated architecture is proposed in this paper which is exclusively used for multiplication of two numbers based on the Vedic sutras. The most significant operation in any signal processing and scientific applications is multiplication. The use of squaring circuits in place of general multipliers can reduce the number of inputs and thereby significantly will reduce the area consumed. To accomplish this, we have implemented Nikhilam Sutra, which is one of the sixteen sutras in Vedic Mathematics. This is dedicated for computing the square of the number. This technique is further extended for finding the product of the binary numbers. The performance for the proposed design is compared with the existing multipliers, on the basis of delay and area utilization. The results prove that the architecture prosed using Nikhilam sutra improves the efficiency considerably. The design has been implemented using Verilog HDL for 8 bit numbers and the synthesis is done using Xilinx ISE 14.5 software.


title: Optimal Trajectory and Schedule Planning for Autonomous Guided Vehicles in Flexible Manufacturing System

abstract: This paper proposed an algorithm to solve major challenges in the domain of autonomous transportation systems. These challenges are trajectory planning and collision avoidance. Generally, in a shared infrastructure where several agents aim to use limited capacity resources, finding a set of optimal and conflict-free paths for each single agent is the most critical part. In such a dynamic environment where continual planning and scheduling are required, we have adopted a coupled (centralized) technique and face the problem by breaking it into two distinct phases: path planning and collision avoidance. In the first phase, the lowest cost path is planned for each agent by a modified version of Dijkstra algorithm. Following that, in the second phase, the head-on collisions can be foreseen and avoided by detecting the agents' common segments of the path choices. Finally, based on the capacity of each resource and the feasibility of each path, the task assignment and scheduling algorithm allocates different tasks between agents.


title: Remote Sensing Scene Classification by Unsupervised Representation Learning

abstract: With the rapid development of the satellite sensor technology, high spatial resolution remote sensing (HSR) data have attracted extensive attention in military and civilian applications. In order to make full use of these data, remote sensing scene classification becomes an important and necessary precedent task. In this paper, an unsupervised representation learning method is proposed to investigate deconvolution networks for remote sensing scene classification. First, a shallow weighted deconvolution network is utilized to learn a set of feature maps and filters for each image by minimizing the reconstruction error between the input image and the convolution result. The learned feature maps can capture the abundant edge and texture information of high spatial resolution images, which is definitely important for remote sensing images. After that, the spatial pyramid model (SPM) is used to aggregate features at different scales to maintain the spatial layout of HSR image scene. A discriminative representation for HSR image is obtained by combining the proposed weighted deconvolution model and SPM. Finally, the representation vector is input into a support vector machine to finish classification. We apply our method on two challenging HSR image data sets: the UCMerced data set with 21 scene categories and the Sydney data set with seven land-use categories. All the experimental results achieved by the proposed method outperform most state of the arts, which demonstrates the effectiveness of the proposed method.


title: Reversed graph embedding resolves complex single-cell trajectories

abstract: Single-cell trajectories can unveil how gene regulation governs cell fate decisions. However, learning the structure of complex trajectories with multiple branches remains a challenging computational problem. We present Monocle 2, an algorithm that uses reversed graph embedding to describe multiple fate decisions in a fully unsupervised manner. We applied Monocle 2 to two studies of blood development and found that mutations in the genes encoding key lineage transcription factors divert cells to alternative fates.


title: Knowledge discovery through directed probabilistic topic models: a survey

abstract: Graphical models have become the basic framework for topic based probabilistic modeling. Especially models with latent variables have proved to be effective in capturing hidden structures in the data. In this paper, we survey an important subclass Directed Probabilistic Topic Models (DPTMs) with soft clustering abilities and their applications for knowledge discovery in text corpora. From an unsupervised learning perspective, “topics are semantically related probabilistic clusters of words in text corpora; and the process for finding these topics is called topic modeling”. In topic modeling, a document consists of different hidden topics and the topic probabilities provide an explicit representation of a document to smooth data from the semantic level. It has been an active area of research during the last decade. Many models have been proposed for handling the problems of modeling text corpora with different characteristics, for applications such as document classification, hidden association finding, expert finding, community discovery and temporal trend analysis. We give basic concepts, advantages and disadvantages in a chronological order, existing models classification into different categories, their parameter estimation and inference making algorithms with models performance evaluation measures. We also discuss their applications, open challenges and future directions in this dynamic area of research.


title: Global Pose Estimation with an Attention-Based Recurrent Network

abstract: The ability for an agent to localize itself within an environment is crucial for many real-world applications. For unknown environments, Simultaneous Localization and Mapping (SLAM) enables incremental and concurrent building of and localizing within a map. We present a new, differentiable architecture, Neural Graph Optimizer, progressing towards a complete neural network solution for SLAM by designing a system composed of a local pose estimation model, a novel pose selection module, and a novel graph optimization process. The entire architecture is trained in an end-to-end fashion, enabling the network to automatically learn domain-specific features relevant to the visual odometry and avoid the involved process of feature engineering. We demonstrate the effectiveness of our system on a simulated 2D maze and the 3D ViZ-Doom environment.


title: DeepBreath: Deep Learning of Breathing Patterns for Automatic Stress Recognition using Low-Cost Thermal Imaging in Unconstrained Settings

abstract: We propose DeepBreath, a deep learning model which automatically recognises people's psychological stress level (mental overload) from their breathing patterns. Using a low cost thermal camera, we track a person's breathing patterns as temperature changes around his/her nostril. The paper's technical contribution is threefold. First of all, instead of creating handcrafted features to capture aspects of the breathing patterns, we transform the uni-dimensional breathing signals into two dimensional respiration variability spectrogram (RVS) sequences. The spectrograms easily capture the complexity of the breathing dynamics. Second, a spatial pattern analysis based on a deep Convolutional Neural Network (CNN) is directly applied to the spectrogram sequences without the need of hand-crafting features. Finally, a data augmentation technique, inspired from solutions for over-fitting problems in deep learning, is applied to allow the CNN to learn with a small-scale dataset from short-term measurements (e.g., up to a few hours). The model is trained and tested with data collected from people exposed to two types of cognitive tasks (Stroop Colour Word Test, Mental Computation test) with sessions of different difficulty levels. Using normalised self-report as ground truth, the CNN reaches 84.59% accuracy in discriminating between two levels of stress and 56.52% in discriminating between three levels. In addition, the CNN outperformed powerful shallow learning methods based on a single layer neural network. Finally, the dataset of labelled thermal images will be open to the community.


title: Impacts of IoT and big data to automotive industry

abstract: With the recent advancement in technologies such as embedded system, wireless distributed sensor, light weight material, smart cognitive radio networks, cloud computing, higher efficiency and ultra-low emission internal combustion engines, intelligent converter, high performance battery and fuel cell technology, the production of smarter, safer, energy efficient and zero emission vehicles is possible in near future. Apart from vehicle technologies, other factors such as road users', well maintained road infrastructure, well maintained vehicles, drivers' attitudes and law and enforcement are also important to be considered and they should work together in order to make our world natural resources can be preserved and maintain cleaner environment and produce sustainable mobility. This paper will discuss the impacts of IoT and Big Data and other emerging technologies mentioned above to the automotive industry. It will include discussion on education, economy, advanced technology, environment, safety and energy.


title: An Energy Efficient Compressed Sensing Framework for the Compression of Electroencephalogram Signals

abstract: The use of wireless body sensor networks is gaining popularity in monitoring and communicating information about a person's health. In such applications, the amount of data transmitted by the sensor node should be minimized. This is because the energy available in these battery powered sensors is limited. In this paper, we study the wireless transmission of electroencephalogram (EEG) signals. We propose the use of a compressed sensing (CS) framework to efficiently compress these signals at the sensor node. Our framework exploits both the temporal correlation within EEG signals and the spatial correlations amongst the EEG channels. We show that our framework is up to eight times more energy efficient than the typical wavelet compression method in terms of compression and encoding computations and wireless transmission. We also show that for a fixed compression ratio, our method achieves a better reconstruction quality than the CS-based state-of-the art method. We finally demonstrate that our method is robust to measurement noise and to packet loss and that it is applicable to a wide range of EEG signal types.


title: Effect of PLC and SCADA in boosting the working of elevator system

abstract: Elevators are used as main connectivity unit of the floors, in malls, offices, industries and, buildings of the hospital. Generally, the chip based controllers are used to control the working of the elevator system. These controllers have some sharp specifications which cannot be maintained at all the time and, that's why faults occur from time to time. So, Programmable Logic Controller (PLC) is implemented in place of embedded system. PLC can easily withstand the faulty situation occurred in industrial environment and easily maintain all the specific requirement of the elevator system. In the present work, the ladder programming is used for PLC. It has very simple and handy programming, i.e., if we want to increase, decrease or skip any floor then program can change as per our requirements. Supervisory Control and Data Acquisition (SCADA) is also used to supervise and control the working of the elevator. If somehow any fault occurs then SCADA itself identifies the fault position and generates an alarm. The response on the fault can be taken within no time by interfacing PLC and SCADA with the elevator system.


title: Artificial bee colony metaheuristic for energy-efficient clustering and routing in wireless sensor networks

abstract: Swarm intelligence (SI)-based metaheuristics are well applied to solve real-time optimization problems of efficient node clustering and energy-aware data routing in wireless sensor networks. This paper presents another superior approach for these optimization problems based on an artificial bee colony metaheuristic. The proposed clustering algorithm presents an efficient cluster formation mechanism with improved cluster head selection criteria based on a multi-objective fitness function, whereas the routing algorithm is devised to consume minimum energy with least hop-count for data transmission. Extensive evaluation and comparison of the proposed approach with existing well-known SI-based algorithms demonstrate its superiority over others in terms of packet delivery ratio, average energy consumed, average throughput and network life.


title: Trends in ship electric propulsion

abstract: Since the 1980s there has been an explosion in the number and variety of electric propulsion ships being built around the world with everything from cruise liners to amphibious assault ships adopting electric propulsion. This technology revolution has occurred, largely un-noticed outside the marine industry. This paper briefly describes what has transpired in recent decades, why the resurgence is occurring and what the future may hold for electrically propelled ships.


title: Facial Expression Recognition

abstract: This chapter introduces recent advances in facial expression analysis and recognition. The first part discusses general structure of AFEA systems. The second part describes the problem space for facial expression analysis. This space includes multiple dimensions: level of description, individual differences in subjects, transitions among expressions, intensity of facial expression, deliberate versus spontaneous expression, head orientation and scene complexity, image acquisition and resolution, reliability of ground truth, databases, and the relation to other facial behaviors or nonfacial behaviors. We note that most work to date has been confined to a relatively restricted region of this space. The last part of this chapter is devoted to a description of more specific approaches and the techniques used in recent advances. They include the techniques for face acquisition, facial data extraction and representation, facial expression recognition, and multimodal expression analysis. The chapter concludes with a discussion assessing the current status, future possibilities, and open questions about automatic facial expression analysis.


title: Who Supported Obama in 2012?: Ecological Inference through Distribution Regression

abstract: We present a new solution to the ``ecological inference'' problem, of learning individual-level associations from aggregate data. This problem has a long history and has attracted much attention, debate, claims that it is unsolvable, and purported solutions. Unlike other ecological inference techniques, our method makes use of unlabeled individual-level data by embedding the distribution over these predictors into a vector in Hilbert space. Our approach relies on recent learning theory results for distribution regression, using kernel embeddings of distributions. Our novel approach to distribution regression exploits the connection between Gaussian process regression and kernel ridge regression, giving us a coherent, Bayesian approach to learning and inference and a convenient way to include prior information in the form of a spatial covariance function. Our approach is highly scalable as it relies on FastFood, a randomized explicit feature representation for kernel embeddings. We apply our approach to the challenging political science problem of modeling the voting behavior of demographic groups based on aggregate voting data. We consider the 2012 US Presidential election, and ask: what was the probability that members of various demographic groups supported Barack Obama, and how did this vary spatially across the country? Our results match standard survey-based exit polling data for the small number of states for which it is available, and serve to fill in the large gaps in this data, at a much higher degree of granularity.


title: Manipulating and Measuring Model Interpretability

abstract: With the increased use of machine learning in decision-making scenarios, there has been a growing interest in creating human-interpretable machine learning models. While many such models have been proposed, there have been relatively few experimental studies of whether these models achieve their intended effects, such as encouraging people to follow the model's predictions when the model is correct and to deviate when it makes a mistake. We present a series of randomized, pre-registered experiments comprising 3,800 participants in which people were shown functionally identical models that varied only in two factors thought to influence interpretability: the number of input features and the model transparency (clear or black-box). Predictably, participants who were shown a clear model with a small number of features were better able to simulate the model's predictions. However, contrary to what one might expect when manipulating interpretability, we found no improvements in the degree to which participants followed the model's predictions when it was beneficial to do so. Even more surprisingly, increased transparency hampered people's ability to detect when the model makes a sizable mistake and correct for it, seemingly due to information overload. These counterintuitive results suggest that decision scientists creating interpretable models should harbor a healthy skepticism of their intuitions and empirically verify that interpretable models achieve their intended effects.


title: Comparison of a high-flow humidified nasal cannula to nasal continuous positive airway pressure in children with acute bronchiolitis: experience in a pediatric intensive care unit

abstract: The objective of the current study is to compare the use of a nasal continuous positive airway pressure (nCPAP) to a high-flow humidified nasal cannula (HFNC) in infants with acute bronchiolitis, who were admitted to a pediatric intensive care unit (PICU) during two consecutive seasons. We retrospectively reviewed the medical records of all infants admitted to a PICU at a tertiary care French hospital during the bronchiolitis seasons of 2010/11 and 2011/12. Infants admitted to the PICU, who required noninvasive respiratory support, were included. The first noninvasive respiratory support modality was nCPAP during the 2010/11 season, while HFNC was used during the 2011/2012 season. We compared the length of stay (LOS) in the PICU; the daily measure of PCO2 and pH; and the mean of the five higher values of heart rate (HR), respiratory rate (RR), FiO2, and SpO2 each day, during the first 5 days. Thirty-four children met the inclusion criteria: 19 during the first period (nCPAP group) and 15 during the second period (HFNC group). Parameters such as LOS in PICU and oxygenation were similar in the two groups. Oxygen weaning occurred during the same time for the two groups. There were no differences between the two groups for RR, HR, FiO2, and CO2 evolution. HFNC therapy failed in three patients, two of whom required invasive mechanical ventilation, versus one in the nCPAP group. Conclusion: We did not find a difference between HFNC and nCPAP in the management of severe bronchiolitis in our PICU. Larger prospective studies are required to confirm these findings.


Online Prediction of Exponential Decay Time Series with Human-Agent Application.


title: Self-aware cyber-physical systems and applications in smart buildings and cities

abstract: The world is facing several challenges that must be dealt within the coming years such as efficient energy management, need for economic growth, security and quality of life of its habitants. The increasing concentration of the world population into urban areas puts the cities in the center of the preoccupations and makes them important actors for the world's sustainable development strategy. ICT has a substantial potential to help cities to respond to the growing demands of more efficient, sustainable, and increased quality of life in the cities, thus to make them "smarter". Smartness is directly proportional with the "awareness". Cyber-physical systems can extract the awareness information from the physical world and process this information in the cyber-world. Thus, a holistic integrated approach, from the physical to the cyber-world is necessary for a successful and sustainable smart city outcome. This paper introduces important research challenges that we believe will be important in the coming years and provides guidelines and recommendations to achieve self-aware smart city objectives.


title: Accelerating Dynamic Graph Analytics on GPUs

abstract: As graph analytics often involves compute-intensive operations, GPUs have been extensively used to accelerate the processing. However, in many applications such as social networks, cyber security, and fraud detection, their representative graphs evolve frequently and one has to perform a rebuild of the graph structure on GPUs to incorporate the updates. Hence, rebuilding the graphs becomes the bottleneck of processing high-speed graph streams. In this paper, we propose a GPU-based dynamic graph storage scheme to support existing graph algorithms easily. Furthermore, we propose parallel update algorithms to support efficient stream updates so that the maintained graph is immediately available for high-speed analytic processing on GPUs. Our extensive experiments with three streaming applications on large-scale real and synthetic datasets demonstrate the superior performance of our proposed approach.


title: LexIt: A Computational Resource on Italian Argument Structure

abstract: The aim of this paper is to introduce LexIt, a computational framework for the automatic acquisition and exploration of distributional information about Italian verbs, nouns and adjectives, freely available through a web interface at the address http://sesia.humnet.unipi.it/lexit. LexIt is the first large-scale resource for Italian in which subcategorization and semantic selection properties are characterized fully on distributional ground: in the paper we describe both the process of data extraction and the evaluation of the subcategorization frames extracted with LexIt.


title: Analysis of the multi-finger dynamics for robot hand system based on EtherCAT

abstract: A multi-finger dynamics model has been presented in this study, which contains a single finger dynamics model equation and a restraint equation between fingers based on Lagrangian multiplier controller. To validate the model, an EtherCAT master and slave platform has been developed based on FPGA. Meanwhile, the multi-finger dynamics algorithm has been designed in the TwinCAT. Finally, the experiments demonstrate this strategy can be implemented and operated by online grasping object.


title: Interpreting Hand-Over-Face Gestures

abstract: People often hold their hands near their faces as a gesture in natural conversation, which can interfere with affective inference from facial expressions. However, these gestures are valuable as an additional channel for multi-modal inference. We analyse hand-over-face gestures in a corpus of naturalistic labelled expressions and propose the use of those gestures as a novel affect cue for automatic inference of cognitive mental states. We define three hand cues for encoding hand-over-face gestures, namely hand shape, hand action and facial region occluded, serving as a first step in automating the interpretation process.


Adding Time-Offsets to Schedulability Analysis


title: SummaryAUC: a tool for evaluating the performance of polygenic risk prediction models in validation datasets with only summary level statistics

abstract: Motivation Polygenic risk score (PRS) methods based on genome-wide association studies (GWAS) have a potential for predicting the risk of developing complex diseases and are expected to become more accurate with larger training data sets and innovative statistical methods. The area under the ROC curve (AUC) is often used to evaluate the performance of PRSs, which requires individual genotypic and phenotypic data in an independent GWAS validation dataset. We are motivated to develop methods for approximating AUC of PRSs based on the summary level data of the validation dataset, which will greatly facilitate the development of PRS models for complex diseases. Results We develop statistical methods and an R package SummaryAUC for approximating the AUC and its variance of a PRS when only the summary level data of the validation dataset are available. SummaryAUC can be applied to PRSs with SNPs either genotyped or imputed in the validation dataset. We examined the performance of SummaryAUC using a large-scale GWAS of schizophrenia. SummaryAUC provides accurate approximations to AUCs and their variances. The bias of AUC is typically less than 0.5% in most analyses. SummaryAUC cannot be applied to PRSs that use all SNPs in the genome because it is computationally prohibitive. Availability https://github.com/lsncibb/SummaryAUC Contact Jianxin.Shi@nih.gov


title: A Digital-Domain Calibration of Split-Capacitor DAC for a Differential SAR ADC Without Additional Analog Circuits

abstract: A digital-domain calibration method is proposed for a split-capacitor DAC (split-CDAC) used in a differential-type 11-bit SAR ADC. It calibrates the nonlinearities of SAR ADC due to the DAC capacitance mismatch as well as the two parasitic capacitances connected in parallel with each of the bridge capacitor and the LSB bank of split-CDAC. The proposed ADC does not require any additional analog circuits for calibration, because it utilizes one of the two split-CDACs to measure the error codes of the other split-CDAC. During the normal A/D conversion step, the 11.5-bit raw SAR code output of ADC is added to the pre-measured error codes to generate the 11-bit calibrated output code. The analog block of the ADC was fabricated in a 0.13- μm CMOS process, and the digital block was implemented in a FPGA. The measured SNDR and SFDR are 61.6 dB (ENOB 9.93 bits) and 78 dB at the Nyquist rate with a 5 kHz sine wave input. INL and DNL are measured to be +0.96/-0.98 LSB, and +0.96/-0.97 LSB, respectively. This work extends the prior work by utilizing an additional 0.5-bit raw SAR code to eliminate the missing code, and by employing a temporal averaging with a FIR LPF to measure the error code reliably in spite of the supply noise.


title: Language Style Matching in Writing: Synchrony in Essays, Correspondence, and Poetry

abstract: Each relationship has its own personality. Almost immediately after a social interaction begins, verbal and nonverbal behaviors become synchronized. Even in asocial contexts, individuals tend to produce utterances that match the grammatical structure of sentences they have recently heard or read. Three projects explore language style matching (LSM) in everyday writing tasks and professional writing. LSM is the relative use of 9 function word categories (e.g., articles, personal pronouns) between any 2 texts. In the first project, 2 samples totaling 1,744 college students answered 4 essay questions written in very different styles. Students automatically matched the language style of the target questions. Overall, the LSM metric was internally consistent and reliable across writing tasks. Women, participants of higher socioeconomic status, and students who earned higher test grades matched with targets more than others did. In the second project, 74 participants completed cliffhanger excerpts from popular fiction. Judges’ ratings of excerpt–response similarity were related to content matching but not function word matching, as indexed by LSM. Further, participants were not able to intentionally increase style or content matching. In the final project, an archival study tracked the professional writing and personal correspondence of 3 pairs of famous writers across their relationships. Language matching in poetry and letters reflected fluctuations in the relationships of 3 couples: Sigmund Freud and Carl Jung, Elizabeth Barrett and Robert Browning, and Sylvia Plath and Ted Hughes. Implications for using LSM as an implicit marker of social engagement and influence are discussed.


title: Physics instruction induces changes in neural knowledge representation during successive stages of learning

abstract: Incremental instruction on the workings of a set of mechanical systems induced a progression of changes in the neural representations of the systems. The neural representations of four mechanical systems were assessed before, during, and after three phases of incremental instruction (which first provided information about the system components, then provided partial causal information, and finally provided full functional information). In 14 participants, the neural representations of four systems (a bathroom scale, a fire extinguisher, an automobile braking system, and a trumpet) were assessed using three recently developed techniques: (1) machine learning and classification of multi-voxel patterns; (2) localization of consistently responding voxels; and (3) representational similarity analysis (RSA). The neural representations of the systems progressed through four stages, or states, involving spatially and temporally distinct multi-voxel patterns: (1) initially, the representation was primarily visual (occipital cortex); (2) it subsequently included a large parietal component; (3) it eventually became cortically diverse (frontal, parietal, temporal, and medial frontal regions); and (4) at the end, it demonstrated a strong frontal cortex weighting (frontal and motor regions). At each stage of knowledge, it was possible for a classifier to identify which one of four mechanical systems a participant was thinking about, based on their brain activation patterns. The progression of representational states was suggestive of progressive stages of learning: (1) encoding information from the display; (2) mental animation, possibly involving imagining the components moving; (3) generating causal hypotheses associated with mental animation; and finally (4) determining how a person (probably oneself) would interact with the system. This interpretation yields an initial, cortically-grounded, theory of learning of physical systems that potentially can be related to cognitive learning theories by suggesting links between cortical representations, stages of learning, and the understanding of simple systems.


title: Research on speech emotion recognition based on deep auto-encoder

abstract: Good features are critical for the research of speech emotion recognition. This paper based on the theory of deep learning, and phonetic features were extracted by using the method of deep auto-encoder (DAE). In this paper, a deep auto-encoder containing five hidden layers was designed. To get the input data, we divided the audio into short frames, each frame of speech emotion signal was then decomposed with wavelet, and was calculated the Fourier transform. Higher features were learned with deep automatic encoder, and some traditional features such as MFCC, LPCC were also extracted. With high-level features and traditional features, support vector machine was used for classification and recognition. Compared with the traditional features, the results show that the highest recognition accuracy rate can be reached 86.41%.


Analysis of Into-Out Wireless Body Area Network Systems : Towards QoS-Aware Health Internet of Things Applications


title: Security Ontology for Adaptive Mapping of Security Standards

abstract: Adoption of security standards has the capability of improving the secu- rity level in an organization as well as to provide additional benefits and possibilities to the organization. However mapping of used standards has to be done when more than one security standard is employed in order to prevent redundant activities, not optimal resource management and unnecessary outlays. Employment of security on- tology to map different standards can reduce the mapping complexity however the choice of security ontology is of high importance and there are no analyses on security ontology suitability for adaptive standards mapping. In this paper we analyze existing security ontologies by comparing their general prop- erties, OntoMetric factors and ability to cover different security standards. As none of the analysed security ontologies were able to cover more than 1/3 of security stan- dards, we proposed a new security ontology, which increased coverage of security standards compared to the existing ontologies and has a better branching and depth properties for ontology visualization purposes. During this research we mapped 4 security standards (ISO 27001, PCI DSS, ISSA 5173 and NISTIR 7621) to the new security ontology, therefore this ontology and mapping data can be used for adaptive mapping of any set of these security standards to optimize usage of multiple security standards in an organization.


title: A Strategy for Identifying Locomotion Modes Using Surface Electromyography

abstract: This study investigated the use of surface electromyography (EMG) combined with pattern recognition (PR) to identify user locomotion modes. Due to the nonstationary characteristics of leg EMG signals during locomotion, a new phase-dependent EMG PR strategy was proposed for classifying the user's locomotion modes. The variables of the system were studied for accurate classification and timely system response. The developed PR system was tested on EMG data collected from eight able-bodied subjects and two subjects with long transfemoral (TF) amputations while they were walking on different terrains or paths. The results showed reliable classification for the seven tested modes. For eight able-bodied subjects, the average classification errors in the four defined phases using ten electrodes located over the muscles above the knee (simulating EMG from the residual limb of a TF amputee) were 12.4% plusmn 5.0%, 6.0% plusmn 4.7%, 7.5% plusmn 5.1%, and 5.2% plusmn 3.7%, respectively. Comparable results were also observed in our pilot study on the subjects with TF amputations. The outcome of this investigation could promote the future design of neural-controlled artificial legs.


title: Big data analytics and firm performance: Effects of dynamic capabilities

abstract: Drawing on the resource-based view and the literature on big data analytics (BDA), information system (IS) success and the business value of information technology (IT), this study proposes a big data analytics capability (BDAC) model. The study extends the above research streams by examining the direct effects of BDAC on firm performance (FPER), as well as the mediating effects of process-oriented dynamic capabilities (PODC) on the relationship between BDAC and FPER. To test our proposed research model, we used an online survey to collect data from 297 Chinese IT managers and business analysts with big data and business analytic experience. The findings confirm the value of the entanglement conceptualization of the hierarchical BDAC model, which has both direct and indirect impacts on FPER. The results also confirm the strong mediating role of PODC in improving insights and enhancing FPER. Finally, implications for practice and research are discussed.


title: Forward-Private Dynamic Searchable Symmetric Encryption with Efficient Search

abstract: Dynamic Searchable Symmetric Encryption (DSSE) allows to delegate keyword search and file update over an encrypted database via encrypted indexes, and therefore provides opportunities to mitigate the data privacy and utilization dilemma in cloud storage platforms. Despite its merits, recent works have shown that efficient DSSE schemes are vulnerable to statistical attacks due to the lack of forward-privacy, whereas forward-private DSSE schemes suffers from practicality concerns as a result of their extreme computation overhead. Due to significant practical impacts of statistical attacks, there is a critical need for new DSSE schemes that can achieve the forward-privacy in a more practical and efficient manner. We propose a new DSSE scheme that we refer to as Forward-private Sublinear DSSE (FS-DSSE). FS-DSSE harnesses special secure update strategies and a novel caching strategy to reduce the computation cost of repeated queries. Therefore, it achieves forward-privacy, sublinear search complexity, low end-to-end delay, and parallelization capability simultaneously. We fully implemented our proposed method and evaluated its performance on a real cloud platform. Our experimental evaluation results showed that the proposed scheme is highly secure and highly efficient compared with state-of-the-art DSSE techniques. Specifically, FS-DSSE is up to three magnitude of times faster than forward-secure DSSE counterparts, depending on the frequency of the searched keyword in the database.


title: The Deep Kernelized Autoencoder

abstract: Abstract Autoencoders learn data representations (codes) in such a way that the input is reproduced at the output of the network. However, it is not always clear what kind of properties of the input data need to be captured by the codes. Kernel machines have experienced great success by operating via inner-products in a theoretically well-defined reproducing kernel Hilbert space, hence capturing topological properties of input data. In this paper, we enhance the autoencoder's ability to learn effective data representations by aligning inner products between codes with respect to a kernel matrix. By doing so, the proposed kernelized autoencoder allows learning similarity-preserving embeddings of input data, where the notion of similarity is explicitly controlled by the user and encoded in a positive semi-definite kernel matrix. Experiments are performed for evaluating both reconstruction and kernel alignment performance in classification tasks and visualization of high-dimensional data. Additionally, we show that our method is capable to emulate kernel principal component analysis on a denoising task, obtaining competitive results at a much lower computational cost.


title: An Efficient End-to-End Neural Model for Handwritten Text Recognition

abstract: Offline handwritten text recognition from images is an important problem for enterprises attempting to digitize large volumes of handmarked scanned documents/reports. Deep recurrent models such as Multi-dimensional LSTMs have been shown to yield superior performance over traditional Hidden Markov Model based approaches that suffer from the Markov assumption and therefore lack the representational power of RNNs. In this paper we introduce a novel approach that combines a deep convolutional network with a recurrent Encoder-Decoder network to map an image to a sequence of characters corresponding to the text present in the image. The entire model is trained end-to-end using Focal Loss, an improvement over the standard Cross-Entropy loss that addresses the class imbalance problem, inherent to text recognition. To enhance the decoding capacity of the model, Beam Search algorithm is employed which searches for the best sequence out of a set of hypotheses based on a joint distribution of individual characters. Our model takes as input a downsampled version of the original image thereby making it both computationally and memory efficient. The experimental results were benchmarked against two publicly available datasets, IAM and RIMES. We surpass the state-of-the-art word level accuracy on the evaluation set of both datasets by 3.5% & 1.1%, respectively.


title: Review on determining number of Cluster in K-Means Clustering

abstract: Clustering is widely used in different field such as biology, psychology, and economics. The result of clustering varies as number of cluster parameter changes hence main challenge of cluster analysis is that the number of clusters or the number of model parameters is seldom known, and it must be determined before clustering. The several clustering algorithm has been proposed. Among them k-means method is a simple and fast clustering technique. We address the problem of cluster number selection by using a k-means approach We can ask end users to provide a number of clusters in advance, but it is not feasible end user requires domain knowledge of each data set. There are many methods available to estimate the number of clusters such as statistical indices, variance based method, Information Theoretic, goodness of fit method etc...The paper explores six different approaches to determine the right number of clusters in a dataset


title: Spin-Transfer Torque Magnetic Memory as a Stochastic Memristive Synapse for Neuromorphic Systems

abstract: Spin-transfer torque magnetic memory (STT-MRAM) is currently under intense academic and industrial development, since it features non-volatility, high write and read speed and high endurance. In this work, we show that when used in a non-conventional regime, it can additionally act as a stochastic memristive device, appropriate to implement a "synaptic" function. We introduce basic concepts relating to spin-transfer torque magnetic tunnel junction (STT-MTJ, the STT-MRAM cell) behavior and its possible use to implement learning-capable synapses. Three programming regimes (low, intermediate and high current) are identified and compared. System-level simulations on a task of vehicle counting highlight the potential of the technology for learning systems. Monte Carlo simulations show its robustness to device variations. The simulations also allow comparing system operation when the different programming regimes of STT-MTJs are used. In comparison to the high and low current regimes, the intermediate current regime allows minimization of energy consumption, while retaining a high robustness to device variations. These results open the way for unexplored applications of STT-MTJs in robust, low power, cognitive-type systems.


title: A Process and Temperature Compensated Two-Stage Ring Oscillator

abstract: Local positive feedback in a delay element enables a ring oscillator with only two stages to oscillate and produce quadrature clocks. Routh-Hurwitz's criterion is applied to prove that such a structure can oscillate. An internally generated power supply from a constant-gm bias keeps the free running frequency to within plusmn 5% from -40 to 125degC over process variations. The 1.25 GHz oscillator in 0.13 mum CMOS draws 3.4 mA and has a phase noise of -88 dBc/Hz at 1MHz offset.