title: Building Expression into Virtual Characters

abstract: Virtual characters are an important part of many 3D graphical simulations. In entertainment or training applications, virtual characters might be one of the main mechanisms for creating and developing content and scenarios. In such applications the user may need to interact with a number of different characters that need to invoke specific responses in the user, so that the user interprets the scenario in the way that the designer intended. Whilst representations of virtual characters have come a long way in recent years, interactive virtual characters tend to be a bit “wooden” with respect to their perceived behaviour. In this STAR we give an overview of work on expressive virtual characters. In particular, we assume that a virtual character representation is already available, and we describe a variety of models and methods that are used to give the characters more “depth” so that they are less wooden and more plausible. We cover models of individual characters’ emotion and personality, models of interpersonal behaviour and methods for generating expression.


title: Multichannel Place Pitch Sensitivity in Cochlear Implant Recipients

abstract: Cochlear implant recipients perceive a rise in pitch when the site of stimulation is moved from the apex toward the base. The place pitch sensitivity is typically measured using the stimulation of single channels. However, all current cochlear implant devices stimulate multiple channels simultaneously or with pulses temporally interleaved. The primary goal of the present study is to test whether the sensitivity of a cochlear implant recipient to changes in perceived pitch associated with changes of place of excitation improves or deteriorates when the number of active channels is increased, compared with stimulation with only one active channel. Place pitch sensitivity was recorded in four Nucleus CI24 subjects as a function of number of active channels (from 1 to 8). Just noticeable differences were estimated from a constant stimuli 2AFC pitch-ranking experiment with roving loudness. Reference and comparison stimuli contained the same number of active channels but were shifted one or two electrodes toward the base or toward the apex. The place pitch sensitivity was measured using monopolar stimulation at two locations along the electrode array. To minimize cues related to loudness, the multichannel stimuli were loudness balanced relative to the single-channel stimuli presented at C-level. The number of active channels did not affect place pitch sensitivity. This is consistent with a model that compares the edges of the excitation pattern irrespective of the overlap between excitation patterns. There was a significant difference in sensitivity to place pitch among subjects. The average just noticeable differences of place pitch, extrapolated from a fitting procedure, for the subjects ranged from 0.25 mm to 0.46 mm.


title: Implementing Synaptic Plasticity in a VLSI Spiking Neural Network Model

abstract: This paper describes an area-efficient mixed-signal implementation of synapse-based long term plasticity realized in a VLSI model of a spiking neural network. The artificial synapses are based on an implementation of spike time dependent plasticity (STDP). In the biological specimen, STDP is a mechanism acting locally in each synapse. The presented electronic implementation succeeds in maintaining this high level of parallelism and simultaneously achieves a synapse density of more than 9k synapses per mm2 in a 180 nm technology. This allows the construction of neural micro-circuits close to the biological specimen while maintaining a speed several orders of magnitude faster than biological real time. The large acceleration factor enhances the possibilities to investigate key aspects of plasticity, e.g. by performing extensive parameter searches.


title: Problematic Video Game Use: Estimated Prevalence and Associations with Mental and Physical Health

abstract: Abstract A nationwide survey was conducted to investigate the prevalence of video game addiction and problematic video game use and their association with physical and mental health. An initial sample comprising 2,500 individuals was randomly selected from the Norwegian National Registry. A total of 816 (34.0 percent) individuals completed and returned the questionnaire. The majority (56.3 percent) of respondents used video games on a regular basis. The prevalence of video game addiction was estimated to be 0.6 percent, with problematic use of video games reported by 4.1 percent of the sample. Gender (male) and age group (young) were strong predictors for problematic use of video games. A higher proportion of high frequency compared with low frequency players preferred massively multiplayer online role-playing games, although the majority of high frequency players preferred other game types. Problematic use of video games was associated with lower scores on life satisfaction and with elevated levels of an...


title: Infant physiological response to the still-face paradigm: contributions of maternal sensitivity and infants' early regulatory behavior.

abstract: Abstract The current study examined the independent and additive contributions of maternal sensitivity measured prior to and following a social stressor, and infant behaviors to infants’ physiological response to the still-face paradigm (SFP) in a sample characterized by poverty-related environmental risk. Ninety-one mother/infant dyads participated in the SFP when their infants were 5 months old. Maternal sensitivity was coded during the play and reunion episodes. Infant behaviors were coded during the reunion episode of the SFP while measures of heart rate (HR) and Respiratory Sinus Arrhythmia (RSA) were collected from the infants at baseline and across the SFP. Maternal sensitivity during the reunion episode predicted infants’ biobehavioral reactivity and regulation, over and above maternal sensitivity during the play episode. Infants’ HR decreased, and RSA increased, with greater levels of maternal sensitivity during the reunion episode. Infants also exhibited greater attentional engagement and fewer resistant behaviors with greater levels of maternal sensitivity during the reunion episode. Finally, infant behaviors predicted change in HR and RSA from the still-face to the reunion episode, above and beyond that of maternal behaviors. These findings further our understanding of the dyadic basis for the development of emotion regulation in infancy.


title: Information technology service performance management using COBIT and an ITIL framework: A systematic literature review

abstract: Information systems play a significant role in assisting and improving a university's operational work performance. The quality of IT service is also needed to provide an information system that matches with a university's needs. As a result, evaluations need to be conducted towards IT services in fulfilling the needs and providing satisfaction for information systems users. The purpose of this paper was to conduct a synthesis of the service work performance provided by the IT Division towards information systems users by using systematic literature review. The methodology used in this research was a literature study related with a COBIT and ITIL framework, as well as finding the interrelatedness between service managements toward an increase of IT work performance.


title: ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events

abstract: Then detection and identification of extreme weather events in large-scale climate simulations is an important problem for risk management, informing governmental policy decisions and advancing our basic understanding of the climate system. Recent work has shown that fully supervised convolutional neural networks (CNNs) can yield acceptable accuracy for classifying well-known types of extreme weather events when large amounts of labeled data are available. However, many different types of spatially localized climate patterns are of interest including hurricanes, extra-tropical cyclones, weather fronts, and blocking events among others. Existing labeled data for these patterns can be incomplete in various ways, such as covering only certain years or geographic areas and having false negatives. This type of climate data therefore poses a number of interesting machine learning challenges. We present a multichannel spatiotemporal CNN architecture for semi-supervised bounding box prediction and exploratory data analysis. We demonstrate that our approach is able to leverage temporal information and unlabeled data to improve the localization of extreme weather events. Further, we explore the representations learned by our model in order to better understand this important data. We present a dataset, ExtremeWeather, to encourage machine learning research in this area and to help facilitate further work in understanding and mitigating the effects of climate change. The dataset is available at extremeweatherdataset.github.io and the code is available at this https URL


title: Controllable Load Management Approaches in Smart Grids

abstract: With rapid smart grid technology development, the customer can actively participate in demand-side management (DSM) with the mutual information communication between the distributor operation company and the smart devices in real-time. Controllable load management not only has the advantage of peak shaving, load balance, frequency regulation, and voltage stability, but is also effective at providing fast balancing services to the renewable energy grid in the distributed power system. The load management faces an enormous challenge as the customer has a large number of both small residential loads and dispersed renewable sources. In this paper, various controllable load management approaches are discussed. The traditional controllable load approaches such as the end users’ controllable appliances, storage battery, Vehicle-to-Grid (V2G), and heat storage are reviewed. The “broad controllable loads” management, such as the microgrid, Virtual Power Plant (VPP), and the load aggregator are also presented. Furthermore, the load characteristics, control strategies, and control effectiveness are analyzed.


Fast Quicksort Implementation Using AVX Instructions


title: Learning to Match Using Local and Distributed Representations of Text for Web Search

abstract: Models such as latent semantic analysis and those based on neural embeddings learn distributed representations of text, and match the query against the document in the latent semantic space. In traditional information retrieval models, on the other hand, terms have discrete or local representations, and the relevance of a document is determined by the exact matches of query terms in the body text. We hypothesize that matching with distributed representations complements matching with traditional local representations, and that a combination of the two is favourable. We propose a novel document ranking model composed of two separate deep neural networks, one that matches the query and the document using a local representation, and another that matches the query and the document using learned distributed representations. The two networks are jointly trained as part of a single neural network. We show that this combination or 'duet' performs significantly better than either neural network individually on a Web page ranking task, and significantly outperforms traditional baselines and other recently proposed models based on neural networks.


title: The Neural Painter: Multi-Turn Image Generation

abstract: In this work we combine two research threads from Vision/ Graphics and Natural Language Processing to formulate an image generation task conditioned on attributes in a multi-turn setting. By multiturn, we mean the image is generated in a series of steps of user-specified conditioning information. Our proposed approach is practically useful and offers insights into neural interpretability. We introduce a framework that includes a novel training algorithm as well as model improvements built for the multi-turn setting. We demonstrate that this framework generates a sequence of images that match the given conditioning information and that this task is useful for more detailed benchmarking and analysis of conditional image generation methods.


title: Cascade Ranking for Operational E-commerce Search

abstract: In the 'Big Data' era, many real-world applications like search involve the ranking problem for a large number of items. It is important to obtain effective ranking results and at the same time obtain the results efficiently in a timely manner for providing good user experience and saving computational costs. Valuable prior research has been conducted for learning to efficiently rank like the cascade ranking (learning) model, which uses a sequence of ranking functions to progressively filter some items and rank the remaining items. However, most existing research of learning to efficiently rank in search is studied in a relatively small computing environments with simulated user queries. This paper presents novel research and thorough study of designing and deploying a Cascade model in a Large-scale Operational E-commerce Search application (CLOES), which deals with hundreds of millions of user queries per day with hundreds of servers. The challenge of the real-world application provides new insights for research: 1). Real-world search applications often involve multiple factors of preferences or constraints with respect to user experience and computational costs such as search accuracy, search latency, size of search results and total CPU cost, while most existing search solutions only address one or two factors; 2). Effectiveness of e-commerce search involves multiple types of user behaviors such as click and purchase, while most existing cascade ranking in search only models the click behavior. Based on these observations, a novel cascade ranking model is designed and deployed in an operational e-commerce search application. An extensive set of experiments demonstrate the advantage of the proposed work to address multiple factors of effectiveness, efficiency and user experience in the real-world application.


title: A Multi-partitioning Approach to Building Fast and Accurate Counting Bloom Filters

abstract: Bloom filters are space-efficient data structures for fast set membership queries. Counting Bloom Filters (CBFs) extend Bloom filters by allowing insertions and deletions to support dynamic sets. The performance of CBFs is critical for various applications and systems. This paper presents a novel approach to building a fast and accurate data structure called Multiple-Partitioned Counting Bloom Filter (MPCBF) that addresses large-scale data processing challenges. MPCBF is based on two ideas: reducing the number of memory accesses from k (for k hash functions) in the standard CBF to only one memory access in the basic MPCBF-1 case, and a hierarchical structure to improve the false positive rate. We also generalize MPCBF-1 to MPCBF-g to accommodate up to g memory accesses. Our simulation and implementation in MapReduce show that MPCBF outperforms the standard CBF in terms of speed and accuracy. Compared to CBF, at the same memory consumption, MPCBF significantly reduces the false positive rate by an order of magnitude, with a reduction of processing overhead by up to 85.9%.


title: Tell-and-Answer: Towards Explainable Visual Question Answering using Attributes and Captions

abstract: Visual Question Answering (VQA) has attracted attention from both computer vision and natural language processing communities. Most existing approaches adopt the pipeline of representing an image via pre-trained CNNs, and then using the uninterpretable CNN features in conjunction with the question to predict the answer. Although such end-to-end models might report promising performance, they rarely provide any insight, apart from the answer, into the VQA process. In this work, we propose to break up the end-to-end VQA into two steps: explaining and reasoning, in an attempt towards a more explainable VQA by shedding light on the intermediate results between these two steps. To that end, we first extract attributes and generate descriptions as explanations for an image using pre-trained attribute detectors and image captioning models, respectively. Next, a reasoning module utilizes these explanations in place of the image to infer an answer to the question. The advantages of such a breakdown include: (1) the attributes and captions can reflect what the system extracts from the image, thus can provide some explanations for the predicted answer; (2) these intermediate results can help us identify the inabilities of both the image understanding part and the answer inference part when the predicted answer is wrong. We conduct extensive experiments on a popular VQA dataset and dissect all results according to several measurements of the explanation quality. Our system achieves comparable performance with the state-of-the-art, yet with added benefits of explainability and the inherent ability to further improve with higher quality explanations.


Linggle Knows: A Search Engine Tells How People Write


title: Low-Profile Magneto-Electric Dipole Antenna

abstract: A novel low-profile magneto-electric dipole antenna is proposed and investigated. The antenna utilizes an obtuse-triangular structure to work as a magnetic dipole, which reduces the antenna thickness to 0.097λ ::: 0 ::: (where λ ::: 0 ::: is the free-space wavelength at the center frequency). A prototype with ground plane size of around 1λ ::: 0 ::: × 1λ ::: 0 ::: is fabricated and measured. Measurement result shows that the proposed design achieves an impedance bandwidth of 28.2% (1.67-2.22 GHz) for SWR <; 2. A stable gain of 9.2 ± 1.1 dBi is realized. The antenna has a unidirectional radiation pattern with low cross polarization (<; -24 dB) and low back-radiation level (<; -13 dB) across the operating frequency band.


title: A Framework for Integrating Non-Functional Requirements into Conceptual Models

abstract: The development of complex information systems calls for conceptual models that describe aspects beyond entities and activities. In particular, recent research has pointed out that conceptual models need to model goals, in order to capture the intentions which underlie complex situations within an organisational context. This paper focuses on one class of goals, namely nonfunctional requirements (NFR), which need to be captured and analysed from the very early phases of the software development process. The paper presents a framework for integrating NFRs into the ER and OO models. This framework has been validated by two case studies, one of which is very large. The results of the case studies suggest that goal modelling during early phases can lead to a more productive and complete modelling activity.


title: Disruption-driven supply chain (re)-planning and performance impact assessment with consideration of pro-active and recovery policies

abstract: In this study, an approach to re-planning the multi-stage supply chain (SC) subject to disruptions is developed. We analyze seven proactive SC structures, compute recovery policies to re-direct material flows in the case of two disruption scenarios, and assess the performance impact for both service level and costs with the help of a SC (re)planning model containing elements of system dynamics and linear programming. In the result, an explicit connection of performance impact assessment and SC plan reconfiguration issues with consideration of the duration of disruptions and the costs of recovery has been achieved.


title: Locomotion of a Modular Worm-like Robot using a FPGA-based embedded MicroBlaze Soft-processor

abstract: Modular reconfigurable robots offer the promise of more versatility, robustness, and low cost. They are composed of simple and small modules, capable of attach and detach one to each other. In this paper, a modular worm-like robot composed of a chain of 8 similar modules is presented. A travelling wave, that moves from the tail to the head, propels the robot forward. The positions of the articulations are calculated using the following parameters: waveform, amplitude, and wavelength. Instead of a conventional architecture, a FPGA-based soft-processor core is utilized. It includes a set of custom peripheral cores, written in VHDL. FPGAs make modular robots more versatile, adding some new featureas to the design of robots like reconfigurable control, hardware reuse, lower cost, fault-recovering, and software/hardware co-design.


title: Neuronal factors determining high intelligence

abstract: Many attempts have been made to correlate degrees of both animal and human intelligence with brain properties. With respect to mammals, a much-discussed trait concerns absolute and relative brain size, either uncorrected or corrected for body size. However, the correlation of both with degrees of intelligence yields large inconsistencies, because although they are regarded as the most intelligent mammals, monkeys and apes, including humans, have neither the absolutely nor the relatively largest brains. The best fit between brain traits and degrees of intelligence among mammals is reached by a combination of the number of cortical neurons, neuron packing density, interneuronal distance and axonal conduction velocity--factors that determine general information processing capacity (IPC), as reflected by general intelligence. The highest IPC is found in humans, followed by the great apes, Old World and New World monkeys. The IPC of cetaceans and elephants is much lower because of a thin cortex, low neuron packing density and low axonal conduction velocity. By contrast, corvid and psittacid birds have very small and densely packed pallial neurons and relatively many neurons, which, despite very small brain volumes, might explain their high intelligence. The evolution of a syntactical and grammatical language in humans most probably has served as an additional intelligence amplifier, which may have happened in songbirds and psittacids in a convergent manner.


title: Consumer-Centric Information Systems : A Literature Review and Avenues for Further Research

abstract: While consumer centricity has been extensively discussed as a concept of organizational transformation in the marketing domain, there is little research on its operationalization as a characteristic of information systems and associated antecedents. ::: ::: We review the marketing literature to understand generic organizational objectives of consumer centricity which are then generalized as characteristics of consumer-centric information systems. In a second step, we draw on socio-technical theory to conceptualize antecedents of consumer centricity as capabilities to align social and technical system components. ::: ::: Our research contributes to the body of knowledge by theoretically deriving an operationalization and antecedents of consumer centricity in IS research. This paper lays the foundation for a structured review of IS literature to theorize on component alignment capabilities as antecedents of consumer centricity. It further is the basis for case study research to construct a nomological network for consumer-centric information systems.


title: Simple analytic rules for model reduction and PID controller tuning §

abstract: The aim of this paper is to present analytic rules for PID controller tuning that are simple and still result in good closed-loop behavior. The starting point has been the IMC-PID tuning rules that have achieved widespread industrial acceptance. The rule for the integral term has been modified to improve disturbance rejection for integrating processes. Furthermore, rather than deriving separate rules for each transfer function model, there is a just a single tuning rule for a first-order or second-order time delay model. Simple analytic rules for model reduction are presented to obtain a model in this form, including the ‘‘half rule’’ for obtaining the effective time delay. # 2002 Elsevier Science Ltd. All rights reserved.


title: Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks

abstract: Both Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) have shown improvements over Deep Neural Networks (DNNs) across a wide variety of speech recognition tasks. CNNs, LSTMs and DNNs are complementary in their modeling capabilities, as CNNs are good at reducing frequency variations, LSTMs are good at temporal modeling, and DNNs are appropriate for mapping features to a more separable space. In this paper, we take advantage of the complementarity of CNNs, LSTMs and DNNs by combining them into one unified architecture. We explore the proposed architecture, which we call CLDNN, on a variety of large vocabulary tasks, varying from 200 to 2,000 hours. We find that the CLDNN provides a 4–6% relative improvement in WER over an LSTM, the strongest of the three individual models.


title: Rollup: Non-Disruptive Rolling Upgrade with Fast Consensus-Based Dynamic Reconfigurations

abstract: Rolling upgrade consists of upgrading progressively the servers of a distributed system to reduce service downtime.Upgrading a subset of servers requires a well-engineered cluster membership protocol to maintain, in the meantime, the availability of the system state. Existing cluster membership reconfigurations, like CoreOS etcd, rely on a primary not only for reconfiguration but also for storing information. At any moment, there can be at most one primary, whose replacement induces disruption. We propose Rollup, a non-disruptive rolling upgrade protocol with a fast consensus-based reconfiguration. Rollup relies on a candidate leader only for the reconfiguration and scalable biquorums for service requests. While Rollup implements a non-disruptive cluster membership protocol, it does not offer a full-fledged coordination service. We analyzed Rollup theoretically and experimentally on an isolated network of 26 physical machines and an Amazon EC2 cluster of 59 virtual machines. Our results show an 8-fold speedup compared to a rolling upgrade based on a primary for reconfiguration.


title: Imaging in lumbar spinal stenosis.

abstract: Lumbar spinal stenosis is a common condition seen in patients presenting to physicians who specialize in pain management or perform spine surgery. The designation of "spinal stenosis" without other qualifiers is vague and as such holds little practical value. Classifications have been created in order to more specifically describe the various etiologies as well as the site(s) of narrowing. For this purpose, diagnostic imaging studies are vital. These include myelography, computed tomography (CT), and magnetic resonance imaging (MRI). Each imaging modality has its own inherent advantages and limitations in demonstrating anatomical structures and how they may contribute to the stenotic process. Since proper treatment follows accurate identification of the pathology, it is important for physicians to have a sound understanding of normal and abnormal spinal elements as they are depicted on various imaging studies.


title: Meme representations for game agents

abstract: The advancement in game technology has served to enrich player's gaming experience in a substantial way. Nowadays, it is common to have blockbuster quality games, with realistic graphics and engaging stories. Despite this, the progress made in incorporating Artificial Intelligence has been slow, and realistic human-like intelligence in games is hardly to be found. There have been some attempts to use Machine Learning in games, but such attempts often ended up impractical or affecting the players enjoyment due to several constraining factors. In this paper, we describe Meme War as a proof-of-concept for practical usage of Machine Learning in games. We introduce Extreme Learning Machine (ELM) as one approach to achieve a better experience in employing Machine Learning in games. Advantages of ELM over Multilayer Perceptron (MLP) are presented in terms of what ELM can offer in a practical point of view of the player.


title: The Evolution of the Web and Implications for an Incremental Crawler

abstract: In this paper we study how to build an effective incremental crawler. The crawler selectively and incrementally updates its index and/or local collection of web pages, instead of periodically refreshing the collection in batch mode. The incremental crawler can improve the ``freshness'' of the collection significantly and bring in new pages in a more timely manner. We first present results from an experiment conducted on more than half million web pages over 4 months, to estimate how web pages evolve over time. Based on these experimental results, we compare various design choices for an incremental crawler and discuss their trade-offs. We propose an architecture for the incremental crawler, which combines the best design choices.


title: Automatic Battery Replacement System for UAVs: Analysis and Design

abstract: Future Unmanned Aircraft Systems (UASs) are expected to be nearly autonomous and composed of heterogeneous Unmanned Aerial Vehicles (UAVs). While most of the current research focuses on UAV avionics and control algorithms, ground task automation has come to the attention of researchers during the past few years. Ground task automation not only relieves human operators, but may also expand the UAS operation area, improve system coverage and enable operation in risky environments without posing a threat to humans. We propose a model to evaluate the coverage of a given UAS. We also compare different solutions for various modules of an automated battery replacement system for UAVs. In addition, we propose a ground station capable of swapping a UAV’s batteries, followed by a discussion of prototype components and tests of some of the prototype modules. The proposed platform is well-suited for high-coverage requirements and is capable of handling a heterogeneous UAV fleet.


title: Virtual reality simulation modeling for a haptic glove

abstract: The recent addition of force and touch feedback to virtual reality simulations has enhanced their realism. Research on haptics interfaces is now extended to physical modeling of contact surfaces, object hardness, surface deformation, etc. This is especially needed when dextrous manipulation of virtual objects is concerned. The paper describes a VR system using a haptic glove (Rutgels Master II) connected to a PC workstation, and a new method for modeling virtual hand haptic interactions. An application example presented here is an orthopedic rehabilitation library. The exercises in this library involve interactions with dynamic objects and physical modeling of plasticity.


title: Physiological time-series analysis using approximate entropy and sample entropy

abstract: Entropy, as it relates to dynamical systems, is the rate of information production. Methods for estimation of the entropy of a system represented by a time series are not, however, well suited to analysis of the short and noisy data sets encountered in cardiovascular and other biological studies. Pincus introduced approximate entropy (ApEn), a set of measures of system complexity closely related to entropy, which is easily applied to clinical cardiovascular and other time series. ApEn statistics, however, lead to inconsistent results. We have developed a new and related complexity measure, sample entropy (SampEn), and have compared ApEn and SampEn by using them to analyze sets of random numbers with known probabilistic character. We have also evaluated cross-ApEn and cross-SampEn, which use cardiovascular data sets to measure the similarity of two distinct time series. SampEn agreed with theory much more closely than ApEn over a broad range of conditions. The improved accuracy of SampEn statistics should make them useful in the study of experimental clinical cardiovascular and other biological time series.


title: Ontology Driven Semantic Search

abstract: The introduction of semantics on the web will lead to a new generation of services based on content rather than on syntax. Search engines will provide topic-based searches, retrieving resources conceptually related to the user informational need. Queries will be expressed in several ways, and will be mapped on the semantic level defining topics that must be retrieved from the web. Moving towards this new Web era, effective semantic search engines will provide means for successful searches avoiding the heavy burden experimented by users in a classical query-string based search task. In this paper we propose a search engine based on web resource semantics. Resources to be retrieved are semantically annotated using an existing open semantic elaboration platform and an ontology is used to describe the knowledge domain into which perform queries. Ontology navigation provides semantic level reasoning in order to retrieve meaningful resources with respect to a given information request.


title: Semi-Supervised Object Detection in Remote Sensing Images Using Generative Adversarial Networks

abstract: Object detection is a challenging task in computer vision. Now many detection networks can get a good detection result when applying large training dataset. However, annotating sufficient amount of data for training is often time-consuming. To address this problem, a semi-supervised learning based method is proposed in this paper. Semi-supervised learning trains detection networks with few annotated data and massive amount of unannotated data. In the proposed method, Generative Adversarial Network is applied to extract data distribution from unannotated data. The extracted information is then applied to improve the performance of detection network. Experiment shows that the method in this paper greatly improves the detection performance compared w1ith supervised learning using only few annotated data. The results prove that it is possible to achieve acceptable detection result when only few target object is annotated in the training dataset.


title: Unified Cloud Computing Adoption Framework

abstract: Cloud computing adoption has represented a big challenge for all kinds of companies all over the world. The challenge involves such questions as where to start, which provider should the company choose or whether it is even worthwhile. With a constantly changing economic environment, businesses must assess current technologies and offerings to remain competitive. The possibility of migrating a company's services and infrastructure to the cloud may seem attractive. However, without proper guidance, the results may not be as expected, leading a loss of time and money. As each company has its own needs and requirements, industry-focused frameworks have been proposed (e.g. for educational or governmental institutions). Although these frameworks are useful, they are not applicable to every business. Hence, a generic, widely-applicable and implementable cloud computing adoption framework is proposed in this paper. It takes the best outcomes of previous studies, best-practice suggestions, as well as authors' additions, and sums them up into a more robust, unified framework. The framework consists of 6 detailed phases carrying the user from knowing the company's current state to successfully migrating the data, services, and infrastructure to the cloud. These steps are intended to help IT directors and other decision-makers to reduce risks and maximize benefits throughout the cloud computing adoption process. Data security risks are not discussed in this paper as other authors have already sufficiently studied them. This framework was developed from a business perspective.


title: White Paper On Natural Language Processing

abstract: We take the ultimate goal of natural language processing (NLP) to be the ability to use natural languages as effectively as humans do. Natural language, whether spoken, written, or typed, is the most natural means of communication between humans, and the mode of expression of choice for most of the documents they produce. As computers play a larger role in the preparation, acquisition, transmission, monitoring, storage, analysis, and transformation of information, endowing them with the ability to understand and generate information expressed in natural languages becomes more and more necessary. Some tasks currently performed by humans cannot be automated without endowing computers with natural language processing capabilities, and these provide two major challenges to NLP systems:1. Reading and writing text, applied to tasks such as message routing, abstracting, monitoring, summarizing, and entering information in databases, with applications, in such areas as intelligence, logistics, office automation, and libraries. Computers should be able to assimilate and compose extended communications.2. Translation, of documents or spoken language, with applications, in such areas as in science, diplomacy, multinational commerce, and intelligence. Computers should be able to understand input in more than one language, provide output in more than one language, and translate between languages.


title: Conditionally correct superoptimization

abstract: The aggressive optimization of heavily used kernels is an important problem in high-performance computing. However, both general purpose compilers and highly specialized tools such as superoptimizers often do not have sufficient static knowledge of restrictions on program inputs that could be exploited to produce the very best code. For many applications, the best possible code is conditionally correct: the optimized kernel is equal to the code that it replaces only under certain preconditions on the kernel's inputs. The main technical challenge in producing conditionally correct optimizations is in obtaining non-trivial and useful conditions and proving conditional equivalence formally in the presence of loops. We combine abstract interpretation, decision procedures, and testing to yield a verification strategy that can address both of these problems. This approach yields a superoptimizer for x86 that in our experiments produces binaries that are often multiple times faster than those produced by production compilers.


title: Bandwidth for the Equivalent Circuit Model in Square-Loop Frequency Selective Surfaces

abstract: This paper presents the results of a study, implemented for the equivalent circuit model (ECM), concerning the bandwidth in square-loop frequency-selective surfaces (FSSs). For the square-loop ECM, there is no formulation to quantify the effects of dielectric supporting layers, geometric features, and incident wave properties on the FSS bandwidth. Hence, it is not possible to control or evaluate the bandwidth of the FSS in the design from the knowledge of these parameters. In this paper, we addressed this issue using the ECM as an analysis method. In the first part of this study, we considered the square-loop FSS with thin dielectric supporting layers, where the bandwidth was expressed as a function of geometric parameters, incidence angle, and polarization of the incident wave as well as a factor that takes into account the effective permittivity of the dielectric support. Particle swarm optimization method was used to synthesize square-loop FSSs via the ECM with a specific resonant frequency and bandwidth requirements. In the second part, the behavior of bandwidth as a function of dielectric thickness was obtained by using a matrix cascading technique (MCT), implemented with ECM and a model for the dielectric effective permittivity. This approach takes into account the modifications on bandwidth due to multiple reflections on dielectric boundaries and allows calculating its value for any dielectric thickness and configuration. It is remarkable the low computational cost and acceptable accuracy obtained with the proposed approaches. All simulation results using ECM and MCT were compared with Ansys-HFSS commercial software simulations.


title: An approach of optimal path generation using ant colony optimization

abstract: Software Testing is one of the indispensable parts of the software development lifecycle and structural testing is one of the most widely used testing paradigms to test various software. Structural testing relies on code path identification, which in turn leads to identification of effective paths. Aim of the current paper is to present a simple and novel algorithm with the help of an ant colony optimization, for the optimal path identification by using the basic property and behavior of the ants. This novel approach uses certain set of rules to find out all the effective/optimal paths via ant colony optimization (ACO) principle. The method concentrates on generation of paths, equal to the cyclomatic complexity. This algorithm guarantees full path coverage.


title: FD-TD analysis of bandwidth characteristics in the asymmetrical ridge rectangular waveguide

abstract: The finite-difference time-domain (FDTD) method is applied to analyzing the influence of the ridge position on the cutoff wavelength and bandwidth of the dominant mode in the ridge waveguide. The cutoff wavelength and bandwidth of the dominant mode of the asymmetrical single- and double-ridge rectangular waveguide have been analyzed. Numerical results demonstrate that FD-TD method is precise and efficient, and the cutoff wavelength and bandwidth can be adjusted by changing the ridge dimensions as well as by adjusting its position.


title: Learning model-based strategies in simple environments with hierarchical q-networks

abstract: Recent advances in deep learning have allowed artificial agents to rival human-level performance on a wide range of complex tasks; however, the ability of these networks to learn generalizable strategies remains a pressing challenge. This critical limitation is due in part to two factors: the opaque information representation in deep neural networks and the complexity of the task environments in which they are typically deployed. Here we propose a novel Hierarchical Q-Network (HQN) motivated by theories of the hierarchical organization of the human prefrontal cortex, that attempts to identify lower dimensional patterns in the value landscape that can be exploited to construct an internal model of rules in simple environments. We draw on combinatorial games, where there exists a single optimal strategy for winning that generalizes across other features of the game, to probe the strategy generalization of the HQN and other reinforcement learning (RL) agents using variations of Wythoff's game. Traditional RL approaches failed to reach satisfactory performance on variants of Wythoff's Game; however, the HQN learned heuristic-like strategies that generalized across changes in board configuration. More importantly, the HQN allowed for transparent inspection of the agent's internal model of the game following training. Our results show how a biologically inspired hierarchical learner can facilitate learning abstract rules to promote robust and flexible action policies in simplified training environments with clearly delineated optimal strategies.


title: Short Synthetic Terminators for Assembly of Transcription Units in Vitro and Stable Chromosomal Integration in Yeast S. cerevisiae

abstract: Assembly of synthetic genetic circuits is central to synthetic biology. Yeast S. cerevisiae, in particular, has proven to be an ideal chassis for synthetic genome assemblies by exploiting its efficient homologous recombination. However, this property of efficient homologous recombination poses a problem for multigene assemblies in yeast, since repeated usage of standard parts, such as transcriptional terminators, can lead to rearrangements of the repeats in assembled DNA constructs in vivo. To address this issue in developing a library of orthogonal genetic components for yeast, we designed a set of short synthetic terminators based on a consensus sequence with random linkers to avoid repetitive sequences. We constructed a series of expression vectors with these synthetic terminators for efficient assembly of synthetic genes using Gateway recombination reactions. We also constructed two BAC (bacterial artificial chromosome) vectors for assembling multiple transcription units with the synthetic terminators in vitro and their integration in the yeast genome. The tandem array of synthetic genes integrated in the genome by this method is highly stable because there are few homologous segments in the synthetic constructs. Using this system of assembly and genomic integration of transcription units, we tested the synthetic terminators and their influence on the proximal transcription units. Although all the synthetic terminators have the common consensus with the identical length, they showed different activities and impacts on the neighboring transcription units.


title: Bayesian Non-Exhaustive Classification A Case Study: Online Name Disambiguation using Temporal Record Streams

abstract: The name entity disambiguation task aims to partition the records of multiple real-life persons so that each partition contains records pertaining to a unique person. Most of the existing solutions for this task operate in a batch mode, where all records to be disambiguated are initially available to the algorithm. However, more realistic settings require that the name disambiguation task be performed in an online fashion, in addition to, being able to identify records of new ambiguous entities having no preexisting records. In this work, we propose a Bayesian non-exhaustive classification framework for solving online name disambiguation task. Our proposed method uses a Dirichlet process prior with a Normal x Normal x Inverse Wishart data model which enables identification of new ambiguous entities who have no records in the training data. For online classification, we use one sweep Gibbs sampler which is very efficient and effective. As a case study we consider bibliographic data in a temporal stream format and disambiguate authors by partitioning their papers into homogeneous groups. Our experimental results demonstrate that the proposed method is better than existing methods for performing online name disambiguation task.


title: Increases in clitoral and vaginal blood flow following clitoral and pelvic plexus nerve stimulations in the female rat

abstract: The objective of this present study is to establish a model in the rat for the study of female clitoral and vaginal vascular changes during sexual excitation. A laser Doppler was used to measure blood flow changes following clitoral and pelvic plexus nerve stimulations. Results show an increase in clitoral blood flow following clitoral nerve (df1=12, df2=108, F=21.4, P<0.001) and pelvic plexus nerve stimulations (n=3). A vaginal blood flow increase is also observed following pelvic plexus nerve stimulations (df1=12, df2=108, F=4.75, P<0.001). The female rat can therefore be used as a model for the study of the physiology, pharmacology and sexual dysfunction relating to blood flow in clitoral and vaginal tissue.


title: 6-DOF Multi-session Visual SLAM using Anchor Nodes

abstract: This paper describes a system for performing multisession ::: visual mapping in large-scale environments. Multi-session ::: mapping considers the problem of combining the results of ::: multiple Simultaneous Localisation and Mapping (SLAM) missions ::: performed repeatedly over time in the same environment. ::: The goal is to robustly combine multiple maps in a common ::: metrical coordinate system, with consistent estimates of uncertainty. ::: Our work employs incremental Smoothing and Mapping ::: (iSAM) as the underlying SLAM state estimator and uses an ::: improved appearance-based method for detecting loop closures ::: within single mapping sessions and across multiple sessions. To ::: stitch together pose graph maps from multiple visual mapping ::: sessions, we employ spatial separator variables, called anchor ::: nodes, to link together multiple relative pose graphs. We provide ::: experimental results for multi-session visual mapping in the MIT ::: Stata Center, demonstrating key capabilities that will serve as ::: a foundation for future work in large-scale persistent visual ::: mapping.


title: Silicon Interposer Warpage Study for 2.5D IC without TSV Utilizing Glass Carrier CTE and Passivation Thickness Tuning

abstract: Warpage control is a key process character for 2.5D IC without Through-Silicon Vias (TSV). For low cost package trend, TSV is not used for advanced 2.5D IC package. Yield is still an issue for 2.5D IC package, Die-Bonding (DB) last process is applied to make sure top die is bonded on good interposer. Therefore, no top die will be sacrificed for low interposer process yield, cost could be more reduced. Re-Distribution Layers (RDL) is first process for DB last interposer, then reversing wafer to do silicone removing, contact pad opening, micro pad plating, die bonding, and compound molding. Because interposer thickness is about 30~50um for 2.5D IC without TSV after silicone removing process, carrier bonding is used before silicone removing. Passivation layer is composited of organic material which coefficient of thermal expansion (CTE) is usually larger than 80ppm. Interposer warpage is substantially increased after silicone removing, which leads that carrier is a major substrate to support RDLs. Glass carrier is discussed in this study and glass CTE and passivation layer thickness are also tuned to optimize interposer warpage after silicone removing. There are two RDL layers and two PBO as passivation material in interposer. Warpage variation is a key evaluation character during this study. From our studies, glass CTE is increased from 3.2ppm to 5.7ppm, and interposer warpage value is reduced and variation could be reduced about 100um at Si removing stage, passivation thickness is increased from X to X+5um, and interposer warpage value is increased and variation could be increased about 50um at Si removing stage. Bi-layer radius formula is used to describe the warpage behavior of under bump metallization (UBM), Bonding, and Si removing stages and is well matched NTI package warpage behavior at UBM and Si removing stages. For Bonding warpage behavior, bi-layer radius theory is interfered by non-perfect adhesion at glue layer, but warpage variation trend is still matched with warpage experiment.


title: SIMPATH: An Efficient Algorithm for Influence Maximization under the Linear Threshold Model

abstract: There is significant current interest in the problem of influence maximization: given a directed social network with influence weights on edges and a number k, find k seed nodes such that activating them leads to the maximum expected number of activated nodes, according to a propagation model. Kempe et al. showed, among other things, that under the Linear Threshold Model, the problem is NP-hard, and that a simple greedy algorithm guarantees the best possible approximation factor in PTIME. However, this algorithm suffers from various major performance drawbacks. In this paper, we propose Simpath, an efficient and effective algorithm for influence maximization under the linear threshold model that addresses these drawbacks by incorporating several clever optimizations. Through a comprehensive performance study on four real data sets, we show that Simpath consistently outperforms the state of the art w.r.t. running time, memory consumption and the quality of the seed set chosen, measured in terms of expected influence spread achieved.


title: SDHM: A hybrid model for spammer detection in Weibo

abstract: As the microblogging service (such as Weibo) is becoming popular, spam becomes a serious problem of affecting the credibility and readability of Online Social Networks. Most existing studies took use of a set of features to identify spam, but without the consideration of the overlap and dependency among different features. In this study, we investigate the problem of spam detection by analyzing real spam dataset collections of Weibo and propose a novel hybrid model of spammer detection, called SDHM, which utilizing significant features, i.e. user behavior information, online social network attributes and text content characteristics, in an organic way. Experiments on real Weibo dataset demonstrate the power of the proposed hybrid model and the promising performance.


title: Genetic Algorithm-Based Motion Estimation Method using Orientations and EMGs for Robot Controls

abstract: Demand for interactive wearable devices is rapidly increasing with the development of smart devices. To accurately utilize wearable devices for remote robot controls, limited data should be analyzed and utilized efficiently. For example, the motions by a wearable device, called Myo device, can be estimated by measuring its orientation, and calculating a Bayesian probability based on these orientation data. Given that Myo device can measure various types of data, the accuracy of its motion estimation can be increased by utilizing these additional types of data. This paper proposes a motion estimation method based on weighted Bayesian probability and concurrently measured data, orientations and electromyograms (EMG). The most probable motion among estimated is treated as a final estimated motion. Thus, recognition accuracy can be improved when compared to the traditional methods that employ only a single type of data. In our experiments, seven subjects perform five predefined motions. When orientation is measured by the traditional methods, the sum of the motion estimation errors is 37.3%; likewise, when only EMG data are used, the error in motion estimation by the proposed method was also 37.3%. The proposed combined method has an error of 25%. Therefore, the proposed method reduces motion estimation errors by 12%.


title: Mechanical design of a compact and dexterous quadruped robot

abstract: This manuscript introduced the modularized structural design of a medium sized quadruped robot with desired compact and dexterous mobility. In detail, the ball screw transmission and the four-bar linkages mechanism were adopted to construct the quadruped robot, and then the relationship between the torque of the knee joint and the torque of the motor was modelled and analyzed. Moreover, the mechanical performance of transmission is analyzed, then the practical result indicates that the shank should be configurated to get close as much as possible to the limited position of extension while walking. The loading analysis further showed that the structural strength of each leg is still enough, when adding 200N external force that similar to the whole weight of the robot to one foot. Based on the optimized design, the mechanical prototype was accomplished, and the corresponding control strategy were briefly illustrated. Finally, locomotory planning in typical walking and when the robot falls down were discussed preliminarily.


title: MEXICA: A computer model of a cognitive account of creative writing

abstract: MEXICA is a computer model that produces frameworks for short stories based on the engagement-reflection cognitive account of writing. During engagement MEXICA generates material guided by content and rhetorical constraints, avoiding the use of explicit goals or story-structure information. During reflection the system breaks impasses, evaluates the novelty and interestingness of the story in progress and verifies that coherence requirements are satisfied. In this way, MEXICA complements and extends those models of computerised story-telling based on traditional problem-solving techniques where explicit goals drive the generation of stories. This paper describes the engagement-reflection account of writing, the general characteristics of MEXICA and reports an evaluation of the program.


title: A Class of Group-Structured LDPC Codes

abstract: 1 This work was supported by NSF grant 99-96222 Abstract A class of graphs is designed using subgroups of the multiplicative group of a prime field GF(p). Instances where (p-1) is divisible by 3 and 5 are used to construct quasi-cyclic LDPC codes with bit degree 3 and parity degree 5, among them a [155,64,20] code. While the girth of a graph in this class cannot be greater than twelve, for many practical lengths the graphs have a relatively large girth and a lowdensity of short cycles. Simulation studies show that at short to moderate lengths these codes’ performance meets or surpasses that of randomly generated regular (3,5) LDPC codes when used with sum-product algorithm decoders.


title: Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning

abstract: Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process and make recommendations following a fixed strategy. In this paper, we propose a novel recommender system with the capability of continuously improving its strategies during the interactions with users. We model the sequential interactions between users and a recommender system as a Markov Decision Process (MDP) and leverage Reinforcement Learning (RL) to automatically learn the optimal strategies via recommending trial-and-error items and receiving reinforcements of these items from users' feedback. Users' feedback can be positive and negative and both types of feedback have great potentials to boost recommendations. However, the number of negative feedback is much larger than that of positive one; thus incorporating them simultaneously is challenging since positive feedback could be buried by negative one. In this paper, we develop a novel approach to incorporate them into the proposed deep recommender system (DEERS) framework. The experimental results based on real-world e-commerce data demonstrate the effectiveness of the proposed framework. Further experiments have been conducted to understand the importance of both positive and negative feedback in recommendations.


title: A Development of HL7 Middleware for Medical Device Communication

abstract: An efficient middleware has been developed to support medical device communication. HL7 is known to be best international standard to facilitate clinical device data transfer to information systems in hospital. In this study, we developed a middleware with capability of receiving data from mCare 300 Vital signs monitoring device and converting to HL7 data type format.


Multi-level Wavelet-CNN for Image Restoration ( Supplementary Material )


title: Network virtualization for cloud computing

abstract: Cloud computing enables a transparent access to information technology (IT) services such that the users do not need to know the location and characteristics of the relevant resources. While IT resource virtualization and service abstraction have been widely investigated, data transport within the cloud and its efficient control have not received much attention in the technical literature. In fact, connectivity is, itself, a service that contributes to the overall performance of the cloud. This paper introduces a novel classification of the Network as a Service (NaaS) such that it can be orchestrated with other cloud services. Then, it proposes a network virtualization platform (NVP) as the mediation layer able to provide NaaS to cloud computing by exploiting the functionality provided by control plane (CP)-enabled networks. In particular, the proposed NVP maps the end-point addresses and perceived Quality of Service parameters of a NaaS requests in the parameters characterizing the connectivity as viewed by transport networks using the information obtained from the CP at the boundary of the network. The NVP uses these parameters to fulfill connectivity requests to the CP. Finally, this paper presents a complete design from both the software implementation and network signaling perspective of two use cases in which NaaS is involved as stand-alone facility for the connectivity service provisioning or is combined with other cloud services for a storage service provisioning.


title: RoboEarth Semantic Mapping: A Cloud Enabled Knowledge-Based Approach

abstract: The vision of the RoboEarth project is to design a knowledge-based system to provide web and cloud services that can transform a simple robot into an intelligent one. In this work, we describe the RoboEarth semantic mapping system. The semantic map is composed of: 1) an ontology to code the concepts and relations in maps and objects and 2) a SLAM map providing the scene geometry and the object locations with respect to the robot. We propose to ground the terminological knowledge in the robot perceptions by means of the SLAM map of objects. RoboEarth boosts mapping by providing: 1) a subdatabase of object models relevant for the task at hand, obtained by semantic reasoning, which improves recognition by reducing computation and the false positive rate; 2) the sharing of semantic maps between robots; and 3) software as a service to externalize in the cloud the more intensive mapping computations, while meeting the mandatory hard real time constraints of the robot. To demonstrate the RoboEarth cloud mapping system, we investigate two action recipes that embody semantic map building in a simple mobile robot. The first recipe enables semantic map building for a novel environment while exploiting available prior information about the environment. The second recipe searches for a novel object, with the efficiency boosted thanks to the reasoning on a semantically annotated map. Our experimental results demonstrate that, by using RoboEarth cloud services, a simple robot can reliably and efficiently build the semantic maps needed to perform its quotidian tasks. In addition, we show the synergetic relation of the SLAM map of objects that grounds the terminological knowledge coded in the ontology.


title: Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification

abstract: Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit certain stationarity properties in time such as smoke, vegetation and fire. The analysis of DT is important for recognition, segmentation, synthesis or retrieval for a range of applications including surveillance, medical imaging and remote sensing. Deep learning methods have shown impressive results and are now the new state of the art for a wide range of computer vision tasks including image and video recognition and segmentation. In particular, Convolutional Neural Networks (CNNs) have recently proven to be well suited for texture analysis with a design similar to a filter bank approach. In this paper, we develop a new approach to DT analysis based on a CNN method applied on three orthogonal planes x y , xt and y t . We train CNNs on spatial frames and temporal slices extracted from the DT sequences and combine their outputs to obtain a competitive DT classifier. Our results on a wide range of commonly used DT classification benchmark datasets prove the robustness of our approach. Significant improvement of the state of the art is shown on the larger datasets.


title: Plane-wave ultrasound beamforming using a nonuniform fast fourier transform

abstract: Beamforming of plane-wave ultrasound echo signals in the Fourier domain provides fast and accurate image reconstruction. Conventional implementations perform a k-space interpolation from the uniform sampled grid to a nonuniform acoustic dispersion grid. In this paper, we demonstrate that this step can be replaced by a nonuniform Fourier transform. We study the performance of the nonuniform fast Fourier transform (NUFFT) in terms of signal-to-noise ratio and computational cost, and show that the NUFFT offers an advantage in the trade-off between speed and accuracy, compared with other frequency-domain beamforming strategies.


title: The Evolving Tree—A Novel Self-Organizing Network for Data Analysis

abstract: The Self-Organizing Map (SOM) is one of the best known and most popular neural network-based data analysis tools. Many variants of the SOM have been proposed, like the Neural Gas by Martinetz and Schulten, the Growing Cell Structures by Fritzke, and the Tree-Structured SOM by Koikkalainen and Oja. The purpose of such variants is either to make a more flexible topology, suitable for complex data analysis problems or to reduce the computational requirements of the SOM, especially the time-consuming search for the best-matching unit in large maps. We propose here a new variant called the Evolving Tree which tries to combine both of these advantages. The nodes are arranged in a tree topology that is allowed to grow when any given branch receives a lot of hits from the training vectors. The search for the best matching unit and its neighbors is conducted along the tree and is therefore very efficient. A comparison experiment with high dimensional real world data shows that the performance of the proposed method is better than some classical variants of SOM.


title: Sign Language Recognition Using Convolutional Neural Networks

abstract: There is an undeniable communication problem between the Deaf community and the hearing majority. Innovations in automatic sign language recognition try to tear down this communication barrier. Our contribution considers a recognition system using the Microsoft Kinect, convolutional neural networks (CNNs) and GPU acceleration. Instead of constructing complex handcrafted features, CNNs are able to automate the process of feature construction. We are able to recognize 20 Italian gestures with high accuracy. The predictive model is able to generalize on users and surroundings not occurring during training with a cross-validation accuracy of 91.7%. Our model achieves a mean Jaccard Index of 0.789 in the ChaLearn 2014 Looking at People gesture spotting competition.


title: Experimental Design of a Nonlinear Control Technique for Three-Phase Shunt Active Power Filter

abstract: This paper presents a nonlinear control technique for a three-phase shunt active power filter (SAPF). The method provides compensation for reactive, unbalanced, and harmonic load current components. A proportional-integral (PI) control law is derived through linearization of the inherently nonlinear SAPF system model, so that the tasks of current control dynamics and dc capacitor voltage dynamics become decoupled. This decoupling allows us to control the SAPF output currents and the dc bus voltage independently of each other, thereby providing either one of these decoupled subsystems a dynamic response that significantly slower than that of the other. To overcome the drawbacks of the conventional method, a computational control delay compensation method, which delaylessly and accurately generates the SAPF reference currents, is proposed. The first step is to extract the SAPF reference currents from the sensed nonlinear load currents by applying the synchronous reference frame method, where a three-phase diode bridge rectifier with R-L load is taken as the nonlinear load, and then, the reference currents are modified, so that the delay will be compensated. The converter, which is controlled by the described control strategy, guarantees balanced overall supply currents, unity displacement power factor, and reduced harmonic load currents in the common coupling point. Various simulation and experimental results demonstrate the high performance of the nonlinear controller.


title: Efficient Learning of Sparse Representations with an Energy-Based Model

abstract: We describe a novel unsupervised method for learning sparse, overcomplete features. The model uses a linear encoder, and a linear decoder preceded by a sparsifying non-linearity that turns a code vector into a quasi-binary sparse code vector. Given an input, the optimal code minimizes the distance between the output of the decoder and the input patch while being as similar as possible to the encoder output. Learning proceeds in a two-phase EM-like fashion: (1) compute the minimum-energy code vector, (2) adjust the parameters of the encoder and decoder so as to decrease the energy. The model produces “stroke detectors” when trained on handwritten numerals, and Gabor-like filters when trained on natural image patches. Inference and learning are very fast, requiring no preprocessing, and no expensive sampling. Using the proposed unsupervised method to initialize the first layer of a convolutional network, we achieved an error rate slightly lower than the best reported result on the MNIST dataset. Finally, an extension of the method is described to learn topographical filter maps


title: Applications of Compressed Sensing in Communications Networks

abstract: This paper presents a tutorial for CS applications in communications networks. The Shannon's sampling theorem states that to recover a signal, the sampling rate must be as least the Nyquist rate. Compressed sensing (CS) is based on the surprising fact that to recover a signal that is sparse in certain representations, one can sample at the rate far below the Nyquist rate. Since its inception in 2006, CS attracted much interest in the research community and found wide-ranging applications from astronomy, biology, communications, image and video processing, medicine, to radar. CS also found successful applications in communications networks. CS was applied in the detection and estimation of wireless signals, source coding, multi-access channels, data collection in sensor networks, and network monitoring, etc. In many cases, CS was shown to bring performance gains on the order of 10X. We believe this is just the beginning of CS applications in communications networks, and the future will see even more fruitful applications of CS in our field.


title: Transformation of rice mediated by Agrobacterium tumefaciens

abstract: Agrobacterium tumefaciens has been routinely utilized in gene transfer to dicotyledonous plants, but monocotyledonous plants including important cereals were thought to be recalcitrant to this technology as they were outside the host range of crown gall. Various challenges to infect monocotyledons including rice with Agrobacterium had been made in many laboratories, but the results were not conclusive until recently. Efficient transformation protocols mediated by Agrobacterium were reported for rice in 1994 and 1996. A key point in the protocols was the fact that tissues consisting of actively dividing, embryonic cells, such as immature embryos and calli induced from scutella, were co-cultivated with Agrobacterium in the presence of acetosyringonc, which is a potent inducer of the virulence genes. It is now clear that Agrobacterium is capable of transferring DNA to monocotyledons if tissues containing 'competent' cells are infected. The studies of transformation of rice suggested that numerous factors including genotype of plants, types and ages of tissues inoculated, kind of vectors, strains of Agrobacterium, selection marker genes and selective agents, and various conditions of tissue culture, are of critical importance. Advantages of the Agrobacterium-mediated transformation in rice, like on dicotyledons, include the transfer of pieces of DNA with defined ends with minimal rearrangements, the transfer of relatively large segments of DNA, the integration of small numbers of copies of genes into plant chromosomes, and high quality and fertility of transgenic plants. Delivery of foreign DNA to rice plants via A. tumefaciens is a routine technique in a growing number of laboratories. This technique will allow the genetic improvement of diverse varieties of rice, as well as studies of many aspects of the molecular biology of rice.


DeepUrbanMomentum: An Online Deep-Learning System for Short-Term Urban Mobility Prediction


title: Modeling Litz-wire winding losses in high-frequency power inductors

abstract: The parasitic effects in stranded, twisted, and Litz wire windings operating at high frequencies are studied. The skin and proximity effects that cause the winding parasitic resistance of an inductor to increase with the operating frequency are considered. An expression for the AC resistance as a function of the operating frequency is given. The measured and calculated values of the inductor AC resistance and quality factor are plotted versus frequency and compared. The theoretical results were in good agreement with those experimentally measured.


title: Is Pretraining Necessary for hyperspectral image classification?

abstract: We address two questions for training a convolutional neural network (CNN) for hyperspectral image classification: i) is it possible to build a pre-trained network? and ii) is the pretraining effective in furthering the performance? To answer the first question, we have devised an approach that pre-trains a network on multiple source datasets that differ in their hyperspectral characteristics and fine-tunes on a target dataset. This approach effectively resolves the architectural issue that arises when transferring meaningful information between the source and the target networks. To answer the second question, we carried out several ablation experiments. Based on the experimental results, a network trained from scratch performs as good as a network fine-tuned from a pre-trained network. However, we observed that pre-training the network has its own advantage in achieving better performances when deeper networks are required.


title: Elastic functional coding of human actions: From vector-fields to latent variables

abstract: Human activities observed from visual sensors often give rise to a sequence of smoothly varying features. In many cases, the space of features can be formally defined as a manifold, where the action becomes a trajectory on the manifold. Such trajectories are high dimensional in addition to being non-linear, which can severely limit computations on them. We also argue that by their nature, human actions themselves lie on a much lower dimensional manifold compared to the high dimensional feature space. Learning an accurate low dimensional embedding for actions could have a huge impact in the areas of efficient search and retrieval, visualization, learning, and recognition. Traditional manifold learning addresses this problem for static points in ℝn, but its extension to trajectories on Riemannian manifolds is non-trivial and has remained unexplored. The challenge arises due to the inherent non-linearity, and temporal variability that can significantly distort the distance metric between trajectories. To address these issues we use the transport square-root velocity function (TSRVF) space, a recently proposed representation that provides a metric which has favorable theoretical properties such as invariance to group action. We propose to learn the low dimensional embedding with a manifold functional variant of principal component analysis (mfPCA). We show that mf-PCA effectively models the manifold trajectories in several applications such as action recognition, clustering and diverse sequence sampling while reducing the dimensionality by a factor of ∼ 250×. The mfPCA features can also be reconstructed back to the original manifold to allow for easy visualization of the latent variable space.


title: Hierarchical reinforcement learning for situated natural language generation

abstract: Natural Language Generation systems in interactive settings often face a multitude of choices, given that the communicative effect of each utterance they generate depends crucially on the interplay between its physical circumstances, addressee and interaction history. This is particularly true in interactive and situated settings. In this paper we present a novel approach for situated Natural Language Generation in dialogue that is based on hierarchical reinforcement learning and learns the best utterance for a context by optimisation through trial and error. The model is trained from human–human corpus data and learns particularly to balance the trade-off between efficiency and detail in giving instructions: the user needs to be given sufficient information to execute their task, but without exceeding their cognitive load. We present results from simulation and a task-based human evaluation study comparing two different versions of hierarchical reinforcement learning: One operates using a hierarchy of policies with a large state space and local knowledge, and the other additionally shares knowledge across generation subtasks to enhance performance. Results show that sharing knowledge across subtasks achieves better performance than learning in isolation, leading to smoother and more successful interactions that are better perceived by human users.


title: CNN-Based Joint Clustering and Representation Learning with Feature Drift Compensation for Large-Scale Image Data

abstract: Given a large unlabeled set of images, how to efficiently and effectively group them into clusters based on extracted visual representations remains a challenging problem. To address this problem, we propose a convolutional neural network (CNN) to jointly solve clustering and representation learning in an iterative manner. In the proposed method, given an input image set, we first randomly pick k samples and extract their features as initial cluster centroids using the proposed CNN with an initial model pre-trained from the ImageNet dataset. Mini-batch k-means is then performed to assign cluster labels to individual input samples for a mini-batch of images randomly sampled from the input image set until all images are processed. Subsequently, the proposed CNN simultaneously updates the parameters of the proposed CNN and the centroids of image clusters iteratively based on stochastic gradient descent. We also proposed a feature drift compensation scheme to mitigate the drift error caused by feature mismatch in representation learning. Experimental results demonstrate the proposed method outperforms start-of-the-art clustering schemes in terms of accuracy and storage complexity on large-scale image sets containing millions of images.


title: Technical features for social commerce website

abstract: In a social commerce website, technical features can be considered as one of the key players in providing the intended online social shopping environment. Amongst them are rating and review tools, share and like buttons, social wish lists, social login buttons, activity feeds, and many more. However, many existing studies tend to emphasize the features in terms of the functionalities in general manners. In other words, there is no clear understanding provided on both functional and non-functional features for social commerce website. Thus, the aim of this study is to explore the potential technical features that a social commerce website should have in both functional and non-functional aspects based on current literature. It is hoped this paper will give some insight to researchers and practitioners as well as a design guideline. The validation of these features will be conducted for future work to secure the needs for selecting effective technical features for a social commerce website.


title: Generalizing Mumford-Shah Model for Multiphase Piecewise Smooth Image Segmentation

abstract: This paper concerns multiphase piecewise smooth image segmentation with intensity inhomogeneities. Traditional methods based on the Mumford–Shah (MS) model require solving complicated diffusion equations evolving in irregular subdomains, leading to significant difficulties in efficient and accurate segmentation, especially in multiphase scenarios. In this paper, we propose a general framework to modify the MS model by using smoothing operators that can avoid the complicated implementation and inaccurate segmentation of traditional approaches. A detailed analysis connecting the smoothing operators and the diffusion equations is given to justify the modification. In addition, we present an efficient algorithm based on the direct augmented Lagrangian method, which requires fewer parameters than the commonly used augmented Lagrangian method. Typically, the smoothing operator in the general model is chosen to be Gaussian kernel, the bilateral kernel, and the directional diffusion kernel, respectively. Ample numerical results are provided to demonstrate the efficiency and accuracy of the modified model and the proposed minimization algorithm through various comparisons with existing approaches.


title: BitTorrent Darknets

abstract: A private BitTorrent site (also known as a "Bit Torrent darknet") is a collection of torrents that can only be accessed by members of the darknet community. The private BitTorrent sites also have incentive policies which encourage users to continue to seed files after completing downloading. Although there are at least 800 independent BitTorrent darknets in the Internet, they have received little attention in the research community to date. We examine BitTorrent darknets from macroscopic, medium-scopic and microscopic perspectives. For the macroscopic analysis, we consider 800+ private sites to obtain a broad picture of the darknet landscape, and obtain a rough estimate of the total number of files, accounts, and simultaneous peers within the entire darknet landscape. Although the size of each private site is relatively small, we find the aggregate size of the darknet landscape to be surprisingly large. For the medium-scopic analysis, we investigate content overlap between four private sites and the public BitTorrent ecosystem. For the microscopic analysis, we explore in-depth one private site and examine its user behavior. We observe that the seed-to-leecher ratios and upload-to-download ratios are much higher than in the public ecosystem. The macroscopic, medium-scopic and microscopic analyses when combined provide a vivid picture of the darknet landscape, and provide insight into how the darknet landscape differs from the public BitTorrent ecosystem.


title: The emerging role of electronic marketplaces on the Internet

abstract: Markets play a central role in the economy, facilitating the exchange of information, goods, services and payments. In the process, they create economic value for buyers, sellers, market intermediaries and for society at large. Recent years have seen a dramatic increase in the role of information technology in markets, both in traditional markets, and in the emergence of electronic marketplaces, such as the multitude of Internet-based online auctions.


title: Sex differences in mate preferences revisited: do people know what they initially desire in a romantic partner?

abstract: In paradigms in which participants state their ideal romantic-partner preferences or examine vignettes and photographs, men value physical attractiveness more than women do, and women value earning prospects more than men do. Yet it remains unclear if these preferences remain sex differentiated in predicting desire for real-life potential partners (i.e., individuals whom one has actually met). In the present study, the authors explored this possibility using speed dating and longitudinal follow-up procedures. Replicating previous research, participants exhibited traditional sex differences when stating the importance of physical attractiveness and earning prospects in an ideal partner and ideal speed date. However, data revealed no sex differences in the associations between participants’ romantic interest in real-life potential partners (met during and outside of speed dating) and the attractiveness and earning prospects of those partners. Furthermore, participants’ ideal preferences, assessed before the speed-dating event, failed to predict what inspired their actual desire at the event. Results are discussed within the context of R. E. Nisbett and T. D. Wilson’s (1977) seminal article: Even regarding such a consequential aspect of mental life as romantic-partner preferences, people may lack introspective awareness of what influences their judgments and behavior.


title: Android anti-forensics through a local paradigm

abstract: Mobile devices are among the most disruptive technologies of the last years, gaining even more diffusion and success in the daily life of a wide range of people categories. Unfortunately, while the number of mobile devices implicated in crime activities is relevant and growing, the capability to perform the forensic analysis of such devices is limited both by technological and methodological problems. In this paper, we focus on Anti-Forensic techniques applied to mobile devices, presenting some fully automated instances of such techniques to Android devices. Furthermore, we tested the effectiveness of such techniques versus both the cursory examination of the device and some acquisition tools.


title: An Efficient Centroid-Based Routing Protocol for Energy Management in WSN-Assisted IoT

abstract: Wireless sensor networks (WSNs) distribute hundreds to thousands of inexpensive micro-sensor nodes in their regions, and these nodes are important parts of Internet of Things (IoT). In WSN-assisted IoT, the nodes are resource constrained in many ways, such as storage resources, computing resources, energy resources, and so on. Robust routing protocols are required to maintain a long network lifetime and achieve higher energy utilization. In this paper, we propose a new energy-efficient centroid-based routing protocol (EECRP) for WSN-assisted IoT to improve the performance of the network. The proposed EECRP includes three key parts: a new distributed cluster formation technique that enables the self-organization of local nodes, a new series of algorithms for adapting clusters and rotating the cluster head based on the centroid position to evenly distribute the energy load among all sensor nodes, and a new mechanism to reduce the energy consumption for long-distance communications. In particular, the residual energy of nodes is considered in EECRP for calculating the centroid′s position. Our simulation results indicate that EECRP performs better than LEACH, LEACH-C, and GEEC. In addition, EECRP is suitable for networks that require a long lifetime and whose base station (BS) is located in the network.


title: mimir: a market-based real-time question and answer service

abstract: Community-based question and answer (Q&A) systems facilitate information exchange and enable the creation of reusable knowledge repositories. While these systems are growing in usage and are changing how people find and share information, current designs are inefficient, wasting the time and attention of their users. Furthermore, existing systems do not support signaling and screening of joking and non-serious questions. Coupling Q&A services with instant and text messaging for faster questions and answers may exacerbate these issues, causing Q&A services to incur high interruption costs on their users. In this paper we present the design and evaluation of a market-based real-time Q&A system. We compared its use to a similar Q&A system without a market. We found that while markets can reduce wasted resources by reducing the number of less important questions and low quality answers, it may also reduce the socially conducive questions and usages that are vital to sustaining a Q&A community.


title: New two-inductor boost converter with auxiliary transformer

abstract: A new, two-inductor, two-switch boost converter topology and its variations suitable for applications with a large difference between the input and output voltage are described. The output voltage regulation of the proposed converters is achieved in a wide load and input-voltage range with constant-frequency control by employing an auxiliary transformer that couples the current paths of the two boost inductors.


title: Dependency Parsing Schemata and Mildly Non-Projective Dependency Parsing

abstract: We introduce dependency parsing schemata, a formal framework based on Sikkel's parsing schemata for constituency parsers, which can be used to describe, analyze, and compare dependency parsing algorithms. We use this framework to describe several well-known projective and non-projective dependency parsers, build correctness proofs, and establish formal relationships between them. We then use the framework to define new polynomial-time parsing algorithms for various mildly non-projective dependency formalisms, including well-nested structures with their gap degree bounded by a constant k in time O(n5+2k), and a new class that includes all gap degree k structures present in several natural language treebanks (which we call mildly ill-nested structures for gap degree k) in time O(n4+3k). Finally, we illustrate how the parsing schema framework can be applied to Link Grammar, a dependency-related formalism.


title: Wideband Properties of Fractal Bowtie Dipoles

abstract: This paper explores constructing fractal bowtie dipoles using the pattern of the fractal binary tree. To achieve desirable wide impedance bandwidth, structural parameters of fractal bowtie antennas are optimized in an automated design, making use of the Genetic Algorithm (GA) in conjunction with NEC (Numerical Electromagnetic Codes) and cluster parallel computation. Several design results are presented and exhibit quite promising wideband properties. Their simulated 2:1 VSWR impedance bandwidths range from 3.2:1 to 5.3:1, which far surpass the bandwidth of a conventional bowtie dipole.


title: Design and control of a multisensory five-finger prosthetic hand

abstract: A five-fingered, multi-sensory and biomimetic prosthetic hand is presented in this paper. The HIT-Hand is a multisensory and integrated five fingered hand with in total 11 DOFs. The hand has five fingers and each of them can be driven individually by the embedded actuation and control system fitted in the palm. Each finger has 2 DOFs and two joints, the two joints are mechanically coupled by a cable driving. The thumb has an additional DOF to realize the motion relative to the palm. All actuators are integrated in the finger's base or the finger's body directly, the electronics and communication controllers are fully integrated in the fingertip in order to realize modularity of the hand and minimize weight and amount of cables needed for a hand. The Prosthetic Hand is controlled by a multiprocessor controller based on FPGA/DSP, the control system is composed of finger control system and palm control system, and each finger control system is integrated in the fingertip. This anthropomorphic prosthetic hand controlled by means of myoelectric commands, and the hierarchical shared control strategies is used. The impedance control approach allows the fingers to behave as virtual springs. Experiments showed that users were able to successfully operate the device in the hierarchical control strategies, and that the grasp success increased with more interactive control.


title: A direct scattering model for tracking vehicles with high-resolution radars

abstract: In advanced driver assistance systems and autonomous driving, reliable environment perception and object tracking based on radar is fundamental. High-resolution radar sensors often provide multiple measurements per object. Since in this case traditional point tracking algorithms are not applicable any more, novel approaches for extended object tracking emerged in the last few years. However, they are primarily designed for lidar applications or omit the additional Doppler information of radars. Classical radar based tracking methods using the Doppler information are mostly designed for point tracking of parallel traffic. The measurement model presented in this paper is developed to track vehicles of approximately rectangular shape in arbitrary traffic scenarios including parallel and cross traffic. In addition to the kinematic state, it allows to determine and track the geometric state of the object. Using the Doppler information is an important component in the model. Furthermore, it neither requires measurement preprocessing, data clustering, nor explicit data association. For object tracking, a Rao-Blackwellized particle filter (RBPF) adapted to the measurement model is presented.


title: Capacity Evaluation for IEEE 802.16e Mobile

abstract: We present a simple analytical method for capacity evaluation of IEEE 802.16e Mobile WiMAX networks. Various overheads that impact the capacity are explained and methods to reduce these overheads are also presented. The advantage of a simple model is that the effect of each decision and sensitivity to various parameters can be seen easily. We illustrate the model by estimating the capacity for three sample applications--Mobile TV, VoIP, and data. The analysis process helps explain various features of IEEE 802.16e Mobile WiMAX. It is shown that proper use of overhead reducing mechanisms and proper scheduling can make an order of magnitude difference in performance. This capacity evaluation method can also be used for validation of simulation models.


title: Deception detection using a multimodal approach

abstract: In this paper we address the automatic identification of deceit by using a multimodal approach. We collect deceptive and truthful responses using a multimodal setting where we acquire data using a microphone, a thermal camera, as well as physiological sensors. Among all available modalities, we focus on three modalities namely, language use, physiological response, and thermal sensing. To our knowledge, this is the first work to integrate these specific modalities to detect deceit. Several experiments are carried out in which we first select representative features for each modality, and then we analyze joint models that integrate several modalities. The experimental results show that the combination of features from different modalities significantly improves the detection of deceptive behaviors as compared to the use of one modality at a time. Moreover, the use of non-contact modalities proved to be comparable with and sometimes better than existing contact-based methods. The proposed method increases the efficiency of detecting deceit by avoiding human involvement in an attempt to move towards a completely automated non-invasive deception detection process.


title: Playful bottle: a mobile social persuasion system to motivate healthy water intake

abstract: This study of mobile persuasion system explores the use of a mobile phone, when attached to an everyday object used by an everyday behavior, becomes a tool to sense and influence that behavior. This mobile persuasion system, called Playful Bottle system, makes use of a mobile phone attached to an everyday drinking mug and motivates office workers to drink healthy quantities of water. A camera and accelerometer sensors in the phone are used to build a vision/motion-based water intake tracker to detect the amount and regularity of water consumed by the user. Additionally, the phone includes hydration games in which natural drinking actions are used as game input. Two hydration games are developed: a single-user TreeGame with automated computer reminders and a multi-user ForestGame with computer-mediated social reminders from members of the group playing the game. Results from 7-week user study with 16 test subjects suggest that both hydration games are effective for encouraging adequate and regular water intake by users. Additionally, results of this study suggest that adding social reminders to the hydration game is more effective than system reminders alone.


title: Building interpreters by composing monads

abstract: We exhibit a set of functions coded in Haskell that can be used as building blocks to construct a variety of interpreters for Lisp-like languages. The building blocks are joined merely through functional composition. Each building block contributes code to support a specific feature, such as numbers, continuations, functions calls, or nondeterminism. The result of composing some number of building blocks is a parser, an interpreter, and a printer that support exactly the expression forms and data types needed for the combined set of features, and no more. The data structures are organized as pseudomonads , a generalization of monads that allows composition. Functional composition of the building blocks implies type composition of the relevant pseudomonads. Our intent was that the Haskell type resolution system ought to be able to deduce the appropriate data types automatically. Unfortunately there is a deficiency in current Haskell implementations related to recursive data types: circularity must be reflected statically in the type definitions. We circumvent this restriction by applying a purpose-built program simplifier that performs partial evaluation and a certain amount of program algebra. We construct a wide variety of interpreters in the style of Wadler by starting with the building blocks and a page of boiler-plate code, writing three lines of code (one to specify the building blocks and two to (redundantly) specify type compositions), and then applying the simplifier. The resulting code is acceptable Haskell code. We have tested a dozen different interpreters with various combinations of features. In this paper we discuss the overall code structuring strategy, exhibit several building blocks, briefly describe the partial evaluator, and present a number of automatically generated interpreters.