title: Factors Influencing Quality of Mobile Apps: Role of Mobile App Development Life Cycle

text: In this paper, The mobile application field has been receiving astronomical attention from the past few years due to the growing number of mobile app downloads and withal due to the revenues being engendered .With the surge in the number of apps, the number of lamentable apps/failing apps has withal been growing.Interesting mobile app statistics are included in this paper which might avail the developers understand the concerns and merits of mobile apps.The authors have made an effort to integrate all the crucial factors that cause apps to fail which include negligence by the developers, technical issues, inadequate marketing efforts, and high prospects of the users/consumers.The paper provides suggestions to eschew failure of apps. As per the various surveys, the number of lamentable/failing apps is growing enormously, primarily because mobile app developers are not adopting a standard development life cycle for the development of apps. In this paper, we have developed a mobile application with the aid of traditional software development life cycle phases (Requirements, Design, Develop, Test, and, Maintenance) and we have used UML, M-UML, and mobile application development technologies.


title: Ranking Scientific Articles in a Dynamically Evolving Citation Network

text: Scientific ranking has long been a hot and important topic in both computer science and scientometrics. A lot of statistics-based and graph-based methods have been proposed for calculating a prestige value as the assessment of each paper's scientific influence. However, being ignorant of the dynamic nature of scientific publication and science evolution, all these methods present a biased point of view of scientific influence. Besides, the ranking results of these methods are not accessible to users because of lack of an explainable model. As an alternative to the state-of-the-art, this paper proposes a cognitively explainable model by integrating three factors in scientific development including knowledge accumulation by individual papers, knowledge diffusion through citation behaviour and knowledge decay with time elapse. Evaluated on ACL Anthology Network using the reference lists of four textbooks or handbooks as the gold standard, the proposed model is proved to be effective in scientific ranking and potential for new insights into the definition and measurement of scientific influence.


title: Semantics, conceptual spaces, and the meeting of minds

text: We present an account of semantics that is not construed as a mapping of language to the world but rather as a mapping between individual meaning spaces. The meanings of linguistic entities are established via a “meeting of minds.” The concepts in the minds of communicating individuals are modeled as convex regions in conceptual spaces. We outline a mathematical framework, based on fixpoints in continuous mappings between conceptual spaces, that can be used to model such a semantics. If concepts are convex, it will in general be possible for interactors to agree on joint meaning even if they start out from different representational spaces. Language is discrete, while mental representations tend to be continuous—posing a seeming paradox. We show that the convexity assumption allows us to address this problem. Using examples, we further show that our approach helps explain the semantic processes involved in the composition of expressions.


title: Equivalent circuit modeling of LLC resonant converter

text: LLC resonant converter is widely used in industry. However, up to now, no simple and accurate small-signal equivalent circuit model is available. This paper proposes an equivalent circuit model of LLC resonant converter. The simple equivalent circuit model is derived based on modification and simplification of extended describing function method. The model can well predicts the small-signal behaviors observed in LLC resonant converter, whenever switching frequency is below, close to or above the resonant frequency. For the first time, analytical expressions for control to output voltage, input to output voltage, input impedance and output impedance are provided to aid close loop feedback design. Simplis simulation and experimental results are presented to prove the accuracy of the model.


title: The Cubli: A reaction wheel based 3D inverted pendulum

text: The Cubli is a 15×15×15 cm cube with reaction wheels mounted on three of its faces. By applying controlled torques to the reaction wheels the Cubli is able to balance on its corner or edge. This paper presents the development of the Cubli. First, the mechatronic design of the Cubli is presented. Then the multi-body system dynamics are derived. The parameters of the nonlinear system are identified using a frequency domain based approach while the Cubli is balancing on its edge with a nominal controller. Finally, the corner balancing using a linear feedback controller is presented along with experimental results.


title: Formalism-Independent Parser Evaluation with CCG and DepBank

text: A key question facing the parsing community is how to compare parsers which use different grammar formalisms and produce different output. Evaluating a parser on the same resource used to create it can lead to non-comparable accuracy scores and an over-optimistic view of parser performance. In this paper we evaluate a CCG parser on DepBank, and demonstrate the difficulties in converting the parser output into DepBank grammatical relations. In addition we present a method for measuring the effectiveness of the conversion, which provides an upper bound on parsing accuracy. The CCG parser obtains an F-score of 81.9% on labelled dependencies, against an upper bound of 84.8%. We compare the CCG parser against the RASP parser, outperforming RASPby over 5% overall and on the majority of dependency types.


title: Learning to Cooperate via Policy Search

text: Cooperative games are those in which both agents share the same payoff structure. Valuebased reinforcement-learning algorithms, such as variants of Q-learning, have been applied to learning cooperative games, but they only apply when the game state is completely observable to both agents. Policy search methods are a reasonable alternative to value-based methods for partially observable environments. In this paper, we provide a gradient-based distributed policysearch method for cooperative games and compare the notion of local optimum to that of Nash equilibrium. We demonstrate the effectiveness of this method experimentally in a small, partially observable simulated soccer domain.


title: Rethinking project management : A structured literature review with a critical look at the brave new world

text: This paper presents the results of a structured review of the rethinking project management (RPM) literature based on the classification and analysis of 74 contributions and in addition takes a critical look at this brave new world. Through the analysis, a total of 6 overarching categories emerged: contextualization, social and political aspects, rethinking practice, complexity and uncertainty, actuality of projects and broader conceptualization. These categories cover a broad range of different contributions with diverse and alternative perspectives on project management. The early RPM literature dates back to the 1980s, while the majority was published in 2006 onwards, and the research stream appears to be still active. A critical look at this brave new world exhibits the overall challenge for RPM to become much more diffused and accepted. © 2014 Elsevier Ltd. APM and IPMA. All rights reserved.


title: UWB Low-Profile Tightly Coupled Dipole Array With Integrated Balun and Edge Terminations

text: Tightly coupled phased arrays (TCPAs) provide UWB performance due to their strong inter-element coupling. However, in finite tightly coupled phased array realizations, mutual coupling is reduced near the array edges, causing the edge elements to become narrowband. To address this issue, a nonuniform array excitation scheme, referred to as “characteristic mode (CM) excitation,” was recently proposed. A more practical alternative for designing UWB tightly coupled phased arrays is proposed here. In this paper, we present a strategy that employs uniform excitation of the central array elements and short-circuits the periphery elements. We report that at least for medium size arrays, this approach provides up to 3 dB more gain and 50% higher efficiency than typical resistive termination. This concept is demonstrated using a 7 × 7 linearly polarized dipole array, 60.96 cm × 60.96 cm (2' × 2') in size, for operation from 200 MHz-600 MHz. Feeding of the active elements is challenging due to several constraints on the feed design, including balanced to unbalanced transitions, impedance transformations, common mode suppression, compact size, low cost, etc. To address these issues we propose a novel array feed using a compact, ultrawideband balun (with 10:1 bandwidth for VSWR ). Simulated and measured data are provided for broadside and 30 scan in the H-plane.


title: Classification and Numbering of Dental Radiographs for an Automated Human Identification System

text: Dental based human identification is commonly used in forensic. In a case of large scale investigation, manual identification needs a large amount of time. In this paper, we developed an automated human identification system based on dental radiographs. The system developed has two main stages. The first stage is to arrange a database consisting of labeled dental radiographs. The second stage is the searching process in the database in order to retrieve the identification result. Both stages use a number of image processing techniques, classification methods, and a numbering system in order to generate dental radiograph’s features and patterns. The first technique is preprocessing which includes image enhancement and binarization, single tooth extraction, and feature extraction. Next, we performed dental classification process which aims to classify the extracted tooth into molar or premolar using the binary support vector machine method. After that, a numbering process is executed in accordance with molar and premolar pattern obtained in the previous process. Our experiments using 16 dental radiographs that consist of 6 bitewing radiographs and 10 panoramic radiographs, 119 teeth objects in total, has shown good performance of classification. The accuracy value of dental pattern classification and dental numbering system are 91.6 % and 81.5% respectively.


title: Quasar: resource-efficient and QoS-aware cluster management

text:
Cloud computing promises flexibility and high performance for users and high cost-efficiency for operators. Nevertheless, most cloud facilities operate at very low utilization, hurting both cost effectiveness and future scalability.
 We present Quasar, a cluster management system that increases resource utilization while providing consistently high application performance. Quasar employs three techniques. First, it does not rely on resource reservations, which lead to underutilization as users do not necessarily understand workload dynamics and physical resource requirements of complex codebases. Instead, users express performance constraints for each workload, letting Quasar determine the right amount of resources to meet these constraints at any point. Second, Quasar uses classification techniques to quickly and accurately determine the impact of the amount of resources (scale-out and scale-up), type of resources, and interference on performance for each workload and dataset. Third, it uses the classification results to jointly perform resource allocation and assignment, quickly exploring the large space of options for an efficient way to pack workloads on available resources. Quasar monitors workload performance and adjusts resource allocation and assignment when needed. We evaluate Quasar over a wide range of workload scenarios, including combinations of distributed analytics frameworks and low-latency, stateful services, both on a local cluster and a cluster of dedicated EC2 servers. At steady state, Quasar improves resource utilization by 47% in the 200-server EC2 cluster, while meeting performance constraints for workloads of all types.


title: What does research reproducibility mean?

text: The language and conceptual framework of “research reproducibility” are nonstandard and unsettled across the sciences. In this Perspective, we review an array of explicit and implicit definitions of reproducibility and related terminology, and discuss how to avoid potential misunderstandings when these terms are used as a surrogate for “truth.”


title: A 120-mV input, fully integrated dual-mode charge pump in 65-nm CMOS for thermoelectric energy harvester

text: In this paper, a fully integrated low voltage charge pump for thermoelectric energy harvesters is presented. The proposed dual-mode architecture achieves both the low startup voltage in a startup mode and high conversion efficiency in a normal operation mode without off-chip inductors and capacitors. In the measurement, the proposed circuit successfully converts 120-mV input to 770-mV output with 38.8% conversion efficiency.


title: Deeply Supervised 3D Recurrent FCN for Salient Object Detection in Videos

text: This paper presents a novel end-to-end 3D fully convolutional network for salient object detection in videos. The proposed network uses 3D filters in the spatiotemporal domain to directly learn both spatial and temporal information to have 3D deep features, and transfers the 3D deep features to pixel-level saliency prediction, outputting saliency voxels. In our network, we combine the refinement at each layer and deep supervision to efficiently and accurately detect salient object boundaries. The refinement module recurrently enhances to learn contextual information into the feature map. Applying deeply-supervised learning to hidden layers, on the other hand, improves details of the intermediate saliency voxel, and thus the saliency voxel is refined progressively to become finer and finer. Intensive experiments using publicly available benchmark datasets confirm that our network outperforms state-of-the-art methods. The proposed saliency model also effectively works for video object segmentation.


title: Fusion of Audio-visual Features using Hierarchical Classifier Systems for the Recognition of Affective States and the State of Depression

text: Reliable prediction of affective states in real world scenarios is very challenging and a significant amount of ongoing research is targeted towards improvement of existing systems. Major problems include the unreliability of labels, variations of the same affective states amongst different persons and in different modalities as well as the presence of sensor noise in the signals. This work presents a framework for adaptive fusion of input modalities incorporating variable degrees of certainty on different levels. Using a strategy that starts with ensembles of weak learners, gradually, level by level, the discriminative power of the system is improved by adaptively weighting favorable decisions, while concurrently dismissing unfavorable ones. For the final decision fusion the proposed system leverages a trained Kalman filter. Besides its ability to deal with missing and uncertain values, in its nature, the Kalman filter is a time series predictor and thus a suitable choice to match input signals to a reference time series in the form of ground truth labels. In the case of affect recognition, the proposed system exhibits superior performance in comparison to competing systems on the analysed dataset.


title: Feature selection based on rough sets and particle swarm optimization

text: We propose a new feature selection strategy based on rough sets and Particle Swarm Optimization (PSO). Rough sets has been used as a feature selection method with much success, but current hill-climbing rough set approaches to feature selection are inadequate at finding optimal reductions as no perfect heuristic can guarantee optimality. On the other hand, complete searches are not feasible for even medium-sized datasets. So, stochastic approaches provide a promising feature selection mechanism. Like Genetic Algorithms, PSO is a new evolutionary computation technique, in which each potential solution is seen as a particle with a certain velocity flying through the problem space. The Particle Swarms find optimal regions of the complex search space through the interaction of individuals in the population. PSO is attractive for feature selection in that particle swarms will discover best feature combinations as they fly within the subset space. Compared with GAs, PSO does not need complex operators such as crossover and mutation, it requires only primitive and simple mathematical operators, and is computationally inexpensive in terms of both memory and runtime. Experimentation is carried out, using UCI data, which compares the proposed algorithm with a GA-based approach and other deterministic rough set reduction algorithms. The results show that PSO is efficient for rough set-based feature selection.


title: From Single User to Multiuser Communications : Shifting the MIMO Paradigm

text: In multiuser MIMO networks, the spatial degrees of freedom o ffered by multiple antennas can be advantageously exploited to enhance the system capacity, b y scheduling multiple users to simultaneously share the spatial channel. This entails a fundamental parad igm shift from single user communications, since multiuser systems can experience substantial benefit from channel state information at the transmitter and, at the same time, require more complex scheduling st rategies and transceiver methodologies. This paper reviews multiuser MIMO communication from an algorit hmic perspective, discussing performance gains, tradeoffs, and practical considerations. Several a pproaches including non-linear and linear channelaware precoding are reviewed, along with more practical lim ited feedback schemes that require only partial channel state information. The interaction betwee n precoding and scheduling is discussed. Several promising strategies for limited multiuser feedback desig n are looked at, some of which are inspired from the single user MIMO precoding scenario while others ar e fully specific to the multiuser setting.


title: Hydrothermal liquefaction of biomass: developments from batch to continuous process.

text: This review describes the recent results in hydrothermal liquefaction (HTL) of biomass in continuous-flow processing systems. Although much has been published about batch reactor tests of biomass HTL, there is only limited information yet available on continuous-flow tests, which can provide a more reasonable basis for process design and scale-up for commercialization. High-moisture biomass feedstocks are the most likely to be used in HTL. These materials are described and results of their processing are discussed. Engineered systems for HTL are described; however, they are of limited size and do not yet approach a demonstration scale of operation. With the results available, process models have been developed, and mass and energy balances determined. From these models, process costs have been calculated and provide some optimism as to the commercial likelihood of the technology.


title: Finding trending local topics in search queries for personalization of a recommendation system

text: In this paper, we present our approach for geographic personalization of a content recommendation system. More specifically, our work focuses on recommending query topics to users. We do this by mining the search query logs to detect trending local topics. For a set of queries we compute their counts and what we call buzz scores, which is a metric for detecting trending behavior. We also compute the entropy of the geographic distribution of the queries as means of detecting their location affinity. We cluster the queries into trending topics and assign the topics to their corresponding location. Human editors then select a subset of these local topics and enter them into a recommendation system. In turn the recommendation system optimizes a pool of trending local and global topics by exploiting user feedback. We present some editorial evaluation of the technique and results of a live experiment. Inclusion of local topics in selected locations into the global pool of topics resulted in more than 6% relative increase in user engagement with the recommendation system compared to using the global topics exclusively.


title: Learning to fly by crashing

text: How do you learn to navigate an Unmanned Aerial Vehicle (UAV) and avoid obstacles? One approach is to use a small dataset collected by human experts: however, high capacity learning algorithms tend to overfit when trained with little data. An alternative is to use simulation. But the gap between simulation and real world remains large especially for perception problems. The reason most research avoids using large-scale real data is the fear of crashes! In this paper, we propose to bite the bullet and collect a dataset of crashes itself! We build a drone whose sole purpose is to crash into objects: it samples naive trajectories and crashes into random objects. We crash our drone 11,500 times to create one of the biggest UAV crash dataset. This dataset captures the different ways in which a UAV can crash. We use all this negative flying data in conjunction with positive data sampled from the same trajectories to learn a simple yet powerful policy for UAV navigation. We show that this simple self-supervised model is quite effective in navigating the UAV even in extremely cluttered environments with dynamic obstacles including humans. For supplementary video see:


title: Selection of the number of clusters via the bootstrap method

text: Here the problem of selecting the number of clusters in cluster analysis is considered. Recently, the concept of clustering stability, which measures the robustness of any given clustering algorithm, has been utilized in Wang (2010) for selecting the number of clusters through cross validation. In this manuscript, an estimation scheme for clustering instability is developed based on the bootstrap, and then the number of clusters is selected so that the corresponding estimated clustering instability is minimized. The proposed selection criterion’s effectiveness is demonstrated on simulations and real examples.


title: Summarizing Search Results with Community-Based Question Answering

text: Previous work on snippet generation focused mainly on how to produce one snippet for an individual search result. This paper aims to generate snippets as a comprehensive overview for an entity query (e.g., flu) in a search-result page. Our approach first extracts the attributes (e.g., Symptom and diagnose) of the categories (e.g., Disease) from a community-based question-answering (CQA) website, and then generates the snippets based on how central a sentence is to the meaning of the query, its category, and how well it diversifies the attributes. Integer Linear Programming (ILP) is adopted to find the optimal sentence set. The experiments are conducted on Wikipedia and Yahoo! Answers. Experimental results demonstrate the effectiveness of our approach, compared to an existing commercial search engine and several summarization baselines.


title: Client-Centred Therapy

text: Our thinking in this area has been influenced by the developments in therapeutic thinking in general, but particularly by the evolution of client-centered therapy. The client-centered orientation was, at first, characterized by a rather specific technique or method: the therapist consistently responded by “reflecting” the client’s feeling. This meant that the therapist avoided diagnosis and deductions about the client, or interpretations of hidden meanings the client did not intend. Instead, the therapist attempted always to state in his own words what he sensed to be the client’s intended message, the client’s perception of his feelings, situation, difficulties, and the like. In this effort the therapist tried to sense not merely what was on the surface but the deeply felt meaning which the client only haltingly approached in what he said. Such responding, when successful, meant that the client’s own presently felt message was more deeply understood and responded to than he would have thought possible.


title: Simple and Effective Multi-Paragraph Reading Comprehension

text: We introduce a method of adapting neural paragraph-level question answering models to the case where entire documents are given as input. Most current question answering models cannot scale to document or multi-document input, and naively applying these models to each paragraph independently often results in them being distracted by irrelevant text. We show that it is possible to significantly improve performance by using a modified training scheme that teaches the model to ignore non-answer containing paragraphs. Our method involves sampling multiple paragraphs from each document, and using an objective function that requires the model to produce globally correct output. We additionally identify and improve upon a number of other design decisions that arise when working with document-level data. Experiments on TriviaQA and SQuAD shows our method advances the state of the art, including a 10 point gain on TriviaQA.


title: Principles of interleukin (IL)-6-type cytokine signalling and its regulation.

text: The IL (interleukin)-6-type cytokines IL-6, IL-11, LIF (leukaemia inhibitory factor), OSM (oncostatin M), ciliary neurotrophic factor, cardiotrophin-1 and cardiotrophin-like cytokine are an important family of mediators involved in the regulation of the acute-phase response to injury and infection. Besides their functions in inflammation and the immune response, these cytokines play also a crucial role in haematopoiesis, liver and neuronal regeneration, embryonal development and fertility. Dysregulation of IL-6-type cytokine signalling contributes to the onset and maintenance of several diseases, such as rheumatoid arthritis, inflammatory bowel disease, osteoporosis, multiple sclerosis and various types of cancer (e.g. multiple myeloma and prostate cancer). IL-6-type cytokines exert their action via the signal transducers gp (glycoprotein) 130, LIF receptor and OSM receptor leading to the activation of the JAK/STAT (Janus kinase/signal transducer and activator of transcription) and MAPK (mitogen-activated protein kinase) cascades. This review focuses on recent progress in the understanding of the molecular mechanisms of IL-6-type cytokine signal transduction. Emphasis is put on the termination and modulation of the JAK/STAT signalling pathway mediated by tyrosine phosphatases, the SOCS (suppressor of cytokine signalling) feedback inhibitors and PIAS (protein inhibitor of activated STAT) proteins. Also the cross-talk between the JAK/STAT pathway with other signalling cascades is discussed.


Airport capacity: representation, estimation, optimization


title: Shaping particle simulations with interaction forces

text: classroom use is granted without fee provided that copies are not made or distributed for commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author. SIGGRAPH 2014, August 10 – 14, 2014, Vancouver, British Columbia, Canada. 2014 Copyright held by the Owner/Author. ACM 978-1-4503-2960-6/14/08 Shaping Particle Simulations with Interaction Forces


title: Kernel estimation for motion blur removal using deep convolutional neural network

text: Blind deblurring can restore the sharp image from the blur version when the blur kernel is unknown, which is a challenging task. Kernel estimation is crucial for blind deblurring. In this paper, a novel blur kernel estimation method based on regression model is proposed for motion blur. The motion blur features are firstly mined through convolutional neural network (CNN), and then mapped to motion length and orientation by support vector regression (SVR). Experiments show that the proposed model, namely CNNSVR, can give more accurate kernel estimation and generate better deblurring result compared with other state-of-the-art algorithms.


title: New Comparative Study Between DES, 3DES and AES within Nine Factors

text: --With the rapid development of various multimedia technologies, more and more multimedia data are generated and transmitted in the medical, also the internet allows for wide distribution of digital media data. It becomes much easier to edit, modify and duplicate digital information .Besides that, digital documents are also easy to copy and distribute, therefore it will be faced by many threats. It is a big security and privacy issue, it become necessary to find appropriate protection because of the significance, accuracy and sensitivity of the information. , which may include some sensitive information which should not be accessed by or can only be partially exposed to the general users. Therefore, security and privacy has become an important. Another problem with digital document and video is that undetectable modifications can be made with very simple and widely available equipment, which put the digital material for evidential purposes under question. Cryptography considers one of the techniques which used to protect the important information. In this paper a three algorithm of multimedia encryption schemes have been proposed in the literature and description. The New Comparative Study between DES, 3DES and AES within Nine Factors achieving an efficiency, flexibility and security, which is a challenge of researchers.


title: Distributed Packet Inspection By Using PIL-2 Language

text: Packet Inspection language PIL-2 incorporates directly support for distributed processing into the language.PIL-2 supports generic header filter, payload scanning, packet replication and traffic flow in network. By distributing PIL-2 based on the possibility created for heterogeneous network of processing node, which gives monitoring architecture facilities with high speed packet processing. The result shows that perform complex processing at terra bit speeds. This proposed frame work can be used to execute distinct task as load balancing, traffic monitoring, firewalls and IDS at high bandwidth links. Keywords—PIL-2,Load balancing, IDS, traffic monitoring.


title: The Digital Sin City: An Empirical Study of Craigslist's Impact on Prostitution Trends

text: Online prostitution has flourished due to presence of online intermediaries. Despite the illegality of selling sexual services online, the Section 230 of Communications Decency Act shields websites from liability for unlawful postings by third parties. Consequently, the websites like Craigslist have become a haven for prostitution-related ads. In this paper, we quantify the economic impact of Craigslist’s entry on prostitution incidence, and identify potential pathways in which the website affects the sex industry. Using a national panel data for 1,796 U.S. counties from 1999 to 2008, our results suggest that entry of Craigslist to a county is related to 26.61 percent increase in prostitution cases. In addition, our analyses investigates four key market mechanisms, namely the relationship to commercial vice, geographical trends, spillover effects, and enhanced policing efficacy. Our results contributes broadly to the emerging literature that researches the societal challenges associated with online intermediaries and Internet penetration.


title: Zero-Knowledge Proofs of Knowledge Without Interaction (Extended Abstract)

text: A zero-knowledge proof system of knowledge is a protocol between two parties called the prover and the verifier. The prover wants to convince the verifier that he “knows” the proof of a given theorem without revealing any additional information. This is different from a zero-knowledge proof system of membership where the prover convinces the verifier only of the veridicity of the statement. Zero-knowledge proofs of knowledge are very useful tools in the design of secure protocols. Though, the concept of a proof of knowledge is a very subtle one and great care is needed to obtain a satisfying formalization. In this paper, we investigate the concept of a zero-knowledge proof of knowledge in the noninteractive model of [5, 61. Here, the prover and the verifier share a short random string and the only communication allowed is from the prover to the verifier. Although this is a simpler model than the interactive one, still formalizing zeroknowledge proofs of knowledge is a delicate task. The main results of the paper are the following e We present formal definitions for the concept of non-interactive zero-knowledge proofs ‘Dipartimento di Informatica ed Applicazioni, Universiti di Salerno, 84081 Baronissi (SA), Italy. Partially supported by Italian Ministry of Universit.y and Research (M.U.R.S.T.) and by National Council for Research (C.N.R.) under grant 91.02326.CT12. Email: ads@udsab.dia.unisa.it ‘Aiken Computation Laboratory, Harvard University, Cambridge, MA 02138, USA and Dipart,imento di Informatica, Universiti di Salerno. Supported at Harvard by NSF Grant # NSF-CCR-90-07677. Email: persiano@endor.harvard.edu GIUSEPPE PERSIANO+


title: Parsing C/C++ Code without Pre-processing

text: It is difficult to develop style-preserving source-to-source transformation engines for C and C++. The main reason is not the complexity of those languages, but the use of the C pre-processor (cpp), especially ifdefs and macros. This has for example hindered the development of refactoring tools for C and C++. In this paper we propose to combine multiple techniques and heuristics to parse C/C++ source files as-is, while still having only a few modifications to the original grammars of C and C++. We rely on the fact that in most C and C++ software, programmers follow a limited number of conventions on the use of cpp which makes it possible to disambiguate different situations by just looking at the context, names, or indentation of cpp constructs. We have implemented a parser, Yacfe, based on these techniques and evaluated it on 16 large open source projects. Yacfe can on average parse 96% of those projects correctly. As a side effect, we also found mistakes in code that was not compiled because it was protected by particular ifdefs, but that was still analyzed by Yacfe. Using Yacfe on new projects may require adapting some of our techniques. We found that as conventions and idioms are shared by many projects, the adaptation time is on average less than 2 hours for a new project.


title: Effects of hippotherapy and therapeutic horseback riding on postural control or balance in children with cerebral palsy: a meta-analysis.

text:
AIM
This research review and meta-analysis presents an overview of the effects of hippotherapy and therapeutic horseback riding (THR) on postural control or balance in children with cerebral palsy (CP).

METHOD
To synthesize previous research findings, a systematic review and meta-analysis were undertaken. Relevant studies were identified by systematic searches of multiple online databases from the inception of the database through to May 2010. Studies were included if they fulfilled the following criteria: (1) quantitative study design, (2) investigation of the effect of hippotherapy or THR on postural control or balance, and (3) the study group comprised children and adults with CP. The selected articles were rated for methodological quality. The treatment effect was coded as a dichotomous outcome (positive effect or no effect) and quantified by odds ratio (OR). The pooled treatment effect was calculated using a random-effects model. Meta-regression of the effect size was performed against study covariates, including study size, publication date, and methodological quality score.

RESULTS
From 77 identified studies, 10 met the inclusion criteria. Two were excluded because they did not include a comparison group. Therapy was found to be effective in 76 out of 84 children with CP included in the intervention groups. The comparison groups comprised 89 children: 50 non-disabled and 39 with CP. A positive effect was shown in 21 of the children with CP in the comparison group regardless of the activity undertaken (i.e. physiotherapy, occupational therapy, sitting on a barrel or in an artificial saddle). The pooled effect size estimate was positive (OR 25.41, 95% CI 4.35, 148.53), demonstrating a statistically significant effectiveness of hippotherapy or THR in children with CP (p<0.001). Meta-regression of study characteristics revealed no study-specific factors.

INTERPRETATION
The eight studies found that postural control and balance were improved during hippotherapy and THR. Although the generalization of our findings may be restricted by the relatively small sample size, the results clearly demonstrate that riding therapy is indicated to improve postural control and balance in children with CP.


title: CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition

text: In this paper, we investigate the robustness of traffic sign recognition algorithms under challenging conditions. Existing datasets are limited in terms of their size and challenging condition coverage, which motivated us to generate the Challenging Unreal and Real Environments for Traffic Sign Recognition (CURE-TSR) dataset. It includes more than two million traffic sign images that are based on real-world and simulator data. We benchmark the performance of existing solutions in realworld scenarios and analyze the performance variation with respect to challenging conditions. We show that challenging conditions can decrease the performance of baseline methods significantly, especially if these challenging conditions result in loss or misplacement of spatial information. We also investigate the effect of data augmentation and show that utilization of simulator data along with real-world data enhance the average recognition performance in real-world scenarios. The dataset is publicly available at https://ghassanalregib.com/cure-tsr/.


title: The KeY Platform for Verification and Analysis of Java Programs

text: The KeY system offers a platform of software analysis tools for sequential Java. Foremost, this includes full functional verification against contracts written in the Java Modeling Language. But the approach is general enough to provide a basis for other methods and purposes: (i) complementary validation techniques to formal verification such as testing and debugging, (ii) methods that reduce the complexity of verification such as modularization and abstract interpretation, (iii) analyses of non-functional properties such as information flow security, and (iv) sound program transformation and code generation. We show that deductive technology that has been developed for full functional verification can be used as a basis and framework for other purposes than pure functional verification. We use the current release of the KeY system as an example to explain and prove this claim.


title: Forecasting Foreign Exchange Rates Using Recurrent Neural Networks

text: This article proposes the use of recurrent neural networks in order to forecast foreign exchange rates. Artificial neural networks have proven to be efficient and profitable in forecasting financial time series. In particular, recurrent networks, in which activity patterns pass through the network more than once before they generate an output pattern, can learn extremely complex temporal sequences. Three recurrent architectures are compared in terms of prediction accuracy of futures forecast for Deutsche mark currency. A trading strategy is then devised and optimized. The profitability of the trading strategy, taking into account transaction costs, is shown for the different architectures. The methods described here, which have obtained promising results in real-time trading, are applicable to other markets.


title: Deciphering Severely Degraded License Plates

text: Extremely low-quality images, on the order of 20 pixels in width, appear with frustrating frequency in many forensic investigations. Even advanced de-noising and super-resolution technologies are unable to extract useful information from such lowquality images. We show, however, that useful information is present in such highly degraded images. We also show that convolutional neural networks can be trained to decipher the contents of highly degraded images of license plates, and that these networks significantly outperform human observers. Introduction Recognizing text in images is a well-studied problem [1]. Text recognition can either be done by recognizing individual characters or by recognizing the full word. For degraded images, however, it is difficult to localize and recognize individual characters in an image. Word recognition, therefore, has become central to text recognition in degraded images. Deep convolutional neural networks [2] have been used for text recognition in natural images. Goodfellow et al. [3] used a deep neural network to localize, segment and recognize multiple digits on street view images. Jaderberg et al. [4] also proposed an end to end text recognition system for natural images using a deep neural network. Jaderberg et al. used a large word dictionary and formulated the text recognition task as a large scale classification problem. Recently, Svoboda et al. [5] used CNN to remove motion blur from images of license plate blurred with a blur kernel of various directions and lengths. Although this approach is able to deblur highly blurred images, it does not contend with extremely low resolution and noisy images. Unlike much of this previous work we focus on extracting text from highly degraded images on the scale of only a few pixels per character. Hsieh et al. [6] were the first to show that information can be extracted from highly degraded license plates. In this work the authors assume a known font type, font size, and character layout, and assume that the degraded image is blurry and perspectively distorted, but does not necessarily contain additive noise. Although the authors only show results on a small set of images, they do show that information is present in license plates as small as 20 pixels in width. Building on these ideas, in this paper we propose to train a CNN for recognizing highly degraded license plates with an unknown background template, font type, size and character location. We also explicitly work in the presence of high amounts of additive noise – a common occurrence in real-world imagery. Recognition by Human Observers We begin by performing a perceptual study to determine how well human observers can decipher degraded license plates. This study provides a baseline against which to compare our computational approaches. Observers were shown images of synthetically Figure 1. An example of the type of degraded license plate that we seek to


title: Prognostics and health management using physics-of-failure

text: This paper presents a physics-of-failure based prognostics and health management approach for effective reliability prediction. This method permits in-situ assessment of system reliability under actual application conditions. The method uses sensor data with models that enable in-situ assessment of the deviation or degradation of a product from an expected normal operating condition (i.e., the system's ldquohealthrdquo) and the prediction of the future state of reliability. The implementation procedure of this approach includes failure modes, mechanisms, and effects analysis, data reduction and feature extraction from the life-cycle loads, damage accumulation, and assessment of uncertainty.


title: Attend More Times for Image Captioning

text: Most attention-based image captioning models attend to the image once per word. However, attending once per word is rigid and is easy to miss some information. Attending more times can adjust the attention position, find the missing information back and avoid generating the wrong word. In this paper, we show that attending more times per word can gain improvements in the image captioning task, without increasing the number of parameters. We propose a flexible two-LSTM merge model to make it convenient to encode more attentions than words. Our captioning model uses two LSTMs to encode the word sequence and the attention sequence respectively. The information of the two LSTMs and the image feature are combined to predict the next word. Experiments on the MSCOCO caption dataset show that our method outperforms the state-of-the-art. Using bottom up features and self-critical training method, our method gets BLEU-4, METEOR, ROUGE-L, CIDEr and SPICE scores of 0.381, 0.283, 0.580, 1.261 and 0.220 on the Karpathy test split.


title: Deep Learning Human Mind for Automated Visual Classification

text: What if we could effectively read the mind and transfer human visual capabilities to computer vision methods? In this paper, we aim at addressing this question by developing the first visual object classifier driven by human brain signals. In particular, we employ EEG data evoked by visual object stimuli combined with Recurrent Neural Networks (RNN) to learn a discriminative brain activity manifold of visual categories in a reading the mind effort. Afterward, we transfer the learned capabilities to machines by training a Convolutional Neural Network (CNN)–based regressor to project images onto the learned manifold, thus allowing machines to employ human brain–based features for automated visual classification. We use a 128-channel EEG with active electrodes to record brain activity of several subjects while looking at images of 40 ImageNet object classes. The proposed RNN-based approach for discriminating object classes using brain signals reaches an average accuracy of about 83%, which greatly outperforms existing methods attempting to learn EEG visual object representations. As for automated object categorization, our human brain–driven approach obtains competitive performance, comparable to those achieved by powerful CNN models and it is also able to generalize over different visual datasets.


title: DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes

text: DisGeNET is a comprehensive discovery platform designed to address a variety of questions concerning the genetic underpinning of human diseases. DisGeNET contains over 380,000 associations between >16,000 genes and 13,000 diseases, which makes it one of the largest repositories currently available of its kind. DisGeNET integrates expert-curated databases with text-mined data, covers information on Mendelian and complex diseases, and includes data from animal disease models. It features a score based on the supporting evidence to prioritize gene-disease associations. It is an open access resource available through a web interface, a Cytoscape plugin and as a Semantic Web resource. The web interface supports user-friendly data exploration and navigation. DisGeNET data can also be analysed via the DisGeNET Cytoscape plugin, and enriched with the annotations of other plugins of this popular network analysis software suite. Finally, the information contained in DisGeNET can be expanded and complemented using Semantic Web technologies and linked to a variety of resources already present in the Linked Data cloud. Hence, DisGeNET offers one of the most comprehensive collections of human gene-disease associations and a valuable set of tools for investigating the molecular mechanisms underlying diseases of genetic origin, designed to fulfill the needs of different user profiles, including bioinformaticians, biologists and health-care practitioners. Database URL: http://www.disgenet.org/


title: A visual tool for ontology alignment to enable geospatial interoperability

text: In distributed geospatial applications with heterogeneous databases, an ontology-driven approach to data integration relies on the alignment of the concepts of a global ontology that describe the domain, with the concepts of the ontologies that describe the data in the distributed databases. Once the alignment between the global ontology and each distributed ontology is established, agreements that encode a variety of mappings between concepts are derived. In this way, users can potentially query hundreds of geospatial databases using a single query. Using our approach, querying can be easily extended to new data sources and, therefore, to new regions. In this paper, we describe the AgreementMaker, a tool that displays the ontologies, supports several mapping layers visually, presents automatically generated mappings, and finally produces the agreements. r 2007 Elsevier Ltd. All rights reserved.


title: How to Prove Yourself: Practical Solutions to Identification and Signature Problems

text: In this paper we describe simple identification and signature schemes which enable any user to prove his identity and the authenticity of his messages to any other user without shared or public keys. The schemes are provably secure against any known or chosen message attack if factoring is difficult, and typical implementations require only 1% to 4% of the number of modular multiplications required by the RSA scheme. Due to their simplicity, security and speed, these schemes are ideally suited for microprocessor-based devices such as smart cards, personal computers, and remote control systems.


Efficient Identification and Signatures for Smart Cards


title: Compact E-Cash

text: This paper presents efficient off-line anonymous e-cash schemes where a user can withdraw a wallet containing 2 coins each of which she can spend unlinkably. Our first result is a scheme, secure under the strong RSA and the y-DDHI assumptions, where the complexity of the withdrawal and spend operations is O( + k) and the user’s wallet can be stored using O( +k) bits, where k is a security parameter. The best previously known schemes require at least one of these complexities to be O(2 · k). In fact, compared to previous e-cash schemes, our whole wallet of 2 coins has about the same size as one coin in these schemes. Our scheme also offers exculpability of users, that is, the bank can prove to third parties that a user has double-spent. We then extend our scheme to our second result, the first e-cash scheme that provides traceable coins without a trusted third party. That is, once a user has double spent one of the 2 coins in her wallet, all her spendings of these coins can be traced. However, the price for this is that the complexity of the spending and of the withdrawal protocols becomes O( · k) and O( · k + k) bits, respectively, and wallets take O( · k) bits of storage. All our schemes are secure in the random oracle model.


title: Chain : Operator Scheduling for Memory Minimization in Data Stream Systems

text: In many applications involving continuous data streams, data arrival is bursty and data rate fluctuates over time. Systems that seek to give rapid or real-time query responses in such an environment must be prepared to deal gracefully with bursts in data arrival without compromising system performance. We discuss one strategy for processing bursty streams --- adaptive, load-aware scheduling of query operators to minimize resource consumption during times of peak load. We show that the choice of an operator scheduling strategy can have significant impact on the run-time system memory usage. We then present Chain scheduling, an operator scheduling strategy for data stream systems that is near-optimal in minimizing run-time memory usage for any collection of single-stream queries involving selections, projections, and foreign-key joins with stored relations. Chain scheduling also performs well for queries with sliding-window joins over multiple streams, and multiple queries of the above types. A thorough experimental evaluation is provided where we demonstrate the potential benefits of Chain scheduling, compare it with competing scheduling strategies, and validate our analytical conclusions.


title: LaBB-CAT: an Annotation Store

text: “ONZE Miner”, an open-source tool for storing and automatically annotating Transcriber transcripts, has been redeveloped to use “annotation graphs” as its data model. The annotation graph framework provides the new software, “LaBB-CAT”, greater flexibility for automatic and manual annotation of corpus data at various independent levels of granularity, and allows more sophisticated annotation structures, opening up new possibilities for corpus mining and conversion between tool formats.


title: Agile in Public Administration: Oxymoron or Reality? An Experience Report

text: In the last 10 years, Agile methods and practices have emerged as an alternative for software development. Different "flavors" of Agile have appeared ranging from project management to tests organization. These approaches have being gaining popularity and involve now a solid option for organizations developing software, but what about Public Administrations? Is Agile a suitable option for developing software in Public Administrations? Even if Public Administrations have been traditionally regarded as changeresistant, Agile approach can also provide them with the benefits of quick adaptation and frequent value delivery. This paper presents the results of two different projects, which use an Agile framework based on Scrum, developed by a Spanish Public Administration. Additionally, after considering the obtained results, it takes out some relevant learned lessons on the suitability of applying Agile approaches to Public Administration environments.


title: Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks

text: In modern industries, machine health monitoring systems (MHMS) have been applied wildly with the goal of realizing predictive maintenance including failures tracking, downtime reduction, and assets preservation. In the era of big machinery data, data-driven MHMS have achieved remarkable results in the detection of faults after the occurrence of certain failures (diagnosis) and prediction of the future working conditions and the remaining useful life (prognosis). The numerical representation for raw sensory data is the key stone for various successful MHMS. Conventional methods are the labor-extensive as they usually depend on handcrafted features, which require expert knowledge. Inspired by the success of deep learning methods that redefine representation learning from raw data, we propose local feature-based gated recurrent unit (LFGRU) networks. It is a hybrid approach that combines handcrafted feature design with automatic feature learning for machine health monitoring. First, features from windows of input time series are extracted. Then, an enhanced bidirectional GRU network is designed and applied on the generated sequence of local features to learn the representation. A supervised learning layer is finally trained to predict machine condition. Experiments on three machine health monitoring tasks: tool wear prediction, gearbox fault diagnosis, and incipient bearing fault detection verify the effectiveness and generalization of the proposed LFGRU.


title: Sparse Probabilistic Principal Component Analysis

text: Principal component analysis (PCA) is a popular dimensionality reduction algorithm. However, it is not easy to interpret which of the original features are important based on the principal components. Recent methods improve interpretability by sparsifying PCA through adding an L1 regularizer. In this paper, we introduce a probabilistic formulation for sparse PCA. By presenting sparse PCA as a probabilistic Bayesian formulation, we gain the benefit of automatic model selection. We examine three different priors for achieving sparsification: (1) a two-level hierarchical prior equivalent to a Laplacian distribution and consequently to an L1 regularization, (2) an inverse-Gaussian prior, and (3) a Jeffrey’s prior. We learn these models by applying variational inference. Our experiments verify that indeed our sparse probabilistic model results in a sparse PCA solution.


title: Template-Free Construction of Poems with Thematic Cohesion and Enjambment

text: Existing poetry generation systems usually focus on particular features of poetry (such as rhythm, rhyme, metaphor) and specific techniques to achieve them. They often resort to template-base solutions, in which it is not always clear how many of the alleged features of the outputs were already present in the template employed. The present paper considers two specific features – thematic consistency, and enjambment – and presents an ngram based construction method that achieves these features without the use of templates. The construction procedure is not intended to produce poetry of high quality, only to achieve the features considered specifically in its design. A set of metrics is defined to capture these features in quantitative terms, and the metrics are applied to system outputs and to samples of both human and computer generated poetry. The results of these tests are discussed in terms of the danger of to ignoring certain features when designing construction procedures but valuing them during evaluation even if they arise from hard-wiring in the resources or serendipitous emergence, and the fundamental need for poets to develop a personal voice – fundamental for human poets and inconsistent with the application of Turing tests.


title: MovieGEN : A Movie Recommendation System

text: In this paper we introduce MovieGEN, an expert system for movie recommendation. We implement the system using machine learning and cluster analysis based on a hybrid recommendation approach. The system takes in the users’ personal information and predicts their movie preferences using well-trained support vector machine (SVM) models. Based on the SVM prediction it selects movies from the dataset, clusters the movies and generates questions to the users. Based on the users’ answers, it refines its movie set and it finally recommends movies for the users. In the process we traverse the parameter space, thus enabling the customizability of the system.


title: A harmonic suppression circularly polarized patch antenna for an RF ambient energy harvesting system

text: This paper presented a new design of harmonic suppression microstrip patch antenna with the circular polarization properties at frequency 900MHz for an RF ambient energy harvesting system. The purpose of harmonic suppression antenna is to eliminate the using of a harmonic filter circuit in filtering the unwanted signal at the harmonic frequencies in a rectenna system. By introducing a truncating to corner of the rectangular microstrip patch antenna to achieved the required axial ratio, an antenna with circular polarization property is presented. The dimension of patch is reduced by applying partially ground plane. Then an implementation of a defected ground structure (DGS) with a circular shape slot, the unwanted radiation at harmonic frequencies are successfully suppressed. The patch antenna is designed on an FR4 substrate with εr=4.7 and a thickness of 1.6 mm has -20dB return loss at 900 MHz and suppress the second and third harmonic up to -2dB. The proposed circularly polarized harmonic suppression antenna is then fabricated and measured for the verification of the design.


title: Conceptualizing and testing formative constructs: tutorial and annotated example

text: Although abundant advice is available for how to develop and validate multi-item scales based on reflective constructs, scant attention has been directed to how to construct and validate formative constructs. Such advice is important because (1) theory suggests many constructs are formative and (2) recent advances in software render testing models with formative constructs more tractable. In this tutorial, our goal is to enhance understanding of formative constructs at the conceptual, statistical and methodological levels. Specifically, we (1) provide general principles for specifying whether a construct should be conceptually modeled as reflective or formative, (2) discuss the statistical logic behind formative constructs, and (3) illustrate how to model and evaluate formative constructs. In particular, we provide a tutorial in which we test and validate professional reward structure, a formative construct, in two popular structural equation modeling programs: EQS and PLS. We conclude with a summary of guidelines for how to conduct and evaluate research using formative constructs.


title: Exemplar-Based Portrait Style Transfer

text: Transferring the style of an example image to a content image opens the door of artistic creation for end users. However, it is especially challenging for portrait photos since human vision system is sensitive to the slight artifacts on portraits. Previous methods use facial landmarks to densely align the content face with the style face to reduce the artifacts. However, they can only handle the facial region. As for the whole image, building the dense correspondence is difficult and may easily introduce errors. In this paper, we propose a robust approach for portrait style transfer that gets rid of dense correspondence. Our approach is based on the guided image synthesis framework. We propose three novel guidance maps for the synthesis process. Contrary to former methods, these maps do not require the dense correspondence between content image and style image, which allows our method to handle the whole portrait photo instead of facial region only. In comparison with recent neural style transfer methods, our method achieves more pleasing results and preserves more texture details. Extensive experiments demonstrate our advantage over former methods on portrait style transfer.


title: Tracking football player movement from a single moving camera using particle filters

text: This paper deals with the problem of tracking football players in a football match using data from a single moving camera. Tracking footballers from a single video source is di cult: not only do the football players occlude each other, but they frequently enter and leave the camera's eld of view, making initialisation and destruction of a player's tracking a di cult task. The system presented here uses particle lters to track players. The multiple state estimates used by a particle lter provide an elegant method for maintaining tracking of players following an occlusion. Automated tracking can be achieved by creating and stopping particle lters depending on the input player data.


title: Compact hashing with joint optimization of search accuracy and time

text: Similarity search, namely, finding approximate nearest neighborhoods, is the core of many large scale machine learning or vision applications. Recently, many research results demonstrate that hashing with compact codes can achieve promising performance for large scale similarity search. However, most of the previous hashing methods with compact codes only model and optimize the search accuracy. Search time, which is an important factor for hashing in practice, is usually not addressed explicitly. In this paper, we develop a new scalable hashing algorithm with joint optimization of search accuracy and search time simultaneously. Our method generates compact hash codes for data of general formats with any similarity function. We evaluate our method using diverse data sets up to 1 million samples (e.g., web images). Our comprehensive results show the proposed method significantly outperforms several state-of-the-art hashing approaches.


title: Blockchain as a Platform for Secure Inter-Organizational Business Processes

text: Today, most of the services one may think of are based on a collaborative paradigm (e.g., social media services, IoT-based services, etc.). One of the most relevant representative of such class of services are inter-organizational processes, where an organized group of joined activities is carried out by two or more organizations to achieve a common business goal. Inter-organizational processes are therefore vital to achieve business partnerships among different organizations. However, they may also pose serious security and privacy threats to the data each organization exposes. This is mainly due to the weak trust relationships that may hold among the collaborating parties, which result in a potential lack of trust on how data/operations are managed. In this paper, we discuss, how blockchain, one of today hottest technology, can be used in support of secure inter-organizational processes. We further point out which additional security issues the use of blockchain can bring, illustrate the ongoing research projects in the area and discuss future research directions.


title: Exploiting 2D Floorplan for Building-Scale Panorama RGBD Alignment

text: This paper presents a novel algorithm that utilizes a 2D floorplan to align panorama RGBD scans. While effective panorama RGBD alignment techniques exist, such a system requires extremely dense RGBD image sampling. Our approach can significantly reduce the number of necessary scans with the aid of a floorplan image. We formulate a novel Markov Random Field inference problem as a scan placement over the floorplan, as opposed to the conventional scan-to-scan alignment. The technical contributions lie in multi-modal image correspondence cues (between scans and schematic floorplan) as well as a novel coverage potential avoiding an inherent stacking bias. The proposed approach has been evaluated on five challenging large indoor spaces. To the best of our knowledge, we present the first effective system that utilizes a 2D floorplan image for building-scale 3D pointcloud alignment. The source code and the data are shared with the community to further enhance indoor mapping research.


title: Impact of singular excessive computer game and television exposure on sleep patterns and memory performance of school-aged children.

text:
OBJECTIVE
Television and computer game consumption are a powerful influence in the lives of most children. Previous evidence has supported the notion that media exposure could impair a variety of behavioral characteristics. Excessive television viewing and computer game playing have been associated with many psychiatric symptoms, especially emotional and behavioral symptoms, somatic complaints, attention problems such as hyperactivity, and family interaction problems. Nevertheless, there is insufficient knowledge about the relationship between singular excessive media consumption on sleep patterns and linked implications on children. The aim of this study was to investigate the effects of singular excessive television and computer game consumption on sleep patterns and memory performance of children.

METHODS
Eleven school-aged children were recruited for this polysomnographic study. Children were exposed to voluntary excessive television and computer game consumption. In the subsequent night, polysomnographic measurements were conducted to measure sleep-architecture and sleep-continuity parameters. In addition, a visual and verbal memory test was conducted before media stimulation and after the subsequent sleeping period to determine visuospatial and verbal memory performance.

RESULTS
Only computer game playing resulted in significant reduced amounts of slow-wave sleep as well as significant declines in verbal memory performance. Prolonged sleep-onset latency and more stage 2 sleep were also detected after previous computer game consumption. No effects on rapid eye movement sleep were observed. Television viewing reduced sleep efficiency significantly but did not affect sleep patterns.

CONCLUSIONS
The results suggest that television and computer game exposure affect children's sleep and deteriorate verbal cognitive performance, which supports the hypothesis of the negative influence of media consumption on children's sleep, learning, and memory.


title: A Nonisolated Three-Port DC–DC Converter and Three-Domain Control Method for PV-Battery Power Systems

text: In order to interface one photovoltaic (PV) port, one bidirectional battery port, and one load port of a PV-battery dc power system, a novel nonisolated three-port dc/dc converter named boost bidirectional buck converter (B3C) and its control method based on three-domain control are proposed in this paper. The power flow and operating principles of the proposed B3C are analyzed in detail, and then, the dc voltage relation between three ports is deduced. The proposed converter features high integration and single-stage power conversion from both PV and battery ports to the load port, thus leading to high efficiency. The current of all three ports is continuous; hence, the electromagnetic noise can be reduced. Furthermore, the control and modulation method for B3C has been proposed for realizing maximum power point tracking (MPPT), battery management, and bus voltage regulation simultaneously. The operation can be transited between conductance mode and MPPT mode automatically according to the load power. Finally, experimental verifications are given to illustrate the feasibility and effectiveness of the proposed topology and control method.


title: A CRISPR/Cas-Mediated Selection-free Knockin Strategy in Human Embryonic Stem Cells

text: The development of new gene-editing tools, in particular the CRISPR/Cas system, has greatly facilitated site-specific mutagenesis in human embryonic stem cells (hESCs), including the introduction or correction of patient-specific mutations for disease modeling. However, integration of a reporter gene into an endogenous locus in hESCs still requires a lengthy and laborious two-step strategy that involves first drug selection to identify correctly targeted clones and then excision of the drug-resistance cassette. Through the use of iCRISPR, an efficient gene-editing platform we recently developed, this study demonstrates a knockin strategy without drug selection for both active and silent genes in hESCs. Lineage-specific hESC reporter lines are useful for real-time monitoring of cell-fate decisions and lineage tracing, as well as enrichment of specific cell populations during hESC differentiation. Thus, this selection-free knockin strategy is expected to greatly facilitate the use of hESCs for developmental studies, disease modeling, and cell-replacement therapy.


title: Computer vision technology for food quality assurance Sundaram Gunasekaran

text: Quality assurance is one of the most important goals of any industry. The ability to manufacture high-quality products consistently is the basis for success in the highly competitive food industry. It encourages loyalty in customers and results in an expanding market share. The quality assurance methods used in the food industry have traditionally involved human visual inspection. Such methods are tedious, laborious, time-consuming and inconsistent. As plant throughput increased and quality tolerance tightened, it became necessary to employ automatic methods for quality assurance and quality control. During the past decade, computers have been introduced widely for automation of the quality assurance task. The food industry has been traditionally slow in adopting this new technology and moving towards programmable automation’. Renard? cited the following barriers to automating food processing plants: diverging opinions of what to measure because manufacturing processes themselves are not well understood; lack of appropriate standards against which properties can be compared; and the age of plants and equipment. However, the speed, reliability and cost savings inherent to process automation have provided sufficient impetus to the food industry to pursue aggressively the automatic, on-line control of all plant operations. Innovation of new technology often undergoes four phases3. The first is a research or discovery phase, in which new knowledge results in advances in the technology. Phase two is the early commercialization phase, in which a small market for the technology develops. Phase three represents the emergence of niche-specific applications of the technology. The last phase is that of widespread proliferation. A key characteristic of phase four is that the technology is transparent to users, that is,


title: Partial entity structure: a compact non-manifold boundary representation based on partial topological entities

text:
Non-manifold boundary representations have gained a great deal of popularity in recent years and various representation schemes have been proposed because they allow an even wider range of objects for various applications than conventional manifold representations. However, since these schemes are mainly interested in describing sufficient adjacency relationships of topological entities, the models represented in these schemes occupy too much storage space redundantly although they are very efficient in answering queries on topological adjacency relationships. Storage requirement can arise as a crucial problem in models in which topological data is more dominant than geometric data, such as tessellated or mesh models.
To solve this problem, in this paper, we propose a compact non-manifold boundary representation, called the partial entity structure, which allows the reduction of the storage size to half that of the radial edge structure, which is known as a time efficient non-manifold data structure, while allowing full topological adjacency relationships to be derived without loss of efficiency. This representation contains not only the conventional primitive entities like the region, face, edge, and vertex, but also the partial topological entities such as the partial-face, partial-edge, and partial-vertex for describing non-manifold conditions at vertices, edges, and faces. In order to verify the time and storage efficiency of the partial entity structure, the time complexity of basic query procedures and the storage requirement for typical geometric models are derived and compared with those of existing schemes. Furthermore, a set of the generalized Euler operators and typical high-level modeling capabilities such as Boolean operations are also implemented to confirm that our data structure is sound and easy to be manipulated.


title: Blood pressure estimation from pulse wave velocity measured on the chest

text: Recently, monitoring of blood pressure fluctuation in the daily life is focused on in the hypertension care area to predict the risk of cardiovascular and cerebrovascular disease events. In this paper, in order to propose an alternative system to the existed ambulatory blood pressure monitoring (ABPM) sphygmomanometer, we have developed a prototype of small wearable device consisting of electrocardiogram (ECG) and photopelthysmograph (PPG) sensors. In addition, it was examined whether blood pressure can be estimated based on pulse wave transit time (PWTT) only by attaching that device on the surface of the chest. We indicated that our system could also sense tendency of time-dependent change of blood pressure by measuring pulse of vessel over the sternum while its propagation distance is short.


title: School burnout and engagement in the context of demands-resources model.

text:
BACKGROUND
A four-wave longitudinal study tested the demands-resources model in the school context.

AIM
To examine the applicability of the demands-resources to the school context.

METHOD
Data of 1,709 adolescents were gathered, once during the transition from comprehensive to post-comprehensive education, twice during post-comprehensive education, and once 2 years later.

RESULTS
The hypotheses were supported, path analysis showing that study demands were related to school burnout a year later, while study resources were related to schoolwork engagement. Self-efficacy was positively related to engagement and negatively to burnout. School burnout predicted schoolwork engagement negatively 1 year later. Engagement was positively related to life satisfaction 2 years later, while burnout was related to depressive symptoms. Finally, burnout mediated the relationship between study demands and mental health outcomes.

CONCLUSIONS
The demands-resources model can usefully be applied to the school context, including the associations between school-related burnout and engagement among adolescents. The model comprises two processes, the energy-depleting process and the motivational process.


title: Search , Obfuscation , and Price Elasticities on the Internet 1

text: We examine the competition between a group of Internet retailers who operate in an environment where a price search engine plays a dominant role. We show that for some products in this environment, the easy price search makes demand tremendously pricesensitive. Retailers, though, engage in obfuscation—practices that frustrate consumer search or make it less damaging to firms—resulting in much less price sensitivity on some other products. We discuss several models of obfuscation and examine its effects on demand and markups empirically.


title: Cost-aware depth map estimation for Lytro camera

text: Since commercial light field cameras became available, the light field camera has aroused much interest from computer vision and image processing communities due to its versatile functions. Most of its special features are based on an estimated depth map, so reliable depth estimation is a crucial step. However, estimating depth on real light field cameras is a challenging problem due to noise and short baselines among sub-aperture images. We propose a depth map estimation method for light field cameras by exploiting correspondence and focus cues. We aggregate costs among all the sub-aperture images on cost volume to alleviate noise effects. With efficiency of the cost volume, cost-aware depth estimation is quickly achieved by discrete-continuous optimization. In addition, we analyze each property of correspondence and focus cues and utilize them to select reliable anchor points. A well reconstructed initial depth map from the anchors is shown to enhance convergence. We show our method outperforms the state-of-the-art methods by validating it on real datasets acquired with a Lytro camera.


title: OpenFlow: enabling innovation in campus networks

text: This whitepaper proposes OpenFlow: a way for researchers to run experimental protocols in the networks they use every day. OpenFlow is based on an Ethernet switch, with an internal flow-table, and a standardized interface to add and remove flow entries. Our goal is to encourage networking vendors to add OpenFlow to their switch products for deployment in college campus backbones and wiring closets. We believe that OpenFlow is a pragmatic compromise: on one hand, it allows researchers to run experiments on heterogeneous switches in a uniform way at line-rate and with high port-density; while on the other hand, vendors do not need to expose the internal workings of their switches. In addition to allowing researchers to evaluate their ideas in real-world traffic settings, OpenFlow could serve as a useful campus component in proposed large-scale testbeds like GENI. Two buildings at Stanford University will soon run OpenFlow networks, using commercial Ethernet switches and routers. We will work to encourage deployment at other schools; and We encourage you to consider deploying OpenFlow in your university network too


title: Software-Defined Wireless Networking Opportunities and Challenges for Internet-of-Things: A Review

text: With the emergence of Internet-of-Things (IoT), there is now growing interest to simplify wireless network controls. This is a very challenging task, comprising information acquisition, information analysis, decision-making, and action implementation on large scale IoT networks. Resulting in research to explore the integration of software-defined networking (SDN) and IoT for a simpler, easier, and strain less network control. SDN is a promising novel paradigm shift which has the capability to enable a simplified and robust programmable wireless network serving an array of physical objects and applications. This paper starts with the emergence of SDN and then highlights recent significant developments in the wireless and optical domains with the aim of integrating SDN and IoT. Challenges in SDN and IoT integration are also discussed from both security and scalability perspectives.


title: Publish/subscribe-enabled software defined networking for efficient and scalable IoT communications

text: The Internet of Things (IoT) is the result of many different enabling technologies such as embedded systems, wireless sensor networks, cloud computing, big-data, etc., which are used to gather, process, infer, and transmit data. Combining all these technologies requires a research effort to address all the challenges of these technologies, especially for sensing and delivering information from the physical world to cloud-hosted services. In this article we outline the most important issues related to standardization efforts, mobility of objects, networking and gateway access, and QoS support. In particular, we describe a novel IoT network architecture that integrates software defined networking (SDN) and data distribution service (DDS) middleware. The proposed architecture will improve service delivery of IoT systems and will bring flexibility to the network.


title: Maestro: A System for Scalable OpenFlow Control

text: The fundamental feature of an OpenFlow network is that the controller is responsible for the initial establishment of every flow by contacting related switches. Thus the performance of the controller could be a bottleneck. This paper shows how this fundamental problem is addressed by parallelism. The state of the art OpenFlow controller, called NOX, achieves a simple programming model for control function development by having a single-threaded event-loop. Yet NOX has not considered exploiting parallelism. We propose Maestro which keeps the simple programming model for programmers, and exploits parallelism in every corner together with additional throughput optimization techniques. We experimentally show that the throughput of Maestro can achieve near linear scalability on an eight core server machine. Keywords-OpenFlow, network management, multithreading, performance optimization


title: A Survey of Fog Computing: Concepts, Applications and Issues

text: Despite the increasing usage of cloud computing, there are still issues unsolved due to inherent problems of cloud computing such as unreliable latency, lack of mobility support and location-awareness. Fog computing can address those problems by providing elastic resources and services to end users at the edge of network, while cloud computing are more about providing resources distributed in the core network. This survey discusses the definition of fog computing and similar concepts, introduces representative application scenarios, and identifies various aspects of issues we may encounter when designing and implementing fog computing systems. It also highlights some opportunities and challenges, as direction of potential future work, in related techniques that need to be considered in the context of fog computing.


title: Grounding New Words on the Physical World in Multi-Domain Human-Robot Dialogues

text: This paper summarizes our ongoing project on developing an architecture for a robot that can acquire new words and their meanings while engaging in multidomain dialogues. These two functions are crucial in making conversational service robots work in real tasks in the real world. Household robots and office robots need to be able to work in multiple task domains and they also need to engage in dialogues in multiple domains corresponding to those task domains. Lexical acquisition is necessary because speech understanding cannot be done without enough knowledge on words that are possibly spoken in the task domain. Our architecture is based on a multi-expert model in which multiple domain experts are employed and one of them is selected based on the user utterance and the situation to engage in the control of the dialogue and physical behaviors. We incorporate experts that have an ability to acquire new lexical entries and their meanings grounded on the physical world through spoken interactions. By appropriately selecting those experts, lexical acquisition in multi-domain dialogues becomes possible. An example robotic system based on this architecture that can acquire object names and location names demonstrates the viability of the architecture.


title: A Brief Tour Through Provenance in Scientific Workflows and Databases

text: Within computer science, the term provenance has multiple meanings, due to different motivations, perspectives, and assumptions prevalent in the respective communities. This chapter provides a high-level “sightseeing tour” of some of those different notions and uses of provenance in scientific workflows and databases.


title: The raven roosting optimisation algorithm

text: A significant stream of literature which draws inspiration from the foraging activities of various organisms to design optimisation algorithms has emerged over the past decade. The success of these algorithms across a wide variety of application domains has spurred interest in the examination of the foraging behaviours of other organisms to develop novel and powerful, optimisation algorithms. A variety of animals, including some species of birds and bats, engage in social roosting whereby large numbers of conspecifics gather together to roost, either overnight or for longer periods. It has been claimed that these roosts can serve as information centres to spread knowledge concerning the location of food resources in the environment. In this paper we look at the social roosting and foraging behaviour of one species of bird, the common raven, and take inspiration from this to design a novel optimisation algorithm which we call the raven roosting optimisation algorithm. The utility of the algorithm is assessed on a series of benchmark problems and the results are found to be competitive. We also provide a novel taxonomy which classifies foraging-inspired optimisation algorithms based on the underlying social communication mechanism embedded in the algorithms.


title: Quantifying driver stress: developing a system for collecting and processing bio-metric signals in natural situations.

text: A system for quantifying the physiological features of emotional stress is being developed for use during a driving task. Two prototypes, using sensors that measure the driver's skin conductance, respiration, muscle activity, and heart activity are presented. The first system allows sampling rates of 200 Hz on two fast channels and 20 Hz on six additional channels. It uses a wearable computer to do real-time processing on the signals and has an attached digital camera which was used to capture images of the driver's facial expression once every minute. The second system uses a car-based computer that allows a sampling rate of 1984 samples per second on eight channels. This system uses multiple video cameras to continuously capture the driver's facial expression and road conditions. The data is then synchronized with the physiological signals using a video quad-splitter. The methods for extracting physiological features in the driving environment are discussed, including measurement of the skin conductance orienting response, muscle activity, pulse, and respiration patterns. Preliminary studies show how using multiple modalities of sensors can help discriminate reactions to driving events and how individual's response to similar driving conditions can vary from day to day.


title: SOCIAL SPACE AND SYMBOLIC POWER *

text: Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at http://www.jstor.org/journals/asa.html. Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission.


title: A customizable pipeline for social media text normalization

text: Social networks are persistently generating text-based data that encapsulate vast amounts of knowledge. However, the presence of non-standard terms and misspellings in texts originating from social networks poses a crucial challenge for natural language processing and machine learning systems that attempt to mine this knowledge. To address this problem, we propose a sequential, modular, and hybrid pipeline for social media text normalization. In the first phase, text preprocessing techniques and social media-specific vocabularies gathered from publicly available sources are used to transform, with high precision, out-of-vocabulary terms into in-vocabulary terms. A sequential language model, generated using the partially normalized texts from the first phase, is then utilized to normalize short, high-frequency, ambiguous terms. A supervised learning module is employed to normalize terms based on a manually annotated training corpus. Finally, a tunable, distributed language model-based backoff module at the end of the pipeline enables further customization of the system to specific domains of text. We performed intrinsic evaluations of the system on a publicly available domain-independent dataset from Twitter, and our system obtained an F-score of 0.836, outperforming other benchmark systems for the task. We further performed brief, task-oriented evaluations of the system to illustrate the customizability of the system to domain-specific tasks and the effects of normalization on downstream applications. The modular design enables the easy customization of the system to distinct types domain-specific social media text, in addition to its off-the-shelf application to generic social media text.


title: Friend or Foe: Cyberbullying in Social Network Sites

text: As the use of social media technologies proliferates in organizations, it is important to understand the nefarious behaviors, such as cyberbullying, that may accompany such technology use and how to discourage these behaviors. We draw from neutralization theory and the criminological theory of general deterrence to develop and empirically test a research model to explain why cyberbullying may occur and how the behavior may be discouraged. We created a research model of three second-order formative constructs to examine their predictive influence on intentions to cyberbully. We used PLS- SEM to analyze the responses of 174 Facebook users in two different cyberbullying scenarios. Our model suggests that neutralization techniques enable cyberbullying behavior and while sanction certainty is an important deterrent, sanction severity appears ineffective. We discuss the theoretical and practical implications of our model and results.


title: Face Recognition: Features Versus Templates

text: Over the last 20 years, several different techniques have been proposed for computer recognition of human faces. The purpose of this paper is to compare two simple but general strategies on a common database (frontal images of faces of 47 people: 26 males and 21 females, four images per person). We have developed and implemented two new algorithms; the first one is based on the computation of a set of geometrical features, such as nose width and length, mouth position, and chin shape, and the second one is based on almost-grey-level template matching. The results obtained on the testing sets (about 90% correct recognition using geometrical features and perfect recognition using template matching) favor our implementation of the template-matching approach.


title: Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond

text: ive Text Summarization using Sequence-to-sequence RNNs and Beyond Ramesh Nallapati IBM Watson nallapati@us.ibm.com Bowen Zhou IBM Watson zhou@us.ibm.com Cicero dos Santos IBM Watson


title: QR Code Security: A Survey of Attacks and Challenges for Usable Security

text: QR (Quick Response) codes are two-dimensional barcodes with the ability to encode different types of information. Because of their high information density and robustness, QR codes have gained popularity in various fields of application. Even though they offer a broad range of advantages, QR codes pose significant security risks. Attackers can encode malicious links that lead e.g. to phishing sites. Such malicious QR codes can be printed on small stickers and replace benign ones on billboard advertisements. Although many real world examples of QR code based attacks have been reported in the media, only little research has been conducted in this field and almost no attention has been paid on the interplay of security and human-computer interaction. In this work, we describe the manifold use cases of QR codes. Furthermore, we analyze the most significant attack scenarios with respect to the specific use cases. Additionally, we systemize the research that has already been conducted and identified usable security and security awareness as the main research challenges. Finally we propose design requirements with respect to the QR code itself, the reader application and usability aspects in order to support further research into to making QR code processing both secure and usable.