title: A comprehensive framework for modeling set-based business rules during conceptual database design

abstract: Business rules are the basis of any organization. From an information systems perspective, these business rules function as constraints on a database helping ensure that the structure and content of the real world sometimes referred to as miniworld--is accurately incorporated into the database. It is important to elicit these rules during the analysis and design stage, since the captured rules are the basis for subsequent development of a business constraints repository. We present a taxonomy for set-based business rules, and describe an overarching framework for modeling rules that constrain the cardinality of sets. The proposed framework results in various types constraints, i.e.. attribute, class, participation, projection, co-occurrence, appearance and overlappinq, on a semantic model that supports abstractions like classification, generalization/specialization, aggregation and association. We formally define the syntax of our proposed framework in Backus-Naur Form and explicate the semantics using first-order logic. We describe partial ordering in the constraints and define the concept of metaconstraints, which can be used for automatic constraint consistency checking during the design stage itself. We demonstrate the practicality of our approach with a case study and show how our approach to modeling business rules seamlessly integrates into existing database design methodology. Via our proposed framework, we show how explicitly capturing data semantics will help bridge the semantic gap between the real world and its representation in an information system.


title: ADVANCES IN NEUROPSYCHIATRY: Neurocognitive models of aggression, the antisocial personality disorders, and psychopathy

abstract: This paper considers neurocognitive models of aggression and relates them to explanations of the antisocial personality disorders. Two forms of aggression are distinguished: reactive aggression elicited in response to frustration/threat and goal directed, instrumental aggression. It is argued that different forms of neurocognitive model are necessary to explain the emergence of these different forms of aggression. Impairments in executive emotional systems (the somatic marker system or the social response reversal system) are related to reactive aggression shown by patients with "acquired sociopathy" due to orbitofrontal cortex lesions. Impairment in the capacity to form associations between emotional unconditioned stimuli, particularly distress cues, and conditioned stimuli (the violence inhibition mechanism model) is related to the instrumental aggression shown by persons with developmental psychopathy.


title: Musical genre classification of audio signals

abstract: Musical genres are categorical labels created by humans to characterize pieces of music. A musical genre is characterized by the common characteristics shared by its members. These characteristics typically are related to the instrumentation, rhythmic structure, and harmonic content of the music. Genre hierarchies are commonly used to structure the large collections of music available on the Web. Currently musical genre annotation is performed manually. Automatic musical genre classification can assist or replace the human user in this process and would be a valuable addition to music information retrieval systems. In addition, automatic musical genre classification provides a framework for developing and evaluating features for any type of content-based analysis of musical signals. In this paper, the automatic classification of audio signals into an hierarchy of musical genres is explored. More specifically, three feature sets for representing timbral texture, rhythmic content and pitch content are proposed. The performance and relative importance of the proposed features is investigated by training statistical pattern recognition classifiers using real-world audio collections. Both whole file and real-time frame-based classification schemes are described. Using the proposed feature sets, classification of 61% for ten musical genres is achieved. This result is comparable to results reported for human musical genre classification.


Clickstream Clustering using Weighted Longest Common Subsequences


title: Harnessing the power of FPGAs using altera's OpenCL compiler

abstract: In recent years, Field-Programmable Gate Arrays have become extremely powerful computational platforms that can efficiently solve many complex problems. The most modern FPGAs comprise effectively millions of programmable elements, signal processing elements and high-speed interfaces, all of which are necessary to deliver a complete solution. The power of FPGAs is unlocked via low-level programming languages such as VHDL and Verilog, which allow designers to explicitly specify the behavior of each programmable element. While these languages provide a means to create highly efficient logic circuits, they are akin to "assembly language" programming for modern processors. This is a serious limiting factor for both productivity and the adoption of FPGAs on a wider scale. In this talk, we use the OpenCL language to explore techniques that allow us to program FPGAs at a level of abstraction closer to traditional software-centric approaches. OpenCL is an industry standard parallel language based on 'C' that offers numerous advantages that enable designers to take full advantage of the capabilities offered by FPGAs, while providing a high-level design entry language that is familiar to a wide range of programmers. To demonstrate the advantages a high-level programming language can offer, we demonstrate how to use Altera's OpenCL Compiler on a set of case studies. The first application is single-precision general-element matrix multiplication (SGEMM). It is an example of a highly-parallel algorithm for which an efficient circuit structures are well known. We show how this application can be implemented in OpenCL and how the high-level description can be optimized to generate the most efficient circuit in hardware. The second application is a Fast Fourier Transform (FFT), which is a classical FPGA benchmark that is known to have a good implementation on FPGAs. We show how we can implement the FFT algorithm, while exploring the many different possible architectural choices that lead to an optimized implementation for a given FPGA. Finally, we discuss a Monte-Carlo Black-Scholes simulation, which demonstrates the computational power of FPGAs. We describe how a random number generator in conjunction with computationally intensive operations can be harnessed on an FPGA to generate a high-speed benchmark, which also consumes far less power than the same benchmark running on a comparable GPU. We conclude the tutorial with a set of live demonstrations. Through this tutorial we show the benefits high-level languages offer for system-level design and productivity. In particular, Altera's OpenCL compiler is shown to enable high-performance application design that fully utilizes capabilities of modern FPGAs.


title: Prevalence of daily breakfast intake, iron deficiency anaemia and awareness of being anaemic among Saudi school students.

abstract: Iron deficiency anaemia is one of most prevalent nutritional disorders worldwide. It is known to affect the health and cognitive ability of children and adolescents. Studies in Saudi Arabia concentrate only on the population of young children and pregnant females or girls. Studies on the whole school student population is lacking. The objectives of this study were to identify the nutritional habits and the prevalence of anaemia among school students in Jeddah, as well as to recognize the students' awareness of their anaemic nutritional status. Data were collected from a sample of Saudi school children in Jeddah City from 42 boys' and 42 girls' schools during the month of April 2000. Data collection was done by an in-person interview to collect socio-demographic factors, nutritional habits, weight and height. Haemoglobin was measured in a sample of 800 students selected at random from both genders and different age groups. Anaemia was defined according to the new WHO cut-off levels for haemoglobin as: blood haemoglobin <11.5 g/dl for the 5-11 years boys and girls; <12.0 g/dl for 12-14 years boys and girls; <12.0 g/dl for 15+ girls and <13.0 g/dl for 15+ years boys. Proportion and 95% confidence intervals (CI) were calculated and significance was considered when the 95% CI did not overlap. Anaemia was reported among 20.5% of school students. Anaemia was more prevalent among students of at least 12 years as compared to the younger age group. Also, anaemia was more marked among governmental school attendees and those born to low-educated mothers. Menstruating girls were at around double the risk of being anaemic than non-menstruating girls. Anaemia was associated with negative impact on school performance and was more marked among those who failed their exams as compared to students with excellent results. Skipping breakfast was reported by 14.9% of students and this habit did not differ by age, sex, body mass index or social class. Skipping breakfast was more marked among students with poor school performance as compared to those with very good or excellent results. Only 34.1% of anaemic school students were aware of being anaemic. Awareness was nearly equal in all age groups and social classes but girls were more aware of their anaemic status than boys. Iron deficiency anaemia appears to be prevalent among school students. At age 12 years and over, low social class and menstruating girls constitute the high-risk groups. Screening is recommended for high-risk groups and school health programs are crucial to improve students' nutritional habits, knowledge and awareness.


title: Facebook self-disclosure: Examining the role of traits, social cohesion, and motives

abstract: Facebook has been shown to be the most popular social network in the United States. Facebook not only has implications in the online world, but face-to-face connections are also affected by this medium. This study explores the uses of Facebook for self-disclosure behavior utilizing the uses and gratifications perspective. Using a convenience sample of Facebook users, this study examines individual and sociological factors as well as Facebook motives to discover the impact on depth, breadth, and amount of user self-disclosure. Path analyses showed that the Big Five personality factors, self-esteem, social cohesion, and motives contribute to self-disclosure dimensions. However, demographic variables did not impact disclosiveness. Limitations are discussed and directions for future research are proposed.


title: State of the art of virtual reality technology

abstract: In the past three years, the so-called second wave of Virtual Reality (VR) has brought us a vast amount of new displays and input devices. Not only new hardware has entered the consumer market providing affordable pricing models but also completely new technologies are being designed and developed. Additionally new concepts for handling existing problems on the hardware and software side of the VR technology are constantly being introduced. This software and hardware development is mainly lead by enthusiasts interested in the domain of VR opposed to the established scientific community, which already partially makes use of the newly available technology. Besides Head-Mounted Displays (HMDs), either cable-based or mobile, other devices like haptics devices, controllers, vests, omnidirectional treadmills, tracking technologies, as well as optical scanners for gesture-based interaction are gaining importance in the field of commodity VR. Most of these technologies are already precise and robust enough to be used for professional operation and scientific experiments. The topics discussed are the common issues with the new technologies including the approaches to solve them as for example motion-to-photon latency, barrel distortion, and low-persistence displays. Additionally an in-depth analysis of the available solutions expected to hit the market is provided. A taxonomy categorising the current developments with the chosen implementation approaches will be given. The paper analyses the state of technological advancements in the field and provides an extensive overview on the current development considering the upcoming devices and the advancements from the software side.


title: Entity Embeddings of Categorical Variables.

abstract: We map categorical variables in a function approximation problem into Euclidean spaces, which are the entity embeddings of the categorical variables. The mapping is learned by a neural network during the standard supervised training process. Entity embedding not only reduces memory usage and speeds up neural networks compared with one-hot encoding, but more importantly by mapping similar values close to each other in the embedding space it reveals the intrinsic properties of the categorical variables. We applied it successfully in a recent Kaggle competition and were able to reach the third position with relative simple features. We further demonstrate in this paper that entity embedding helps the neural network to generalize better when the data is sparse and statistics is unknown. Thus it is especially useful for datasets with lots of high cardinality features, where other methods tend to overfit. We also demonstrate that the embeddings obtained from the trained neural network boost the performance of all tested machine learning methods considerably when used as the input features instead. As entity embedding defines a distance measure for categorical variables it can be used for visualizing categorical data and for data clustering.


title: Review article: Polymer-matrix Nanocomposites, Processing, Manufacturing, and Application: An Overview

abstract: This review is designed to be a comprehensive source for polymer nanocomposite research, including fundamental structure/property relationships, manufacturing techniques, and applications of polymer nanocomposite materials. In addition to presenting the scientific framework for the advances in polymer nanocomposite research, this review focuses on the scientific principles and mechanisms in relation to the methods of processing and manufacturing with a discussion on commercial applications and health/safety concerns (a critical issue for production and scale-up). Hence, this review offers a comprehensive discussion on technology, modeling, characterization, processing, manufacturing, applications, and health/safety concerns for polymer nanocomposites.


title: Deep Learning Based Oil Palm Tree Detection and Counting for High-Resolution Remote Sensing Images

abstract: Oil palm trees are important economic crops in Malaysia and other tropical areas. The number of oil palm trees in a plantation area is important information for predicting the yield of palm oil, monitoring the growing situation of palm trees and maximizing their productivity, etc. In this paper, we propose a deep learning based framework for oil palm tree detection and counting using high-resolution remote sensing images for Malaysia. Unlike previous palm tree detection studies, the trees in our study area are more crowded and their crowns often overlap. We use a number of manually interpreted samples to train and optimize the convolutional neural network (CNN), and predict labels for all the samples in an image dataset collected through the sliding window technique. Then, we merge the predicted palm coordinates corresponding to the same palm tree into one palm coordinate and obtain the final palm tree detection results. Based on our proposed method, more than 96% of the oil palm trees in our study area can be detected correctly when compared with the manually interpreted ground truth, and this is higher than the accuracies of the other three tree detection methods used in this study.


title: Babylon Parallel Text Builder: Gathering Parallel Texts for Low-Density Languages

abstract: This paper describes BABYLON, a system that attempts to overcome the shortage of parallel texts in low-density languages by supplementing existing parallel texts with texts gathered automatically from the Web. In addition to the identification of entire Web pages, we also propose a new feature specifically designed to find parallel text chunks within a single document. Experiments carried out on the Quechua-Spanish language pair show that the system is successful in automatically identifying a significant amount of parallel texts on the Web. Evaluations of a machine translation system trained on this corpus indicate that the Web-gathered parallel texts can supplement manually compiled parallel texts and perform significantly better than the manually compiled texts when tested on other Web-gathered


title: Parsing with Compositional Vector Grammars

abstract: Natural language parsing has typically been done with small sets of discrete categories such as NP and VP, but this representation does not capture the full syntactic nor semantic richness of linguistic phrases, and attempts to improve on this by lexicalizing phrases or splitting categories only partly address the problem at the cost of huge feature spaces and sparseness. Instead, we introduce a Compositional Vector Grammar (CVG), which combines PCFGs with a syntactically untied recursive neural network that learns syntactico-semantic, compositional vector representations. The CVG improves the PCFG of the Stanford Parser by 3.8% to obtain an F1 score of 90.4%. It is fast to train and implemented approximately as an efficient reranker it is about 20% faster than the current Stanford factored parser. The CVG learns a soft notion of head words and improves performance on the types of ambiguities that require semantic information such as PP attachments.


title: A Framework for Resilience-Based Governance of Social-Ecological Systems

abstract: Panarchy provides a heuristic to characterize the cross-scale dynamics of social-ecological systems and a framework for how governance institutions should behave to be compatible with the ecosystems they manage. Managing for resilience will likely require reform of law to account for the dynamics of social-ecological systems and achieve a substantive mandate that accommodates the need for adaptation. In this paper, we suggest expansive legal reform by identifying the principles of reflexive law as a possible mechanism for achieving a shift to resilience-based governance and leveraging cross-scale dynamics to provide resilience-based responses to increasingly challenging environmental conditions.


title: Linking GloVe with word2vec

abstract: The Global Vectors for word representation (GloVe), introduced by Jeffrey Pennington et al. is reported to be an efficient and effective method for learning vector representations of words. State-of-the-art performance is also provided by skip-gram with negative-sampling (SGNS) implemented in the word2vec tool. In this note, we explain the similarities between the training objectives of the two models, and show that the objective of SGNS is similar to the objective of a specialized form of GloVe, though their cost functions are defined differently.


title: 3D technology in libraries: Applications for teaching and research

abstract: As emerging technologies appear on the horizon of education every day, they are changing the world of teaching and research continuously with their adoption and integration into libraries, classrooms and research labs. Most of us are familiar with the libraries inside a lab, but hosting a lab inside a library holds tremendous potential for research and instruction. In this paper, we will feature a successful effort of integrating 3D technology and a visualization wall in the Marston Science Library at the University of Florida. We will describe how 3D printing was achieved and will highlight the benefit of the technologies related to the data visualization wall as well as recent 3D models made at the library used for research and teaching. The paper will also describe the lessons learned such as the challenges of maintaining these technologies staffing, maintenance, and environmental health and safety requirements.


title: A framework for analysing migrant health policies in Europe

abstract: Evidence suggests migrants experience inequalities in health and access to health care. However, to date there has been little analysis of the policies employed to address these inequalities. This article develops a framework to compare migrant health policies, focusing on England, Italy, the Netherlands and Sweden. The first issue addressed in the framework is data collection. All four countries collect migrant health data, but many methodological limitations remain. The second issue is targeting of population groups. Countries typically focus either on first generation immigrants or on ethnic minorities, but not both, despite the often divergent needs of the two groups. Another issue is whether specific diseases should take priority in migrant health policy. While communicable diseases, sexual and reproductive health and mental health have been targeted, there may be a lack of attention paid to lifestyle related risk factors and preventive care. Fourthly, decisions about the mix of demand and supply-side interventions need to be made and evaluated. Finally, the challenge of implementation is discussed. Although migrant health policy has been elaborated in the four countries, implementation has not necessarily reflected this on the ground. These experiences signal important policy issues and options in the development of migrant health policies in Europe.


Enterotypes of thehumangutmicrobiome


title: Uplink Vs. Downlink NOMA in Cellular Networks: Challenges and Research Directions

abstract: Non-orthogonal multiple access (NOMA) is a promising multiple access technique for 5G wireless technology. In this paper, we first discuss the fundamentals of uplink and downlink NOMA transmissions in a cellular system and outline their key distinctions in terms of implementation complexity, detection and decoding at the SIC receiver(s), and the intra-cell and inter-cell interferences. Later, for both downlink and uplink NOMA, for each individual user in a two-user NOMA cluster, we theoretically derive the NOMA dominant condition, which refers to the condition under which the spectral efficiency gains of NOMA are guaranteed compared to conventional orthogonal multiple access (OMA). The conditions, which are distinct for uplink and downlink as well as for each individual user, provide direct insights into selecting appropriate users in two-user NOMA clusters. Numerical results show the significance of the derived conditions for user selection in uplink/downlink NOMA clusters and provide a comparison to the random user selection. Finally, a brief summary of the recent research investigations is provided which is followed by a discussion on the research challenges and future research directions.


title: The Semantic Distance Task: Quantifying Semantic Distance With Semantic Network Path Length

abstract: Semantic distance is a determining factor in cognitive processes, such as semantic priming, operating upon semantic memory. The main computational approach to compute semantic distance is through latent semantic analysis (LSA). However, objections have been raised against this approach, mainly in its failure at predicting semantic priming. We propose a novel approach to computing semantic distance, based on network science methodology. Path length in a semantic network represents the amount of steps needed to traverse from 1 word in the network to the other. We examine whether path length can be used as a measure of semantic distance, by investigating how path length affect performance in a semantic relatedness judgment task and recall from memory. Our results show a differential effect on performance: Up to 4 steps separating between word-pairs, participants exhibit an increase in reaction time (RT) and decrease in the percentage of word-pairs judged as related. From 4 steps onward, participants exhibit a significant decrease in RT and the word-pairs are dominantly judged as unrelated. Furthermore, we show that as path length between word-pairs increases, success in free- and cued-recall decreases. Finally, we demonstrate how our measure outperforms computational methods measuring semantic distance (LSA and positive pointwise mutual information) in predicting participants RT and subjective judgments of semantic strength. Thus, we provide a computational alternative to computing semantic distance. Furthermore, this approach addresses key issues in cognitive theory, namely the breadth of the spreading activation process and the effect of semantic distance on memory retrieval. (PsycINFO Database Record


title: Augmenting a voice and facial expression control of a robotic wheelchair with assistive navigation

abstract: In this work, we are presenting a navigation framework for electric wheelchairs which integrates various alternative input interfaces (voice control with Google Glass, voice control with CMU Sphinx, and facial expression control with Emotiv EPOC) with assistive navigation. Assistive navigation compensates for the limitations of the alternative interfaces by implementing obstacle avoidance. This enables people with limited control over their limbs to freely navigate within indoor environments. A set of use cases designed to ensure safe and reliable navigation is also presented.


title: A 1.25 ps Resolution 8b Cyclic TDC in 0.13 $\mu$m CMOS

abstract: This paper describes the first implementation of the well-known cyclic ADC architecture into a time-to-digital converter. With an asynchronous clocking scheme, an all-digital 1.5b time-domain multiplying DAC (MDAC) is repetitively used for 8b conversion. The MDAC is based on a 2 × time amplifier with an offset-compensated gain calibration scheme. The proposed cyclic TDC, fabricated in a 0.13 μm CMOS, shows a resolution of 1.25 ps with a total conversion range of ±160 ps, the maximum operating frequency of 100 MHz, and a power consumption of 4.3 mW at 50 MHz. The measured DNL and INL are ± 0.7 LSB and - 3 to + 1 LSB, respectively.


title: Acquiring competitive intelligence from social media

abstract: Competitive intelligence is the art of defining, gathering and analyzing intelligence about competitor's products, promotions, sales etc. from external sources. The Web comes across as an important source for gathering competitive intelligence. News, blogs, as well as social media not only provide competitors information but also provide direct comparison of customer behaviors with respect to different verticals among competing organizations. This paper discusses methodologies to obtain competitive intelligence from different types of web resources including social media using a wide array of text mining techniques. It provides some results from case-studies to show how the gathered information can be integrated with structured data and used to explain business facts and thereby adopted for future decision making.


www.elsevier.com/locate/acha 3D Fourier based discrete Radon transform


title: An Online Learning Framework for Refining Recency Search Results with User Click Feedback

abstract: Traditional machine-learned ranking systems for Web search are often trained to capture stationary relevance of documents to queries, which have limited ability to track nonstationary user intention in a timely manner. In recency search, for instance, the relevance of documents to a query on breaking news often changes significantly over time, requiring effective adaptation to user intention. In this article, we focus on recency search and study a number of algorithms to improve ranking results by leveraging user click feedback. Our contributions are threefold. First, we use commercial search engine sessions collected in a random exploration bucket for reliable offline evaluation of these algorithms, which provides an unbiased comparison across algorithms without online bucket tests. Second, we propose an online learning approach that reranks and improves the search results for recency queries near real-time based on user clicks. This approach is very general and can be combined with sophisticated click models. Third, our empirical comparison of a dozen algorithms on real-world search data suggests importance of a few algorithmic choices in these applications, including generalization across different query-document pairs, specialization to popular queries, and near real-time adaptation of user clicks for reranking.


title: An Effective Approach for Video-on-Demand Streaming Services Based on BitTorrent

abstract: In this paper, an effective solution for video-on- demand (VoD) streaming services based on BitTorrent mechanism is present. In this solution, several modifications have been incorporated into BitTorrent to support high quality streaming content delivery. First, pieces are selected based on the double windows algorithm instead of the rarest-first policy. Second, some neighbor peers are selected based on the time when the peers join in the system not randomly. Finally, each peer gives more free tries to the early joined peers to let them participate sooner in the media distribution. The result shows that our modifications shorten the startup latency, improve the continuity as well as the bandwidth utilization, and a good streaming quality can be achieved. Keywords-BitTorrent;VoD;streaming;piece selection I. INTRODUCITON In the recent years, with the rapid development of network technology and increasing of the demand for streaming applications, P2P streaming has captured the interest of the research community as well as industry. P2P networks are autonomous system with the advantage of self-organization and self-adaption, and can overcome many limitations of traditional client-server architecture. P2P applications are categorized into tree-based approach and mesh-based approach. DirectStream and ZIGZAG(1) try to construct an overlay tree in order to minimize the end-to-end delay and maximize the utilization of the bandwidth of the peers. SplitStream(2) and CoopNET transfer data using multiple distribution trees and assume the presence of uninterested nodes to forward traffic. CoolStreaming(3) adpots mesh-based overlay network technology.


title: Exception handling in workflow-driven Web applications

abstract: As the Web becomes a platform for implementing B2B applications, the need arises of Web conceptual models for describing Web oriented workflow applications implementing business processes. In this context, new problems about process correctness arise, due to the loose control of Web applications upon the behavior of their Web clients. Indeed, incoherent user's behavior can lead to inconsistent processes.This paper presents a high level approach to the management of exceptions that occur during the execution of processes on the Web. We present a classification of exceptions that can be raised inside workflow-driven Web applications, and recovery policies to retrieve coherent status and data after an exception. We devise these concepts at high level and then we exploit them using a Web modeling language (WebML) that in turn provides development facilities like automatic code generation, validation of hypertext models, and so on. An industrial implementation experience is briefly presented too.


title: A TAXONOMY OF MIXED REALITY VISUAL DISPLAYS

abstract: Paul Milgram received the B.A.Sc. degree from the University of Toronto in 1970, the M.S.E.E. degree from the Technion (Israel) in 1973 and the Ph.D. degree from the University of Toronto in 1980. From 1980 to 1982 he was a ZWO Visiting Scientist and a NATO Postdoctoral in the Netherlands, researching automobile driving behaviour. From 1982 to 1984 he was a Senior Research Engineer in Human Engineering at the National Aerospace Laboratory (NLR) in Amsterdam, where his work involved the modelling of aircraft flight crew activity, advanced display concepts and control loops with human operators in space teleoperation. Since 1986 he has worked at the Industrial Engineering Department of the University of Toronto, where he is currently an Associate Professor and Coordinator of the Human Factors Engineering group. He is also cross appointed to the Department of Psychology. In 1993-94 he was an invited researcher at the ATR Communication Systems Research Laboratories, in Kyoto, Japan. His research interests include display and control issues in telerobotics and virtual environments, stereoscopic video and computer graphics, cognitive engineering, and human factors issues in medicine. He is also President of Translucent Technologies, a company which produces "Plato" liquid crystal visual occlusion spectacles (of which he is the inventor), for visual and psychomotor research.


title: Design science and the accumulation of knowledge in the information systems discipline

abstract: Design science has emerged as an important research paradigm in the information systems (IS) discipline, and much has been written on how it should be conducted and evaluated (e.g., Hevner et al. [2004]; Walls et al. [1992]; Vaishnavi and Kuechler [2007]; Kuechler and Vaishnavi [2008]; Peffers et al. [2007]; Iivari [2010]; Pigneur [2011]). We contend that, as a socio-technical discipline, IS research must address the interaction between design and behavior. We begin with a background discussion of what we mean by IS research and the nature of the relationship between design and behavioral approaches to IS research. We discuss the nature of design, design science, and IT artifacts within information systems research and describe the importance of linking design and behavioral perspectives. We illustrate several key points using selected articles recently published in ACM Transactions on Management Information Systems [Schmidt-Rauch and Schwabe 2011; Lau et al. 2011]. We conclude with a vision of IS research in which the capabilities and affordances of IT artifacts are incorporated into behavioral studies; the results of behavioral studies are utilized in the development and evaluation of IT artifacts; and both behavioral and design perspectives are used to address the important problems of our constituent community.


title: The Sheaf-Theoretic Structure Of Non-Locality and Contextuality

abstract: We use the mathematical language of sheaf theory to give a unified treatment of non-locality and contextuality, in a setting that generalizes the familiar probability tables used in non-locality theory to arbitrary measurement covers; this includes Kochen–Specker configurations and more. We show that contextuality, and non-locality as a special case, correspond exactly to obstructions to the existence of global sections. We describe a linear algebraic approach to computing these obstructions, which allows a systematic treatment of arguments for non-locality and contextuality. We distinguish a proper hierarchy of strengths of no-go theorems, and show that three leading examples—due to Bell, Hardy and Greenberger, Horne and Zeilinger, respectively—occupy successively higher levels of this hierarchy. A general correspondence is shown between the existence of local hidden-variable realizations using negative probabilities, and no-signalling; this is based on a result showing that the linear subspaces generated by the non-contextual and no-signalling models, over an arbitrary measurement cover, coincide. Maximal non-locality is generalized to maximal contextuality, and characterized in purely qualitative terms, as the non-existence of global sections in the support. A general setting is developed for the Kochen–Specker-type results, as generic, model-independent proofs of maximal contextuality, and a new combinatorial condition is given, which generalizes the ‘parity proofs’ commonly found in the literature. We also show how our abstract setting can be represented in quantum mechanics. This leads to a strengthening of the usual no-signalling theorem, which shows that quantum mechanics obeys no-signalling for arbitrary families of commuting observables, not just those represented on different factors of a tensor product.


title: Augmenting Wikipedia with Named Entity Tags

abstract: Wikipedia is the largest organized knowledge repository on the Web, increasingly employed by natural language processing and search tools. In this paper, we investigate the task of labeling Wikipedia pages with standard named entity tags, which can be used further by a range of information extraction and language processing tools. To train the classifiers, we manually annotated a small set of Wikipedia pages and then extrapolated the annotations using the Wikipedia category information to a much larger training set. We employed several distinct features for each page: bag-of-words, page structure, abstract, titles, and entity mentions. We report high accuracies for several of the classifiers built. As a result of this work, a Web service that classifies any Wikipedia page has been made available to the academic community.


title: Conditional variable importance for random forests

abstract: BackgroundRandom forests are becoming increasingly popular in many scientific fields because they can cope with "small n large p" problems, complex interactions and even highly correlated predictor variables. Their variable importance measures have recently been suggested as screening tools for, e.g., gene expression studies. However, these variable importance measures show a bias towards correlated predictor variables.ResultsWe identify two mechanisms responsible for this finding: (i) A preference for the selection of correlated predictors in the tree building process and (ii) an additional advantage for correlated predictor variables induced by the unconditional permutation scheme that is employed in the computation of the variable importance measure. Based on these considerations we develop a new, conditional permutation scheme for the computation of the variable importance measure.ConclusionThe resulting conditional variable importance reflects the true impact of each predictor variable more reliably than the original marginal approach.


title: Computational methods in drug discovery

abstract: The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein–ligand docking, pharmacophore modeling and QSAR techniques are reviewed.


title: Symbolic representations of time series applied to biometric recognition based on ECG signals

abstract: One reason for researching new biometric modalities is to improve the capabilities of security systems against threats. Biometric modalities based on biomedical signals, in particular the electrocardiogram signal (ECG), have been widely adopted. These can be represented by time series. However, in this context, a critical issue is how to extract features from ECG signals effectively. Several techniques have been put forward regarding the best way to represent time series, in particular techniques that are based on symbolic values, and significant results have been achieved by means of these techniques for addressing different types of problems. In this paper, we present twenty symbolic representations of time series applied to the extraction of nonfiducial features from ECG signals that aim at biometric recognition. In addition, we put forward three novel symbolic representations, namely, Representation based on Kmeans (R-Kmeans), Symbolic Aggregate approXimation based on Kmeans (SAX-Kmeans), and Extended Symbolic Aggregate approXimation based on Kmeans (ESAX-Kmeans). Experimental results conducted on two publicly available datasets indicate that the novel representations can improve the performance of recognition compared with the others.


title: Early-onset social anxiety disorder in adults: clinical and therapeutic features

abstract: OBJECTIVE: To investigate possible differences in clinical and treatment response in patients suffering from early-onset ( 18 years) social anxiety disorder. METHODS: Patients diagnosed with social anxiety disorder of early-onset (n = 47; 75.8%) were compared to those diagnosed with late-onset social anxiety disorder (n = 15; 24.2%) in terms of age, mode of onset, subtype, psychiatric comorbidities (according to the Structured Clinical Interview for DSM-IV), symptom severity and response (assessed according to the Clinical Global Impression scale) after at least ten weeks of drug treatment. The statistical analyses included c² tests with Yates correction or Fisher's exact test, as well as Student's t-test or Mann-Whitney test. The level of statistic significance adopted was 5%. RESULTS: Patients presenting early-onset phobic symptoms more frequently: were inactive (c² = 4.28; df = 1; p = 0.04); suffered from the generalized subtype of social phobia (c² = 6.53; df = 1; p = 0.01); and presented psychiatric comorbidity (c² = 6.71; df = 1; p = 0.01). No differences were observed between the groups in severity of symptoms and therapeutic response. CONCLUSION: The findings suggest the existence of a possible social anxiety disorder subtype characterized by early onset of symptoms, higher rates of absenteeism, a wider range of social phobia symptoms and psychiatric complications.


INTRINSICALLY MOTIVATED GOAL EXPLORATION


title: The 3-DoF bicycle model with the simplified piecewise linear tire model

abstract: The paper proposed a 3-DoF bicycle model, that considering the longitudinal acceleration or deceleration motion, the lateral motion and the yaw motion of the vehicle. A simplified piecewise tire model is proposed to improve the precise of the tire force. The bicycle model uses the wheel rolling speed information from the real car that is usually used in the ABS or other active safety devices. The simulation results show that compared with the 14-DoF full car model, the proposed bicycle model is good and can be used for the chassis control.


title: A method for building self-folding machines

abstract: Origami can turn a sheet of paper into complex three-dimensional shapes, and similar folding techniques can produce structures and mechanisms. To demonstrate the application of these techniques to the fabrication of machines, we developed a crawling robot that folds itself. The robot starts as a flat sheet with embedded electronics, and transforms autonomously into a functional machine. To accomplish this, we developed shape-memory composites that fold themselves along embedded hinges. We used these composites to recreate fundamental folded patterns, derived from computational origami, that can be extrapolated to a wide range of geometries and mechanisms. This origami-inspired robot can fold itself in 4 minutes and walk away without human intervention, demonstrating the potential both for complex self-folding machines and autonomous, self-controlled assembly.


title: System Level Performance Evaluation of LTE-V2X Network

abstract: Vehicles are among the fastest growing type of connected devices. Therefore, there is a need for Vehicle-toEverything (V2X) communication i.e. passing of information from a Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) and vice versa. In this paper, the main focus is on the communication between vehicles and road side units (RSUs) commonly referred to as V2I communication in a multi-lane freeway scenario. Moreover, we analyze network related bottlenecks such as the maximum number of vehicles that can be supported when coverage is provided by the Long Term Evolution Advanced (LTE-A) network. The performance evaluation is assessed through extensive system-level simulations. Results show that new resource allocation and interference mitigation techniques are needed in order to achieve the required high reliability requirements, especially when network load is high.


title: The robust design of parallel spherical robots

abstract: In this paper a robust methodology, reported in a previous work, is applied to the design of the unactuated joints of a spherical three-degree-of-freedom parallel robot, the Agile Wrist (AW). Robustness is needed because of the extremely difficult task of manufacturing a spherical mechanism with all its joint axes concurrent at a single point. In order to account for the unavoidable manufacturing errors, it is proposed here to replace the unactuated revolute (R) joints of an existing design by cylindrical (C) joints. The latter function even in the presence of nonconcurrent axes. A procedure, based on dual numbers, is used to solve the inverse kinematics of the entire mechanism in order to determine the rotations and translations of each C joint. Based on statistical results of the kinematic analysis, the C joints are suitably dimensioned.


Survey on Quality of Primary Health Cares in Zanjan District Health Centers


title: Design considerations of flat patterns analysis techniques when applied for folding 3-D sheet metal geometries

abstract: This paper discusses a systematic approach to implement the principles of Flat Pattern Analysis FPA for folding sheet metal products. The paper starts by highlighting the needs for the vehicular structure forming process with respect to the main production line requirements through using Quality Function Deployment QFD matrix. Additionally, the potentials of fold forming for sheet metal parts in achieving the major production needs will then be benchmarked against other forming techniques through a decision making tool namely; the Analytical Hierarchy Process AHP. The study investigates the application of flat pattern tools for sheet metal products derived from analysis for thin or zero thickness sheets (i.e. paper origami). The analysis sets an approach to generate all possible configurations of flat patterns that result in a specific 3-D structure profile. Secondly, a set of optimality selection metrics are developed and applied to these configurations to help determine the most optimized flat pattern. These optimality measures are a metric based on compactness, a metric for nesting efficiency to describe the strip layout planning, and two measures to assess the manufacturing aspect i.e. bending operation in terms of number and orientation of bend lines.


title: Causal Mediation Analysis

abstract: Estimating the mechanisms that connect explanatory variables with the explained variable, also known as "mediation analysis," is central to a variety of social-science fields, especially psychology, and increasingly to fields like epidemiology. Recent work on the statistical methodology behind mediation analysis points to limitations in earlier methods. We implement in Stata computational approaches based on recent developments in the statistical methodology of mediation analysis. In particular, we provide functions for the correct calculation of causal mediation effects using several different types of parametric models, as well as the calculation of sensitivity analyses for violations to the key identifying assumption required for interpreting mediation results causally.


title: Application of machine learning methods in post-silicon yield improvement

abstract: In nanometer scale manufacturing, process variations have a significant impact on circuit performance. To handle them, post-silicon clock tuning buffers can be included into the circuit to balance timing budgets of neighboring critical paths. The state of the art is a sampling-based approach, in which an integer linear programming (ILP) problem must be solved for every sample. The runtime complexity of this approach is the number of samples multiplied by the required time for an ILP solution. Existing work tries to reduce the number of samples but still leaves the problem of a long runtime unsolved. In this paper, we propose a machine learning approach to reduce the runtime by learning the positions and sizes of post-silicon tuning buffers. Experimental results demonstrate that we can predict buffer locations and sizes with a very good accuracy (90% and higher) and achieve a significant yield improvement (up to 18.8%) with a significant speed-up (up to almost 20 times) compared to existing work.


title: A 3-DOF knee joint angle measurement system with inertial and magnetic sensors

abstract: Quantitative diagnosis of the knee joint dynamics is required to decrease the inner- and intra-observer variability. This paper proposes a noninvasive, unconstrained and free field of measurement system of 3 degree-of-freedom knee joint angles. The proposed system employs a compound sensor of inertial and magnetic sensors. Based on a rigid-body link model, the proposed method enables a measurement system. The experimental results showed that the proposed method estimated the flexion of knee joint angle with a mean displacement of 1.3 deg.


title: A prospective ten-year clinical trial of porcelain veneers

abstract: Abstract The aim of this in vivo study was to evaluate the clinical performance of porcelain veneers after 5 and 10 years of clinical service. A single operator placed porcelain laminates on 87 maxillary anterior teeth in 25 patients. All restorations were recalled at 5 years and 93% of the restorations at 10 years. Clinical performance was assessed in terms of esthetics, marginal integrity, retention, clinical microleakage, caries recurrence, fracture, vitality, and patient satisfaction. Failures were recorded either as "clinically unacceptable but repairable" or as "clinically unacceptable with replacement needed". Porcelain veneers maintained their esthetic appearance after 10 years of clinical service. None of the veneers were lost. The percentage of restorations that remained "clinically acceptable" (without need for intervention) significantly decreased from an average of 92% (95 CI: 90% to 94%) at 5 years to 64% (95 CI: 51% to 77%) at 10 years. Fractures of porcelain (11%) and large marginal defects (20%) were the main reason for failure. Marginal defects were especially noticed at locations where the veneer ended in an existing composite filling. At such vulnerable locations, severe marginal discoloration (19%) and caries recurrence (10%) were frequently observed. Most of the restorations that present one or more "clinically unacceptable" problems (28%) were repairable. Only 4% of the restorations needed to be replaced at the 10-year recall. It was concluded that labial porcelain veneers represent a reliable, effective procedure for conservative treatment of unesthetic anterior teeth. Occlusion, preparation design, presence of composite fillings, and the adhesive used to bond veneers to tooth substrate are covariables that contribute to the clinical outcome of these restorations in the long-term.


title: A recursive random search algorithm for large-scale network parameter configuration

abstract: Parameter configuration is a common procedure used in large-scale network protocols to support multiple operational goals. It can be formulated as a black-box optimization problem and solved with an efficient search algorithm. This paper proposes a new heuristic search algorithm, Recursive Random Search(RRS), for large-scale network parameter optimization. The RRS algorithm is based on the initial high-efficiency feature of random sampling and it attempts to maintain this high efficiency by constantly "restarting" random sampling with adjusted sample spaces. Besides the high efficiency, the RRS algorithm is robust to the effect of random noise and trivial parameters in the objective function because of its root in random sampling. These features are very important for the efficient optimization of network protocol configuration. The performance of RRS is demonstrated with the tests on a suite of benchmark functions. The algorithm has been applied to the configuration of several network protocols, such as RED, OSPF and BGP. One example application in OSPF routing algorithm is presented.


title: XLink: An Unsupervised Bilingual Entity Linking System

abstract: Entity linking is a task of linking mentions in text to the corresponding entities in a knowledge base. Recently, entity linking has received considerable attention and several online entity linking systems have been published. In this paper, we build an online bilingual entity linking system XLink, which is based on Wikipeida and Baidu Baike. XLink conducts two steps to link the mentions in the input document to entities in knowledge base, namely mention parsing and entity disambiguation. To eliminate dependency of language, we conduct mention parsing without any named entity recognition tools. To ensure the correctness of linking results, we propose an unsupervised generative probabilistic method and utilize text and knowledge joint representations to perform entity disambiguation. Experiments show that our system gets a state-of-the-art performance and a high time efficiency.


title: Management and Follow-Up of a Case of Gestational Gigantomastia in a Brazilian Hospital

abstract: Gigantomastia is a breast disorder that is associated with an exaggerated, rapid growth of the breasts, generally bilaterally. Since the pathology is rare and has seldom been described, its etiology has yet to be fully established, although there are speculations that a hormonal component may play an important role. Treatment is aimed at improving the clinical and psychological symptoms; however, the best therapeutic option varies from case to case. The present report describes a case of gestational gigantomastia seen at the Department of Obstetrics and Gynecology of the Hospital da Santa Casa de Misericórdia, Vitória, Espírito Santo, Brazil, in a primigravida in the second trimester of pregnancy. The report follows this patient from her diagnosis until the completion of treatment with a third and final surgical procedure.


title: New horizons for the methodology and physiology of training periodization.

abstract: The theory of training was established about five decades ago when knowledge of athletes' preparation was far from complete and the biological background was based on a relatively small amount of objective research findings. At that time, traditional 'training periodization', a division of the entire seasonal programme into smaller periods and training units, was proposed and elucidated. Since then, international sport and sport science have experienced tremendous changes, while the traditional training periodization has remained at more or less the same level as the published studies of the initial publications. As one of the most practically oriented components of theory, training periodization is intended to offer coaches basic guidelines for structuring and planning training. However, during recent decades contradictions between the traditional model of periodization and the demands of high-performance sport practice have inevitably developed. The main limitations of traditional periodization stemmed from: (i) conflicting physiological responses produced by 'mixed' training directed at many athletic abilities; (ii) excessive fatigue elicited by prolonged periods of multi-targeted training; (iii) insufficient training stimulation induced by workloads of medium and low concentration typical of 'mixed' training; and (iv) the inability to provide multi-peak performances over the season. The attempts to overcome these limitations led to development of alternative periodization concepts. The recently developed block periodization model offers an alternative revamped approach for planning the training of high-performance athletes. Its general idea proposes the sequencing of specialized training cycles, i.e. blocks, which contain highly concentrated workloads directed to a minimal number of targeted abilities. Unlike the traditional model, in which the simultaneous development of many athletic abilities predominates, block-periodized training presupposes the consecutive development of reasonably selected target abilities. The content of block-periodized training is set down in its general principles, a taxonomy of mesocycle blocks, and guidelines for compiling an annual plan.


title: METAMODEL FOR SERVICE DESIGN AND SERVICE INNOVATION : INTEGRATING SERVICE ACTIVITIES , SERVICE SYSTEMS , AND VALUE CONSTELLATIONS

abstract: This paper presents a metamodel that addresses service system design and innovation by traversing and integrating three essential layers, service activities, service systems, and value constellations. The metamodel's approach to service systems as service-in-operation is an alternative to another currently used approach that views service systems as systems of economic exchange. The metamodel addresses service science topics including basic concepts of service science, design thinking for service systems, decomposition within service systems, and integration of IT service architecture with customer services. This paper's contributions to service science include clarifications concerning concepts such as service, service system, customer, product/service, coproduction and cocreation of value, actor roles, resources, symmetrical treatment of automated and non-automated service systems, and the relationship between service-dominant logic and service systems. Many articles have discussed these topics individually. Few, if any, have tied them together using an integrated metamodel.


title: Gender and the Use of Exclamation Points in Computer-Mediated Communication: An Analysis of Exclamations Posted to Two Electronic Discussion Lists

abstract: Past research has reported that females use exclamation points more frequently than do males. Such research often characterizes exclamation points as ‘‘markers of excitability,’’ a term that suggests instability and emotional randomness, yet it has not necessarily examined the contexts in which exclamation points appeared for evidence of ‘‘excitability.’’ The present study uses a 16-category coding frame in a content analysis of 200 exclamations posted to two electronic discussion groups serving the library and information science profession. The results indicate that exclamation points rarely function as markers of excitability in these professional forums, but may function as markers of friendly interaction, a finding with implications for understanding gender styles in email and other forms of computer-mediated communication.


title: Breakage of non-Newtonian character in flow through a porous medium: Evidence from numerical simulation

abstract: We study the flow, through a model two-dimensional porous medium, of Newtonian fluids, power-law fluids, and viscoplastic fluids in the laminar regime and with moderate or dominant effects of the yielding term. A numerical technique able to take properly into account yielding effects in viscoplastic flows without any regularization is used to determine the detailed flow characteristics. We show that as soon as the distance between the disks forming the porous medium is sufficiently small, the velocity field and in particular the distribution function of the velocity of these different fluids in a wide range of flow regimes are similar. Moreover, the volume fraction of fluid at rest is negligible even at low flow rate. Thus the non-Newtonian character of a fluid flowing through such a complex geometry tends to be broken. We suggest that this is due to the fact that in a flow through a channel of rapidly varying cross section, the deformation, and thus the flow field, is imposed on the fluid, a situation that is encountered almost everywhere in a porous medium. These results make it possible to deduce a general expression for Darcy's law of these fluid types and estimate the parameters appearing in this expression.


title: Conceptual framework for personal recovery in mental health: systematic review and narrative synthesis

abstract: Background ::: ::: No systematic review and narrative synthesis on personal recovery in mental illness has been undertaken. ::: ::: Aims ::: ::: To synthesise published descriptions and models of personal recovery into an empirically based conceptual framework. ::: ::: Method ::: ::: Systematic review and modified narrative synthesis. ::: ::: Results ::: ::: Out of 5208 papers that were identified and 366 that were reviewed, a total of 97 papers were included in this review. The emergent conceptual framework consists of: (a) 13 characteristics of the recovery journey; (b) five recovery processes comprising: connectedness; hope and optimism about the future; identity; meaning in life; and empowerment (giving the acronym CHIME); and (c) recovery stage descriptions which mapped onto the transtheoretical model of change. Studies that focused on recovery for individuals of Black and minority ethnic (BME) origin showed a greater emphasis on spirituality and stigma and also identified two additional themes: culturally specific facilitating factors and collectivist notions of recovery. ::: ::: Conclusions ::: ::: The conceptual framework is a theoretically defensible and robust synthesis of people’s experiences of recovery in mental illness. This provides an empirical basis for future recovery-oriented research and practice.


title: Structured Transforms for Small-Footprint Deep Learning

abstract: We consider the task of building compact deep learning pipelines suitable for deployment on storage and power constrained mobile devices. We propose a unified framework to learn a broad family of structured parameter matrices that are characterized by the notion of low displacement rank. Our structured transforms admit fast function and gradient evaluation, and span a rich range of parameter sharing configurations whose statistical modeling capacity can be explicitly tuned along a continuum from structured to unstructured. Experimental results show that these transforms can significantly accelerate inference and forward/backward passes during training, and offer superior accuracy-compactness-speed tradeoffs in comparison to a number of existing techniques. In keyword spotting applications in mobile speech recognition, our methods are much more effective than standard linear low-rank bottleneck layers and nearly retain the performance of state of the art models, while providing more than 3.5-fold compression.


title: On rapid releases and software testing: a case study and a semi-systematic literature review

abstract: Large open and closed source organizations like Google, Facebook and Mozilla are migrating their products towards rapid releases. While this allows faster time-to-market and user feedback, it also implies less time for testing and bug fixing. Since initial research results indeed show that rapid releases fix proportionally less reported bugs than traditional releases, this paper investigates the changes in software testing effort after moving to rapid releases in the context of a case study on Mozilla Firefox, and performs a semi-systematic literature review. The case study analyzes the results of 312,502 execution runs of the 1,547 mostly manual system-level test cases of Mozilla Firefox from 2006 to 2012 (5 major traditional and 9 major rapid releases), and triangulates our findings with a Mozilla QA engineer. We find that rapid releases have a narrower test scope that enables a deeper investigation of the features and regressions with the highest risk. Furthermore, rapid releases make testing more continuous and have proportionally smaller spikes before the main release. However, rapid releases make it more difficult to build a large testing community , and they decrease test suite diversity and make testing more deadline oriented. In addition, our semi-systematic literature review presents the benefits, problems and enablers of rapid releases from 24 papers found using systematic search queries and a similar amount of papers found through other means. The literature review shows that rapid releases are a prevalent industrial practice that are utilized even in some highly critical domains of software engineering, and that rapid releases originated from several software development methodologies such as agile, open source, lean and internet-speed software development. However, empirical studies proving evidence of the claimed advantages and disadvantages of rapid releases are scarce.


title: Jena property table implementation

abstract: A common approach to providing persistent storage for RDF is to store statements in a three-column table in a relational database system. This is commonly referred to as a triple store. Each table row represents one RDF statement. For RDF graphs with frequent patterns, an alternative storage scheme is a property table. A property table comprises one column containing a statement subject plus one or more columns containing property values for that subject. In this approach, a single table row may store many RDF statements. This paper describes a property table design and implementation for Jena, an RDF Semantic Web toolkit. A design goal is to make Jena property tables look like normal relational database tables. This enables relational database tools such as loaders, report writers and query optimizers to work well with Jena property tables. This paper includes a basic performance analysis comparing a triple store with property tables for dataset load time and query response time. Depending on the application and data characteristics, Jena property tables may provide a performance advantage compared to a triple store for large RDF graphs with frequent patterns. The disadvantage is some loss in flexibility.


title: Training deep neural networks with low precision multiplications

abstract: Multipliers are the most space and power-hungry arithmetic operators of the digital implementation of deep neural networks. We train a set of state-of-the-art neural networks (Maxout networks) on three benchmark datasets: MNIST, CIFAR-10 and SVHN. They are trained with three distinct formats: floating point, fixed point and dynamic fixed point. For each of those datasets and for each of those formats, we assess the impact of the precision of the multiplications on the final error after training. We find that very low precision is sufficient not just for running trained networks but also for training them. For example, it is possible to train Maxout networks with 10 bits multiplications.


title: Deep Video Code for Efficient Face Video Retrieval

abstract: In this paper, we address the problem of face video retrieval. Given one face video of a person as query, we search the database and return the most relevant face videos, i.e., ones have same class label with the query. Such problem is of great challenge. For one thing, faces in videos have large intra-class variations. For another, it is a retrieval task which has high request on efficiency of space and time. To handle such challenges, this paper proposes a novel Deep Video Code (DVC) method which encodes face videos into compact binary codes. Specifically, we devise a multi-branch CNN architecture that takes face videos as training inputs, models each of them as a unified representation by temporal feature pooling operation, and finally projects the high-dimensional representations into Hamming space to generate a single binary code for each video as output, where distance of dissimilar pairs is larger than that of similar pairs by a margin. To this end, a smooth upper bound on triplet loss function which can avoid bad local optimal solution is elaborately designed to preserve relative similarity among face videos in the output space. Extensive experiments with comparison to the state-of-the-arts verify the effectiveness of our method.


title: Enhanced online subspace estimation via adaptive sensing

abstract: This work investigates the problem of adaptive measurement design for online subspace estimation from compressive linear measurements. We study the previously proposed Grassmannian rank-one online subspace estimation (GROUSE) algorithm with adaptively designed compressive measurements. We propose an adaptive measurement scheme that biases the measurement vectors towards the current subspace estimate and prove a global convergence result for the resulting algorithm. Our experiments on synthetic data demonstrate the effectiveness of the adaptive measurement scheme over non-adaptive compressive random measurements.


title: Improving Human Action Recognition with Two-Stream 3D Convolutional Neural Network

abstract: Human action recognition has became a hot topic in recent years because it opens a wide range of applications such as video surveillance, assisted living, entertainment. Recently, advanced techniques relying on convolutional neural networks produced impressive improvement compared to traditional handcrafted features based techniques. Besides, literature researches also showed that the use of different streams of data will help to increase recognition performance. This paper proposes a method that exploits both RGB and optical flow for human action recognition. Specifically, we deploy a two stream convolutional neural network that takes RGB and optical flow computed from RGB stream as inputs. Each stream has architecture of an existing 3D convolutional neural network (C3D) which has been shown to be compact but efficient for the task of action recognition from video. Each stream works independently then is combined by early fusion or late fusion to output the recognition results. We show that the proposed two-stream 3D convolutional neural network (2stream C3D) outperforms one stream C3D on two benchmark datasets UCF101 (from 82.37% to 88.79%) and HMDB51 (from 48.43 % to 62.54%).


title: Modularity and Architecture of PLC-based Software for Automated Production Systems: An analysis in industrial companies

abstract: Adaptive and flexible production systems require modular and reusable software especially considering their long-term life cycle of up to 50 years. SWMAT4aPS, an approach to measure Software Maturity for automated Production Systems is introduced. The approach identifies weaknesses and strengths of various companies’ solutions for modularity of software in the design of automated Production Systems (aPS). At first, a self-assessed questionnaire is used to evaluate a large number of companies concerning their software maturity. Secondly, we analyze PLC code, architectural levels, workflows and abilities to configure code automatically out of engineering information in four selected companies. In this paper, the questionnaire results from 16 German world-leading companies in machine and plant manufacturing and four case studies validating the results from the detailed analyses are introduced to prove the applicability of the approach and give a survey of the state of the art in industry.


title: Physicochemical Characterization of Biofield Energy Treated Calcium Carbonate Powder

abstract: Calcium carbonate (CaCO3) is widely used in pharmaceutical industries and as a supplement in probiotics. The present study was designed to evaluate the effect of biofield energy treatment on the physicochemical properties of the CaCO3. The CaCO3 powder was divided into two parts and referred as control and treated. The control part was remained untreated, whereas treated part was subjected to Trivedi’s biofield treatment. The control and biofield treated samples were characterized using X-ray diffraction (XRD), particle size analyzer, surface area analyzer, thermogravimetric analysis (TGA) and Fourier transform infrared spectroscopy (FT-IR).


title: Teaching cryptography with open-source software

abstract: Cryptography has become an important topic in undergraduate curricula in mathematics and computer science, not just for its intrinsic interest---``about the most fun you can have with mathematics''\cite{ferg04, but for its current standing as the basis for almost all computer security. From wireless networking to secure email to password protection, cryptographic methods are used to secure information, to protect users, and to protect data. At Victoria University, cryptography has been taught as part of a mathematics and computer science degree for several years. The students all have had at least a year of tertiary mathematics, and some exposure to a computer algebra system (Maple). However, the cost of Maple, and the current licensing agreement, means that students are unable to experiment with the software away from the computer laboratories at the University. For this reason we have decided to investigate the use of open-source computer algebra systems Maxima and Axiom. Although not as full-featured and powerful as the commercial systems Maple and Mathematica, we show they are in fact admirably suited for a subject such as cryptography. In some ways Maxima and Axiom even surpass Maple and Mathematica. Student response to the introduction of these systems has been very positive.


title: End-to-End Training of Hybrid CNN-CRF Models for Stereo

abstract: We propose a novel and principled hybrid CNN+CRF model for stereo estimation. Our model allows to exploit the advantages of both, convolutional neural networks (CNNs) and conditional random fields (CRFs) in an unified approach. The CNNs compute expressive features for matching and distinctive color edges, which in turn are used to compute the unary and binary costs of the CRF. For inference, we apply a recently proposed highly parallel dual block descent algorithm which only needs a small fixed number of iterations to compute a high-quality approximate minimizer. As the main contribution of the paper, we propose a theoretically sound method based on the structured output support vector machine (SSVM) to train the hybrid CNN+CRF model on large-scale data end-to-end. Our trained models perform very well despite the fact that we are using shallow CNNs and do not apply any kind of post-processing to the final output of the CRF. We evaluate our combined models on challenging stereo benchmarks such as Middlebury 2014 and Kitti 2015 and also investigate the performance of each individual component.


title: English-to-Sanskrit Statistical Machine Translation with Ubiquitous Application

abstract: In this paper by utilizing the capabilities of modern ubiquitous operating systems we introduce a comprehensive framework for a ubiquitous translation and language learning environment for English to Sanskrit Machine Translation. We present an application for learning Sanskrit characters, sentences and English Sanskrit translation. For the implementation, we have used the open-source Android platform on the Samsung Mini2440, a state-of-the-art development board. We present our current state of implementation, the architecture of our framework,and the findings we have gathered so far. In addition to this, here we describes the Phrase-Based Statistical Machine Translation Decoder for English to Sanskrit translation in ubiquitous environment. Our goal is to improve the translation quality by enhancing the translation table and by preprocessing the Sanskrit language text .


title: GraphReduce: processing large-scale graphs on accelerator-based systems

abstract: Recent work on real-world graph analytics has sought to leverage the massive amount of parallelism offered by GPU devices, but challenges remain due to the inherent irregularity of graph algorithms and limitations in GPU-resident memory for storing large graphs. We present GraphReduce, a highly efficient and scalable GPU-based framework that operates on graphs that exceed the device's internal memory capacity. GraphReduce adopts a combination of edge- and vertex-centric implementations of the Gather-Apply-Scatter programming model and operates on multiple asynchronous GPU streams to fully exploit the high degrees of parallelism in GPUs with efficient graph data movement between the host and device. GraphReduce-based programming is performed via device functions that include gatherMap, gatherReduce, apply, and scatter, implemented by programmers for the graph algorithms they wish to realize. Extensive experimental evaluations for a wide variety of graph inputs and algorithms demonstrate that GraphReduce significantly outperforms other competing out-of-memory approaches.


title: Nonsmooth nonnegative matrix factorization (nsNMF)

abstract: We propose a novel nonnegative matrix factorization model that aims at finding localized, part-based, representations of nonnegative multivariate data items. Unlike the classical nonnegative matrix factorization (NMF) technique, this new model, denoted "nonsmooth nonnegative matrix factorization" (nsNMF), corresponds to the optimization of an unambiguous cost function designed to explicitly represent sparseness, in the form of nonsmoothness, which is controlled by a single parameter. In general, this method produces a set of basis and encoding vectors that are not only capable of representing the original data, but they also extract highly focalized patterns, which generally lend themselves to improved interpretability. The properties of this new method are illustrated with several data sets. Comparisons to previously published methods show that the new nsNMF method has some advantages in keeping faithfulness to the data in the achieving a high degree of sparseness for both the estimated basis and the encoding vectors and in better interpretability of the factors.


title: Special care dentistry: part 1. dental management of patients with inherited bleeding disorders

abstract: UNLABELLED ::: The most common cause of excessive bleeding is idiopathic, but bleeding can also be caused by inherited or acquired conditions associated with vascular defects, platelet defects or coagulation disorders. This paper will cover inherited bleeding disorders. Every clinician will encounter a patient who complains of prolonged bleeding following certain procedures; most commonly dental extractions. In the majority of cases the cause is often a local one and can be managed using simple local measures. However, prolonged post-operative bleeding following dental treatment might be one of the first signs of a bleeding disorder in an undiagnosed patient, necessitating referral for further investigations. Some patients may present with an already confirmed diagnosis of a bleeding disorder, requiring appropriate treatment planning and dental management in an appropriate setting with haematological advice. This paper will provide guidance on how to achieve this. ::: ::: ::: CLINICAL RELEVANCE ::: To update clinicians on the dental management of patients with inherited bleeding disorders and how to decide the most appropriate setting for the provision of dental care.


title: Cyber Security Risks Assessment with Bayesian Defense Graphs and Architectural Models

abstract: To facilitate rational decision making regarding cyber security investments, decision makers need to be able to assess expected losses before and after potential investments. This paper presents a model based assessment framework for analyzing the cyber security provided by different architectural scenarios. The framework uses the Bayesian statistics based Extended Influence Diagrams to express attack graphs and related countermeasures. In this paper it is demonstrated how this structure can be captured in an abstract model to support analysis based on architectural models. The approach allows calculating the probability that attacks will succeed and the expected loss of these given the instantiated architectural scenario. Moreover, the framework can handle the uncertainties that are accompanied to the analyses. In architectural analysis there are uncertainties acquainted both to the scenario and its properties, as well as to the analysis framework that stipulates how security countermeasures contribute to cyber security.


title: Understanding the Experience of Stigma for Parents of Children with Autism Spectrum Disorder and the Role Stigma Plays in Families’ Lives

abstract: Stigma is widely perceived in the lives of families with autism spectrum disorder (ASD) yet large, systematic studies have not been undertaken. Following Link and Phelan’s (Ann Rev Sociol 27:363–385, 2001) model, this study of 502 Simons Simplex Collection families details how different factors contribute to stigma and how each appears to increase the overall difficulty of raising a child with ASD. The model begins with the child’s behavioral symptoms and then specifies stigma processes of stereotyping, rejection, and exclusion. Autism behaviors contribute both to the difficulty families experience raising a child with autism and to the stigma processes associated with those behaviors. Stigma also plays a significant role (.282, p < .001) in predicting how difficult life is overall for parents.


MinIE: Minimizing Facts in Open Information Extraction


title: Robust 3D Shape Correspondence in the Spectral Domain

abstract: We present an algorithm for finding a meaningful vertexto- vertex correspondence between two 3D shapes given as triangle meshes. Our algorithm operates on embeddings of the two shapes in the spectral domain so as to normalize them with respect to uniform scaling and rigid-body transformation. Invariance to shape bending is achieved by relying on geodesic point proximities on a mesh to capture its shape. To deal with stretching, we propose to use non-rigid alignment via thin-plate splines in the spectral domain. This is combined with a refinement step based on the geodesic proximities to improve dense correspondence. We show empirically that our algorithm outperforms previous spectral methods, as well as schemes that compute correspondence in the spatial domain via non-rigid iterative closest points or the use of local shape descriptors, e.g., 3D shape context.


title: Unsupervised adversarial domain adaptation for acoustic scene classification

abstract: A general problem in acoustic scene classification task is the mismatched conditions between training and testing data, which significantly reduces the performance of the developed methods on classification accuracy. As a countermeasure, we present the first method of unsupervised adversarial domain adaptation for acoustic scene classification. We employ a model pre-trained on data from one set of conditions and by using data from other set of conditions, we adapt the model in order that its output cannot be used for classifying the set of conditions that input data belong to. We use a freely available dataset from the DCASE 2018 challenge Task 1, subtask B, that contains data from mismatched recording devices. We consider the scenario where the annotations are available for the data recorded from one device, but not for the rest. Our results show that with our model agnostic method we can achieve $\sim 10\%$ increase at the accuracy on an unseen and unlabeled dataset, while keeping almost the same performance on the labeled dataset.


Determining factors influencing information security culture among ICT librarians


African-American Music as Rebellion: From Slavesong to Hip-Hop


title: Evolution of the Icarus Cognitive Architecture

abstract: Abstract Cognitive architectures serve as both unified theories of the mind and as computational infrastructures for constructing intelligent agents. In this article, we review the evolution of one such framework, I carus , over the three decades of its development. We discuss the representational and processing assumptions made by different versions of the architecture, their relation to alternative theories, and some promising directions for future research.


title: Agricultural Production System Based on IoT

abstract: There has been much research and various attempts to apply new IoT technology to agricultural areas. However, IoT for the agriculture should be considered differently against the same areas such as industrial, logistics. This paper presents the IoT-based agricultural production system for stabilizing supply and demand of agricultural products while developing the environment sensors and prediction system for the growth and production amount of crops by gathering its environmental information. Currently, the demand by consumption of agricultural products could be predicted quantitatively, however, the variation of harvest and production by the change of farm's cultivated area, weather change, disease and insect damage etc. could not be predicted, so that the supply and demand of agricultural products has not been controlled properly. To overcome it, this paper designed the IoT-based monitoring system to analyze crop environment, and the method to improve the efficiency of decision making by analyzing harvest statistics. Therefore, this paper developed the decision support system to forecast agricultural production using IoT sensors. This system was also a unified system that supports the processes sowing seeds through selling agricultural products to consumers. 3 Corresponding author The IoT-based agricultural production system through correlation analysis between the crop statistical information and agricultural environment information has enhanced the ability of farmers, researchers, and government officials to analyze current conditions and predict future harvest. Additionally, agricultural products quality can be improved because farmers observe whole cycle from seeding to selling using this IoT-based decision support system.


title: NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection

abstract: The labeling cost of large number of bounding boxes is one of the main challenges for training modern object detectors. To reduce the dependence on expensive bounding box annotations, we propose a new semi-supervised object detection formulation, in which a few seed box level annotations and a large scale of image level annotations are used to train the detector. We adopt a training-mining framework, which is widely used in weakly supervised object detection tasks. However, the mining process inherently introduces various kinds of labelling noises: false negatives, false positives and inaccurate boundaries, which can be harmful for training the standard object detectors (e.g. Faster RCNN). We propose a novel NOise Tolerant Ensemble RCNN (NOTE-RCNN) object detector to handle such noisy labels. Comparing to standard Faster RCNN, it contains three highlights: an ensemble of two classification heads and a distillation head to avoid overfitting on noisy labels and improve the mining precision, masking the negative sample loss in box predictor to avoid the harm of false negative labels, and training box regression head only on seed annotations to eliminate the harm from inaccurate boundaries of mined bounding boxes. We evaluate the methods on ILSVRC 2013 and MSCOCO 2017 dataset; we observe that the detection accuracy consistently improves as we iterate between mining and training steps, and state-of-the-art performance is achieved.


title: Autonomous Seed Sowing Agricultural Robot

abstract: In agriculture there is need for a technology that is more easily understood, implemented and used by the farmers. Equipment that requires less human effort and time with less cost of implementation is much required for success in agricultural industry. Autonomous robots built with less maintainance and that are portable as well as customizable according to the requirements might serve the purpose here and thus this paper presents you the design of a four wheel drive robot that does the work of seed sowing in ploughed agricultural land avoiding the human effort by tracing the path and sowing seeds at equal intervals using the field area parameters(length and breadth) and seed spacing intervals as inputs specified by the user. It also takes you through the design process of the robot, explains in detail the protype and the equipment used for building it and the difficulties faced during the different phases of the project. The paper also presents the sequence of steps to be followed to come up with designing a wheel drive robot and the parameters that has to be taken into consideration before building the prototype.


title: Analyzing the composition of cities using spatial clustering

abstract: Cities all around the world are in constant evolution due to numerous factors, such as fast urbanization and new ways of communication and transportation. Since understanding the composition of cities is the key to intelligent urbanization, there is a growing need to develop urban computing and analysis tools to guide the orderly development of cities, as well as to enhance their smooth and beneficiary evolution. This paper presents a spatial clustering approach to discover interesting regions and regions which serve different functions in cities. Spatial clustering groups the objects in a spatial dataset and identifies contiguous regions in the space of the spatial attributes. We formally define the task of finding uniform regions in spatial data as a maximization problem of a plug-in measure of uniformity and introduce a prototype-based clustering algorithm named CLEVER to find such regions. Moreover, polygon models which capture the scope of a spatial cluster and histogram-style distribution signatures are used to annotate the content of a spatial cluster in the proposed methodology; they play a key role in summarizing the composition of a spatial dataset. Furthermore, algorithms for identifying popular distribution signatures and approaches for identifying regions which express a particular distribution signature will be presented. The proposed methodology is demonstrated and evaluated in a challenging real-world case study centering on analyzing the composition of the city of Strasbourg in France.


title: Ultra-Low-Noise High-Input Impedance Amplifier for Low-Frequency Measurement Applications

abstract: The design of a low-frequency high-input-impedance amplifier having probably the lowest noise ever reported is presented. The amplifier's frequency range is from about 0.07 Hz to about 110 kHz at the -3-dB level. The equivalent input noise voltage spectral density is about 5.6, 1.4, 0.6, and 0.5 nV/radicHz at frequencies 0.1, 1, 10, and 1000 Hz, respectively. Gain of the amplifier is about 83 dB. Noise analysis is made for active-type, capacitive-type, and low impedance signal sources. The contribution from different noise sources in the amplifier and JFET to the overall noise is shown.


title: Modified range-Doppler processing for FM-CW synthetic aperture radar

abstract: The combination of compact frequency-modulated continuous-wave (FM-CW) technology and high-resolution synthetic aperture radar (SAR) processing techniques should pave the way for the development of a lightweight, cost-effective, high-resolution, airborne imaging radar. Regarding FM-CW SAR signal processing, the motion during the transmission of a sweep and the reception of the corresponding echo were expected to be one of the major problems. In FM-CW SAR, the so-called stop-and-go approximation is no longer valid due to the relatively long sweeps that FM-CW radars transmit. The main effect of the continuous motion is a Doppler frequency shift throughout the SAR observation time. This Doppler frequency shift can be compensated for by modifying the range migration compensation.


title: Cross-Modal Retrieval with Implicit Concept Association

abstract: Traditional cross-modal retrieval assumes explicit association of concepts across modalities, where there is no ambiguity in how the concepts are linked to each other, e.g., when we do the image search with a query"dogs", we expect to see dog images. In this paper, we consider a different setting for cross-modal retrieval where data from different modalities are implicitly linked via concepts that must be inferred by high-level reasoning; we call this setting implicit concept association. To foster future research in this setting, we present a new dataset containing 47K pairs of animated GIFs and sentences crawled from the web, in which the GIFs depict physical or emotional reactions to the scenarios described in the text (called"reaction GIFs"). We report on a user study showing that, despite the presence of implicit concept association, humans are able to identify video-sentence pairs with matching concepts, suggesting the feasibility of our task. Furthermore, we propose a novel visual-semantic embedding network based on multiple instance learning. Unlike traditional approaches, we compute multiple embeddings from each modality, each representing different concepts, and measure their similarity by considering all possible combinations of visual-semantic embeddings in the framework of multiple instance learning. We evaluate our approach on two video-sentence datasets with explicit and implicit concept association and report competitive results compared to existing approaches on cross-modal retrieval.


Cellular basis of working memory


title: Compression of Neural Machine Translation Models via Pruning

abstract: Neural Machine Translation (NMT), like many other deep learning domains, typically suffers from over-parameterization, resulting in large storage sizes. This paper examines three simple magnitude-based pruning schemes to compress NMT models, namely class-blind, class-uniform, and class-distribution, which differ in terms of how pruning thresholds are computed for the different classes of weights in the NMT architecture. We demonstrate the efficacy of weight pruning as a compression technique for a state-of-the-art NMT system. We show that an NMT model with over 200 million parameters can be pruned by 40% with very little performance loss as measured on the WMT'14 English-German translation task. This sheds light on the distribution of redundancy in the NMT architecture. Our main result is that with retraining, we can recover and even surpass the original performance with an 80%-pruned model.


title: Group Recommendation Based on the PageRank

abstract: Social network greatly improve the social recommendation application, especially the study of group recommendation. The group recommendation, analyze the social factors of the group, such as social and trust relationship between users, as the factors for the prediction model established. In this paper, PageRank algorithm is introduced in the recommendation method to calculate the member’s importance in the group respectively, and to amend the aggregate function of individual preferences. The aggregate function consider the relationship between various users in the group, and optimize the aggregate function according to users different influence on the group, which can better reflect the social characteristics of group. In short, the study on group recommended model and algorithm can take the initiative to find the user's needs. Extensive experiments demonstrate the effectiveness and efficiency of the methods, which improve the prediction accuracy of the group recommended algorithms.


title: DGLB: Distributed Stochastic Geographical Load Balancing over Cloud Networks

abstract: Contemporary cloud networks are being challenged by the rapid increase of user demands and growing concerns about global warming, due to their substantial energy consumption. This requires future data centers to be both energy efficient and sustainable, which calls for leveraging cutting-edge features and the flexibility provided by the modern smart grids. To fulfill those goals, this paper puts forward a systematic approach to designing energy-aware traffic-efficient geographical load balancing schemes for data-center networks that are not only optimal, but also computationally efficient and amenable to distributed implementation. Under this comprehensive approach, workload and power balancing schemes are designed jointly across the network, both delay-tolerant and interactive workloads are accommodated, novel smart-grid features such as energy storage units are incorporated to cope with renewables, and incentive pricing mechanisms are adopted in the design. To further account for the spatio-temporal variation of demands, energy prices and renewables, the task is formulated as a two-timescale stochastic optimization. Leveraging dual stochastic approximation and the fast iterative shrinkage-thresholding algorithm (FISTA), the proposed optimization is decomposed across time slots (first-stage) and data centers (second-stage). While the resultant online algorithm is strictly feasible and provably optimal under a Markovian assumption for the underlying random processes, extensive numerical tests further demonstrate that it also works well in real-data scenarios, where the underlying randomness is highly correlated across time.


title: FACTORS INFLUENCING MOBILE SERVICES ADOPTION: A BRAND-EQUITY PERSPECTIVE

abstract: The purpose of this study is to develop and validate empirically a research model that depicts the relationships between the identified key value proposition attributes of mobile value-added services and the core factors of brand equity. Survey data collected from 497 mobile value-added service consumers were examined using structural equation modeling to validate the research model. The results show that the mobile service attributes of personalization, and identifiability have significant positive determines  on the key brand equity factors, including brand loyalty, perceived quality, and brand awareness. Additionally, the results approve the significance of all three of the brand equity factors in interpreting consumer purchase goal in the context of mobile value-added service consumption. The research results provide insights into how mobile value-added services may be better designed and delivered to increase brand equity and, in turn, profits.