title: Minimally Invasive Techniques in Distal Tibial Fractures

abstract: A series of 20 patients of distal tibial fractures treated with minimally invasive techniques have been reviewed after surgery. The technique involves open reduction and internal fixation of the associated fibular fracture when present, followed by minimally plate osteosynthesis of the tibia utilizing precontoured tubular plates and percutaneously placed cortical screws. Out of 20 patients, 14 had excellent results, 4 had good results and 2 patient had a fair result. Two patients had superficial wound infection and one patient had palpable screw.This minimally invasive technique for treatment of distal tibial fractures proves to be a feasible and worthwhile method of stabilization while avoiding the severe complications associated with the more standard methods of internal or external fixation of those fractures


title: Sex differences in anxiety and depression clinical perspectives

abstract: Sex differences are prominent in mood and anxiety disorders and may provide a window into mechanisms of onset and maintenance of affective disturbances in both men and women. With the plethora of sex differences in brain structure, function, and stress responsivity, as well as differences in exposure to reproductive hormones, social expectations and experiences, the challenge is to understand which sex differences are relevant to affective illness. This review will focus on clinical aspects of sex differences in affective disorders including the emergence of sex differences across developmental stages and the impact of reproductive events. Biological, cultural, and experiential factors that may underlie sex differences in the phenomenology of mood and anxiety disorders are discussed.


title: Formal Evaluation of an Instructional ODL Tool

abstract: This paper presents the application and evaluation by means of a controlled experiment of an instructional tool during an open and distance learning (ODL) course. The core issue of investigation is whether this instructional aid can support, guide and scaffold the distant student in his/her study. For this purpose, a controlled experiment was conducted with the participation of 191 undergraduate students. Descriptive statistics as well as a variety of statistical methods have been applied to the collected data, in order to test the research hypotheses. The results have shown a statistical significant difference in performance for the student group that used the tool. Finally, concerns about the application of the tool in a broader context and further research on the area are also presented


title: Powering the planet: Chemical challenges in solar energy utilization

abstract: Global energy consumption is projected to increase, even in the face of substantial declines in energy intensity, at least 2-fold by midcentury relative to the present because of population and economic growth. This demand could be met, in principle, from fossil energy resources, particularly coal. However, the cumulative nature of CO(2) emissions in the atmosphere demands that holding atmospheric CO(2) levels to even twice their preanthropogenic values by midcentury will require invention, development, and deployment of schemes for carbon-neutral energy production on a scale commensurate with, or larger than, the entire present-day energy supply from all sources combined. Among renewable energy resources, solar energy is by far the largest exploitable resource, providing more energy in 1 hour to the earth than all of the energy consumed by humans in an entire year. In view of the intermittency of insolation, if solar energy is to be a major primary energy source, it must be stored and dispatched on demand to the end user. An especially attractive approach is to store solar-converted energy in the form of chemical bonds, i.e., in a photosynthetic process at a year-round average efficiency significantly higher than current plants or algae, to reduce land-area requirements. Scientific challenges involved with this process include schemes to capture and convert solar energy and then store the energy in the form of chemical bonds, producing oxygen from water and a reduced fuel such as hydrogen, methane, methanol, or other hydrocarbon species.


title: Personality and Learning Motivation

abstract: In this study I investigated the relationship between personality traits and learning motivations by correlating Big Five model of personality, Core Self-evaluation, achievement and affiliation motivation, and intrinsic and extrinsic motivations for leaning. Data were collected from 298 participants using a questionnaire. Regression analysis results indicated that extraversion, agreeableness, openness to experience and conscientiousness were positively associated with intrinsic motivation, but neuroticism was positively associated with extrinsic motivation. Core self-evaluation was also positively related with intrinsic motivation and negatively related to extrinsic motivation. Furthermore, intrinsic motivation and extrinsic motivation are two concept that was mutually exclusive. Implications and further research directions are then discussed. Keywords: five-factor model, core self-evaluation, intrinsic motivation, extrinsic motivations


title: An Artificial Bee Colony-Based COPE Framework for Wireless Sensor Network

abstract: In wireless communication, network coding is one of the intelligent approaches to process the packets before transmitting for efficient information exchange. The goal of this work is to enhance throughput by using the intelligent technique, which may give comparatively better optimization. This paper introduces a biologically-inspired coding approach called Artificial Bee Colony Network Coding (ABC-NC), a modification in the COPE framework. The existing COPE and its variant are probabilistic approaches, which may not give good results in all of the real-time scenarios. Therefore, it needs some intelligent technique to find better packet combinations at intermediate nodes before forwarding to optimize the energy and maximize the throughput in wireless networks. This paper proposes ABC-NC over the existing COPE framework for the wireless environment.


title: YouTube Movie Reviews: Sentiment Analysis in an Audio-Visual Context

abstract: This work focuses on automatically analyzing a speaker's sentiment in online videos containing movie reviews. In addition to textual information, this approach considers adding audio features as typically used in speech-based emotion recognition as well as video features encoding valuable valence information conveyed by the speaker. Experimental results indicate that training on written movie reviews is a promising alternative to exclusively using (spoken) in-domain data for building a system that analyzes spoken movie review videos, and that language-independent audio-visual analysis can compete with linguistic analysis.


title: A cascade framework for masked face detection

abstract: Accurately and efficiently detecting masked faces is increasingly meaningful, since it can be applied on tracking and identifying criminals or terrorists. As a unique face detection task, masked face detection is much more difficult because of extreme occlusions which leads to the loss of face details. Besides, there is almost no existing large-scale accurately labeled masked face dataset, which increase the difficulty of masked face detection. The CNN-based deep learning algorithms has made great breakthroughs in many computer vision areas including face detection. In this paper, we propose a new CNN-based cascade framework, which consists of three carefully designed convolutional neural networks to detect masked faces. Besides, because of the shortage of masked face training samples, we propose a new dataset called "MASKED FACE dataset" to fine-tune our CNN models. We evaluate our proposed masked face detection algorithm on the MASKED FACE testing set, and it achieves satisfactory performance.


title: Pyoderma gangrenosum, acne, and suppurative hidradenitis (PASH)--a new autoinflammatory syndrome distinct from PAPA syndrome.

abstract: BACKGROUND ::: PAPA syndrome is a recently identified hereditary autoinflammatory syndrome clinically characterized by pyogenic arthritis, severe acne, and pyoderma gangrenosum. It is caused by mutations in the PSTPIP1 gene and may be closely linked to the aseptic abscesses syndrome, which has been shown to be associated with CCTG repeat amplification in the promoter region of PSTPIP1. ::: ::: ::: OBJECTIVE ::: We describe two unrelated patients with a clinical presentation quite similar to, yet distinct from, PAPA syndrome. ::: ::: ::: RESULTS ::: Both patients had pyoderma gangrenosum and acute or remittent acne conglobata, but, in contrast to PAPA syndrome, lacked any episodes of pyogenic arthritis. Instead, they had suppurative hidradenitis. Mutations in PSTPIP1 exons 1 to 15 were excluded. In the promoter region, an increased repetition of the CCTG microsatellite motif was present on one allele in both patients. Alterations of the most commonly affected exons of the MEFV, NLRP3, and TNFRSF1A genes also were not detectable. One patient was treated with the interleukin (IL)-1 receptor antagonist anakinra and responded well, although without complete remission. This implies that IL-1ß may be of pathogenetic importance. ::: ::: ::: LIMITATIONS ::: Small number of patients, no gene mutation identified, and unclear efficacy of therapy are limitations. ::: ::: ::: CONCLUSIONS ::: The clinical triad of pyoderma gangrenosum, acne, and suppurative hidradenitis represents a new disease entity within the spectrum of autoinflammatory syndromes, similar to PAPA and aseptic abscesses syndrome. For this disease, we propose the acronym "PASH" syndrome. PASH syndrome may respond to IL-1ß blockade.


title: Early pattern of neuronal differentiation in theXenopus embryonic brainstem and spinal cord

abstract: Wholemount antibody labeling techniques and horseradish peroxidase backfilling were used to analyze the pattern of neuronal differentiation in the embryonic Xenopus central nervous system between stages 22 and 35/36. In the spinal cord, the first neurons to differentiate are the Rohon-Beard neurons; they are followed by ventral neurons with descending axons (descending interneurons, motoneurons) and lateral interneurons with commissural axons. The somata and axons of these primary neurons form dorsal, ventral, and lateral columns, respectively; the ventral and lateral columns uninterruptedly continue forward into the brainstem. The distribution and projection patterns of spinal neurons were analyzed quantitatively. Rohon-Beard neurons, commissural interneurons, and primary motoneurons vary in number from segment to segment. Thus, these neurons are not distributed in a segmental pattern. In each segment, neurons of a given type project axons whose length varies over a wide range. The numerical distribution of length of axons formed by a population of neurons of a given type was calculated and expressed as the projection profile of these neurons. For each type of neuron and spinal segment, the projection profile is different. Furthermore, the projection profiles change in a systematic way along the spinal cord. For example, the fraction of Rohon-Beard neurons with long ascending axons steadily increases if one moves towards caudal spinal levels. The findings suggest that suprasegmental cues with a graded distribution along the spinal cord determine the number and projection profile of a particular cell type in a given segment. © 1993 Wiley-Liss, Inc.


A place theory of sound localization.


title: Action recognition by dense trajectories

abstract: Feature trajectories have shown to be efficient for representing videos. Typically, they are extracted using the KLT tracker or matching SIFT descriptors between frames. However, the quality as well as quantity of these trajectories is often not sufficient. Inspired by the recent success of dense sampling in image classification, we propose an approach to describe videos by dense trajectories. We sample dense points from each frame and track them based on displacement information from a dense optical flow field. Given a state-of-the-art optical flow algorithm, our trajectories are robust to fast irregular motions as well as shot boundaries. Additionally, dense trajectories cover the motion information in videos well. We, also, investigate how to design descriptors to encode the trajectory information. We introduce a novel descriptor based on motion boundary histograms, which is robust to camera motion. This descriptor consistently outperforms other state-of-the-art descriptors, in particular in uncontrolled realistic videos. We evaluate our video description in the context of action classification with a bag-of-features approach. Experimental results show a significant improvement over the state of the art on four datasets of varying difficulty, i.e. KTH, YouTube, Hollywood2 and UCF sports.


From Grayscale to Color : Digital Image Colorization using Machine Learning


title: Organizational Ambidexterity: Past, Present and Future

abstract: Organizational ambidexterity refers to the ability of an organization to both explore and exploit--to compete in mature technologies and markets where efficiency, control, and incremental improvement are prized and to also compete in new technologies and markets where flexibility, autonomy, and experimentation are needed. In the past 15 years there has been an explosion of interest and research on this topic. We briefly review the current state of the research, highlighting what we know and don't know about the topic. We close with a point of view on promising areas for ongoing research.


title: Typing haskell with an attribute grammar

abstract: A great deal has been written about type systems. Much less has been written about implementing them. Even less has been written about implementations of complete compilers in which all aspects come together. This paper fills this gap by describing the implementation of a series of compilers for a simplified variant of Haskell. By using an attribute grammar system, aspects of a compiler implementation can be described separately and added in a sequence of steps, thereby giving a series of increasingly complex (working) compilers. Also, the source text of both this paper and the executable compilers come from the same source files by an underlying minimal weaving system. Therefore, source and explanation is kept consistent.


title: Incorporating Advice into Neuroevolution of Adaptive Agents

abstract: Neuroevolution is a promising learning method in tasks with extremely large state and action spaces and hidden states. Recent advances allow neuroevolution to take place in real time, making it possible to e.g. construct video games with adaptive agents. Often some of the desired behaviors for such agents are known, and it would make sense to prescribe them, rather than requiring evolution to discover them. This paper presents a technique for incorporating human-generated advice in real time into neuroevolution. The advice is given in a formal language and converted to a neural network structure through KBANN. The NEAT neuroevolution method then incorporates the structure into existing networks through evolution of network weights and topology. The method is evaluated in the NERO video game, where it makes learning faster even when the tasks change and novel ways of making use of the advice are required. Such ability to incorporate human knowledge into neuroevolution in real time may prove useful in several interactive adaptive domains in the future.


title: Hierarchical attribute-based encryption for fine-grained access control in cloud storage services

abstract: Cloud computing, as an emerging computing paradigm, enables users to remotely store their data into a cloud so as to enjoy scalable services on-demand. Especially for small and medium-sized enterprises with limited budgets, they can achieve cost savings and productivity enhancements by using cloud-based services to manage projects, to make collaborations, and the like. However, allowing cloud service providers (CSPs), which are not in the same trusted domains as enterprise users, to take care of confidential data, may raise potential security and privacy issues. To keep the sensitive user data confidential against untrusted CSPs, a natural way is to apply cryptographic approaches, by disclosing decryption keys only to authorized users. However, when enterprise users outsource confidential data for sharing on cloud servers, the adopted encryption system should not only support fine-grained access control, but also provide high performance, full delegation, and scalability, so as to best serve the needs of accessing data anytime and anywhere, delegating within enterprises, and achieving a dynamic set of users. In this paper, we propose a scheme to help enterprises to efficiently share confidential data on cloud servers. We achieve this goal by first combining the hierarchical identity-based encryption (HIBE) system and the ciphertext-policy attribute-based encryption (CP-ABE) system, and then making a performance-expressivity tradeoff, finally applying proxy re-encryption and lazy re-encryption to our scheme.


title: Media Effects: Theory and Research

abstract: This review analyzes trends and commonalities among prominent theories of media effects. On the basis of exemplary meta-analyses of media effects and bibliometric studies of well-cited theories, we identify and discuss five features of media effects theories as well as their empirical support. Each of these features specifies the conditions under which media may produce effects on certain types of individuals. Our review ends with a discussion of media effects in newer media environments. This includes theories of computer-mediated communication, the development of which appears to share a similar pattern of reformulation from unidirectional, receiver-oriented views, to theories that recognize the transactional nature of communication. We conclude by outlining challenges and promising avenues for future research.


title: A 0.13-$\mu$ m CMOS 6 Gb/s/pin Memory Transceiver Using Pseudo-Differential Signaling for Removing Common-Mode Noise Due to SSN

abstract: A 6 Gb/s/pin transceiver for DRAM interfaces is implemented in a 0.13-mum CMOS process. Pseudo-differential signaling to overcome problems of conventional single-ended signaling is proposed. In a conventional single-ended signaling, the reference signal from a transmitter is generally used to reduce the common-mode noise which is induced by simultaneous switching noise (SSN). However, as supply voltage becomes low and data rate increases, the reference signal reduces the voltage and timing margin of receiver inputs. The proposed transceiver uses the pseudo-differential signaling without the reference signal and encoding schemes using the relation between neighboring data are proposed to remove the reference signal. In the receiver, transition-check circuit (TCC) is used to convert encoded data into original data before encoding. The proposed pseudo-differential signaling increases the eye-opening of 1:2 demuxed outputs of the receiver by 65 ps. The transceiver dissipates 242.5 mW and its active area is 1.0 mm times 0.3 mm.


title: Gist of the Scene

abstract: ABSTRACT Studies in scene perception have shown that observers recognize a real-world scene at a single glance. During this expeditious process of seeing, the visual system forms a spatial representation of the outside world that is rich enough to grasp the meaning of the scene, to recognize a few objects and other salient information in the image, and to facilitate object detection and the deployment of attention. This representation refers to the gist of a scene, which includes all levels of processing, from low-level features (e.g., color, spatial frequencies) to intermediate image properties (e.g., surface, volume) and high-level information (e.g., objects, activation of semantic knowledge). Therefore, gist can be studied at both perceptual and conceptual levels.


title: Efficient MPC for permanent magnet synchronous motor

abstract: The permanent magnet synchronous motor (PMSM) control poses a challenging task since the dynamics of the system is nonlinear, there are hard constraints, and computational costs should be kept as low as possible. One way how to satisfy all of the requirements is to use the model predictive control (MPC) with a small length of short horizon in the MPC criterion and then try to replace the remaining long-horizon part with an appropriate approximation. This paper develops the idea of the long-horizon part approximation by the expected time to reach the required state of the PMSM. The reason of using this expected time is properly justified here which simultaneously causes that the number of user-defined parameters is reduced to minimum. Then, the MPC controller for the PMSM utilizing this approximation is designed for continuous region of admissible control actions. The advantages of the designed control algorithm are demonstrated by numerical simulations.


title: Leveraging digital technologies: how information quality leads to localized capabilities and customer service performance

abstract: With the growing recognition of the customer's role in service creation and delivery, there is an increased impetus on building customer-centric organizations. Digital technologies play a key role in such organizations. Prior research studying digital business strategies has largely focused on building production-side competencies and there has been little focus on customer-side digital business strategies to leverage these technologies. We propose a theory to understand the effectiveness of a customer-side digital business strategy focused on localized dynamics--here, a firm's customer service units (CSUs). Specifically, we use a capabilities perspective to propose digital design as an antecedent to two customer service capabilities--namely, customer orientation capability and customer response capability--across a firm's CSUs. These two capabilities will help a firm to locally sense and respond to customer needs, respectively. Information quality from the digital design of the CSU is proposed as the antecedent to the two capabilities. Proposed capability-building dynamics are tested using data collected from multiple respondents across 170 branches of a large bank. Findings suggest that the impacts of information quality in capability-building are contingent on the local process characteristics. We offer implications for a firm's customer-side digital business strategy and present new areas for future examination of such strategies.


title: A local fuzzy thresholding methodology for multiregion image segmentation

abstract: Thresholding is a direct and simple approach to extract different regions from an image. In its basic formulation, thresholding searches for a global value that maximizes the separation between output classes. The use of a single hard threshold value is precisely the source of important segmentation errors in many scenarios like noisy images or uneven illumination. If no connectivity or closed objects are considered, the method is prone to produce isolated pixels. In this paper a new multiregion thresholding methodology is presented to overcome the common drawbacks of thresholding methods when images are corrupted with artifacts and noise. It is based on relating each pixel in the image to different output centroids via a fuzzy membership function, avoiding any initial hard decision. The starting point of the technique is the definition of the output centroids using a clustering method compatible with most thresholding techniques in the literature. The method makes use of the spatial information through a local aggregation step where the membership degree of each pixel is modified by local information that takes into account the memberships of the surrounding pixels. This makes the method robust to noise and artifacts. The general formulation of the proposed methodology allows the design of spatial aggregations for multiple applications, including the possibility of including heuristic information via a fuzzy inference rule base.


title: An Algorithm For Sketching Design with Pencil and Eraser

abstract: We have investigated interaction behavior with physical tools - pencil and eraser together in sketching design and conclude that interaction with pencil and eraser is more natural than with pen alone. We present a new algorithm supporting both sketching design and interaction with pencil and eraser just like designers naturally do on the paper. This interaction technique belongs to the most pervasive computing technology. The algorithm adopts a new data structure, which integrates data structure for image and graph. The graph modification and recognition is based on image along with dynamic information of strokes or context. Experiments for illustrating the efficiency of this algorithm are also included


title: Azure Data Lake Store: A Hyperscale Distributed File Service for Big Data Analytics

abstract: Azure Data Lake Store (ADLS) is a fully-managed, elastic, scalable, and secure file system that supports Hadoop distributed file system (HDFS) and Cosmos semantics. It is specifically designed and optimized for a broad spectrum of Big Data analytics that depend on a very high degree of parallel reads and writes, as well as collocation of compute and data for high bandwidth and low-latency access. It brings together key components and features of Microsoft?s Cosmos file system-long used by internal customers at Microsoft and HDFS, and is a unified file storage solution for analytics on Azure. Internal and external workloads run on this unified platform. Distinguishing aspects of ADLS include its design for handling multiple storage tiers, exabyte scale, and comprehensive security and data sharing features. We present an overview of ADLS architecture, design points, and performance.


title: A social network-based system for supporting interactive collaboration in knowledge sharing over peer-to-peer network

abstract: Knowledge sharing enables people in virtual communities to access relevant knowledge (explicit or tacit) from broader scope of resources. The performance in such environments is fundamentally based on how effectively the explicit and tacit knowledge can be shared across people, and how efficiently the created knowledge can be organized and disseminated to enrich digital content. This study will address how to apply social network-based system to support interactive collaboration in knowledge sharing over peer-to-peer networks. Results of this study demonstrate that applying such social network-based collaboration support to knowledge sharing helps people find relevant content and knowledgeable collaborators who are willing to share their knowledge.


title: Tacit Knowledge and Knowledge Conversion: Controversy and Advancement in Organizational Knowledge Creation Theory

abstract: Nonaka's paper [1994. A dynamic theory of organizational knowledge creation. Organ. Sci.5(1) 14--37] contributed to the concepts of “tacit knowledge” and “knowledge conversion” in organization science. We present work that shaped the development of organizational knowledge creation theory and identify two premises upon which more than 15 years of extensive academic work has been conducted: (1) tacit and explicit knowledge can be conceptually distinguished along a continuum; (2) knowledge conversion explains, theoretically and empirically, the interaction between tacit and explicit knowledge. Recently, scholars have raised several issues regarding the understanding of tacit knowledge as well as the interaction between tacit and explicit knowledge in the theory. The purpose of this article is to introduce and comment on the debate about organizational knowledge creation theory. We aim to help scholars make sense of this debate by synthesizing six fundamental questions on organizational knowledge creation theory. Next, we seek to elaborate and advance the theory by responding to questions and incorporating new research. Finally, we discuss implications of our endeavor for organization science.


title: Improving the agility of keyframe-based SLAM

abstract: The ability to localise a camera moving in a previously unknown environment is desirable for a wide range of applications. In computer vision this problem is studied as monocular SLAM. Recent years have seen improvements to the usability and scalability of monocular SLAM systems to the point that they may soon find uses outside of laboratory conditions. However, the robustness of these systems to rapid camera motions (we refer to this quality as agility) still lags behind that of tracking systems which use known object models. In this paper we attempt to remedy this. We present two approaches to improving the agility of a keyframe-based SLAM system: Firstly, we add edge features to the map and exploit their resilience to motion blur to improve tracking under fast motion. Secondly, we implement a very simple inter-frame rotation estimator to aid tracking when the camera is rapidly panning --- and demonstrate that this method also enables a trivially simple yet effective relocalisation method. Results show that a SLAM system combining points, edge features and motion initialisation allows highly agile tracking at a moderate increase in processing time.


title: A Multi-Agent Systems Approach to Autonomic Computing

abstract: The goal of autonomic computing is to create computing systems capable of managing themselves to a far greater extent than they do today. This paper presents Unity, a decentralized architecture for autonomic computing based on multiple interacting agents called autonomic elements. We illustrate how the Unity architecture realizes a number of desired autonomic system behaviors including goal-driven self-assembly, self-healing, and real-time self-optimization. We then present a realistic prototype implementation, showing how a collection of Unity elements self-assembles, recovers from certain classes of faults, and manages the use of computational resources (e.g. servers) in a dynamic multi-application environment. In Unity, an autonomic element within each application environment computes a resource-level utility function based on information specified in that applicationýs service-level utility function. Resource-level utility functions from multiple application environments are sent to a Resource Arbiter element, which computes a globally optimal allocation of servers across the applications. We present illustrative empirical data showing the behavior of our implemented system in handling realistic Web-based transactional workloads running on a Linux cluster.


title: Growth of Loudness in Listeners with Cochlear Hearing Losses: Recruitment Reconsidered

abstract: listeners with hearing losses. Such loudness functions are consistent with the steep functions shown by classical data on loudness matches between tones for which This article examines how loudness grows with increasing intensity near threshold in five listeners with hear- thresholds are normal and tones for which thresholds are elevated. Thus, the present data indicate that an inglossesofpredominantlycochlearorigin.Itprovides evidence against the pervasive and long-held notion abnormally large loudness at an elevated threshold is likely to be a better definition of recruitment than the classical that such listeners show abnormally rapid loudness growth near their elevated thresholds. As in a previous definition of it as an abnormally rapid growth of loudness above an elevated threshold. study for listeners with normal hearing, loudness functions near threshold were derived from loudness Keywords: loudness, cochlear hearing losses, recruitment, threshold, models matches between a pure tone and four- or ten-tone complexes using a simple model of loudness summation. This study assumed that the loudness function had the same form for any component of a complex, but a scale factor that depended on the amount of hearing loss allowed the loudness at threshold to vary withfrequency.Thebest-fittingloudnessfunctionshad INTRODUCTION low-level local exponents [i.e., slopes of the low-level loudness function plotted as log(loudness) versus Listeners with cochlear hearing losses cannot hear a log(intensity)] that were within the normal range. At low-SPL tone in the frequency range of their hearing 0d BSL, the average local exponents were 1.26 for loss. However, once the tone is about 30 dB above thelistenerswithhearinglossescomparedwith1.31for threshold, its loudness level is usually reduced by only normal listeners, which indicates that loudness near a fraction of the amount of hearing loss. That is, the threshold growsat similar ratesin normallisteners and level difference between equally loud tones in regions listeners with hearing losses. The model also indicated of cochlear hearing loss and in regions of normal that, on average, the loudness at threshold doubled hearing almost always decreases as sound level forevery16 dBofhearingloss.The increasedloudness increases. This fact has led to one of the longest-standat threshold, together with somewhat enlarged local ing “truths” about cochlear hearing losses, i.e., loudexponents around 20 dB SL, accounts for the near- ness grows abnormally fast as the sound intensity normal loudness often obtained for high-SPL tones in increases above the elevated threshold of a listener witha cochlearhearing loss.Indeed, loudnessmatches between tones for which the threshold is normal and


title: Issues in robot ethics seen through the lens of a moral Turing test

abstract: Purpose – The purpose of this paper is to explore artificial moral agency by reflecting upon the possibility of a Moral Turing Test (MTT) and whether its lack of focus on interiority, i.e. its behaviouristic foundation, counts as an obstacle to establishing such a test to judge the performance of an Artificial Moral Agent (AMA). Subsequently, to investigate whether an MTT could serve as a useful framework for the understanding, designing and engineering of AMAs, we set out to address fundamental challenges within the field of robot ethics regarding the formal representation of moral theories and standards. Here, typically three design approaches to AMAs are available: top-down theory-driven models and bottom-up approaches which set out to model moral behaviour by means of models for adaptive learning, such as neural networks, and finally, hybrid models, which involve components from both top-down and bottom-up approaches to the modelling of moral agency. With inspiration from Allen and Wallace (2009, 2000...


title: Learning Semantic Maps from Natural Language Descriptions

abstract: This paper proposes an algorithm that enables robots to efficiently learn human-centric models of their environment from natural language descriptions. Typical semantic mapping approaches augment metric maps with higher-level properties of the robot’s surroundings (e.g., place type, object locations), but do not use this information to improve the metric map. The novelty of our algorithm lies in fusing high-level knowledge, conveyed by speech, with metric information from the robot’s low-level sensor streams. Our method jointly estimates a hybrid metric, topological, and semantic representation of the environment. This semantic graph provides a common framework in which we integrate concepts from natural language descriptions (e.g., labels and spatial relations) with metric observations from low-level sensors. Our algorithm efficiently maintains a factored distribution over semantic graphs based upon the stream of natural language and low-level sensor information. We evaluate the algorithm’s performance and demonstrate that the incorporation of information from natural language increases the metric, topological and semantic accuracy of the recovered environment model.


User Embedding for Scholarly Microblog Recommendation


title: Topological Structural Analysis of Digitized Binary Images by Border Following

abstract: Abstract Two border following algorithms are proposed for the topological analysis of digitized binary images. The first one determines the surroundness relations among the borders of a binary image. Since the outer borders and the hole borders have a one-to-one correspondence to the connected components of 1-pixels and to the holes, respectively, the proposed algorithm yields a representation of a binary image, from which one can extract some sort of features without reconstructing the image. The second algorithm, which is a modified version of the first, follows only the outermost borders (i.e., the outer borders which are not surrounded by holes). These algorithms can be effectively used in component counting, shrinking, and topological structural analysis of binary images, when a sequential digital computer is used.


title: Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Probe and Learn Neural Networks

abstract: In this paper, we propose a novel model for high-dimensional data, called the Hybrid Orthogonal Projection and Estimation (HOPE) model, which combines a linear orthogonal projection and a finite mixture model under a unified generative modeling framework. The HOPE model itself can be learned unsupervised from unlabelled data based on the maximum likelihood estimation as well as discriminatively from labelled data. More interestingly, we have shown the proposed HOPE models are closely related to neural networks (NNs) in a sense that each hidden layer can be reformulated as a HOPE model. As a result, the HOPE framework can be used as a novel tool to probe why and how NNs work, more importantly, to learn NNs in either supervised or unsupervised ways. In this work, we have investigated the HOPE framework to learn NNs for several standard tasks, including image recognition on MNIST and speech recognition on TIMIT. Experimental results have shown that the HOPE framework yields significant performance gains over the current state-of-the-art methods in various types of NN learning problems, including unsupervised feature learning, supervised or semi-supervised learning.


title: Multi-tenancy authorization models for collaborative cloud services

abstract: The cloud service model intrinsically caters to multiple tenants, most obviously not only in public clouds but also in private clouds for large organizations. Currently, most cloud service providers isolate user activities and data within a single tenant boundary with no or minimum cross-tenant interaction. It is anticipated that this situation will evolve soon to foster cross-tenant collaboration supported by Authorization as a Service. At present, there is no widely accepted model for cross-tenant authorization. Recently, Calero et al. informally presented a multi-tenancy authorization system MTAS, which extends the well-known role-based access control model by building trust relations among collaborating tenants. In this paper, we formalize this MTAS model and propose extensions for finer-grained cross-tenant trust. We also develop an administration model for MTAS. We demonstrate the utility and practical feasibility of MTAS by means of an example policy specification in extensible access control markup language. To further test the metrics of the model, we develop a prototype system and conduct experiments on it. The result shows that the prototype has 12-ms policy decision overhead on average and is scalable. We anticipate that researchers will develop additional multi-tenant authorization models before eventual consolidation and convergence to standard industry practice. Copyright © 2014 John Wiley & Sons, Ltd.


title: Generalized spectral bounds for sparse LDA

abstract: We present a discrete spectral framework for the sparse or cardinality-constrained solution of a generalized Rayleigh quotient. This NP-hard combinatorial optimization problem is central to supervised learning tasks such as sparse LDA, feature selection and relevance ranking for classification. We derive a new generalized form of the Inclusion Principle for variational eigenvalue bounds, leading to exact and optimal sparse linear discriminants using branch-and-bound search. An efficient greedy (approximate) technique is also presented. The generalization performance of our sparse LDA algorithms is demonstrated with real-world UCI ML benchmarks and compared to a leading SVM-based gene selection algorithm for cancer classification.


title: Postcolonial computing: a lens on design and development

abstract: As our technologies travel to new cultural contexts and our designs and methods engage new constituencies, both our design and analytical practices face significant challenges. We offer postcolonial computing as an analytical orientation to better understand these challenges. This analytic orientation inspires four key shifts in our approach to HCI4D efforts: generative models of culture, development as a historical program, uneven economic relations, and cultural epistemologies. Then, through reconsideration of the practices of engagement, articulation and translation in other contexts, we offer designers and researchers ways of understanding use and design practice to respond to global connectivity and movement.


title: The Impact of Using Multiple Antennas on Wireless Localization

abstract: We show that signal strength variability can be reduced by employing multiple low-cost antennas at fixed locations. We further explore the impact of this reduction on wireless localization by analyzing a representative set of algorithms ranging from fingerprint matching, to statistical maximum likelihood estimation, and to multilateration. We provide experimental evaluation using an indoor wireless testbed of the localization performance under multiple antennas. We found that in nearly all cases the performance of localization algorithms improved when using multiple antennas. Specifically, the median and the 90th percentile error can be reduced up to 70%. Additionally, we found that multiple antennas improve the localization stability significantly, up to 100% improvement, when there are small scale 3-dimensional movements of a mobile device around a given location.


title: Diagnostic Study of VHF Plasma and Deposition of Hydrogenated Amorphous Silicon Films

abstract: The role of the excitation frequency in processing plasma is investigated through comparative experiments of VHF and RF plasma by means of plasma diagnostics and deposition charaterisitcs of a-Si:H films. Electron temperature, electron density and the shape of the tail of the electron energy distribution function (EEDF) are evaluated from the Langmuir probe measurement. EEDF is also discussed based on optical emission spectra measured by He, H2 SiH4 plasma. The frequency effect is explained in terms of the effect of ω/ν on EEDF. Structural and electrical properties of a-Si:H films deposited both by VHF and RF plasma are also investigated. The favorable condition for obtaining device-quality a-Si:H films is discussed in connection with the plasma structure.


title: Specialized entailment engines: approaching linguistic aspects of textual entailment

abstract: Textual Entailment (TE), one of the current hot topics in Computational Linguistics, has been proposed as a task to address the problem of language variability. Since TE is due to the combination of different linguistic phenomena which interact among them in a complex way, this paper proposes to experiment the use of specialized entailment engines, each addressing a specific phenomenon relevant to entailment.


title: Temporal Convolutional Networks and Dynamic Time Warping can Drastically Improve the Early Prediction of Sepsis

abstract: Motivation: Sepsis is a life-threatening host response to infection associated with high mortality, morbidity and health costs. Its management is highly time-sensitive since each hour of delayed treatment increases mortality due to irreversible organ damage. Meanwhile, despite decades of clinical research robust biomarkers for sepsis are missing. Therefore, detecting sepsis early by utilizing the affluence of high-resolution intensive care records has become a challenging machine learning problem. Recent advances in deep learning and data mining promise a powerful set of tools to efficiently address this task. ::: Results: This paper proposes two approaches for the early detection of sepsis: a new deep learning model (MGP-TCN) and a data mining model (DTW-KNN). MGP-TCN employs a temporal convolutional network as embedded in a Multitask Gaussian Process Adapter framework, making it directly applicable to irregularly spaced time series data. Our DTW-KNN is an ensemble approach that employs dynamic time warping. We then frame the timely detection of sepsis as a supervised time series classification task. For this, we derive the most recent sepsis definition in an hourly resolution to provide the first fully accessible early sepsis detection environment. Seven hours before sepsis onset, our methods MGP-TCN/DTW-KNN improve area under the precision--recall curve from 0.25 to 0.35/0.40 over the state of the art. This demonstrates that they are well-suited for detecting sepsis in the crucial earlier stages when management is most effective.


title: Dietary practices and associated factors during pregnancy in northwestern Ethiopia

abstract: Pregnancy is the most crucial nutritionally demanding period of every woman’s life. The high demand of nutrients to deposit energy in the form of new tissue, growth of existing maternal tissues such as breast and uterus and increased energy requirements for tissue synthesis makes pregnant women more vulnerable to malnutrition. Dietary practice is defined as an observable actions or behavior of dietary habit and can be classified as good dietary practices and poor dietary practices. The incidence of dietary inadequacies as a result of dietary habits and patterns in pregnancy is higher during pregnancy when compared to any other stage of the life cycle. Thus, this study aimed to assess dietary practices and associated factors during pregnancy in Bahir Dar town, Northwest Ethiopia. A community based cross sectional study was conducted from March 1 to April 1, 2016. A total of 616 pregnant women were participated in the study. All eligible pregnant women were identified through house-to-house visit with the help of health extension workers. Cluster sampling was used to select eligible pregnant women. The data were collected using interviewer administered questionnaire prepared in English and translated in to Amharic. Data were analyzed by using Statistical Package for Social Sciences (SPSS) version 20. Multivariate logistic regression analysis was employed to identify factors associated with dietary practices. This study has shown that 39.3% of the study participants had good dietary practices and the rest 60.7% of pregnant women reported poor dietary practices. Concerning dietary knowledge, 61.4% of the study participants had good dietary knowledge while 38.6% had poor dietary knowledge. Husband income, ownership of radio, history of disease and dietary knowledge were shown to have significant association (P < 0.05) with dietary practices. Dietary practices of pregnant women in the study area was suboptimal. Husband income, ownership of radio, history of disease and dietary knowledge were independent predictors of women dietary practices. Awareness should be created among pregnant women by concerned bodies such as governmental and non-governmental organization through local mass media.


title: Using encryption for authentication in large networks of computers

abstract: Use of encryption to achieve authenticated communication in computer networks is discussed. Example protocols are presented for the establishment of authenticated connections, for the management of authenticated mail, and for signature verification and document integrity guarantee. Both conventional and public-key encryption algorithms are considered as the basis for protocols.


title: DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference

abstract: We present a novel deep learning architecture to address the natural language inference (NLI) task. Existing approaches mostly rely on simple reading mechanisms for independent encoding of the premise and hypothesis. Instead, we propose a novel dependent reading bidirectional LSTM network (DR-BiLSTM) to efficiently model the relationship between a premise and a hypothesis during encoding and inference. We also introduce a sophisticated ensemble strategy to combine our proposed models, which noticeably improves final predictions. Finally, we demonstrate how the results can be improved further with an additional preprocessing step. Our evaluation shows that DR-BiLSTM obtains the best single model and ensemble model results achieving the new state-of-the-art scores on the Stanford NLI dataset.


Therapy with Ulipristal Acetate in a Hypertensive Patient


title: The Roots of Empathy : The Shared Manifold Hypothesis and the Neural Basis of Intersubjectivity

abstract: Starting from a neurobiological standpoint, I will propose that our capacity to understand others as intentional agents, far from being exclusively dependent upon mentalistic/linguistic abilities, be deeply grounded in the relational nature of our interactions with the world. According to this hypothesis, an implicit, prereflexive form of understanding of other individuals is based on the strong sense of identity binding us to them. We share with our conspecifics a multiplicity of states that include actions, sensations and emotions. A new conceptual tool able to capture the richness of the experiences we share with others will be introduced: the shared manifold of intersubjectivity. I will posit that it is through this shared manifold that it is possible for us to recognize other human beings as similar to us. It is just because of this shared manifold that intersubjective communication and ascription of intentionality become possible. It will be argued that the same neural structures that are involved in processing and controlling executed actions, felt sensations and emotions are also active when the same actions, sensations and emotions are to be detected in others. It therefore appears that a whole range of different ‘mirror matching mechanisms’ may be present in our brain. This matching mechanism, constituted by mirror neurons originally discovered and described in the domain of action, could well be a basic organizational feature of our brain, enabling our rich and diversified intersubjective experiences. This perspective is in a position to offer a global approach to the understanding of the vulnerability to major psychoses such as schizophrenia.


title: Single-chip 5.8 GHz ETC transceiver IC with PLL and demodulation circuits using SiGe HBT/CMOS

abstract: A single-chip 5.8 GHz ETC transceiver IC with PLL and demodulator uses SiGe HBT/CMOS. The fully integrated ETC chip includes a 31 dB-gain RX stage, an ASK demodulator, and a high-precision RSSI. The PLL is constructed with a varactor-tuned LC-VCO and a low-power BiCMOS synthesizer. The TX stage incorporates a transformer-transferred single-ended PA.


title: Dot Matrix Alphanumeric Display System for Arabic

abstract: The layouts for Arabic alphanumeric characters for implementation on 5×7 dot matrix displays have been presented in this paper. The organization of the ROM code converter for the display system has been given.


title: From Point to Set: Extend the Learning of Distance Metrics

abstract: Most of the current metric learning methods are proposed for point-to-point distance (PPD) based classification. In many computer vision tasks, however, we need to measure the point-to-set distance (PSD) and even set-to-set distance (SSD) for classification. In this paper, we extend the PPD based Mahalanobis distance metric learning to PSD and SSD based ones, namely point-to-set distance metric learning (PSDML) and set-to-set distance metric learning (SSDML), and solve them under a unified optimization framework. First, we generate positive and negative sample pairs by computing the PSD and SSD between training samples. Then, we characterize each sample pair by its covariance matrix, and propose a covariance kernel based discriminative function. Finally, we tackle the PSDML and SSDML problems by using standard support vector machine solvers, making the metric learning very efficient for multiclass visual classification tasks. Experiments on gender classification, digit recognition, object categorization and face recognition show that the proposed metric learning methods can effectively enhance the performance of PSD and SSD based classification.


title: Neonatal brainstem dysfunction risks infant social engagement

abstract: The role of the brainstem in mediating social signaling in phylogenetic ancestral organisms has been demonstrated. Evidence for its involvement in social engagement in human infants may deepen the understanding of the evolutionary pathway of humans as social beings. In this longitudinal study, neonatal brainstem functioning was measured by auditory brainstem-evoked responses (ABRs) in 125 healthy neonates born prematurely before 35 weeksgestational age. At 4 months, infants were tested in a set of structured vignettes that required varying levels of social engagement and cardiac vagal tone was assessed. Data show that neonates with a disrupted I-V waveform, evident mostly by delayed wave V, exhibit shorter latencies to gaze averts in episodes involving direct face-to-face interactions but engage gaze as controls when interacting with masked agents or with agents whose faces are partly veiled by toys. Analysis of variance of infantssocial engagement with ABR, neonatal risk, maternal stress and cardiac vagal tone showed a main effect for ABR and an ABR by gestational age interaction. The integrity of brainstem trans- mission of sensory information during the final weeks of gestation may scaffold the development of social disengagement, thereby attesting to the brainstem's preserved evolutionary role in developing humans as social organisms prior to engaging in social encounters.


title: Loneliness, social isolation and behavioral and biological health indicators in older adults.

abstract: Objective: A number of mechanisms have been proposed through which social isolation and loneliness may affect health, including health-related behavioral and biological factors. However, it is unclear to what extent isolation and loneliness are independently associated with these pathways. The objective of the present analysis was to determine the impact of social isolation and loneliness, individually as well as simultaneously, on health-related behavioral and biological factors using data from the English Longitudinal Study of Ageing (ELSA). Method: Data on health behaviors (smoking and physical activity) were analyzed from 8,688 participants and data on blood pressure, cholesterol, and inflammatory markers were analyzed from over 5,000 of these participants who were eligible for a nurse visit and blood sampling. Loneliness was measured using the short form of the Revised UCLA scale and an index of social isolation was computed incorporating marital status; frequency of contact with friends, family, and children; and participation in social activities. Results: Fewer than 2% of participants reported being lonely all the time, while nearly 7% had the highest possible scores on social isolation. Both social isolation and loneliness were associated with a greater risk of being inactive, smoking, as well as reporting multiple health-risk behaviors. Social isolation was also positively associated with blood pressure, C-reactive protein, and fibrinogen levels. Conclusions: Loneliness and social isolation may affect health independently through their effects on health behaviors. In addition, social isolation may also affect health through biological processes associated with the development of cardiovascular disease.


title: Secure and resilient distributed machine learning under adversarial environments

abstract: With a large number of sensors and control units in networked systems, the decentralized computing algorithms play a key role in scalable and efficient data processing for detection and estimation. The well-known algorithms are vulnerable to adversaries who can modify and generate data to deceive the system to misclassify or misestimate the information from the distributed data processing. This work aims to develop secure, resilient and distributed machine learning algorithms under adversarial environment. We establish a game-theoretic framework to capture the conflicting interests between the adversary and a set of distributed data processing units. The Nash equilibrium of the game allows predicting the outcome of learning algorithms in adversarial environment, and enhancing the resilience of the machine learning through dynamic distributed learning algorithms. We use Spambase Dataset to illustrate and corroborate our results.


title: On the Minimization of Glass-to-Glass and Glass-to-Algorithm Delay in Video Communication

abstract: Video cameras are increasingly used to provide real-time feedback in automatic control systems, such as autonomous driving and robotics systems. For such highly dynamic applications, the glass-to-glass (G2G) and glass-to-algorithm (G2A) latencies are critical. In this paper, we analyze the latencies in a point-to-point video transmission system and propose novel frame skipping and preemption approaches to reduce the G2G and G2A delays. We implement the proposed approaches in a prototype that shows significantly reduced G2G and G2A latencies as well as reduced transmission bitrate requirements compared with traditional video transmission schemes. In our low-delay video communication prototype, a VGA resolution video is transmitted with average G2G and G2A delays of 21.2 and 11.5 ms, respectively, with off-the-shelf hardware.


title: Requiem for online harassers: Identifying racism from political tweets

abstract: During the last five years, the amount of users of online social networks has increased exponentially. With the growing of users, social problems also arise. Due to the nature of these platforms, specifically Twitter, users can express their ideas in the way they prefer no matter if it is racist or not. As the Twitter CEO says, one of the most difficult things for them is to detect and ban people who harass others. Researches have addressed this issue in recent years. However, it is needed a wider range of strategies to detect racist users and content. In this work, we collect tweets produced by the ego networks of the two former 2016 US Presidential Candidates: Hillary Clinton and Donald Trump, grouped in four datasets. After deleting spammers, we get 84,371 unique users labeled by using two different metrics: Sentiment Word Count and Racist Score. Both of them let us not only to identify users as racists, but also to detect the level of negativism by analyzing their most recent 200 tweets, increasing the effectiveness of the method. Using it, we find the most negative and racist user and the most positive and non-racist user from all datasets. Taking advantage of the topological properties of the ego networks we analyzed, we also verify that our results satisfy the sociologist theory of homophily; where the followers of each candidate represent their homophilous. For a nation as the United States of America, detecting online harassers might help to decrease racism and cyberbullying, social problems that affect their society. A world without online harassers is an utopia, but this is one step to achieve it.


title: Design Methodology for Phase-Locked Loops Using Binary (Bang-Bang) Phase Detectors

abstract: We present a linearized analysis of bang-bang phase-locked loops (PLLs) in the frequency domain that is complete and self-consistent. It enables the manual design of frequency synthesis PLLs for loop bandwidth, output phase noise and minimum jitter. Tradeoffs between various parameters of the loop become clear. The analysis is validated against measurements on four very different loops, and helps to answer long-standing questions on aspects of these circuits attributable a hard nonlinearity. A brief designer’s guide is included.


Multi-Granularity Chinese Word Embedding


title: Asymmetric multi-task learning based on task relatedness and loss

abstract: We propose a novel multi-task learning method that minimizes the effect of negative transfer by allowing asymmetric transfer between the tasks based on task relatedness as well as the amount of individual task losses, which we refer to as Asymmetric Multi-task Learning (AMTL). To tackle this problem, we couple multiple tasks via a sparse, directed regularization graph, that enforces each task parameter to be reconstructed as a sparse combination of other tasks selected based on the task-wise loss. We present two different algorithms that jointly learn the task predictors as well as the regularization graph. The first algorithm solves for the original learning objective using alternative optimization, and the second algorithm solves an approximation of it using curriculum learning strategy, that learns one task at a time. We perform experiments on multiple datasets for classification and regression, on which we obtain significant improvements in performance over the single task learning and existing multitask learning models.


title: Localization Systems Using Passive UHF RFID

abstract: Since gathering spatial information of objects often provides a large number of extended functions in RFID based applications, many different localization systems have been developed in recent years. Determining and estimating the physical location of tagged objects in an interrogating area is known as localization. In this paper, we study properties of passive UHF RFID systems such as the relationship between distance and RSSI (Received Signal Strength Indictor), performance variations among the same type of passive tags, and readabilities of tags. We propose a received signal strength based Localization Algorithm using passive UHF RFID system. The localization uses the k-nearest neighbor algorithm to estimate the physical position of the target tag. To improve the accuracy of the passive tag attached object location, the properties and characteristics are applied to the localization algorithm. According to the analysis of experimentation, our proposed approach shows over 34% improvement compared with the k-Nearest Neighbor algorithm with the use of single reader and a single antenna, and 13 reference tags.


title: Resin infiltration of deproteinised natural occlusal subsurface lesions improves initial quality of fissure sealing

abstract: A low-viscosity resin infiltrant combined with a flowable composite resin can effectively seal fissures in premolars and molars. A team headed by Andrej Kielbassa at Austria’s Danube Private University in Krems compared two treatments for sealing fissures in extracted human premolars and molars having occlusal caries (decay on biting surface of teeth). Comparing the infiltration and microleakage of a low-viscosity resin combined with a flowable composite resin (RI/CR) with the flowable composite resin (CR) alone, they found that no parts of the caries lesions were infiltrated in the CR group. In the RI/CR group, infiltration was uniformly substantial and microleakage was lower. The resin infiltrant evidently provided a stable basis for the flowable CR, enhancing the quality of fissure sealing. The combination treatment appears promising for clinical control of occlusal caries.


title: A low-cost ultrasonic wind speed and direction measurement system

abstract: The paper deals with a low-cost transducer that implements an improved technique for wind speed measurement in open air. The instrument is a 2-axis ultrasonic anemometer, capable of monitoring both wind speed and direction. Its main advantage is that using the ultrasonic technique it has no moving parts, allowing for the utilization in a variety of applications requiring low maintenance and fast response to rapid wind gusts. Wind speed measurement for power turbine control can be considered as a reference application of it. The proposed instrument presents a simple design and is constructed using commercially available components, to reduce development costs. In the paper the proposed architecture is presented, discussing the algorithm applied to process the transduced ultrasonic signals. Some results obtained during the first tests are also presented and discussed, suggesting for successive improvements.


title: HISTORIC BUILDING INFORMATION MODELLING – ADDING INTELLIGENCE TO LASER AND IMAGE BASED SURVEYS

abstract: Abstract. Historic Building Information Modelling (HBIM) is a novel prototype library of parametric objects based on historic data and a system of cross platform programmes for mapping parametric objects onto a point cloud and image survey data. The HBIM process begins with remote collection of survey data using a terrestrial laser scanner combined with digital photo modelling. The next stage involves the design and construction of a parametric library of objects, which are based on the manuscripts ranging from Vitruvius to 18th century architectural pattern books. In building parametric objects, the problem of file format and exchange of data has been overcome within the BIM ArchiCAD software platform by using geometric descriptive language (GDL). The plotting of parametric objects onto the laser scan surveys as building components to create or form the entire building is the final stage in the reverse engin- eering process. The final HBIM product is the creation of full 3D models including detail behind the object's surface concerning its methods of construction and material make-up. The resultant HBIM can automatically create cut sections, details and schedules in addition to the orthographic projections and 3D models (wire frame or textured).


title: High purity distillation column: Simulation and optimization

abstract: Distillation columns with a high-purity product (down to 7 ppm) have been studied. A steady state model is developed using a commercial process simulator. The model is validated against industrial data. Based on the model, three major optimal operational changes are identified. These are, lowering the location of the feed and side draw streams, increasing the pressure at the top of the distillation column and changing the configuration of the products draw. It is estimated that these three changes will increase the throughput of each column by 5%. The validated model is also used to quantify the effects on key internal column parameters such as the flooding factor, in the event of significant changes to product purity and throughput.


title: Deep learning for object identification in ROS-based mobile robots

abstract: In this paper, an open source robotic middleware ROS is applied to identify objects with a Raspberry Pi based mobile robot. Faster R-CNN algorithm is considered for enhanced deep learning. The capabilities of robot control and object detection are verified through experimental validations. Based on a low-cost Raspberry Pi control kernel, an easy-to-use interface is developed to integrate front-end and back-end applications. Firstly some ROS packages for the mobile robot are needed to be designed. Then the captured environment information will be provided for objective detection using GPU-accelerated computing. In this paper, Kinect sensor is used to capture images and the deep learning algorithm is implemented based on ROS middleware. All previous mentioned function modules are contained in a cloud service.


title: Architectural Styles, Design Patterns, And Objects

abstract: Architectural styles, object-oriented design and design patterns all hold promise as approaches that simplify software design and reuse by capturing and exploiting system design knowledge. This article explores the capabilities and roles of the various approaches, their strengths and their limitations.


title: Practical perfect hashing in nearly optimal space

abstract: A hash function is a mapping from a key universe U to a range of integers, i.e., h:U@?{0,1,...,m-1}, where m is the range's size. A perfect hash function for some set S@?U is a hash function that is one-to-one on S, where m>=|S|. A minimal perfect hash function for some set S@?U is a perfect hash function with a range of minimum size, i.e., m=|S|. This paper presents a construction for (minimal) perfect hash functions that combines theoretical analysis, practical performance, expected linear construction time and nearly optimal space consumption for the data structure. For n keys and m=n the space consumption ranges from 2.62n+o(n) to 3.3n+o(n) bits, and for m=1.23n it ranges from 1.95n+o(n) to 2.7n+o(n) bits. This is within a small constant factor from the theoretical lower bounds of 1.44n bits for m=n and 0.89n bits for m=1.23n. We combine several theoretical results into a practical solution that has turned perfect hashing into a very compact data structure to solve the membership problem when the key set S is static and known in advance. By taking into account the memory hierarchy we can construct (minimal) perfect hash functions for over a billion keys in 46min using a commodity PC. An open source implementation of the algorithms is available at http://cmph.sf.net under the GNU Lesser General Public License (LGPL).


A Coherent Healthcare System with RDBMS, NoSQL and GIS Databases


title: The Chemistry Development Kit (CDK): An Open-Source Java Library for Chemo-and Bioinformatics

abstract: The Chemistry Development Kit (CDK) is a freely available open-source Java library for Structural Chemo- and Bioinformatics. Its architecture and capabilities as well as the development as an open-source project by a team of international collaborators from academic and industrial institutions is described. The CDK provides methods for many common tasks in molecular informatics, including 2D and 3D rendering of chemical structures, I/O routines, SMILES parsing and generation, ring searches, isomorphism checking, structure diagram generation, etc. Application scenarios as well as access information for interested users and potential contributors are given.


title: Robust perceptual image hashing via matrix invariants

abstract: In this paper we suggest viewing images (as well as attacks on them) as a sequence of linear operators and propose novel hashing algorithms employing transforms that are based on matrix invariants. To derive this sequence, we simply cover a two dimensional representation of an image by a sequence of (possibly overlapping) rectangles R/sub i/ whose sizes and locations are chosen randomly/sup 1/ from a suitable distribution. The restriction of the image (representation) to each R/sub i/ gives rise to a matrix A/sub i/. The fact that A/sub i/'s will overlap and are random, makes the sequence (respectively) a redundant and non-standard representation of images, but is crucial for our purposes. Our algorithms first construct a secondary image, derived from input image by pseudo-randomly extracting features that approximately capture semi-global geometric characteristics. From the secondary image (which does not perceptually resemble the input), we further extract the final features which can be used as a hash value (and can be further suitably quantized). In this paper, we use spectral matrix invariants as embodied by singular value decomposition. Surprisingly, formation of the secondary image turns out be quite important since it not only introduces further robustness (i.e., resistance against standard signal processing transformations), but also enhances the security properties (i.e. resistance against intentional attacks). Indeed, our experiments reveal that our hashing algorithms extract most of the geometric information from the images and hence are robust to severe perturbations (e.g. up to %50 cropping by area with 20 degree rotations) on images while avoiding misclassification. Our methods are general enough to yield a watermark embedding scheme, which will be studied in another paper.


title: A Symbolic Execution Framework for JavaScript

abstract: As AJAX applications gain popularity, client-side JavaScript code is becoming increasingly complex. However, few automated vulnerability analysis tools for JavaScript exist. In this paper, we describe the first system for exploring the execution space of JavaScript code using symbolic execution. To handle JavaScript code’s complex use of string operations, we design a new language of string constraints and implement a solver for it. We build an automatic end-to-end tool, Kudzu, and apply it to the problem of finding client-side code injection vulnerabilities. In experiments on 18 live web applications, Kudzu automatically discovers 2 previously unknown vulnerabilities and 9 more that were previously found only with a manually-constructed test suite.


A Hybrid CNN-RNN Alignment Model for Phrase-Aware Sentence Classification.


title: How to Combine Homomorphic Encryption and Garbled Circuits Improved Circuits and Computing the Minimum Distance Efficiently

abstract: We show how two existing paradigms for two-party secure function evaluation (SFE) in the semi-honest model can be combined securely and eciently { those based on additively homomorphic en- cryption (HE) with those based on garbled circuits (GC) and vice versa. Additionally, we propose new GC constructions for addition, subtrac- tion, multiplication, and comparison functions. Our circuits are approx- imately two times smaller (in terms of garbled tables) than previous constructions. This implies corresponding computation and communica- tion improvements in SFE of functions using the above building blocks (including the protocols for combining HE and GC). Based on these results, we present ecient constant-round protocols for secure integer comparison, and the related problems of minimum selec- tion and minimum distance, which are crucial building blocks of many cryptographic schemes such as privacy preserving biometric authentica- tion (e.g., face recognition, ngerprint matching, etc).


Semantic Segmentation for Line Drawing Vectorization Using Neural Networks


title: $\textsf{LoPub}$ : High-Dimensional Crowdsourced Data Publication With Local Differential Privacy

abstract: High-dimensional crowdsourced data collected from a large number of users produces rich knowledge for our society. However, it also brings unprecedented privacy threats to participants. Local privacy, a variant of differential privacy, is proposed as a means to eliminate the privacy concern. Unfortunately, achieving local privacy on high-dimensional crowdsourced data raises great challenges on both efficiency and effectiveness. Here, based on EM and Lasso regression, we propose efficient multi-dimensional joint distribution estimation algorithms with local privacy. Then, we develop a Locally privacy-preserving high-dimensional data Publication algorithm, LoPub, by taking advantage of our distribution estimation techniques. In particular, both correlations and joint distribution among multiple attributes can be identified to reduce the dimension of crowdsourced data, thus achieving both efficiency and effectiveness in locally private high-dimensional data publication. Extensive experiments on real-world datasets demonstrated that the efficiency of our multivariate distribution estimation scheme and confirm the effectiveness of our LoPub scheme in generating approximate datasets with local privacy.


title: Dual Circularly Polarized Broadside Beam Metasurface Antenna

abstract: This paper presents the design of a modulated metasurface (MTS) antenna capable to provide both right-hand (RH) and left-hand (LH) circularly polarized (CP) boresight radiation at Ku-band (13.5 GHz). This antenna is based on the interaction of two cylindrical-wavefront surface wave (SW) modes of transverse electric (TE) and transverse magnetic (TM) types with a rotationally symmetric, anisotropic-modulated MTS placed on top of a grounded slab. A properly designed centered circular waveguide feed excites the two orthogonal (decoupled) SW modes and guarantees the balance of the power associated with each of them. By a proper selection of the anisotropy and modulation of the MTS pattern, the phase velocities of the two modes are synchronized, and leakage is generated in broadside direction with two orthogonal linear polarizations. When the circular waveguide is excited with two mutually orthogonal TE11 modes in phase–quadrature, an LHCP or RHCP antenna is obtained. This paper explains the feeding system and the MTS requirements that guarantee the balanced conditions of the TM/TE SWs and consequent generation of dual CP boresight radiation.


title: Framing the holocaust online : Memory of the Babi Yar Massacres on Wikipedia

abstract: The article explores how a notorious case of Second World War atrocities in Ukraine – the Babi Yar massacres of 1941-1943 – is represented and interpreted on Wikipedia. Using qualitative content analysis, it examines what frames and content features are used in different language versions of Wikipedia to transcribe the traumatic narrative of Babi Yar as an online encyclopedia entry. It also investigates how these frames are constructed by scrutinising the process of collaborative frame-building on discussion pages of Wikipedia and exploring how Wikipedia users employ different power play strategies to promote their vision of the events at Babi Yar.


title: Large Scale Agile Transformation in an On-Demand World

abstract: Salesforce.com has recently completed an agile transformation of a two hundred person team within a three month window. This is one of the largest and fastest "big-bang" agile rollouts. This experience report discusses why we chose to move to an agile process, how we accomplished the transformation and what we learned from applying agile at scale.


title: A review of Parkinson's disease.

abstract: INTRODUCTION ::: Parkinson's disease (PD) is one of the most common neurodegenerative disorders. Sources of data Literature search using Medline with keywords Parkinson's disease supplemented with previously published papers known to the author. ::: ::: ::: AREAS OF AGREEMENT ::: There have been significant recent advances in the understanding of the pathogenesis of the disease. There has also been a greater realization that the disorder may be associated with significant non-motor disturbances in addition to the more commonly recognized motor complications. ::: ::: ::: AREAS OF CONTROVERSY ::: Although there is growing circumstantial evidence, it remains to be proven whether any of the current treatments for PD have a neuroprotective effect. ::: ::: ::: AREAS TIMELY FOR DEVELOPING RESEARCH ::: Although there is no cure, there are several management options for the early treatment of PD. As the disease progresses, further treatment options are available; however, the management of late-stage motor complications and non-motor symptoms remains particularly challenging and will benefit from further clinical research.


title: Fieldbus Technology in Industrial Automation

abstract: Fieldbus technology in industrial automation is not only relatively complex because of the number of solutions possible, but also, and above all, because of the variety of applications. Ironically, these in turn are responsible for the multitude of solutions available. If the analysis of the basic needs is relatively standard, as they will always involve connecting sensors, actuators, and field controllers with each other, the options in architecture are numerous and can impose the need for certain services. The required performances themselves and the QoS expected fundamentally depend on the applications. This paper traces this technology from its beginnings, which go back to the first industrial networks in the 1970s. The principal stages of development are recounted, from the initial requirement specifications to the current state of international standardization. The diverse technical solutions are then analyzed and classified. In particular, we study the temporal aspects, the medium access control protocols, and application relationships.


title: Fairness Of And Satisfaction With Performance Appraisal Process

abstract: Literature in organizational justice suggest that organizational fairness in decision making processes may encourage employees’ acceptance of and positive reaction to many organizational decisions including decision in human resource interventions such as performance appraisal system. This study seeks to understand how perceived fairness in the performance appraisal process affects employees’ satisfaction. The perception of fairness on performance appraisal was discussed and analysed in the light of theory of organizational justice which consist of procedural, distributive, interpersonal and interaction justice as the independent variables, and employees’ satisfaction as the dependent variable. This study explored employees’ satisfaction as consist of satisfaction with the last performance appraisal ratings, satisfaction toward supervisor in relation with appraisal process and satisfaction toward the performance appraisal system. Data from companies in Malaysia were collected to test these relationships. The findings showed that distributive and informational justice are significantly related to satisfaction with the last appraisal ratings; distributive, interpersonal and informational justice are significantly related to satisfaction with supervision; and distributive and informational justice are related to satisfaction toward the performance appraisal system. It also showed that distributive, interpersonal and informational justice are related to overall employees’ satisfaction. Implication of the justice concerns and practice of a more just performance appraisals are discussed.


title: Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour

abstract: Recent years have witnessed the rise of the gut microbiota as a major topic of research interest in biology. Studies are revealing how variations and changes in the composition of the gut microbiota influence normal physiology and contribute to diseases ranging from inflammation to obesity. Accumulating data now indicate that the gut microbiota also communicates with the CNS — possibly through neural, endocrine and immune pathways — and thereby influences brain function and behaviour. Studies in germ-free animals and in animals exposed to pathogenic bacterial infections, probiotic bacteria or antibiotic drugs suggest a role for the gut microbiota in the regulation of anxiety, mood, cognition and pain. Thus, the emerging concept of a microbiota-gut-brain axis suggests that modulation of the gut microbiota may be a tractable strategy for developing novel therapeutics for complex CNS disorders.


title: Characterizing user sessions on YouTube

abstract: In this study, we characterize user sessions of the popular multimedia Web 2.0 site, YouTube. We observe ::: YouTube user sessions by making measurements from an edge network perspective. Several characteristics of user ::: sessions are considered, including session duration, inter-transaction times, and the types of content transferred by ::: user sessions. We compare and contrast our results with "traditional" Web user sessions. We found that YouTube ::: users transfer more data and have longer think times than traditional Web workloads. These differences have ::: implications for network capacity planning and design of next generation synthetic Web workloads.


title: A NOVEL EVALUATION MODEL OF USER ACCEPTANCE OF SOFTWARE TECHNOLOGY IN HEALTHCARE SECTOR

abstract: Effective evaluation of healthcare information systems is necessary in order to ensure systems adequately ::: meet the requirements and information processing needs of the users and healthcare organisations. A number ::: of studies have proposed evaluation frameworks to aid in evaluation work. However, some of them can be ::: improved upon. This paper proposes a novel evaluation model to evaluate user acceptance of software technology ::: within the healthcare sector. It describes the theoretical basis behind the development of the research ::: model and methodology being employed to validate the model.


title: Framing the frameworks: a review of IT governance research

abstract: With the passage of the Sarbanes-Oxley Act in the United States in 2002, and an ever-increasing corporate focus on ensuring prudent returns on technology investments, the notion of IT governance became a major issue for both business practitioners and academics. Although the term “IT governance” is a relatively new addition to the syntax of academic research, significant previous work is reported on IT decisions rights and IT loci of control, notions that are synonymous with the current understanding of IT governance. This paper presents a literature review for existing research in IT governance. A framework, named the Conceptual Framework For IT Governance Research is proposed to provide a logical structure for existing research results. Using this framework, we classify the previous literature on governance into two separate streams that follow parallel paths of advancement. A popular contemporary notion of IT governance is then presented, together with the argument that this new notion, by implicitly extending both streams of research, represents an initial amalgamation of the two paths of literature. We conclude that even with the consideration of contemporary structures, academicians and practitioners alike continue to explore the concept of IT governance in an attempt to find appropriate mechanisms to govern corporate IT decisions.


title: Risk factors for HIV infection among injection drug users: results of blinded surveys in drug treatment centers, King County, Washington 1988-1991.

abstract: Among injection drug users (IDUs) entering drug treatment in King County, Washington between 1988 and 1991, we investigated HIV seroprevalence in relationship to demographic, sexual, and drug-use characteristics. Eighty-two of 3,039 (2.7%) IDUs tested HIV positive. Gay or bisexual men had the highest HIV prevalence (37.1%), followed by lesbian or bisexual women (8.3%), heterosexual men (2.3%), and heterosexual women (1.5%). American Indians were more likely to be infected with HIV than were whites. Those with no permanent address were more likely to be infected than those with an address. Unexpectedly, the prevalence of HIV infection among amphetamine injectors (13.1% of 168) was higher than among those who did not report using amphetamines. After adjustment for sexual orientation, HIV prevalence was four times higher among primary amphetamine injectors and three times higher among secondary amphetamine injectors than among injectors of other drugs. The basis for the strong association observed between HIV infection and a history of injection of amphetamines is not known and should be clarified through further research that obtains more detailed information on IDUs.


title: Active Learning by Querying Informative and Representative Examples

abstract: Most active learning approaches select either informative or representative unlabeled instances to query their labels. Although several active learning algorithms have been proposed to combine the two criteria for query selection, they are usually ad hoc in finding unlabeled instances that are both informative and representative. We address this challenge by a principled approach, termed QUIRE, based on the min-max view of active learning. The proposed approach provides a systematic way for measuring and combining the informativeness and representativeness of an instance. Extensive experimental results show that the proposed QUIRE approach outperforms several state-of -the-art active learning approaches.


title: Balance control of two-wheeled self-balancing mobile robot based on TS fuzzy model

abstract: Two-wheeled self-balancing robot is a mechanically unstable, nonholonomic constraint robot. This paper has established a dynamics and kinematics for nonholonomic mobile robot. In order to control more effective for self-balancing robot's balance model, has designed fuzzy controller based on the T-S fuzzy model with the parallel distribution compensator (PDC) the structure. It realizes the robot balance and the movement control. Finally, validity and feasibility of the system modeling and the designed controller were confirmed through the actual test results and the simulation results contrast analysis.


title: Utilization Pattern of Mobile Phone Technology (Smartphone) among the Farmers of Nagaur District in Rajasthan

abstract: Now-a-days, one of the most important emerging tools of ICT is the mobile phones with a remarkable growth during last decade. Mobile phone technologies have provided a good platform for farmers to share their knowledge and information among each other in time such as weather information, crop and variety selection, fertilizer and irrigation management, disease and pest management, market prices of the product and on-going government programmes. Keeping this in view, a study was conducted assess the utilization pattern of mobile phones forsharing agricultural information among farmers in Merta block of Nagaur district of Rajasthan. A total of 110 respondents were selected for the study. This study has shown that a majority (83.63%) of the farmer used call to retailor than call to relative farmer (80%), internet user (29.09) and perceived as most information on weedicide, pesticide and disease control (89.09%) through mobile phones. The correlation studies indicated a significant association between educations, with extension agency contact and mass media of the farmer. The analysis showed that the poor connectivity, economic problem, high cost internet services and lack of updated information were some of the problems reported by the respondents.


title: THE IMPACT OF VIDEO GAMES ON STUDENT GPA, STUDY HABITS, AND TIME MANAGEMENT SKILLS: WHAT'S THE BIG DEAL?

abstract: Playing video games has become one of the largest leisure activities in the world. This study examines the effects video games have on college students, their grade point averages, time management, and study habits. Existing literature has linked video game usage as being negatively correlated with each of these three variables. This research, using a sample of the undergraduate student population at a private university in northeast Ohio, found a statistically significant correlation between video game usage and grade point average. Statistically significant relationships were not found between video game usage and the variables of time management skills or study habits. It is important that college students are aware of these possible negative effects of video game usage on their academic performance. This research can serve as a foundation for future research on the impact of video game playing and student performance.


title: A survey of image retargeting techniques

abstract: Advances in imaging technology have made the capture and display of digital images ubiquitous. A variety ::: of displays are used to view them, ranging from high-resolution computer monitors to low-resolution mobile ::: devices, and images often have to undergo changes in size and aspect ratio to adapt to different screens. Also, ::: displaying and printing documents with embedded images frequently entail resizing of the images to comply with ::: the overall layout. Straightforward image resizing operators, such as scaling, often do not produce satisfactory ::: results, since they are oblivious to image content. In this work, we review and categorize algorithms for contentaware ::: image retargeting, i.e., resizing an image while taking its content into consideration to preserve important ::: regions and minimize distortions. This is a challenging problem, as it requires preserving the relevant information ::: while maintaining an aesthetically pleasing image for the user. The techniques typically start by computing an ::: importance map which represents the relevance of every pixel, and then apply an operator that resizes the image ::: while taking into account the importance map and additional constraints. We intend this review to be useful to ::: researchers and practitioners interested in image retargeting.


title: Automatic Room Segmentation From Unstructured 3-D Data of Indoor Environments

abstract: We present an automatic approach for the task of reconstructing a 2-D floor plan from unstructured point clouds of building interiors. Our approach emphasizes accurate and robust detection of building structural elements and, unlike previous approaches, does not require prior knowledge of scanning device poses. The reconstruction task is formulated as a multiclass labeling problem that we approach using energy minimization. We use intuitive priors to define the costs for the energy minimization problem and rely on accurate wall and opening detection algorithms to ensure robustness. We provide detailed experimental evaluation results, both qualitative and quantitative, against state-of-the-art methods and labeled ground-truth data.


title: Topic Modeling over Short Texts by Incorporating Word Embeddings

abstract: Inferring topics from the overwhelming amount of short texts becomes a critical but challenging task for many content analysis tasks, such as content charactering, user interest profiling, and emerging topic detecting. Existing methods such as probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA) cannot solve this prob- lem very well since only very limited word co-occurrence information is available in short texts. This paper studies how to incorporate the external word correlation knowledge into short texts to improve the coherence of topic modeling. Based on recent results in word embeddings that learn se- mantically representations for words from a large corpus, we introduce a novel method, Embedding-based Topic Model (ETM), to learn latent topics from short texts. ETM not only solves the problem of very limited word co-occurrence information by aggregating short texts into long pseudo- texts, but also utilizes a Markov Random Field regularized model that gives correlated words a better chance to be put into the same topic. The experiments on real-world datasets validate the effectiveness of our model comparing with the state-of-the-art models.


title: A generic and distributed privacy preserving classification method with a worst-case privacy guarantee

abstract: It is often necessary for organizations to perform data mining tasks collaboratively without giving up their own data. This necessity has led to the development of privacy preserving distributed data mining. Several protocols exist which deal with data mining methods in a distributed scenario but most of these methods handle a single data mining task. Therefore, if the participating parties are interested in more than one classification methods they will have to go through a series of distributed protocols every time, thus increasing the overhead substantially. A second significant drawback with existing methods is that they are often quite expensive due to the use of encryption operations. In this paper a method has been proposed that addresses both these issues and provides a generic approach to efficient privacy preserving classification analysis in a distributed setting with a worst-case privacy guarantee. The experimental results demonstrate the effectiveness of this method.


title: California Verbal Learning Test: performance by patients with focal frontal and non‐frontal lesions

abstract: Although frontal lobe lesions do not cause classic amnesia, they may disrupt learning and memory in a number of ways. To investigate in finer detail the regions of frontal injury that are associated with impaired learning and to define the cognitive processing deficits specific to each region that disrupt memory, we compared 33 patients with focal frontal injury with patients with non‐frontal injury and with normal controls on a standard neuropsychological instrument, the California Verbal Learning Test (CVLT). Subgroups of patients with distinct lesion site profiles were compared in a number of learning measures. All of the subgroups of patients with frontal lesions (with one exception) had inefficient learning due to poor implementation of a strategy of subjective organization. Despite this organizational deficiency, the performance of patients with frontopolar lesions normalized across trials. Only the subgroups with lesions centred either on the left posterior dorsolateral frontal region or the posterior medial frontal region had overall impaired learning and recall. The left posterior dorsolateral frontal group was most significantly impaired on all measures. This recall impairment was secondary to a mild lexical–semantic deficit. A recognition memory deficit in the same group was due to an abnormal response bias. Several groups had a modest increase in perseverative recalls; the underlying mechanisms differed. Disruption of different cognitive processes associated with specific frontal regions underlies the varied patterns of memory impairment. This study has demonstrated even finer differentiations within the frontal region than previously known. ::: ::: Received June 25, 2002. Revised November 20, 2002. Second revision January 2, 2003. Accepted January 6, 2003


title: Story Plot Generation based on CBR

abstract: In this paper we present a system for automatic story generation that reuses existing stories to produce a new story that matches a given user query. The plot structure is obtained by a case-based reasoning (CBR) process over a case base of tales and an ontology of explicitly declared relevant knowledge. The resulting story is generated as a sketch of a plot described in natural language by means of natural language generation (NLG) techniques.