title: Why agents? On the varied motivations for agent computing in the social sciences

abstract: The many motivations for employing agent-based computation in the social sciences are reviewed. It is argued that there exist three distinct uses of agent modeling techniques. One such use — the simplest — is conceptually quite close to traditional simulation in operations research. This use arises when equations can be formulated that completely describe a social process, and these equations are explicitly soluble, either analytically or numerically. In the former case, the agent model is merely a tool for presenting results, while in the latter it is a novel kind of Monte Carlo analysis. A second, more commonplace usage of computational agent models arises when mathematical models can be written down but not completely solved. In this case the agent-based model can shed significant light on the solution structure, illustrate dynamical properties of the model, serve to test the dependence of results on parameters and assumptions, and be a source of counter-examples. Finally, there are important classes of problems for which writing down equations is not a useful activity. In such circumstances, resort to agent-based computational models may be the only way available to explore such processes systematically, and constitute a third distinct usage of such models. ∗ Corresponding author address: Robert Axtell, Center on Social and Economic Dynamics, The Brookings Institution, 1775 Massachusetts Ave. NW, Washington, DC 20036; e-mail: raxtell@brook.edu; web: http://www.brook.edu/es/dynamics.


title: Secure Databases: An Analysis of Clark-Wilson Model in a Database Environment

abstract: Information systems are vulnerable to accidental or malicious attacks. Security models for commercial computer systems exist, but information systems security is often ignored or added at or after implementation. The paper explores common security models, and their relevance to databases. It demonstrates how security-relevant concepts can be extracted during a conventional database development.


title: Steady State of a SAW Stabilized Bipolar Oscillator Obtained by Solution of a Boundary Value Problem

abstract: A time-domain boundary value (TDBV) - method for the self - consistent determination of the steady state solution of oscillators is applied to the simulation of a SAW stabilized bipolar oscillator including transmission lines. The boundary value problem was solved with the multiple shooting algorithm, which has a wider region of convergence than conventional single shooting. Comparison of measurement and simulation shows agreement within 50 ppm for the frequency of the fundamental oscillation and 0.5 dB for the output power.


title: Phytochemical, Antimicrobial, and Toxicological Evaluation of Traditional Herbs Used to Treat Sore Throat

abstract: The in vitro antibacterial activities of 29 traditional medicinal plants used in respiratory ailments were assessed on multidrug resistant Gram-positive and Gram-negative bacteria isolated from the sore throat patients and two reference strains. The methanolic, n-hexane, and aqueous extracts were screened by the agar well diffusion assay. Bioactive fractions of effective extracts were identified on TLC coupled with bioautography, while their toxicity was determined using haemolytic assay against human erythrocytes. Qualitative and quantitative phytochemical analysis of effective extracts was also performed. Methanolic extract of 18 plants showed antimicrobial activity against test strains. Adhatoda vasica (ZI = 17-21 mm, MIC: 7.12-62.5 μg/mL), Althaea officinalis (ZI = 16-20 mm, MIC: 15.62-31.25 μg/mL), Cordia latifolia (ZI = 16-20 mm, MIC: 12.62-62.5 μg/mL), Origanum vulgare (ZI = 20-22 mm, MIC: 3-15.62 μg/mL), Thymus vulgaris (ZI = 21-25 mm, MIC: 7.81-31.25 μg/mL), and Ziziphus jujuba (ZI = 14-20 mm, MIC: 7.81-31.25 μg/mL) showed significant antibacterial activity. Alkaloid fractions of Adhatoda vasica, Cordia latifolia, and Origanum vulgare and flavonoid fraction of the Althaea officinalis, Origanum vulgare, Thymus Vulgaris, and Ziziphus jujuba exhibited antimicrobial activity. Effective plant extracts show 0.93-0.7% erythrocyte haemolysis. The results obtained from this study provide a scientific rationale for the traditional use of these herbs and laid the basis for future studies to explore novel antimicrobial compounds.


title: Scalable Distributed Computing Hierarchy: Cloud, Fog and Dew Computing

abstract: The paper considers the conceptual approach for organization of the vertical hierarchical links between the scalable distributed computing paradigms: Cloud Computing, Fog Computing and Dew Computing. In this paper, the Dew Computing is described and recognized as a new structural layer in the existing distributed computing hierarchy. In the existing computing hierarchy, the Dew computing is positioned as the ground level for the Cloud and Fog computing paradigms. Vertical, complementary, hierarchical division from Cloud to Dew Computing satisfies the needs of high- and low-end computing demands in everyday life and work. These new computing paradigms lower the cost and improve the performance, particularly for concepts and applications such as the Internet of Things (IoT) and the Internet of Everything (IoE). In addition, the Dew computing paradigm will require new programming models that will efficiently reduce the complexity and improve the productivity and usability of scalable distributed computing, following the principles of High-Productivity computing.


title: Cognitive Dissonance, Sentiment, and Momentum

abstract: We consider whether sentiment affects the profitability of momentum strategies. We hypothesize that news that contradicts investors’ sentiment causes cognitive dissonance, slowing the diffusion of such news. Thus, losers (winners) become underpriced under optimism (pessimism). Short-selling constraints may impede arbitraging of losers and thus strengthen momentum during optimistic periods. Supporting this notion, we empirically show that momentum profits arise only under optimism. An analysis of net order flows from small and large trades indicates that small investors are slow to sell losers during optimistic periods. Momentum-based hedge portfolios formed during optimistic periods experience long-run reversals.


title: Variational Inference for Uncertainty on the Inputs of Gaussian Process Models

abstract: The Gaussian process latent variable model (GP-LVM) provides a flexible approach for non-linear dimensionality reduction that has been widely applied. However, the current approach for training GP-LVMs is based on maximum likelihood, where the latent projection variables are maximized over rather than integrated out. In this paper we present a Bayesian method for training GP-LVMs by introducing a non-standard variational inference framework that allows to approximately integrate out the latent variables and subsequently train a GP-LVM by maximizing an analytic lower bound on the exact marginal likelihood. We apply this method for learning a GP-LVM from iid observations and for learning non-linear dynamical systems where the observations are temporally correlated. We show that a benefit of the variational Bayesian procedure is its robustness to overfitting and its ability to automatically select the dimensionality of the nonlinear latent space. The resulting framework is generic, flexible and easy to extend for other purposes, such as Gaussian process regression with uncertain inputs and semi-supervised Gaussian processes. We demonstrate our method on synthetic data and standard machine learning benchmarks, as well as challenging real world datasets, including high resolution video data.


title: Task-oriented generalization of dynamic movement primitive

abstract: An important question in imitation learning is how to generalize a learned motion to novel situations. The motion generalization depends on a set of features which can be represented as feature vectors spanning a feature space, called query space. The purpose of generalization is to find a mapping from this query space to the motion primitive space (MP space). In this paper, we address the problem of generalization of dynamic movement primitives (DMPs) to new queries by applying locally weighted regression (LWR) with radial basis functions (RBF). Since two DMPs differ only in their non-linear part, we transform the problem of DMP generalization to a regression analysis problem. We introduce a task-oriented regression algorithm with a cost function that takes task constraints into consideration and which relies on model switching to solve the problem of poor DMP generalization when using a single regression model for the entire query space. The evaluation shows that our algorithm outperforms related approaches in the literature in terms of generalization capabilities.


title: Accuracy in random number generation

abstract: The generation of continuous random variables on a digital computer encounters a problem of accuracy caused by approximations and discretization error. These in turn impose a bias on the simulation results. An ideal discrete approximation of a continuous distribution and a measure of error are proposed. Heuristic analysis of common methods for transforming uniform deviates to other continuous random variables is discussed. Comments and recom- mendations are made for the design of algorithms to reduce the bias and avoid overflow problems.


title: Analyzing Customer Journey with Process Mining: From Discovery to Recommendations

abstract: Customer journey analysis is a hot topic in marketing. Understanding how the customers behave is crucial and is considered as one of the key drivers of business success. To the best of our knowledge, a data-driven approach to analyze the customer journey is still missing. For instance, web analytics tools like Google Analytics provide an oversimplified version of the user behavior, focusing more on the frequency of page visits rather than discovering the process of the visit itself. On the other hand, customer journey maps have shown their usefulness, but they need to be created manually by domain experts. This paper contributes a novel approach for applying process mining techniques to web log customer journey analysis. Through process mining we are able to (i) discover the process that better describes the user behavior, (ii) find useful insights, (iii) compare the processes of different clusters of users, and then (iv) use this analysis to improve the journey by optimizing some KPIs (Key Performance Indicators) via personalized recommendations based on the user behavior. We show through a real-life case study a proof of the correctness of the introduced concept by improving the recommender accuracy when incorporating additional context information about the journey as extracted from the process model.


title: Indoor Positioning in High Speed OFDM Visible Light Communications

abstract: Visible Light Communication (VLC) technology using light emitting diodes (LEDs) has been gaining increasing attention in recent years as it is appealing for a wide range of applications such as indoor positioning. Orthogonal frequency division multiplexing (OFDM) has been applied to indoor wireless optical communications in order to mitigate the effect of multipath distortion of the optical channel as well as increasing data rate. In this paper, we investigate the indoor positioning accuracy of optical based OFDM techniques used in VLC systems. A positioning algorithm based on power attenuation is used to estimate the receiver coordinates. We further calculate the positioning errors in all the locations of a room and compare them with those of single carrier modulation scheme, i.e., on-off keying (OOK) modulation. We demonstrate that OFDM positioning system outperforms its conventional counterpart.


title: Awareness Is Not Enough: Pitfalls of Learning Analytics Dashboards in the Educational Practice

abstract: It has been long argued that learning analytics has the potential to act as a “middle space” between the learning sciences and data analytics, creating technical possibilities for exploring the vast amount of data generated in online learning environments. One common learning analytics intervention is the learning dashboard, a support tool for teachers and learners alike that allows them to gain insight into the learning process. Although several related works have scrutinised the state-of-the-art in the field of learning dashboards, none have addressed the theoretical foundation that should inform the design of such interventions. In this systematic literature review, we analyse the extent to which theories and models from learning sciences have been integrated into the development of learning dashboards aimed at learners. Our critical examination reveals the most common educational concepts and the context in which they have been applied. We find evidence that current designs foster competition between learners rather than knowledge mastery, offering misguided frames of reference for comparison.


title: INFORMATION SECURITY CULTURE – FROM ANALYSIS TO CHANGE

abstract: Information Security Culture includes all socio-cultural measures that support technical security methods, so that information security becomes a natural aspect in the daily activity of every employee. To apply these socio-cultural measures in an effective and efficient way, certain management models and tools are needed. In our research we developed a framework analyzing the security culture of an organization which we then applied in a pre-evaluation survey. This paper is based on the results of this survey. We will develop a management model for creating, changing and maintaining Information Security Culture. This model will then be used to define explicit sociocultural measures, based on the concept of internal marketing.


title: BASS: Improving I/O Performance for Cloud Block Storage via Byte-Addressable Storage Stack

abstract: In an Infrastructure-as-a-Service cloud, cloud block storage offers conventional, block-level storage resources via a storage area network. However, compared to local storage, this multilayered cloud storage model imposes considerable I/O overheads due to much longer I/O path in the virtualized cloud. In this paper, we propose a novel byte-addressable storage stack, BASS, to bridge the addressability gap between the storage and network stacks in cloud, and in return boost I/O performance for cloud block storage. Equipped with byte-addressability, BASS not only avails the benefits of using variable-length I/O requests that avoid unnecessary data transfer, but also enables a highly efficient non-blocking approach that eliminates the blocking of write processes. We have developed a generic prototype of BASS based on Linux storage stack, which is applicable to traditional VMs, lightweight containers and physical machines. Our extensive evaluation with micro-benchmarks, I/O traces and real-world applications demonstrates the effectiveness of BASS, with significantly improved I/O performance and reduced storage network usage.


title: Will C++ Be Faster than Fortran?

abstract: Typical benchmarks showed C++ lagging behind Fortran's performance by 20% to a factor of ten. Performance was so poor that mixed-language programming was necessary : a program's framework could be written in C++, but any speed-critical routines had to be coded in Fortran. We'll start by locking at reasons why C++ used to be slower, and then describe techniques which can make it faster than Fortran. Ideas will be illustrated with examples and benchmarks from the Blitz++ class library.


title: The Study on the Self-Similarity and Simulation of CPS Traffic

abstract: CPS traffic characteristics is one of key techniques of Cyber-Physical Systems (CPS). A deep research of CPS network traffic characteristics can help to better plan and design CPS networks. A brief overview of the key concepts of CPS is firstly presented. Then CPS application scenarios are analyzed in details and classified. The characteristics of CPS traffic is analyzed theoretically for different CPS application scenarios. At last, the characteristics of CPS traffic is verified using NS-2 simulation.


title: Low complexity precoding for large millimeter wave MIMO systems

abstract: Millimeter wave (mmWave) systems must overcome heavy signal attenuation to support high-throughput wireless communication links. The small wavelength in mmWave systems enables beamforming using large antenna arrays to combat path loss with directional transmission. Beamforming with multiple data streams, known as precoding, can be used to achieve even higher performance. Both beamforming and precoding are done at baseband in traditional microwave systems. In mmWave systems, however, the high cost of mixed-signal and radio frequency chains (RF) makes operating in the passband and analog domains attractive. This hardware limitation places additional constraints on precoder design. In this paper, we consider single user beamforming and precoding in mmWave systems with large arrays. We exploit the structure of mmWave channels to formulate the precoder design problem as a sparsity constrained least squares problem. Using the principle of basis pursuit, we develop a precoding algorithm that approximates the optimal unconstrained precoder using a low dimensional basis representation that can be efficiently implemented in RF hardware. We present numerical results on the performance of the proposed algorithm and show that it allows mmWave systems to approach waterfilling capacity.


title: A Hierarchical Generative Model for Eye Image Synthesis and Eye Gaze Estimation

abstract: In this work, we introduce a Hierarchical Generative Model (HGM) to enable realistic forward eye image synthesis, as well as effective backward eye gaze estimation. The proposed HGM consists of a hierarchical generative shape model (HGSM), and a conditional bidirectional generative adversarial network (c-BiGAN). The HGSM encodes eye geometry knowledge and relates eye gaze with eye shape, while c-BiGAN leverages on big data and captures the dependency between eye shape and eye appearance. As an intermediate component, eye shape connects knowledge-based model (HGSM) with data-driven model (c-BiGAN) and enables bidirectional inference. Through a top-down inference, the HGM can synthesize eye images consistent with the given eye gaze. Through a bottom-up inference, HGM can infer eye gaze effectively from a given eye image. Qualitative and quantitative evaluations on benchmark datasets demonstrate our model's effectiveness on both eye image synthesis and eye gaze estimation. In addition, the proposed model is not restricted to eye images only. It can be adapted to face images and any shape-appearance related fields.


title: Thai speech recognition using Neuro-fuzzy system

abstract: There are many popular algorithms to recognize the human voice. The good algorithm not only results the high recognition accuracy, but also robust to noises. Several experiments are done in this research to verify the performance of the Neuro-fuzzy system to recognize the human voice. Eight words in Thai language recorded in a different environment, syllable and pronunciations are used as a data set in the experiments. All words are in machine command category, the meaning of words such as forward, back, left, right and so on, with the purpose of applying to some instrument for disabled people in the future. The preliminary results show that each factor has different effects to the recognition accuracy. However the experimental results show that the Neuro-fuzzy system is quite robust to noise and can yield the higher recognition results compare with other popular algorithms. The methodology of speech recognition proposed in this paper can be one of the best alternative choices to apply to a relative work in the future.


title: A Survey on Applications of Model-Free Strategy Learning in Cognitive Wireless Networks

abstract: The framework of cognitive wireless networks is expected to endow the wireless devices with the cognition-intelligence ability with which they can efficiently learn and respond to the dynamic wireless environment. In many practical scenarios, the complexity of network dynamics makes it difficult to determine the network evolution model in advance. Thus, the wireless decision-making entities may face a black-box network control problem and the model-based network management mechanisms will be no longer applicable. In contrast, model-free learning enables the decision-making entities to adapt their behaviors based on the reinforcement from their interaction with the environment and (implicitly) build their understanding of the system from scratch through trial-and-error. Such characteristics are highly in accordance with the requirement of cognition-based intelligence for devices in cognitive wireless networks. Therefore, model-free learning has been considered as one key implementation approach to adaptive, self-organized network control in cognitive wireless networks. In this paper, we provide a comprehensive survey on the applications of the state-of-the-art model-free learning mechanisms in cognitive wireless networks. According to the system models on which those applications are based, a systematic overview of the learning algorithms in the domains of single-agent system, multiagent systems, and multiplayer games is provided. The applications of model-free learning to various problems in cognitive wireless networks are discussed with the focus on how the learning mechanisms help to provide the solutions to these problems and improve the network performance over the model-based, non-adaptive methods. Finally, a broad spectrum of challenges and open issues is discussed to offer a guideline for the future research directions.


title: Analysis and detection of low quality information in social networks

abstract: With social networks like Facebook, Twitter and Google+ attracting audiences of millions of users, they have been an important communication platform in daily life. This in turn attracts malicious users to the social networks as well, causing an increase in the incidence of low quality information. Low quality information such as spam and rumors is a nuisance to people and hinders them from consuming information that is pertinent to them or that they are looking for. Although individual social networks are capable of filtering a significant amount of low quality information they receive, they usually require large amounts of resources (e.g, personnel) and incur a delay before detecting new types of low quality information. Also the evolution of various low quality information posts lots of challenges to defensive techniques. My PhD thesis work focuses on the analysis and detection of low quality information in social networks. We introduce social spam analytics and detection framework SPADE across multiple social networks showing the efficiency and flexibility of cross-domain classification and associative classification. For evolutionary study of low quality information, we present the results on large-scale study on Web spam and email spam over a long period of time. Furthermore, we provide activity-based detection approaches to filter out low quality information in social networks: click traffic analysis of short URL spam, behavior analysis of URL spam and information diffusion analysis of rumor. Our framework and detection techniques show promising results in analyzing and detecting low quality information in social networks.


Advancements in Gingival Retraction Techniques in Restorative Dentistry


title: Towards Real-Time Traffic Sign Detection and Classification

abstract: Traffic sign recognition plays an important role in driver assistant systems and intelligent autonomous vehicles. Its real-time performance is highly desirable in addition to its recognition performance. This paper aims to deal with real-time traffic sign recognition, i.e., localizing what type of traffic sign appears in which area of an input image at a fast processing time. To achieve this goal, we first propose an extremely fast detection module, which is 20 times faster than the existing best detection module. Our detection module is based on traffic sign proposal extraction and classification built upon a color probability model and a color HOG. Then, we harvest from a convolutional neural network to further classify the detected signs into their subclasses within each superclass. Experimental results on both German and Chinese roads show that both our detection and classification methods achieve comparable performance with the state-of-the-art methods, with significantly improved computational efficiency.


title: SPREAD: a balancing constraint based on statistics

abstract: Many combinatorial problems require of their solutions that they achieve a certain balance of given features. In the constraint programming literature, little has been written to specifically address this issue, particularly at the modeling level. We propose a new constraint dedicated to balancing, based on well-known and well-understood concepts in statistics. We show how it can be used to model different situations in which balance is important. We also design efficient filtering algorithms to guide the search towards balanced solutions.


title: AudioSentibank: Large-scale Semantic Ontology of Acoustic Concepts for Audio Content Analysis

abstract: Audio carries substantial information about the content of our surroundings. The content has been explored at the semantic level using acoustic concepts, but rarely on concept pairs such as happy crowd and angry crowd. Concept pairs convey unique information and complement other audio and multimedia applications. Hence, in this work we explored for the first time the classification's performance of acoustic concepts pairs. For this study, we introduce the AudioSentiBank corpus, which is a large-scale folksology containing over 1,123 adjective and verb noun pairs. Our contribution consists on providing the research corpus, the benchmark for classification of acoustic concept pairs, and an analysis on the pairs.


title: Data compression for energy efficient IoT solutions

abstract: Energy is the most important resource in state-of-the-art Internet of Things solutions. There are a lot of concepts and techniques dedicated to save energy, mainly focused to reduce transmission, since the energy used for preprocessing (encoding) is incomparable smaller than energy used for broadcasting. If applications do not require real-time measurements, data compression is one solution to energy saving problem. The goal of this paper was to develop new coding scheme for delta compression, that can be used for efficient data compression of temporally correlated data, such as temperature measurements coming from different smart devices. We proved that our coding scheme can achieve up to 85% energy saving. Compared to other coding techniques, our scheme has greater compression ratio and lower memory requirements.


title: A Regression Approach to Music Emotion Recognition

abstract: Content-based retrieval has emerged in the face of content explosion as a promising approach to information access. In this paper, we focus on the challenging issue of recognizing the emotion content of music signals, or music emotion recognition (MER). Specifically, we formulate MER as a regression problem to predict the arousal and valence values (AV values) of each music sample directly. Associated with the AV values, each music sample becomes a point in the arousal-valence plane, so the users can efficiently retrieve the music sample by specifying a desired point in the emotion plane. Because no categorical taxonomy is used, the regression approach is free of the ambiguity inherent to conventional categorical approaches. To improve the performance, we apply principal component analysis to reduce the correlation between arousal and valence, and RReliefF to select important features. An extensive performance study is conducted to evaluate the accuracy of the regression approach for predicting AV values. The best performance evaluated in terms of the R 2 statistics reaches 58.3% for arousal and 28.1% for valence by employing support vector machine as the regressor. We also apply the regression approach to detect the emotion variation within a music selection and find the prediction accuracy superior to existing works. A group-wise MER scheme is also developed to address the subjectivity issue of emotion perception.


title: A novel CMOS operational transconductance amplifier based on a mobility compensation technique

abstract: This brief describes a new linear operational transconductance amplifier (OTA) and its application to a ninth-order Bessel filter. To improve the linearity of the OTA, we employ a mobility compensation circuit which combines the transistors operating in the triode and the subthreshold regions. The proposed technique enhances the linearity of the transconductance without loss of the input swing range. The proposed OTA shows /spl plusmn/0.5% Gm variation and the total harmonic distortion of less than - 60-dB over the input range of /spl plusmn/0.8-V. The ninth-order Bessel filter employing the proposed OTA has been implemented in a 0.35-/spl mu/m n-well CMOS process under 3.3-V supply voltage. It shows the cutoff frequency of 8 MHz and the power consumption of 65 mW.


Semi-distantly Supervised Neural Model for Generating Compact Answers to Open-domain Why Questions


title: A Study of Encryption Algorithms (RSA, DES, 3DES and AES) for Information Security

abstract: Encryption is the process of scrambling a message so that only the intended recipient can read it. Encryption can provide a means of securing information. As more and more information is stored on computers or communicated via computers, the need to insure that this information is invulnerable to snooping and/or tampering becomes more relevant. With the fast progression of digital data exchange in electronic way, Information Security is becoming much more important in data storage and transmission. Information Confidentiality has a prominent significance in the study of ethics, law and most recently in Information Systems. With the evolution of human intelligence, the art of cryptography has become more complex in order to make information more secure. Arrays of Encryption systems are being deployed in the world of Information Systems by various organizations. In this paper, a survey of various Encryption Algorithms is presented. General Terms Information Security, Encryption


title: Anatomy of a Crowdsourcing Platform - Using the Example of Microworkers.com

abstract: Since Jeff Howe introduced the term "crowdsourcing" in 2006 for the first time, crowd sourcing has be come a growing market in the current Internet. Thousands of workers categorize images, write articles or perform other small tasks on platforms like Amazon Mechanical Turk (MTurk), Micro workers or Short Task. In this work, we want to give an inside view of the usage data from Micro workers and show that there are significant differences to the well studied MTurk. Further, we have a look at Micro workers from the perspective for a worker, an employer and the platform owner, in order to answer their most important questions: What jobs are most paid? How do I get my work done most quickly? When are the users of my platform active?


title: Modeling and analysis of phase-shift controlled LCL resonant converter in wireless charging systems

abstract: In this paper, a simplified low order linear model for LCL compensated WCS is presented using Extended Describing Function (EDF) technique. It turns out that the six-order LCL type WCS behaves as a second-order under-damped system within low frequency range. The simplified reduced order model provides intuitive understanding into both steady and dynamic performance of the converter and also facilitates controller design. The stability boundary and ripple suppression condition using PI controller is given. Considering the limitation of conventional PI controller, the improved PI combining notch filter is proposed to acquire minimal charging ripple and quick dynamic response simultaneously. Experimental results are presented to verify the accuracy and robustness of proposed model and control design.


title: Cell Segmentation in Phase Contrast Microscopy Images Via Semi-Supervised Classification Over Optics-Related Features

abstract: Abstract Phase-contrast microscopy is one of the most common and convenient imaging modalities to observe long-term multi-cellular processes, which generates images by the interference of lights passing through transparent specimens and background medium with different retarded phases. Despite many years of study, computer-aided phase contrast microscopy analysis on cell behavior is challenged by image qualities and artifacts caused by phase contrast optics. Addressing the unsolved challenges, the authors propose (1) a phase contrast microscopy image restoration method that produces phase retardation features, which are intrinsic features of phase contrast microscopy, and (2) a semi-supervised learning based algorithm for cell segmentation, which is a fundamental task for various cell behavior analysis. Specifically, the image formation process of phase contrast microscopy images is first computationally modeled with a dictionary of diffraction patterns; as a result, each pixel of a phase contrast microscopy image is represented by a linear combination of the bases, which we call phase retardation features. Images are then partitioned into phase-homogeneous atoms by clustering neighboring pixels with similar phase retardation features. Consequently, cell segmentation is performed via a semi-supervised classification technique over the phase-homogeneous atoms. Experiments demonstrate that the proposed approach produces quality segmentation of individual cells and outperforms previous approaches.


title: On the Power of Over-parametrization in Neural Networks with Quadratic Activation

abstract: We provide new theoretical insights on why over-parametrization is effective in learning neural networks. For a $k$ hidden node shallow network with quadratic activation and $n$ training data points, we show as long as $ k \ge \sqrt{2n}$, over-parametrization enables local search algorithms to find a \emph{globally} optimal solution for general smooth and convex loss functions. Further, despite that the number of parameters may exceed the sample size, using theory of Rademacher complexity, we show with weight decay, the solution also generalizes well if the data is sampled from a regular distribution such as Gaussian. To prove when $k\ge \sqrt{2n}$, the loss function has benign landscape properties, we adopt an idea from smoothed analysis, which may have other applications in studying loss surfaces of neural networks.


title: Social Robots for Long-Term Interaction: A Survey

abstract: As the field of HRI evolves, it is important to understand how users interact with robots over long periods. This paper reviews the current research on long-term interaction between users and social robots. We describe the main features of these robots and highlight the main findings of the existing long-term studies. We also present a set of directions for future research and discuss some open issues that should be addressed in this field.


title: Learning multilingual named entity recognition from Wikipedia

abstract: We automatically create enormous, free and multilingual silver-standard training annotations for named entity recognition (ner) by exploiting the text and structure of Wikipedia. Most ner systems rely on statistical models of annotated data to identify and classify names of people, locations and organisations in text. This dependence on expensive annotation is the knowledge bottleneck our work overcomes. We first classify each Wikipedia article into named entity (ne) types, training and evaluating on 7200 manually-labelled Wikipedia articles across nine languages. Our cross-lingual approach achieves up to 95% accuracy. We transform the links between articles into ne annotations by projecting the target [email protected]?s classifications onto the anchor text. This approach yields reasonable annotations, but does not immediately compete with existing gold-standard data. By inferring additional links and heuristically tweaking the Wikipedia corpora, we better align our automatic annotations to gold standards. We annotate millions of words in nine languages, evaluating English, German, Spanish, Dutch and Russian Wikipedia-trained models against conll shared task data and other gold-standard corpora. Our approach outperforms other approaches to automatic ne annotation (Richman and Schone, 2008 [61], Mika et al., 2008 [46]) competes with gold-standard training when tested on an evaluation corpus from a different source; and performs 10% better than newswire-trained models on manually-annotated Wikipedia text.


title: How evolution learns to generalise: Using the principles of learning theory to understand the evolution of developmental organisation

abstract: One of the most intriguing questions in evolution is how organisms exhibit suitable phenotypic variation to rapidly adapt in novel selective environments. Such variability is crucial for evolvability, but poorly understood. In particular, how can natural selection favour developmental organisations that facilitate adaptive evolution in previously unseen environments? Such a capacity suggests foresight that is incompatible with the short-sighted concept of natural selection. A potential resolution is provided by the idea that evolution may discover and exploit information not only about the particular phenotypes selected in the past, but their underlying structural regularities: new phenotypes, with the same underlying regularities, but novel particulars, may then be useful in new environments. If true, we still need to understand the conditions in which natural selection will discover such deep regularities rather than exploiting ‘quick fixes’ (i.e., fixes that provide adaptive phenotypes in the short term, but limit future evolvability). Here we argue that the ability of evolution to discover such regularities is formally analogous to learning principles, familiar in humans and machines, that enable generalisation from past experience. Conversely, natural selection that fails to enhance evolvability is directly analogous to the learning problem of over-fitting and the subsequent failure to generalise. We support the conclusion that evolving systems and learning systems are different instantiations of the same algorithmic principles by showing that existing results from the learning domain can be transferred to the evolution domain. Specifically, we show that conditions that alleviate over-fitting in learning systems successfully predict which biological conditions (e.g., environmental variation, regularity, noise or a pressure for developmental simplicity) enhance evolvability. This equivalence provides access to a well-developed theoretical framework from learning theory that enables a characterisation of the general conditions for the evolution of evolvability.


title: Motion correction with PROPELLER MRI: application to head motion and free-breathing cardiac imaging

abstract: A method for motion correction, involving both data collection and reconstruction, is presented. The PROPELLER MRI method collects data in concentric rectangular strips rotated about the k-space origin. The central region of k-space is sampled for every strip, which (a) allows one to correct spatial inconsistencies in position, rotation, and phase between strips, (b) allows one to reject data based on a correlation measure indicating through-plane motion, and (c) further decreases motion artifacts through an averaging effect for low spatial frequencies. Results are shown in which PROPELLER MRI is used to correct for bulk motion in head images and respiratory motion in nongated cardiac images. Magn Reson Med 42:963-969, 1999.


title: Data-Center Architecture Impacts on Virtualized Network Functions Service Chain Embedding with High Availability Requirements

abstract: Network Functions Virtualization (NFV) is a recent networking trend gaining a lot of attention from telecom operators and vendors. It promises to virtualize entire classes of network node functions within a data-center and to deliver network services in the form of Virtualized Network Function (VNF) service chains using commercial off-the-shelf hardware and IT virtualization technologies. However, availability gets an important issue when purpose-built telecom hardware designed for the "fives nines" standard via built-in failure protection and recovery mechanisms is replaced by the off-the shelf hardware. With commercial off-the-shelf data-center hardware, failure probabilities could be higher than in traditional physical network infrastructure. Thus with NFV, infrastructure availability has to be considered all the way from the physical right up to the hypervisor layer and resilience mechanisms need to be built into the software and service provisioning design. In this work, we model different backup strategies for VNF service chains and provide algorithms for their resilient embedding in the data-center. Further, we answer the question which data-center topologies offer the best cost-per-throughput relation for a given resilience/availability for VNF service chains.


title: Assessing robustness of radiomic features by image perturbation

abstract: Image features need to be robust against differences in positioning, acquisition and segmentation to ensure reproducibility. Radiomic models that only include robust features can be used to analyse new images, whereas models with non-robust features may fail to predict the outcome of interest accurately. Test-retest imaging is recommended to assess robustness, but may not be available for the phenotype of interest. We therefore investigated 18 combinations of image perturbations to determine feature robustness, based on noise addition (N), translation (T), rotation (R), volume growth/shrinkage (V) and supervoxel-based contour randomisation (C). Test-retest and perturbation robustness were compared for combined total of 4032 morphological, statistical and texture features that were computed from the gross tumour volume in two cohorts with computed tomography imaging: I) 31 non-small-cell lung cancer (NSCLC) patients; II): 19 head-and-neck squamous cell carcinoma (HNSCC) patients. Robustness was determined using the 95% confidence interval (CI) of the intraclass correlation coefficient (1, 1). Features with CI ≥ 0:90 were considered robust. The NTCV, TCV, RNCV and RCV perturbation chain produced similar results and identified the fewest false positive robust features (NSCLC: 0.2–0.9%; HNSCC: 1.7–1.9%). Thus, these perturbation chains may be used as an alternative to test-retest imaging to assess feature robustness.


title: A distributed beaconless routing protocol for real-time video dissemination in multimedia VANETs

abstract: We design a framework for video (and in general high data rate applications) transmission over a VANET.Our framework includes both application and routing layer design.We analyze MAC layer behavior and improve its utilization by solving the Spurious Forwarding problem.We tested our protocols on a real scenario, and considered both QoS and QoE, including MOS experiments in a real car.We grant a high data rate in ad-hoc mode, also for long distance, through our backbone beaconless approach. Vehicular Ad-Hoc Networks (VANETs) will play an important role in Smart Cities and will support the development of not only safety applications, but also car smart video surveillance services. Recent improvements in multimedia over VANETs allow drivers, passengers, and rescue teams to capture, share, and access on-road multimedia services. Vehicles can cooperate with each other to transmit live flows of traffic accidents or disasters and provide drivers, passengers, and rescue teams rich visual information about a monitored area. Since humans will watch the videos, their distribution must be done by considering the provided Quality of Experience (QoE) even in multi-hop, multi-path, and dynamic environments. This article introduces an application framework to handle this kind of services and a routing protocol, the DBD (Distributed Beaconless Dissemination), that enhances the dissemination of live video flows on multimedia highway VANETs. DBD uses a backbone-based approach to create and maintain persistent and high quality routes during the video delivery in opportunistic Vehicle to Vehicle (V2V) scenarios. It also improves the performance of the IEEE 802.11p MAC layer, by solving the Spurious Forwarding (SF) problem, while increasing the packet delivery ratio and reducing the forwarding delay. Performance evaluation results show the benefits of DBD compared to existing works in forwarding videos over VANETs, where main objective and subjective QoE results are measured.


title: Synchronous Boost Converter With High Efficiency at Light Load Using QSW-ZVS and SiC mosfets

abstract: A converter intended to be used for the interconnection of battery-based energy storage systems with the cells of a multilevel converter is addressed in this paper. High efficiency at light and medium loads is important in these applications and it can be achieved using soft switching techniques. Two control techniques with fixed and variable switching frequency are proposed and compared. The use of silicon carbide (SiC) mosfet s provides a higher attainable switching frequency, which is especially interesting in variable frequency control techniques, allowing the operation at high voltages and high switching frequencies, with high efficiencies over a wide power range. A synchronous boost DC/DC converter rated for 400 to 800 V and 10 kW is designed and developed with SiC mosfet s obtaining efficiencies higher than 97% from 100% to 3.5% of full load using a variable switching frequency (up to 200 kHz) control. Significant efficiency improvement is achieved at medium and light loads.


title: The Abstract Expressionists and Les Automatistes: A shared multi-fractal depth?

abstract: Statistical analysis of abstract paintings is becoming an increasingly important tool for understanding the creative process of visual artists. We present a multifractal analysis of 'poured' paintings from the Abstract Expressionism and Les Automatistes movements. The box-counting dimension (D"0) is measured for the analyzed paintings, as is the associated multifractal depth @DD=D"0-D"~, where D"~ is the asymptotic dimension. We investigate the role of depth by plotting a 'phase space' diagram that examines the relationship between D"0 and D"~. We show that, although the D"0 and D"~ values vary between individual paintings, the collection of paintings exhibit a similar depth, suggesting a shared visual characteristic for this genre. We discuss the visual implications of this result.


title: Novel electrostatic discharge (ESD) protection solution in GaAs pHEMT technology

abstract: This paper develops an effective electrostatic discharge (ESD) protection solution for GaAs-based integrated circuits based on a novel multi-gate pHEMT. With approximately the same layout area, the proposed ESD protection clamp can carry an ESD current three times higher than the conventional single-gate pHEMT clamp under the human body model (HBM) stress. Moreover, the new ESD clamp shows promising results when characterized under the charged device model (CDM) stress. The parasitic capacitance of the new ESD clamp is also measured to assess its suitability for high-frequency ESD applications.


Realtime Visual and Point Cloud SLAM Nicola Fioraio Willow Garage


title: Risk Analysis for Large Engineering Projects: Modeling Cost Uncertainty for Ship Production Activities

abstract:  Monte Carlo simulation of activity networks has generated interest as a method for cost/schedule risk analysis during the design and contracting stages of large engineering projects. Commercial software for project risk analysis is now widely available as extensions to many popular project management/scheduling software packages. However, many cost and uncertainty modeling issues remain to be addressed, in terms of both practical implementation and underlying methodology. Several of these issues were examined in the course of developing a risk analysis methodology for the domain of commercial ship production. These include: statistical dependence of riskrelated activities; expert elicitation of activity duration uncertainty; net present value representation for production costs, contract penalty costs, etc.; and determination of contract payments at project milestones.


title: GaN HEMT based Doherty amplifier for 3.5-GHz WiMAX Applications

abstract: We have implemented a Doherty amplifier for 3.5-GHz World Interoperability for Microwave Access (WiMAX) applications using Eudyna 90-W (P3 dB) Gallium-Nitride (GaN) High Electron Mobility Transistor (HEMT) because of the poor efficiency of a standard class AB amplifier when the linearity performance is good. The load modulation performance of the GaN HEMT device for the Doherty operation is rather moderate but workable. The linearity is improved using the in-band error cancellation technique of the Doherty amplifier. The implemented Doherty amplifier has been designed at an average output power of 43 dBm, backed-off about 8 dB from the 51 dBm (P3 dB). For WiMAX signal with 28 MHz signal bandwidth, the measured drain efficiency of the amplifier is 27.8%, and the measured Relative Constellation Error (RCE) is -33.17 dB, while those of the comparable class AB amplifier are 19.42% and -24.26 dB, respectively, at the same average output power level.


title: Multi-DOF counterbalance mechanism for low-cost, safe and easy-usable robot arm

abstract: Recently, various types of robot arms have been developed due to the increasing needs of the robot in various fields. However, these robot arms are still too much expensive and dangerous since high capacity motors and gear reducers are used to provide sufficient torques to support the robot arm mass. This makes robot market still limited to industrial fields. To this end, we propose a novel counterbalance mechanism based on the spring-wire and a double parallelogram mechanism, which can completely cancel out the gravitational torques applied at each joint due to the robot mass. Thus the robot would need much smaller torques, which can be delivered by cheap and low capacity actuators. Furthermore, a 6-DOF counterbalance robot arm was built to demonstrate the performance of the proposed mechanism. Simulation and experimental results showed that the proposed mechanism had effectively decreased the torques required to operate the robot arm and thereby allowing the prospective use of low capacity motors and gear reducers for high performance robot arms.


title: Attention and the detection of signals.

abstract: Michael I. Posner, Charles R. R. Snyder, and Brian J. DavidsonUniversity of OregonSUMMARYDetection of a visual signal requires information to reach a system capable ofeliciting arbitrary responses required by the experimenter. Detection latencies arereduced when subjects receive a cue that indicates where in the visual field thesignal will occur. This shift in efficiency appears to be due to an alignment (orient-ing) of the central attentional system with the pathways to be activated by thevisual input.It would also be possible to describe these results as being due to a reducedcriterion at the expected target position. However, this description ignores impor-tant constraints about the way in which expectancy improves performance. First,when subjects are cued on each trial, they show stronger expectancy effects thanwhen a probable position is held constant for a block, indicating the active natureof the expectancy. Second, while information on spatial position improves per-formance, information on the form of the stimulus does not. Third, expectancymay lead to improvements in latency without a reduction in accuracy. Fourth,there appears to be little ability to lower the criterion at two positions that arenot spatially contiguous.A framework involving the employment of a limited-capacity attentional mech-anism seems to capture these constraints better than the more general language ofcriterion setting. Using this framework, we find that attention shifts are not closelyrelated to the saccadic eye movement system. For luminance detection the retinaappears to be equipotential with respect to attention shifts, since costs to unex-pected stimuli are similar whether foveal or peripheral. These results appear toprovide an important model system for the study of the relationship between atten-tion and the structure of the visual system.Detecting the presence of a clear signal controlling our awareness of environmentalin an otherwise noise-free environment is events. Although there are a number of em-probably the simplest perceptual act of which pirical approaches to the study of detection,the human is capable. For this reason it may most have not clearly separated between at-serve as an ideal model task for investigating tentional factors and sensory factors and arethe role of sensory and attentional factors in thus incapable of providing an analysis of therelationship between the two.The classical psychophysical approach tothe use ofUniversity of Oregon. Portions of the data were near-threshold signals (e.g., Hecht, Schlaer,adapted from Chronometrk Explorations of Mind & Pirenne, 1942). This approach has been(Posner, 1978). Parts of these experiments were concerned with such stimulus factors as in-rdllV


title: Rate-distortion methods for image and video compression

abstract: In this article we provide an overview of rate-distortion (R-D) based optimization techniques and their practical application to image and video coding. We begin with a short discussion of classical rate-distortion theory and then we show how in many practical coding scenarios, such as in standards-compliant coding environments, resource allocation can be put in an R-D framework. We then introduce two popular techniques for resource allocation, namely, Lagrangian optimization and dynamic programming. After a discussion of these techniques as well as some of their extensions, we conclude with a quick review of literature in these areas citing a number of applications related to image and video compression and transmission.


title: Robots as Powerful Allies for the Study of Embodied Cognition from the Bottom Up

abstract: A large body of compelling evidence has been accumulated demonstrating that embodiment - the agent's physical setup, including its shape, materials, sensors and actuators - is constitutive for any form of cognition and as a consequence, models of cognition need to be embodied. In contrast to methods from empirical sciences to study cognition, robots can be freely manipulated and virtually all key variables of their embodiment and control programs can be systematically varied. As such, they provide an extremely powerful tool of investigation. We present a robotic bottom-up or developmental approach, focusing on three stages: (a) low-level behaviors like walking and reflexes, (b) learning regularities in sensorimotor spaces, and (c) human-like cognition. We also show that robotic based research is not only a productive path to deepening our understanding of cognition, but that robots can strongly benefit from human-like cognition in order to become more autonomous, robust, resilient, and safe.


title: Sophisticated Honeypot mechanism - the autonomous hybrid solution for enhancing computer system security

abstract: Computer system security is one of the most popular and the fastest evolving Information Technology (IT) areas. Protection of information access, availability and data integrity represents the basic security characteristics desired on information sources. Any disruption of these properties would result in system intrusion and the related security risk. Advanced decoy based technology called Honeypot has a huge potential for the security community and can achieve several goals of other security technologies, which makes it almost universal. Paper is devoted to sophisticated hybrid Honeypot with autonomous feature that allows to, based on the collected system parameters, adapt to the system of deployment. By its presence Honeypot attracts attacker by simulating vulnerabilities and poor security. After initiation of interaction Honeypot will record all attacker activities and after data analysis allows improving security in computer systems.


title: Multivariable-PI-Based $dq$ Current Control of Voltage Source Converters With Superior Axis Decoupling Capability

abstract: This paper presents a linear direct-quadrature current control strategy for voltage source converters (VSCs) in a rotating reference frame (RRF). The described method is based on multivariable-proportional-integral (PI) regulators and provides fast dynamics and a zero steady-state error. Contrary to the well-known conventional PI-based control strategies in RRFs, the presented method provides practically decoupled axes with a superior disturbance rejection capability. Moreover, its implementation is relatively simple and does not impose excessive structural complexity compared to its conventional PI-based competitors. The method is applicable to both single- and three-phase systems and also to anisotropic three-phase systems, e.g., synchronous motors with different direct and quadrature impedances driven by VSCs. Implementing a three-phase test system, the performance of the presented method is experimentally evaluated.


title: High performance low power array multiplier using temporal tiling

abstract: Digital multipliers are a major source power dissipation in digital signal processors. Array architecture is a popular technique to implement these multipliers due to its regular compact structure. High power dissipation in these structures is mainly due to the switching of a large number of gates during multiplication. In addition, much power is also dissipated due to a large number of spurious transitions on internal nodes. Timing analysis of a full adder, which is a basic building block in array multipliers, has resulted in a different array connection pattern that reduces power dissipation due to the spurious transition activity. Furthermore, this connection pattern also improves the multiplier throughput. This array pattern is based on creating a compact tiled structure, wherein the shape of a tile represents the delay through that tile. That is, a compact structure created using these tiles is nothing but a structure with high throughput. Such a temporal tiling technique can also be applied to other digital circuits. Based on our simulation studies, a temporally tiled array multiplier achieves 50% and 35% improvements in delay and power dissipation compared to a conventional array multiplier. Improvement in delay can be traded for power using voltage reduction techniques.


title: A Web-Based Intelligent Tutoring System for Computer Programming

abstract: Web Intelligence is a direction for scientific research that explores practical applications of Artificial Intelligence to the next generation of Web-empowered systems. In this paper, we present a Web-based intelligent tutoring system for computer programming. The decision making process conducted in our intelligent system is guided by Bayesian networks, which are a formal framework for uncertainty management in Artificial Intelligence based on probability theory. Whereas many tutoring systems are static HTML Web pages of a class textbook or lecture notes, our intelligent system can help a student navigate through the online course materials, recommend learning goals, and generate appropriate reading sequences.


title: A PSO-based multi-robot cooperation method for target searching in unknown environments

abstract: In this paper, we study the problem of multi-robot target searching in unknown environments. For target searching, robots need an efficient method with respect to their limitations and characteristics of the workspace. Every robotic search algorithm has several constraints. Our goal is to propose a distributed algorithm based on Particle Swarm Optimization (PSO) for target searching which satisfies the before-mentioned constraints. This extension of PSO is named A-RPSO (Adaptive Robotic PSO). A-RPSO acts as the controlling mechanism for robots. It is similar to PSO with two modifications: firstly it takes into account obstacle avoidance, secondly A-RPSO has a mechanism to escape from local optima. Various experimental results obtained in a simulated environment, show that A-RPSO is able to outperform other state of-the-art techniques in target searching problems. The performance of A-RPSO is much more significant compared with other approaches in two distinctive states particularly: large environments and small number of robots.


title: Mitochondrial dysfunction in bipolar disorder: evidence from magnetic resonance spectroscopy research

abstract: Magnetic resonance spectroscopy (MRS) affords a noninvasive window on in vivo brain chemistry and, as such, provides a unique opportunity to gain insight into the biochemical pathology of bipolar disorder. Studies utilizing proton (1H) MRS have identified changes in cerebral concentrations of N-acetyl aspartate, glutamate/glutamine, choline-containing compounds, myo-inositol, and lactate in bipolar subjects compared to normal controls, while studies using phosphorus (31P) MRS have examined additional alterations in levels of phosphocreatine, phosphomonoesters, and intracellular pH. We hypothesize that the majority of MRS findings in bipolar subjects can be fit into a more cohesive bioenergetic and neurochemical model of bipolar illness that is both novel and yet in concordance with findings from complementary methodological approaches. In this review, we propose a hypothesis of mitochondrial dysfunction in bipolar disorder that involves impaired oxidative phosphorylation, a resultant shift toward glycolytic energy production, a decrease in total energy production and/or substrate availability, and altered phospholipid metabolism.


title: Treatment of Obesity and Diabetes Using Oxytocin or Analogs in Patients and Mouse Models

abstract: Obesity is important for the development of type-2 diabetes as a result of obesity-induced insulin resistance accompanied by impaired compensation of insulin secretion from pancreatic beta cells. Here, based on a randomized pilot clinical trial, we report that intranasal oxytocin administration over an 8-week period led to effective reduction of obesity and reversal of related prediabetic changes in patients. Using mouse models, we further systematically evaluated whether oxytocin and its analogs yield therapeutic effects against prediabetic or diabetic disorders regardless of obesity. Our results showed that oxytocin and two analogs including [Ser4, Ile8]-oxytocin or [Asu1,6]-oxytocin worked in mice to reverse insulin resistance and glucose intolerance prior to reduction of obesity. In parallel, using streptozotocin-induced diabetic mouse model, we found that treatment with oxytocin or its analogs reduced the magnitude of glucose intolerance through improving insulin secretion. The anti-diabetic effects of oxytocin and its analogs in these animal models can be produced similarly whether central or peripheral administration was used. In conclusion, oxytocin and its analogs have multi-level effects in improving weight control, insulin sensitivity and insulin secretion, and bear potentials for being developed as therapeutic peptides for obesity and diabetes.


title: Automatically designing CNN architectures using genetic algorithm for image classification

abstract: Convolutional Neural Networks (CNNs) have gained a remarkable success on many image classification tasks in recent years. However, the performance of CNNs highly relies upon their architectures. For most state-of-the-art CNNs, their architectures are often manually-designed with expertise in both CNNs and the investigated problems. Therefore, it is difficult for users, who have no extended expertise in CNNs, to design optimal CNN architectures for their own image classification problems of interest. In this paper, we propose an automatic CNN architecture design method by using genetic algorithms, to effectively address the image classification tasks. The most merit of the proposed algorithm remains in its"automatic"characteristic that users do not need domain knowledge of CNNs when using the proposed algorithm, while they can still obtain a promising CNN architecture for the given images. The proposed algorithm is validated on widely used benchmark image classification datasets, by comparing to the state-of-the-art peer competitors covering eight manually-designed CNNs, seven automatic+manually tuning and five automatic CNN architecture design algorithms. The experimental results indicate the proposed algorithm outperforms the existing automatic CNN architecture design algorithms in terms of classification accuracy, parameter numbers and consumed computational resources. The proposed algorithm also shows the very comparable classification accuracy to the best one from manually-designed and automatic+manually tuning CNNs, while consumes much less of computational resource.


title: A comparative study of combined feature selection methods for Arabic text classification

abstract: Text classification is a very important task due to the huge amount of electronic documents. One of the problems of text classification is the high dimensi onality of feature space. Researchers proposed many algorithms to select related features from text. Th ese algorithms have been studied extensively for En glish text, while studies for Arabic are still limited. This st udy introduces an investigation on the performance of five widely used feature selection methods namely Chi-square, Correlation, GSS Coefficient, Information Gai n and Relief F. In addition, this study also introduces a n approach of combination of feature selection meth ods based on the average weight of the features. The experime nts are conducted using Naive Bayes and Support Vector Machine classifiers to classify a published Arabic corpus. The results show that the best results were obtained when using Information Gain method. The results also show that the combination of multiple feature sel ection methods outperforms the best results obtain by the individual methods.


title: Bot Classification for Real-Life Highly Class-Imbalanced Dataset

abstract: Botnets are networks formed with a number of machines infected by malware called bots. Detection of these malicious networks is becoming a major concern as they pose a serious threat to the network security. Most of the research on bot detection is based on particular botnet characteristics which fail to detect other types of botnets and bots. Furthermore, there are very few bot detection methods that considered real-life class-imbalanced dataset. A dataset is class-imbalanced if there are significantly more instances in one class than the other classes. In this paper, we develop three generic features to detect different types of bots regardless of their botnet characteristics. We develop five classification models based on those features to classify bots from a large, real-life, class-imbalanced network dataset. Results show that our methodology can detect bots more accurately than the existing methods. Experimental results also demonstrate that the developed methodology can successfully detect bots when the proportion of bots to normal activity is very small. We also provide a performance comparison of our methodology with a recent study on bot detection in a real-life, large, imbalanced dataset.


A survey of disease impact and awareness in pond aquaculture in Bangladesh, the Fisheries Training and Extension Project - Phase II


title: Study of user QoE improvement for dynamic adaptive streaming over HTTP (MPEG-DASH)

abstract: Video streaming over HTTP is becoming the de facto dominating paradigm for today's video applications. HTTP as an over-the-top (OTT) protocol has been leveraged for quality video traversal over the Internet. High user-received quality-of-experience (QoE) is driven not only by the new technology, but also by a wide range of user demands. Given the limitation of a traditional TCP/IP network for supporting video transmission, the typical on-off transfer pattern is inevitable. Dynamic adaptive streaming over HTTP (DASH) establishes a simple architecture and enables new video applications to fully utilize the exiting physical network infrastructure. By deploying robust adaptive algorithms at the client side, DASH can provide a smooth streaming experience. We propose a dynamic adaptive algorithm in order to keep a high QoE for the average user's experience. We formulated our QoE optimization in a set of key factors. The results obtained by our empirical network traces show that our approach not only achieves a high average QoE but it also works stably under different network conditions.


title: Mechatronic design of innovative fingers for anthropomorphic robot hands

abstract: In this paper, a novel design approach for the development of robot hands is presented. This approach, that can be considered alternative to the "classical" one, takes into consideration compliant structures instead of rigid ones. Compliance effects, which were considered in the past as a "defect" to be mechanically eliminated, can be viceversa regarded as desired features and can be properly controlled in order to achieve desired properties from the robotic device. In particular, this is true for robot hands, where the mechanical complexity of "classical" design solutions has always originated complicated structures, often with low reliability and high costs. In this paper, an alternative solution to the design of dexterous robot hand is illustrated, considering a "mechatronic approach" for the integration of the mechanical structure, the sensory and electronic system, the control and the actuation part. Moreover, the preliminary experimental activity on a first prototype is reported and discussed. The results obtained so far, considering also reliability, costs and development time, are very encouraging, and allows to foresee a wider diffusion of dexterous hands for robotic applications.


title: Review of the Impact of Vehicle-to-Grid Technologies on Distribution Systems and Utility Interfaces

abstract: Plug-in vehicles can behave either as loads or as a distributed energy and power resource in a concept known as vehicle-to-grid (V2G) connection. This paper reviews the current status and implementation impact of V2G/grid-to-vehicle (G2V) technologies on distributed systems, requirements, benefits, challenges, and strategies for V2G interfaces of both individual vehicles and fleets. The V2G concept can improve the performance of the electricity grid in areas such as efficiency, stability, and reliability. A V2G-capable vehicle offers reactive power support, active power regulation, tracking of variable renewable energy sources, load balancing, and current harmonic filtering. These technologies can enable ancillary services, such as voltage and frequency control and spinning reserve. Costs of V2G include battery degradation, the need for intensive communication between the vehicles and the grid, effects on grid distribution equipment, infrastructure changes, and social, political, cultural, and technical obstacles. Although V2G operation can reduce the lifetime of vehicle batteries, it is projected to become economical for vehicle owners and grid operators. Components and unidirectional/bidirectional power flow technologies of V2G systems, individual and aggregated structures, and charging/recharging frequency and strategies (uncoordinated/coordinated smart) are addressed. Three elements are required for successful V2G operation: power connection to the grid, control and communication between vehicles and the grid operator, and on-board/off-board intelligent metering. Success of the V2G concept depends on standardization of requirements and infrastructure decisions, battery technology, and efficient and smart scheduling of limited fast-charge infrastructure. A charging/discharging infrastructure must be deployed. Economic benefits of V2G technologies depend on vehicle aggregation and charging/recharging frequency and strategies. The benefits will receive increased attention from grid operators and vehicle owners in the future.


title: Beyond Traditional DTN Routing: Social Networks for Opportunistic Communication

abstract: This article examines the evolution of routing protocols for intermittently connected ad hoc networks and discusses the trend toward socialbased routing protocols. A survey of current routing solutions is presented, where routing protocols for opportunistic networks are classified based on the network graph employed. The need to capture performance trade-offs from a multi-objective perspective is highlighted.


title: Performance limitations of passive UHF RFID systems

abstract: This paper presents an overview of limitations imposed on the range performance of passive UHF RFID systems by such factors as tag characteristics, propagation environment, and RFID reader parameters.


title: Motion adaptation on a wheelchair driving simulator

abstract: The objective of the AccesSim project is to design a wheelchair simulator using Virtual Reality and a robotic platform to detect and illustrate accessibility issues in complex environments. In order to be efficient, the robotic platform must provide haptic and vestibular feedbacks to different profile of end users: urbanists to wheelchair users. It must be modular and adaptable to each one of them. In this paper we focus our robotic platform capable of adapting its configuration and feedback rendering based on the user. The design of the platform is described. The capacity of the platform to reproduce the motion of a wheelchair in a specific study case is tested experimen-tally. Finally the results introduce the possibility of adapting the dynamic feedback rendering based on a specific user and environmental situations.


title: Detection of unhealthy region of plant leaves using image processing and genetic algorithm

abstract: Agricultural productivity is that thing on which Indian Economy highly depends. This is the one of the reasons that disease detection in plants plays an important role in agriculture field, as having disease in plants are quite natural. If proper care is not taken in this area then it causes serious effects on plants and due to which respective product quality, quantity or productivity is affected. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops, and at very early stage itself it detects the symptoms of diseases means when they appear on plant leaves. This paper presents an algorithm for image segmentation technique used for automatic detection as well as classification of plant leaf diseases and survey on different diseases classification techniques that can be used for plant leaf disease detection. Image segmentation, which is an important aspect for disease detection in plant leaf disease, is done by using genetic algorithm.


title: A wideband CPW-to-microstrip transition for millimeter-wave packaging

abstract: A broadband single layer vertical transition from CPW to microstrip has been developed for multilayer millimeter-wave circuits. The transition has exceptionally wide bandwidth and presents low insertion and return losses. A transition has been fabricated on a 100 /spl mu/m silicon wafer that shows approximately 0.3 dB of insertion loss and better than 10 dB return loss over 75-110 GHz. This transition can be used for the vertical integration of multi-layer millimeter wave circuits and packaging.


title: Reaching Consensus Using Raft

abstract: This thesis project implements and evaluates log replication using the Raft consensus algorithm. Raft presents a new and easier to understand consensus protocol for log replication in a distributed ...


title: Feature Detection with Automatic Scale Selection

abstract: The fact that objects in the world appear in different ways depending on the scale of observation has important implications if one aims at describing them. It shows that the notion of scale is of utmost importance when processing unknown measurement data by automatic methods. In their seminal works, Witkin (1983) and Koenderink (1984) proposed to approach this problem by representing image structures at different scales in a so-called scale-space representation. Traditional scale-space theory building on this work, however, does not address the problem of how to select local appropriate scales for further analysis. This article proposes a systematic methodology for dealing with this problem. A framework is presented for generating hypotheses about interesting scale levels in image data, based on a general principle stating that local extrema over scales of different combinations of γ-normalized derivatives are likely candidates to correspond to interesting structures. Specifically, it is shown how this idea can be used as a major mechanism in algorithms for automatic scale selection, which adapt the local scales of processing to the local image structure. ::: ::: Support for the proposed approach is given in terms of a general theoretical investigation of the behaviour of the scale selection method under rescalings of the input pattern and by integration with different types of early visual modules, including experiments on real-world and synthetic data. Support is also given by a detailed analysis of how different types of feature detectors perform when integrated with a scale selection mechanism and then applied to characteristic model patterns. Specifically, it is described in detail how the proposed methodology applies to the problems of blob detection, junction detection, edge detection, ridge detection and local frequency estimation. ::: ::: In many computer vision applications, the poor performance of the low-level vision modules constitutes a major bottleneck. It is argued that the inclusion of mechanisms for automatic scale selection is essential if we are to construct vision systems to automatically analyse complex unknown environments.


title: Radiation Characteristics of Tunable Graphennas in the Terahertz Band

abstract: Graphene-enabled wireless communications constitute a novel paradigm which has been proposed to implement wireless communications among nanosystems. Indeed, graphene-based plasmonic nano-antennas, or graphennas, just a few micrometers in size have been predicted to radiate electromagnetic waves at the terahertz band. In this work, the important role of the graphene conductivity in the characteristics of graphennas is analyzed, and their radiation performance both in transmission and reception is numerically studied. The resonance frequency of graphennas is calculated as a function of their length and width, both analytically and by simulation. Moreover, the influence of a dielectric substrate with a variable size, and the position of the patch with respect to the substrate is also evaluated. Further, important properties of graphene, such as its chemical potential or its relaxation time, are found to have a profound impact in the radiation properties of graphennas. Finally, the radiation pattern of a graphenna is compared to that of an equivalent metallic antenna. These results will prove useful for designers of future graphennas, which are expected to enable wireless communications among nanosystems.


title: Globally optimal shape-based tracking in real-time

abstract: Most algorithms for real-time tracking of deformable shapes provide sub-optimal solutions for a suitable energy minimization task: The search space is typically considered too large to allow for globally optimal solutions. In this paper we show that - under reasonable constraints on the object motion - one can guarantee global optimality while maintaining real-time requirements. The problem is cast as finding the optimal cycle in a graph spanned by the prior template and the image. The underlying combinatorial algorithm is implemented on state-of- the-art graphics hardware. Solutions on FPGAs are conceivable. Experimental results demonstrate long-term tracking of cars in real-time, while coping with challenging weather conditions. In particular, we show that the proposed tracking algorithm is highly robust to illumination changes and that it outperforms local tracking methods such as the level set method.


Corrections to “The Performance of the Maximum Ratio Combining Method in Correlated Rician Fading Channels for Antenna-Diversity Signal Combining”


title: Embedded vision-based nighttime driver assistance system

abstract: This study presents an effective method for detecting vehicles in front of the camera-assisted car during nighttime driving and implements it on an embedded system. The proposed method detects vehicles based on detecting and locating vehicle headlights and taillights using techniques of image segmentation and pattern analysis. Firstly, to effectively extract bright objects of interest, a segmentation process based on automatic multilevel thresholding applied on the grabbed road-scene images. Then the extracted bright objects are processed by to identify the vehicles by locating and analyzing their vehicle light patterns and to estimate their distances to the cameraassisted car by a rule-based procedure. Finally, we also implement the above vision-based techniques on a real-time system mounted in the host car. The proposed vision-based techniques are integrated and implemented on an ARMLinux embedded platform, as well as the peripheral devices, including image grabbing devices, voice reporting module, and other in-vehicle control devices, will be also integrated to accomplish an in-vehicle embedded vision-based nighttime driver assistance system.


title: Alemtuzumab for patients with relapsing multiple sclerosis after disease-modifying therapy: a randomised controlled phase 3 trial

abstract: BACKGROUND ::: The anti-CD52 monoclonal antibody alemtuzumab reduces disease activity in previously untreated patients with relapsing-remitting multiple sclerosis. We aimed to assess efficacy and safety of alemtuzumab compared with interferon beta 1a in patients who have relapsed despite first-line treatment. ::: ::: ::: METHODS ::: In our 2 year, rater-masked, randomised controlled phase 3 trial, we enrolled adults aged 18-55 years with relapsing-remitting multiple sclerosis and at least one relapse on interferon beta or glatiramer. Eligible participants were randomly allocated in a 1:2:2 ratio by an interactive voice response system, stratified by site, to receive subcutaneous interferon beta 1a 44 μg, intravenous alemtuzumab 12 mg per day, or intravenous alemtuzumab 24 mg per day. Interferon beta 1a was given three-times per week and alemtuzumab was given once per day for 5 days at baseline and for 3 days at 12 months. The 24 mg per day group was discontinued to aid recruitment, but data are included for safety assessments. Coprimary endpoints were relapse rate and time to 6 month sustained accumulation of disability, comparing alemtuzumab 12 mg and interferon beta 1a in all patients who received at least one dose of study drug. This study is registered with ClinicalTrials.gov, number NCT00548405. ::: ::: ::: FINDINGS ::: 202 (87%) of 231 patients randomly allocated interferon beta 1a and 426 (98%) of 436 patients randomly allocated alemtuzumab 12 mg were included in the primary analyses. 104 (51%) patients in the interferon beta 1a group relapsed (201 events) compared with 147 (35%) patients in the alemtuzumab group (236 events; rate ratio 0·51 [95% CI 0·39-0·65]; p<0·0001), corresponding to a 49·4% improvement with alemtuzumab. 94 (47%) patients in the interferon beta 1a group were relapse-free at 2 years compared with 278 (65%) patients in the alemtuzumab group (p<0·0001). 40 (20%) patients in the interferon beta 1a group had sustained accumulation of disability compared with 54 (13%) in the alemtuzumab group (hazard ratio 0·58 [95% CI 0·38-0·87]; p=0·008), corresponding to a 42% improvement in the alemtuzumab group. For 435 patients allocated alemtuzumab 12 mg, 393 (90%) had infusion-associated reactions, 334 (77%) had infections (compared with 134 [66%] of 202 patients in the interferon beta 1a group) that were mostly mild-moderate with none fatal, 69 (16%) had thyroid disorders, and three (1%) had immune thrombocytopenia. ::: ::: ::: INTERPRETATION ::: For patients with first-line treatment-refractory relapsing-remitting multiple sclerosis, alemtuzumab could be used to reduce relapse rates and sustained accumulation of disability. Suitable risk management strategies allow for early identification of alemtuzumab's main adverse effect of secondary autoimmunity. ::: ::: ::: FUNDING ::: Genzyme (Sanofi) and Bayer Schering Pharma.


title: Do People Ask Good Questions?

abstract: People ask questions in order to efficiently learn about the world. But do people ask good questions? In this work, we designed an intuitive, game-based task that allowed people to ask natural language questions to resolve their uncertainty. Question quality was measured through Bayesian ideal observer models that considered large spaces of possible game states. During free-form question generation, participants asked a creative variety of useful and goal-directed questions, yet they rarely asked the best questions as identified by the Bayesian ideal observers (Experiment 1). In subsequent experiments, participants strongly preferred the best questions when evaluating questions that they did not generate themselves (Experiments 2 and 3). On one hand, our results show that people can accurately evaluate question quality, even when the set of questions is diverse and an ideal observer analysis has large computational requirements. On the other hand, people have a limited ability to synthesize maximally informative questions from scratch, suggesting a bottleneck in the question asking process.


title: Fully parallel RRAM synaptic array for implementing binary neural network with (+1, −1) weights and (+1, 0) neurons

abstract: Binary Neural Networks (BNNs) have been recently proposed to improve the area-/energy-efficiency of the machine/deep learning hardware accelerators, which opens an opportunity to use the technologically more mature binary RRAM devices to effectively implement the binary synaptic weights. In addition, the binary neuron activation enables using the sense amplifier instead of the analog-to-digital converter to allow bitwise communication between layers of the neural networks. However, the sense amplifier has intrinsic offset that affects the threshold of binary neuron, thus it may degrade the classification accuracy. In this work, we analyze a fully parallel RRAM synaptic array architecture that implements the fully connected layers in a convolutional neural network with (+1, −1) weights and (+1, 0) neurons. The simulation results with TSMC 65 nm PDK show that the offset of current mode sense amplifier introduces a slight accuracy loss from ∼98.5% to ∼97.6% for MNIST dataset. Nevertheless, the proposed fully parallel BNN architecture (P-BNN) can achieve 137.35 TOPS/W energy efficiency for the inference, improved by ∼20X compared to the sequential BNN architecture (S-BNN) with row-by-row read-out scheme. Moreover, the proposed P-BNN architecture can save the chip area by ∼16% as it eliminates the area overhead of MAC peripheral units in the S-BNN architecture.


title: History of CRISPR-Cas from encounter with a mysterious repeated sequence to genome editing technology

abstract: ABSTRACT Clustered regularly interspaced short palindromic repeat (CRISPR)-Cas systems are well-known acquired immunity systems that are widespread in archaea and bacteria. The RNA-guided nucleases from CRISPR-Cas systems are currently regarded as the most reliable tools for genome editing and engineering. The first hint of their existence came in 1987, when an unusual repetitive DNA sequence, which subsequently was defined as a CRISPR, was discovered in the Escherichia coli genome during an analysis of genes involved in phosphate metabolism. Similar sequence patterns were then reported in a range of other bacteria as well as in halophilic archaea, suggesting an important role for such evolutionarily conserved clusters of repeated sequences. A critical step toward functional characterization of the CRISPR-Cas systems was the recognition of a link between CRISPRs and the associated Cas proteins, which were initially hypothesized to be involved in DNA repair in hyperthermophilic archaea. Comparative genomics, structural biology, and advanced biochemistry could then work hand in hand, not only culminating in the explosion of genome editing tools based on CRISPR-Cas9 and other class II CRISPR-Cas systems but also providing insights into the origin and evolution of this system from mobile genetic elements denoted casposons. To celebrate the 30th anniversary of the discovery of CRISPR, this minireview briefly discusses the fascinating history of CRISPR-Cas systems, from the original observation of an enigmatic sequence in E. coli to genome editing in humans.


title: Light Field Image Processing: An Overview

abstract: Light field imaging has emerged as a technology allowing to capture richer visual information from our world. As opposed to traditional photography, which captures a 2D projection of the light in the scene integrating the angular domain, light fields collect radiance from rays in all directions, demultiplexing the angular information lost in conventional photography. On the one hand, this higher dimensional representation of visual data offers powerful capabilities for scene understanding, and substantially improves the performance of traditional computer vision problems such as depth sensing, post-capture refocusing, segmentation, video stabilization, material classification, etc. On the other hand, the high-dimensionality of light fields also brings up new challenges in terms of data capture, data compression, content editing, and display. Taking these two elements together, research in light field image processing has become increasingly popular in the computer vision, computer graphics, and signal processing communities. In this paper, we present a comprehensive overview and discussion of research in this field over the past 20 years. We focus on all aspects of light field image processing, including basic light field representation and theory, acquisition, super-resolution, depth estimation, compression, editing, processing algorithms for light field display, and computer vision applications of light field data.


title: Breaking good: Breaking ties with social groups may be good for recovery from substance misuse

abstract: According to the Social Identity Model of Identity Change, maintaining social identities and support over time is good for health and well-being, particularly during stressful transitions. However, in this study we explore the circumstances under which maintaining social identities – such as ‘substance user’ – may be harmful to health, and when a successful transition constitutes identity change, rather than maintenance. This prospective study examined social identities of 132 adults entering a drug and alcohol therapeutic community (TC) at admission, three fortnightly intervals and exit, as well as a representative subsample of 60 participants at follow-up. Repeated measures ANOVA results showed that user identity decreased significantly over time, such that 76% of the sample decreased in user identity strength over the first month in the TC. At the same time, recovery identity ratings increased significantly over time, with 64% of the sample staying the same or increasing their recovery identity ratings over the first month. Identity change, indexed by the change in the difference score between user identity and recovery identity over the treatment period, accounted for 34% of the variance in drinking quantity, 41% of the variance in drinking frequency, 5% of the variance in other drug use frequency, and 49% of the variance in life satisfaction at follow-up, after accounting for initial substance abuse severity and social identity ratings at entry to the TC. The findings indicate that moving from a substance using identity towards a recovery identity constitutes an important step in substance abuse treatment.


title: Byakkokaninjinto prevents body water loss by increasing the expression of kidney aquaporin-2 and skin aquaporin-3 in KKAy mice.

abstract: Byakkokaninjinto (BKN) is an herbal medicine used for the relief of diuresis, thirst and dermal pruritus that are associated with diabetes. The effects of BKN on the expression of aquaporins (AQPs) in the kidney, salivary gland and skin were investigated in order to clarify the mechanism of drug action. Seven-week-old KKAy mice were given feed containing 4.5% BKN for 4 weeks. Compared with the control group, BKN administration did not affect the blood glucose and insulin concentration. However, water intake and urine volume were significantly reduced. AQP2 protein expression in the kidney inner medullary was significantly increased after BKN administration. AQP3 mRNA and protein expression in skin tissue was significantly increased after BKN administration. However, BKN administration did not affect AQP5 mRNA expression in the salivary gland. These results suggest that BKN treatment relieves diuresis, thirst, and dermal pruritis by increasing kidney AQP2 expression and skin AQP3 expression.


Declarative Metadata Management : A Missing Piece in End-To-End Machine Learning


title: Hybrid RF/IR transcutaneous telemetry for power and high-bandwidth data

abstract: As neuroprosthetic control systems continue to advance and increase in channel density, there will be a constant need to deliver data at higher bandwidths in and out of the body. Currently, RF telemetry and inductive coupling are the most commonly used methods for transmitting power and electronic data between implants and external systems, and state of the art systems can deliver data rates up to hundreds of kilobits per second. However, it is difficult to operate implanted medical RF links at higher data rates due to electromagnetic compatibility (EMC) constraints. In this study, we investigate the potential for hybrid telemetry systems that use constant-frequency RF inductive links for power and transcutaneous infrared (IR) signals for data. We show that with commercially available infrared communication components, data rates of up to 40 Mbits per second can be transmitted out across 5 mm of skin with an internal device power dissipation under 100 mW.


title: Combining evidence from residual phase and MFCC features for speaker recognition

abstract: The objective of this letter is to demonstrate the complementary nature of speaker-specific information present in the residual phase in comparison with the information present in the conventional mel-frequency cepstral coefficients (MFCCs). The residual phase is derived from speech signal by linear prediction analysis. Speaker recognition studies are conducted on the NIST-2003 database using the proposed residual phase and the existing MFCC features. The speaker recognition system based on the residual phase gives an equal error rate (EER) of 22%, and the system using the MFCC features gives an EER of 14%. By combining the evidence from both the residual phase and the MFCC features, an EER of 10.5% is obtained, indicating that speaker-specific excitation information is present in the residual phase. This information is useful since it is complementary to that of MFCCs.


title: Grasping reality through illusion—interactive graphics serving science

abstract: I treat three related subjects: virtual-worlds research—the construction of real-time 3-D illusions by computer graphics; some observations about interfaces to virtual worlds; and the coming application of virtual-worlds techniques to the enhancement of scientific computing. We need to design generalized interfaces for visualizing, exploring, and steering scientific computations. Our interfaces must be direct-manipulation, not command-string; interactive, not batch; 3-D, not 2-D; multisensory, not just visual. We need generalized research results for 3-D interactive interfaces. More is known than gets reported, because of a reluctance to share “unproven” results. I propose a shells-of-certainty model for such knowledge.


title: Computational Curiosity (A Book Draft)

abstract: This book discusses computational curiosity, from the psychology of curiosity to the computational models of curiosity, and then showcases several interesting applications of computational curiosity. A brief overview of the book is given as follows. Chapter 1 discusses the underpinnings of curiosity in human beings, including the major categories of curiosity, curiosity-related emotions and behaviors, and the benefits of curiosity. Chapter 2 reviews the arousal theories of curiosity in psychology and summarizes a general two-step process model for computational curiosity. Base on the perspective of the two-step process model, Chapter 3 reviews and analyzes some of the traditional computational models of curiosity. Chapter 4 introduces a novel generic computational model of curiosity, which is developed based on the arousal theories of curiosity. After the discussion of computational models of curiosity, we outline the important applications where computational curiosity may bring significant impacts in Chapter 5. Chapter 6 discusses the application of the generic computational model of curiosity in a machine learning framework. Chapter 7 discusses the application of the generic computational model of curiosity in a recommender system. In Chapter 8 and Chapter 9, the generic computational model of curiosity is studied in two types of pedagogical agents. In Chapter 8, a curious peer learner is studied. It is a non-player character that aims to provide a believable virtual learning environment for users. In Chapter 9, a curious learning companion is studied. It aims to enhance users' learning experience through providing meaningful interactions with them. Chapter 10 discusses open questions in the research field of computation curiosity.