title: Profiling relational data: a survey

abstract: Profiling data to determine metadata about a given dataset is an important and frequent activity of any IT professional and researcher and is necessary for various use-cases. It encompasses a vast array of methods to examine datasets and produce metadata. Among the simpler results are statistics, such as the number of null values and distinct values in a column, its data type, or the most frequent patterns of its data values. Metadata that are more difficult to compute involve multiple columns, namely correlations, unique column combinations, functional dependencies, and inclusion dependencies. Further techniques detect conditional properties of the dataset at hand. This survey provides a classification of data profiling tasks and comprehensively reviews the state of the art for each class. In addition, we review data profiling tools and systems from research and industry. We conclude with an outlook on the future of data profiling beyond traditional profiling tasks and beyond relational databases.


title: Outliers detection methods in wireless sensor networks

abstract: Detection and accommodation of outliers are crucial in a number of contexts, in which collected data from a given environment is subsequently used for assessing its running conditions or for data-based decision-making. Although a significant number of studies on this subject can be found in literature, a comprehensive empirical assessment in the context of local online detection in wireless sensor networks is still missing. The present work aims at filling this gap by offering an empirical evaluation of two state-of-the-art online detection methods. The first methodology is based on a Least Squares-Support Vector Machine technique, along with a sliding window-based learning algorithm, while the second approach relies on Principal Component Analysis and on the robust orthonormal projection approximation subspace tracking with rank-1 modification. The performance and implementability of these methods are evaluated using a generated non-stationary time-series and a test-bed consisting of a benchmark three-tank system and a wireless sensor network, where deployed algorithms are implemented under a multi-agent framework.


title: Upright Time and Sit-To-Stand Transition Progression After Total Hip Arthroplasty: An Inhospital Longitudinal Study

abstract: Abstract Background Although early mobilization in hospital is a key element of post–total hip arthroplasty rehabilitation, it is poorly documented. Methods To gain quantitative insight into inhospital mobilization, upright times and sit-to-stand transitions (STS) were measured using a thigh-mounted movement sensor in 44 participants (13 males and 31 females), age 50 to 82 years, in an observational, postsurgery, inhospital, longitudinal study. Results Some participants performed no activity in the first 24 hours after surgery. However, in the last 24 hours before discharge, participants performed a median of 40 (interquartile range [IQR], 15) STS and spent 134 minutes (IQR, 74 minutes) upright. Activity in rehabilitation constituted 19.4% (IQR, 15.8%) of STS and 13.3% (IQR, 5.5%) of upright time. Females spent longer in hospital (80 hours; IQR, 24) compared to males (54 hours; IQR, 26). Conclusion Although there was considerable activity within rehabilitation periods, a large majority of STS and upright time occurred outside rehabilitation. Within the last 24 hours in hospital, all participants were upright for prolonged periods and completed numerous STS.


title: A Survey of Technologies and Applications for Climbing Robots Locomotion and Adhesion

abstract: The interest in the development of climbing robots has grown rapidly in the last years. Climbing robots are useful devices that can be adopted in a variety of applications, such as maintenance and inspection in the process and construction industries. These systems are mainly adopted in places where direct access by a human operator is very expensive, because of the need for scaffolding, or very dangerous, due to the presence of an hostile environment. The main motivations are to increase the operation efficiency, by eliminating the costly assembly of scaffolding, or to protect human health and safety in hazardous tasks. Several climbing robots have already been developed, and other are under development, for applications ranging from cleaning to inspection of difficult to reach constructions. A wall climbing robot should not only be light, but also have large payload, so that it may reduce excessive adhesion forces and carry instrumentations during navigation. These machines should be capable of travelling over different types of surfaces, with different inclinations, such as floors, walls, or ceilings, and to walk between such surfaces (Elliot et al. (2006); Sattar et al. (2002)). Furthermore, they should be able of adapting and reconfiguring for various environment conditions and to be self-contained. Up to now, considerable research was devoted to these machines and various types of experimental models were already proposed (according to Chen et al. (2006), over 200 prototypes aimed at such applications had been developed in the world by the year 2006). However, we have to notice that the application of climbing robots is still limited. Apart from a couple successful industrialized products, most are only prototypes and few of them can be found in common use due to unsatisfactory performance in on-site tests (regarding aspects such as their speed, cost and reliability). Chen et al. (2006) present the main design problems affecting the system performance of climbing robots and also suggest solutions to these problems. The major two issues in the design of wall climbing robots are their locomotion and adhesion methods. With respect to the locomotion type, four types are often considered: the crawler, the wheeled, the legged and the propulsion robots. Although the crawler type is able to move relatively faster, it is not adequate to be applied in rough environments. On the other hand, the legged type easily copes with obstacles found in the environment, whereas generally its speed is lower and requires complex control systems. Regarding the adhesion to the surface, the robots should be able to produce a secure gripping force using a light-weight mechanism. The adhesion method is generally classified into four 1


title: Deep bi-directional recurrent networks over spectral windows

abstract: Long short-term memory (LSTM) acoustic models have recently achieved state-of-the-art results on speech recognition tasks. As a type of recurrent neural network, LSTMs potentially have the ability to model long-span phenomena relating the spectral input to linguistic units. However, it has not been clear whether their observed performance is actually due to this capability, or instead if it is due to a better modeling of short term dynamics through the recurrence. In this paper. we answer this question by applying a windowed (truncated) LSTM to conversational speech transcription, and find that a limited context is adequate, and that it is not necessaary to scan the entire utterance. The sliding window approach allows not only incremental (online) recognition with a bidirectional model, but also frame-wise randomization (as opposed to utterance randomization), which results in faster convergence. On the SWBD/Fisher corpus, applying bidirectional LSTM RNNs to spectral windows of about 0.5s improves WER on the Hub5'00 benchmark set by 16% relative compared to our best sequence-trained DNN. On an extended 3850h training set that that also includes lectures, the relative gain becomes 28% (Hub5'00 WER 9.2%). In-house conversational data improves by 12 to 17% relative.


title: Encouraging user behaviour with achievements: An empirical study

abstract: Stack Overflow, a question and answer Web site, uses a reward system called badges to publicly reward users for their contributions to the community. Badges are used alongside a reputation score to reward positive behaviour by relating a user's site identity with their perceived expertise and respect in the community. A greater number of badges associated with a user profile in some way indicates a higher level of authority, leading to a natural incentive for users to attempt to achieve as many badges as possible. In this study, we examine the publicly available logs for Stack Overflow to examine three of these badges in detail. We look at the effect of one badge in context on an individual user level and at the global scope of three related badges across all users by mining user behaviour around the time that the badge is awarded. This analysis supports the claim that badges can be used to influence user behaviour by demonstrating one instance of an increase in user activity related to a badge immediately before it is awarded when compared to the period afterwards.


title: The Road to Obesity or the Path to Prevention: Motorized Transportation and Obesity in China

abstract: BELL, A. COLIN, KEYOU GE, AND BARRY M. POPKIN. The road to obesity or the path to prevention: motorized transportation and obesity in China. Obes Res. 2002;10:277–283. Objective: Dependence on motorized forms of transportation may contribute to the worldwide obesity epidemic. Shifts in transportation patterns occurring in China provide an ideal opportunity to study the association between vehicle ownership and obesity. Our objective was to determine whether motorized forms of transportation promote obesity. Research Methods and Procedures: A multistage randomcluster sampling process was used to select households from eight provinces in China. Data were included on household vehicle ownership and individual anthropometric and sociodemographic status. Cross-sectional data (1997) from 4741 Chinese adults aged 20 to 55 years were used to explore the association between vehicle ownership and obesity. Cohort data (1989 to 1997) from 2485 adults aged 20 to 45 years in 1989 (59% follow-up) were used to measure the impact of vehicle acquisition on the odds of becoming obese. Results: Our main outcome measure was current obesity status and the odds of becoming obese over an 8-year period. In 1997, 84% of adults did not own motorized transportation. However, the odds of being obese were 80% higher (p 0.05) for men and women in households who owned a motorized vehicle compared with those who did not own a vehicle. Fourteen percent of households acquired a motorized vehicle between 1989 and 1997. Compared with those whose vehicle ownership did not change, men who acquired a vehicle experienced a 1.8-kg greater weight gain (p 0.05) and had 2 to 1 odds of becoming obese. Discussion: Encouraging active forms of transportation may be one way to protect against obesity.


title: A self-organizing neural network structure for motif identification in DNA sequences

abstract: In this paper, we study the problem of subtle signal discoveries in unaligned DNA and protein sequences. Motifs, also known as approximate common substrings, are good examples of subtle signals in DNA and protein sequences. The problem of motif identification in DNA and protein sequences has been studied for many years in the literature. Major hurdles at this point include computational complexity and reliability of the searching algorithms. We would develop a self-organizing neural network for solving the problem of motif identification in DNA and protein sequences. Our network contains several layers with each layer performing classifications at different level. The top layer divide the input space into a small number of regions and the bottom layer classifies all input patterns into motifs and non-motif patterns. Depending on the number of input patterns to be classified, several layers between the top layer and the bottom layer are needed to perform intermediate classification. We maintain a low computational complexity through the use of the layered structure so that each pattern's classification is performed with respect to a small subspace of the whole input space. We also maintain a high reliability using our self-organizing neural network since the network would grow as needed to make sure all input patterns are considered and are given the same amount of attention. Finally, simulation results show that our algorithm significantly outperforms existing algorithms, especially in the reliability aspect Our algorithm can identify motifs with higher accuracy than existing algorithms.


title: Digital Innovation and Strategic Transformation

abstract: Digital technologies and how we use them are changing the face of business, even as those technologies that underlie computers, robots, and smart equipment are rapidly evolving, becoming more powerful, and transforming organizations much faster than in the past. Significant competitive advantage is now achievable from digital innovation and transformation. This special issue aims to generate understanding of some of today's issues and looks at opportunities for tomorrow.


title: Toward a Common Event Model for Multimedia Applications

abstract: Although events are ubiquitous in multimedia, no common notion of events has emerged. Events appear in multimedia presentation formats, programming frameworks, and databases, as well as in next-generation multimedia applications such as eChronicles, life logs, or the Event Web. A common event model for multimedia could serve as a unifying foundation for all of these applications


title: Spectrum management in cognitive radio ad hoc networks

abstract: The problem of spectrum scarcity and inefficiency in spectrum usage will be addressed by the newly emerging cognitive radio paradigm that allows radios to opportunistically transmit in the vacant portions of the spectrum already assigned to licensed users. For this, the ability for spectrum sensing, spectrum sharing, choosing the best spectrum among the available options, and dynamically adapting transmission parameters based on the activity of the licensed spectrum owners must be integrated within cognitive radio users. Specifically in cognitive radio ad hoc networks, distributed multihop architecture, node mobility, and spatio-temporal variance in spectrum availability are some of the key distinguishing factors. In this article the important features of CRAHNs are presented, along with the design approaches and research challenges that must be addressed. Spectrum management in CRAHNs comprises spectrum sensing, sharing, decision, and mobility. In this article each of these functions are described in detail from the viewpoint of multihop infra-structureless networks requiring cooperation among users.


title: A Situation-Based Dialogue Management using Dialogue Examples

abstract: In this paper, we present POSTECH Situation-Based Dialogue Manager (POSSDM) for a spoken dialogue system using both example- and rule-based dialogue management techniques for effective generation of appropriate system responses. A spoken dialogue system should generate cooperative responses to smoothly control dialogue flow with the users. We introduce a new dialogue management technique incorporating dialogue examples and situation-based rules for the Electronic Program Guide (EPG) domain. For the system response generation, we automatically construct and index a dialogue example database from the dialogue corpus, and the proper system response is determined by retrieving the best dialogue example for the current dialogue situation, which includes a current user utterance, dialogue act, semantic frame and discourse history. When the dialogue corpus is not enough to cover the domain, we also apply manually constructed situation-based rules mainly for meta-level dialogue management. Experiments show that our example-based dialogue modeling is very useful and effective in domain-oriented dialogue processing.


Demographic Predictors of Critical Thinking Ability in Undergraduate Animal Science Students


Automatic Brain Tumor Segmentation


title: Signals in social supernets

abstract: Social network sites SNSs provide a new way to organize and navigate an egocentric social network. Are they a fad, briefly popular but ultimately useless? Or are they the harbingers of a new and more powerful social world, where the ability to maintain an immense network-a social "supernet"-fundamentally changes the scale of human society? This article presents signaling theory as a conceptual framework with which to assess the transformative potential of SNSs and to guide their design to make them into more effective social tools. It shows how the costs associated with adding friends and evaluating profiles affect the reliability of users' self-presentation; examines strategies such as information fashion and risk-taking; and shows how these costs and strategies affect how the publicly-displayed social network aids the establishment of trust, identity, and cooperation-the essential foundations for an expanded social world.


title: GRAPHENE: Packing and Dependency-Aware Scheduling for Data-Parallel Clusters

abstract: We present a newcluster scheduler, GRAPHENE, aimed at jobs that have a complex dependency structure and heterogeneous resource demands. Relaxing either of these challenges, i.e., scheduling a DAG of homogeneous tasks or an independent set of heterogeneous tasks, leads to NP-hard problems. Reasonable heuristics exist for these simpler problems, but they perform poorly when scheduling heterogeneous DAGs. Our key insights are: (1) focus on the long-running tasks and those with tough-to-pack resource demands, (2) compute a DAG schedule, offline, by first scheduling such troublesome tasks and then scheduling the remaining tasks without violating dependencies. These offline schedules are distilled to a simple precedence order and are enforced by an online component that scales to many jobs. The online component also uses heuristics to compactly pack tasks and to trade-off fairness for faster job completion. Evaluation on a 200-server cluster and using traces of production DAGs at Microsoft, shows that GRAPHENE improves median job completion time by 25% and cluster throughput by 30%.


title: Personalizing Dialogue Agents: I have a dog, do you have pets too?

abstract: Chit-chat models are known to have several problems: they lack specificity, do not display a consistent personality and are often not very captivating. In this work we present the task of making chit-chat more engaging by conditioning on profile information. We collect data and train models to (i) condition on their given profile information; and (ii) information about the person they are talking to, resulting in improved dialogues, as measured by next utterance prediction. Since (ii) is initially unknown our model is trained to engage its partner with personal topics, and we show the resulting dialogue can be used to predict profile information about the interlocutors.


title: Single- and dual-band multimode hard horn antennas with partly corrugated walls

abstract: We introduce a partly corrugated hard horn, consisting of a smooth-walled horn with an attached longitudinally corrugated outer section. This alleviates the problems with the manufacturing when the longitudinal corrugations extend into the throat of the horn. The transition between the inner smooth walled part and the outer corrugated part is abrupt. This is used and controlled to design better and shorter single-band horns than otherwise possible. Furthermore, it enables the design of dual-band horns with low cross-polarization and high gain, for multifunction use at Ka-band with transmit and receive frequencies in the same antenna. Measurements on a model of such a dual-band horn are presented and discussed.


A Review on Comparative Analysis of Path Planning and Collision Avoidance Algorithms


title: Modeling Beats, Accents, Beams, And Time Signatures Hierarchically With Music21 Meter Objects

abstract: The music21 TimeSignature object represents meters hierarchically, through independent display, beam, beat, and accent attributes capable of unlimited partitioning and nesting. This model, designed for applications in computer-aided musicology, accommodates any variety of compound, complex, or additive meters, can report beat position and accent levels, and can algorithmically perform multi-level beaming or various types of metrical analysis. As part of the music21 Python toolkit, the meter module can read input from Humdrum and MusicXML and output to MusicXML and Lilypond.


title: Android based Portable Hand Sign Recognition System

abstract: These days mobile devices like phones or tablets are very common among people of all age. They are connected with network and provide seamless communications through internet or cellular services. These devices can be a big help for the people who are not able to communicate properly and even in emergency conditions. A disabled person who is not able to speak or a person who speak a different language, these devices can be a boon for them as understanding, translating and speaking systems for these people. This chapter discusses a portable android based hand sign recognition system which can be used by disabled people. This chapter shows a part of on-going project. Computer Vision based techniques were used for image analysis and PCA was used after image tokenizer for recognition. This method was tested with webcam results to make system more robust.


Positive Design : An Introduction to Design for Subjective Well-Being


title: Educational psychology and the effectiveness of inclusive education/mainstreaming

abstract: Background. Inclusive education/mainstreaming is a key policy objective for the education of children and young people with special educational needs (SEN) and disabilities. ::: ::: Aims. This paper reviews the literature on the effectiveness of inclusive education/mainstreaming. The focus is on evidence for effects in terms of child outcomes with examination also of evidence on processes that support effectiveness. ::: ::: Samples. The review covers a range of SEN and children from pre-school to the end of compulsory education. ::: ::: Method. Following an historical review of evidence on inclusive education/mainstreaming, the core of the paper is a detailed examination of all the papers published in eight journals from the field of special education published 2001-2005 (N = 1373): journal of Special Education, Exceptional Children, Learning Disabilities Research and Practice, journal of Learning Disabilities, Remedial and Special Education, British journal of Special Education, European journal of Special Needs Education, and the International journal of Inclusive Education. The derived categories were: comparative studies of outcomes: other outcome studies; non-comparative qualitative studies including non-experimental case studies; teacher practice and development; teacher attitudes; and the use of teaching assistants. ::: ::: Results. Only 14 papers (1.0%) were identified as comparative outcome studies of children with some form of SEN. Measures used varied but included social as well as educational outcomes. Other papers included qualitative studies of inclusive practice, some of which used a non-comparative case study design while others were based on respondent's judgements, or explored process factors including teacher attitudes and the use of teaching assistants. ::: ::: Conclusions. Inclusive education/mainstreaming has been promoted on two bases: the rights of children to be included in mainstream education and the proposition that inclusive education is more effective. This review focuses on the latter issue. The evidence from this review does not provide a clear endorsement for the positive effects of inclusion. There is a lack of evidence from appropriate studies and, where evidence does exist, the balance was only marginally positive. It is argued that the policy has been driven by a concern for children's rights. The important task now is to research more thoroughly the mediators and moderators that support the optimal education for children with SEN and disabilities and, as a consequence, develop an evidence-based approach to these children's education.


title: Automatic mapping of NES games with mappy

abstract: Game maps are useful for human players, general-game-playing agents, and data-driven procedural content generation. These maps are generally made by hand-assembling manually-created screen-shots of game levels. Besides being tedious and error-prone, this approach requires additional effort for each new game and level to be mapped. The results can still be hard for humans or computational systems to make use of, privileging visual appearance over semantic information. We describe a software system, Mappy, that produces a good approximation of a linked map of rooms given a Nintendo Entertainment System game program and a sequence of button inputs exploring its world. In addition to visual maps, Mappy outputs grids of tiles (and how they change over time), positions of non-tile objects, clusters of similar rooms that might in fact be the same room, and a set of links between these rooms. We believe this is a necessary step towards developing larger corpora of high-quality semantically-annotated maps for PCG via machine learning and other applications.


title: A Framework for Generalizing Graph-based Representation Learning Methods

abstract: Random walks are at the heart of many existing deep learning algorithms for graph data. However, such algorithms have many limitations that arise from the use of random walks, e.g., the features resulting from these methods are unable to transfer to new nodes and graphs as they are tied to node identity. In this work, we introduce the notion of attributed random walks which serves as a basis for generalizing existing methods such as DeepWalk, node2vec, and many others that leverage random walks. Our proposed framework enables these methods to be more widely applicable for both transductive and inductive learning as well as for use on graphs with attributes (if available). This is achieved by learning functions that generalize to new nodes and graphs. We show that our proposed framework is effective with an average AUC improvement of 16.1% while requiring on average 853 times less space than existing methods on a variety of graphs from several domains.


title: Transfer learning with partial related “instance-feature” knowledge

abstract: Abstract Transfer learning, which applies the knowledge from related but different source domains to improve the learning of the target domains, has attracted much attention in recent years. Lots of transfer learning methods have been proposed in literature, and many concentrate on selecting related source (-domain) instances or features for boosting learning. However, in each source instance, possibly only part of the features are related or helpful for transfer. That is, when a source instance is selected by relativeness, its unrelated feature knowledge can also be introduced. At the same time, some related feature knowledge may be discarded when an unrelated source instance is dropped. As a result, these methods ignore the partial related/unrelated knowledge in each source instance. In this paper, we attempt to discover such partial related “instance-feature” knowledge in transfer, and propose a new transfer learning method with partial related “instance-feature” knowledge (PRIF for short). Specifically, the partial “instance-feature” structure is first discovered by co-clustering over both instances and features, then the source instances are reconstructed considering both the related “instance-feature” knowledge and the related target (-domain) instances, so that the source instances can be more related to the target ones. Finally, transfer learning is performed with these newly-constructed source instances. Empirical results over several real-world datasets demonstrate the effectiveness of PRIF.


title: SuperParsing: Scalable nonparametric image parsing with superpixels

abstract: This paper presents a simple and effective nonparametric approach to the problem of image parsing, or labeling image regions (in our case, superpixels produced by bottom-up segmentation) with their categories. This approach requires no training, and it can easily scale to datasets with tens of thousands of images and hundreds of labels. It works by scene-level matching with global image descriptors, followed by superpixel-level matching with local features and efficient Markov random field (MRF) optimization for incorporating neighborhood context. Our MRF setup can also compute a simultaneous labeling of image regions into semantic classes (e.g., tree, building, car) and geometric classes (sky, vertical, ground). Our system outperforms the state-of-the-art non-parametric method based on SIFT Flow on a dataset of 2,688 images and 33 labels. In addition, we report per-pixel rates on a larger dataset of 15,150 images and 170 labels. To our knowledge, this is the first complete evaluation of image parsing on a dataset of this size, and it establishes a new benchmark for the problem.


title: 60GHz passive and active RF-path phase shifters in silicon

abstract: Integrated 60-GHz active and passive phase shifters for RF-path phase-shifting phased array transceivers are demonstrated in this paper. The reflection-type passive phase shifter achieves ≫180° phase variation across the 57GHz–64GHz band with insertion loss varying from 4.2dB–7.5dB at 60GHz. The active phase shifter employs vector-interpolation architecture and achieves 360° phase variation, −2dB gain, 12GHz 3dB bandwidth and 16.5dB noise figure at 60GHz. Measurements over process and temperature are also discussed and comparisons are drawn between active and passive phase shifting approach for 60GHz phased arrays.


title: Opportunistic sensing: Security challenges for the new paradigm

abstract: We study the security challenges that arise in opportunistic people-centric sensing, a new sensing paradigm leveraging humans as part of the sensing infrastructure. Most prior sensor-network research has focused on collecting and processing environmental data using a static topology and an application-aware infrastructure, whereas opportunistic sensing involves collecting, storing, processing and fusing large volumes of data related to everyday human activities. This highly dynamic and mobile setting, where humans are the central focus, presents new challenges for information security, because data originates from sensors carried by people— not tiny sensors thrown in the forest or attached to animals. In this paper we aim to instigate discussion of this critical issue, because opportunistic people-centric sensing will never succeed without adequate provisions for security and privacy. To that end, we outline several important challenges and suggest general solutions that hold promise in this new sensing paradigm.


title: Big Data for Industry 4.0: A Conceptual Framework

abstract: Exponential growth in data volume originating from Internet of Things sources and information services drives the industry to develop new models and distributed tools to handle big data. In order to achieve strategic advantages, effective use of these tools and integrating results to their business processes are critical for enterprises. While there is an abundance of tools available in the market, they are underutilized by organizations due to their complexities. Deployment and usage of big data analysis tools require technical expertise which most of the organizations don't yet possess. Recently, the trend in the IT industry is towards developing prebuilt libraries and dataflow based programming models to abstract users from low-level complexities of these tools. After briefly analyzing trends in the literature and industry, this paper presents a conceptual framework which offers a higher level of abstraction to increase adoption of big data techniques as part of Industry 4.0 vision in future enterprises.


title: Stationary and moving targets detection on FMCW radar using GNU radio-based software defined radio

abstract: This paper discusses the implementation of GNU radio-based software defined radio (SDR) for designing a frequency modulated continuous wave (FMCW) radar to detect stationary and moving targets. The use of SDR system in which its components are implemented by means of software is to reduce cost and complexity in the design and implementation. Whilst the signal processing of FMCW radar is carried out using Matlab R® with triangular linear frequency modulation (LFM) waveform to obtain the target distance and the target relative speed for stationary and moving target, respectively. From the result, it is shown that the radar is successfully implemented using GNU radio-based SDR with the capability in distance target detection of 14.79km for a moving target away from the radar with the relative speed of 50m/s.


title: Identifying salient entities in web pages

abstract: We propose a system that determines the salience of entities within web documents. Many recent advances in commercial search engines leverage the identification of entities in web pages. However, for many pages, only a small subset of entities are central to the document, which can lead to degraded relevance for entity triggered experiences. We address this problem by devising a system that scores each entity on a web page according to its centrality to the page content. We propose salience classification functions that incorporate various cues from document content, web search logs, and a large web graph. To cost-effectively train the models, we introduce a soft labeling methodology that generates a set of annotations based on user behaviors observed in web search logs. We evaluate several variations of our model via a large-scale empirical study conducted over a test set, which we release publicly to the research community. We demonstrate that our methods significantly outperform competitive baselines and the previous state of the art, while keeping the human annotation cost to a minimum.


title: Ranging in a dense multipath environment using an UWB radio link

abstract: A time-of-arrival (ToA)-based ranging scheme using an ultra-wideband (UWB) radio link is proposed. This ranging scheme implements a search algorithm for the detection of a direct path signal in the presence of dense multipath, utilizing generalized maximum-likelihood (GML) estimation. Models for critical parameters in the algorithm are based on statistical analysis of propagation data and the algorithm is tested on another independent set of propagation measurements. The proposed UWB ranging system uses a correlator and a parallel sampler with a high-speed measurement capability in each transceiver to accomplish two-way ranging between them in the absence of a common clock.


title: The Staircase Mechanism in Differential Privacy

abstract: Adding Laplacian noise is a standard approach in differential privacy to sanitize numerical data before releasing it. In this paper, we propose an alternative noise adding mechanism: the staircase mechanism, which is a geometric mixture of uniform random variables. The staircase mechanism can replace the Laplace mechanism in each instance in the literature and for the same level of differential privacy, the performance in each instance improves; the improvement is particularly stark in medium-low privacy regimes. We show that the staircase mechanism is the optimal noise adding mechanism in a universal context, subject to a conjectured technical lemma (which we also prove to be true for one and two dimensional data).


Intelligent Systems in Accounting, Finance and Management: ISI journal and proceeding citations, and research issues from most-cited papers


title: ViSQOL: an objective speech quality model

abstract: This paper presents an objective speech quality model, ViSQOL, the Virtual Speech Quality Objective Listener. It is a signal-based, full-reference, intrusive metric that models human speech quality perception using a spectro-temporal measure of similarity between a reference and a test speech signal. The metric has been particularly designed to be robust for quality issues associated with Voice over IP (VoIP) transmission. This paper describes the algorithm and compares the quality predictions with the ITU-T standard metrics PESQ and POLQA for common problems in VoIP: clock drift, associated time warping, and playout delays. The results indicate that ViSQOL and POLQA significantly outperform PESQ, with ViSQOL competing well with POLQA. An extensive benchmarking against PESQ, POLQA, and simpler distance metrics using three speech corpora (NOIZEUS and E4 and the ITU-T P.Sup. 23 database) is also presented. These experiments benchmark the performance for a wide range of quality impairments, including VoIP degradations, a variety of background noise types, speech enhancement methods, and SNR levels. The results and subsequent analysis show that both ViSQOL and POLQA have some performance weaknesses and under-predict perceived quality in certain VoIP conditions. Both have a wider application and robustness to conditions than PESQ or more trivial distance metrics. ViSQOL is shown to offer a useful alternative to POLQA in predicting speech quality in VoIP scenarios.


title: The potential for machine learning algorithms to improve and reduce the cost of 3-dimensional printing for surgical planning

abstract: INTRODUCTION ::: 3D-printed anatomical models play an important role in medical and research settings. The recent successes of 3D anatomical models in healthcare have led many institutions to adopt the technology. However, there remain several issues that must be addressed before it can become more wide-spread. Of importance are the problems of cost and time of manufacturing. Machine learning (ML) could be utilized to solve these issues by streamlining the 3D modeling process through rapid medical image segmentation and improved patient selection and image acquisition. The current challenges, potential solutions, and future directions for ML and 3D anatomical modeling in healthcare are discussed. Areas covered: This review covers research articles in the field of machine learning as related to 3D anatomical modeling. Topics discussed include automated image segmentation, cost reduction, and related time constraints. Expert commentary: ML-based segmentation of medical images could potentially improve the process of 3D anatomical modeling. However, until more research is done to validate these technologies in clinical practice, their impact on patient outcomes will remain unknown. We have the necessary computational tools to tackle the problems discussed. The difficulty now lies in our ability to collect sufficient data.


title: Performance of Biometric Quality Measures

abstract: We document methods for the quantitative evaluation of systems that produce a scalar summary of a biometric sample's quality. We are motivated by a need to test claims that quality measures are predictive of matching performance. We regard a quality measurement algorithm as a black box that converts an input sample to an output scalar. We evaluate it by quantifying the association between those values and observed matching results. We advance detection error trade-off and error versus reject characteristics as metrics for the comparative evaluation of sample quality measurement algorithms. We proceed this with a definition of sample quality, a description of the operational use of quality measures. We emphasize the performance goal by including a procedure for annotating the samples of a reference corpus with quality values derived from empirical recognition scores.


Libyan healthcare system during the armed conflict: Challenges and restoration


title: Robust Computer Algebra, Theorem Proving, and Oracle AI

abstract: In the context of superintelligent AI systems, the term “oracle” has two meanings. One refers to modular systems queried for domain-specific tasks. Another usage, referring to a class of systems which may be useful for addressing the value alignment and AI control problems, is a superintelligent AI system that only answers questions. The aim of this manuscript is to survey contemporary research problems related to oracles which align with long-term research goals of AI safety. We examine existing question answering systems and argue that their high degree of architectural heterogeneity makes them poor candidates for rigorous analysis as oracles. On the other hand, we identify computer algebra systems (CASs) as being primitive examples of domain-specific oracles for mathematics and argue that efforts to integrate computer algebra systems with theorem provers, systems which have largely been developed independent of one another, provide a concrete set of problems related to the notion of provable safety that has emerged in the AI safety community. We review approaches to interfacing CASs with theorem provers, describe well-defined architectural deficiencies that have been identified with CASs, and suggest possible lines of research and practical software projects for scientists interested in AI safety.


title: Genetic algorithm optimization of a convolutional neural network for autonomous crack detection

abstract: Detecting cracks is an important function in building, tunnel and bridge structural analysis. Successful automation of crack detection can provide a uniform and timely means for preventing further damage to structures. This laboratory has successfully applied convolutional neural networks (CNNs) to online crack detection. CNNs represent an interesting method for adaptive image processing and form a link between artificial neural networks, and finite impulse response filters. As with most artificial neural networks, the CNN is susceptible to multiple local minima, thus complexity and time must be applied in order to avoid becoming trapped within the local minima. This paper employs a standard genetic algorithm (GA) to train the weights of a 4-5x5 filter CNN in order to pass through the local minima. This technique resulted in a 92.3/spl plusmn/1.4% average success rate using 25 GA-trained CNNs presented with 100 crack (320x240 pixel) images.


title: Hand wrist cumulative trauma disorders in industry.

abstract: A total of 574 active workers from six different industrial sites were categorised into four force repetitive exposure groups. Workers in low force-low repetitive jobs served as an internal comparison population for the three other groups. Videotapes and surface electromyography were used to estimate hand force and repetitiveness. The presence of cumulative trauma disorders (CTD) was determined by structured interview and standardised non-invasive physical examination. Only workers who had been working on the study jobs for at least one year at the time of evaluation were eligible for selection. Categorisation of jobs and identification of CTDs were carried out independently by investigators who were appropriately blinded to exposure and outcome. The analysis of associations between CTDs and exposure categories were performed using Mantel-Haenszel plant adjusted odds ratios and unconditional multiple logistic regression. Significant positive associations were observed between hand wrist CTDs and high force-high repetitive jobs. These associations were independent of age, sex, years on the specific job, and plant.


title: The Stability of Hedonic Coalition Structures

abstract: We consider the partitioning of a society into coalitions in purely hedonic settings, i.e., where each player's payoff is completely determined by the identity of other members of her coalition. We first discuss how hedonic and nonhedonic settings differ and some sufficient conditions for the existence of core stable coalition partitions in hedonic settings. We then focus on a weaker stability condition: individual stability, where no player can benefit from moving to another coalition while not hurting the members of that new coalition. We show that if coalitions can be ordered according to some characteristic over which players have single-peaked preferences, or where players have symmetric and additively separable preferences, then there exists an individually stable coalition partition. Examples show that without these conditions, individually stable coalition partitions may not exist. We also discuss some other stability concepts, and the incompatibility of stability with other normative properties. Journal of Economic Literature Classification Numbers: C71, A14, D20.


title: Analysis of position and speed control of sensorless BLDC motor using zero crossing back-EMF technique

abstract: Motor is the heart of many industrial automation and motion control applications. Brushless DC (BLDC) motor drives are widely used in a many applications, ranging from low to high power applications, because of their high efficiency, low maintenance, require small space, salient operation and high torque to power ratio, etc. The rotor position must be acquired in order to drive a BLDC motor. The position of the rotor is known by hall sensors and encoders, but when these types of sensors are used to drive BLDC motor, the circuits become very complex and large. Therefore accuracy and performance of BLDC motor drivers should be enhanced through sensorless technology. Many sensorless methods are existing in literature to sense the rotor position of motor, but this paper explain and proposed zero crossing back-EMF detection method. This paper work on analysis of sensorless techniques to sense the rotor position and PWM based speed control of inverter-fed sensorless BLDC motor. Modeling and simulation of three-phase star connected sensorless BLDC motor is done in MATLAB/Simulink environment. An accurate speed estimation has been achieved by using back EMF zero crossing speed estimation method. Fast response with negligible settling time has been obtained by this method of sensorless speed estimation of BLDC motor.


title: Fable II: Design of a modular robot for creative learning

abstract: Robotic systems have a high potential for creative learning if they are flexible, accessible and engaging for the user in the experimental process of building and programming robots. In this paper we describe the Fable modular robotic system for creative learning which we develop to enable and motivate anyone to build and program their own robots. The Fable system consists of self-contained modules equipped with sensors and actuators, which users can use to easily assemble a wide range of robots in a matter of seconds. The robots are user-programmable on several levels of abstraction ranging from a simple visual programming language to powerful conventional ones. This paper provides an overview of the design of Fable for different user groups and an evaluation of critical issues when we attempt to integrate the system into an everyday teaching context.


title: A back-illuminated global-shutter CMOS image sensor with pixel-parallel 14b subthreshold ADC

abstract: Rolling-shutter CMOS image sensors (CISs) are widely used [1,2]. However, the distortion of moving subjects remains an unresolved problem, regardless of the speed at which these sensors are operated. It has been reported that by adopting in-pixel analog memory (MEM) in pixels, a global shutter (GS) can be achieved by saving all pixels simultaneously as stored charges [3,4]. However, as signals from a storage unit are read in a column-wise sequence, a light-shielding structure is required for the MEM to suppress the influence of parasitic light during the reading period. Pixel-parallel ADCs have been reported as methods of implementing GS on a circuit [5,6]. However, these techniques have not been successful in operations on megapixels because they do not address issues such as the timing constraint for reading and writing a digital signal to and from an ADC in a pixel owing to increase in the number of pixels and the increase in the total power consumption of massively parallel comparators (CMs).


title: Simultaneous mosaicing and tracking with an event camera

abstract: © 2014. The copyright of this document resides with its authors. An event camera is a silicon retina which outputs not a sequence of video frames like a standard camera, but a stream of asynchronous spikes, each with pixel location, sign and precise timing, indicating when individual pixels record a threshold log intensity change. By encoding only image change, it offers the potential to transmit the information in a standard video but at vastly reduced bitrate, and with huge added advantages of very high dynamic range and temporal resolution. However, event data calls for new algorithms, and in particular we believe that algorithms which incrementally estimate global scene models are best placed to take full advantages of its properties. Here, we show for the first time that an event stream, with no additional sensing, can be used to track accurate camera rotation while building a persistent and high quality mosaic of a scene which is super-resolution accurate and has high dynamic range. Our method involves parallel camera rotation tracking and template reconstruction from estimated gradients, both operating on an event-by-event basis and based on probabilistic filtering.


title: Ontology-Based systems dedicated to human resources management: an application in e-recruitment

abstract: This paper presents the CommOn framework (Competency Management through Ontologies) which aims at developing operational Knowledge-Based Systems founded on ontologies and dedicated to the management of competencies Based on two different models (implemented within specific tools developped with the Protege-2000 framework), Common allows a Knowledge Engineering (i) to build competency reference systems related to particular domains such as Healthcare or Information and Telecommunication, (ii) to identify and formally represent competency profiles (related to a job seeker, a job offer or a training offer) and (iii) to automatically match competency profiles Developed in the context of Semantic Web Technology, the CommOn framework permits the building of domain ontologies and knowledge bases represented with Semantic Web Languages and the development of Competency-Based Web Services dedicated to Human Resources Management The use of CommOn is illustrated in the context of a project (related to e-recruitment) which aims at developing the first Macedonian web-based platform dedicated to the definition of an efficient networking of employment and training operators However, Common is not limited to e-recruitment application and it can be used for different purposes such as staff development and deployment, job analysis or economic evaluation.


title: Sex Discrimination: How Do We Tell the Difference between Male and Female Faces?

abstract: People are remarkably accurate (approaching ceiling) at deciding whether faces are male or female, even when cues from hairstyle, makeup, and facial hair are minimised. Experiments designed to explore the perceptual basis of our ability to categorise the sex of faces are reported. Subjects were considerably less accurate when asked to judge the sex of three-dimensional (3-D) representations of faces obtained by laser-scanning, compared with a condition where photographs were taken with hair concealed and eyes closed. This suggests that cues from features such as eyebrows, and skin texture, play an important role in decisionmaking. Performance with the laser-scanned heads remained quite high with 3/4-view faces, where the 3-D shape of the face should be easiest to see, suggesting that the 3-D structure of the face is a further source of information contributing to the classification of its sex. Performance at judging the sex from photographs (with hair concealed) was disrupted if the photographs were inver...


Big Data from CT Scanning


title: Vertebrae Localization in Pathological Spine CT via Dense Classification from Sparse Annotations

abstract: Accurate localization and identification of vertebrae in spinal imaging is crucial for the clinical tasks of diagnosis, surgical planning, and post-operative assessment. The main difficulties for automatic methods arise from the frequent presence of abnormal spine curvature, small field of view, and image artifacts caused by surgical implants. Many previous methods rely on parametric models of appearance and shape whose performance can substantially degrade for pathological cases.


title: A Literature Review of IGBT Fault Diagnostic and Protection Methods for Power Inverters

abstract: This paper presents a survey on existing methods for fault diagnosis and protection of insulated gate bipolar transistors with special focus on those used in three-phase power inverters. Twenty-one methods for open-circuit faults and ten methods for short-circuit faults are evaluated and summarized, based on their performance and implementation efforts. The gate-misfiring faults and their diagnostic methods are also briefly discussed. Finally, the promising methods are recommended for future work.


title: Actor-Oriented Design of Embedded Hardware and Software Systems

abstract: In this paper, we argue that model-based design and platform-based design are two views of the same thing. A platform is an abstraction layer in the design flow. For example, a core-based architecture and an instruction set architecture are platforms. We focus on the set of designs induced by this abstraction layer. For example, the set of all ASICs based on a particular core-based architecture and the set of all x86 programs are induced sets. Hence, a platform is equivalently a set of designs. Model-based design is about using platforms with useful modeling properties to specify designs, and then synthesizing implementations from these specifications. Hence model-based design is the view from above (more abstract, closer to the problem domain) and platform-based design is the view from below (less abstract, closer to the implementation technology). One way to define a platform is to provide a design language. Any valid expression in the language is an element of the set. A platform provides a set of cons...


title: A model for selecting an ERP system based on linguistic information processing

abstract: An enterprise resource planning (ERP) is an enterprise-wide application software package that integrates all necessary business functions into a single system with a common database. In order to implement an ERP project successfully in an organization, it is necessary to select a suitable ERP system. This paper presents a new model, which is based on linguistic information processing, for dealing with such a problem. In the study, a similarity degree based algorithm is proposed to aggregate the objective information about ERP systems from some external professional organizations, which may be expressed by different linguistic term sets. The consistency and inconsistency indices are defined by considering the subject information obtained from internal interviews with ERP vendors, and then a linear programming model is established for selecting the most suitable ERP system. Finally, a numerical example is given to demonstrate the application of the proposed method.


title: Exploiting Sentence Embedding for Medical Question Answering

abstract: Despite the great success of word embedding, sentence embedding remains a not-well-solved problem. In this paper, we present a supervised learning framework to exploit sentence embedding for the medical question answering task. The learning framework consists of two main parts: 1) a sentence embedding producing module, and 2) a scoring module. The former is developed with contextual self-attention and multi-scale techniques to encode a sentence into an embedding tensor. This module is shortly called Contextual self-Attention Multi-scale Sentence Embedding (CAMSE). The latter employs two scoring strategies: Semantic Matching Scoring (SMS) and Semantic Association Scoring (SAS). SMS measures similarity while SAS captures association between sentence pairs: a medical question concatenated with a candidate choice, and a piece of corresponding supportive evidence. The proposed framework is examined by two Medical Question Answering(MedicalQA) datasets which are collected from real-world applications: medical exam and clinical diagnosis based on electronic medical records (EMR). The comparison results show that our proposed framework achieved significant improvements compared to competitive baseline approaches. Additionally, a series of controlled experiments are also conducted to illustrate that the multi-scale strategy and the contextual self-attention layer play important roles for producing effective sentence embedding, and the two kinds of scoring strategies are highly complementary to each other for question answering problems.


A "No Recipe" Approach to PDS.


title: Employee Commitment and Motivation: A Conceptual Analysis and Integrative Model.

abstract: Theorists and researchers interested in employee commitment and motivation have not made optimal use of each other’s work. Commitment researchers seldom address the motivational processes through which commitment affects behavior, and motivation researchers have not recognized important distinctions in the forms, foci, and bases of commitment. To encourage greater cross-fertilization, the authors present an integrative framework in which commitment is presented as one of several energizing forces for motivated behavior. E. A. Locke’s (1997) model of the work motivation process and J. P. Meyer and L. Herscovitch’s (2001) model of workplace commitments serve as the foundation for the development of this new framework. To facilitate the merger, a new concept, goal regulation, is derived from selfdetermination theory (E. L. Deci & R. M. Ryan, 1985) and regulatory focus theory (E. I. Higgins, 1997). By including goal regulation, it is acknowledged that motivated behavior can be accompanied by different mindsets that have particularly important implications for the explanation and prediction of discretionary work behavior. Organizational scientists and practitioners have long been interested in employee motivation and commitment. This interest derives from the belief and evidence that there are benefits to having a motivated and committed workforce (Locke & Latham, 1990; Meyer & Allen, 1997; Pinder, 1998). Surprisingly, however, the commitment and motivation literatures in organizational psychology have evolved somewhat independently. Although commitment is discussed by motivation researchers and motivation by commitment researchers, neither concept is dealt with at the level of complexity that it is within its own domain. Moreover, there have been few attempts to integrate the two literatures; to demonstrate how the concepts are similar, different, and related; or to examine how commitment and motivation combine to influence behavior. These are our objectives. One explanation for the relative independence of the two bodies of work might be the differences in origin and objectives. Theories of work motivation evolved out of more general theories of motivation (see Steers, Porter, & Bigley, 1996) and have largely been applied to explain task performance. This emphasis is perhaps


title: Top- k high utility pattern mining with effective threshold raising strategies

abstract: In pattern mining, users generally set a minimum threshold to find useful patterns from databases. As a result, patterns with higher values than the user-given threshold are discovered. However, it is hard for the users to determine an appropriate minimum threshold. The reason for this is that they cannot predict the exact number of patterns mined by the threshold and control the mining result precisely, which can lead to performance degradation. To address this issue, top-k mining has been proposed for discovering patterns from ones with the highest value to ones with the kth highest value with setting the desired number of patterns, k. Top-k utility mining has emerged to consider characteristics of real-world databases such as relative importance of items and item quantities with the advantages of top-k mining. Although a relevant algorithm has been suggested in recent years, it generates a huge number of candidate patterns, which results in an enormous amount of execution time. In this paper, we propose an efficient algorithm for mining top-k high utility patterns with highly decreased candidates. For this purpose, we develop three strategies that can reduce the search space by raising a minimum threshold effectively in the construction of a global tree, where they utilize exact and pre-evaluated utilities of itemsets. Moreover, we suggest a strategy to identify actual top-k high utility patterns from candidates with the exact and pre-calculated utilities. Comprehensive experimental results on both real and synthetic datasets show that our algorithm with the strategies outperforms state-of-the-art methods.


title: Automatic rate adaptation

abstract: Rate adaptation is a fundamental primitive in wireless networks. Since wireless channel strength varies quickly and unpredictably, senders have to constantly measure the channel and correspondingly adapt the bitrate so that the transmitted packet gets correctly decoded. Prior approaches to this problem can be divided into two classes: those that require constant and expensive feedback from the receiver about channel strength, or those that use coarse and often inaccurate inference based on packet losses to measure channel strength and decide what bitrate to use. In this paper we take the opposite approach. Instead of actively adapting the bitrate based on receiver or packet loss feedback, we present a technique where the sender does no measurement or adaptation, yet the receiver manages to receive packets at a bitrate corresponding to whatever channel conditions exist at that point. The technique works with existing coding and modulation techniques (e.g. convolutional codes in WiFi), and requires no changes to them. Our preliminary evaluation shows that our proposed feedback-free technique achieves a performance that is nearly as good as if the sender knew exactly what the channel strength was in advance.


title: Unified Confidence Estimation Networks for Robust Stereo Matching

abstract: We present a deep architecture that estimates a stereo confidence, which is essential for improving the accuracy of stereo matching algorithms. In contrast to existing methods based on deep convolutional neural networks (CNNs) that rely on only one of the matching cost volume or estimated disparity map, our network estimates the stereo confidence by using the two heterogeneous inputs simultaneously. Specifically, the matching probability volume is first computed from the matching cost volume with residual networks and a pooling module in a manner that yields greater robustness. The confidence is then estimated through a unified deep network that combines confidence features extracted both from the matching probability volume and its corresponding disparity. In addition, our method extracts the confidence features of the disparity map by applying multiple convolutional filters with varying sizes to an input disparity map. To learn our networks in a semi-supervised manner, we propose a novel loss function that use confident points to compute the image reconstruction loss. To validate the effectiveness of our method in a disparity post-processing step, we employ three post-processing approaches; cost modulation, ground control points-based propagation, and aggregated ground control points-based propagation. Experimental results demonstrate that our method outperforms state-of-the-art confidence estimation methods on various benchmarks.


title: A local fingerprinting approach for audio copy detection

abstract: This study proposes an audio copy detection system that is robust to various attacks. These include the severe pitch shift and tempo change attacks which existing systems fail to detect. First, we propose a novel two dimensional representation for audio signals called the time-chroma image. This image is based on a modification of the concept of chroma in the music literature and is shown to achieve better performance in song identification. Then, we propose a novel fingerprinting algorithm that extracts local fingerprints from the time-chroma image. The proposed local fingerprinting algorithm is invariant to time/frequency scale changes in audio signals. It also outperforms existing methods like SIFT to a great extent. Finally, we introduce a song identification algorithm that uses the proposed fingerprints. The resulting copy detection system is shown to significantly outperform existing methods. Besides being able to detect whether a song (or a part of it) has been copied, the proposed system can accurately estimate the amount of pitch shift and/or tempo change that were applied to a song.


title: Determination of the PID Controller Parameters by Modified Genetic Algorithm for Improved Performance

abstract: This paper presents an efficient and fast tuning method based on a modified genetic algorithm (MGA) structure to find the optimal parameters of the proportional-integralderivative (PID) controller so that the desired system specifications are satisfied. To achieve more effective search, the optimization algorithm is based on the integration of classical genetic algorithm structure and a systematic neighbourhood structure. To demonstrate the effectiveness of presented method, the step responses of closed loop system were compared with that of the existing methods in the literature. Simulation results indicate that the performance of the PID controlled system can be significantly improved by the MGA-based method.


title: New two-hop multiple relay protocol with H-ARQ in the absence of a direct link

abstract: In this paper, we devise and comprehensively analyze an efficient transmission protocol for multiple half-duplex relay communication without a direct link but with inter-relay interference. The proposed protocol achieves high diversity gain with the help of Hybrid Automatic Retransmission reQuest (H-ARQ) even when multiplexing gain is quite high. As the maximum allowable number of retransmission rounds (L) increases, an additional time diversity gain is obtained in short-term static channels. Compared to pre-existing protocols which adopt H-ARQ in a similar manner, its diversity gain at high multiplexing gain is much improved. As L increases, the dominating region become wider. In addition, in long-term static channels, we show that more than two retransmissions (i.e., L > 2) are unnecessary for improvement of the diversity gain.


title: Bayesian network structures and inference techniques for automatic speech recognition,” Computer Speech and Language

abstract: This paper describes the theory and implementation of Bayesian networks in the context of automatic speech recognition. Bayesian networks provide a succinct and expressive graphical language for factoring joint probability distributions, and we begin by presenting the structures that are appropriate for doing speech recognition training and decoding. This approach is notable because it expresses all the details of a speech recognition system in a uniform way using only the concepts of random variables and conditional probabilities. A powerful set of computational routines complements the representational utility of Bayesian networks, and the second part of this paper describes these algorithms in detail. We present a novel view of inference in general networks – where inference is done via a change-of-variables that renders the network tree-structured and amenable to a very simple form of inference. We present the technique in terms of straightforward dynamic programming recursions analogous to HMM α–β computation, and then extend it to handle deterministic constraints amongst variables in an extremely efficient manner. The paper concludes with a sequence of experimental results that show the range of effects that can be modeled, and that significant reductions in error-rate can be expected from intelligently factored state representations.


title: SentiWordNet for Indian Languages

abstract: The discipline where sentiment/ opinion/ emotion has been identified and classified in human written text is well known as sentiment analysis. A typical computational approach to sentiment analysis starts with prior polarity lexicons where entries are tagged with their prior out of context polarity as human beings perceive using their cognitive knowledge. Till date, all research efforts found in sentiment lexicon literature deal mostly with English texts. In this article, we propose multiple computational techniques like, WordNet based, dictionary based, corpus based or generative approaches for generating SentiWordNet(s) for Indian languages. Currently, SentiWordNet(s) are being developed for three Indian languages: Bengali, Hindi and Telugu. An online intuitive game has been developed to create and validate the developed SentiWordNet(s) by involving Internet population. A number of automatic, semi-automatic and manual validations and evaluation methodologies have been adopted to measure the coverage and credibility of the developed SentiWordNet(s).


title: Exploring autoencoders for unsupervised feature selection

abstract: Feature selection plays an important role in pattern classification. It is especially an important preprocessing task when there are large number of features in comparison to number of patterns as is the case with gene expression data. A new unsupervised feature selection method has been evolved using autoencoders since autoencoders have the capacity to learn the input features without class information. In order to prevent the autoencoder from overtraining, masking has been used and the reconstruction error of masked input features has been used to compute feature weights in moving average manner. A new aggregation function for autoencoder has also been introduced by incorporating correlation between input features to remove the redundancy in selected features set. Comparative performance evaluation on benchmark image and gene expression datasets shows that the proposed method outperforms other unsupervised feature selection methods.


title: Car parking occupancy detection using smart camera networks and Deep Learning

abstract: This paper presents an approach for real-time car parking occupancy detection that uses a Convolutional Neural Network (CNN) classifier running on-board of a smart camera with limited resources. Experiments show that our technique is very effective and robust to light condition changes, presence of shadows, and partial occlusions. The detection is reliable, even when tests are performed using images captured from a viewpoint different than the viewpoint used for training. In addition, it also demonstrates its robustness when training and tests are executed on different parking lots. We have tested and compared our solution against state of the art techniques, using a reference benchmark for parking occupancy detection. We have also produced and made publicly available an additional dataset that contains images of the parking lot taken from different viewpoints and in different days with different light conditions. The dataset captures occlusion and shadows that might disturb the classification of the parking spaces status.


title: Recommender systems based on user reviews: the state of the art

abstract: In recent years, a variety of review-based recommender systems have been developed, with the goal of incorporating the valuable information in user-generated textual reviews into the user modeling and recommending process. Advanced text analysis and opinion mining techniques enable the extraction of various types of review elements, such as the discussed topics, the multi-faceted nature of opinions, contextual information, comparative opinions, and reviewers' emotions. In this article, we provide a comprehensive overview of how the review elements have been exploited to improve standard content-based recommending, collaborative filtering, and preference-based product ranking techniques. The review-based recommender system's ability to alleviate the well-known rating sparsity and cold-start problems is emphasized. This survey classifies state-of-the-art studies into two principal branches: review-based user profile building and review-based product profile building. In the user profile sub-branch, the reviews are not only used to create term-based profiles, but also to infer or enhance ratings. Multi-faceted opinions can further be exploited to derive the weight/value preferences that users place on particular features. In another sub-branch, the product profile can be enriched with feature opinions or comparative opinions to better reflect its assessment quality. The merit of each branch of work is discussed in terms of both algorithm development and the way in which the proposed algorithms are evaluated. In addition, we discuss several future trends based on the survey, which may inspire investigators to pursue additional studies in this area.


title: VPN gate: a volunteer-organized public VPN relay system with blocking resistance for bypassing government censorship firewalls

abstract: VPN Gate is a public VPN relay service designed to achieve blocking resistance to censorship firewalls such as the Great Firewall (GFW) of China. To achieve such resistance, we organize many volunteers to provide a VPN relay service, with many changing IP addresses. To block VPN Gate with their firewalls, censorship authorities must find the IP addresses of all the volunteers. To prevent this, we adopted two techniques to improve blocking resistance. The first technique is to mix a number of innocent IP addresses into the relay server list provided to the public. The second technique is collaborative spy detection. The volunteer servers work together to create a list of spies, meaning the computers used by censorship authorities to probe the volunteer servers. Using this list, each volunteer server ignores packets from spies. We launched VPN Gate on March 8, 2013. By the end of August it had about 3,000 daily volunteers using 6,300 unique IP addresses to facilitate 464,000 VPN connections from users worldwide, including 45,000 connections and 9,000 unique IP addresses from China. At the time VPN Gate maintained about 70% of volunteer VPN servers as unblocked by the GFW.


Evaluation of usability and user experience of an m-learning environment, custom-designed for a tertiary educational context


title: Lies, Damn Lies, and Fake News

abstract: The Oxford Dictionaries selected "post-truth" as the 2016 international word of the year. This is such a preposterous recognition from a heretofore august publication that I just can't allow it to pass in silence.


title: Sparsely Aggregated Convolutional Networks

abstract: We explore a key architectural aspect of deep convolutional neural networks: the pattern of internal skip connections used to aggregate outputs of earlier layers for consumption by deeper layers. Such aggregation is critical to facilitate training of very deep networks in an end-to-end manner. This is a primary reason for the widespread adoption of residual networks, which aggregate outputs via cumulative summation. While subsequent works investigate alternative aggregation operations (e.g. concatenation), we focus on an orthogonal question: which outputs to aggregate at a particular point in the network. We propose a new internal connection structure which aggregates only a sparse set of previous outputs at any given depth. Our experiments demonstrate this simple design change offers superior performance with fewer parameters and lower computational requirements. Moreover, we show that sparse aggregation allows networks to scale more robustly to 1000+ layers, thereby opening future avenues for training long-running visual processes.


title: Classification in Big Image Datasets Using Layered-SOM

abstract: Adequately classifying big image datasets containing images of arbitrary domains is getting more and more important nowadays. However the above mentioned problem has yet to be solved generally. The most suitable descriptors recognizing possible underlying structures and similar characteristics within large image datasets have to be selected and combined in order to carry out multi-feature analysis and thus image classification. This paper presents an enhancement of the original SOM via developing an unsupervised learning method using multiple layers. This method is appropriate of analyzing and classifying big image datasets with combining multiple image descriptors nonlinearly. It increases the precision of image clustering as well as reducing the time required for computation.


title: Explore, Visualize, and Analyze Functional Cancer Proteomic Data Using the Cancer Proteome Atlas

abstract: Reverse-phase protein arrays (RPPA) represent a powerful functional proteomic approach to elucidate cancer-related molecular mechanisms and to develop novel cancer therapies. To facilitate community-based investigation of the large-scale protein expression data generated by this platform, we have developed a user-friendly, open-access bioinformatic resource, The Cancer Proteome Atlas (TCPA, http://tcpaportal.org), which contains two separate web applications. The first one focuses on RPPA data of patient tumors, which contains >8,000 samples of 32 cancer types from The Cancer Genome Atlas and other independent patient cohorts. The second application focuses on the RPPA data of cancer cell lines and contains >650 independent cell lines across 19 lineages. Many of these cell lines have publicly available, high-quality DNA, RNA, and drug screening data. TCPA provides various analytic and visualization modules to help cancer researchers explore these datasets and generate testable hypotheses in an effective and intuitive manner. Cancer Res; 77(21); e51-54. ©2017 AACR.


title: Incremental Open Set Intrusion Recognition Using Extreme Value Machine

abstract: Typically, most network intrusion detection systems use supervised learning techniques to identify network anomalies. A problem exists when identifying the unknowns and automatically updating a classifier with new query classes. This is defined as an open set incremental learning problem and we propose to extend a recently introduced method, the Extreme Value Machine (EVM) to address the issue of identifying new classes during query time. The EVM is derived from the statistical extreme value theory and is the first classifier that can perform kernel-free, nonlinear, variable bandwidth outlier detection combined with incremental learning. In this paper, we utilize the EVM for intrusion detection and measure the open set recognition performance of identifying known and unknown classes. Additionally, we evaluate the performance on the KDDCUP’99 dataset and compare the results with the state-ofthe- art Weibull-SVM (W-SVM). Our findings demonstrate that the EVM mirrors the performance of the W-SVM classifier, while it supports incremental learning.


title: Learning to Remember Rare Events

abstract: Despite recent advances, memory-augmented deep neural networks are still limited ::: when it comes to life-long and one-shot learning, especially in remembering rare events. ::: We present a large-scale life-long memory module for use in deep learning. ::: The module exploits fast nearest-neighbor algorithms for efficiency and ::: thus scales to large memory sizes. ::: Except for the nearest-neighbor query, the module is fully differentiable ::: and trained end-to-end with no extra supervision. It operates in ::: a life-long manner, i.e., without the need to reset it during training. ::: ::: Our memory module can be easily added to any part of a supervised neural network. ::: To show its versatility we add it to a number of networks, from simple ::: convolutional ones tested on image classification to deep sequence-to-sequence ::: and recurrent-convolutional models. ::: In all cases, the enhanced network gains the ability to remember ::: and do life-long one-shot learning. ::: Our module remembers training examples shown many thousands ::: of steps in the past and it can successfully generalize from them. ::: We set new state-of-the-art for one-shot learning on the Omniglot dataset ::: and demonstrate, for the first time, life-long one-shot learning in ::: recurrent neural networks on a large-scale machine translation task.


title: Understanding individual mobility patterns from urban sensing data: A mobile phone trace example

abstract: Large-scale urban sensing data such as mobile phone traces are emerging as an important data source for urban modeling. This study represents a first step towards building a methodology whereby mobile phone data can be more usefully applied to transportation research. In this paper, we present techniques to extract useful mobility information from the mobile phone traces of millions of users to investigate individual mobility patterns within a metropolitan area. The mobile-phone-based mobility measures are compared to mobility measures computed using odometer readings from the annual safety inspections of all private vehicles in the region to check the validity of mobile phone data in characterizing individual mobility and to identify the differences between individual mobility and vehicular mobility. The empirical results can help us understand the intra-urban variation of mobility and the non-vehicular component of overall mobility. More importantly, this study suggests that mobile phone trace data represent a reasonable proxy for individual mobility and show enormous potential as an alternative and more frequently updatable data source and a compliment to the conventional travel surveys in mobility study.


Emergence of Functional Hierarchy in a Multiple Timescales Recurrent Neural Network Model : A Humanoid Robot Experiment


Design and Implementation of Deep Neural Network for Edge Computing


title: Distributed Ledger

abstract: Distributed Ledger (DL) has gained huge attention in the last years and will shift the conduction of business in the future. Until today, because of the great dynamics in the field a systematic approach to structure and define the field of DL does not exist. This led to a heterogeneous landscape of DL definitions and thus, misleading discussions. This paper gives a systematic overview of principles relevant to understand and structure the DL field. Furthermore, based on this it describes DL concepts and shows examples of protocols. The result can then be used for further research but also as a basis for successful collaborations in the field of DL.


title: Digital Rights Management

abstract: Upon flaws of current blockchain platforms of heavyweight, large capacity of ledger, and time-consuming of synchronization of data, in this paper, we proposed a new paradigm of master-slave blockchain scheme (MSB) for pervasive computing that suitable for general PC, mobile device such as smart phones or PADs to participants in the working of mining and verification, in which we separated traditional blockchain model in 2 layer defined as master node layer and a series of slavery agents layer, then we proposed 2 approaches for partially computing model(P-CM) and non-computing of model(NCM) in the MSB blockchain, Finally large amounts of simulations manifest the proposed master-slave blockchain scheme is feasible, extendible and suitable for pervasive computing especially in the 5G generation environment, and can apply in the DRM-related applications.


title: Optimal design of UHF TV band log-periodic antenna using invasive weed optimization

abstract: A powerful evolutionary method called Invasive Weed Optimization (IWO) is applied to achieve optimal designs of log-periodic antennas. The antennas are designed for operation in the UHF-TV band, i.e. 470–860 MHz, and are optimized with respect to the standing wave ratio (SWR), the front-to-rear (F/R) ratio, and the forward gain. The parameters under optimization are the dipole lengths, the dipole diameters, the distances between adjacent dipoles and the characteristic impedance of the transmission line that feeds the dipoles. The optimized antenna geometries that resulted from the above method seem to be significantly better than the respective ones derived from the classical design method.


title: Cultural Circuits of Climate Change in U.K. Broadsheet Newspapers, 1985–2003

abstract: This article argues for a cultural perspective to be brought to bear on studies of climate change risk perception. Developing the “circuit of culture” model, the article maintains that the producers and consumers of media texts are jointly engaged in dynamic, meaning-making activities that are context-specific and that change over time. A critical discourse analysis of climate change based on a database of newspaper reports from three U.K. broadsheet papers over the period 1985‐2003 is presented. This empirical study identifies three distinct circuits of climate change—1985‐1990, 1991‐1996, 1997‐2003—which are characterized by different framings of risks associated with climate change. The article concludes that there is evidence of social learning as actors build on their experiences in relation to climate change science and policy making. Two important factors in shaping the U.K.’s broadsheet newspapers’ discourse on “dangerous” climate change emerge as the agency of top political figures and the dominant ideological standpoints in different newspapers.


title: Pursuit of the muscular ideal: Physical and psychological consequences and putative risk factors

abstract: Developing a lean muscular figure for the purposes of sports and/or appearance has become a central issue for males. Concern has been raised because the desire to develop such a body build may lead to the adoption of numerous health-threatening behaviors. Consequently, this review presents a comprehensive analysis of the physical and psychological consequences that result from the use of steroids (legal and illegal), ephedrine, and deleterious dieting strategies specific to males. Putative risk factors for these behaviors will be identified, and the clinical disorder associated with the extreme abuse of these behaviors, muscle dysmorphia, will be examined.


title: Exploitation of spectral redundancy in cyclostationary signals

abstract: It is shown that the cyclostationarity attribute, as it is reflected in the periodicities of (second-order) moments of the signal, can be interpreted in terms of the property that allows generation of spectral lines from the signal by putting it through a (quadratic) nonlinear transformation. The fundamental link between the spectral-line generation property and the statistical property called spectral correlation, which corresponds to the correlation that exists between the random fluctuations of components of the signal residing in distinct spectral bands, is explained. The effects on the spectral-correlation characteristics of some basic signal processing operations, such as filtering, product modulation, and time sampling, are examined. It is shown how to use these results to derive the spectral-correlation characteristics for various types of man-made signals. Some ways of exploiting the inherent spectral redundancy associated with spectral correlation to perform various signal processing tasks involving detection and estimation of highly corrupted man-made signals are described. >


title: Towards a theory of semantic communication

abstract: This paper studies methods of quantitatively measuring semantic information in communication. We review existing work on quantifying semantic information, then investigate a model-theoretical approach for semantic data compression and reliable semantic communication. We relate our approach to the statistical measurement of information by Shannon, and show that Shannon's source and channel coding theorems have semantic counterparts.


title: Organic Computing – Addressing Complexity by Controlled Self-Organization

abstract: In the past, the focus of the computer industry has been to improve hardware performance and add more and more features to the software. As a result, more and more appliances surrounding us are equipped with embedded computational power and wireless communication. As such, they become ever more flexible and multifunctional, and almost indispensable in daily life. On the other hand, the resulting systems become increasingly complex and unreliable, posing new challenges to designer and user. Organic Computing (OC) has the vision to address the challenges of complex distributed systems by making them more life-like (organic), i.e. endowing them with abilities such as self- organization, self-configuration, self-repair, or adaptation. The designer's task is simplified, because it is no longer necessary to exactly specify the low-level system behavior in all possible situations that might occur, but instead leaving the system with a certain degree of freedom which allows it to react in an intelligent way to new situations. Also, use of such systems is simplified, as they can be controlled by setting few high-level goals, rather than having to manipulate many low-level parameters with unclear influence. In this paper, we give a general introduction to OC, and propose a generic observer-controller architecture as a framework for designing OC systems. Then, it is shown how to use this architecture at the example of a traffic light controller. The paper concludes with a summary and a discussion of future challenges.


Convolutional Neural Networks for Fashion Classification and Object Detection


title: Gaussian Processes for Data-Efficient Learning in Robotics and Control

abstract: Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. In this paper, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.


title: Time-based calibration of effectiveness measures

abstract: Many current effectiveness measures incorporate simplifying assumptions about user behavior. These assumptions prevent the measures from reflecting aspects of the search process that directly impact the quality of retrieval results as experienced by the user. In particular, these measures implicitly model users as working down a list of retrieval results, spending equal time assessing each document. In reality, even a careful user, intending to identify as much relevant material as possible, must spend longer on some documents than on others. Aspects such as document length, duplicates and summaries all influence the time required. In this paper, we introduce a time-biased gain measure, which explicitly accommodates such aspects of the search process. By conducting an appropriate user study, we calibrate and validate the measure against the TREC 2005 Robust Track test collection. We examine properties of the measure, contrasting it to traditional effectiveness measures, and exploring its extension to other aspects and environments. As its primary benefit, the measure allows us to evaluate system performance in human terms, while maintaining the simplicity and repeatability of system-oriented tests. Overall, we aim to achieve a clearer connection between user-oriented studies and system-oriented tests, allowing us to better transfer insights and outcomes from one to the other.


title: Highly integrated drivetrain solution: Integration of motor, inverter and gearing

abstract: Addressing power density and costs as major success factors for drivetrain solutions for e-mobility, a holistic system concept is necessary. Approaches which integrate e-machine, power electronics, control, gearing and cooling into a common housing offer high potential for compact and cost efficient (H)EV drivetrains. The transition to distributed multiphase inverter and motor concepts enables a novel level of system integration. A new smart stator tooth concept is presented. It comprises the integration of a segmented stator tooth together with dedicated power electronics and control electronics. This enables new degrees of freedom in terms of motor design, assembly and control.


title: ENDOGENOUS CORRUPTION, INEQUALITY AND GROWTH: ECONOMETRIC EVIDENCE

abstract: This paper empirically addresses three questions. First, what is corruption's effect on economic growth? Second, what are the factors that determine corruption? Third, what is the relationship among corruption, economic growth, and income distribution? I use a cross section of countries, both developed and underdeveloped. I find that corruption is an important determinant of both per-capita real growth and of the distribution of income. Corruption is positively and significantly correlated with growth, implying that corruption has efficiency-enhancing qualities. Corruption is positively and significantly correlated with inequality, implying that increased income inequality is associated with greater corruption. The most robust specification, which associates three jointly dependent equations using a two-stage least squares estimation technique, reinforces the proposition that corruption enhances efficiency, justifies inequality's role in determining growth rates, and lends support to the theory of international convergence of growth rates.


title: Bayesian Optimization with Inequality Constraints

abstract: Bayesian optimization is a powerful framework for minimizing expensive objective functions while using very few function evaluations. It has been successfully applied to a variety of problems, including hyperparameter tuning and experimental design. However, this framework has not been extended to the inequality-constrained optimization setting, particularly the setting in which evaluating feasibility is just as expensive as evaluating the objective. Here we present constrained Bayesian optimization, which places a prior distribution on both the objective and the constraint functions. We evaluate our method on simulated and real data, demonstrating that constrained Bayesian optimization can quickly find optimal and feasible points, even when small feasible regions cause standard methods to fail.


title: Supporting smart construction with dependable edge computing infrastructures and applications

abstract: Abstract The Internet of Things (IoT) such as the use of robots, sensors, actuators, electronic signalization and a variety of other Internet enabled physical devices may provide for new advanced smart applications to be used in construction in the very near future. Such applications require real-time responses and are therefore time-critical. Therefore, in order to support collaboration, control, monitoring, supply management, safety and other construction processes, they have to meet dependability requirements, including requirements for high Quality of Service (QoS). Dependability and high QoS can be achieved by using adequate number and quality of computing resources, such as processing, memory and networking elements, geographically close to the smart environments. The goal of this study is to develop a practical edge computing architecture and design, which can be used to support smart construction environments with high QoS. This study gives particular attention to the solution design, which relies on latest cloud and software engineering approaches and technologies, and provides elasticity, interoperability and adaptation to companies' specific needs. Two edge computing applications supporting video communications and construction process documentation are developed and demonstrate a viable edge computing design for smart construction.


Higher education and the new professionalism for teachers : realising the potential of partnership


title: A fully automatic algorithm for contour detection of bones in hand radiographs using active contours

abstract: This paper presents an algorithm for automatically detecting bone contours from hand radiographs using active contours. Prior knowledge is first used to locate initial contours for the snakes inside each bone of interest. Next, an adaptive snake algorithm is applied so that parameters are properly adjusted for each bone specifically. We introduce a novel truncation technique to prevent the external forces of the snake from pulling the contour outside the bones boundaries, yielding excellent results.


title: Assert(!defined(sequential I/O))

abstract: The term sequential I/O is widely used in systems research with the intuitive understanding that it means consecutive access. From a survey of the literature, though, this intuitive understanding has translated into numerous, inconsistent definitions. Since sequential I/O is such a fundamental concept in systems research, we believe that a sequentiality metric should allow us to compare access patterns in a meaningful way. ::: ::: We explore access properties that could be incorporated into potential metrics for sequential I/O including: access size, gaps between accesses, multi-stream, and inter-arrival time. We then analyze hundreds of large-scale storage traces and discuss how potential metrics compare. Interestingly, we find I/O traces considered highly sequential by one metric can be highly random to another metric. We further demonstrate that many plausible metrics are weakly correlated, though metrics weighted by size have more consistency. While there may not be a single metric for sequential I/O that is best in all cases, we believe systems researchers should more carefully consider, and state, which definition they use.