title: Secure Image Encryption without Size Limitation Using Arnold Transform and Random Strategies

abstract: Encryption is an efficient way to protect the contents of digital media. Arnold transform is a significant technique of image encryption, but has weaknesses in security and applications to images of any size. To solve these problems, we propose an image encryption scheme using Arnold transform and random strategies. It is achieved by dividing the image into random overlapping square blocks, generating random iterative numbers and random encryption order, and scrambling pixels of each block using Arnold transform. Experimental results show that the proposed encryption scheme is robust and secure. It has no size limitation, indicating the application to any size images.


Privacy preserving biometrics using partially homomorphic encryption


title: Text Stemming: Approaches, Applications, and Challenges

abstract: Stemming is a process in which the variant word forms are mapped to their base form. It is among the basic text pre-processing approaches used in Language Modeling, Natural Language Processing, and Information Retrieval applications. In this article, we present a comprehensive survey of text stemming techniques, evaluation mechanisms, and application domains. The main objective of this survey is to distill the main insights and present a detailed assessment of the current state of the art. The performance of some well-known rule-based and statistical stemming algorithms in different scenarios has been analyzed. In the end, we highlighted some open issues and challenges related to unsupervised statistical text stemming. This research work will help the researchers to select the most suitable text stemming technique in a specific application and will also serve as a guide to identify the areas that need attention from the research community.


title: TwinsCoin: A Cryptocurrency via Proof-of-Work and Proof-of-Stake

abstract: We design and implement TwinsCoin, the first cryptocurrency based on a provably secure and scalable public blockchain design using both proof-of-work and proof-of-stake mechanisms. Different from the proof-of-work based Bitcoin, our construction uses two types of resources, computing power and coins (i.e., stake). The blockchain in our system is more robust than that in a pure proof-of-work based system; even if the adversary controls the majority of mining power, we can still have the chance to secure the system by relying on honest stake. In contrast, Bitcoin blockchain will be insecure if the adversary controls more than 50% of mining power. Our design follows a recent provably secure proof-of-work/proof-of-stake hybrid blockchain[11]. In order to make our construction practical, we considerably enhance its design. In particular, we introduce a new strategy for difficulty adjustment in the hybrid blockchain and provide a theoretical analysis of it. We also show how to construct a light client for proof-of-stake cryptocurrencies and evaluate the proposal practically. We implement our new design. Our implementation uses a recent modular development framework for blockchains, called Scorex. It allows us to change only certain parts of an application leaving other codebase intact. In addition to the blockchain implementation, a testnet is deployed. Source code is publicly available.


title: Learning Quick Fixes from Code Repositories

abstract: Code analyzers such as Error Prone and FindBugs detect code patterns symptomatic of bugs, performance issues, or bad style. These tools express patterns as quick fixes that detect and rewrite unwanted code. However, it is difficult to come up with new quick fixes and decide which ones are useful and frequently appear in real code. We propose to rely on the collective wisdom of programmers and learn quick fixes from revision histories in software repositories. We present REVISAR, a tool for discovering common Java edit patterns in code repositories. Given code repositories and their revision histories, REVISAR (i) identifies code edits from revisions and (ii) clusters edits into sets that can be described using an edit pattern. The designers of code analyzers can then inspect the patterns and add the corresponding quick fixes to their tools. We ran REVISAR on nine popular GitHub projects, and it discovered 89 useful edit patterns that appeared in 3 or more projects. Moreover, 64% of the discovered patterns did not appear in existing tools. We then conducted a survey with 164 programmers from 124 projects and found that programmers significantly preferred eight out of the nine of the discovered patterns. Finally, we submitted 16 pull requests applying our patterns to 9 projects and, at the time of the writing, programmers accepted 6 (60%) of them. The results of this work aid toolsmiths in discovering quick fixes and making informed decisions about which quick fixes to prioritize based on patterns programmers actually apply in practice.


title: The role of aesthetic, cultural, utilitarian and branding factors in young Chinese consumers' repurchase intention of smartphone brands

abstract: Despite the exponential growth of smartphone consumption, to date, very few studies have investigated the factors that influence consumers' repurchase intention of smartphone brands. China has become the world's largest consumer markets for smartphones, therefore understanding young Chinese consumers' repurchase intention in the smartphone market is of crucial importance to smartphone companies. A preliminary qualitative study based on 30 face-to-face interviews has led to the development of a new conceptual framework including aesthetic (design appeal), functional (perceived quality), brand value (brand popularity), social (subjective norm) and cultural influences (mianzi). The newly developed framework has been tested through partial least squares structural equation modeling with a sample of 321 young Chinese smartphone users. The results show that young Chinese customer's smartphone repurchase intention is mainly determined by mianzi, perceived quality, brand popularity, and design appeal. Furthermore, findings also highlight that subjective norm, perceived quality and design appeal affect Chinese people's mianzi. Display Omitted This study investigates young Chinese consumers' repurchase intention of smartphone.The study adopts mixed method (interviews+quantitative analysis, PLS-SEM).Develops a new framework including aesthetic, functional, branding, socio-cultural influences.Subjective norm, perceived quality and design appeal influence young Chinese consumers' mianzi.Mianzi, perceived quality, design appeal predict repurchase intentions.


title: Learning Visually Grounded Sentence Representations

abstract: We introduce a variety of models, trained on a supervised image captioning corpus to predict the image features for a given caption, to perform sentence representation grounding. We train a grounded sentence encoder that achieves good performance on COCO caption and image retrieval and subsequently show that this encoder can successfully be transferred to various NLP tasks, with improved performance over text-only models. Lastly, we analyze the contribution of grounding, and show that word embeddings learned by this system outperform non-grounded ones.


title: Evaluation of nurse's attitudes toward adult patients of size

abstract: Abstract Background Obesity is a growing epidemic in the United States, with waistlines expanding (overweight) for almost 66% of the population (National Health and Nutrition Examination Survey 1999–2004). The attitude of society, which includes healthcare providers, toward people of size has traditionally been negative, regardless of their own gender, age, experience, and occupation. The purpose of the present study was to determine whether bariatric sensitivity training could improve nursing attitudes and beliefs toward adult obese patients and whether nurses' own body mass index (BMI) affected their attitude and belief scores. Methods An on-line survey was conducted of nursing attitudes and beliefs regarding adult obese patients. The responses were compared between 1 hospital that offered bariatric sensitivity training and 1 that did not. The primary study measures were 2 scales that have been validated to assess weight bias: Attitudes Toward Obese Persons (ATOP) and Beliefs Against Obese Persons (BAOP). The primary outcome measures were the scores derived from the ATOP and BAOP scales. Results Data were obtained from 332 on-line surveys, to which 266 nurses responded with complete data, 145 from hospital 1 (intervention) and 121 from hospital 2 (control). The mean ATOP scores for hospital 1 were modestly greater than those for hospital 2 (18.0 versus 16.1, P = .03). However, no differences were found between the 2 hospitals for the mean BAOP scores (67.1 versus 67.1, P = .86). No statistically significant differences were found between the 2 hospitals among the BMI groups for either ATOP or BAOP. Within each hospital, no statistically significant trend was found among the BMI groups for either ATOP or BAOP. The association of BMI with the overall ATOP ( r = .13, P = .04) and BOAP ( r = .12, P = .05) scores was very weak, although marginally significant. The association of the overall ATOP score with the BAOP score was weak, although significant ( r = .26, P Conclusion Annual bariatric sensitivity training might improve nursing attitudes toward obese patients, but it does not improve nursing beliefs, regardless of the respondent's BMI.


title: Augmented Reality Projects in Automotive and Aerospace Industry

abstract: In 2003 the International Symposium on Mixed and Augmented Reality (ISMAR) was accompanied by a workshop on Potential Industrial Applications (PIA). The organizers wisely called it "potential" because the real use of augmented reality (AR) in an industrial context is still in its infancy. Our own experience in this field clearly supports this viewpoint. We have been actively involved in the research, development and deployment of AR systems in the automotive, aviation and astronautics industries for more than five years and have developed and implemented AR systems in a wide variety of environments. In this paper we have selected ten AR projects from those we have managed and implemented in the past to examine the main challenges faced and to share some of the lessons learned. We will conclude with some guidelines for successfully deploying AR in an industrial context.


title: Electrophysiological Evidence for Two Steps in Syntactic Analysis: Early Automatic and Late Controlled Processes

abstract: In this study we examined the properties of the processes involved in the structural analysis of sentences using eventrelated brain potential measures (ERP). Previous research had shown two ERP components to correlate with phrase structure violations: an early left anterior negativity (ELAN), which is assumed to reflect first-pass parsing processes, and a late parietally distributed positivity (P600), assumed to reflect second-pass parsing processes. We hypothesized that the first-pass parsing processes are highly automatic, whereas second-pass parsing processes are more controlled. To test this hypothesis we varied the proportion of correct sentences and sentences containing phrase structure violations with incorrect sentences being either of a low (20p violation) or a high (80p violation) proportion. Results showed that the early left anterior negativity was elicited and equally pronounced under both proportion conditions. By contrast, the late positivity was elicited for a low proportion of incorrect sentences only.This data pattern suggests that first-pass parsing processes are automatic, whereas second-pass parsing processes are under participants' strategic control.


title: Estimation of omnidirectional camera model from epipolar geometry

abstract: We generalize the method of simultaneous linear estimation of multiple view geometry and lens distortion, introduced by Fitzgibbon at CVPR 2001, to an omnidirectional (angle of view larger than 180/spl deg/) camera. The perspective camera is replaced by a linear camera with a spherical retina and a nonlinear mapping of the sphere into the image plane. Unlike the previous distortion-based models, the new camera model is capable to describe a camera with an angle of view larger than 180/spl deg/ at the cost of introducing only one extra parameter. A suitable linearization of the camera model and of the epipolar constraint is developed in order to arrive at a quadratic eigenvalue problem for which efficient algorithms are known. The lens calibration is done from automatically established image correspondences only. Besides rigidity, no assumptions about the scene are made (e.g. presence of a calibration object). We demonstrate the method in experiments with Nikon FC-E8 fish-eye converter for COOLPIX digital camera. In practical situations, the proposed method allows to incorporate the new omnidirectional camera model into RANSAC - a robust estimation technique.


title: Universal serial bus enhances virtual instrument based distributed power monitoring

abstract: The Universal Serial Bus (USB) links a distributed multi-circuit power monitoring system to its virtual instrument (VI) host computer. The distributed interconnection provides enhanced system performance at reduced cost, since the USB host controller is a standard function of up-to-date personal computers (PCs) and low-cost high-performance USB hardware is readily available. The 12 Mbps, recently updated to 480 Mbps, transfer rate of USB improves data transmission capability and boosts the real-time performance of the whole VI monitoring system when compared with conventional serial communications. In addition, the USB dynamic attachment and removal feature allows each meter to be connected or disconnected from its VI host computer without any system interruption. However, the complete VI solutions are not currently supported by any USB hosts and devices. This paper presents an innovative USB VI strategy to implement integrated power monitoring applications and shows its superior performance.


title: Precise timing analysis for direct-mapped caches

abstract: Safety-critical systems require guarantees on their worst-case execution times. This requires modelling of speculative hardware features such as caches that are tailored to improve the average-case performance, while ignoring the worst case, which complicates the Worst Case Execution Time (WCET) analysis problem. Existing approaches that precisely compute WCET suffer from state-space explosion. In this paper, we present a novel cache analysis technique for direct-mapped instruction caches with the same precision as the most precise techniques, while improving analysis time by up to 240 times. This improvement is achieved by analysing individual control points separately, and carrying out optimisations that are not possible with existing techniques.


title: Graph Analytics-Lessons Learned and Challenges Ahead

abstract: Lessons learned from developing four graph analytics applications reveal good research practices and grand challenges for future research. The application domains include electric-power-grid analytics, social-network and citation analytics, text and document analytics, and knowledge domain analytics.


title: A practical approach to HFI based sensorless control of PM-assisted synchronous reluctance machines applied to EVs and HEVs

abstract: Sensorless control is a promising alternative for controlling Electric Vehicle (EV) and Hybrid Electric Vehicle (HEV) propulsion systems without the need of complex devices, such as resolvers or encoders. As the usage of a physical sensor is avoided, this allows significant cost reductions of the drive, and the reliability of the system is also improved. EVs require an operation range from standstill to high speeds. At low speeds, the back-EMF of the electric machine is low, and signal injection techniques are required in order to estimate the position and speed of the machine. This paper presents practical implementation details of the High Frequency Injection (HFI) technique, giving special attention to signal processing, offset compensation due to filtering delays and robust speed estimation. The approach is validated in an automotive Permanent Magnet Assisted Synchronous Reluctance Machine (PM-assisted SynRM) of 51 kW.


Best Practices in Testing and Reporting Performance of Biometric Devices Produced for the Biometrics Working Group By


title: Cache Coherence Protocol and Memory Performance of the Intel Haswell-EP Architecture

abstract: A major challenge in the design of contemporary microprocessors is the increasing number of cores in conjunction with the persevering need for cache coherence. To achieve this, the memory subsystem steadily gains complexity that has evolved to levels beyond comprehension of most application performance analysts. The Intel Has well-EP architecture is such an example. It includes considerable advancements regarding memory hierarchy, on-chip communication, and cache coherence mechanisms compared to the previous generation. We have developed sophisticated benchmarks that allow us to perform in-depth investigations with full memory location and coherence state control. Using these benchmarks we investigate performance data and architectural properties of the Has well-EP micro-architecture, including important memory latency and bandwidth characteristics as well as the cost of core-to-core transfers. This allows us to further the understanding of such complex designs by documenting implementation details the are either not publicly available at all, or only indirectly documented through patents.


title: A Simple Edit-Time Metaobject Protocol

abstract: present a simple edit-time metaobject protocol (ET- MOP) which runs as part of a code editor and enables meta- data annotations to customize the rendering and editing of code. The protocol is layered, so that simple render/edit customizations are easy to implement, while more substan- tial customizations are still manageable. Experiments with a prototype implementation of the proto- col as an Eclipse plug-in show that the ETMOP is flexible enough to allow easy customization of the rendering and editing of a number of annotations being used in current practice. The flexibility and performance of the prototype suggest that the ETMOP approach is viable and warrants further study.


title: Automated test input generation for Android: are we really there yet in an industrial case?

abstract: Given the ever increasing number of research tools to automatically generate inputs to test Android applications (or simply apps), researchers recently asked the question "Are we there yet?" (in terms of the practicality of the tools). By conducting an empirical study of the various tools, the researchers found that Monkey (the most widely used tool of this category in industrial practices) outperformed all of the research tools that they studied. In this paper, we present two significant extensions of that study. First, we conduct the first industrial case study of applying Monkey against WeChat, a popular messenger app with over 762 million monthly active users, and report the empirical findings on Monkey's limitations in an industrial setting. Second, we develop a new approach to address major limitations of Monkey and accomplish substantial code-coverage improvements over Monkey, along with empirical insights for future enhancements to both Monkey and our approach.


title: Serratus Anterior Muscle Activity During Selected Rehabilitation Exercises

abstract: The purpose of this study was to document the electromyographic activity and applied resistance associated with eight scapulohumeral exercises performed below shoulder height. We used this information to design a continuum of serratus anterior muscle exercises for progressive rehabilitation or training. Five muscles in 20 healthy subjects were studied with surface electrodes for the following exercises: shoulder extension, forward punch, serratus anterior punch, dynamic hug, scaption (with external rotation), press-up, push-up plus, and knee push-up plus. Electromyographic data were collected from the middle serratus anterior, upper and middle trapezius, and anterior and posterior deltoid muscles. Each exercise was partitioned into phases of increasing and decreasing force and analyzed for average and peak electromyographic amplitude. Resistance was provided by body weight, an elastic cord, or dumbbells. The serratus anterior punch, scaption, dynamic hug, knee push-up plus, and push-up plus exercises consistently elicited serratus anterior muscle activity greater than 20% maximal voluntary contraction. The exercises that maintained an upwardly rotated scapula while accentuating scapular protraction, such as the push-up plus and the newly designed dynamic hug, elicited the greatest electromyographic activity from the serratus anterior muscle. Normal shoulder motion results from a complex interplay of the scapulohumeral, acromioclavicular, sternoclavicular, and scapulothoracic articulations. The coordination of these articulations provides the shoulder with an ample range of motion necessary for overhead sporting activities. Proper positioning of the humerus in the glenoid cavity, known as scapulohumeral rhythm, 6 is critical to the proper function of the glenohumeral joint during overhead motion. A disturbance in normal scapulohumeral rhythm may cause inappropriate positioning of the glenoid relative to the humeral head, resulting in injury. 16, 18, 22 One of the primary muscles responsible for maintaining normal rhythm and shoulder motion is the serratus anterior. 8, 32 Lack of strength or endurance in this muscle


Effective shared representations with Multitask Learning for Community Question Answering


Analysis of Data Mining Techniques for Agriculture Data


title: Competence measure in social networks

abstract: The current research aims to gain insight on user competence in detecting security threats in the context of online social networks (OSNs) and investigates the multidimensional space that determines this user competence level. The role of user competence and its dimensions in facilitating the detection of online threats is still a controversial topic in the information security field. The dimensions used to measure the concept are self-efficacy, security awareness, privacy awareness, and cybercrime experience. The scales used to measure those factors can determine the level of user competence in evaluating risks associated with social network usage. The measurement scales employed here have been validated using an item-categorization approach that, to our knowledge, has never before been used in information security research. The result of this study provides evidence for the suitability and validity of the user competence dimensions and associated measurement scales.


title: A Meta-Learning Perspective on Cold-Start Recommendations for Items

abstract: Matrix factorization (MF) is one of the most popular techniques for product recommendation, but is known to suffer from serious cold-start problems. Item cold-start problems are particularly acute in settings such as Tweet recommendation where new items arrive continuously. In this paper, we present a meta-learning strategy to address item cold-start when new items arrive continuously. We propose two deep neural network architectures that implement our meta-learning strategy. The first architecture learns a linear classifier whose weights are determined by the item history while the second architecture learns a neural network whose biases are instead adjusted. We evaluate our techniques on the real-world problem of Tweet recommendation. On production data at Twitter, we demonstrate that our proposed techniques significantly beat the MF baseline and also outperform production models for Tweet recommendation.


title: Simon and Speck: Block Ciphers for the Internet of Things

abstract: The U.S. National Security Agency (NSA) developed the Simon and Speck families of lightweight block ciphers as an aid for securing applications in very constrained environments where AES may not be suitable. This paper summarizes the algorithms, their design rationale, along with current cryptanalysis and implementation results.


title: RRED Indices: Reduced Reference Entropic Differencing for Image Quality Assessment

abstract: We study the problem of automatic “reduced-reference” image quality assessment (QA) algorithms from the point of view of image information change. Such changes are measured between the reference- and natural-image approximations of the distorted image. Algorithms that measure differences between the entropies of wavelet coefficients of reference and distorted images, as perceived by humans, are designed. The algorithms differ in the data on which the entropy difference is calculated and on the amount of information from the reference that is required for quality computation, ranging from almost full information to almost no information from the reference. A special case of these is algorithms that require just a single number from the reference for QA. The algorithms are shown to correlate very well with subjective quality scores, as demonstrated on the Laboratory for Image and Video Engineering Image Quality Assessment Database and the Tampere Image Database. Performance degradation, as the amount of information is reduced, is also studied.


title: - 11 July 2007 Using Cartoons to Teach Internet Security by Sukamol Srikwan

abstract: While good user education can hardly secure a system, we believe that poor user education can put it at serious risk. The current problem of online fraud is exasperated by the fact that most users make security decisions, such as whether to install a given piece of software or not, based on a very rudimentary understanding of risk. We describe the design principles behind SecurityCartoon.com, the first cartoon-based approach aimed at improving the understanding of risk among typical Internet users. We argue why an approach like ours is likely to produce better long-term effects than currently practiced educational efforts with the same general goals. This belief is based on the apparent difference between our approach and currently used alternatives. At the heart of these differences are the four guiding principles of our approach: (1) A research driven content selection, according to which we select educational messages based on user studies; (2) accessibility of the material, to reach and maintain a large readership; (3) user immersion in the material, based on repetitions on a theme; and (4) adaptability to a changing threat.


title: Lensless computational imaging through deep learning

abstract: Deep learning has been proven to yield reliably generalizable answers to numerous classification and decision tasks. Here, we demonstrate for the first time, to our knowledge, that deep neural networks (DNNs) can be trained to solve inverse problems in computational imaging. We experimentally demonstrate a lens-less imaging system where a DNN was trained to recover a phase object given a raw intensity image recorded some distance away.


ChunkCast: An Anycast Service for Large Content Distribution


title: Reverse logistics network design for the collection of End-of-Life Vehicles in Mexico

abstract: This paper seeks to describe several features of establishing a closed-loop supply chain for the collection of End-of-Life Vehicles in Mexico. To address this task, the problem is handled through Reverse Logistics and is modelled through an Uncapacitated Facility Location Problem. The solution of this model is obtained using software SITATIONÂ©. Furthermore, this work also presents a brief description of the current Mexican ELV management system and the future trends in ELV generation in Mexico. The main result is the configuration of three collection networks within Mexico, which correspond to three possible scenarios that consider 100%, 90% and 75%, respectively, of collection coverage. Regions with high ELV generation are identified as well as relevant factors affecting total costs in the reverse supply chain.


title: Generalized Linear Discriminant Analysis: A Unified Framework and Efficient Model Selection

abstract: High-dimensional data are common in many domains, and dimensionality reduction is the key to cope with the curse-of-dimensionality. Linear discriminant analysis (LDA) is a well-known method for supervised dimensionality reduction. When dealing with high-dimensional and low sample size data, classical LDA suffers from the singularity problem. Over the years, many algorithms have been developed to overcome this problem, and they have been applied successfully in various applications. However, there is a lack of a systematic study of the commonalities and differences of these algorithms, as well as their intrinsic relationships. In this paper, a unified framework for generalized LDA is proposed, which elucidates the properties of various algorithms and their relationships. Based on the proposed framework, we show that the matrix computations involved in LDA-based algorithms can be simplified so that the cross-validation procedure for model selection can be performed efficiently. We conduct extensive experiments using a collection of high-dimensional data sets, including text documents, face images, gene expression data, and gene expression pattern images, to evaluate the proposed theories and algorithms.


title: Obesity prevention: the case for action

abstract: Contents1. Obesity and the global burden of disease2. Prevalence, trends and economics3. Targets for action4. The action agenda5. Potential solutions6. Tracking outcomes7. Glossary of terms8. Key references and further reading9. Case studies: Available on Nature website at www.naturesj.com/ijo/index.html


title: How Auto-Encoders Could Provide Credit Assignment in Deep Networks via Target Propagation

abstract: We propose to exploit {\em reconstruction} as a layer-local training signal for deep learning. Reconstructions can be propagated in a form of target propagation playing a role similar to back-propagation but helping to reduce the reliance on derivatives in order to perform credit assignment across many levels of possibly strong non-linearities (which is difficult for back-propagation). A regularized auto-encoder tends produce a reconstruction that is a more likely version of its input, i.e., a small move in the direction of higher likelihood. By generalizing gradients, target propagation may also allow to train deep networks with discrete hidden units. If the auto-encoder takes both a representation of input and target (or of any side information) in input, then its reconstruction of input representation provides a target towards a representation that is more likely, conditioned on all the side information. A deep auto-encoder decoding path generalizes gradient propagation in a learned way that can could thus handle not just infinitesimal changes but larger, discrete changes, hopefully allowing credit assignment through a long chain of non-linear operations. In addition to each layer being a good auto-encoder, the encoder also learns to please the upper layers by transforming the data into a space where it is easier to model by them, flattening manifolds and disentangling factors. The motivations and theoretical justifications for this approach are laid down in this paper, along with conjectures that will have to be verified either mathematically or experimentally, including a hypothesis stating that such auto-encoder mediated target propagation could play in brains the role of credit assignment through many non-linear, noisy and discrete transformations.


title: Containers and Clusters for Edge Cloud Architectures -- A Technology Review

abstract: Cloud technology is moving towards more distribution across multi-clouds and the inclusion of various devices, as evident through IoT and network integration in the context of edge cloud and fog computing. Generally, lightweight virtualisation solutions are beneficial for this architectural setting with smaller, but still virtualised devices to host application and platform services, and the logistics required to manage this. Containerisation is currently discussed as a lightweight virtualisation solution. In addition to having benefits over traditional virtual machines in the cloud in terms of size and flexibility, containers are specifically relevant for platform concerns typically dealt with Platform-as-a-Service (PaaS) clouds such as application packaging and orchestration. For the edge cloud environment, application and service orchestration can help to manage and orchestrate applications through containers as an application packaging mechanism. We review edge cloud requirements and discuss the suitability container and cluster technology of that arise from having to facilitate applications through distributed multi-cloud platforms build from a range of networked nodes ranging from data centres to small devices, which we refer to here as edge cloud.


title: Mapping flagellar genes in Chlamydomonas using restriction fragment length polymorphisms

abstract: To correlate cloned nuclear DNA sequences with previously characterized mutations in Chlamydomonas and, to gain insight into the organization of its nuclear genome, we have begun to map molecular markers using restriction fragment length polymorphisms (RFLPs). A Chlamydomonas reinhardtii strain (CC-29) containing phenotypic markers on nine of the 19 linkage groups was crossed to the interfertile species Chlamydomonas smithii. DNA from each member of 22 randomly selected tetrads was analyzed for the segregation of RFLPs associated with cloned genes detected by hybridization with radioactive DNA probes. The current set of markers allows the detection of linkage to new molecular markers over approximately 54% of the existing genetic map. This study focused on mapping cloned flagellar genes and genes whose transcripts accumulate after deflagellation. Twelve different molecular clones have been assigned to seven linkage groups. The α-1 tubulin gene maps to linkage group III and is linked to the genomic sequence homologous to pcf6-100, a cDNA clone whose corresponding transcript accumulates after deflagellation. The α-2 tubulin gene maps to linkage group IV. The two β-tubulin genes are linked, with the β-1 gene being approximately 12 cM more distal from the centromere than the β-2 gene. A clone corresponding to a 73-kD dynein protein maps to the opposite arm of the same linkage group. The gene corresponding to the cDNA clone pcf6-187, whose mRNA accumulates after deflagellation, maps very close to the tightly linked pf-26 and pf-1 mutations on linkage group V.


Gate Diffusion Input: A technique for fast digital circuits (implemented on 180 nm technology)


title: New explicit conditions of elliptic curve traces for FR-reduction

abstract: Elliptic curve cryptosystems([19],[25]) are based on the elliptic curve discrete logarithm problem(ECDLP). If elliptic curve cryptosystems avoid FRreduction([11],[17]) and anomalous elliptic curve over Fq ([3], [33], [35]), then with current knowledge we can construct elliptic curve cryptosystems over a smaller definition field. ECDLP has an interesting property that the security deeply depends on elliptic curve traces rather than definition fields, which does not occur in the case of the discrete logarithm problem(DLP). Therefore it is important to characterize elliptic curve traces explicitly from the security point of view. As for FR-reduction, supersingular elliptic curves or elliptic curve E/Fq with trace 2 have been reported to be vulnerable. However unfortunately these have been only results that characterize elliptic curve traces explicitly for FRand MOV-reductions. More importantly, the secure trace against FR-reduction has not been reported at all. Elliptic curves with the secure trace means that the reduced extension degree is always higher than a certain level. In this paper, we aim at characterizing elliptic curve traces by FR-reduction and investigate explicit conditions of traces vulnerable or secure against FR-reduction. We show new explicit conditions of elliptic curve traces for FRreduction. We also present algorithms to construct such elliptic curves, which have relation to famous number theory problems. key words: elliptic curve cryptosystems, trace, FRreduction


title: A 990-$\mu\hbox{W}$ 1.6-GHz PLL Based on a Novel Supply-Regulated Active-Loop-Filter VCO

abstract: A low-power 1.6-GHz phase-locked loop (PLL) based on a novel supply-regulated voltage-controlled oscillator (SR-VCO) including an active-loop filter (ALF) is realized. In this PLL, an active RC filter is combined with SR-VCO, achieving the advantages of ALF PLL without penalties in power consumption or phase noises. The PLL has measured rms jitter of 4.82 ps, and its core consumes 990 μW from 1-V supply while the chip area is 420 × 570 μm2 including on-chip passive components required for the ALF and the supply regulator.


title: A Framework for the Structural Analysis of REST APIs

abstract: Today, REST APIs have established as a means for realizing distributed systems and are supposed to gain even more importance in the context of Cloud Computing, Internet of Things, and Microservices. Nevertheless, many existing REST APIs are known to be not well-designed, resulting in the absence of desirable quality attributes that truly RESTful systems entail. Although existing analysis show, that many REST APIs are not fully REST compliant, it is still an open issue how to improve this deficit and where to start. In this work, we introduce a framework for the structural analysis of REST APIs based on their description documents, as this allows for a comprehensive, well-structured analysis approach that also includes analyzing the corresponding API description languages. A first validation builds on a set of 286 real world API descriptions available as Swagger documents, and comprises their transformation into a canonical metamodel for REST APIs as well as a metrics-based analysis and discussion of their structural characteristics with respect to compliance with the REST architectural style.


title: Canto: A Hypertext Data Model

abstract: SUMMARY The Canto hypertext data model is characterized by a hierarchical schema mechanism that allows a predetermined, open-ended schema to be embedded in the hyperdocument. Canto uses two types of nodes: concept nodes, which provide organizational structure, and information nodes, which contain text and other data. The operations provided by the Canto Data Model are defined formally using the Z specification language. The Canto Schema Language gives the hypertext designer access to these operations. We show that the operations defined in Canto preserve properties of accessibility and data integrity. We evaluated Canto with a controlled experiment involving human users. These tests showed that a hypertext system developed with Canto was easier to use than an otherwise similar system that did not employ a schema mechanism. Several applications have been developed using Canto. One such application, which we describe in some detail, involves the use of Canto to teach students the skill of program reading.


title: Scalable Anomaly Ranking of Attributed Neighborhoods

abstract: Given a graph with node attributes, what neighborhoods are anomalous? To answer this question, one needs a quality score that utilizes both structure and attributes. Popular existing measures either quantify the structure only and ignore the attributes (e.g., conductance), or only consider the connectedness of the nodes inside the neighborhood and ignore the cross-edges at the boundary (e.g., density). ::: In this work we propose normality, a new quality measure for attributed neighborhoods. Normality utilizes structure and attributes together to quantify both internal consistency and external separability. It exhibits two key advantages over other measures: (1) It allows many boundary-edges as long as they can be "exonerated"; i.e., either (i) are expected under a null model, and/or (ii) the boundary nodes do not exhibit the subset of attributes shared by the neighborhood members. Existing measures, in contrast, penalize boundary edges irrespectively. (2) Normality can be efficiently maximized to automatically infer the shared attribute subspace (and respective weights) that characterize a neighborhood. This efficient optimization allows us to process graphs with millions of attributes. ::: We capitalize on our measure to present a novel approach for Anomaly Mining of Entity Neighborhoods (AMEN). Experiments on real-world attributed graphs illustrate the effectiveness of our measure at anomaly detection, outperforming popular approaches including conductance, density, OddBall, and SODA. In addition to anomaly detection, our qualitative analysis demonstrates the utility of normality as a powerful tool to contrast the correlation between structure and attributes across different graphs.


title: System Z: a natural ordering of defaults with tractable applications to default reasoning

abstract: Recent progress towards unifying the probabilistic and preferential models semantics for non-monotonic reasoning has led to a remarkable observation: Any consistent system of default rules imposes an unambiguous and natural ordering on these rules which, to emphasize its simple and basic character, we term "Z-ordering." This ordering can be used with various levels of refinement, to prioritize conflicting arguments, to rank the degree of abnormality of states of the world, and to define plausible consequence relationships. This paper defines the Z-ordering, briefly mentions its semantical origins, and illustrates two simple entailment relationships induced by the ordering. Two extensions are then described, maximum-entropy and conditional entailment, which trade in computational simplicity for semantic refinements.


title: The role of anterior insular cortex in social emotions

abstract: Functional neuroimaging investigations in the fields of social neuroscience and neuroeconomics indicate that the anterior insular cortex (AI) is consistently involved in empathy, compassion, and interpersonal phenomena such as fairness and cooperation. These findings suggest that AI plays an important role in social emotions, hereby defined as affective states that arise when we interact with other people and that depend on the social context. After we link the role of AI in social emotions to interoceptive awareness and the representation of current global emotional states, we will present a model suggesting that AI is not only involved in representing current states, but also in predicting emotional states relevant to the self and others. This model also proposes that AI enables us to learn about emotional states as well as about the uncertainty attached to events, and implies that AI plays a dominant role in decision making in complex and uncertain environments. Our review further highlights that dorsal and ventro-central, as well as anterior and posterior subdivisions of AI potentially subserve different functions and guide different aspects of behavioral regulation. We conclude with a section summarizing different routes to understanding other people’s actions, feelings and thoughts, emphasizing the notion that the predominant role of AI involves understanding others’ feeling and bodily states rather than their action intentions or abstract beliefs.


title: An Adaptive Mixed Reality Training System for Stroke Rehabilitation

abstract: This paper presents a novel mixed reality rehabilitation system used to help improve the reaching movements of people who have hemiparesis from stroke. The system provides real-time, multimodal, customizable, and adaptive feedback generated from the movement patterns of the subject's affected arm and torso during reaching to grasp. The feedback is provided via innovative visual and musical forms that present a stimulating, enriched environment in which to train the subjects and promote multimodal sensory-motor integration. A pilot study was conducted to test the system function, adaptation protocol and its feasibility for stroke rehabilitation. Three chronic stroke survivors underwent training using our system for six 75-min sessions over two weeks. After this relatively short time, all three subjects showed significant improvements in the movement parameters that were targeted during training. Improvements included faster and smoother reaches, increased joint coordination and reduced compensatory use of the torso and shoulder. The system was accepted by the subjects and shows promise as a useful tool for physical and occupational therapists to enhance stroke rehabilitation.


title: Permission based Android security: Issues and countermeasures

abstract: Abstract Android security has been a hot spot recently in both academic research and public concerns due to numerous instances of security attacks and privacy leakage on Android platform. Android security has been built upon a permission based mechanism which restricts accesses of third-party Android applications to critical resources on an Android device. Such permission based mechanism is widely criticized for its coarse-grained control of application permissions and difficult management of permissions by developers, marketers, and end-users. In this paper, we investigate the arising issues in Android security, including coarse granularity of permissions, incompetent permission administration, insufficient permission documentation, over-claim of permissions, permission escalation attack, and TOCTOU (Time of Check to Time of Use) attack. We illustrate the relationships among these issues, and investigate the existing countermeasures to address these issues. In particular, we provide a systematic review on the development of these countermeasures, and compare them according to their technical features. Finally, we propose several methods to further mitigate the risk in Android security.


title: Storing and Indexing Massive RDF Datasets

abstract: The resource description framework (RDF for short) provides a flexible method for modeling information on the Web [34, 40]. All data items in RDF are uniformly represented as triples of the form (subject, predicate, object), sometimes also referred to as (subject, property, value)triples.


title: Efficient Garbling from a Fixed-Key Blockcipher

abstract: We advocate schemes based on fixed-key AES as the best route to highly efficient circuit-garbling. We provide such schemes making only one AES call per garbled-gate evaluation. On the theoretical side, we justify the security of these methods in the random-permutation model, where parties have access to a public random permutation. On the practical side, we provide the Just Garble system, which implements our schemes. Just Garble evaluates moderate-sized garbled-circuits at an amortized cost of 23.2 cycles per gate (7.25 nsec), far faster than any prior reported results.


title: Layer-Wise Floorplan Extraction for Automatic Urban Building Reconstruction

abstract: Urban building reconstruction is an important step for urban digitization and realisticvisualization. In this paper, we propose a novel automatic method to recover urban building geometry from 3D point clouds. The proposed method is suitable for buildings composed of planar polygons and aligned with the gravity direction, which are quite common in the city. Our key observation is that the building shapes are usually piecewise constant along the gravity direction and determined by several dominant shapes. Based on this observation, we formulate building reconstruction as an energy minimization problem under the Markov Random Field (MRF) framework. Specifically, point clouds are first cutinto a sequence of slices along the gravity direction. Then, floorplans are reconstructed by extracting boundaries of these slices, among which dominant floorplans are extracted and propagated to other floors via MRF. To guarantee correct propagation, a new distance measurement for floorplans is designed, which first encodes floorplans into strings and then calculates distances between their corresponding strings. Additionally, an image based editing method is also proposed to recover detailed window structures. Experimental results on both synthetic and real data sets have validated the effectiveness of our method.


title: www.elsevier.com/locate/neucom EEG classification using generative independent component analysis

abstract: We present an application of independent component analysis (ICA) to the discrimination of mental tasks for EEG-based brain computer interface systems. ICA is most commonly used with EEG for artifact identification with little work on the use of ICA for direct discrimination of different types of EEG signals. By viewing ICA as a generative model, we can use Bayes' rule to form a classifier. We fit spatial filters and source distribution parameters simultaneously and investigate whether these are sufficiently informative to produce good results when compared to more traditional methods based on using temporal features as inputs to off-the-shelf classifiers. Experiments suggest that state-of-the-art results may indeed be found without explicitly using temporal features. We extend the method to using a mixture of ICA models, consistent with the assumption that subjects may have more than one approach to thinking about a specific mental task.


title: A Survey on Recent Advances in Organic Visible Light Communications

abstract: Visible light communication (VLC) employs light emitting diodes (LEDs) to provide illumination and data communications simultaneously. Organic LEDs (OLEDs) employing small molecules and long-chain polymers PLEDs, have been gaining attention within the VLC research community due to their inherent advantages such as flexible substrates and low-cost manufacturing. However, the carrier mobility of organic semiconductors is much slower than the devices composed of metal alloys, such as gallium nitride, thus leading to a restriction in the OLED modulation bandwidth. The manufacturing processes, materials and the photoactive size of the devices can affect the raw bandwidth of OLEDs. To increase the transmission speeds, novel approaches have been proposed including equalization techniques, signalling schemes and the optimum driver circuits. The paper provides a survey on the evolution of OLED-based VLC systems, and the respective challenges and recent progresses.


title: Abstractive Summarization using Graph Based Methods

abstract: In the era of big data, resources are bountiful. Text summarization is a necessary and research area that minifies text such that repeated data are removed and important information is extracted and represented in the concise way which can help us to understand the information instantly. There are two approaches of text summarization mainly classified into Extractive and Abstractive. However, Extractive summary can be generated by extracting key features from the original document and fuse them into a shorter version but it has limitation that it never generates human level summaries for large documents. Abstractive Summarization(AS) understands the whole document and creates a concise and instructive version of a text document by extracting important information from the original document and generating new sentences using that information. Structured and Semantic are the two basic approaches of Abstractive Summarization. Graph based method from both the approaches are the one of the method used for summarization. Our work will mainly focus on the analysis details of how Graph based technique can be used to generate a condensed version of original document in abstractive summarization.


Calibration Drift of Clinical Prediction Models Across Modeling Methods.


title: Global Increases in Individualism

abstract: Individualism appears to have increased over the past several decades, yet most research documenting this shift has been limited to the study of a handful of highly developed countries. Is the world becoming more individualist as a whole? If so, why? To answer these questions, we examined 51 years of data on individualist practices and values across 78 countries. Our findings suggest that individualism is indeed rising in most of the societies we tested. Despite dramatic shifts toward greater individualism around the world, however, cultural differences remain sizable. Moreover, cultural differences are primarily linked to changes in socioeconomic development, and to a lesser extent to shifts in pathogen prevalence and disaster frequency.


title: An efficient cryptographic protocol verifier based on prolog rules

abstract: Abstract: We present a new automatic cryptographic protocol verifier based on a simple representation of the protocol by Prolog rules, and on a new efficient algorithm that determines whether a fact can be proved from these rules or not. This verifier proves secrecy properties of the protocols. Thanks to its use of unification, it avoids the problem of the state space explosion. Another advantage is that we do not need to limit the number of runs of the protocol to analyze it. We have proved the correctness of our algorithm, and have implemented it. The experimental results show that many examples of protocols of the literature, including Skeme [24], can be analyzed by our tool with very small resources: the analysis takes from less than 0.1 s for simple protocols to 23 s for the main mode of Skeme. It uses less than 2 Mb of memory in our tests.


title: How to effectively use topic models for software engineering tasks? an approach based on genetic algorithms

abstract: Information Retrieval (IR) methods, and in particular topic models, have recently been used to support essential software engineering (SE) tasks, by enabling software textual retrieval and analysis. In all these approaches, topic models have been used on software artifacts in a similar manner as they were used on natural language documents (e.g., using the same settings and parameters) because the underlying assumption was that source code and natural language documents are similar. However, applying topic models on software data using the same settings as for natural language text did not always produce the expected results. Recent research investigated this assumption and showed that source code is much more repetitive and predictable as compared to the natural language text. Our paper builds on this new fundamental finding and proposes a novel solution to adapt, configure and effectively use a topic modeling technique, namely Latent Dirichlet Allocation (LDA), to achieve better (acceptable) performance across various SE tasks. Our paper introduces a novel solution called LDA-GA, which uses Genetic Algorithms (GA) to determine a near-optimal configuration for LDA in the context of three different SE tasks: (1) traceability link recovery, (2) feature location, and (3) software artifact labeling. The results of our empirical studies demonstrate that LDA-GA is ableto identify robust LDA configurations, which lead to a higher accuracy on all the datasets for these SE tasks as compared to previously published results, heuristics, and the results of a combinatorial search.


title: Automatic Single Document Text Summarization Using Key Concepts in Documents

abstract: Many previous research studies on extractive text summarization consider a subset of words in a document as keywords and use a sentence ranking function that ranks sentences based on their similarities with the list of extracted keywords. But the use of key concepts in automatic text summarization task has received less attention in literature on summarization. The proposed work uses key concepts identified from a document for creating a summary of the document. We view single-word or multi-word keyphrases of a document as the important concepts that a document elaborates on. Our work is based on the hypothesis that an extract is an elaboration of the important concepts to some permissible extent and it is controlled by the given summary length restriction. In other words, our method of text summarization chooses a subset of sentences from a document that maximizes the important concepts in the final summary. To allow diverse information in the summary, for each important concept, we select one sentence that is the best possible elaboration of the concept. Accordingly, the most important concept will contribute first to the summary, then to the second best concept, and so on. To prove the effectiveness of our proposed summarization method, we have compared it to some state-of-the art summarization systems and the results show that the proposed method outperforms the existing systems to which it is compared.


title: Switching techniques for brushless DC motors

abstract: This paper presents a Simulink model for BLDC (Brushless DC) motors, using trapezoidal commutation. First, presents some common switching techniques for sensored Brushless DC motors type, using Hall Effect sensors, followed by a description of brushless DC motors' characteristics, its manufacturing structure, and their operation, torque curves comparison for several commutation techniques. Concluding with simulation for speed control, using trapezoidal commutation.


title: Client-Centric Benchmarking of Eventual Consistency for Cloud Storage Systems

abstract: Eventually-consistent key-value storage systems sacrifice the ACID semantics of conventional databases to achieve superior latency and availability. However, this means that client applications, and hence end-users, can be exposed to stale data. The degree of staleness observed depends on various tuning knobs set by application developers (customers of key-value stores) and system administrators (providers of key-value stores). Both parties must be cognizant of how these tuning knobs affect the consistency observed by client applications in the interest of both providing the best end-user experience and maximizing revenues for storage providers. Quantifying consistency in a meaningful way is a critical step toward both understanding what clients actually observe, and supporting consistency-aware service level agreements (SLAs) in next generation storage systems. This paper proposes a novel consistency metric called Gamma that captures client-observed consistency. This metric provides quantitative answers to questions regarding observed consistency anomalies, such as how often they occur and how bad they are when they do occur. We argue that Gamma is more useful and accurate than existing metrics. We also apply Gamma to benchmark the popular Cassandra key-value store. Our experiments demonstrate that Gamma is sensitive to both the workload and client-level tuning knobs, and is preferable to existing techniques which focus on worst-case behavior.


title: Relationship quality, community promotion and brand loyalty in virtual communities: Evidence from free software communities

abstract: The Internet has favored the growth of collaborative platforms where marketers and consumers interact to develop more engaging products and services. These platforms are usually centered in a specific brand/product and their members are linked by a shared admiration to that brand. This paper analyzes one of the most powerful online collaborative platforms, the free software (FS) case, which involves a lot of virtual communities developed around products such as Linux or Android, the new Google's mobile operating system. Our purpose is to determine some of the main antecedents and consequences of the consumer involvement in this type of communities. Results have shown that satisfaction with a virtual community may increase the level of consumer participation in that community. At the same time, a greater identification with the virtual community may increase indirectly the consumer participation thanks to the enhancement of his/her satisfaction with the community. We have also found positive and significant effects of consumer identification and participation on the level of community promotion. Finally, positive and significant effects of consumer participation and satisfaction with the community on loyalty to the FS were also found. These findings allow us to conclude some interesting managerial implications.


title: Oriented colloidal-crystal thin films of polystyrene spheres via spin coating

abstract: We developed a simple and inexpensive synthesis of a large-scale close-packed monolayer of polystyrene sphere arrays, which have a variety of applications. The influence of three step spin speeds, spinning time, solution quantity and relative humidity is studied in order to achieve a large area close-packed monolayer. A relatively high surface coverage and uniform monolayer of PS spheres in the range of 85%—90% are achieved by appropriate control of the preparative parameters. Also the effect of the oxygen plasma etching process on the reduction of PS spheres has been studied. We conclude that it can be useful in industrial applications, because of the fabrication speed, surface coverage, control over PS spheres and cost of the process.


Virtual Storage System based on Multiple Embedded Devices in IoT Environments


title: The relationship between trauma and beliefs about hearing voices: a study of psychiatric and non-psychiatric voice hearers

abstract: Background Cognitive models suggest that distress associated with auditory hallucinations is best understood in terms of beliefs about voices. What is less clear is what factors govern such beliefs. This study aimed to explore the way in which traumatic life events contribute towards beliefs about voices and any associated distress. ::: ::: Method The difference in the nature and prevalence of traumatic life events and associated psychological sequelae was compared in two groups of voice hearers: psychiatric voice hearers with predominantly negative beliefs about voices (PVH) and non-psychiatric voice hearers with predominantly positive beliefs about voices (NPVH). The data from the two groups were then combined in order to examine which factors could significantly account for the variance in beliefs about voices and therefore levels of distress. ::: ::: Results Both groups reported a high prevalence of traumatic life events although significantly more PVH reported trauma symptoms sufficient for a diagnosis of post-traumatic stress disorder (PTSD). Furthermore, significantly more PVH reported experiencing childhood sexual abuse. Current trauma symptoms (re-experiencing, avoidance and hyperarousal) were found to be a significant predictor of beliefs about voices. Trauma variables accounted for a significant proportion of the variance in anxiety and depression. ::: ::: Conclusions The results suggest that beliefs about voices may be at least partially understood in the context of traumatic life events.


Zero-Shot Learning Across Heterogeneous Overlapping Domains.


title: Aberrant recombination involving the granzyme locus occurs in Atm-/- T-cell lymphomas.

abstract: Ataxia telangiectasia (A-T) is an autosomal recessive disease caused by loss of function of the serine/threonine protein kinase ATM (ataxia telangiectasia mutated). A-T patients have a 250-700-fold increased risk of developing lymphomas and leukemias which are typically highly invasive and proliferative. In addition, a subset of adult acute lymphoblastic leukemias and aggressive B-cell chronic lymphocytic leukemias that occur in the general population show loss of heterozygosity for ATM. To define the specific role of ATM in lymphomagenesis, we studied T-cell lymphomas isolated from mice with mutations in ATM and/or p53 using cytogenetic analysis and mRNA transcriptional profiling. The analyses identified genes misregulated as a consequence of the amplifications, deletions and translocation events arising as a result of ATM loss. A specific recurrent disruption of the granzyme gene family locus was identified resulting in an aberrant granzyme B/C fusion product. The combined application of cytogenetic and gene expression approaches identified specific loci and genes that define the pathway of initiation and progression of lymphoreticular malignancies in the absence of ATM.


title: Iterative unified clustering in big data

abstract: We propose a novel iterative unified clustering algorithm for data with both continuous and categorical variables, in the big data environment. Clustering is a well-studied problem and finds several applications. However, none of the big data clustering works discuss the challenge of mixed attribute datasets, with both categorical and continuous attributes. We study an application in the health care domain namely Case Based Reasoning (CBR), which refers to solving new problems based on solutions to similar past problems. This is particularly useful when there is a large set of clinical records with several types of attributes, from which similar patients need to be identified. We go one step further and include the genomic components of patient records to enhance the CBR discovery. Thus, our contributions in this paper spans across the big data algorithmic research and a key contribution to the domain of heath care information technology research. First, our clustering algorithm deals with both continuous and categorical variables in the data; second, our clustering algorithm is iterative where it finds the clusters which are not well formed and iteratively drills down to form well defined clusters at the end of the process; third we provide a novel approach to CBR across clinical and genomic data. Our research has implications for clinical trials and facilitating precision diagnostics in large and heterogeneous patient records. We present extensive experimental results to show the efficacy or our approach.


title: How do agile practices support automotive SPICE compliance?

abstract: In the automotive domain, Automotive SPICE® is a common requirement for software and system development. Most automotive companies face the problem of complying with this model, especially, when dealing with agile development in order to benefit from advantages, such as higher customer collaboration or faster development. This paper describes the evaluation of how agile practices support Automotive SPICE requirements, based on literature reviews and expert opinion and including 772 mappings. 103 of 155 agile practices including Scrum and XP were used to cover 173 of 185 Automotive SPICE requirements (base practices and work products). Most Automotive SPICE base practices are supported (96%), while there is less support for work products (87%). The results are coherent with existing more high-level mappings but show more details. These results enable companies to benefit from agile without compromising Automotive SPICE conformity.


title: Dissimilar circular patch size antenna array with enhanced bandwidth and broadside gain

abstract: Design of microstrip antenna arrays using dissimilar circular patch elements is presented in this paper to cover the band 2.400–2.483 GHz required in IEEE802.11b,g applications. The following antenna prototypes were designed and built: circular patch antenna, 1×2 and 1×4 arrays. Proposed arrays presented an impedance bandwidth above to 100 MHz and 1×4 array synthetizes a broadside radiation pattern with gain more than 8 dBi and front-to-back ratio greater than 20 dB.


Clustering scRNA-Seq Data using TF-IDF


nowledge management : An information science perspective


title: Facial Expression Recognition with deep age

abstract: This paper presents a novel deep learning framework for facial expression recognition (FER). Our framework is derived from Convolutional Neural Networks (CNNs), adopting outline, texture, and angle raw data to generate 3 different convolutional feature maps for deep learning. In so doing, the proposed method is cable of robustly classifying expressions, by emphasizing the facial deformation, muscle movement, and outline feature descriptions. Therefore, our method makes the facial expression task not only more tractable, but also more reliable in the case of expression images, which leads to an overall improvement of classification performance compared to conventional methods. The proposed method is valid for the Static Facial Expression Recognition (SFEW) database, improving the baseline results by 6.98%.


A SIMULATION STUDY OF LOGISTICS AND MANUFACTURING ACTIVITIES IN AN AUTOMOBILE SUPPLY CHAIN


Estimation of Maximum Loadability in Power Systems By Using Elliptic Properties of P-e Curve


title: A novel chaotic salp swarm algorithm for global optimization and feature selection

abstract: Salp Swarm Algorithm (SSA) is one of the most recently proposed algorithms driven by the simulation behavior of salps. However, similar to most of the meta-heuristic algorithms, it suffered from stagnation in local optima and low convergence rate. Recently, chaos theory has been successfully applied to solve these problems. In this paper, a novel hybrid solution based on SSA and chaos theory is proposed. The proposed Chaotic Salp Swarm Algorithm (CSSA) is applied on 14 unimodal and multimodal benchmark optimization problems and 20 benchmark datasets. Ten different chaotic maps are employed to enhance the convergence rate and resulting precision. Simulation results showed that the proposed CSSA is a promising algorithm. Also, the results reveal the capability of CSSA in finding an optimal feature subset, which maximizes the classification accuracy, while minimizing the number of selected features. Moreover, the results showed that logistic chaotic map is the optimal map of the used ten, which can significantly boost the performance of original SSA.


title: Connection-Oriented DNS to Improve Privacy and Security

abstract: The Domain Name System (DNS) seems ideal for connectionless UDP, yet this choice results in challenges of eavesdropping that compromises privacy, source-address spoofing that simplifies denial-of-service (DoS) attacks on the server and third parties, injection attacks that exploit fragmentation, and reply-size limits that constrain key sizes and policy choices. We propose T-DNS to address these problems. It uses TCP to smoothly support large payloads and to mitigate spoofing and amplification for DoS. T-DNS uses transport-layer security (TLS) to provide privacy from users to their DNS resolvers and optionally to authoritative servers. TCP and TLS are hardly novel, and expectations about DNS suggest connections will balloon client latency and overwhelm server with state. Our contribution is to show that T-DNS significantly improves security and privacy: TCP prevents denial-of-service (DoS) amplification against others, reduces the effects of DoS on the server, and simplifies policy choices about key size. TLS protects against eavesdroppers to the recursive resolver. Our second contribution is to show that with careful implementation choices, these benefits come at only modest cost: end-to-end latency from TLS to the recursive resolver is only about 9% slower when UDP is used to the authoritative server, and 22% slower with TCP to the authoritative. With diverse traces we show that connection reuse can be frequent (60 -- 95% for stub and recursive resolvers, although half that for authoritative servers), and after connection establishment, experiments show that TCP and TLS latency is equivalent to UDP. With conservative timeouts (20 s at authoritative servers and 60 s elsewhere) and estimated per-connection memory, we show that server memory requirements match current hardware: a large recursive resolver may have 24k active connections requiring about 3.6 GB additional RAM. Good performance requires key design and implementation decisions we identify: query pipelining, out-of-order responses, TCP fast-open and TLS connection resumption, and plausible timeouts.


title: On the interpretation of results from the NIST statistical test suite

abstract: NIST Statistical Test Suite is an important testing suite for randomness analysis often used for formal certifications or approvals. Documentation of the NIST STS gives some guidance on how to interpret results of the NIST STS but interpretation is not clear enough or it uses just approximated values. Moreover NIST considers data to be random if all tests are passed yet even truly random data shows a high probability (80%) of failing at least one NIST STS test. If data fail some tests the NIST STS recommends the analysis of different samples. We analysed 819200 sequences (100 GB of data) produced by a physical source of randomness (quantum random number generator) in order to interpret results computed without analysing any additional samples. The results indicate that data can be still considered random for the significance level a = 0.01 if they fail less than 7 NIST STS tests, 7 tests of uniformity of p-values (100 sequences) or 10 tests of proportion of passing sequences. We have also defined a more accurate interval of acceptable proportions computed with a new constant (2.6 instead of 3) for which 1000 sequences can be considered random if they fail less than 7 tests of proportion.


title: A functional approach to the assessment of self-mutilative behavior.

abstract: Self-mutilative behavior (SMB) refers to deliberate damage to one’s own body tissue without suicidal intent and is part of the larger class of self-injurious behavior that includes actions ranging from stereotypic skin rubbing to completed suicide. SMB is a pervasive public health problem, with an estimated rate of 4% of the general adult population and 21% of adult psychiatric inpatient populations displaying these behaviors (Briere & Gil, 1998). As with suicidal thoughts and behaviors, adolescence appears to be a period of increased risk for SMB. Studies suggest that 14%–39% of adolescents in the community (Lloyd, 1998; Ross & Heath, 2002) and 40%– 61% of adolescents in psychiatric inpatient settings perform these behaviors (Darche, 1990; DiClemente, Ponton, & Hartley, 1991). The alarmingly high rate at which adolescent self-injurious behaviors occur, coupled with the psychopathology and dysfunction often associated with such behaviors (e.g., Nock & Kazdin, 2002), underscores the need for a better understanding of how to effectively assess and treat these behaviors. Despite the dangerousness and pervasiveness of SMB, surprisingly little is known about the nature of this problem. Limitations in the literature exist at both descriptive and theoretical levels. For instance, basic aspects of SMB, including the frequency of different methods and the average age of onset, have not been well established. Previous work has focused primarily on describing the psychosocial constructs associated with the presence of SMB. The result of this research is a heterogeneous list of diagnostic and psychosocial characteristics associated with SMB, including higher scores on measures of suicidal thoughts and behaviors, depression, anxiety, posttraumatic stress, anger, aggressiveness, impulsiveness, loneliness, social isolation, and hopelessness


title: Enhancing situation awareness in power system control centers

abstract: Due to the growing size and complexity of modern grids, power system operators have to deal with several challenges to maintain the reliability of the electrical supply. Developing and maintaining a sufficient situation awareness is a key element in preserving this reliability. However, because numerous factors govern its formation in a power system control center, this can be a very complicated process. The aim of this paper is to demonstrate the impact of insufficient situation awareness on power systems operational reliability and provide an overview of best practices in supporting human operators. It first identifies the main sources of inadequate situation awareness in a control room, and then presents its impact on operational decision-making. Several measures for dealing with the problems related to situation awareness are discussed and their applicability to power systems is illustrated.


title: Plasticity from muscle to brain

abstract: Recognition that the entire central nervous system (CNS) is highly plastic, and that it changes continually throughout life, is a relatively new development. Until very recently, neuroscience has been dominated by the belief that the nervous system is hardwired and changes at only a few selected sites and by only a few mechanisms. Thus, it is particularly remarkable that Sir John Eccles, almost from the start of his long career nearly 80 years ago, focused repeatedly and productively on plasticity of many different kinds and in many different locations. He began with muscles, exploring their developmental plasticity and the functional effects of the level of motor unit activity and of cross-reinnervation. He moved into the spinal cord to study the effects of axotomy on motoneuron properties and the immediate and persistent functional effects of repetitive afferent stimulation. In work that combined these two areas, Eccles explored the influences of motoneurons and their muscle fibers on one another. He studied extensively simple spinal reflexes, especially stretch reflexes, exploring plasticity in these reflex pathways during development and in response to experimental manipulations of activity and innervation. In subsequent decades, Eccles focused on plasticity at central synapses in hippocampus, cerebellum, and neocortex. His endeavors extended from the plasticity associated with CNS lesions to the mechanisms responsible for the most complex and as yet mysterious products of neuronal plasticity, the substrates underlying learning and memory. At multiple levels, Eccles' work anticipated and helped shape present-day hypotheses and experiments. He provided novel observations that introduced new problems, and he produced insights that continue to be the foundation of ongoing basic and clinical research. This article reviews Eccles' experimental and theoretical contributions and their relationships to current endeavors and concepts. It emphasizes aspects of his contributions that are less well known at present and yet are directly relevant to contemporary issues.


title: Early Diagnosis of Dementia from Clinical Data by Machine Learning Techniques

abstract: Dementia is the most prevalent degenerative disease in seniors in which progression can be prevented or delayed by early diagnosis. In this study, we proposed a two-layer model inspired by the method used in dementia support centers for the early diagnosis of dementia and using machine learning techniques. Data were collected from patients who received dementia screening from 2008 to 2013 at the Gangbuk-Gu center for dementia in the Republic of Korea. The data consisted of the patient’s gender, age, education, the Mini-Mental State Examination in the Korean version of the CERAD Assessment Packet (MMSE-KC) for dementia screening test, and the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD-K) for the dementia precise test. In the proposed model, MMSE-KC data are initially classified into normal and abnormal. In the second stage, CERAD-K data are used to classify dementia and mild cognitive impairment. The performance of each algorithm is compared with that of Naive Bayes, Bayes Network, Begging, Logistic Regression, Random Forest, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) using Precision, Recall and F-measure. Comparing the F-measure values of normal, mild cognitive impairment (MCI), and dementia, the MLP was the highest in the F-measure values of normal with 0.97, while the SVM appear to be the highest in MCI and dementia with 0.739. Using the proposed early diagnosis model for dementia reduces the time and economic burden and can help simplify the diagnosis method for dementia.


title: Model-based predictions for dopamine

abstract: Phasic dopamine responses are thought to encode a prediction-error signal consistent with model-free reinforcement learning theories. However, a number of recent findings highlight the influence of model-based computations on dopamine responses, and suggest that dopamine prediction errors reflect more dimensions of an expected outcome than scalar reward value. Here, we review a selection of these recent results and discuss the implications and complications of model-based predictions for computational theories of dopamine and learning.


The results on vertex domination in Fuzzy graphs


title: User‐oriented quality for OER: understanding teachers' views on re‐use, quality, and trust

abstract: We analysed how teachers as users of open educational resources (OER) repositories act in the re-use process and how they perceive quality. Based on a quantitative empirical study, we also surveyed which quality requirements users have and how they would contribute to the quality process. Trust in resources, organizations, and technologies seem to be of particular importance when looking at quality. In our findings, we derive recommendations for learning object repositories and OER user-oriented quality assurance.


Modeling Visit Potential of Geographic Locations Based on Mobility Data


title: Fraud detection in international calls using fuzzy logic

abstract: Fraud is a problem that affects operators and telecommunication companies all around the world. One of the most known fraud is the bypass fraud, which is used in international calls in order to avoid access charges and making profits. This kind of fraud causes severe losses in revenue to operators with other negative side effects. In this paper a new fraud detection technique is proposed. The proposed technique depends on user profiling and using fuzzy logic. Fuzzy logic was used in decision making process by utilizing fuzzy logic membership function. A real database from mobile operator company Almadar Aljadid in Libya is used in this investigation. Five features are extracted and employed as detection patterns in the proposed technique. The five features or detection patterns are, subscriber's mobility, incoming to outgoing calls ratio, suspicious cell activity, irregular calls, and service type.


title: Pogamut 3 Can Assist Developers in Building AI (Not Only) for Their Videogame Agents

abstract: Many research projects oriented on control mechanisms of virtual agents in videogames have emerged in recent years. However, this boost has not been accompanied with the emergence of toolkits supporting development of these projects, slowing down the progress in the field. Here, we present Pogamut 3, an open source platform for rapid development of behaviour for virtual agents embodied in a 3D environment of the Unreal Tournament 2004 videogame. Pogamut 3 is designed to support research as well as educational projects. The paper also briefly touches extensions of Pogamut 3; the ACT-R integration, the emotional model ALMA integration, support for control of avatars at the level of gestures, and a toolkit for developing educational scenarios concerning orientation in urban areas. These extensions make Pogamut 3 applicable beyond the domain of computer games.


title: NPC multilevel multistring topology for large scale grid connected photovoltaic systems

abstract: Large scale photovoltaic energy conversion systems are currently stepping into the multi-megawatt range. Therefore high power multilevel converters are becoming an attractive alternative for this application. In this work, the two-level voltage source converter based multistring topology concept is extended and explored for the three-level neutral point clamped (NPC) converter. The split dc-link of the NPC enables the use of two series connected dc-buses, naturally doubling the voltage level of the system. Hence, less voltage step-up effort is required to reach medium voltage operation by the dc-dc stage or by the line side transformer. In addition, the system allows to connect as many strings to both dc-buses as permitted by the total power rating of the NPC, which is capable of concentrating a large PV farm into a single converter, power control system, line filter and transformer, with lower losses, size and cost than several two-level converters needed to handle the same power. In addition, the inherent superior output voltage waveforms of the NPC enables operation with lower switching frequency improving the efficiency of the system, and provides better power quality that complies with the more demanding grid codes at higher power levels.


title: Developing Business Models for eBusiness

abstract: The paper introduces first insights into a methodology for developing eBusiness business models, which was elaborated at evolaris and is currently validated in various business cases. This methodology relies upon a definition of the term business model, which is first examined and upon which prerequisites for such a methodology are presented. A business model is based on a mental representation of certain aspects of the real world that are relevant for the business. Supporting this change of the mental model is therefore a major prerequisite for a methodology for developing business models. This paper demonstrates that it addition, a business model discussion should be theory based, able to handle complex systems, provide a way for risk free experiments and be practically applicable. In order to fulfill the above critieria, the evolaris methodology is grounded on system theory and combines aspects of system dynamics and action research.


title: EMI Coupling Paths and Mitigation in Optical Transceiver Modules

abstract: Optical transceiver modules are commonly used in telecommunication and data communication systems, and are among the most troublesome electromagnetic interference (EMI) problems for regulatory compliance at their operation frequencies and/or harmonics. In this study, simulations and measurements are performed on an optical subassembly module, including the silicon photonics submodule assembly, in order to identify and characterize the EMI coupling paths. The total radiated power is measured and computed. Through simulation and corroborating measurements, the dominant EMI coupling paths at high frequencies above 10 GHz are identified and characterized, and currents on the silicon photonic subassembly conductor housing and optical fiber connection ferrule are a dominant radiating source. EMI mitigation methods are developed and shown to be effective in reducing the radiated emissions from real product hardware.


title: Substrate integrated waveguide power divider based on multimode interference imaging

abstract: A 1×N (with arbitrary N) power divider based on multimode interference imaging in substrate integrated waveguide (SIW) technology is proposed. This divider is demonstrated by fully-wave analysis and experimental results in the case of a division ratio of 1/6. It is implemented at Ka band with the use of continuous wall SIW geometry. The insertion loss from the input to each output of the divider at 25 GHz is −9 dB. Compared with traditional corporate dividing structures, this divider has a much simpler configuration and also a lower design complexity. This device may find applications in the design of millimeter-wave and quasi-optical spatial power combiners, antenna feeding networks and amplifiers.


title: Correlation analysis of MQTT loss and delay according to QoS level

abstract: MQTT is an open protocol developed and released by IBM. To ensure the reliability of message transmission, MQTT supports three levels of QoS. In this paper, we analyze MQTT message transmission process which consists of real wired/wireless publish client, broker server and Subscribe client. By transmitting messages through 3 levels of QoS with various sizes of payloads, we have captured packets to analyze end-to-end delays and message loss.


title: Beamforming for Millimeter Wave Communications: An Inclusive Survey

abstract: The remarkable growth of wireless data traffic in recent times has driven the need to explore suitable regions in the radio spectrum to meet the projected requirements. In pursuance of this, millimeter wave communications have received considerable attention in the research fraternity. Due to the high path and penetration losses at millimeter wavelengths, antenna beamforming assumes a pivotal role in establishing and maintaining a robust communication link. Beamforming for millimeter wave communications poses a multitude of diverse challenges due to the large channel bandwidth, unique channel characteristics, and hardware constraints. In this paper, we track the evolution and advancements in antenna beamforming for millimeter wave communications in the context of the distinct requirements for indoor and outdoor communication scenarios. We expand the scope of discussion by including the developments in radio frequency system design and implementation for millimeter wave beamforming. We explore the suitability of millimeter wave beamforming methods, both, existing and proposed till midyear 2015, and identify the exciting new prospects unfolding in this domain.


title: Disentangled Representation Learning for Non-Parallel Text Style Transfer

abstract: This paper tackles the problem of disentangling the latent variables of style and content in language models. We propose a simple yet effective approach, which incorporates auxiliary multi-task and adversarial objectives, for label prediction and bag-of-words prediction, respectively. We show, both qualitatively and quantitatively, that the style and content are indeed disentangled in the latent space. This disentangled latent representation learning method is applied to style transfer on non-parallel corpora. We achieve substantially better results in terms of transfer accuracy, content preservation and language fluency, in comparison to previous state-of-the-art approaches.


End-to-End Speech Command Recognition with Capsule Network


title: PacketCloud: A Cloudlet-Based Open Platform for In-Network Services

abstract: The Internet was designed with the end-to-end principle where the network layer provided merely the best-effort forwarding service. This design makes it challenging to add new services into the Internet infrastructure. However, as the Internet connectivity becomes a commodity, users and applications increasingly demand new in-network services. This paper proposes PacketCloud, a cloudlet-based open platform to host in-network services. Different from standalone, specialized middleboxes, cloudlets can efficiently share a set of commodity servers among different services, and serve the network traffic in an elastic way. PacketCloud can help both Internet Service Providers (ISPs) and emerging application/content providers deploy their services at strategic network locations. We have implemented a proof-of-concept prototype of PacketCloud. PacketCloud introduces a small additional delay, and can scale well to handle high-throughput data traffic. We have evaluated PacketCloud in both a fully functional emulated environment, and the real Internet.


title: CASENet: Deep Category-Aware Semantic Edge Detection

abstract: Boundary and edge cues are highly beneficial in improving a wide variety of vision tasks such as semantic segmentation, object recognition, stereo, and object proposal generation. Recently, the problem of edge detection has been revisited and significant progress has been made with deep learning. While classical edge detection is a challenging binary problem in itself, the category-aware semantic edge detection by nature is an even more challenging multi-label problem. We model the problem such that each edge pixel can be associated with more than one class as they appear in contours or junctions belonging to two or more semantic classes. To this end, we propose a novel end-to-end deep semantic edge learning architecture based on ResNet and a new skip-layer architecture where category-wise edge activations at the top convolution layer share and are fused with the same set of bottom layer features. We then propose a multi-label loss function to supervise the fused activations. We show that our proposed architecture benefits this problem with better performance, and we outperform the current state-of-the-art semantic edge detection methods by a large margin on standard data sets such as SBD and Cityscapes.


Do sharks have a new antibody lineage


title: Limitations of Highly-Available Eventually-Consistent Data Stores

abstract: Modern replicated data stores aim to provide high availability, by immediately responding to client requests, often by implementing objects that expose concurrency. Such objects, for example, multi-valued registers (MVRs), do not have sequential specifications. This paper explores a recent model for replicated data stores that can be used to precisely specify causal consistency for such objects, and liveness properties like eventual consistency, without revealing details of the underlying implementation. The model is used to prove the following results: An eventually consistent data store implementing MVRs cannot satisfy a consistency model strictly stronger than observable causal consistency (OCC). OCC is a model somewhat stronger than causal consistency, which captures executions in which client observations can use causality to infer concurrency of operations. This result holds under certain assumptions about the data store. Under the same assumptions, an eventually consistent and causally consistent replicated data store must send messages of unbounded size: If s objects are supported by n replicas, then, for every k > 1, there is an execution in which an Ω({n,s} k)-bit message is sent.


title: The case for the reduced instruction set computer

abstract: One of the primary goals of computer architects is to design computers that are more costeffective than their predecessors. Cost-effectiveness includes the cost of hardware to manufacture the machine, the cost of programming, and costs incurred related to the architecture in debugging both the initial hardware and subsequent programs. If we review the history of computer families we find that the most common architectural change is the trend toward ever more complex machines. Presumably this additional complexity has a positive tradeoff with regard to the costeffectiveness of newer models. In this paper we propose that this trend is not always cost-effective, and in fact, may even do more harm than good. We shall examine the case for a Reduced Instruction Set Computer (RISC) being as cost-effective as a Complex Instruction Set Computer (CISC). This paper will argue that the next generation of VLSI computers may be more effectively implemented as RISC's than CISC's.


title: Weakly Supervised Object Detection with Posterior Regularization

abstract: This paper focuses on the problem of object detection when the annotation at training time is restricted to presence or absence of object instances at image level. We present a method based on features extracted from a Convolutional Neural Network and latent SVM that can represent and exploit the presence of multiple object instances in an image. Moreover, the detection of the object instances in the image is improved by incorporating in the learning procedure additional constraints that represent domain-specific knowledge such as symmetry and mutual exclusion. We show that the proposed method outperforms the state-of-the-art in weakly-supervised object detection and object classification on the Pascal VOC 2007 dataset.


title: Multithreaded Sliding Window Approach to Improve Exact Pattern Matching Algorithms

abstract: In this paper an efficient pattern matching ap-proach, based on a multithreading sliding window technique, is proposed to improve the efficiency of the common sequential exact pattern matching algorithms including: (i) Brute Force, (ii) Knuth-Morris-Pratt and (iii) Boyer-Moore. The idea is to divide the text under-search into blocks, each block is allocated one or two threads running concurrently. Reported experimental results indicated that the proposed approach improves the performance of the well-known pattern matching algorithms, in terms of search time, especially when the searched patterns are located at the middle or at the end of the text.


title: Cultural differences, experience with social networks and the nature of true commitment in Facebook

abstract: Formed on an analysis of design practices, the behaviour chain model stipulates that social network designer's ultimate aim is to encourage users to adopt the social network site by entering a phase of true commitment. During this phase, social network users are driven to connect to known or unknown others by engaging in instrumental uses that create value and content and involve others, while staying active and loyal by investing time in the site. This paper investigates how designer's intentions, as captured by the behaviour chain model, materialise through users' reported practices in the social network site Facebook. A total of 423 Facebook users from 5 countries answered a questionnaire that allowed us to examine how 2 user characteristics, experience with the site, and culture, shape the nature of true commitment. Our findings show that experience with the site and even more so, culture, have an effect on users' motivations for using Facebook, as well as their instrumental uses and the time they invest on the site. This analysis reifies the behaviour chain model by allowing designers to understand how the features they design are embodied in users' practices.


title: Hybrid Modular Multilevel DSCC Inverter for Open-End Winding Induction Motor Drives

abstract: This paper proposes a hybrid modular multilevel drive system based on a modular multilevel cascade inverter (MMCI) with double-star chopper cells (DSCC) and a two-level (2L) inverter to feed three-phase induction motors with open-end windings (OEWs). The proposed system is an alternative for high-speed motor drives based on a small-scale DSCC topology. A voltage reference technique is presented based on the analysis of the degrees of freedom of the system for generating the output voltage references, considering any voltage ratios of the dc links. The proposed system is evaluated in a symmetric scenario, in which the dc-link voltages are equal. Thus, the 2L inverter is set up to operate at the fundamental frequency, while the DSCC inverter synthesizes the output voltage references from high-quality waveform with a significant number of voltage levels. Experimental results obtained from a downscaled 10-kVA 380-V prototype driving a full loaded 3.7-kW OEW induction motor are provided to verify the viability of the proposed system.


Deep Learning for Hate Speech Detection in


Triaging Checklists: a Substitute for a PhD in Static Analysis