title: Beam-Scanning Performance of Leaky-Wave Slot-Array Antenna on Variable Stub-Loaded Left-Handed Waveguide

abstract: Metamaterial is an artificial material in which the effective phase constant can be controlled by the internal physical periodic structure of the transmission line. Effective phase constant is designed to be of any value, which may be small, large, approximately zero, or negative, attained by changing the dimensions of the periodic structure. Miniaturization of the antenna and wide design flexibility of radiation patterns can be expected from the use of metamaterials. In this work, we developed a leaky-wave slot-array antenna on a variable stub-loaded left-handed waveguide. The radiation pattern of the fabricated antenna was measured and the characteristics of the left-handed transmission line were observed. Moreover, performance of beam-scanning by changing the length of the stubs on the waveguide was confirmed experimentally.


title: An overview of wind power probabilistic forecasts

abstract: Over the past one to two decades, probabilistic forecasts have become more popular. Probabilistic forecasts take uncertainty into account and predict a probability distribution function (pdf). This study provides an overview of state-of-the-art technologies on wind-power probabilistic forecasts, and describes the fundamental concepts on those forecasting methods. Additionally, this study also summarizes various methods to evaluate the performance of a probabilistic forecast model. Finally, this study presents a case study using the ensemble prediction model, in which the wind-speed data were provided by the numerical weather prediction system (NWP) of the Central Weather Bureau (CWB) of Taiwan.


title: Language Resources for Studying Argument

abstract: This paper describes the development of a written corpus of argumentative reasoning. Arguments in the corpus have been analysed using state of the art techniques from argumentation theory and have been marked up using an open, reusable markup language. A number of the key challenges enountered during the process are explored, and preliminary observations about features such as intercoder reliability and corpus statistics are discussed. In addition, several examples are offered of how this kind of language resource can be used in linguistic, computational and philosophical research, and in particular, how the corpus has been used to initiate a programme investigating the automatic detection of argumentative srtucture.


title: A Performance Comparison of Data Encryption Algorithms

abstract: The principal goal guiding the design of any encryption algorithm must be security against unauthorized attacks. However, for all practical applications, performance and the cost of implementation are also important concerns. A data encryption algorithm would not be of much use if it is secure enough but slow in performance because it is a common practice to embed encryption algorithms in other applications such as e-commerce, banking, and online transaction processing applications. Embedding of encryption algorithms in other applications also precludes a hardware implementation, and is thus a major cause of degraded overall performance of the system. In this paper, the four of the popular secret key encryption algorithms, i.e., DES, 3DES, AES (Rijndael), and the Blowfish have been implemented, and their performance is compared by encrypting input files of varying contents and sizes, on different Hardware platforms. The algorithms have been implemented in a uniform language, using their standard specifications, to allow a fair comparison of execution speeds. The performance results have been summarized and a conclusion has been presented. Based on the experiments, it has been concluded that the Blowfish is the best performing algorithm among the algorithms chosen for implementation.


title: An improved image scrambling algorithm based on magic cube rotation and chaotic sequences

abstract: This paper improves an image scrambling algorithm based on Rubik's cube rotation and logistic sequence. The improved algorithm resizes original image and partitions a resized image into six blocks to generate cubes. Rotate these cubes in 25 steps by using 30 different rotating methods which are controlled by a chaotic system. After rotating, remap those cubes to a chaotic image. Then use Chebyshev chaotic system to generate the rotating methods and scramble the chaotic image again in order to get better result. As the rotation methods are totally generated by the chaotic system, the encrypted image can not be retrieval unless the arguments of the chaotic system are exactly the same. Compared with an algorithm based on Arnold transform and the algorithm based on Rubik's cube rotation and logistic sequence, this improved algorithm has better scrambling degree and less correlation between adjacent pixels.


title: TreeNetViz: Revealing Patterns of Networks over Tree Structures

abstract: Network data often contain important attributes from various dimensions such as social affiliations and areas of expertise in a social network. If such attributes exhibit a tree structure, visualizing a compound graph consisting of tree and network structures becomes complicated. How to visually reveal patterns of a network over a tree has not been fully studied. In this paper, we propose a compound graph model, TreeNet, to support visualization and analysis of a network at multiple levels of aggregation over a tree. We also present a visualization design, TreeNetViz, to offer the multiscale and cross-scale exploration and interaction of a TreeNet graph. TreeNetViz uses a Radial, Space-Filling (RSF) visualization to represent the tree structure, a circle layout with novel optimization to show aggregated networks derived from TreeNet, and an edge bundling technique to reduce visual complexity. Our circular layout algorithm reduces both total edge-crossings and edge length and also considers hierarchical structure constraints and edge weight in a TreeNet graph. These experiments illustrate that the algorithm can reduce visual cluttering in TreeNet graphs. Our case study also shows that TreeNetViz has the potential to support the analysis of a compound graph by revealing multiscale and cross-scale network patterns.


title: Anger is More Influential Than Joy: Sentiment Correlation in Weibo

abstract: Recent years have witnessed the tremendous growth of the online social media. In China, Weibo, a Twitter-like service, has attracted more than 500 million users in less than five years. Connected by online social ties, different users might share similar affective states. We find that the correlation of anger among users is significantly higher than that of joy. While the correlation of sadness is surprisingly low. Moreover, there is a stronger sentiment correlation between a pair of users if they share more interactions. And users with larger number of friends possess more significant sentiment correlation with their neighborhoods. Our findings could provide insights for modeling sentiment influence and propagation in online social networks.


title: Influence of the cortical midline structures on moral emotion and motivation in moral decision-making

abstract: Abstract The present study aims to examine the relationship between the cortical midline structures (CMS), which have been regarded to be associated with selfhood, and moral decision making processes at the neural level. Traditional moral psychological studies have suggested the role of moral self as the moderator of moral cognition, so activity of moral self would present at the neural level. The present study examined the interaction between the CMS and other moral-related regions by conducting psycho-physiological interaction analysis of functional images acquired while 16 subjects were solving moral dilemmas. Furthermore, we performed Granger causality analysis to demonstrate the direction of influences between activities in the regions in moral decision-making. We first demonstrate there are significant positive interactions between two central CMS seed regions—i.e., the medial prefrontal cortex (MPFC) and posterior cingulate cortex (PCC)—and brain regions associated with moral functioning including the cerebellum, brainstem, midbrain, dorsolateral prefrontal cortex, orbitofrontal cortex and anterior insula (AI); on the other hand, the posterior insula (PI) showed significant negative interaction with the seed regions. Second, several significant Granger causality was found from CMS to insula regions particularly under the moral-personal condition. Furthermore, significant dominant influence from the AI to PI was reported. Moral psychological implications of these findings are discussed. The present study demonstrated the significant interaction and influence between the CMS and morality-related regions while subject were solving moral dilemmas. Given that, activity in the CMS is significantly involved in human moral functioning.


title: Accurate freeway travel time prediction with state-space neural networks under missing data

abstract: Accuracy and robustness with respect to missing or corrupt input data are two key characteristics for any travel time prediction model that is to be applied in a real-time environment (e.g. for display on variable message signs on freeways). This article proposes a freeway travel time prediction framework that exhibits both qualities. The framework exploits a recurrent neural network topology, the so-called state-space neural network (SSNN), with preprocessing strategies based on imputation. Although the SSNN model is a neural network, its design (in terms of input- and model selection) is not “black box” nor location-specific. Instead, it is based on the lay-out of the freeway stretch of interest. In this sense, the SSNN model combines the generality of neural network approaches, with traffic related (“white-box”) design. Robustness to missing data is tackled by means of simple imputation (data replacement) schemes, such as exponential forecasts and spatial interpolation. Although there are clear theoretical shortcomings to “simple” imputation schemes to remedy input failure, our results indicate that their use is justified in this particular application. The SSNN model appears to be robust to the “damage” done by these imputation schemes. This is true for both incidental (random) and structural input failure. We demonstrate that the SSNN travel time prediction framework yields good accurate and robust travel time predictions on both synthetic and real data.


title: Arbitrary Discrete Sequence Anomaly Detection with Zero Boundary LSTM

abstract: We propose a simple mathematical definition and new neural architecture for finding anomalies within discrete sequence datasets. Our model comprises of a modified LSTM autoencoder and an array of One-Class SVMs. The LSTM takes in elements from a sequence and creates context vectors that are used to predict the probability distribution of the following element. These context vectors are then used to train an array of One-Class SVMs. These SVMs are used to determine an outlier boundary in context space.We show that our method is consistently more stable and also outperforms standard LSTM and sliding window anomaly detection systems on two generated datasets.


title: Opportunistic Flooding in Low-Duty-Cycle Wireless Sensor Networks with Unreliable Links

abstract: Flooding service has been investigated extensively in wireless networks to efficiently disseminate network-wide commands, configurations, and code binaries. However, little work has been done on low-duty-cycle wireless sensor networks in which nodes stay asleep most of the time and wake up asynchronously. In this type of network, a broadcasting packet is rarely received by multiple nodes simultaneously, a unique constraining feature that makes existing solutions unsuitable. In this paper, we introduce Opportunistic Flooding, a novel design tailored for low-duty-cycle networks with unreliable wireless links and predetermined working schedules. Starting with an energy-optimal tree structure, probabilistic forwarding decisions are made at each sender based on the delay distribution of next-hop receivers. Only opportunistically early packets are forwarded via links outside the tree to reduce the flooding delay and redundancy in transmission. We further propose a forwarder selection method to alleviate the hidden terminal problem and a link-quality-based backoff method to resolve simultaneous forwarding operations. We show by extensive simulations and test-bed implementations that Opportunistic Flooding is close to the optimal performance achievable by oracle flooding designs. Compared with Improved Traditional Flooding, our design achieves significantly shorter flooding delay while consuming only 20-60% of the transmission energy.


title: ‘Am I employable?’: Understanding students' employability confidence and their perceived barriers to gaining employment

abstract: This paper addresses employability among undergraduate Marine Sport Science students' at a post-92 HE Institution in the UK, focusing on perceptions of their own employability and their confidence in gaining graduate employment after having careers education embedded within their programme. Mixed method surveys (69% of cohort/n=57) produced results that showed that Marine Sport Science students' perception of their employability increased year on year whilst conversely, confidence in gaining graduate employment decreased year on year. This was due to seven perceived barriers: competition, experience, location, degree quality, qualifications, economy and confidence. The ‘diving board theory’ was established explaining the juxtaposition of improving perceived employability alongside decreasing confidence in gaining employment.


title: Bowel obstruction and pseudo-obstruction

abstract: The gastroenterologist is frequently involved in the care of patients with bowel obstruction and pseudo-obstruction. In the case of obstruction, the central problem is determining which patients should be managed surgically. In both SBO and LBO, evidence of vascular compromise to the gut mandates surgical intervention. Most patients with pseudo-obstruction respond to conservative therapy or neostigmine. Endoscopic decompression is indicated in recalcitrant cases, with surgery reserved as a last resort.


title: Falcons Concept Search: A Practical Search Engine for Web Ontologies

abstract: Web ontologies provide shared concepts for describing do- main entities and thus enable semantic interoperability between applica- tions. To facilitate concept sharing and ontology reusing, we developed Falcons Concept Search, a novel keyword-based ontology search engine. In this paper, we illustrate how the proposed mode of interaction helps users quickly find ontologies that satisfy their needs and present several supportive techniques including a new method of constructing virtual documents of concepts for keyword search, a popularity-based scheme to rank concepts and ontologies, and a way to generate query-relevant structured snippets. We also report the results of a usability evaluation as well as user feedback.


title: Branch-and-cut-and-price for the pickup and delivery problem with time windows

abstract: In the pickup and delivery problem with time windows vehicle routes must be designed to satisfy a set of transportation requests, each involving a pickup and delivery location, under capacity, time window, and precedence constraints. This paper introduces a new branch-and-cut-and-price algorithm in which lower bounds are computed by solving through column generation the linear programming relaxation of a set partitioning formulation. Two pricing subproblems are considered in the column generation algorithm: an elementary and nonelementary shortest path problem. Valid inequalities are added dynamically to strengthen the relaxations. Some of the previously proposed inequalities for the pickup and delivery problem with time windows are also shown to be implied by the set partitioning formulation. Computational experiments indicate that the proposed algorithm outperforms a recent branch-and-cut algorithm.


Biological Therapy in the Treatment of Ulcerative Colitis


title: Development of an Assessment Model for Industry 4.0: Industry 4.0-MM

abstract: The application of new technologies in the manufacturing environment is ushering a new era referred to as the 4th industrial revolution, and this digital transformation appeals to companies due to various competitive advantages it provides. Accordingly, there is a fundamental need for assisting companies in the transition to Industry 4.0 technologies/practices, and guiding them for improving their capabilities in a standardized, objective, and repeatable way. Maturity Models (MMs) aim to assist organizations by providing comprehensive guidance. Therefore, the literature is reviewed systematically with the aim of identifying existing studies related to MMs proposed in the context of Industry 4.0. Seven identified MMs are analyzed by comparing their characteristics of scope, purpose, completeness, clearness, and objectivity. It is concluded that none of them satisfies all expected criteria. In order to satisfy the need for a structured Industry 4.0 assessment/maturity model, SPICE-based Industry 4.0-MM is proposed in this study. Industry 4.0-MM has a holistic approach consisting of the assessment of process transformation, application management, data governance, asset management, and organizational alignment areas. The aim is to create a common base for performing an assessment of the establishment of Industry 4.0 technologies, and to guide companies towards achieving a higher maturity stage in order to maximize the economic benefits of Industry 4.0. Hence, Industry 4.0-MM provides standardization in continuous benchmarking and improvement of businesses in the manufacturing industry.


title: Accelerating magnetic resonance imaging via deep learning

abstract: This paper proposes a deep learning approach for accelerating magnetic resonance imaging (MRI) using a large number of existing high quality MR images as the training datasets. An off-line convolutional neural network is designed and trained to identify the mapping relationship between the MR images obtained from zero-filled and fully-sampled k-space data. The network is not only capable of restoring fine structures and details but is also compatible with online constrained reconstruction methods. Experimental results on real MR data have shown encouraging performance of the proposed method for efficient and accurate imaging.


title: Gaming well: links between videogames and flourishing mental health

abstract: This paper is a review of the state of play of research linking videogaming and flourishing, and explores the role of videogames and technology to improve mental health and well-being. Its purpose is to develop understandings about the positive intersection of gaming and well-being, to document evidence regarding links between videogames and positive mental health, and to provide guidelines for use by other researchers as they design and use tools and games to improve mental health and well-being. Using Huppert's (Huppert and So, 2013) proposition that to flourish is more than the absence of mental disorder but rather a combination of feeling good and functioning effectively, resulting in high levels of mental well-being, and Seligman's (Seligman, 2011) PERMA theory of well-being, the paper identifies strengths in existing games that generate positive affect, positive functioning, and positive social functioning, contributing to, and supporting mental health and well-being.


title: COMPARISON OF FOUR CLASSIFICATION METHODS FOR BRAIN-COMPUTER INTERFACE

abstract: This paper examines the performance of four classiflers for Brain Com- puter Interface (BCI) systems based on multichannel EEG recordings. The clas- siflers are designed to distinguish EEG patterns corresponding to performance of several mental tasks. The flrst one is the basic Bayesian classifler (BC) which exploits only interchannel covariance matrices corresponding to difierent mental tasks. The second classifler is also based on Bayesian approach but it takes into account EEG frequency structure by exploiting interchannel covariance matrices estimated separately for several frequency bands (Multiband Bayesian Classifler, MBBC). The third one is based on the method of Multiclass Common Spatial Pat- terns (MSCP) exploiting only interchannel covariance matrices as BC. The fourth one is based on the Common Tensor Discriminant Analysis (CTDA), which is a generalization of MCSP, taking EEG frequency structure into account. The MBBC and CTDA classiflers are shown to perform signiflcantly better than the two other methods. Computational complexity of the four methods is estimated. It is shown that for all classiflers the increase in the classifying quality is always accompanied by a signiflcant increase of computational complexity.


title: A review of European research on consumer response to nutrition information on food labels

abstract: The aim of this study was to review research conducted in 2003–2006 in the EU-15 countries on how consumers perceive, understand, like and use nutrition information on food labels. Based on a search of databases on academic publications, Google-based search, and enquiries directed to a range of food retailers, food companies, consumer associations and government agencies, a total of 58 studies were identified. These studies were summarised using a standard format guided by a model of consumer information processing, and these summaries were subsequently processed using the MAXqda software in order to identify key findings and common themes across the studies. The studies show widespread consumer interest in nutrition information on food packages, though this interest varies across situations and products. Consumers like the idea of simplified front of pack information but differ in their liking for the various formats. Differences can be related to conflicting preferences for ease of use, being fully informed and not being pressurised into behaving in a particular way. Most consumers understand the most common signposting formats in the sense that they themselves believe that they understand them and they can replay key information presented to them in an experimental situation. There is, however, virtually no insight into how labelling information is, or will be, used in a real-world shopping situation, and how it will affect consumers’ dietary patterns. Results are largely in line with an earlier review by Cowburn and Stockley (Public Health Nutr 8:21–28, 2005), covering research up to 2002, but provide new insights into consumer liking and understanding of simplified front of pack signposting formats. There is an urgent need for more research studying consumer use of nutritional information on food labels in a real-world setting.


title: Advanced zinc-air batteries based on high-performance hybrid electrocatalysts

abstract: Metal-air batteries are promising for energy storage because of their high theoretical energy density, but their realization is hampered by the lack of efficient and robust air catalysts. Li et al. construct stable zinc-air batteries using novel catalysts for oxygen reduction and evolution reactions.


title: Empirical studies of negative political advertising: A quantitative review using a method of combined citation and content analysis

abstract: This study quantitatively reviews the empirical studies of negative political advertising. A method of the combination of citation analysis and content analysis is used. The citation analysis examines each cited work in 20 selected studies with respect to its citation information; and the content analysis investigates these 20 selected studies (citing sources) in terms of their hypotheses, research questions, and methodologies. The aggregated information from the individual cited works and the citing works show that scholars from communication and other disciplines have strong influence on the development of the empirical studies on negative political ads, but communication scholars remain as the driving force. Facing continuously increased literatures in the area, communication scholars need to develop a theory or theories to guide the research. The direction of the research has been moving toward focusing on the boarder and more general effects of negative political ads.


title: Co-ranking Authors and Documents in a Heterogeneous Network

abstract: Recent graph-theoretic approaches have demonstrated remarkable successes for ranking networked entities, but most of their applications are limited to homogeneous networks such as the network of citations between publications. This paper proposes a novel method for co-ranking authors and their publications using several networks: the social network connecting the authors, the citation network connecting the publications, as well as the authorship network that ties the previous two together. The new co-ranking framework is based on coupling two random walks, that separately rank authors and documents following the PageRankparadigm. As a result, improved rankings of documents and their authors depend on each other in a mutually reinforcing way, thus taking advantage of the additional information implicit in the heterogeneous network of authors and documents.


title: Distributed representation learning for knowledge graphs with entity descriptions

abstract: Abstract Recent studies of knowledge representation attempt to project both entities and relations, which originally compose a high-dimensional and sparse knowledge graph, into a continuous low-dimensional space. One canonical approach TransE [2] which represents entities and relations with vectors (embeddings), achieves leading performances solely with triplets, i.e. (head_entity, relation, tail_entity), in a knowledge base. The cutting-edge method DKRL [23] extends TransE via enhancing the embeddings with entity descriptions by means of deep neural network models. However, DKRL requires extra space to store parameters of inner layers, and relies on more hyperparameters to be tuned. Therefore, we create a single-layer model which requests much fewer parameters. The model measures the probability of each triplet along with corresponding entity descriptions, and learns contextual embeddings of entities, relations and words in descriptions simultaneously, via maximizing the loglikelihood of the observed knowledge. We evaluate our model in the tasks of knowledge graph completion and entity type classification with two benchmark datasets: FB500K and EN15K, respectively. Experimental results demonstrate that the proposed model outperforms both TransE and DKRL, indicating that it is both efficient and effective in learning better distributed representations for knowledge bases.


title: Camera-based calibration techniques for seamless multiprojector displays

abstract: Multiprojector, large-scale displays are used in scientific visualization, virtual reality, and other visually intensive applications. In recent years, a number of camera-based computer vision techniques have been proposed to register the geometry and color of tiled projection-based display. These automated techniques use cameras to "calibrate" display geometry and photometry, computing per-projector corrective warps and intensity corrections that are necessary to produce seamless imagery across projector mosaics. These techniques replace the traditional labor-intensive manual alignment and maintenance steps, making such displays cost-effective, flexible, and accessible. In this paper, we present a survey of different camera-based geometric and photometric registration techniques reported in the literature to date. We discuss several techniques that have been proposed and demonstrated, each addressing particular display configurations and modes of operation. We overview each of these approaches and discuss their advantages and disadvantages. We examine techniques that address registration on both planar (video walls) and arbitrary display surfaces and photometric correction for different kinds of display surfaces. We conclude with a discussion of the remaining challenges and research opportunities for multiprojector displays


title: Biosynthesis And Production Of Sophorolipids

abstract: Area and speed are the most important design objectives in integrated circuits. As addition is the basic operation of all computer arithmetic, adders are one of the widely used components in digital integrated circuit design. Since propagation of carry is of major concern in designing efficient adders, this paper presents different fast adders and their performance analysis. Among all the adders discussed Square root Carry Select Adder (SQCSA) provides a good compromise between cost and performance. As, Conventional SQCSA is still area consuming due to dual Ripple Carry Adder(RCA)structures, modifications are done at gate level to reduce area. Modified SQCSA is designed using fast adders like Carry Skip Adder (CSA) and Carry Look-Ahead Adder (CLA) to increase the speed of operation. 1. INTODUCTION In rapidly growing electronic industry, faster units are not only of concern for design but also smaller area and less power become major concerns for design of VLSI circuits. So a VLSI designer has to optimize area delay and power constraints for increasing portability and battery life of portable devices. As we know millions of instructions per second are performed in micro processors speed of operation is the most important constraint to be considered while designing multipliers. These constraints are difficult to achieve so depending on application compromise between constraints has to be made. There are many ways to design a adder. The Ripple carry Adder (RCA) exhibits the most compact design but slowest in speed because for an N-bit RCA, the delay is linearly proportional to N. Thus for large values of N the Ripple Carry Adder gives greater delay of all adders. An N-Carry Look-ahead Adder (CLA) gives fast results when compared to RCA for N ≤ 4, but for large values of N its delay increases. The structure of Carry Select Adder is built using dual Ripple Carry Adders which increase area. In order to reduce the area and power Modified Carry Select Adder(MCSA) is implemented where one ripple carry adder at each stage of addition is replaced with BEC (Binary to Excess-1 Converter). To further increase the performance of MCSA the other RCA's are replaced with still faster adders like Carry Skip Adder and Carry Look-ahead Adders long. The results show a greater improvement in terms of delay and number of logic levels is reduced. The square-root carry select adder is constructed by equalizing the delay through two carry chains and the block multiplexer signal from previous stage. It is also called as non-linear carry select adder. The existing modified SQRT CSLA uses Binary to Excess-1 Converter (BEC) instead of RCA with Cin-=1 to achieve lower delay and less area. The RCA with Cin=0 is also replaced with fast adders like CLA CSKA and CSA and analysis is done in terms of logic levels and delay.


title: Laws of Software Evolution Revisited

abstract: Data obtained during a 1968 study of the software process [8] led to an investigation of the evolution of OS/360 [13] and and, over a period of twenty years, to formulation of eight Laws of Software Evolution. The FEAST project recently initiated (see sections 4–6 below) is expected to throw additional light on the phenomenology underlying these laws, to increase understanding of them, to explore their finer detail, to expose their wider relevance and implications and to develop means for their beneficial exploitation. This paper is intended to trigger wider interest in the laws and in the FEAST study of feedback and feedback control in the context of the software process and its improvement to ensure beneficial exploitation of their potential.


title: Communicating While Computing: Distributed mobile cloud computing over 5G heterogeneous networks

abstract: Current estimates of mobile data traffic in the years to come foresee a 1,000 increase of mobile data traffic in 2020 with respect to 2010, or, equivalently, a doubling of mobile data traffic every year. This unprecedented growth demands a significant increase of wireless network capacity. Even if the current evolution of fourth-generation (4G) systems and, in particular, the advancements of the long-term evolution (LTE) standardization process foresees a significant capacity improvement with respect to third-generation (3G) systems, the European Telecommunications Standards Institute (ETSI) has established a roadmap toward the fifth-generation (5G) system, with the aim of deploying a commercial system by the year 2020 [1]. The European Project named ?Mobile and Wireless Communications Enablers for the 2020 Information Society? (METIS), launched in 2012, represents one of the first international and large-scale research projects on fifth generation (5G) [2]. In parallel with this unparalleled growth of data traffic, our everyday life experience shows an increasing habit to run a plethora of applications specifically devised for mobile devices, (smartphones, tablets, laptops)for entertainment, health care, business, social networking, traveling, news, etc. However, the spectacular growth in wireless traffic generated by this lifestyle is not matched with a parallel improvement on mobile handsets? batteries, whose lifetime is not improving at the same pace [3]. This determines a widening gap between the energy required to run sophisticated applications and the energy available on the mobile handset. A possible way to overcome this obstacle is to enable the mobile devices, whenever possible and convenient, to offload their most energy-consuming tasks to nearby fixed servers. This strategy has been studied for a long time and is reported in the literature under different names, such as cyberforaging [4] or computation offloading [5], [6]. In recent years, a strong impulse to computation offloading has come through cloud computing (CC), which enables the users to utilize resources on demand. The resources made available by a cloud service provider are: 1) infrastructures, such as network devices, storage, servers, etc., 2) platforms, such as operating systems, offering an integrated environment for developing and testing custom applications, and 3) software, in the form of application programs. These three kinds of services are labeled, respectively, as infrastructure as a service, platform as a service, and software as a service. In particular, one of the key features of CC is virtualization, which makes it possible to run multiple operating systems and multiple applications over the same machine (or set of machines), while guaranteeing isolation and protection of the programs and their data. Through virtualization, the number of virtual machines (VMs) can scale on ?demand, thus improving the overall system computational efficiency. Mobile CC (MCC) is a specific case of CC where the user accesses the cloud services through a mobile handset [5]. The major limitations of today?s MCC are the energy consumption associated to the radio access and the latency experienced in reaching the cloud provider through a wide area network (WAN). Mobile users located at the edge of macrocellular networks are particularly disadvantaged in terms of power consumption and, furthermore, it is very difficult to control latency over a WAN. As pointed out in [7]?[9], humans are acutely sensitive to delay and jitter: as latency increases, interactive response suffers. Since the interaction times foreseen in 5G systems, in particular in the so-called tactile Internet [10], are quite small (in the order of milliseconds), a strict latency control must be somehow incorporated in near future MCC. Meeting this constraint requires a deep ?rethinking of the overall service chain, from the physical layer up to virtualization.


title: Identifying moving objects in a video using modified background subtraction and optical flow method

abstract: To identify the object in a video that changes its position from time to time in a video is amongst the crucial steps in various application of computer vision like extraction of objects, video surveillance, classification and pattern identification as well. In this paper a novel approach is proposed that is used to detect the objects that are not stationary in noisy environment. In the proposed algorithm Gaussian mixture model is used to extract the moving object and then the Optical flow method is used to acquire error free results quickly followed by frame differencing to obtain accurate extraction of shapes. The algorithm has been analyzed and tested over various videos in different environmental circumstances. The result acquired proves the algorithm to be more reliable, the detection is more precise and even low in terms of cost and memory requirement as well.


title: Ball tracking and goal detection for middle size soccer robot using omnidirectional camera

abstract: The RoboCup Middle Size League competition is a standard real-world test bed for autonomous multi-robot control, robot vision and other relative research subjects. In the past decade, omnidirectional vision system has become one of the most important sensors for the RoboCup soccer robots, for it can provide a 360° view of the robot's surrounding environment in a single image. The robot can use it for tracking and self-localization which very important for robot's control, strategy, and coordination. This paper will discuss the vision system to detect ball, goals, and calculate the angle and real distance from those objects. Based on the research that has been done, the system can detect the ball and the goal, and calculate the angle and the actual distance with a maximum error distance is 5%.


title: Towards goal-driven behaviour control of multi-robot systems

abstract: The task-level control of mobile robots in uncertain environments demand high adaptivity and cognitive capabilities that allow for flexible decision making and planning. This work introduces the ROS Hybrid Behaviour Planner (RHBP). RHBP combines the advantages of a reactive and adaptive behaviour-based layer with a formal symbolic planner that enables appropriate goal pursuance. Here, we present the first development stage of our system that enables more adaptive multi-robot decision making and planning in the widely used Robot Operating System (ROS) ecosystem.


title: Malicious web page detection based on on-line learning algorithm

abstract: The Internet has become an indispensable tool in peoples' daily life. It also bring us serious computer security problem. One big security threat comes from malicious webpages. In this paper we study how to detect malicious pages. Since malicious webpages are generated inconstantly, we use on line learning methods to detect malicious webpages. To keep the client side as safe as possible, we do not download the webpages, and analysis webpages' content. We only use URL information to determine if the URL links to a malicious pages. The feature selection methods for URL are discussed, and the performances of different on line learning methods are compared. To improve the performance of on line learning classifiers, an improved on line learning method is proposed, experiments show that this method is effective.


title: Offline Handwritten Signature Verification - Literature Review

abstract: The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5–10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.


title: ECG signal compression using analysis by synthesis coding

abstract: An electrocardiogram (ECG) compression algorithm, called analysis by synthesis ECG compressor (ASEC), is introduced. The ASEC algorithm is based on analysis by synthesis coding, and consists of a beat codebook, long and short-term predictors, and an adaptive residual quantizer. The compression algorithm uses a defined distortion measure in order to efficiently encode every heartbeat, with minimum bit rate, while maintaining a predetermined distortion level. The compression algorithm was implemented and tested with both the percentage rms difference (PRD) measure and the recently introduced weighted diagnostic distortion (WDD) measure. The compression algorithm has been evaluated with the MIT-BIH Arrhythmia Database. A mean compression rate of approximately 100 bits/s (compression ratio of about 30:1) has been achieved with a good reconstructed signal quality (WDD below 4% and PRD below 8%). The ASEC was compared with several well-known ECG compression algorithms and was found to be superior at all tested bit rates. A mean opinion score (MOS) test was also applied. The testers were three independent expert cardiologists. As In the quantitative test, the proposed compression algorithm was found to be superior to the other tested compression algorithms.


ARM Cortex-A 8 vs . Intel Atom : Architectural and Benchmark Comparisons


title: Semantic segmentation with heterogeneous sensor coverages

abstract: We propose a new approach to semantic parsing, which can seamlessly integrate evidence from multiple sensors with overlapping but possibly different fields of view (FOV), account for missing data and predict semantic labels over the spatial union of sensors coverages. The existing approaches typically carry out semantic segmentation using only one modality, incorrectly interpolate measurements of other modalities or at best assign semantic labels only to the spatial intersection of coverages of different sensors. In this work we remedy these problems by proposing an effective and efficient strategy for inducing the graph structure of Conditional Random Field used for inference and a novel method for computing the sensor domain dependent potentials. We focus on RGB cameras and 3D data from lasers or depth sensors. The proposed approach achieves superior performance, compared to state of the art and obtains labels for the union of spatial coverages of both sensors, while effectively using appearance or 3D cues when they are available. The efficiency of the approach is amenable to realtime implementation. We quantitatively validate our proposal in two publicly available datasets from indoors and outdoors real environments. The obtained semantic understanding of the acquired sensory information can enable higher level tasks for autonomous mobile robots and facilitate semantic mapping of the environments.


title: Iris Localization using Daugman's Intero-Differential Operator

abstract: recognition is regarded as the most reliable and accurate biometric identification system. Most commercial iris recognition systems use patented algorithms developed by Daugman and these algorithms are able to produce perfect recognition rates. These algorithms are based on linear search methods which make the identification process extremely slow and also raise the false acceptance rate beyond the acceptable range. The proposed iris recognition approach consists of an automatic segmentation system that is based on the various algorithms and is able to localise the circular iris and pupil region, occluding eyelids and eyelashes and reflections. Our proposed method has shown out performing results than existing Houghman algorithms.


title: Variational methods for the Dirichlet process

abstract: Variational inference methods, including mean field methods and loopy belief propagation, have been widely used for approximate probabilistic inference in graphical models. While often less accurate than MCMC, variational methods provide a fast deterministic approximation to marginal and conditional probabilities. Such approximations can be particularly useful in high dimensional problems where sampling methods are too slow to be effective. A limitation of current methods, however, is that they are restricted to parametric probabilistic models. MCMC does not have such a limitation; indeed, MCMC samplers have been developed for the Dirichlet process (DP), a nonparametric distribution on distributions (Ferguson, 1973) that is the cornerstone of Bayesian nonparametric statistics (Escobar & West, 1995; Neal, 2000). In this paper, we develop a mean-field variational approach to approximate inference for the Dirichlet process, where the approximate posterior is based on the truncated stick-breaking construction (Ishwaran & James, 2001). We compare our approach to DP samplers for Gaussian DP mixture models.


title: Information security culture: A management perspective

abstract: Information technology has become an integral part of modern life. Today, the use of information permeates every aspect of both business and private lives. Most organizations need information systems to survive and prosper and thus need to be serious about protecting their information assets. Many of the processes needed to protect these information assets are, to a large extent, dependent on human cooperated behavior. Employees, whether intentionally or through negligence, often due to a lack of knowledge, are the greatest threat to information security. It has become widely accepted that the establishment of an organizational sub-culture of information security is key to managing the human factors involved in information security. This paper briefly examines the generic concept of corporate culture and then borrows from the management and economical sciences to present a conceptual model of information security culture. The presented model incorporates the concept of elasticity from the economical sciences in order to show how various variables in an information security culture influence each other. The purpose of the presented model is to facilitate conceptual thinking and argumentation about information security culture.


title: Students Want to Create Apps: Leveraging Computational Thinking to Teach Mobile Software Development

abstract: Computational Thinking (CT) is recognised as one of the fundamental skills of all graduates. Nevertheless, some issues can emerge when trying to introduce CT into schools; for example, teachers might not be willing to add topics to their intensive syllabi. Therefore, out-of-school venues such as summer schools can be considered a great opportunity for exposure to CT. Moreover, summer schools allow students to meet first hand researchers and help them pursue their interest far from the regular school climate. High school students in general are very curious for the creation of mobile apps; however, most of them get discouraged because they perceive this activity as a very difficult task. Here we describe the MobileDev summer school, a one-week training and hands-on experience in current topics of software development for mobile devices. The curiosity of the students for developing mobile apps is used to introduce and train CT via programming mobile applications through exercises (also with "pen and paper'") of increasing difficulty. The school was repeated twice at our university and was targeted to a reduced class of students concluding the third and the fourth year of high school. Participants were in total 19 and coming from different types of schools. This paper describes the structure of MobileDev and discusses the results to provide directions for further research.


title: Kinetic study of l-isoleucine transport through a liquid membrane containing di(2-ethylhexyl) phosphoric acid in kerosene

abstract: A kinetic study of l-isoleucine transport through a liquid membrane containing di(2-ethylhexyl) phosphoric acid (D2EHPA) in kerosene is presented. The influences of pH in the aqueous feed solution, D2EHPA concentration in the organic phase, the stripping solution composition and H2SO4 concentration in the stripping solution were investigated, and the effects of stirring speed and temperature on the transport of l-isoleucine through a bulk liquid membrane (BLM) were studied. The kinetics of l-isoleucine transport could be analyzed in the formalism of a reversible pseudo-first-order reaction followed by an irreversible pseudo-first-order reaction. The pseudo-first-order apparent rate constants of the interfacial transport of l-isoleucine species are determined for the liquid membrane, at various temperatures. The apparent activation energy values are 21.3 ± 1.9, 57.6 ± 5.1 and 31.8 ± 2. 7k J mol −1 for the extraction reaction, extraction back reaction and stripping reaction, respectively. © 2002 Elsevier Science B.V. All rights reserved.


Highmark's P4P program uses 2 different approaches.


title: The Impact of Information Technology on Library Anxiety: The Role of Computer Attitudes

abstract: Over the past two decades, computer-based technologies have become dominant forces to shape and reshape the products and services the academic library has to offer. The applicationo of library technologies has had a profound impact on the way library resources are being used. Although many students continue to experience high levels of library anxiety, it is likely that the new technologies in the library have led to them experiencing other forms of negative affective states that may be, in part, a function of their attitude towards computers. This study investigates whether students' computer attitudes predict levels of library anxiety.


title: Prevalence and distribution of odontogenic cysts in a Mexican sample. A 753 cases study

abstract: BACKGROUND ::: Odontogenic cysts (OC) are the most frequent lesions of the jaws and their constant epidemiological update is necessary and indispensable. Therefore the principal objective of this report was To determine prevalence and clinical-demographical characteristics of OC in a Mexican sample. ::: ::: ::: MATERIAL AND METHODS ::: 753 cases of OC coming from the archive of a head and neck histopathological teaching service, from January 2000 to December 2013, were included. OC cases were re-assessed according 2005 WHO classification. Chi square test was used to establish possible associations (p<0.05IC95%). ::: ::: ::: RESULTS ::: From 753 OC, 369 were female and 384 male; 52.9% of them were in their 2nd- 4th decade of life. The most common location (41%) was the mandibular posterior area. Radicular cysts were more frequent in maxillary anterior zone of females (p 0.0002) at their fourth decade of life. Dentigerous cysts were more frequent in the mandibular posterior zone of males (p 0.0000) in their second decade of life. Six cases of periodontal lateral cyst; 4 cases of paradental cysts; 4 eruption cysts and 4 cases of adult gingival cyst, as well were identified. ::: ::: ::: CONCLUSIONS ::: Radicular cyst and dentigerous cyst are the most prevalent odontogenic cyst in this Mexican sample. Due to their etiology, dental pulpar necrosis and impacted teeth, radicular cyst and dentigerous cyst could be prevenible. Therefore, it is necessary to establish preventive strategies to diminish dental decay and programs of prophylactic extractions of impacted teeth, to in consequence decrease the prevalence of odontogenic cysts. Key words:Cyst, dentigerous cyst, mexican, odontogenic cyst, radicular cyst.


title: Sleep-Quality among Girls with Different Involvement in Competitive Sports during Fall in the Arctic Circle:

abstract: Eight high-school girls participating in competitive sports and six non-participating girls living north of the polar circle recorded their sleep once a week during the Fall, including the period when the sun does not rise above the horizon. Sleep-quality scores were computed as the sum of answers identical to a preset “right” answer indicating good sleep-quality. Significant differences on sleep-quality between the two groups were found, but a significant influence of amount of daylight or exercise could not be confirmed. When sleep-quality was ranked during three periods through the Fall, the girls participating in competitive sports showed a trend of increasing sleep-quality and the nonparticipants a decreasing one. An explanation based on influence of exercise thresholds upon sleep-quality is presented.


title: Essential Oils in Combination and Their Antimicrobial Properties

abstract: Essential oils (EOs) have been long recognized for their antibacterial, antifungal, antiviral, insecticidal and antioxidant properties. They are widely used in medicine and the food industry for these purposes. The increased interest in alternative natural substances is driving the research community to find new uses and applications of these substances. EOs and their components show promising activities against many food-borne pathogens and spoilage microorganisms when tested in vitro. In food systems, higher concentrations of EOs are needed to exert similar antibacterial effects as those obtained in in vitro assays. The use of combinations of EOs and their isolated components are thus new approaches to increase the efficacy of EOs in foods, taking advantage of their synergistic and additive effects. The purpose of this review is to provide an overview on the antimicrobial efficacy of these combinations. A survey of the methods used for the determination of the interactions and mechanisms involved in the antimicrobial activities of these combinations are also reported.


title: Minimum-time speed optimisation over a fixed path

abstract: In this paper we investigate the problem of optimising the speed of a vehicle over a fixed path for minimum time traversal. We utilise a change of variables that has been known since the 1980s, although the resulting convexity of the problem was not noted until recently. The contributions of this paper are three fold. First, we extend the convexification of the problem to a more general framework. Second, we identify a wide range of vehicle models and constraints which can be included in this expanded framework. Third, we develop and implement an algorithm that allows these problems to be solved in real time, on embedded systems, with a high degree of accuracy.


Human Identification via Gait Recognition Using Accelerometer Gyro Forces


title: SegMap: 3D Segment Mapping using Data-Driven Descriptors

abstract: When performing localization and mapping, working at the level of structure can be advantageous in terms of robustness to environmental changes and differences in illumination. This paper presents SegMap: a map representation solution to the localization and mapping problem based on the extraction of segments in 3D point clouds. In addition to facilitating the computationally intensive task of processing 3D point clouds, working at the level of segments addresses the data compression requirements of real-time single- and multi-robot systems. While current methods extract descriptors for the single task of localization, SegMap leverages a data-driven descriptor in order to extract meaningful features that can also be used for reconstructing a dense 3D map of the environment and for extracting semantic information. This is particularly interesting for navigation tasks and for providing visual feedback to end-users such as robot operators, for example in search and rescue scenarios. These capabilities are demonstrated in multiple urban driving and search and rescue experiments. Our method leads to an increase of area under the ROC curve of 28.3% over current state of the art using eigenvalue based features. We also obtain very similar reconstruction capabilities to a model specifically trained for this task. The SegMap implementation will be made available open-source along with easy to run demonstrations at www.github.com/ethz-asl/segmap. A video demonstration is available at https://youtu.be/CMk4w4eRobg.


title: 3-HOP: a high-compression indexing scheme for reachability query

abstract: Reachability queries on large directed graphs have attracted much attention recently. The existing work either uses spanning structures, such as chains or trees, to compress the complete transitive closure, or utilizes the 2-hop strategy to describe the reachability. Almost all of these approaches work well for very sparse graphs. However, the challenging problem is that as the ratio of the number of edges to the number of vertices increases, the size of the compressed transitive closure grows very large. In this paper, we propose a new 3-hop indexing scheme for directed graphs with higher density. The basic idea of 3-hop indexing is to use chain structures in combination with hops to minimize the number of structures that must be indexed. Technically, our goal is to find a 3-hop scheme over dense DAGs (directed acyclic graphs) with minimum index size. We develop an efficient algorithm to discover a transitive closure contour, which yields near optimal index size. Empirical studies show that our 3-hop scheme has much smaller index size than state-of-the-art reachability query schemes such as 2-hop and path-tree when DAGs are not very sparse, while our query time is close to path-tree, which is considered to be one of the best reachability query schemes.


title: A Model for Understanding How Virtual Reality Aids Complex Conceptual Learning

abstract: Designers and evaluators of immersive virtual reality systems have many ideas concerning how virtual reality can facilitate learning. However, we have little information concerning which of virtual reality's features provide the most leverage for enhancing understanding or how to customize those affordances for different learning environments. In part, this reflects the truly complex nature of learning. Features of a learning environment do not act in isolation; other factors such as the concepts or skills to be learned, individual characteristics, the learning experience, and the interaction experience all play a role in shaping the learning process and its outcomes. Through Project Science Space, we have been trying to identify, use, and evaluate immersive virtual reality's affordances as a means to facilitate the mastery of complex, abstract concepts. In doing so, we are beginning to understand the interplay between virtual reality's features and other important factors in shaping the learning process and learning outcomes for this type of material. In this paper, we present a general model that describes how we think these factors work together and discuss some of the lessons we are learning about virtual reality's affordances in the context of this model for complex conceptual learning.


title: The First Launch of an Autonomous Thrust-Driven Microrobot Using Nanoporous Energetic Silicon

abstract: As the capability and complexity of robotic platforms continue to evolve from the macro to the micron scale, the challenge of achieving autonomy requires the development of robust, lightweight architectures. These architectures must provide a platform upon which actuators, control, sensing, power, and communication modules are integrated for optimal performance. In this paper, the first autonomous jumping microrobotic platform is demonstrated using a hybrid integration approach to assemble on-board control, sensing, power, and actuation directly onto a polymer chassis. For the purposes of this paper, jumping is defined as brief parabolic motion achieved via an actuation pulse at takeoff. In this paper, the actuation pulse comes from the rapid release of chemical energy to create propulsion. The actuation pulse lasts several microseconds and is achieved using a novel high-force/low-power thrust actuator, nanoporous energetic silicon, resulting in 250 μJ of kinetic energy delivered to the robot and a vertical height of approximately 8 cm.


title: Cloud Computing Security: What Changes with Software-Defined Networking?

abstract: Broadly construed, Software-Defined Networking (SDN) refers to the use of a standards-based open architecture and its supporting open source and open interfaces technologies to enable the deployment, management, and operation of networks. While traditional network management relies on vendor-specific hardware, protocols, and software, SDN systems are architected to have well-defined control and data planes offering flexible management interfaces. The enhanced control enabled by SDN opens opportunities for better cloud security engineering. At the same time, new vulnerabilities are potentially exposed as new technologies are introduced. This chapter discusses how SDN impacts cloud security, and potential risks that need to be addressed when SDN is deployed within and across clouds.


title: Malware Detection in Adversarial Settings: Exploiting Feature Evolutions and Confusions in Android Apps

abstract: Existing techniques on adversarial malware generation employ feature mutations based on feature vectors extracted from malware. However, most (if not all) of these techniques suffer from a common limitation: feasibility of these attacks is unknown. The synthesized mutations may break the inherent constraints posed by code structures of the malware, causing either crashes or malfunctioning of malicious payloads. To address the limitation, we present Malware Recomposition Variation (MRV), an approach that conducts semantic analysis of existing malware to systematically construct new malware variants for malware detectors to test and strengthen their detection signatures/models. In particular, we use two variation strategies (i.e., malware evolution attack and malware confusion attack) following structures of existing malware to enhance feasibility of the attacks. Upon the given malware, we conduct semantic-feature mutation analysis and phylogenetic analysis to synthesize mutation strategies. Based on these strategies, we perform program transplantation to automatically mutate malware bytecode to generate new malware variants. We evaluate our MRV approach on actual malware variants, and our empirical evaluation on 1,935 Android benign apps and 1,917 malware shows that MRV produces malware variants that can have high likelihood to evade detection while still retaining their malicious behaviors. We also propose and evaluate three defense mechanisms to counter MRV.


title: Cytosolic DNA Sensing Promotes Macrophage Transformation and Governs Myocardial Ischemic Injury

abstract: BACKGROUND ::: Myocardium irreversibly injured by ischemic stress must be efficiently repaired to maintain tissue integrity and contractile performance. Macrophages play critical roles in this process. These cells transform across a spectrum of phenotypes to accomplish diverse functions ranging from mediating the initial inflammatory responses that clear damaged tissue to subsequent reparative functions that help rebuild replacement tissue. Although macrophage transformation is crucial to myocardial repair, events governing this transformation are poorly understood. ::: ::: ::: METHODS ::: Here, we set out to determine whether innate immune responses triggered by cytoplasmic DNA play a role. ::: ::: ::: RESULTS ::: We report that ischemic myocardial injury, along with the resulting release of nucleic acids, activates the recently described cyclic GMP-AMP synthase-stimulator of interferon genes pathway. Animals lacking cyclic GMP-AMP synthase display significantly improved early survival after myocardial infarction and diminished pathological remodeling, including ventricular rupture, enhanced angiogenesis, and preserved ventricular contractile function. Furthermore, cyclic GMP-AMP synthase loss of function abolishes the induction of key inflammatory programs such as inducible nitric oxide synthase and promotes the transformation of macrophages to a reparative phenotype, which results in enhanced repair and improved hemodynamic performance. ::: ::: ::: CONCLUSIONS ::: These results reveal, for the first time, that the cytosolic DNA receptor cyclic GMP-AMP synthase functions during cardiac ischemia as a pattern recognition receptor in the sterile immune response. Furthermore, we report that this pathway governs macrophage transformation, thereby regulating postinjury cardiac repair. Because modulators of this pathway are currently in clinical use, our findings raise the prospect of new treatment options to combat ischemic heart disease and its progression to heart failure.


title: Learning movement primitives

abstract: This paper discusses a comprehensive framework for modular motor control based on a recently developed theory of dynamic movement primitives (DMP). DMPs are a formulation of movement primitives with autonomous nonlinear differential equations, whose time evolution creates smooth kinematic control policies. Model-based control theory is used to convert the outputs of these policies into motor commands. By means of coupling terms, on-line modifications can be incorporated into the time evolution of the differential equations, thus providing a rather flexible and reactive framework for motor planning and execution. The linear parameterization of DMPs lends itself naturally to supervised learning from demonstration. Moreover, the temporal, scale, and translation invariance of the differential equations with respect to these parameters provides a useful means for movement recognition. A novel reinforcement learning technique based on natural stochastic policy gradients allows a general approach of improving DMPs by trial and error learning with respect to almost arbitrary optimization criteria. We demonstrate the different ingredients of the DMP approach in various examples, involving skill learning from demonstration on the humanoid robot DB, and learning biped walking from demonstration in simulation, including self-improvement of the movement patterns towards energy efficiency through resonance tuning.


Model-Based Tracking of Multiple Worms and


title: Competition and Selection in Language Evolution

abstract: The primary thesis of this paper is that selection plays a role in language evolution. Underlying this position is the assumption that a language is a Lamarckian species, a construct extrapolated from idiolects spoken by individuals who acknowledge using the same verbal code to communicate with each other. There is no perfect replication in any case of language “acquisition”, which is actually a recreation process in which the learner makes a system out of features selected from utterances of different individuals with whom he/she has interacted. In a way similar to gene recombination in biology, each learner gradually and selectively reintegrates into new system features which are often modified in the process. At the population level, the congruence of some divergent idiolectal selections is often strong enough for a language to evolve into a new communal system. A fundamental question for my hypothesis is: What principles regulate selection? I also assume hybridism in language “transmission”, which is polyploidic, as features of every idiolect originate not only in various competing idiolects, but possibly also in different dialects or languages in contact. The question about feature selection remains the same.


A Comparison and Analysis of Some Ontology Visualization Tools.


title: Formal Modeling and Verification of Blockchain System

abstract: As a decentralized and distributed secure storage technology, the notion of blockchain is now widely used for electronic trading in finance, for issuing digital certificates, for copyrights management, and for many other security-critical applications. With applications in so many domains with high-assurance requirements, the formalization and verification of safety and security properties of blockchain becomes essential, and the aim of the present paper. We present the model-based formalization, simulation and verification of a blockchain protocol by using the SDL formalism of Telelogic Tau. We consider the hierarchical and modular SDL model of the blockchain protocol and exercise a methodology to formally simulate and verify it. This way, we show how to effectively increase the security and safety of blockchain in order to meet high assurance requirements demanded by its application domains. Our work also provides effective support for assessing different network consensus algorithms, which are key components in blockchain protocols, as well as on the topology of blockchain networks. In conclusion, our approach contributes to setting up a verification methodology for future blockchain standards in digital trading.


title: Optical character recognition for South African languages

abstract: Optical Character Recognition (OCR) is an essential technology in the digitisation of printed media. Many OCR engines are language-specific and available for common languages such as English and other European languages, but less so for smaller languages such as Tshivenda and the other South African languages. In this paper, we describe the process of training OCR engines for South African languages using Tesseract, and compare the accuracy of these engines with the accuracy of using an English engine on the other South African languages. We find that our language-specific engines achieve a high accuracy, with 50% less errors as when using an English engine.


title: Personalized Medical Treatments Using Novel Reinforcement Learning Algorithms

abstract: In both the fields of computer science and medicine there is very strong interest in developing personalized treatment policies for patients who have variable responses to treatments. In particular, I aim to find an optimal personalized treatment policy which is a non-deterministic function of the patient specific covariate data that maximizes the expected survival time or clinical outcome. I developed an algorithmic framework to solve multistage decision problem with a varying number of stages that are subject to censoring in which the "rewards" are expected survival times. In specific, I developed a novel Q-learning algorithm that dynamically adjusts for these parameters. Furthermore, I found finite upper bounds on the generalized error of the treatment paths constructed by this algorithm. I have also shown that when the optimal Q-function is an element of the approximation space, the anticipated survival times for the treatment regime constructed by the algorithm will converge to the optimal treatment path. I demonstrated the performance of the proposed algorithmic framework via simulation studies and through the analysis of chronic depression data and a hypothetical clinical trial. The censored Q-learning algorithm I developed is more effective than the state of the art clinical decision support systems and is able to operate in environments when many covariate parameters may be unobtainable or censored.


title: Localizing Parts of Faces Using a Consensus of Exemplars

abstract: We present a novel approach to localizing parts in images of human faces. The approach combines the output of local detectors with a nonparametric set of global models for the part locations based on over 1,000 hand-labeled exemplar images. By assuming that the global models generate the part locations as hidden variables, we derive a Bayesian objective function. This function is optimized using a consensus of models for these hidden variables. The resulting localizer handles a much wider range of expression, pose, lighting, and occlusion than prior ones. We show excellent performance on real-world face datasets such as Labeled Faces in the Wild (LFW) and a new Labeled Face Parts in the Wild (LFPW) and show that our localizer achieves state-of-the-art performance on the less challenging BioID dataset.


title: 280GHz and 860GHz image sensors using Schottky-barrier diodes in 0.13μm digital CMOS

abstract: Millimeter and sub-millimeter-wave imaging using solid-state circuits is gaining attention for security and medical applications. To lower cost and increase integration, MOSFETs in CMOS are being investigated for implementing broadband detectors [1–3]. However, neither measured noise-equivalent power (NEP) nor noise floor of the imager was given in [1]. Although NEP of 17pW/Hz1/2 was achieved at 650GHz in [2], an external lens was attached to the 65nm SOI CMOS chip. In [3], an NEP of 66pW/Hz1/2 was measured at 1.05THz using 65nm CMOS without a lens attached to the chip. Additionally, although the efforts reported in [1–3] realized an array, none demonstrated the image-array function. As an alternative, polysilicon-gate-separation (PGS) Schottky-barrier diodes (SBD) with cut-off frequency of ∼2THz were fabricated in CMOS without process modifications [4] and were used to demonstrate a 280GHz detector with NEP of 30pW/Hz1/2 [5,6]. To significantly enhance the scanning speed, a 16-pixel 280GHz SBD imager is fabricated and its array function is reported in this paper. The imager including baseband amplifiers achieves responsivity of 5.1kV/W and NEP of 29pW/Hz1/2. More importantly, its operation was demonstrated in a setup that requires no mirror or lens that is bulky and costly. Next, an 860GHz SBD detector is demonstrated with a measured non-amplified responsivity of 355V/W and NEP of 32pW/Hz1/2. This NEP is ∼2X lower than the best reported work in CMOS [3]. Both chips are fabricated in a 0.13μm logic CMOS. The results suggest a path for high performance, compact and affordable sub-millimeter-wave and terahertz CMOS imagers using SBDs.


title: Anomaly detector fusion processing for advanced military aircraft

abstract: Automated Prognostics and Health Management (PHM) is a requirement for advanced military aircraft. PHM is the key to achieving true condition-based maintenance. PHM processing strategies include modules for the processing of known nominal and fault conditions. However in real operations there will also occur faults and other off-nominal operations that were never anticipated nor ever encountered before. We call these events anomalies. Missing the presence of an anomaly could potentially be catastrophic with the loss of the pilot and aircraft. Several different anomaly detectors (ADs) have been developed for advanced military aircraft to solve this problem. Fusion of these ADs can significantly reduce false alarms while at the same time substantially improving detection performance. Fusion is a way of approaching the goal of perfect detection with zero false alarms. We have developed a neural net approach for performing AD fusion. Presented is a description of that technique and the application to military aircraft subsystem data.


title: An Edge-Sensing Generic Demosaicing Algorithm With Application to Image Resampling

abstract: In this paper, we introduce a new demosaicing algorithm that can be used for various sensor images captured by digital cameras equipped with various red-green-blue color filter arrays. Our algorithm enhances the universal demosaicing algorithm of Lukac et al by defining a new spectral interpolation model that exploits not only the information on the color of pixels but also the relative distance between neighboring pixels within an image. Moreover, we include an edge-detection model that makes our algorithm adaptive and reduces the presence of color shifts and artifacts. A series of tests has been made on images of the Kodak database, and our algorithm performs better than the universal demosaicing algorithm with regard to both subjective and objective evaluation. The versatility of our demosaicing algorithm is also highlighted through an application to the issue of color image resampling, and we obtain conclusive experimental results.


Detection of Steel Surface Defect Based on Machine Learning Using Deep Auto-encoder Network


title: Verification and validation of MapReduce program model for parallel K-means algorithm on Hadoop cluster

abstract: With the development of information technology, a large volume of data is growing and getting stored electronically. Thus, the data volumes processing by many applications will routinely cross the petabyte threshold range, in that case it would increase the computational requirements. Efficient processing algorithms and implementation techniques are the key in meeting the scalability and performance requirements in such scientific data analyses. So for the same here, we have p analyzed the various MapReduce Programs and a parallel clustering algorithm (PKMeans) on Hadoop cluster, using the Concept of MapReduce. Here, in this experiment we have verified and validated various MapReduce applications like wordcount, grep, terasort and parallel K-Means Clustering Algorithm. We have found that as the number of nodes increases the execution time decreases, but also some of the interesting cases has been found during the experiment and recorded the various performance change and drawn different performance graphs. This experiment is basically a research study of above MapReduce applications and also to verify and validate the MapReduce Program model for Parallel K-Means algorithm on Hadoop Cluster having four nodes.


title: IntentStreams: Smart Parallel Search Streams for Branching Exploratory Search

abstract: The user's understanding of information needs and the information available in the data collection can evolve during an exploratory search session. Search systems tailored for well-defined narrow search tasks may be suboptimal for exploratory search where the user can sequentially refine the expressions of her information needs and explore alternative search directions. A major challenge for exploratory search systems design is how to support such behavior and expose the user to relevant yet novel information that can be difficult to discover by using conventional query formulation techniques. We introduce IntentStreams, a system for exploratory search that provides interactive query refinement mechanisms and parallel visualization of search streams. The system models each search stream via an intent model allowing rapid user feedback. The user interface allows swift initiation of alternative and parallel search streams by direct manipulation that does not require typing. A study with 13 participants shows that IntentStreams provides better support for branching behavior compared to a conventional search system.


title: Short-Interval Detailed Production Scheduling in 300mm Semiconductor Manufacturing using Mixed Integer and Constraint Programming

abstract: Fully automated 300mm manufacturing requires the adoption of a real-time lot dispatching paradigm. Automated dispatching has provided significant improvements over manual dispatching by removing variability from the thousands of dispatching decisions made every day in a fab. Real-time resolution of tool queues, with consideration of changing equipment states, process restrictions, physical and logical location of WIP, supply chain objectives and a myriad of other parameters, is required to ensure successful dispatching in the dynamic fab environment. However, the real-time dispatching decision in semiconductor manufacturing generally remains a reactive, heuristic response in existing applications, limited to the current queue of each tool. The shortcomings of this method of assigning WIP to tools, aptly named "opportunistic scavenging" as stated in G. Sullivan (1987), have become more apparent in lean manufacturing environments where lower WIP levels present fewer obvious opportunities for beneficial lot sequencing or batching. Recent advancements in mixed integer programming (MIP) and constraint programming (CP) have raised the possibility of integrating optimization software, commonly used outside of the fab environment to compute optimal solutions for scheduling scenarios ranging from order fulfillment systems to crew-shift-equipment assignments, with a real-time dispatcher to create a short-interval scheduler. The goal of such a scheduler is to optimize WIP flow through various sectors of the fab by expanding the analysis beyond the current WIP queue to consider upstream and downstream flow across the entire tool group or sector. This article describes the production implementation of a short-interval local area scheduler in IBM's leading-edge 300mm fab located in East Fishkill, New York, including motivation, approach, and initial results


title: A comparative study on software vulnerability static analysis techniques and tools

abstract: Using static analysis tools can detect software vulnerabilities, which is important for improving the security of software. Static analysis technology has developed rapidly, but the comparison and evaluation of static analysis techniques and tools are not much. This paper focuses on software vulnerability static analysis techniques and tools. First we discuss the commonly-used static analysis techniques and tools, and compare these tools in a technical perspective, and then we analyze the characteristics of these tools through the experiment, finally, combining dynamic analysis, we propose an efficient software vulnerability detection method.


title: 28 GHz channel measurements and modeling in a ski resort town in pyeongchang for 5G cellular network systems

abstract: In this paper, radio propagation measurements and analysis are presented to investigate the channel characteristics of millimeter wave (mmWave) transmission for outdoor cellular communication systems. The measurement campaign is performed in a ski resort town placed in Pyeongchang, South Korea, which is the venue of the coming Winter Olympics in 2018. A 28 GHz synchronous channel sounder system is used in conducting the channel measurements. From the measurements, the spatio-temporal channel characteristics such as path loss, multipath delay, angular statistics are analyzed. The mmWave radio propagation parameters and the corresponding channel models based on the 3GPP spatial channel model (SCM) are also presented. Since a trial service is planned during the 2018 Pyeongchang Winter Olympic Games, this research is significant to demonstrate the emerging 5G technologies. Additionally, blocking tests, which measure the path loss variation when there are some obstacles, are discussed in this paper.


title: Two-Level Spatial Multiplexing using Hybrid Beamforming Antenna Arrays for mmWave Communications

abstract: In this work, we consider a two-level hierarchical MIMO antenna array system, where each antenna of the upper level is made up of a subarray on the lower one. The concept of spatial multiplexing is applied twice in this situation: Firstly, the spatial multiplexing of a Line-of-Sight (LoS) MIMO system is exploited. It is based on appropriate (sub-)array distances and achieves multiplexing gain due to phase differences among the signals at the receive (sub-)arrays. Secondly, one or more additional reflected paths of different angles (separated from the LoS path by different spatial beams at the subarrays) are used to exploit spatial multiplexing between paths. By exploiting the above two multiplexing kinds simultaneously, a high dimensional system with maximum spatial multiplexing is proposed by jointly using 'phase differences' within paths and 'angular differences' between paths. The system includes an advanced hybrid beamforming architecture with large subarray separation, which could occur in millimeter wave backhaul scenarios. The possible gains of the system w.r.t. a pure LOS MIMO system are illustrated by evaluating the capacities with total transmit power constraints.


title: Big data analytics on traditional HPC infrastructure using two-level storage

abstract: Data-intensive computing has become one of the major workloads on traditional high-performance computing (HPC) clusters. Currently, deploying data-intensive computing software framework on HPC clusters still faces performance and scalability issues. In this paper, we develop a new two-level storage system by integrating Tachyon, an in-memory file system with OrangeFS, a parallel file system. We model the I/O throughputs of four storage structures: HDFS, OrangeFS, Tachyon and two-level storage. We conduct computational experiments to characterize I/O throughput behavior of two-level storage and compare its performance to that of HDFS and OrangeFS, using TeraSort benchmark. Theoretical models and experimental tests both show that the two-level storage system can increase the aggregate I/O throughputs. This work lays a solid foundation for future work in designing and building HPC systems that can provide a better support on I/O intensive workloads with preserving existing computing resources.


title: Efficient Representation, Measurement, and Recovery of Large-scale Networks

abstract: Real-world networks have millions of users and are complex in structure. Efficient techniques are required to capture the network characteristics as compact data. The overall purpose of this research is to mine information from partially or completely available graph data and obtain optimum data representation for networks. The first phase involves studying network data to draw meaningful relations and properties about the network. We extend this work to introduce a novel way of sampling graphs in lossless manner. The second phase involves observing and processing the partial data available to complete the graph data by estimation methods. We also leverage our knowledge about graphs to design an optimal network sampling technique. Subsequently, these techniques will be applied to both static as well as dynamic graphs.


title: A survey on SQL injection: Vulnerabilities, attacks, and prevention techniques

abstract: In this paper, we present a detailed review on various types of SQL injection attacks, vulnerabilities, and prevention techniques. Alongside presenting our findings from the survey, we also note down future expectations and possible development of countermeasures against SQL injection attacks.


title: Secure Management of Low Power Fitness Trackers

abstract: The increasing popular interest in personal telemetry, also called the Quantified Self or “lifelogging”, has induced a popularity surge for wearable personal fitness trackers. Fitness trackers automatically collect sensor data about the user throughout the day, and integrate it into social network accounts. Solution providers have to strike a balance between many constraints, leading to a design process that often puts security in the back seat. Case in point, we reverse engineered and identified security vulnerabilities in Fitbit Ultra and Gammon Forerunner 610, two popular and representative fitness tracker products. We introduce FitBite and GarMax, tools to launch efficient attacks against Fitbit and Garmin. We devise SensCrypt, a protocol for secure data storage and communication, for use by makers of affordable and lightweight personal trackers. SensCrypt thwarts not only the attacks we introduced, but also defends against powerful JTAG Read attacks. We have built Sens.io, an Arduino Uno based tracker platform, of similar capabilities but at a fraction of the cost of current solutions. On Sens.io, SensCrypt imposes a negligible write overhead and significantly reduces the end-to-end sync overhead of Fitbit and Garmin.


title: CRM Adoption in a Higher Education Institution

abstract: More and more organisations, from private to public sectors, are pursuing higher levels of customer satisfaction, loyalty and retention. With this intent, higher education institutions (HEI) have adopted CRM – Customer Relationship Management. In order to analyse some of the interesting aspects of this phenomenon n, we conducted an action research in a European Institute. The main research question we answered is “how to adopt a CRM strategy in a Higher Education Institution?” Some of the main findings of this study are (1) even though HEI’s main customer is the student, there are others stakeholders that a CRM project must consider; (2) universities can use their internal resources to implement a CRM project successfully; and (3) using Agile software methodology is an effective way to define clearer, more objective and more assertive technical requirements which result in a CRM software that meet send user’s expectations and organizational strategic goals. These findings can help other HEIs planning to adopt CRM as a strategic tool to improve their relationship with the stakeholders´ community and expand their student body.


title: Toward a Quantitative Theory of Self-Generated Complexity

abstract: Quantities are defined operationally which qualify as measures of complexity of patterns arising in physical situations. Their main features, distinguishing them from previously used quantities, are the following: (1) they are measuretheoretic concepts, more closely related to Shannon entropy than to computational complexity; and (2) they are observables related to ensembles of patterns, not to individual patterns. Indeed, they are essentially Shannon information needed to specify not individual patterns, but either measure-theoretic or algebraic properties of ensembles of patterns arising ina priori translationally invariant situations. Numerical estimates of these complexities are given for several examples of patterns created by maps and by cellular automata.


Richer Interpolative Smoothing Based on Modified Kneser-Ney Language Modeling


title: Model-driven Generation of Microservice Architectures for Benchmarking Performance and Resilience Engineering Approaches

abstract: Microservice architectures are steadily gaining adoption in industrial practice. At the same time, performance and resilience are important properties that need to be ensured. Even though approaches for performance and resilience have been developed (e.g., for anomaly detection and fault tolerance), there are no benchmarking environments for their evaluation under controlled conditions. In this paper, we propose a generative platform for benchmarking performance and resilience engineering approaches in microservice architectures, comprising an underlying metamodel, a generation platform, and supporting services for workload generation, problem injection, and monitoring.


title: Sketchsegnet: A Rnn Model for Labeling Sketch Strokes

abstract: We investigate the problem of stroke-level sketch segmentation, which is to train machines to assign strokes with semantic part labels given a input sketch. Solving the problem of sketch segmentation opens the door for fine-grained sketch interpretation, which can benefit many novel sketch-based applications, including sketch recognition and sketch-based image retrieval. In this paper, we treat the problem of stroke-level sketch segmentation as a seqence-to-sequence generation problem, and a reccurent nueral networks (RNN)-based model SketchSegNet is presented to translate sequence of strokes into thier semantic part labels. In addition, for the first time a large-scale stroke-level sketch segmentation dataset is proposed, which is composed of 57K annotated free-hand human sketch selected from QuickDraw. Experimental results of stroke-level sketch segmentation on this novel dataset shows that our approach offers an average accuracy over 90% for stroke labeling.


title: The effect of exercise in clinically depressed adults: systematic review and meta-analysis of randomized controlled trials.

abstract: OBJECTIVE ::: To assess the effectiveness of exercise in adults with clinical depression. ::: ::: ::: DATA SOURCES ::: The databases CINAHL, Embase, Cochrane Database of Systematic reviews, Cochrane Controlled Trials Register, MEDLINE, and PsycINFO were searched (1806-2008) using medical subject headings (MeSH) and text word terms depression, depressive disorder and exercise, aerobic, non-aerobic, physical activity, physical fitness, walk*, jog*, run*, bicycling, swim*, strength, and resistance. ::: ::: ::: STUDY SELECTION ::: Randomized trials including adults with clinical depression according to any diagnostic system were included. ::: ::: ::: DATA EXTRACTION ::: Two investigators evaluated trials using a prepiloted structured form. ::: ::: ::: DATA SYNTHESIS ::: Thirteen trials were identified that fulfilled the inclusion criteria. Eight had adequate allocation concealment, 6 had a blinded outcome, and 5 used intention-to-treat analyses. The pooled standardized mean difference (SMD) calculated using a random-effects model was -0.40 (95% CI, -0.66 to -0.14), with evidence of heterogeneity between trials (I(2) = 57.2%, P = .005). There was an inverse association between duration of intervention and the magnitude of the association of exercise with depression (P = .002). No other characteristics were related to between-study heterogeneity. Pooled analysis of 5 trials with long-term follow-up (ie, that examined outcomes beyond the end of the intervention) suggested no long-term benefit (SMD, -0.01; 95% CI, -0.28 to 0.26), with no strong evidence of heterogeneity in this pooled analysis (I(2) = 23.4%, P = .27). There was no strong statistical evidence for small study bias (P > .27). Only 3 studies were assessed as high quality (adequately concealed random allocation, blinded outcome assessment, and intention-to-treat analysis). When we pooled results from these, the estimated beneficial effect of exercise was more modest (SMD, -0.19; 95% CI, -0.70 to 0.31) than the pooled result for all 13 studies, with no strong evidence of benefit. ::: ::: ::: CONCLUSIONS ::: Our results suggest a short-term effect of exercise on depression: on average, depression scores 0.4 of a standard deviation lower in clinically depressed patients randomly assigned to an exercise intervention at the end of that intervention compared to those randomly assigned to a none exercise group. There is little evidence of a long-term beneficial effect of exercise in patients with clinical depression.


title: Open Information Extraction for Spanish Language based on Syntactic Constraints

abstract: Open Information Extraction (Open IE) serves for the analysis of vast amounts of texts by extraction of assertions, or relations, in the form of tupleshargument 1; relation; argument 2i. Various approaches to Open IE have been designed to perform in a fast, unsupervised manner. All of them require language specific information for their implementation. In this work, we introduce an approach to Open IE based on syntactic constraints over POS tag sequences targeted at Spanish language. We describe the rules specific for Spanish language constructions and their implementation in EXTRHECH, an Open IE system for Spanish. We also discuss language-specific issues of implementation. We compare EXTRHECH’s performance with that of REVERB, a similar Open IE system for English, on a parallel dataset and show that these systems perform at a very similar level. We also compare EXTRHECH’s performance on a dataset of grammatically correct sentences against its performance on a dataset of random texts extracted from the Web, drastically different in their quality from the first dataset. The latter experiment shows robustness of EXTRHECH on texts from the Web.


Skin necrosis of the nasal ala after injection of dermal fillers.


title: Dynamic Video Stitching via Shakiness Removing

abstract: Stitching videos captured by hand-held mobile cameras can essentially enhance entertainment experience of ordinary users. However, such videos usually contain heavy shakiness and large parallax, which are challenging to stitch. In this paper, we propose a novel approach of video stitching and stabilization for videos captured by mobile devices. The main component of our method is a unified video stitching and stabilization optimization that computes stitching and stabilization simultaneously rather than does each one individually. In this way, we can obtain the best stitching and stabilization results relative to each other without any bias to one of them. To make the optimization robust, we propose a method to identify background of input videos, and also common background of them. This allows us to apply our optimization on background regions only, which is the key to handle large parallax problem. Since stitching relies on feature matches between input videos, and there inevitably exist false matches, we thus propose a method to distinguish between right and false matches, and encapsulate the false match elimination scheme and our optimization into a loop, to prevent the optimization from being affected by bad feature matches. We test the proposed approach on videos that are causally captured by smartphones when walking along busy streets, and use stitching and stability scores to evaluate the produced panoramic videos quantitatively. Experiments on a diverse of examples show that our results are much better than (challenging cases) or at least on par with (simple cases) the results of previous approaches.


title: Learning Effective and Interpretable Semantic Models using Non-Negative Sparse Embedding

abstract: In this paper, we introduce an application of matrix factorization to produce corpus-derived, distributional models of semantics that demonstrate cognitive plausibility. We find that word representations learned by Non-Negative Sparse Embedding (NNSE), a variant of matrix factorization, are sparse, effective, and highly interpretable. To the best of our knowledge, this is the first approach which yields semantic representation of words satisfying these three desirable properties. Though extensive experimental evaluations on multiple real-world tasks and datasets, we demonstrate the superiority of semantic models learned by NNSE over other state-of-the-art baselines.


title: POINT CLOUD NON LOCAL DENOISING USING LOCAL SURFACE DESCRIPTOR SIMILARITY

abstract: This article addresses the problem of denoising 3D data from LIDAR. It is a step often required to allow a good reconstruction of surfaces represented by point clouds. In this paper, we present an original algorithm inspired by a recent method developed by (Buades and Morel, 2005) in the field of image processing, the Non Local Denoising (NLD). With a local geometric descriptor, we look for points that have similarities in order to reduce noise while preserving the surface details. We describe local geometry by MLS surfaces and we use a local reference frame invariant by rotation for denoising points. We present our results on synthetic and real data.