title: OBFS: A File System for Object-Based Storage Devices

abstract: The object-based storage model, in which files are made up of o ne or more data objects stored on self-contained Object-Based Storage Devices (OSDs), is em erging as an architecture for distributed storage systems. The workload presented to the OSDs will be q uite different from that of generalpurpose file systems, yet many distributed file systems employ general-purpose file systems as their underlying file system. We present OBFS, a small and highly ef ficient file system designed for use in OSDs. Our experiments show that our user-level implement ation of OBFS outperforms Linux Ext2 and Ext3 by a factor of two or three, and while OBFS is 1/25 the size of XFS, it provides only slightly lower read performance and 10%‐40% higher write pe rformance.


title: Single- and Dual-Port 50-100-GHz Integrated Vector Network Analyzers With On-Chip Dielectric Sensors

abstract: This work presents a single- and dual-port fully integrated millimeter-wave ultra-broadband vector network analyzer. Both circuits, realized in a commercial 0.35-μm SiGe:C technology with an ft/fmax of 170/250 GHz, cover an octave frequency bandwidth between 50-100 GHz. The presented chips can be configured to measure complex scattering parameters of external devices or determine the permittivity of different materials using an integrated millimeter-wave dielectric sensor. Both devices are based on a heterodyne architecture that achieves a receiver dynamic range of 57-72.5 dB over the complete design frequency range. Two integrated frequency synthesizer modules are included in each chip that enable the generation of the required test and local-oscillator millimeter-wave signals. A measurement 3σ statistical phase error lower than 0.3 ° ::: is achieved. Automated measurement of changes in the dielectric properties of different materials is demonstrated using the proposed systems. The single- and dual-port network analyzer chips have a current consumption of 600 and 700 mA, respectively, drawn from a single 3.3-V supply.


title: A survey of outlier detection methodologies and their applications

abstract: Outlier detection is a data analysis method and has been used to detect and remove anomalous observations from data. In this paper, we firstly introduced some current mainstream outlier detection methodologies, i.e. statistical-based, distance-based, and density-based. Especially, we analyzed distance-based approach and reviewed several kinds of peculiarity factors in detail. Then, we introduced sampled peculiarity factor (SPF) and a SPF-based outlier detection algorithm in order to explore a lower-computational complexity approach to compute peculiarity factor for real world needs in our future work.


title: Data Driven Energy Efficiency in Buildings

abstract: Buildings across the world contribute significantly to the overall energy consumption and are thus stakeholders in grid operations. Towards the development of a smart grid, utilities and governments across the world are encouraging smart meter deployments. High resolution (often at every 15 minutes) data from these smart meters can be used to understand and optimize energy consumptions in buildings. In addition to smart meters, buildings are also increasingly managed with Building Management Systems (BMS) which control different sub-systems such as lighting and heating, ventilation, and air conditioning (HVAC). With the advent of these smart meters, increased usage of BMS and easy availability and widespread installation of ambient sensors, there is a deluge of building energy data. This data has been leveraged for a variety of applications such as demand response, appliance fault detection and optimizing HVAC schedules. Beyond the traditional use of such data sets, they can be put to effective use towards making buildings smarter and hence driving every possible bit of energy efficiency. Effective use of this data entails several critical areas from sensing to decision making and participatory involvement of occupants. Picking from wide literature in building energy efficiency, we identify five crust areas (also referred to as 5 Is) for realizing data driven energy efficiency in buildings : i) instrument optimally; ii) interconnect sub-systems; iii) inferred decision making; iv) involve occupants and v) intelligent operations. We classify prior work as per these 5 Is and dis-cuss challenges, opportunities and applications across them. Building upon these 5 Is we discuss a well studied problem in building energy efficiency -non-intrusive load monitoring (NILM) and how research in this area spans across the 5 Is.


title: A fourth law of robotics? Copyright and the law and ethics of machine co-production

abstract: Jon Bing was not only a pioneer in the field of artificial intelligence and law and the legal regulation of technology. He was also an accomplished author of fiction, with an oeuvre spanning from short stories and novels to theatre plays and even an opera. As reality catches up with the imagination of science fiction writers who have anticipated a world shared by humans and non-human intelligences of their creation, some of the copyright issues he has discussed in his academic capacity take on new resonance. How will we regulate copyright when robots are producers and consumers of art? This paper tries to give a sketch of the problem and hints at possible answers that are to a degree inspired by Bing's academic and creative writing.


title: Wireless Sensors Networks for Internet of Things

abstract: The Internet is smoothly migrating from an Internet of people towards an Internet of Things (IoT). By 2020, it is expected to have 50 billion things connected to the Internet. However, such a migration induces a strong level of complexity when handling interoperability between the heterogeneous Internet things, e.g., RFIDs (Radio Frequency Identification), mobile handheld devices, and wireless sensors. In this context, a couple of standards have been already set, e.g., IPv6, 6LoWPAN (IPv6 over Low power Wireless Personal Area Networks), and M2M (Machine to Machine communications). In this paper, we focus on the integration of wireless sensor networks into IoT, and shed further light on the subtleties of such integration. We present a real-world test bed deployment where wireless sensors are used to control electrical appliances in a smart building. Encountered problems are highlighted and suitable solutions are presented.


title: Leadership styles in nursing management: preferred and perceived

abstract: AIM ::: The aim was to explore nursing leadership regarding what nurse managers and subordinates see as important and to explore subordinates' opinions of their nurse manager's performance in reality. Background The manager's style can be fundamental for subordinates' acceptance of change and in motivating them to achieve stated visions and goals and high quality of care. ::: ::: ::: METHODS ::: Nurse managers (n=77) and 10 of each included nurse manager's subordinates received a questionnaire to assess 'preferred' leadership behaviour in three dimensions: change, production and employee/relation orientations. The same questionnaire was used to assess subordinates' opinions of their manager's leadership behaviour. ::: ::: ::: RESULTS ::: There are statistically significant differences in opinions of preferred leadership between managers and subordinates, especially related to production and relation orientation. The subordinates' perception of real leadership behaviour has lower mean values than their preferred leadership behaviour in all three dimensions. ::: ::: ::: CONCLUSIONS ::: Subordinates prefer managers with more clearly expressed leadership behaviour than managers themselves prefer and demonstrate.


title: ERNN: A Biologically Inspired Feedforward Neural Network to Discriminate Emotion From EEG Signal

abstract: Emotions play an important role in human cognition, perception, decision making, and interaction. This paper presents a six-layer biologically inspired feedforward neural network to discriminate human emotions from EEG. The neural network comprises a shift register memory after spectral filtering for the input layer, and the estimation of coherence between each pair of input signals for the hidden layer. EEG data are collected from 57 healthy participants from eight locations while subjected to audio-visual stimuli. Discrimination of emotions from EEG is investigated based on valence and arousal levels. The accuracy of the proposed neural network is compared with various feature extraction methods and feedforward learning algorithms. The results showed that the highest accuracy is achieved when using the proposed neural network with a type of radial basis function.


title: ORIGINAL ARTICLE Effects of Novelty and Its Dimensions on Aesthetic Preference in Product Design

abstract: IntroductionAn understanding of how to create beautiful objects, ones that elicit aesthetic responses such as "sensory pleasure and delight" (Hekkert & Leder, 2008), is fundamental to the profession of design. All design disciplines - including architecture, product design, visual communications design, interface design, animation - combine a knowledge of aesthetics with knowledge of the different embodying technologies of that discipline. In a more commercial context, product appearance has been recognized as an important factor in the success of a product (Bloch, 1995; Hertenstein, Platt, & Veryzer, 2005). By changing different aspects of product appearance, including form, material and color, designers try to communicate messages and elicit responses from consumers (Crilly, Moultrie, & Clarkson, 2004; Creusen & Schoormans, 2005; Hsiao & Chen, 2006). As pointed out by Raymond Loewy in his famous MAYA - "Most Advanced, Yet Acceptable" - principle, a successful design must be as innovative as possible, but not so much as to be considered unacceptable (Loewy, 1951). To achieve higher aesthetic preference among consumers, designers should aim for the best combination of typicality and novelty (Hekkert, Snelders, & van Wieringen, 2003; Hekkert & Leder, 2008).Several studies in the past have investigated the relationship between novelty and aesthetic preference in product design (e.g., Whitfield, 1983; Hekkert et al., 2003; Blijlevens, Carbon, Mugge, & Schoormans, 2012). In this paper, we re-visit the relationship between novelty and aesthetic preference, but look further into what novelty means in product design. In particular, we employed three fundamental dimensions of product semantics - trendiness, complexity, and emotion - (Hsiao & Chen, 2006) and explored how changes in product semantics affect judgment of product novelty and, in turn, judgment of aesthetic preference. By linking product appearance characteristics to novelty, we hope to offer operational information to designers that can be used to achieve an optimal level of novelty in product design.Typicality/Novelty and Aesthetic PreferencePrototypicality or typicality is usually defined as the degree to which an object represents a category (Loken & Ward, 1990; Veryzer & Hutchinson, 1998; Hekkert et al., 2003). Barsalou (1985) discussed three possible determinants of typicality: similarity to the ideal of the category, similarity to the central tendency of the category, and frequency of encounters with the object as a category member. To investigate the influences of typicality, some studies have employed the "prototype distortion" approach by systematically varying a given prototypical stimulus (e.g., Veryzer & Hutchinson, 1998; Blijlevens et al., 2012), while other studies have measured perceived typicality by "goodness of example" ratings (e.g., Barsalou, 1985; Hekkert et al., 2003). The concept of novelty, furthermore, is related to typicality. Berlyne (1971) classified two kinds of novelty: absolute novelty - an object that has never been experienced before - and relative novelty - an object that consists of a new combination of previously experienced elements.Past studies, however, have obtained inconsistent results concerning the relationship between typicality/novelty and aesthetic preference. To examine this relationship, a preference for prototype theory was proposed by Whitfield (1983; Whitfield & Slatter, 1979). Using different styles of chairs as stimuli, Whitfield and his colleagues found a positive linear relationship between typicality and preference. By systematically distorting the stimuli, Veryzer and Hutchinson (1998) found a negative linear relationship between prototype distortion and aesthetic response. Other studies, using houses, paintings, and music performances, have also found a positive relationship between typicality and preference (Purcell, 1984; Hekkert & van Wieringen, 1990; Repp, 1997; Hekkert & Leder, 2008). …


title: From AESA radar to digital radar for surface-based applications

abstract: This paper describes how Thales Surface Radar Product Policy is evolving from Active Electronically Steered Antenna (AESA) radar to digital radar in order to improve its capabilities when operating in challenging electromagnetic environments. It shows how digital receiver technology (with an analogue-to-digital converter behind every receive antenna element) tailored for the digital beam forming array application is a key enabling technology for enhancing the detection of radiofrequency signals of interest mixed with strong interference. After a description of the architecture and packaging design choices, some recent developments of digital tile receivers in S band are presented. Examples using digital technology are given including M3R, GM400 and SM400. The conclusion focuses on future perspectives in digital radar technology.


title: The impact of motor impairment on the processing of sensory information

abstract: Abstract Sensorimotor adaptation is driven by mismatch errors between desired movements and actual movement outcomes. A mismatch error can be minimized by adjusting movements or by altering the interpretation of sensory information. While the effect of mismatch errors on the motor system has received much attention, the contribution of somatosensory feedback, particularly the sensory-motor interplay in the process of adaptation, remains poorly understood. Our study analyzes the impact of peripheral deefferentation on the plasticity of the brain networks responsible for sensory-motor adaptation, focusing particularly on changes in the processing of somatosensory information. For this aim, task-based and resting-state functional MRI was performed on 24 patients in the acute state of a left-sided idiopathic peripheral facial nerve palsy. The functional connectivity of cortical and subcortical networks was analyzed and compared to a healthy control group. We found a strong involvement of the somatosensory system and the thalamus in the adaptation process following an acute peripheral deefferentation. The investigated network shows the principal pattern of a reduced connectivity between cortical areas, while the connectivity to subcortical areas (the basal ganglia and the thalamus) is increased. We suggest that the increased connectivity between the subcortical and cortical structures indicates an active sensory-motor adaptation process. We further hypothesize that the decreased functional connectivity at the cortical level reflects an unsuccessful sensorimotor adaptation process due to the inability to solve the somatosensory-motor mismatch. These results extend our understanding of the somatosensory-motor interaction in response to a mismatch signal and highlight the importance of the thalamus in this process.


title: Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization

abstract: Flexible manufacturing system (FMS) enhances the firm’s flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs’ battery charge. Assessment of the numerical examples’ scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.


title: A Reconfigurable Graphene Reflectarray for Generation of Vortex THz Waves

abstract: A reconfigurable graphene reflectarray is proposed for the generation of vortex radio waves at THz. First, a simple sectored circular reflective surface model with a plane wave at normal incidence is constructed to illustrate how vortex radio waves can be generated. Then, a graphene reflective cell is examined to demonstrate that the reflection coefficient can be controlled by changing the chemical potential and size of the graphene patch. Next, the sectored circular reflective surface is realized with the graphene reflective cells that are properly sized, arranged, and biased to satisfy the required reflection coefficients for various modes of vortex radio waves. Finally, the graphene reflectarray is excited with a horn antenna, showing from simulations that it can be dynamically reconfigured to generate the 0, $\pm 1$ , and $\pm 2$ modes of vortex radio waves at 1.6 THz.


title: Explaining Black Boxes on Sequential Data using Weighted Automata

abstract: Understanding how a learned black box works is of crucial interest for the future of Machine Learning. In this paper, we pioneer the question of the global interpretability of learned black box models that assign numerical values to symbolic sequential data. To tackle that task, we propose a spectral algorithm for the extraction of weighted automata (WA) from such black boxes. This algorithm does not require the access to a dataset or to the inner representation of the black box: the inferred model can be obtained solely by querying the black box, feeding it with inputs and analyzing its outputs. Experiments using Recurrent Neural Networks (RNN) trained on a wide collection of 48 synthetic datasets and 2 real datasets show that the obtained approximation is of great quality.


title: Characterizing the behavior of handheld devices and its implications

abstract: Abstract The Bring-Your-Own-Handheld-device (BYOH) phenomenon continues to make inroads as more people bring their own handheld devices to work or school. While convenient to device owners, this trend presents novel management challenges to network administrators as they have no control over these devices and no solid understanding of the behavior of these emerging devices. In order to cope with the impact of these BYOHs on current existing network management infrastructures, we identify two tightly-coupled questions that network administrators need to answer: (a) how do these BYOHs behave? and (b) how can we manage them more effectively based on the understanding of their behaviors? In response, we design and deploy Brofiler, a framework that could enable network administrators to effectively manage BYOHs via behavior-aware profiling. Our behavior-aware profiling captures the behaviors of each individual BYOH and improves the visibility on managing these BYOHs. In detail, the contributions of our work are three-fold. First, we present Brofiler, a time-aware device-centric approach for grouping devices into intuitive behavioral groups from multiple perspectives, including data plane, temporal behavior, and the protocol and control plane. Second, we conduct an extensive study of BYOHs using our approach with real data collected over a year, and highlight several novel insights on the behavior of BYOHs. For example, we find that 70% of the BYOHs generate 50% of their monthly data traffic in one day, while remaining mostly idle the rest of the month. In addition, 68% of BYOHs do not conform to DHCP protocol specifications. Third, we present the implications of our study based on the framework in DHCP management, bandwidth management and access control. Overall, our approach could enable network administrators better understand and manage these new emerging devices for their networks in the post-PC era.


title: Attention-based encoder-decoder model for answer selection in question answering

abstract: One of the key challenges for question answering is to bridge the lexical gap between questions and answers because there may not be any matching word between them. Machine translation models have been shown to boost the performance of solving the lexical gap problem between question-answer pairs. In this paper, we introduce an attention-based deep learning model to address the answer selection task for question answering. The proposed model employs a bidirectional long short-term memory (LSTM) encoder-decoder, which has been demonstrated to be effective on machine translation tasks to bridge the lexical gap between questions and answers. Our model also uses a step attention mechanism which allows the question to focus on a certain part of the candidate answer. Finally, we evaluate our model using a benchmark dataset and the results show that our approach outperforms the existing approaches. Integrating our model significantly improves the performance of our question answering system in the TREC 2015 LiveQA task.


title: Feature selection, L1 vs. L2 regularization, and rotational invariance

abstract: We consider supervised learning in the presence of very many irrelevant features, and study two different regularization methods for preventing overfitting. Focusing on logistic regression, we show that using L1 regularization of the parameters, the sample complexity (i.e., the number of training examples required to learn "well,") grows only logarithmically in the number of irrelevant features. This logarithmic rate matches the best known bounds for feature selection, and indicates that L1 regularized logistic regression can be effective even if there are exponentially many irrelevant features as there are training examples. We also give a lower-bound showing that any rotationally invariant algorithm---including logistic regression with L2 regularization, SVMs, and neural networks trained by backpropagation---has a worst case sample complexity that grows at least linearly in the number of irrelevant features.


title: Controlling a Car Through OBD Injection

abstract: Internet of vehicles(IOV) is an application of Internet of things in Intelligent Transport System, and has attracted high attention of researchers. IOV brings network connectivity to traditional vehicles, while also introduces security risks. This paper presents experimental analysis on the security of vehicles with Internet connections and propose an approach to Controlling a Car Through OBD Injection. In the experiments, we successfully penetrated several types of cars in a wireless way. We also put out a multi-level safety model of cars, which divides cars into different groups and gives analysis and explanations of each group. All of these things are done for indicating a point of view that traditional cars are not safe enough on information security. It is surely risky to put a car without the ability to resist the attack of informational ways into the Internet of vehicles.


title: Metabolism of compounds with nitro-functions by Klebsiella pnuemoniae isolated from a regional wetland

abstract: Abstract Metabolism of nitroaromatic compounds by a Klebsiella pneumoniae isolated from a regional wetland was studied. When it was grown with nitrophenol as the sole nitrogen source, the isolate had the metabolic capability of transforming nitrophenol to phenol; however, it did not use nitrophenol as the sole carbon source. The metabolic pathway showed that the K. pneumoniae reduced the nitro group to an amino group forming aminophenol as a major metabolite. This aminophenol was oxidatively deaminated to phenol. For every mole of nitrophenol metabolized, 1 mol of phenol was produced and the phenol did not undergo further metabolism. The isolate also used several other nitroaromatic compounds including nitrotoluenes and nitrobenzenes as its nitrogen source. Even though this organism did not degrade the nitroaromatics completely, it may be useful in degrading nitroaromatics in contaminated soil and water containing other aromatic degraders in a syntrophic condition in nature.


title: Language and conceptual development.

abstract: Linguistic and conceptual development converge crucially in the process of early word learning. Acquiring a new word requires the child to identify a conceptual unit, identify a linguistic unit, and establish a mapping between them. On the conceptual side, the child has to not only identify the relevant part of the scene being labeled, but also isolate a concept at the correct level of abstraction—the word ‘dog’ must be mapped to the concept dog and not to the concepts petting or collie, for example. On the linguistic side, the child must use the syntactic context in which the word appears to determine its grammatical category (e.g., noun, verb, adjective). But she also uses syntactic information, along with observation of the world and social-communicative cues, to make guesses at which concept the word picks out as well as its level of abstraction. We present evidence that young learners learn new words rapidly and extend them appropriately. However, the relative import of observational and linguistic cues varies as a function of the kind of word being acquired, with verbs requiring a richer set of conceptual and linguistic cues than nouns. Copyright © 2010 John Wiley & Sons, Ltd. ::: ::: For further resources related to this article, please visit the WIREs website


title: The SAGE project: a storage centric approach for exascale computing: invited paper

abstract: SAGE (Percipient StorAGe for Exascale Data Centric Computing) is a European Commission funded project towards the era of Exascale computing. Its goal is to design and implement a Big Data/Extreme C ...


Constant-Time Sliding Window Aggregation Kanat


title: Receptive fields and functional architecture of monkey striate cortex

abstract: 1. The striate cortex was studied in lightly anaesthetized macaque and spider monkeys by recording extracellularly from single units and stimulating the retinas with spots or patterns of light. Most cells can be categorized as simple, complex, or hypercomplex, with response properties very similar to those previously described in the cat. On the average, however, receptive fields are smaller, and there is a greater sensitivity to changes in stimulus orientation. A small proportion of the cells are colour coded.2. Evidence is presented for at least two independent systems of columns extending vertically from surface to white matter. Columns of the first type contain cells with common receptive-field orientations. They are similar to the orientation columns described in the cat, but are probably smaller in cross-sectional area. In the second system cells are aggregated into columns according to eye preference. The ocular dominance columns are larger than the orientation columns, and the two sets of boundaries seem to be independent.3. There is a tendency for cells to be grouped according to symmetry of responses to movement; in some regions the cells respond equally well to the two opposite directions of movement of a line, but other regions contain a mixture of cells favouring one direction and cells favouring the other.4. A horizontal organization corresponding to the cortical layering can also be discerned. The upper layers (II and the upper two-thirds of III) contain complex and hypercomplex cells, but simple cells are virtually absent. The cells are mostly binocularly driven. Simple cells are found deep in layer III, and in IV A and IV B. In layer IV B they form a large proportion of the population, whereas complex cells are rare. In layers IV A and IV B one finds units lacking orientation specificity; it is not clear whether these are cell bodies or axons of geniculate cells. In layer IV most cells are driven by one eye only; this layer consists of a mosaic with cells of some regions responding to one eye only, those of other regions responding to the other eye. Layers V and VI contain mostly complex and hypercomplex cells, binocularly driven.5. The cortex is seen as a system organized vertically and horizontally in entirely different ways. In the vertical system (in which cells lying along a vertical line in the cortex have common features) stimulus dimensions such as retinal position, line orientation, ocular dominance, and perhaps directionality of movement, are mapped in sets of superimposed but independent mosaics. The horizontal system segregates cells in layers by hierarchical orders, the lowest orders (simple cells monocularly driven) located in and near layer IV, the higher orders in the upper and lower layers.


DESIGN AND DEVELOPMENT OF A PERSONAL HEALTH MONITORING SYSTEM ON ANDROID MOBILE PLATFORM


title: Feature mining for localised crowd counting

abstract: This paper presents a multi-output regression model for crowd counting in public scenes. Existing counting by regression methods either learn a single model for global counting, or train a large number of separate regressors for localised density estimation. In contrast, our single regression model based approach is able to estimate people count in spatially localised regions and is more scalable without the need for training a large number of regressors proportional to the number of local regions. In particular, the proposed model automatically learns the functional mapping between interdependent low-level features and multi-dimensional structured outputs. The model is able to discover the inherent importance of different features for people counting at different spatial locations. Extensive evaluations on an existing crowd analysis benchmark dataset and a new more challenging dataset demonstrate the effectiveness of our approach.


title: Test orchestration a framework for Continuous Integration and Continuous deployment

abstract: Theenterprises follow Agile Software Development methodology to because business requirements changes frequently. InAgile Software Developmentmethodology, it is essential to continuously integrate the component into a main trunk of a project to test the new component of the system. Then test all the component of the project; this happens frequently so it needs to streamlineprocesses to orchestrate the tests. So it is difficult to manage the software development life cycle for those changes and maintain the software code quality. To maintain the product quality it is essential to integrate the product component and need to deploy a product on pre-production environment and test the product. Hence the need for Continuous Integration and Continuous Delivery process for software product. The popularization of DevOps, and cloud computing has revolutionized the software delivery process- making it faster and affordable for business to release their software continuously. Hence Enterprises need for reliable and predictable delivery process of software. The objective of the paper is to design an effective framework for automated testing and deployment to help to automate the code analysis, test selection, test scheduling, environment provisioning, test execution,results analysis and deployment pipeline. Test orchestration framework typically very complicated to develop such pipeline to make software reliable, and bug free. For environment provisioning can be provided through virtualization and cloud computing.


The Psychological Flexibility Questionnaire (PFQ): Development, Reliability And Validity


title: Mindfulness Training Improves Working Memory Capacity and GRE Performance While Reducing Mind Wandering

abstract: Given that the ability to attend to a task without distraction underlies performance in a wide variety of contexts, training one's ability to stay on task should result in a similarly broad enhancement of performance. In a randomized controlled investigation, we examined whether a 2-week mindfulness-training course would decrease mind wandering and improve cognitive performance. Mindfulness training improved both GRE reading-comprehension scores and working memory capacity while simultaneously reducing the occurrence of distracting thoughts during completion of the GRE and the measure of working memory. Improvements in performance following mindfulness training were mediated by reduced mind wandering among participants who were prone to distraction at pretesting. Our results suggest that cultivating mindfulness is an effective and efficient technique for improving cognitive function, with wide- reaching consequences.


A Look at Agenda-setting: past, present and future


title: THE VELOCITY CONTROL OF THE ELECTRO-HYDRAULIC SERVO SYSTEM

abstract: In general two basic methods are used for controlling the velocity of a hydraulic cylinder. First by an axial variable-displacement pump for controls flow to the cylinder. This configuration is commonly known as a hydrostatic transmission. Second by proportional valve powered by a constant-pressure source, such as a pressure compensated pump, drives the hydraulic cylinder. In this study, the electro-hydraulic servo system (EHSS) for velocity control of hydraulic cylinder is investigated experimentally and its analysis theoretically. Where the controlled hydraulic cylinder is altered by a swashplate axial piston pump or by proportional valve to achieve velocity control. The theoretical part includes the derivation of the mathematical model equations of combination system. Velocity control system for hydraulic cylinder using simple (PID) controller to get constant velocity range of hydraulic cylinder under applied external variable loads . An experimental set-up is constructed, which consists of the hydraulic test pump unit, the electro-hydraulic proportional valve unit, the hydraulic actuator unit , the external load control unit and interfacing electronic unit. The experimental results show that PID controller can be achieve good velocity control by variable displacement axial piston pump and also by proportional valve under external loads variations.


title: On the Complexity of Value Iteration

abstract: Value iteration is a fundamental algorithm for solving Markov Decision Processes (MDPs). It computes the maximal $n$-step payoff by iterating $n$ times a recurrence equation which is naturally associated to the MDP. At the same time, value iteration provides a policy for the MDP that is optimal on a given finite horizon $n$. In this paper, we settle the computational complexity of value iteration. We show that, given a horizon $n$ in binary and an MDP, computing an optimal policy is EXP-complete, thus resolving an open problem that goes back to the seminal 1987 paper on the complexity of MDPs by Papadimitriou and Tsitsiklis. As a stepping stone, we show that it is EXP-complete to compute the $n$-fold iteration (with $n$ in binary) of a function given by a straight-line program over the integers with $\max$ and $+$ as operators.


Elder Neglect and Abuse: An Annotated Bibliography


title: A novel approach for automatic common-centroid pattern generation

abstract: This paper introduces a novel placement methodology for a common-centroid (CC) pattern generator. It can be applied to various integrated circuit (IC) elements, such as transistors, capacitors, diodes, and resistors. The proposed method consists of a constructive algorithm which generates an initial, close to the optimum, solution, and an iterative algorithm which is used subsequently, if the output of constructive algorithm does not satisfy the desired criteria. The outcome of this work is an automatic CC placement algorithm for IC element arrays. Additionally, the paper presents a method for the CC arrangement evaluation. It allows for evaluating the quality of an array, and a comparison of different placement methods.


title: CSI-SF: Estimating wireless channel state using CSI sampling & fusion

abstract: One of the key features of high speed WLAN such as 802.11n is the use of MIMO (Multiple Input Multiple Output) antenna technology. The MIMO channel is described with fine granularity by Channel State Information (CSI) that can be utilized in many ways to improve network performance. Many complex parameters of a MIMO system require numerous samples to obtain CSI for all possible channel configurations. As a result, measuring the complete CSI space requires excessive sampling overhead and thus degrades network performance. We propose CSI-SF (CSI Sampling & Fusion), a method for estimating CSI for every MIMO configuration by sampling a small number of frames transmitted with different settings and extrapolating data for the remaining settings. For instance, we predict CSI of multi-stream settings using CSI obtained only from single stream packets. We evaluate the effectiveness of CSI-SF in various scenarios using our 802.11n testbed and show that CSI-SF provides an accurate, complete knowledge of the MIMO channel with reduced overhead from traditional sampling. We also show that CSI-SF can be applied to network algorithms such as rate adaptation, antenna selection and association control to significantly improve their performance and efficiency.


title: Adding navigation to the equation: Turning decisions for end-to-end vehicle control

abstract: Navigation and obstacle avoidance are two problems that are not easily incorporated into direct control of autonomous vehicles solely based on visual input. However, they are required if lane following given proper lane markings is not enough to incorporate trained systems into larger architectures. We present a method to allow for obstacle avoidance while driving using a single, front-facing camera as well as navigation capabilities such as taking turns at junctions and lane changes by feeding turn indicator signals into a Convolutional Neural Network. Both situations share the difficulty intrinsic to single camera setups of limited field of views. This problem is handled by using a spatial history of input images to extend the field of view regarding static obstacles. The trained model, referred to as DriveNet, is evaluated in real world driving scenarios, using the same model for lateral vehicle control to both dynamically drive around obstacles as well as perform lane changing and turning in intersections.


title: ATM: A distributed, collaborative, scalable system for automated machine learning

abstract: In this paper, we present Auto-Tuned Models, or ATM, a distributed, collaborative, scalable system for automated machine learning. Users of ATM can simply upload a dataset, choose a subset of modeling methods, and choose to use ATM's hybrid Bayesian and multi-armed bandit optimization system. The distributed system works in a load-balanced fashion to quickly deliver results in the form of ready-to-predict models, confusion matrices, cross-validation results, and training timings. By automating hyperparameter tuning and model selection, ATM returns the emphasis of the machine learning workflow to its most irreducible part: feature engineering. We demonstrate the usefulness of ATM on 420 datasets from OpenML and train over 3 million classifiers. Our initial results show ATM can beat human-generated solutions for 30% of the datasets, and can do so in 1/100th of the time.


Conversion Disorder


title: Wrong, but useful: regional species distribution models may not be improved by range‐wide data under biased sampling

abstract: Species distribution modeling (SDM) is an essential method in ecology and conservation. SDMs are often calibrated within one country's borders, typically along a limited environmental gradient with biased and incomplete data, making the quality of these models questionable. In this study, we evaluated how adequate are national presence-only data for calibrating regional SDMs. We trained SDMs for Egyptian bat species at two different scales: only within Egypt and at a species-specific global extent. We used two modeling algorithms: Maxent and elastic net, both under the point-process modeling framework. For each modeling algorithm, we measured the congruence of the predictions of global and regional models for Egypt, assuming that the lower the congruence, the lower the appropriateness of the Egyptian dataset to describe the species' niche. We inspected the effect of incorporating predictions from global models as additional predictor ("prior") to regional models, and quantified the improvement in terms of AUC and the congruence between regional models run with and without priors. Moreover, we analyzed predictive performance improvements after correction for sampling bias at both scales. On average, predictions from global and regional models in Egypt only weakly concur. Collectively, the use of priors did not lead to much improvement: similar AUC and high congruence between regional models calibrated with and without priors. Correction for sampling bias led to higher model performance, whatever prior used, making the use of priors less pronounced. Under biased and incomplete sampling, the use of global bats data did not improve regional model performance. Without enough bias-free regional data, we cannot objectively identify the actual improvement of regional models after incorporating information from the global niche. However, we still believe in great potential for global model predictions to guide future surveys and improve regional sampling in data-poor regions.


title: Matching large ontologies: A divide-and-conquer approach

abstract: Ontologies proliferate with the progress of the Semantic Web. Ontology matching is an important way of establishing interoperability between (Semantic) Web applications that use different but related ontologies. Due to their sizes and monolithic nature, large ontologies regarding real world domains bring a new challenge to the state of the art ontology matching technology. In this paper, we propose a divide-and-conquer approach to matching large ontologies. We develop a structure-based partitioning algorithm, which partitions entities of each ontology into a set of small clusters and constructs blocks by assigning RDF Sentences to those clusters. Then, the blocks from different ontologies are matched based on precalculated anchors, and the block mappings holding high similarities are selected. Finally, two powerful matchers, V-Doc and Gmo, are employed to discover alignments in the block mappings. Comprehensive evaluation on both synthetic and real world data sets demonstrates that our approach both solves the scalability problem and achieves good precision and recall with significant reduction of execution time.


title: Learning to tell brake and turn signals in videos using CNN-LSTM structure

abstract: We present a method that learns to tell rear signals from a number of frames using a deep learning framework. The proposed framework extracts spatial features with a convolution neural network (CNN), and then applies a long short term memory (LSTM) network to learn the long-term dependencies. The brake signal classifier is trained using RGB frames, while the turn signal is recognized via a two-step localization approach. The two separate classifiers are learned to recognize the static brake signals and the dynamic turn signals. As a result, our recognition system can recognize 8 different rear signals via the combined two classifiers in real-world traffic scenes. Experimental results show that our method is able to obtain more accurate predictions than using only the CNN to classify rear signals with time sequence inputs.


title: Beyond Fine Tuning: A Modular Approach to Learning on Small Data

abstract: In this paper we present a technique to train neural network models on small amounts of data. Current methods for training neural networks on small amounts of rich data typically rely on strategies such as fine-tuning a pre-trained neural network or the use of domain-specific hand-engineered features. Here we take the approach of treating network layers, or entire networks, as modules and combine pre-trained modules with untrained modules, to learn the shift in distributions between data sets. The central impact of using a modular approach comes from adding new representations to a network, as opposed to replacing representations via fine-tuning. Using this technique, we are able surpass results using standard fine-tuning transfer learning approaches, and we are also able to significantly increase performance over such approaches when using smaller amounts of data.


title: Antisocial personality disorder is on a continuum with psychopathy

abstract: Abstract Background Antisocial personality disorder (ASPD) and psychopathy are different diagnostic constructs. It is unclear whether they are separate clinical syndromes or whether psychopathy is a severe form of ASPD. Methods A representative sample of 496 prisoners in England and Wales was interviewed in the second phase of a survey carried out in 1997 using the Schedules for Clinical Assessment in Neuropsychiatry, the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition Axis II personality disorders, and the Psychopathy Checklist–Revised. Results Among those 18 years and older (n = 470), 211 (44.9%) received a diagnosis of ASPD, of whom 67 (31.8%) were classified as psychopaths, indicated by Psychopathy Checklist–Revised scores of 25 and above. Symptoms of ASPD and psychopathy both demonstrated low diagnostic contrast when comparing subgroups of ASPD above and below the cutoff for psychopathy. There were no differences in demography, Axis I comorbidity, and treatment-seeking behavior. Psychopathic individuals with ASPD demonstrated comorbid schizoid and narcissistic personality disorder, more severe conduct disorder and adult antisocial symptoms, and more violent convictions. Conclusions Psychopathy and ASPD are not separate diagnostic entities, but psychopathic ASPD is a more severe form than ASPD alone with greater risk of violence. Dimensional scores of Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition personality disorders (other than ASPD) may be helpful in identifying this specific subgroup.


title: Comparative Analysis of LPCC, MFCC and BFCC for the Recognition of Hindi Words using Artificial Neural Networks

abstract: Most important way of communication among humans is language and primary medium used for the said is speech. The speech recognizers make use of a parametric form of a signal to obtain the most important distinguishable features of speech signal for recognition purpose. In this paper, Linear Prediction Cepstral Coefficient (LPCC), Mel Frequency Cepstral Coefficient (MFCC) and Bark frequency Cepstral coefficient (BFCC) feature extraction techniques for recognition of Hindi Isolated, Paired and Hybrid words have been studied and the corresponding recognition rates are compared. Artifical Neural Network is used as back end processor. The experimental results show that the better recognition rate is obtained for MFCC as compared to LPCC and BFCC for all the three types of words. General terms Speech Recognition, Recognition rate, Bit-wise, Zero crossing rate.


title: Towards a unified design theory for creativity support systems

abstract: A magnitude of predominantly qualitative empirical and conceptual work has indentified design principles that provide for the design of creativity support systems (CSS). Numerous kernel theories have been utilized to inform CSS design principles. However, the logical next step for design research is pending: this rich field of research may now allow for more quantitative empirical research on the actual effects of particular CSS design features on creative performance. Against the background of this research gap, we first analyze existing CSS design theories applying an analysis framework encompassing obligatory design theory components. On that basis, we extract the underlying independent (latent) variables addressed in design principles. Our contribution entails a unified design theory for CSS, laying the basis for future research in IS design science on creativity-support. Furthermore, we reflect on our approach to develop a unified design theory and discuss its implications for the philosophy of design science.


title: Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels

abstract: The use of space-division multiple access (SDMA) in the downlink of a multiuser multiple-input, multiple-output (MIMO) wireless communications network can provide a substantial gain in system throughput. The challenge in such multiuser systems is designing transmit vectors while considering the co-channel interference of other users. Typical optimization problems of interest include the capacity problem - maximizing the sum information rate subject to a power constraint-or the power control problem-minimizing transmitted power such that a certain quality-of-service metric for each user is met. Neither of these problems possess closed-form solutions for the general multiuser MIMO channel, but the imposition of certain constraints can lead to closed-form solutions. This paper presents two such constrained solutions. The first, referred to as "block-diagonalization," is a generalization of channel inversion when there are multiple antennas at each receiver. It is easily adapted to optimize for either maximum transmission rate or minimum power and approaches the optimal solution at high SNR. The second, known as "successive optimization," is an alternative method for solving the power minimization problem one user at a time, and it yields superior results in some (e.g., low SNR) situations. Both of these algorithms are limited to cases where the transmitter has more antennas than all receive antennas combined. In order to accommodate more general scenarios, we also propose a framework for coordinated transmitter-receiver processing that generalizes the two algorithms to cases involving more receive than transmit antennas. While the proposed algorithms are suboptimal, they lead to simpler transmitter and receiver structures and allow for a reasonable tradeoff between performance and complexity.


title: A technique to build a secret key in integrated circuits for identification and authentication applications

abstract: This paper describes a technique that exploits the statistical delay variations of wires and transistors across ICs to build a secret key unique to each IC. To explore its feasibility, we fabricated a candidate circuit to generate a response based on its delay characteristics. We show that there exists enough delay variation across ICs implementing, the proposed circuit to identify individual ICs. Further. the circuit, functions reliably over a practical range of environmental variation such as temperature and voltage.


title: Constructing School Timetables using Simulated Annealing: Sequential and Parallel Algorithms

abstract: This paper considers a solution to the school timetabling problem. The timetabling problem involves scheduling a number of tuples, each consisting of class of students, a teacher, a subject and a room, to a fixed number of time slots. A Monte Carlo scheme called simulated annealing is used as an optimisation technique. The paper introduces the timetabling problem, and then describes the simulated annealing method. Annealing is then applied to the timetabling problem. A prototype timetabling environment is described followed by some experimental results. A parallel algorithm which can be implemented on a multiprocessor is presented. This algorithm can provide a faster solution than the equivalent sequential algorithm. Some further experimental results are given.


title: Sparse Recovery Using Sparse Matrices

abstract: In this paper, we survey algorithms for sparse recovery problems that are based on sparse random matrices. Such matrices has several attractive properties: they support algorithms with low computational complexity, and make it easy to perform incremental updates to signals. We discuss applications to several areas, including compressive sensing, data stream computing, and group testing.


Very greedy crossover in a genetic algorithm for the traveling salesman problem


title: Combining nonlinear independent component analysis and neural network for the prediction of Asian stock market indexes

abstract: With the economic successes of several Asian economies and their increasingly important roles in the global financial market, the prediction of Asian stock markets has becoming a hot research area. As Asian stock markets are highly dynamic and exhibit wide variation, it may more realistic and practical that assumed the stock indexes of Asian stock markets are nonlinear mixture data. In this research, a time series prediction model by combining nonlinear independent component analysis (NLICA) and neural network is proposed to forecast Asian stock markets. NLICA is a novel feature extraction technique to find independent sources from observed nonlinear mixture data where no relevant data mixing mechanisms are available. In the proposed method, we first use NLICA to transform the input space composed of original time series data into the feature space consisting of independent components representing underlying information of the original data. Then, the ICs are served as the input variables of the neural network to build prediction model. Among the Asian stock markets, Japanese and China's stock markets are the biggest two in Asia and they respectively represent the two types of stock markets. Therefore, in order to evaluate the performance of the proposed approach, the Nikkei 225 closing index and Shanghai B-share closing index are used as illustrative examples. Experimental results show that the proposed forecasting model not only improves the prediction accuracy of the neural network approach but also outperforms the three comparison methods. The proposed stock index prediction model can be therefore a good alternative for Asian stock market indexes.


title: Development of Marathi part of speech tagger using statistical approach

abstract: Part-of-speech (POS) tagging is a process of assigning the words in a text corresponding to a particular part of speech. A fundamental version of POS tagging is the identification of words as nouns, verbs, adjectives etc. For processing natural languages, Part of Speech tagging is a prominent tool. It is one of the simplest as well as most constant and statistical model for many NLP applications. POS Tagging is an initial stage of linguistics, text analysis like information retrieval, machine translator, text to speech synthesis, information extraction etc. In POS Tagging we assign a Part of Speech tag to each word in a sentence and literature. Various approaches have been proposed to implement POS taggers. In this paper we present a Marathi part of speech tagger. It is morphologically rich language. Marathi is spoken by the native people of Maharashtra. The general approach used for development of tagger is statistical using Unigram, Bigram, Trigram and HMM Methods. It presents a clear idea about all the algorithms with suitable examples. It also introduces a tag set for Marathi which can be used for tagging Marathi text. In this paper we have shown the development of the tagger as well as compared to check the accuracy of taggers output. The three Marathi POS taggers viz. Unigram, Bigram, Trigram and HMM gives the accuracy of 77.38%, 90.30%, 91.46% and 93.82% respectively.


title: Transaction level modeling: an overview

abstract: Recently, the transaction-level modeling has been widely referred to in system-level design community. However, the transaction-level models (TLMs) are not well defined and the usage of TLMs in the existing design domains, namely modeling, validation, refinement, exploration, and synthesis, is not well coordinated. This paper introduces a TLM taxonomy and compares the benefits of TLMs' use.


title: Rivastigmine: the advantages of dual inhibition of acetylcholinesterase and butyrylcholinesterase and its role in subcortical vascular dementia and Parkinson’s disease dementia

abstract: Several studies have demonstrated clinical benefits of sustained cholinesterase inhibition with rivastigmine in Alzheimer's disease (AD) and Parkinson's disease dementia (PDD). Unlike donepezil and galantamine that selectively inhibit acetylcholinesterase (AChE; EC 3.1.1.7), rivastigmine is a unique cholinesterase inhibitor with both AChE and butyrylcholinesterase (BuChE; EC 3.1.1.8) inhibitory activity. Rivastigmine is also available as transdermal patch that has been approved by the US Food and Drug Administration for the treatment of mild, moderate, and severe AD as well as mild-to-moderate PDD. In this review, we explore the role of BuChE inhibition in addition to AChE inhibition with rivastigmine in the outcomes of cognition, global function, behavioral symptoms, and activities of daily living. Additionally, we review the evidence supporting the use of dual AChE-BuChE inhibitory activity of rivastigmine as a therapeutic strategy in the treatment of neurological disorders, with a focus on the role of rivastigmine in subcortical dementias such as vascular dementia (VaD) and PDD. Toward this objective, we performed a literature search in PubMed and Ovid with limits to articles published in the English language before June 2016. The available evidence from the literature suggests that the dual inhibition of AChE and BuChE may afford additional therapeutic potential of rivastigmine in subcortical dementias (subcortical VaD and PDD) with benefits on cognition and behavioral symptoms. Rivastigmine was found to specifically benefit executive dysfunction frequently observed in subcortical dementias; however, large randomized clinical studies are warranted to support these observations.


title: Cyberbullying or Cyber Aggression?: A Review of Existing Definitions of Cyber-Based Peer-to-Peer Aggression

abstract: Due to the ongoing debate regarding the definitions and measurement of cyberbullying, the present article critically appraises the existing literature and offers direction regarding the question of how best to conceptualise peer-to-peer abuse in a cyber context. Variations across definitions are problematic as it has been argued that inconsistencies with regard to definitions result in researchers examining different phenomena, whilst the absence of an agreed conceptualisation of the behaviour(s) involved hinders the development of reliable and valid measures. Existing definitions of cyberbullying often incorporate the criteria of traditional bullying such as intent to harm, repetition, and imbalance of power. However, due to the unique nature of cyber-based communication, it can be difficult to identify such criteria in relation to cyber-based abuse. Thus, for these reasons cyberbullying may not be the most appropriate term. Rather than attempting to “shoe-horn” this abusive behaviour into the preconceived conceptual framework that provides an understanding of traditional bullying, it is timely to take an alternative approach. We argue that it is now time to turn our attention to the broader issue of cyber aggression, rather than persist with the narrow focus that is cyberbullying.


title: Realistic User Behavior Modeling for Energy Saving in Residential Buildings

abstract: Due to the high costs of live research, performance simulation has become a widely accepted method of assessment for the quality of proposed solutions in this field. Additionally, being able to simulate the behavior of the future occupants of a residential building can be very useful since it can support both design-time and run-time decisions leading to reduced energy consumption through, e.g., the design of model predictive controllers that incorporate user behavior predictions. In this work, we provide a framework for simulating user behavior in residential buildings. In fact, we are interested in how to deal with all user behavior aspects so that these computer simulations can provide a realistic framework for testing alternative policies for energy saving.


title: DESIGN OF A BALUN FOR A BOW TIE ANTENNA IN RECONFIGURABLE GROUND PENETRATING RADAR SYSTEMS

abstract: DESIGN OF A BALUN FOR A BOW TIE ANTENNA INRECONFIGURABLE GROUND PENETRATING RADARSYSTEMSR. PersicoIstituto per il Beni Archeologici e MonumentaliConsiglio Nazionale delle RicercheVia Monteroni, Campus Universitario, Lecce 73100, ItalyN. RomanoDipartimento di Ingegneria dell’InformazioneSeconda Universitµa degli studi di NapoliVia Roma 29, Aversa 81031, ItalyF. SoldovieriIstituto per il Rilevamento Elettromagnetico dell’AmbienteConsiglio Nazionale delle RicercheVia Diocleziano 328, Napoli 80124, ItalyAbstract|This paper deals with the design of a reconﬂgurableantenna that resembles a monolithic UWB bow-tie antenna for GroundPenetrating Radar (GPR) applications. In particular, the attentionis focussed on the design of the balun system able to work in thefrequency band 0.3{1GHz; the eﬁectiveness of the design is shown byexamining the behaviour of the scattering parameters


title: Use of assistance and therapy dogs for children with autism spectrum disorders: a critical review of the current evidence.

abstract: Abstract Background: Autism spectrum disorders (ASD) are characterized by deficits in social reciprocity and communication, and by unusually restricted, repetitive behaviors. Intervention strategies based on the exploitation of the emotional aspects of human–dog relationships hold the potential to overcome the difficulty of subjects with ASD to relate and interact effectively with others, targeting core symptoms of this disorder. Methods: This review summarizes the results of six published studies on the effects of brief interactions with dogs and the effects of introducing dogs in families with a child diagnosed with ASD, with an emphasis on social behaviors and language use. Furthermore, the possible mechanisms responsible for the beneficial effects observed are discussed. Conclusions: Although the studies described here are encouraging, further research with better designs and using larger samples is needed to strengthen translation of such interventions to the clinic. In addition, potential applicatio...


A Model for Competence and Integrity in Variable Payoff Games


title: A Linear Correction for Principal Component Analysis of Dynamic Fluorescence Diffuse Optical Tomography Images

abstract: The analysis of dynamic fluorescence diffuse optical tomography (D-FDOT) is important both for drug delivery research and for medical diagnosis and treatment. The low spatial resolution and complex kinetics, however, limit the ability of FDOT in resolving drug distributions within small animals. Principal component analysis (PCA) provides the capability of detecting and visualizing functional structures with different kinetic patterns from D-FDOT images. A particular challenge in using PCA is to reduce the level of noise in D-FDOT images. This is particularly relevant in drug study, where the time-varying fluorophore concentration (drug concentration) will result in the reconstructed images containing more noise and, therefore, affect the performance of PCA. In this paper, a new linear corrected method is proposed for modeling these time-varying fluorescence measurements before performing PCA. To evaluate the performance of the new method in resolving drug biodistribution, the metabolic processes of indocyanine green within mouse is dynamically simulated and used as the input data of PCA. Simulation results suggest that the principal component (PC) images generated using the new method improve SNR and discrimination capability, compared to the PC images generated using the uncorrected D-FDOT images.


title: Evaluation of face recognition system in heterogeneous environments (visible vs NIR)

abstract: Performing facial recognition between Near Infrared (NIR) and visible-light (VIS) images has been established as a common method of countering illumination variation problems in face recognition. In this paper we present a new database to enable the evaluation of cross-spectral face recognition. A series of preprocessing algorithms, followed by Local Binary Pattern Histogram (LBPH) representation and combinations with Linear Discriminant Analysis (LDA) are used for recognition. These experiments are conducted on both NIR→VIS and the less common VIS→NIR protocols, with permutations of uni-modal training sets. 12 individual baseline algorithms are presented. In addition, the best performing fusion approaches involving a subset of 12 algorithms are also described.


title: Tiled-based adaptive streaming using MPEG-DASH

abstract: Interactive spatial navigation within a video is getting attention in the research community, especially in scenarios such as 360° video, video surveillance or region-of-interest viewing. With more content being delivered over the top, the complexity of spatial access to the video is increased by the need to perform bitrate adaptation to react to network variations. In this paper, we describe how spatial access can be performed in an adaptive HTTP streaming context, using tiling of the source content, MPEG-DASH and its SRD extensions. We describe a configurable implementation of these technologies, within the GPAC open-source player, allowing experimentations of different adaptation policies for tiled video content.


title: A Research on 3D Model Construction from 2D DICOM

abstract: In recent years, the development of high techniques in the field of computer graphics, computer vision and images processing has been widely studied and applied in geometric modeling. Especially, in the field of 3D modeling and reconstruction of a 3D object from 2D images. One of these researches is the field of medical image processing. It has been invested and developed by researchers, both in computer science and medicine. This paper presents a survey research on construction and reconstruction of a 3D object from a set of 2D medical images. We first review the existing methods for building a 3D object from 2D medical image slices in DICOM (Digital Imaging and Communications in Medicine) format. They come from measurement devices in medicine like X-ray Computerized Tomography (X-ray CT), Magnetic Resonance Imaging (MRI), etc. Thereafter, we construct an application based on the VTK library for building, visualizing and handling a 3D object from these CT scanned slices. This application can support medical staffs and doctors in diagnostic and treatment of patients.


title: The Problematic Internet Entertainment Use Scale for Adolescents: Prevalence of Problem Internet Use in Spanish High School Students

abstract: Abstract Many researchers and professionals have reported nonsubstance addiction to online entertainments in adolescents. However, very few scales have been designed to assess problem Internet use in this population, in spite of their high exposure and obvious vulnerability. The aim of this study was to review the currently available scales for assessing problematic Internet use and to validate a new scale of this kind for use, specifically in this age group, the Problematic Internet Entertainment Use Scale for Adolescents. The research was carried out in Spain in a gender-balanced sample of 1131 high school students aged between 12 and 18 years. Psychometric analyses showed the scale to be unidimensional, with excellent internal consistency (Cronbach's alpha of 0.92), good construct validity, and positive associations with alternative measures of maladaptive Internet use. This self-administered scale can rapidly measure the presence of symptoms of behavioral addiction to online videogames and social netw...


title: Semi-supervised Node Splitting for Random Forest Construction

abstract: Node splitting is an important issue in Random Forest but robust splitting requires a large number of training samples. Existing solutions fail to properly partition the feature space if there are insufficient training data. In this paper, we present semi-supervised splitting to overcome this limitation by splitting nodes with the guidance of both labeled and unlabeled data. In particular, we derive a nonparametric algorithm to obtain an accurate quality measure of splitting by incorporating abundant unlabeled data. To avoid the curse of dimensionality, we project the data points from the original high-dimensional feature space onto a low-dimensional subspace before estimation. A unified optimization framework is proposed to select a coupled pair of subspace and separating hyper plane such that the smoothness of the subspace and the quality of the splitting are guaranteed simultaneously. The proposed algorithm is compared with state-of-the-art supervised and semi-supervised algorithms for typical computer vision applications such as object categorization and image segmentation. Experimental results on publicly available datasets demonstrate the superiority of our method.


title: Statistical feature matrix for texture analysis

abstract: Abstract A new approach using the statistical feature matrix, which measures the statistical properties of pixel pairs at several distances, within an image, is proposed for texture analysis. The major properties of this approach are that (1) the size of the matrix is dependent on the maximum distance used instead of the number of gray-levels, (2) the matrix can be expanded easily and (3) some physical properties can be evaluated from the matrix. These properties have enhanced the practical applications of the matrix. In this paper, the matrix is applied to texture classification and visual-perceptual feature extraction. For texture classification, two experiments are performed. First, 16 Brodatz textures are employed to evaluate the performance of the matrix. A simple distance measure is defined to determine the similarity between two statistical feature matrices. Texture discrimination in an additive noise environment is also considered. Second, we apply the matrix to the classification of 150 sampled ultrasonic liver images. From experimental results it can be found that our approach is better than the spatial gray-level dependence method and the spatial frequency-based method. For visual-perceptual feature extraction, we evaluate five basic texture features, namely, coarseness, contrast, regularity, periodicity and roughness, from the statistical feature matrix. It is shown that the statistical feature matrix is an excellent tool for texture analysis.


title: Community Detection in Social Networks Based on Influential Nodes

abstract: Large-scale social networks emerged rapidly in recent years. Social networks have become complex networks. The structure of social networks is an important research area and has attracted much scientific interest. Community is an important structure in social networks. In this paper, we propose a community detection algorithm based on influential nodes. First, we introduce how to find influential nodes based on random walk. Then we combine the algorithm with order statistics theory to find community structure. We apply our algorithm in three classical data sets and compare to other algorithms. Our community detection algorithm is proved to be effective in the experiments. Our algorithm also has applications in data mining and recommendations.


International Journal of Advanced Robotic Systems Design of a Control System for an Autonomous Vehicle Based on Adaptive-PID Regular Paper


title: Sensitive couture for interactive garment modeling and editing

abstract: We present a novel interactive tool for garment design that enables, for the first time, interactive bidirectional editing between 2D patterns and 3D high-fidelity simulated draped forms. This provides a continuous, interactive, and natural design modality in which 2D and 3D representations are simultaneously visible and seamlessly maintain correspondence. Artists can now interactively edit 2D pattern designs and immediately obtain stable accurate feedback online, thus enabling rapid prototyping and an intuitive understanding of complex drape form.


title: Twenty Years of Attacks on the RSA Cryptosystem

abstract: Introduction The RSA cryptosystem, invented by Ron Rivest, Adi Shamir, and Len Adleman [18], was first publicized in the August 1977 issue of Scientific American. The cryptosystem is most commonly used for providing privacy and ensuring authenticity of digital data. These days RSA is deployed in many commercial systems. It is used by Web servers and browsers to secure Web traffic, it is used to ensure privacy and authenticity of e-mail, it is used to secure remote login sessions, and it is at the heart of electronic credit card payment systems. In short, RSA is frequently used in applications where security of digital data is a concern. Since its initial publication, the RSA system has been analyzed for vulnerability by many researchers. Although twenty years of research have led to a number of fascinating attacks, none of them is devastating. They mostly illustrate the dangers of improper use of RSA. Indeed, securely implementing RSA is a nontrivial task. Our goal is to survey some of these attacks and describe the underlying mathematical tools they use. Throughout the survey we follow standard naming conventions and use “Alice” and “Bob” to denote two generic parties wishing to communicate with each other. We use “Marvin” to denote a malicious attacker wishing to eavesdrop or tamper with the communication between Alice and Bob. We begin by describing a simplified version of RSA encryption. Let N = pq be the product of two large primes of the same size (n/2 bits each). A typical size for N is n = 1024 bits, i.e., 309 decimal digits. Each of the factors is 512 bits. Let e, d be two integers satisfying ed = 1 mod φ(N) where φ(N) = (p − 1)(q − 1) is the order of the multiplicative group ZN. We call N the RSA modulus, e the encryption exponent, and d the decryption exponent. The pair 〈N, e〉 is the public key. As its name suggests, it is public and is used to encrypt messages. The pair 〈N,d〉 is called the secret key or private key and is known only to the recipient of encrypted messages. The secret key enables decryption of ciphertexts. A message is an integer M ∈ ZN. To encrypt M, one computes C =Me mod N . To decrypt the ciphertext, the legitimate receiver computes Cd mod N. Indeed, Cd =Med =M mod N,


title: A Survey of Methods for Collective Communication Optimization and Tuning

abstract: New developments in HPC technology in terms of increasing computing power on multi/many core processors, high-bandwidth memory/IO subsystems and communication interconnects, pose a direct impact on software and runtime system development. These advancements have become useful in producing high-performance collective communication interfaces that integrate efficiently on a wide variety of platforms and environments. However, number of optimization options that shows up with each new technology or software framework has resulted in a \emph{combinatorial explosion} in feature space for tuning collective parameters such that finding the optimal set has become a nearly impossible task. Applicability of algorithmic choices available for optimizing collective communication depends largely on the scalability requirement for a particular usecase. This problem can be further exasperated by any requirement to run collective problems at very large scales such as in the case of exascale computing, at which impractical tuning by brute force may require many months of resources. Therefore application of statistical, data mining and artificial Intelligence or more general hybrid learning models seems essential in many collectives parameter optimization problems. We hope to explore current and the cutting edge of collective communication optimization and tuning methods and culminate with possible future directions towards this problem.


Automatic image retargeting


title: An exploratory study of visual information analysis

abstract: To design information visualization tools for collaborative use, we need to understand how teams engage with visualizations during their information analysis process. We report on an exploratory study of individuals, pairs, and triples engaged in information analysis tasks using paper-based visualizations. From our study results, we derive a framework that captures the analysis activities of co-located teams and individuals. Comparing this framework with existing models of the information analysis process suggests that information visualization tools may benefit from providing a flexible temporal flow of analysis actions.


title: Matrix factorization and neighbor based algorithms for the netflix prize problem

abstract: Collaborative filtering (CF) approaches proved to be effective for recommender systems in predicting user preferences in item selection using known user ratings of items. This subfield of machine learning has gained a lot of popularity with the Netflix Prize competition started in October 2006. Two major approaches for this problem are matrix factorization (MF) and the neighbor based approach (NB). In this work, we propose various variants of MF and NB that can boost the performance of the usual ensemble based scheme. First, we investigate various regularization scenarios for MF. Second, we introduce two NB methods: one is based on correlation coefficients and the other on linear least squares. At the experimentation part, we show that the proposed approaches compare favorably with existing ones in terms of prediction accuracy and/or required training time. We present results of blending the proposed methods.


On the Influence of Gender on Interruptions in Multiparty Dialogue.


title: Context-aware body area networks (CABAN) for interactive smart environments: interference characterization

abstract: Body Area Networks are unique in that the large-scale mobility of users allows the network itself to travel across a diverse range of operating domains or even to enter new and unknown environments. This network mobility is unlike node mobility in that sensed changes in inter-network interference level may be used to identify opportunities for intelligent inter-networking, for example, by merging or splitting from other networks, thus providing an extra degree of freedom. This paper introduces the concept of context-aware bodynets for interactive environments using inter-network interference sensing. New ideas are explored at both the physical and link layers with an investigation based on a 'smart' office environment. A series of carefully controlled measurements of the mesh interconnectivity both within and between an ambulatory body area network and a stationary desk-based network were performed using 2.45 GHz nodes. Received signal strength and carrier to interference ratio time series for selected node to node links are presented. The results provide an insight into the potential interference between the mobile and static networks and highlight the possibility for automatic identification of network merging and splitting opportunities.


title: Practical Fully Homomorphic Encryption without Noise Reduction

abstract: We present a new fully homomorphic encryption (FHE) scheme that is efficient for practical applications. The main feature of our scheme is that noise reduction considered essential in current FHE schemes, such as boot strapping and modulus switching, is not needed in our scheme, because it allows arbitrarily large noises in its ciphertexts. A ciphertext in our scheme is a vector with its dimension specified as a security parameter of the encryption key. The dimension of ciphertexts does not change with homomorphic operations and all ciphertext elements are in a finite domain, so our scheme is compact. In addition, our scheme can directly encrypt big integers, rather than only bit messages. We proved the hardness of recovering encryption keys from any number of ciphertexts with chosen plaintexts and then the semantic security of our scheme. The hardness of recovering keys from ciphertexts is based on the approximate greatest common divisors problem. We implemented a prototype of our scheme and evaluated its concrete performance extensively from the aspects of encryption, decryption, homomorphic operations, and bitwise operators over ciphertexts. The efficiency of our scheme is confirmed by the evaluation result.


title: PS-LSTM: Capturing Essential Sequential Online Information with Path Signature and LSTM for Writer Identification

abstract: Writer identification plays a significant role in several applications including forensic trace evidence identification, mobile bank transaction authentication, and handwritten character/text recognition. However, performing writer identification requires a considerable amount of experimental work and labor, as well as professional skills. In this paper, we propose a novel path-signature long short-term memory (PS-LSTM) recurrent neural network for writer identification that contributes as follows: 1) A mathematical feature set, path signature, is successfully applied to writer identification to characterize the essential geometric and analytic properties of pen-tip trajectory, which help distinguish between the diverse writing styles of different people, especially in confusing situations. 2) Different iterated integrals of path signature are investigated to model the local curvature nature of the pen-tip trajectory for writer identification. 3) Our study is the first to demonstrate the significance of integrating path signature with long short-term memory for writer identification. On Database CASIA-OLHWDN1.0, the proposed PS-LSTM writer identification achieves significantly superior results and outperforms previous works.


title: TransPhoner: automated mnemonic keyword generation

abstract: We present TransPhoner: a system that generates keywords for a variety of scenarios including vocabulary learning, phonetic transliteration, and creative word plays. We select effective keywords by considering phonetic, orthographic and semantic word similarity, and word concept imageability. We show that keywords provided by TransPhoner improve learner performance in an online vocabulary learning study, with the improvement being more pronounced for harder words. Participants rated TransPhoner keywords as more helpful than a random keyword baseline, and almost as helpful as manually selected keywords. Comments also indicated higher engagement in the learning task, and more desire to continue learning. We demonstrate additional applications to tasks such as pure phonetic transliteration, generation of mnemonics for complex vocabulary, and topic-based transformation of song lyrics.


title: Ensemble Diversity in Evolving Data Streams

abstract: While diversity of ensembles has been studied in the context of static data, it has not still received such research interest for evolving data streams. This paper aims at analyzing the impact of concept drift on diversity measures calculated for streaming ensembles. We consider six popular diversity measures and adapt their calculations to data stream requirements. A comprehensive series of experiments reveals the potential of each measure for visualizing ensemble performance over time. Measures highlighted as capable of depicting sudden and virtual drifts over time are used as basis for detecting changes with the Page-Hinkley test. Experimental results demonstrate that the \(\kappa \) interrater agreement, disagreement, and double fault measures, although designed to quantify diversity, provide a means of detecting changes competitive to that using classification accuracy.


title: Ontology-based data integration in EPNet : Production and distribution of food during the Roman Empire

abstract: Semantic technologies are rapidly changing the historical research. Over the last decades, an immense amount of new quantifiable data have been accumulated, and made available in interchangeable formats, in social sciences and humanities, opening up new possibilities for solving old questions and posing new ones. This paper introduces a framework that eases the access of scholars to historical and cultural data about food production and commercial trade system during the Roman Empire, distributed across different data sources. The proposed approach relies on the Ontology-Based Data Access (OBDA) paradigm, where the different datasets are virtually integrated by a conceptual layer (an ontology) that provides to the user a clear point of access and a unified and unambiguous conceptual view.


title: Activity of striatal neurons reflects dynamic encoding and recoding of procedural memories

abstract: Habit formation is an important factor in both human and animal behaviour patterns. These stereotyped behaviours are hard won, requiring extensive repetition, and difficult to lose. But once lost a habit can be quickly regained. The basal ganglia are known as a key part of the brain's habit system, but it is not known what they do. Now a study of rats navigating a maze demonstrates that as animals acquire a habit through training and then lose the habit and again acquire it through further bouts of training, waves of changing activity occur in the basal ganglia. Surprisingly, the research suggests that a main role of the basal ganglia may be to change the habit memories, not simply to store them. Learning to perform a behavioural procedure as a well-ingrained habit requires extensive repetition of the behavioural sequence, and learning not to perform such behaviours is notoriously difficult. Yet regaining a habit can occur quickly, with even one or a few exposures to cues previously triggering the behaviour1,2,3. To identify neural mechanisms that might underlie such learning dynamics, we made long-term recordings from multiple neurons in the sensorimotor striatum, a basal ganglia structure implicated in habit formation4,5,6,7,8, in rats successively trained on a reward-based procedural task, given extinction training and then given reacquisition training. The spike activity of striatal output neurons, nodal points in cortico-basal ganglia circuits, changed markedly across multiple dimensions during each of these phases of learning. First, new patterns of task-related ensemble firing successively formed, reversed and then re-emerged. Second, task-irrelevant firing was suppressed, then rebounded, and then was suppressed again. These changing spike activity patterns were highly correlated with changes in behavioural performance. We propose that these changes in task representation in cortico-basal ganglia circuits represent neural equivalents of the explore–exploit behaviour characteristic of habit learning.


Top-k Skyline Groups Queries.


title: Abstract The Top 5 Reasons For Lurking: Improving Community Experiences For Everyone

abstract: Even in busy online communities, usually only a small fraction of members post messages. Why do so many people prefer not to contribute publicly? From an online survey that generated 1,188 responses from posters and lurkers from 375 MSN bulletin board communities, 219 lurkers spoke out about their reasons for not posting. While lurkers did not participate publicly, they did seek answers to questions. However, lurkers’ satisfaction with their community experience was lower than those who post. Data from 19 checkbox items and over 490 open-ended responses were analyzed. From this analysis, the main reasons why lurkers lurk were concerned with: not needing to post; needing to find out more about the group before participating; thinking that they were being helpful by not posting; not being able to make the software work (i.e., poor usability); and not liking the group dynamics or the community was a poor fit for them. Two key conclusions were drawn from this analysis. First, there are many reasons why people lurk in online discussion communities. Second, and most important, most lurkers are not selfish free-riders. From these findings, it is clear that there are many ways to improve online community experiences for both posters and lurkers. Some solutions require improved software and better tools, but moderation and better interaction support will produce dramatic improvements. # 2003 Elsevier Ltd. All rights reserved.


title: Deficits of cognitive executive functions in patients with sleep apnea syndrome

abstract: Impairment of cognitive executive functions previously has been suspected to occur in association with sleep apnea syndrome (SAS), as suggested by some neuropsychological studies. However, such functions have not been assessed directly. In the present study, 17 patients with SAS were evaluated with various focused frontal loberelated tests in comparison with 17 normal controls. Such tasks explored attention, short-term memory spans, learning abilities, planning and programming capacities, categorizing activities and verbal fluency. Patients were found to have a significantly decreased ability to initiate new mental processes and to inhibit automatic ones, in conjunction with a tendency for perseverative errors. They were also affected with deficits of verbal and visual learning abilities and had reduced memory spans. Such defects were further evaluated via logistic regression against two criteria of the severity of the disease: the number of apneas and hypopneas per hour of sleep and the level of nocturnal hypoxemia. Memory deficits were rather related to the former, whereas typical frontal lobe-related abnormalities seemed rather consistent with the latter. These findings are discussed in light of data from the literature concerning cognitive impairments described for patients with isolated daytime sleepiness versus hypoxemia, as illustrated in other pathological or physiological circumstances


A surprisingly effective out-of-the-box char2char model on the E2E NLG Challenge dataset


title: Hybrid Taguchi-genetic algorithm for global numerical optimization

abstract: In this paper, a hybrid Taguchi-genetic algorithm (HTGA) is proposed to solve global numerical optimization problems with continuous variables. The HTGA combines the traditional genetic algorithm (TGA), which has a powerful global exploration capability, with the Taguchi method, which can exploit the optimum offspring. The Taguchi method is inserted between crossover and mutation operations of a TGA. Then, the systematic reasoning ability of the Taguchi method is incorporated in the crossover operations to select the better genes to achieve crossover, and consequently, enhance the genetic algorithm. Therefore, the HTGA can be more robust, statistically sound, and quickly convergent. The proposed HTGA is effectively applied to solve 15 benchmark problems of global optimization with 30 or 100 dimensions and very large numbers of local minima. The computational experiments show that the proposed HTGA not only can find optimal or close-to-optimal solutions but also can obtain both better and more robust results than the existing algorithm reported recently in the literature.


title: Torque estimation of robot joint with harmonic drive transmission using a redundant adaptive robust extended Kalman filter

abstract: A torque estimation method with adaptive robustness and optimality adjustment according to the load for modular and reconfigurable robot joint with harmonic drive transmission is proposed, on the basis of harmonic drive compliance model and redundant adaptive robust extended Kalman filter (RAREKF). The proposed approach can adapt torque estimation filtering optimality and robustness to the load variations by self-tuning the filtering gain and self-switching the filtering modes between optimal and robust. The redundant factor of RAREKF is designed as a function of the load to provide desirable tolerant capability to the modeling error and load-dependent filtering mode switching. The proposed joint torque estimation method has been experimentally studied in comparison with a commercial sensor, and the results have demonstrated the effectiveness of the proposed torque estimation technique.


title: Motor point map of upper body muscles

abstract: Purpose ::: The purpose of the present study was to systematically investigate the upper body motor point (MP) positions of selected muscles and to create an atlas of the identified MPs.


title: IP2Vec: Learning Similarities Between IP Addresses

abstract: IP Addresses are a central part of packet- and flow-based network data. However, visualization and similarity computation of IP Addresses are challenging to due the missing natural order. This paper presents a novel similarity measure IP2Vec for IP Addresses that builds on ideas from Word2Vec, a popular approach in text mining. The key idea is to learn similarities by extracting available context information from network data. IP Addresses are similar if they appear in similar contexts. Thus, IP2Vec is automatically derived from the given network data set. The proposed approach is evaluated experimentally on two public flow-based data sets. In particular, we demonstrate the effectiveness of clustering IP Addresses within a botnet data set. In addition, we use visualization methods to analyse the learned similarities in more detail. These experiments indicate that IP2Vec is well suited to capture the similarity of IP Addresses based on their network communications.


title: Schur Number Five

abstract: We present the solution of a century-old problem known as Schur Number Five: What is the largest (natural) number $n$ such that there exists a five-coloring of the positive numbers up to $n$ without a monochromatic solution of the equation $a + b = c$? We obtained the solution, $n = 160$, by encoding the problem into propositional logic and applying massively parallel satisfiability solving techniques on the resulting formula. We constructed and validated a proof of the solution to increase trust in the correctness of the multi-CPU-year computations. The proof is two petabytes in size and was certified using a formally verified proof checker, demonstrating that any result by satisfiability solvers---no matter how large---can now be validated using highly trustworthy systems.


TrueSkill 2: An improved Bayesian skill rating system


In vitro antioxidant activity and cytotoxicity of sequential extracts from selected black pepper ( Piper nigrum L . ) varieties and Piper species


title: Mathematical Morphology Based ECG Feature Extraction for the Purpose of Heartbeat Classification

abstract: The paper presents the classification performance of an automatic classifier of the electrocardiogram (ECG) for the detection abnormal beats with new concept of feature extraction stage. Feature sets were based on ECG morphology and RR-intervals. Configuration adopted a Kohonen self-organizing maps (SOM) for analysis of signal features and clustering. In this study, a classifier was developed with SOM and learning vector quantization (LVQ) algorithms using the data from the records recommended by ANSI/AAMI EC57 standard. This paper compares two strategies for classification of annotated QRS complexes: based on original ECG morphology features and proposed new approach - based on preprocessed ECG morphology features. The mathematical morphology filtering is used for the preprocessing of ECG signal. The problem of choosing an appropriate structuring element of mathematical morphology filtering for ECG signal processing was studied. The performance of the algorithm is evaluated on the MIT-BIH Arrhythmia Database following the AAMI recommendations. Using this method the results of recognition beats either as normal or arrhythmias was improved.


title: Collaborative Multi-domain Sentiment Classification

abstract: Sentiment classification is a hot research topic in both industrial and academic fields. The mainstream sentiment classification methods are based on machine learning and treat sentiment classification as a text classification problem. However, sentiment classification is widely recognized as a highly domain-dependent task. The sentiment classifier trained in one domain may not perform well in another domain. A simple solution to this problem is training a domain-specific sentiment classifier for each domain. However, it is difficult to label enough data for every domain since they are in a large quantity. In addition, this method omits the sentiment information in other domains. In this paper, we propose to train sentiment classifiers for multiple domains in a collaborative way based on multi-task learning. Specifically, we decompose the sentiment classifier in each domain into two components, a general one and a domain-specific one. The general sentiment classifier can capture the global sentiment information and is trained across various domains to obtain better generalization ability. The domain-specific sentiment classifier is trained using the labeled data in one domain to capture the domain-specific sentiment information. In addition, we explore two kinds of relations between domains, one based on textual content and the other one based on sentiment word distribution. We build a domain similarity graph using domain relations and encode it into our approach as regularization over the domain-specific sentiment classifiers. Besides, we incorporate the sentiment knowledge extracted from sentiment lexicons to help train the general sentiment classifier more accurately. Moreover, we introduce an accelerated optimization algorithm to train the sentiment classifiers efficiently. Experimental results on two benchmark sentiment datasets show that our method can outperform baseline methods significantly and consistently.