Query: A Neural Network Approach to Missing Marker Reconstruction

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Recurrent Network Models for Human Dynamics <=SEP=> We propose the Encoder-Recurrent-Decoder (ERD) model for recognition and prediction of human body pose in videos and motion capture. The ERD model is a recurrent neural network that incorporates nonlinear encoder and decoder networks before and after recurrent layers. We test instantiations of ERD architectures in the tasks of motion capture (mocap) generation, body pose labeling and body pose forecasting in videos. Our model handles mocap training data across multiple subjects and activity domains, and synthesizes novel motions while avoiding drifting for long periods of time. For human pose labeling, ERD outperforms a per frame body part detector by resolving left-right body part confusions. For video pose forecasting, ERD predicts body joint displacements across a temporal horizon of 400ms and outperforms a first order motion model based on optical flow. ERDs extend previous Long Short Term Memory (LSTM) models in the literature to jointly learn representations and their dynamics. Our experiments show such representation learning is crucial for both labeling and prediction in space-time. We find this is a distinguishing feature between the spatio-temporal visual domain in comparison to 1D text, speech or handwriting, where straightforward hard coded representations have shown excellent results when directly combined with recurrent units [31].

Structural-RNN: Deep Learning on Spatio-Temporal Graphs <=SEP=> Deep Recurrent Neural Network architectures, though remarkably capable at modeling sequences, lack an intuitive high-level spatio-temporal structure. That is while many problems in computer vision inherently have an underlying high-level structure and can benefit from it. Spatiotemporal graphs are a popular tool for imposing such high-level intuitions in the formulation of real world problems. In this paper, we propose an approach for combining the power of high-level spatio-temporal graphs and sequence learning success of Recurrent Neural Networks (RNNs). We develop a scalable method for casting an arbitrary spatio-temporal graph as a rich RNN mixture that is feedforward, fully differentiable, and jointly trainable. The proposed method is generic and principled as it can be used for transforming any spatio-temporal graph through employing a certain set of well defined steps. The evaluations of the proposed approach on a diverse set of problems, ranging from modeling human motion to object interactions, shows improvement over the state-of-the-art with a large margin. We expect this method to empower new approaches to problem formulation through high-level spatio-temporal graphs and Recurrent Neural Networks.

Adam: A Method for Stochastic Optimization <=SEP=> We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.

Deep Representation Learning for Human Motion Prediction and Classification <=SEP=> Generative models of 3D human motion are often restricted to a small number of activities and can therefore not generalize well to novel movements or applications. In this work we propose a deep learning framework for human motion capture data that learns a generic representation from a large corpus of motion capture data and generalizes well to new, unseen, motions. Using an encoding-decoding network that learns to predict future 3D poses from the most recent past, we extract a feature representation of human motion. Most work on deep learning for sequence prediction focuses on video and speech. Since skeletal data has a different structure, we present and evaluate different network architectures that make different assumptions about time dependencies and limb correlations. To quantify the learned features, we use the output of different layers for action classification and visualize the receptive fields of the network units. Our method outperforms the recent state of the art in skeletal motion prediction even though these use action specific training data. Our results show that deep feedforward networks, trained from a generic mocap database, can successfully be used for feature extraction from human motion data and that this representation can be used as a foundation for classification and prediction.

On Human Motion Prediction Using Recurrent Neural Networks <=SEP=> Human motion modelling is a classical problem at the intersection of graphics and computer vision, with applications spanning human-computer interaction, motion synthesis, and motion prediction for virtual and augmented reality. Following the success of deep learning methods in several computer vision tasks, recent work has focused on using deep recurrent neural networks (RNNs) to model human motion, with the goal of learning time-dependent representations that perform tasks such as short-term motion prediction and long-term human motion synthesis. We examine recent work, with a focus on the evaluation methodologies commonly used in the literature, and show that, surprisingly, state of the art performance can be achieved by a simple baseline that does not attempt to model motion at all. We investigate this result, and analyze recent RNN methods by looking at the architectures, loss functions, and training procedures used in state-of-the-art approaches. We propose three changes to the standard RNN models typically used for human motion, which results in a simple and scalable RNN architecture that obtains state-of-the-art performance on human motion prediction.

Trial for RDF: adapting graph query languages for RDF data <=SEP=> Querying RDF data is viewed as one of the main applications of graph query languages, and yet the standard model of graph databases -- essentially labeled graphs -- is different from the triples-based model of RDF. While encodings of RDF databases into graph data exist, we show that even the most natural ones are bound to lose some functionality when used in conjunction with graph query languages. The solution is to work directly with triples, but then many properties taken for granted in the graph database context (e.g., reachability) lose their natural meaning.
 Our goal is to introduce languages that work directly over triples and are closed, i.e., they produce sets of triples, rather than graphs. Our basic language is called TriAL, or Triple Algebra: it guarantees closure properties by replacing the product with a family of join operations. We extend TriAL with recursion, and explain why such an extension is more intricate for triples than for graphs. We present a declarative language, namely a fragment of datalog, capturing the recursive algebra. For both languages, the combined complexity of query evaluation is given by low-degree polynomials. We compare our languages with relational languages, such as finite-variable logics, and previously studied graph query languages such as adaptations of XPath, regular path queries, and nested regular expressions; many of these languages are subsumed by the recursive triple algebra. We also provide examples of the usefulness of TriAL in querying graph and RDF data.

Clinical application of the Hybrid Assistive Limb (HAL) for gait training—a systematic review <=SEP=> OBJECTIVE
The aim of this study was to review the literature on clinical applications of the Hybrid Assistive Limb system for gait training.


METHODS
A systematic literature search was conducted using Web of Science, PubMed, CINAHL and clinicaltrials.gov and additional search was made using reference lists in identified reports. Abstracts were screened, relevant articles were reviewed and subject to quality assessment.


RESULTS
Out of 37 studies, 7 studies fulfilled inclusion criteria. Six studies were single group studies and 1 was an explorative randomized controlled trial. In total, these studies involved 140 participants of whom 118 completed the interventions and 107 used HAL for gait training. Five studies concerned gait training after stroke, 1 after spinal cord injury (SCI) and 1 study after stroke, SCI or other diseases affecting walking ability. Minor and transient side effects occurred but no serious adverse events were reported in the studies. Beneficial effects on gait function variables and independence in walking were observed.


CONCLUSIONS
The accumulated findings demonstrate that the HAL system is feasible when used for gait training of patients with lower extremity paresis in a professional setting. Beneficial effects on gait function and independence in walking were observed but data do not allow conclusions. Further controlled studies are recommended.

Expressing Interactivity with States and Constraints <=SEP=> ......................................................................................................................... iii Acknowledgements ......................................................................................................... iv Table of

Keyphrase Extraction Using Knowledge Graphs <=SEP=> Extracting keyphrases from documents automatically is an important and interesting task since keyphrases provide a quick summarization for documents. Although lots of efforts have been made on keyphrase extraction, most of the existing methods (the co-occurrence-based methods and the statistic-based methods) do not take semantics into full consideration. The co-occurrence-based methods heavily depend on the co-occurrence relations between two words in the input document, which may ignore many semantic relations. The statistic-based methods exploit the external text corpus to enrich the document, which introduce more unrelated relations inevitably. In this paper, we propose a novel approach to extract keyphrases using knowledge graphs, based on which we could detect the latent relations of two keyterms (i.e., noun words and named entities) without introducing many noises. Extensive experiments over real data show that our method outperforms the state-of-the-art methods including the graph-based co-occurrence methods and statistic-based clustering methods.

ValueCharts: analyzing linear models expressing preferences and evaluations <=SEP=> In this paper we propose ValueCharts, a set of visualizations and interactive techniques intended to support decision-makers in inspecting linear models of preferences and evaluation. Linear models are popular decision-making tools for individuals, groups and organizations. In Decision Analysis, they help the decision-maker analyze preferential choices under conflicting objectives. In Economics and the Social Sciences, similar models are devised to rank entities according to an evaluative index of interest. The fundamental goal of building models expressing preferences and evaluations is to help the decision-maker organize all the information relevant to a decision into a structure that can be effectively analyzed. However, as models and their domain of application grow in complexity, model analysis can become a very challenging task. We claim that ValueCharts will make the inspection and application of these models more natural and effective. We support our claim by showing how ValueCharts effectively enable a set of basic tasks that we argue are at the core of analyzing and understanding linear models of preferences and evaluation.

Artificial Neural Networks to Forecast Air Pollution 221 Artificial Neural Networks to Forecast Air Pollution <=SEP=> European laws concerning urban and suburban air pollution requires the analysis and implementation of automatic operating procedures in order to prevent the risk for the principal air pollutants to be above alarm thresholds (e.g. the Directive 2002/3/EC for ozone or the Directive 99/30/CE for the particulate matter with an aerodynamic diameter of up to 10 μm, called PM10). As an example of European initiative to support the investigation of air pollution forecast, the COST Action ES0602 (Towards a European Network on Chemical Weather Forecasting and Information Systems) provides a forum for standardizing and benchmarking approaches in data exchange and multi-model capabilities for air quality forecast and (near) real-time information systems in Europe, allowing information exchange between meteorological services, environmental agencies, and international initiatives. Similar efforts are also proposed by the National Oceanic and Atmospheric Administration (NOAA) in partnership with the United States Environmental Protection Agency (EPA), which are developing an operational, nationwide Air Quality Forecasting (AQF) system. Critical air pollution events frequently occur where the geographical and meteorological conditions do not permit an easy circulation of air and a large part of the population moves frequently between distant places of a city. These events require drastic measures such as the closing of the schools and factories and the restriction of vehicular traffic. Indeed, many epidemiological studies have consistently shown an association between particulate air pollution and cardiovascular (Brook et al., 2007) and respiratory (Pope et al., 1991) diseases. The forecasting of such phenomena with up to two days in advance would allow taking more efficient countermeasures to safeguard citizens’ health. Air pollution is highly correlated with meteorological variables (Cogliani, 2001). Indeed, pollutants are usually entrapped into the planetary boundary layer (PBL), which is the lowest part of the atmosphere and has behaviour directly influenced by its contact with the ground. It responds to surface forcing in a timescale of an hour or less. In this layer, physical quantities such as flow velocity, temperature, moisture and pollutants display rapid fluctuations (turbulence) and vertical mixing is strong. Different automatic procedures have been developed to forecast the time evolution of the concentration of air pollutants, using also meteorological data. Mathematical models of the 10

Grace: safe multithreaded programming for C/C++ <=SEP=> The shift from single to multiple core architectures means that programmers must write concurrent, multithreaded programs in order to increase application performance. Unfortunately, multithreaded applications are susceptible to numerous errors, including deadlocks, race conditions, atomicity violations, and order violations. These errors are notoriously difficult for programmers to debug.
 This paper presents Grace, a software-only runtime system that eliminates concurrency errors for a class of multithreaded programs: those based on fork-join parallelism. By turning threads into processes, leveraging virtual memory protection, and imposing a sequential commit protocol, Grace provides programmers with the appearance of deterministic, sequential execution, while taking advantage of available processing cores to run code concurrently and efficiently. Experimental results demonstrate Grace's effectiveness: with modest code changes across a suite of computationally-intensive benchmarks (1-16 lines), Grace can achieve high scalability and performance while preventing concurrency errors.

Structural Data De-anonymization: Quantification, Practice, and Implications <=SEP=> In this paper, we study the quantification, practice, and implications of structural data (e.g., social data, mobility traces) De-Anonymization (DA). First, we address several open problems in structural data DA by quantifying perfect and (1-ε)-perfect structural data DA}, where ε is the error tolerated by a DA scheme. To the best of our knowledge, this is the first work on quantifying structural data DA under a general data model, which closes the gap between structural data DA practice and theory. Second, we conduct the first large-scale study on the de-anonymizability of 26 real world structural datasets, including Social Networks (SNs), Collaborations Networks, Communication Networks, Autonomous Systems, and Peer-to-Peer networks. We also quantitatively show the conditions for perfect and (1-ε)-perfect DA of the 26 datasets. Third, following our quantification, we design a practical and novel single-phase cold start Optimization based DA} (ODA) algorithm. Experimental analysis of ODA shows that about 77.7% - 83.3% of the users in Gowalla (.2M users and 1M edges) and 86.9% - 95.5% of the users in Google+ (4.7M users and 90.8M edges) are de-anonymizable in different scenarios, which implies optimization based DA is implementable and powerful in practice. Finally, we discuss the implications of our DA quantification and ODA and provide some general suggestions for future secure data publishing.

Active Learning in Multi-objective Evolutionary Algorithms for Sustainable Building Design <=SEP=> Residential and commercial buildings are responsible for about 40% of primary energy consumption in the US. The design of a building has tremendous effect on its energy profile, and recently there has been an increased interest in developing optimization methods that support the design of high performance buildings. Previous approaches are either based on simulation optimization or on training an accurate predictive model to replace expensive energy simulations during the optimization. We propose a method, suitable for expensive multiobjective optimization in very large search spaces. In particular, we use a Gaussian Process (GP) model for the prediction and devise an active learning scheme in a multi-objective genetic algorithm to preferentially simulate only solutions that are very informative to the model's predictions for the current generation. We develop a comprehensive and publicly available benchmark for building design optimization. We show that the GP model is highly competitive as a surrogate for building energy simulations, in addition to being well-suited for the active learning setting. Our results show that our approach clearly outperforms surrogate-based optimization, and produces solutions close in hypervolume to simulation optimization, while using only a fraction of the simulations and time.

Technology , globalization , and international competitiveness : Challenges for developing countries <=SEP=> This paper traces the role of technology in economic growth and competitiveness, summarizes the strategies of the fastest growing economies over the last 50 years from the perspective of their technology strategy, summarizes some of the key global trends which are making it more difficult for developing countries to replicate the fast growth experience of the countries mentioned, and traces the impact of the rise of China on developing countries. The main argument of this paper is that technology is an increasingly important element of globalisation and of competitiveness and that the acceleration in the rate of technological change and the pre-requisites necessary to participate effectively in globalisation are making it more difficult for many developing countries to compete. Section 2 gives a long-term perspective on technology and economic growth. Section 3 presents a global overview of changes in regional competitiveness as revealed by economic growth. Section 4 identifies some of the high performers in the last 50 years and reviews the strategies of the high performing East Asian economies comprising the well known “gang of four”, plus three South East Asian countries. Section 5 reviews the strategies of the BRICM countries, the largest developing country economies (Brazil, Russia, India, China and Mexico). It also argues that it is harder for developing countries to replicate the success of the high performing East Asian countries for two main reasons. One relates to new elements in the global competitive environment. These are summarized in section 6. The other is the rapid rise of China (and to a lesser extent India). This is covered in Section 7, which also includes a preliminary analysis of the effects of the rapid rise of China on the rest of the world. Finally, Section 8 draws some conclusions. Developing countries must develop more technological capability and greater flexibility to succeed in the more demanding and asymmetric global environment. It is likely that the pressures of globalisation and greater international competition generate strong protectionist retrenchment in both developed and developing countries. These should be resisted. The world as a whole will be better off if developed countries focus on increasing their flexibility to adjust to changing comparative advantage resulting from rapid technical change, and developing countries focus on increasing their education, infrastructure, and technological capability. There remain however large asymmetries in the global system and greater efforts need to be made to provide some global balancing and transfer mechanisms.

A Novel Multi-task Deep Learning Model for Skin Lesion Segmentation and Classification <=SEP=> In this study, a multi-task deep neural network is proposed for skin lesion analysis. The proposed multi-task learning model solves different tasks (e.g., lesion segmentation and two independent binary lesion classifications) at the same time by exploiting commonalities and differences across tasks. This results in improved learning efficiency and potential prediction accuracy for the task-specific models, when compared to training the individual models separately. The proposed multi-task deep learning model is trained and evaluated on the dermoscopic image sets from the International Skin Imaging Collaboration (ISIC) 2017 Challenge “Skin Lesion Analysis towards Melanoma Detection”, which consists of 2000 training samples and 150 evaluation samples. The experimental results show that the proposed multi-task deep learning model achieves promising performances on skin lesion segmentation and classification. The average value of Jaccard index for lesion segmentation is 0.724, while the average values of area under the receiver operating characteristic curve (AUC) on two individual lesion classifications are 0.880 and 0.972, respectively.

Checking Business Process Evolution <=SEP=> Business processes support the modeling and the implementation of software as workflows of local and inter-process activities. Taking over structuring and composition, evolution has become a central concern in software development. We advocate it should be taken into account as soon as the modeling of business processes, which can thereafter be made executable using process engines or model-to-code transformations. We show here that business process evolution needs formal analysis in order to compare different versions of processes, identify precisely the differences between them, and ensure the desired consistency. To reach this objective, we first present a model transformation from the BPMN standard notation to the LNT process algebra. We then propose a set of relations for comparing business processes at the formal model level. With reference to related work, we propose a richer set of comparison primitives supporting renaming, refinement, propertyand context-awareness. Thanks to an implementation of our approach that can be used through a Web application, we put the checking of evolution within the reach of business process designers.

Multi-Task Learning with Multi-View Attention for Answer Selection and Knowledge Base Question Answering <=SEP=> Answer selection and knowledge base question answering (KBQA) are two important tasks of question answering (QA) systems. Existing methods solve these two tasks separately, which requires large number of repetitive work and neglects the rich correlation information between tasks. In this paper, we tackle answer selection and KBQA tasks simultaneously via multi-task learning (MTL), motivated by the following motivations. First, both answer selection and KBQA can be regarded as a ranking problem, with one at text-level while the other at knowledge-level. Second, these two tasks can benefit each other: answer selection can incorporate the external knowledge from knowledge base (KB), while KBQA can be improved by learning contextual information from answer selection. To fulfill the goal of jointly learning these two tasks, we propose a novel multi-task learning scheme that utilizes multi-view attention learned from various perspectives to enable these tasks to interact with each other as well as learn more comprehensive sentence representations. The experiments conducted on several real-world datasets demonstrate the effectiveness of the proposed method, and the performance of answer selection and KBQA is improved. Also, the multi-view attention scheme is proved to be effective in assembling attentive information from different representational perspectives.

A Unified Approach for Learning the Parameters of Sum-Product Networks <=SEP=> • We present a unified approach for learning the parameters of Sum-Product networks (SPNs). • We construct a more efficient factorization of complete and decomposable SPN into a mixture of trees, with each tree being a product of univariate distributions. • We show that the MLE problem for SPNs can be formulated as a signomial program. • We construct two parameter learning algorithms for SPNs by using sequential monomial approximations (SMA) and the concave-convex procedure (CCCP). Both SMA and CCCP admit multiplicative weight updates. • We prove the convergence of CCCP on SPNs.

Illuminating cell signalling with optogenetic tools <=SEP=> The light-based control of ion channels has been transformative for the neurosciences, but the optogenetic toolkit does not stop there. An expanding number of proteins and cellular functions have been shown to be controlled by light, and the practical considerations in deciding between reversible optogenetic systems (such as systems that use light-oxygen-voltage domains, phytochrome proteins, cryptochrome proteins and the fluorescent protein Dronpa) are well defined. The field is moving beyond proof of concept to answering real biological questions, such as how cell signalling is regulated in space and time, that were difficult or impossible to address with previous tools.

A survey on skin detection in colored images <=SEP=> Color is an efficient feature for object detection as it has the advantage of being invariant to changes in scaling, rotation, and partial occlusion. Skin color detection is an essential required step in various applications related to computer vision. The rapidly-growing research in human skin detection is based on the premise that information about individuals, intent, mode, and image contents can be extracted from colored images, and computers can then respond in an appropriate manner. Detecting human skin in complex images has proven to be a challenging problem because skin color can vary dramatically in its appearance due to many factors such as illumination, race, aging, imaging conditions, and complex background. However, many methods have been developed to deal with skin detection problem in color images. The purpose of this study is to provide an up-to-date survey on skin color modeling and detection methods. We also discuss relevant issues such as color spaces, cost and risks, databases, testing, and benchmarking. After investigating these methods and identifying their strengths and limitations, we conclude with several implications for future direction.

The evolution of cooperation. <=SEP=> Cooperation in organisms, whether bacteria or primates, has been a difficulty for evolutionary theory since Darwin. On the assumption that interactions between pairs of individuals occur on a probabilistic basis, a model is developed based on the concept of an evolutionarily stable strategy in the context of the Prisoner's Dilemma game. Deductions from the model, and the results of a computer tournament show how cooperation based on reciprocity can get started in an asocial world, can thrive while interacting with a wide range of other strategies, and can resist invasion once fully established. Potential applications include specific aspects of territoriality, mating, and disease.

Visual Graphs from Motion (VGfM): Scene understanding with object geometry reasoning <=SEP=> Recent approaches on visual scene understanding attempt to build a scene graph – a computational representation of objects and their pairwise relationships. Such rich semantic representation is very appealing, yet difficult to obtain from a single image, especially when considering complex spatial arrangements in the scene. Differently, an image sequence conveys useful information using the multi-view geometric relations arising from camera motions. Indeed, object relationships are naturally related to the 3D scene structure. To this end, this paper proposes a system that first computes the geometrical location of objects in a generic scene and then efficiently constructs scene graphs from video by embedding such geometrical reasoning. Such compelling representation is obtained using a new model where geometric and visual features are merged using an RNN framework. We report results on a dataset we created for the task of 3D scene graph generation in multiple views.

A Lightweight Virtualization Solution for Android Devices <=SEP=> Mobile virtualization has emerged fairly recently and is considered a valuable way to mitigate security risks on Android devices. However, major challenges in mobile virtualization include runtime, hardware, resource overhead, and compatibility. In this paper, we propose a lightweight Android virtualization solution named Condroid, which is based on container technology. Condroid utilizes resource isolation based on namespaces feature and resource control based on cgroups feature. By leveraging them, Condroid can host multiple independent Android virtual machines on a single kernel to support mutilple Android containers. Furthermore, our implementation presents both a system service sharing mechanism to reduce memory utilization and a filesystem sharing mechanism to reduce storage usage. The evaluation results on Google Nexus 5 demonstrate that Condroid is feasible in terms of runtime, hardware resource overhead, and compatibility. Therefore, we find that Condroid has a higher performance than other virtualization solutions.

Chip design of a 12-bit 5MS/s fully differential SAR ADC with resistor-capacitor array DAC technique for wireless application <=SEP=> A 1.8-V 12-bit 5MS/s successive approximation register (SAR) analog-to-digital converter (ADC) implemented in TSMC 0.18-um CMOS process is presented. To reduce DAC switching energy and chip area, a hybrid resistor-capacitor DAC is applied. To save energy, asynchronous control logic to drive the ADC is used. A pre-amplifier based comparator circuit is built to reduce the kickback noise from the dynamic latch designs. With 1.8 V supply voltage and 5.0 MHz sampling rate, measured results achieve -0.55/0.72 LSB (Least Significant Bit) of DNL (differential nonlinearity) and -0.78/0.92 LSB of integral nonlinearity (INL) respectively, and 10.76 bits of an effective number of bits (ENOB) at 1MHz input frequency. The chip area is 0.83 mm2 including pads and the power consumption is 490μW for optical and wireless communications.

Captcha as graphical passwords-enhanced with video-based captcha for secure services <=SEP=> CAPTCHAs, also known as reverse Turing tests are real-time assessments that are commonly used by programs to tell humans and machines apart. This can be achieved by assigning and assessing or evaluating hard AI problems such that these problems could only be solved easily by human but not by machines. A new security approach based on hard AI problems and Captcha technology is known as Captcha as gRaphical Passwords (CaRP). This scheme can address many security problems such as dictionary attacks, online guessing attacks and shoulder-surfing attacks etc. In this paper, we present an enhanced security for the CaRP scheme i,e CaRP with motion-based Captcha. The motion can be done by using video. The movement of Captcha ensures high security over the normal CaRP scheme. Video-based captcha can provide a great challenge for humans in exploiting the remarkable perceptual abilities or skills of humans to unravel structure-from-motion. The scheme can resist attack caused due to moving object detection. Thus it can thwart many attacks, can enforce more security and it stipulates security along with usability to legitimate users in dealing with real time applications.

Quantum-inspired evolutionary algorithm for a class of combinatorial optimization <=SEP=> This paper proposes a novel evolutionary algorithm inspired by quantum computing, called a quantum-inspired evolutionary algorithm (QEA), which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Like other evolutionary algorithms, QEA is also characterized by the representation of the individual, the evaluation function, and the population dynamics. However, instead of binary, numeric, or symbolic representation, QEA uses a Q-bit, defined as the smallest unit of information, for the probabilistic representation and a Q-bit individual as a string of Q-bits. A Q-gate is introduced as a variation operator to drive the individuals toward better solutions. To demonstrate its effectiveness and applicability, experiments are carried out on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that QEA performs well, even with a small population, without premature convergence as compared to the conventional genetic algorithm.

Towards secure mobile cloud computing: A survey <=SEP=> Mobile cloud computing is gaining popularity among mobile users. The ABI Research predicts that the number of mobile cloud computing subscribers is expected to grow from 42.8 million (1.1% of total mobile users) in 2008 to 998 million (19% of total mobile users) in 2014. Despite the hype achieved by mobile cloud computing, the growth of mobile cloud computing subscribers is still below expectations. According to the recent survey conducted by the International Data Corporation, most IT Executives and CEOs are not interested in adopting such services due to the risks associatedwith security and privacy. The security threats have become a hurdle in the rapid adaptability of the mobile cloud computing paradigm. Significant efforts have been devoted in research organizations and academia to build securemobile cloud computing environments and infrastructures. In spite of the efforts, there are a number of loopholes and challenges that still exist in the security policies of mobile cloud computing. This literature review: (a)highlights the current state of the artwork proposed to securemobile cloud computing infrastructures, (b) identifies the potential problems, and (c) provides a taxonomy of the state of the art. © 2012 Elsevier B.V. All rights reserved.

Kinematics and workspace analysis for a novel 6-PSS parallel manipulator <=SEP=> In this paper, a novel 6-PSS parallel manipulator is designed and the three dimensional structure model is obtained with the Solidworks. The inverse kinematics analysis is performed based on the designed geometric parameters. And according to this analysis, we obtain the theoretical simulation results of virtual structures with different positions and orientations by using MATLAB software. These simulation structures verify the feasibility of this novel 6-PSS parallel manipulator. With a given orientation, a numerical search method is adopted in finding the reachable workspace with the judgment conditions of physical constraints, and the relationship between the reachable workspace and the size of the structure is studied in virtual simulation. Therefore, the range including the largest reachable workspace is achieved and a possible further application of this novel 6-PSS parallel manipulator is proposed, especially in some fields of different structure size requirements.

Design and Implementation of a Pure Sine Wave Single Phase Inverter for Photovoltaic Applications <=SEP=> This paper aims at developing the control circuit for a single phase inverter which produces a pure sine wave with an output voltage that has the same magnitude and frequency as a grid voltage. A microcontroller, based on an advanced technology to generate a sine wave with fewer harmonics, less cost and a simpler design. The technique used is the sinusoidal pulse width modulation signal (SPWM) which is generated by microcontroller. The designed inverter is tested on various AC loads and is essentially focused upon low power electronic applications such as a lamp, a fan and chargers etc. The proposed model of the inverter can improve the output wave forms of the inverter and the dead time control reduced to 63μs. The finished design is simulated in Proteus and PSIM software to ensure output results which is verified practically.