title: Examining Factors Affecting College Students' Intention to Use Web-Based Instruction Systems: Towards an Integrated Model.

abstract: With accelerated progress of information and communication technologies (ICT), web-based instruction (WBI) is becoming a popular method for education resources distributing and delivering. This study was conducted to explore what factors influence college students’ behavioral intentions to utilize WBI systems. To achieve this aim, a WBI system was developed and employed in a vocational college in Taiwan to support undergraduate courses learning. Drawing on the concepts from Technology Acceptance Model (TAM), Theory of Reasoned Action (TRA), and Social Cognitive Theory (SCT), this study proposes a nomological framework and develops an instrument for measuring college students’ intention to use the WBI platform. The empirical results indicate that students show great readiness and positive intentions towards the system for their web-based learning activities and expose a possible benefit from its use in the long term. The research findings can provide instrumental suggestions for web-based instruction practices and may serve as instrumental guidelines for WBI systems to be effectively designed to advance college students’ interests and activations in the virtual learning environment.


title: Mapping Urban Land Use by Using Landsat Images and Open Social Data

abstract: High-resolution urban land use maps have important applications in urban planning and management, but the availability of these maps is low in countries such as China. To address this issue, we have developed a protocol to identify urban land use functions over large areas using satellite images and open social data. We first derived parcels from road networks contained in Open Street Map (OSM) and used the parcels as the basic mapping unit. We then used 10 features derived from Points of Interest (POI) data and two indices obtained from Landsat 8 Operational Land Imager (OLI) images to classify parcels into eight Level I classes and sixteen Level II classes of land use. Similarity measures and threshold methods were used to identify land use types in the classification process. This protocol was tested in Beijing, China. The results showed that the generated land use map had an overall accuracy of 81.04% and 69.89% for Level I and Level II classes, respectively. The map revealed significantly more details of the spatial pattern of land uses in Beijing than the land use map released by the government.


title: Co-analysis of RAS Log and Job Log on Blue Gene/P

abstract: With the growth of system size and complexity, reliability has become of paramount importance for petascale systems. Reliability, Availability, and Serviceability (RAS) logs have been commonly used for failure analysis. However, analysis based on just the RAS logs has proved to be insufficient in understanding failures and system behaviors. To overcome the limitation of this existing methodologies, we analyze the Blue Gene/P RAS logs and the Blue Gene/P job logs in a cooperative manner. From our co-analysis effort, we have identified a dozen important observations about failure characteristics and job interruption characteristics on the Blue Gene/P systems. These observations can significantly facilitate the research in fault resilience of large-scale systems.


title: Investigating Electronic Word-of-Mouth Effects on Online Discussion Forums: The Role of Perceived Positive Electronic Word-of-Mouth Review Credibility

abstract: Abstract Electronic word of mouth (eWOM) has been an important factor influencing consumer purchase decisions. Using the ABC model of attitude, this study proposes a model to explain how eWOM affects online discussion forums. Specifically, we propose that platform (Web site reputation and source credibility) and customer (obtaining buying-related information and social orientation through information) factors influence purchase intentions via perceived positive eWOM review credibility, as well as product and Web site attitudes in an online community context. A total of 353 online discussion forum users in an online community (Fashion Guide) in Taiwan were recruited, and structural equation modeling (SEM) was used to test the research hypotheses. The results indicate that Web site reputation, source credibility, obtaining buying-related information, and social orientation through information positively influence perceived positive eWOM review credibility. In turn, perceived positive eWOM review credibility...


title: FMS receptor for M-CSF (CSF-1) is sensitive to the kinase inhibitor imatinib and mutation of Asp-802 to Val confers resistance

abstract: The kinase inhibitor imatinib is used in the treatment of chronic myeloid leukaemia, where it targets the intracellular Bcr-Abl tyrosine kinase, and gastrointestinal stromal tumours, where it targets either the KIT or PDGF tyrosine kinase receptors. Here, we report that imatinib is also an effective inhibitor of the closely related FMS receptor for macrophage colony stimulating factor and that mutation of Asp 802 of FMS to Val confers imatinib resistance. Imatinib readily reverted the transformed phenotype of haemopoietic and fibroblast cell lines that express the oncogene v-fms and also inhibited the growth of the Bacl.2F5 macrophage cell line. The cellular IC50 value of imatinib for FMS was similar to those for Bcr-Abl and KIT. Consequently, imatinib may also prove effective for the treatment of diseases whose progression is dependent upon macrophage-colony stimulating factor, this includes certain aspects of cancer and inflammation.


title: Theory of current source-density analysis and determination of conductivity tensor for anuran cerebellum.

abstract: The theoretical basis of current source-density (CSD) analysis in the central nervous system is described. Equations relating CSD, the current flow vector, and the extracellular field potential are given. It is shown that the CSD provides superior resolution of neuronal events when compared to conventional field-potential analysis. Expressions for the CSD in rectangular Cartesian coordinates are derived, including the general case of anisotropic, inhomogeneous conductive tissue, and a coordinate system rotated with respect to the principal axes (APPENDIX). The minimum number of spatial dimensions for accurate CSD analysis is discussed. The conductivity tensor was experimentally measured in frog and toad cerebella. All three principal components of the tensor were evaluated and their spatial gradients determined to be negligible. It was also shown that the conductivity was independent of potential. Thus the anuran cerebellum is anisotropic, homogeneous, and ohmic. On the basis of these results the appropriate mathematical expression for the CSD was selected.


title: The Impact of Service Quality on Customer Satisfaction, Customer Loyalty and Brand Image: Evidence from Hotel Industry of Pakistan

abstract: The rationale of this study is to look at the association between services quality of Pakistani hotel and to scrutinize cause on customer satisfaction, brand image and customer loyalty. The primary data was collected from 5 and 8 star hotels of Pakistan. The response rate was 86%. Structural equation modeling (SEM) technique was used to analyze the data. The findings suggested that high quality of services boost up the customer satisfaction and then afterward this satisfaction will strengthen the customer loyalty, our results also matched with Brodie et al., (2009). Last but not the least strong customer loyalty directly related to strong brand image.


title: A comparison of Pedestrian Dead-Reckoning algorithms using a low-cost MEMS IMU

abstract: Human localization is a very valuable information for smart environments. State-of-the-art Local Positioning Systems (LPS) require a complex sensor-network infrastructure to locate with enough accuracy and coverage. Alternatively, Inertial Measuring Units (IMU) can be used to estimate the movement of a person, by detecting steps, estimating stride lengths and the directions of motion; a methodology that is called Pedestrian Dead-Reckoning (PDR). In this paper, we use low-performance Micro-Electro-Mechanical (MEMS) inertial sensors attached to the foot of a person. This sensor has triaxial orthogonal accelerometers, gyroscopes and magnetometers. We describe, implement and compare several of the most relevant algorithms for step detection, stride length, heading and position estimation. The challenge using MEMS is to provide location estimations with enough accuracy and a limited drift. Several tests were conducted outdoors and indoors, and we found that the stride length estimation errors were about 1%. The positioning errors were almost always below 5% of the total travelled distance. The main source of positioning errors are the absolute orientation estimation.


title: Process Mining Software Repositories

abstract: Software developers’ activities are in general recorded in software repositories such as version control systems, bug trackers and mail archives. While abundant information is usually present in such repositories, successful information extraction is often challenged by the necessity to simultaneously analyze different repositories and to combine the information obtained. We propose to apply process mining techniques, originally developed for business process analysis, to address this challenge. However, in order for process mining to become applicable, different software repositories should be combined, and “related” software development events should be matched: e.g., mails sent about a file, modifications of the file and bug reports that can be traced back to it. The combination and matching of events has been implemented in FRASR (Framework for Analyzing Software Repositories), augmenting the process mining framework ProM. FRASR has been successfully applied in a series of case studies addressing such aspects of the development process as roles of different developers and the way bug reports are handled.


title: `N-Body' Problems in Statistical Learning

abstract: We present efficient algorithms for all-point-pairs problems, or 'N-body'- like problems, which are ubiquitous in statistical learning. We focus on six examples, including nearest-neighbor classification, kernel density estimation, outlier detection, and the two-point correlation. These include any problem which abstractly requires a comparison of each of the N points in a dataset with each other point and would naively be solved using N2 distance computations. In practice N is often large enough to make this infeasible. We present a suite of new geometric techniques which are applicable in principle to any 'N-body' computation including large-scale mixtures of Gaussians, RBF neural networks, and HMM's. Our algorithms exhibit favorable asymptotic scaling and are empirically several orders of magnitude faster than the naive computation, even for small datasets. We are aware of no exact algorithms for these problems which are more efficient either empirically or theoretically. In addition, our framework yields simple and elegant algorithms. It also permits two important generalizations beyond the standard all-point-pairs problems, which are more difficult. These are represented by our final examples, the multiple two-point correlation and the notorious n-point correlation.


title: Development of Student Information System

abstract: Information system in any organization is an essential element. For this purposed, many organization spend budget to buy a good system in order to have a reliable system to be operated in their organization. Many systems that invented are focused on the user friendly element e.g. easy to print out the document, available in cellphone and easy to access from anywhere. The proposed project developed student information that be used in Faculty of Electronics & Computer Engineering (FKEKK) and is known as the Student Information System (SIS). This system focused on recording and updating the student data. It is also will provide report for the lecturer who wanted to check their student status. Besides, this system was developed based on the forms oriented which used by the existing Staff Information System before. This new system used database concept to store all the information, which related to land application processes. As guidance for this project development, Rapid Application Design (RAD) methodology is used. As the conclusion, the software was fully functional and achieved the objectives. This system will contribute a new knowledge to the student information system and give the ease to the user for making arrangement or scheduling the student matters. This system will give a better performance in arranging the lecturer and student information without having to do it manually. For the future improvement, it is suggested that this system available and able to be used through cellular phone as the nowadays demand.


title: Marked point process framework for living probabilistic safety assessment and risk follow-up

abstract: We construct a model for living probabilistic safety assessment (PSA) by applying the general framework of marked point processes. The framework provides a theoretically rigorous approach for considering risk follow-up of posterior hazards. In risk follow-up, the hazard of core damage is evaluated synthetically at time points in the past, by using some observed events as logged history and combining it with re-evaluated potential hazards. There are several alternatives for doing this, of which we consider three here, calling them initiating event approach, hazard rate approach, and safety system approach. In addition, for a comparison, we consider a core damage hazard arising in risk monitoring. Each of these four definitions draws attention to a particular aspect in risk assessment, and this is reflected in the behaviour of the consequent risk importance measures. Several alternative measures are again considered. The concepts and definitions are illustrated by a numerical example.


title: Lymphocyte activation gene 3 (Lag3) is upregulated by prions but does not contribute to disease

abstract: Prion diseases (PrDs), Alzheimer’s disease and Parkinson’s disease (PD) are fatal neurodegenerative disorders that share neuropathological and biochemical features, including the aggregation of pathological protein conformers. Lymphocyte activation gene 3 (Lag3, also known as CD223) is a member of the immunoglobulin superfamily of receptors expressed on immune cells and neurons, which serves as a receptor for α-synuclein aggregates in PD. Here we examined the possible role of Lag3 in the pathogenesis of PrDs. Through quantitative real-time PCR and RNA-sequencing, we found that the expression levels of Lag3 were relatively low in adult mouse brains, yet its expression was significantly upregulated after prion infection. However, we failed finding significant differences regarding the incubation time, PrPSc load, neurodegeneration and inflammatory gene expression profiles between the Lag3 knockout mice and wild-type littermate controls after prion infection. We conclude that loss of Lag3 has no significant influence on PrD pathogenesis. Considering that Lag3 is an immune checkpoint receptor, our results also suggest that immune checkpoint inhibition (an increasingly prevalent therapeutic modality against many types of cancer) might not exert positive or negative effects on the progression of PrDs. Author Summary Prion diseases (PrDs) are fatal neurological disorders that can be transmitted between individuals. Their causative agent is called the prion, and is a conformationally changed, aggregated form of a normal cellular protein called PrPC. How prions spread from one cell to another is still largely unknown, but this spreading behavior is common to several additional proteins including α-synuclein. In this study, we test the hypothesis that lymphocyte activation gene 3 (Lag3), a neuronal receptor for α-synuclein aggregates, may serve as a prion receptor and facilitate its cell-to-cell transmission. We found that prion-infected Lag3-deficient mice progressed to terminal disease with the same kinetics and attack rates as wild-type mice, and exhibited the same type of CNS pathology. Therefore, Lag3 is not a mediator of prion transmission during PrD pathogenesis. Because Lag3 is an immune checkpoint receptor, our study also suggests that immune checkpoint inhibition, a promising therapeutic intervention to combat neurodegenerative disorders, might not be an effective way to halt PrD progression. Currently, several phase II/III clinical trials are exploring Lag3 inhibition as an anticancer strategy. Our results suggest that it may be safe to administer Lag3 antibodies (and perhaps also other checkpoint inhibitors) to those patients who suffer from pathogenic prion mutations and may need to undergo cancer immunotherapy.


title: Analysis of Reactive Spectrum Handoff in Cognitive Radio Networks

abstract: In this paper, we present an analytical framework to evaluate the effects of multiple spectrum handoffs on channel utilization and latency performances in cognitive radio (CR) networks. During the transmission period of a secondary connection, multiple interruptions from the primary users result in multiple spectrum handoffs. In order to decide the target channel for each spectrum handoff and resume the unfinished transmission, wideband sensing is performed in an on-demand reactive manner. Although spectrum handoff procedure can enhance channel utilization, transmission latency of the secondary users is prolonged due to multiple handoffs. Thus, two fundamental issues in CR networks with multiple spectrum handoffs arise: (1) to what extent the channel utilization can be improved; and (2) how long the transmission latency will be extended for the secondary users. To solve the first problem, we introduce the preemptive resume priority (PRP) M/G/1 queueing network to characterize the channel usage behaviors of CR networks. Based on this queueing network, channel utilization under various traffic arrival rates and service time distributions can be evaluated. Furthermore, on top of the proposed queueing network, a state diagram is developed to characterize the effects of multiple handoff delay on the transmission latency of the secondary users. The analytical results can provide a helpful insight to study the effects of traffic arrival rates and service time on channel utilization and transmission latency and then facilitate the designs of admission control rules for the secondary users subject to their performance requirements.


title: Query-Efficient Black-box Adversarial Examples (superceded)

abstract: Note that this paper is superceded by "Black-Box Adversarial Attacks with Limited Queries and Information." ::: Current neural network-based image classifiers are susceptible to adversarial examples, even in the black-box setting, where the attacker is limited to query access without access to gradients. Previous methods --- substitute networks and coordinate-based finite-difference methods --- are either unreliable or query-inefficient, making these methods impractical for certain problems. ::: We introduce a new method for reliably generating adversarial examples under more restricted, practical black-box threat models. First, we apply natural evolution strategies to perform black-box attacks using two to three orders of magnitude fewer queries than previous methods. Second, we introduce a new algorithm to perform targeted adversarial attacks in the partial-information setting, where the attacker only has access to a limited number of target classes. Using these techniques, we successfully perform the first targeted adversarial attack against a commercially deployed machine learning system, the Google Cloud Vision API, in the partial information setting.


title: Design of a parabolic reflector antenna with a compact splash-plate feed

abstract: A parabolic reflector antenna with a compact splash-plate feed is designed and optimized in this paper. A dielectric connection portion is used to combine the waveguide and splash-plate sub-reflector. The use of dielectric material not only can achieve low blockage as no struts are needed to support the feed, but also can adjust the impedance of the antenna. The designed antenna is able to achieve a low VSWR <; 1.17 within the frequency range of 21.2 GHz-23 GHz. In addition, some chokes are added on the back of the splash-plate to suppress the radiation of the undesired directions. A corrugated fence is added along the edge of parabolic reflector to achieve better performance, in which the front to back ratio is significantly improved. The gain of the designed parabolic reflector antenna is 40.1-41.06 dBi within 21.2 GHz-23 GHz. Moreover, by using the designed splash-plate feed, simulation results show that the radiation efficiency of the parabolic reflector antenna is about 57.7%-62.5% within the operation bandwidth.


title: Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization

abstract: Many clustering algorithms, including cluster ensembles, rely on a random component. Stability of the results across different runs is considered to be an asset of the algorithm. The cluster ensembles considered here are based on k-means clusterers. Each clusterer is assigned a random target number of clusters, k and is started from a random initialization. Here, we use 10 artificial and 10 real data sets to study ensemble stability with respect to random k, and random initialization. The data sets were chosen to have a small number of clusters (two to seven) and a moderate number of data points (up to a few hundred). Pairwise stability is defined as the adjusted Rand index between pairs of clusterers in the ensemble, averaged across all pairs. Nonpairwise stability is defined as the entropy of the consensus matrix of the ensemble. An experimental comparison with the stability of the standard k-means algorithm was carried out for k from 2 to 20. The results revealed that ensembles are generally more stable, markedly so for larger k. To establish whether stability can serve as a cluster validity index, we first looked at the relationship between stability and accuracy with respect to the number of clusters, k. We found that such a relationship strongly depends on the data set, varying from almost perfect positive correlation (0.97, for the glass data) to almost perfect negative correlation (-0.93, for the crabs data). We propose a new combined stability index to be the sum of the pairwise individual and ensemble stabilities. This index was found to correlate better with the ensemble accuracy. Following the hypothesis that a point of stability of a clustering algorithm corresponds to a structure found in the data, we used the stability measures to pick the number of clusters. The combined stability index gave best results.


title: Evidence-based practice models for organizational change: overview and practical applications

abstract: Aim. To provide an overview, summary of key features and evaluation of usefulness of six evidence-based practice models frequently discussed in the literature. Background. The variety of evidence-based practice models and frameworks, complex terminology and organizational culture challenges nurses in selecting the model that best fits their practice setting. Data sources. The authors: (1) initially identified models described in a predominant nursing text; (2) searched the literature through CINAHL from 1998 to current year, using combinations of ‘evidence’, ‘evidence-based practice’, ‘models’, ‘nursing’ and ‘research’; (3) refined the list of selected models based on the initial literature review; and (4) conducted a second search of the literature on the selected models for all available years to locate both historical and recent articles on their use in nursing practice. Discussion. Authors described model key features and provided an evaluation of model usefulness based on specific criteria, which focused on facilitating the evidence-based practice process and guiding practice change. Implications for nursing. The evaluation of model usefulness can be used to determine the best fit of the models to the practice setting. Conclusion. The Johns Hopkins Model and the Academic Center for EvidenceBased Practice Star Model emphasize the processes of finding and evaluating evidence that is likely to appeal to nursing educators. Organizations may prefer the Promoting Action on Research Implementation in Health Services Framework, Advancing Research and Clinical Practice Through Close Collaboration, or Iowa models for their emphasis on team decision-making. An evidence-based practice model that is clear to the clinician and fits the organization will guide a systematic approach to evidence review and practice


title: A Game-Theoretic Approach to Generating Spatial Descriptions

abstract: Language is sensitive to both semantic and pragmatic effects. To capture both effects, we model language use as a cooperative game between two players: a speaker, who generates an utterance, and a listener, who responds with an action. Specifically, we consider the task of generating spatial references to objects, wherein the listener must accurately identify an object described by the speaker. We show that a speaker model that acts optimally with respect to an explicit, embedded listener model substantially outperforms one that is trained to directly generate spatial descriptions.


title: Stereophonic Acoustic Echo Suppression Incorporating Spectro-Temporal Correlations

abstract: In this letter, we propose an enhanced stereophonic acoustic echo suppression (SAES) algorithm incorporating spectral and temporal correlations in the short-time Fourier transform (STFT) domain. Unlike traditional stereophonic acoustic echo cancellation, SAES estimates the echo spectra in the STFT domain and uses a Wiener filter to suppress echo without performing any explicit double-talk detection. The proposed approach takes account of interdependencies among components in adjacent time frames and frequency bins, which enables more accurate estimation of the echo signals. Experimental results show that the proposed method yields improved performance compared to that of conventional SAES.


title: Active Learning for Human Pose Estimation

abstract: Annotating human poses in realistic scenes is very time consuming, yet necessary for training human pose estimators. We propose to address this problem in an active learning framework, which alternates between requesting the most useful annotations among a large set of unlabelled images, and re-training the pose estimator. To this end, (1) we propose an uncertainty estimator specific for body joint predictions, which takes into account the spatial distribution of the responses of the current pose estimator on the unlabelled images; (2) we propose a dynamic combination of influence and uncertainty cues, where their weights vary during the active learning process according to the reliability of the current pose estimator; (3) we introduce a computer assisted annotation interface, which reduces the time necessary for a human annotator to click on a joint by discretizing the image into regions generated by the current pose estimator. Experiments using the MPII and LSP datasets with both simulated and real annotators show that (1) the proposed active selection scheme outperforms several baselines; (2) our computer-assisted interface can further reduce annotation effort; and (3) our technique can further improve the performance of a pose estimator even when starting from an already strong one.


title: A note on undecidable properties of formal languages

abstract: A general set of conditions is given under which a property is undecidable for a family of languages. Examples are given of the application of this result to wellknown families of languages.


title: Bayesian Inference for Nonnegative Matrix Factorisation Models

abstract: We describe nonnegative matrix factorisation (NMF) with a Kullback-Leibler (KL) error measure in a statistical framework, with a hierarchical generative model consisting of an observation and a prior component. Omitting the prior leads to the standard KL-NMF algorithms as special cases, where maximum likelihood parameter estimation is carried out via the Expectation-Maximisation (EM) algorithm. Starting from this view, we develop full Bayesian inference via variational Bayes or Monte Carlo. Our construction retains conjugacy and enables us to develop more powerful models while retaining attractive features of standard NMF such as monotonic convergence and easy implementation. We illustrate our approach on model order selection and image reconstruction.


title: Text and Structural Data Mining of Influenza Mentions in Web and Social Media

abstract: Text and structural data mining of web and social media (WSM) provides a novel disease surveillance resource and can identify online communities for targeted public health communications (PHC) to assure wide dissemination of pertinent information. WSM that mention influenza are harvested over a 24-week period, 5 October 2008 to 21 March 2009. Link analysis reveals communities for targeted PHC. Text mining is shown to identify trends in flu posts that correlate to real-world influenza-like illness patient report data. We also bring to bear a graph-based data mining technique to detect anomalies among flu blogs connected by publisher type, links, and user-tags.


title: Blink detection robust to various facial poses

abstract: Applications based on eye-blink detection have increased, as a result of which it is essential for eye-blink detection to be robust and non-intrusive irrespective of the changes in the user's facial pose. However, most previous studies on camera-based blink detection have the disadvantage that their performances were affected by the facial pose. They also focused on blink detection using only frontal facial images. To overcome these disadvantages, we developed a new method for blink detection, which maintains its accuracy despite changes in the facial pose of the subject. This research is novel in the following four ways. First, the face and eye regions are detected by using both the AdaBoost face detector and a Lucas-Kanade-Tomasi (LKT)-based method, in order to achieve robustness to facial pose. Secondly, the determination of the state of the eye (being open or closed), needed for blink detection, is based on two features: the ratio of height to width of the eye region in a still image, and the cumulative difference of the number of black pixels of the eye region using an adaptive threshold in successive images. These two features are robustly extracted irrespective of the lighting variations by using illumination normalization. Thirdly, the accuracy of determining the eye state - open or closed - is increased by combining the above two features on the basis of the support vector machine (SVM). Finally, the SVM classifier for determining the eye state is adaptively selected according to the facial rotation. Experimental results using various databases showed that the blink detection by the proposed method is robust to various facial poses.


title: Comparison of different shapes in microstrip patch antenna for X-band applications

abstract: A Microstrip Patch antenna for radar application is presented. There is a variety of shapes available for Microstrip patch antenna. In this paper, conventional shapes like Rectangular, Triangular and Circular Microstrip patch antenna are designed and analyzed. The antenna is designed to resonate at X-band frequency. The X-band frequency range lies between 8 to 12 Gigahertz and are widely used in radar applications. The substrate used by the antenna is the low cost FR4 (Flame Retardant) Epoxy. The Ansoft HFSS (High Frequency Structural Simulator) Version 12 software is used to analyze the results of different shapes of Microstrip patch antenna. The parameters like return loss, bandwidth, gain and directivity are compared and discussed in this paper.


title: Transverse vibrations of auger conveyors

abstract: Abstract The low flexural rigidity of auger conveyors often results in the critical speed for transverse vibrations occurring well within the working speed range of the conveyor. Operation at or near the critical speed should be avoided, not only for the safeguard of the conveying machinery itself, but also because of the increased damage which is inflicted upon the material being conveyed. It is shown that the computation of critical auger speeds can be carried out, with sufficient accuracy for practical purposes, by neglecting the stiffening effect of the helical auger blade. An approximate theoretical analysis of the blade stiffness is presented which serves to explain why the stiffness is negligible in certain cases. An experimental study was performed to correlate the theory and to obtain critical speed measurements of a 6 in auger for comparison with values computed from a theoretical equation.


title: Collaboratively Regularized Nearest Points for Set Based Recognition.

abstract: Set based recognition has been attracting more and more attention in recent years, benefitting from two facts: the difficulty of collecting sets of images for recognition fades quickly, and set based recognition models generally outperform the ones for single instance based recognition. In this paper, we propose a novel model called collaboratively regularized nearest points (CRNP) for solving this problem. The proposal inherits the merits of simplicity, robustness, and high-efficiency from the very recently introduced regularized nearest points (RNP) method on finding the set-to-set distance using the l2-norm regularized affine hulls. Meanwhile, CRNP makes use of the powerful discriminative ability induced by collaborative representation, following the same idea as that in sparse recognition for classification (SRC) for image-based recognition and collaborative sparse approximation (CSA) for set-based recognition. However, CRNP uses l2-norm instead of the expensive l1-norm for coefficients regularization, which makes it much more efficient. Extensive experiments on five benchmark datasets for face recognition and person re-identification demonstrate that CRNP is not only more effective but also significantly faster than other state-of-the-art methods, including RNP and CSA.


title: difficulties: Perspectives of speech and language therapy service managers

abstract: Background: Children with specific speech and language difficulties (SSLD) pose a challenge to the education system, and to speech and language therapists who support them, as a result of their language needs and associated educational and social–behavioural difficulties. The development of inclusion raises questions regarding appropriate provision, whether the tradition of language units or full inclusion into mainstream schools. Aims: To gather the views of speech and language therapy service managers in England and Wales regarding approaches to service delivery, terminology and decision-making for educational provision, and the use of direct and indirect (consultancy) models of intervention. Method & Procedures: The study reports on a national survey of speech and language therapy (SLT) services in England and Wales (129 respondents, 72.1% response rate) and interviews with 39 SLT service managers. Outcomes & Results: Provision varied by age group with support to children in the mainstream common from pre-school to the end of Key Stage 2 (up to 11 years), and to those in designated specialist provision, common at Key Stages 1/ 2 (ages 5–11 years), but less prevalent at Key Stages 3/4 (11–16 years). Decision-making regarding provision was influenced by the lack of common terminology, with SSLD and specific language impairment (SLI) the most common, and criteria, including the use of the discrepancy model for defining SSLD. Practice was influenced by the difficulties in distinguishing children with


title: Long short-term memory neural network for network traffic prediction

abstract: This paper proposes a model of neural network which can be used to combine Long Short Term Memory networks (LSTM) with Deep Neural Networks (DNN). Autocorrelation coefficient is added to model to improve the accuracy of prediction model. It can provide better than the other traditional precision of the model. And after considering the autocorrelation features, the neural network of LSTM and DNN has certain advantages in the accuracy of the large granularity data sets. Several experiments were held using real-world data to show effectivity of LSTM model and accuracy were improve with autocorrelation considered.


title: Learning Cross-Domain Landmarks for Heterogeneous Domain Adaptation

abstract: While domain adaptation (DA) aims to associate the learning tasks across data domains, heterogeneous domain adaptation (HDA) particularly deals with learning from cross-domain data which are of different types of features. In other words, for HDA, data from source and target domains are observed in separate feature spaces and thus exhibit distinct distributions. In this paper, we propose a novel learning algorithm of Cross-Domain Landmark Selection (CDLS) for solving the above task. With the goal of deriving a domain-invariant feature subspace for HDA, our CDLS is able to identify representative cross-domain data, including the unlabeled ones in the target domain, for performing adaptation. In addition, the adaptation capabilities of such cross-domain landmarks can be determined accordingly. This is the reason why our CDLS is able to achieve promising HDA performance when comparing to state-of-the-art HDA methods. We conduct classification experiments using data across different features, domains, and modalities. The effectiveness of our proposed method can be successfully verified.


title: [From crisis to adjustment disorder: a medicalization of a concept?].

abstract: This review aimed to compare two concepts in the psychiatric literature: crisis and adjustment disorder. The two concepts stem from different theoretical perspectives, rely upon different (though relatively loose) bodies of data, and may serve different purposes. The concept of crisis originated from an approach that could be considered psychodynamic, humanistic, and community oriented. Treatment, according to this approach, is known as crisis intervention and is characterized as being principally psychological, social, humanistic, and systemic. The generic approach to crisis calls for immediate aid rather than for a diagnosis and regular appointments, as is customary in psychiatric practice. The concept of adjustment disorder, on the other hand, is a rather medical nosological approach, which strives to achieve a phenomenological and objective description of the patient, and which may lead to ordinary psychiatric treatment, such as pharmacotherapy.


title: Phase-type distribution of customer relationship with Markovian response and marketing expenditure decision on the customer lifetime value

abstract: In recent years, more and more companies have adopted relationship marketing (RM). At the core of RM is the development and maintenance of long-term relationships with valuable customers. In RM, the customer lifetime value (CLV) is the discounted profit streams of a customer across the entire customer life cycle. The CLV plays a key role in customer acquisition and retention decisions. In this paper, we present a general mathematical framework for RM, and introduce a Markov chain model which is appropriate in modeling RM because of its flexibility and probabilistic nature. We also develop the life distribution of the customer relationship, which is a phase-type distribution since it is the distribution of the first arrival stopping state. And we obtain the expectation of the CLV, which is an important statistic for good decision-making. Finally, we illustrate how to find the optimal remarketing policy numerically. The framework developed for RM systems in this paper should be seen as a practical approach to RM where one can directly apply the results of phase-type distribution and expectation of CLV to marketing decisions.


title: Epidemiological data challenges: planning for a more robust future through data standards

abstract: Accessible epidemiological data are of great value for emergency preparedness and response, understanding disease progression through a population, and building statistical and mechanistic disease models that enable forecasting. The status quo, however, renders acquiring and using such data difficult in practice. In many cases, a primary way of obtaining epidemiological data is through the internet, but the methods by which the data are presented to the public often differ drastically among institutions. As a result, there is a strong need for better data sharing practices. This paper identifies, in detail and with examples, the three key challenges one encounters when attempting to acquire and use epidemiological data: 1) interfaces, 2) data formatting, and 3) reporting. These challenges are used to provide suggestions and guidance for improvement as these systems evolve in the future. If these suggested data and interface recommendations were adhered to, epidemiological and public health analysis, modeling, and informatics work would be significantly streamlined, which can in turn yield better public health decision-making capabilities.


title: Global Relation Embedding for Relation Extraction

abstract: We study the problem of textual relation embedding with distant supervision. To combat the wrong labeling problem of distant supervision, we propose to embed textual relations with global statistics of relations, i.e., the co-occurrence statistics of textual and knowledge base relations collected from the entire corpus. This approach turns out to be more robust to the training noise introduced by distant supervision. On a popular relation extraction dataset, we show that the learned textual relation embedding can be used to augment existing relation extraction models and significantly improve their performance. Most remarkably, for the top 1,000 relational facts discovered by the best existing model, the precision can be improved from 83.9% to 89.3%.


title: Maximum likelihood analysis of conflicting observations in social sensing

abstract: This article addresses the challenge of truth discovery from noisy social sensing data. The work is motivated by the emergence of social sensing as a data collection paradigm of growing interest, where humans perform sensory data collection tasks. Unlike the case with well-calibrated and well-tested infrastructure sensors, humans are less reliable, and the likelihood that participants' measurements are correct is often unknown a priori. Given a set of human participants of unknown trustworthiness together with their sensory measurements, we pose the question of whether one can use this information alone to determine, in an analytically founded manner, the probability that a given measurement is true. In our previous conference paper, we offered the first maximum likelihood solution to the aforesaid truth discovery problem for corroborating observations only. In contrast, this article extends the conference paper and provides the first maximum likelihood solution to handle the cases where measurements from different participants may be conflicting. The article focuses on binary measurements. The approach is shown to outperform our previous work used for corroborating observations, the state-of-the-art fact-finding baselines, as well as simple heuristics such as majority voting.


title: Silhouettes: A graphical aid to the interpretation and validation of cluster analysis

abstract: A new graphical display is proposed for partitioning techniques. Each cluster is represented by a so-called silhouette, which is based on the comparison of its tightness and separation. This silhouette shows which objects lie well within their cluster, and which ones are merely somewhere in between clusters. The entire clustering is displayed by combining the silhouettes into a single plot, allowing an appreciation of the relative quality of the clusters and an overview of the data configuration. The average silhouette width provides an evaluation of clustering validity, and might be used to select an ‘appropriate’ number of clusters.


title: Multivariate-Time-Series-Driven Real-time Anomaly Detection Based on Bayesian Network

abstract: Anomaly detection is an important research direction, which takes the real-time information system from different sensors and conditional information sources into consideration. Based on this, we can detect possible anomalies expected of the devices and components. One of the challenges is anomaly detection in multivariate-sensing time-series in this paper. Based on this situation, we propose RADM, a real-time anomaly detection algorithm based on Hierarchical Temporal Memory (HTM) and Bayesian Network (BN). First of all, we use HTM model to evaluate the real-time anomalies of each univariate-sensing time-series. Secondly, a model of anomalous state detection in multivariate-sensing time-series based on Naive Bayesian is designed to analyze the validity of the above time-series. Lastly, considering the real-time monitoring cases of the system states of terminal nodes in Cloud Platform, the effectiveness of the methodology is demonstrated using a simulated example. Extensive simulation results show that using RADM in multivariate-sensing time-series is able to detect more abnormal, and thus can remarkably improve the performance of real-time anomaly detection.


title: Deep neural networks based user interface detection for mobile applications using symbol marker

abstract: The UI storyboard is a design drawing for the development of mobile application. The designer uses a storyboard as a tool to help communication between the developer and the designer. However, the developer can make an unintended widget which is different from the intention of the designer. In this paper, we propose a DNN (Deep Convolutional Neural Network) based automatic UI detection method using symbol markers to improve the accuracy of UI identification. The symbol marker can be useful for UI detection and developing the UI Code.


title: Credit Card Fraud Detection Using Hidden Markov Model

abstract: Due to a rapid advancement in the electronic commerce technology, the use of credit cards has dramatically increased. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In this paper, we model the sequence of operations in credit card transaction processing using a hidden Markov model (HMM) and show how it can be used for the detection of frauds. An HMM is initially trained with the normal behavior of a cardholder. If an incoming credit card transaction is not accepted by the trained HMM with sufficiently high probability, it is considered to be fraudulent. At the same time, we try to ensure that genuine transactions are not rejected. We present detailed experimental results to show the effectiveness of our approach and compare it with other techniques available in the literature.


title: Deep Learning in Radiology: Does One Size Fit All?

abstract: Abstract Deep learning (DL) is a popular method that is used to perform many important tasks in radiology and medical imaging. Some forms of DL are able to accurately segment organs (essentially, trace the boundaries, enabling volume measurements or calculation of other properties). Other DL networks are able to predict important properties from regions of an image—for instance, whether something is malignant, molecular markers for tissue in a region, even prognostic markers. DL is easier to train than traditional machine learning methods, but requires more data and much more care in analyzing results. It will automatically find the features of importance, but understanding what those features are can be a challenge. This article describes the basic concepts of DL systems and some of the traps that exist in building DL systems and how to identify those traps.


title: Support vector machines for histogram-based image classification

abstract: Traditional classification approaches generalize poorly on image classification tasks, because of the high dimensionality of the feature space. This paper shows that support vector machines (SVM's) can generalize well on difficult image classification problems where the only features are high dimensional histograms. Heavy-tailed RBF kernels of the form K(x, y) = e(-rho)Sigma(i)/xia-yia/b with a < or = 1 and b < or = 2 are evaluated on the classification of images extracted from the Corel stock photo collection and shown to far outperform traditional polynomial or Gaussian radial basis function (RBF) kernels. Moreover, we observed that a simple remapping of the input x(i)-->x(i)(a) improves the performance of linear SVM's to such an extend that it makes them, for this problem, a valid alternative to RBF kernels.


title: Seven challenges for RESTful transaction models

abstract: The REpresentational State Transfer (REST) architectural style describes the design principles that made the World Wide Web scalable and the same principles can be applied in enterprise context to do loosely coupled and scalable application integration. In recent years, RESTful services are gaining traction in the industry and are commonly used as a simpler alternative to SOAP Web Services. However, one of the main drawbacks of RESTful services is the lack of standard mechanisms to support advanced quality-of-service requirements that are common to enterprises. Transaction processing is one of the essential features of enterprise information systems and several transaction models have been proposed in the past years to fulfill the gap of transaction processing in RESTful services. The goal of this paper is to analyze the state-of-the-art RESTful transaction models and identify the current challenges.


title: Studying aesthetics in photographic images using a computational approach

abstract: Aesthetics, in the world of art and photography, refers to the principles of the nature and appreciation of beauty. Judging beauty and other aesthetic qualities of photographs is a highly subjective task. Hence, there is no unanimously agreed standard for measuring aesthetic value. In spite of the lack of firm rules, certain features in photographic images are believed, by many, to please humans more than certain others. In this paper, we treat the challenge of automatically inferring aesthetic quality of pictures using their visual content as a machine learning problem, with a peer-rated online photo sharing Website as data source. We extract certain visual features based on the intuition that they can discriminate between aesthetically pleasing and displeasing images. Automated classifiers are built using support vector machines and classification trees. Linear regression on polynomial terms of the features is also applied to infer numerical aesthetics ratings. The work attempts to explore the relationship between emotions which pictures arouse in people, and their low-level content. Potential applications include content-based image retrieval and digital photography.


title: Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning

abstract: Many real-world problems, such as network packet routing and urban traffic control, are naturally modeled as multi-agent reinforcement learning (RL) problems. However, existing multi-agent RL methods typically scale poorly in the problem size. Therefore, a key challenge is to translate the success of deep learning on single-agent RL to the multi-agent setting. A major stumbling block is that independent Q-learning, the most popular multi-agent RL method, introduces nonstationarity that makes it incompatible with the experience replay memory on which deep Q-learning relies. This paper proposes two methods that address this problem: 1) using a multi-agent variant of importance sampling to naturally decay obsolete data and 2) conditioning each agent's value function on a fingerprint that disambiguates the age of the data sampled from the replay memory. Results on a challenging decentralised variant of StarCraft unit micro-management confirm that these methods enable the successful combination of experience replay with multi-agent RL.


Mobile Augmented Reality Art and the Politics of Re-assembly


title: Extinguishing Ransomware - A Hybrid Approach to Android Ransomware Detection

abstract: Mobile ransomware is on the rise and effective defense from it is of utmost importance to guarantee security of mobile users’ data. Current solutions provided by antimalware vendors are signature-based and thus ineffective in removing ransomware and restoring the infected devices and files. Also, current state-of-the art literature offers very few solutions to effectively detecting and blocking mobile ransomware. Starting from these considerations, we propose a hybrid method able to effectively counter ransomware. The proposed method first examines applications to be used on a device prior to their installation (static approach) and then observes their behavior at runtime and identifies if the system is under attack (dynamic approach). To detect ransomware, the static detection method uses the frequency of opcodes while the dynamic detection method considers CPU usage, memory usage, network usage and system call statistics. We evaluate the performance of our hybrid detection method on a dataset that contains both ransomware and legitimate applications. Additionally, we evaluate the performance of the static and dynamic stand-alone methods for comparison. Our results show that although both static and dynamic detection methods perform well in detecting ransomware, their combination in a form of a hybrid method performs best, being able to detect ransomware with 100% precision and having a false positive rate of less than 4%.


title: Zipf’s Law in Passwords

abstract: Despite three decades of intensive research efforts, it remains an open question as to what is the underlying distribution of user-generated passwords. In this paper, we make a substantial step forward toward understanding this foundational question. By introducing a number of computational statistical techniques and based on 14 large-scale data sets, which consist of 113.3 million real-world passwords, we, for the first time, propose two Zipf-like models (i.e., PDF-Zipf and CDF-Zipf) to characterize the distribution of passwords. More specifically, our PDF-Zipf model can well fit the popular passwords and obtain a coefficient of determination larger than 0.97; our CDF-Zipf model can well fit the entire password data set, with the maximum cumulative distribution function (CDF) deviation between the empirical distribution and the fitted theoretical model being 0.49%~4.59% (on an average 1.85%). With the concrete knowledge of password distributions, we suggest a new metric for measuring the strength of password data sets. Extensive experimental results show the effectiveness and general applicability of the proposed Zipf-like models and security metric.


title: Fine-grained recognition without part annotations

abstract: Scaling up fine-grained recognition to all domains of fine-grained objects is a challenge the computer vision community will need to face in order to realize its goal of recognizing all object categories. Current state-of-the-art techniques rely heavily upon the use of keypoint or part annotations, but scaling up to hundreds or thousands of domains renders this annotation cost-prohibitive for all but the most important categories. In this work we propose a method for fine-grained recognition that uses no part annotations. Our method is based on generating parts using co-segmentation and alignment, which we combine in a discriminative mixture. Experimental results show its efficacy, demonstrating state-of-the-art results even when compared to methods that use part annotations during training.


title: Properties of Turbulence in the Very Local Interstellar Clouds

abstract: We have investigated the degree to which turbulence in the Very Local Interstellar Clouds resembles the highly-studied turbulence in the solar corona and the solar wind. The turbulence diagnostics for the Local Clouds are the absorption line widths measured along 32 lines of sight to nearby stars, yielding measurements for 53 absorption components (Redfield and Linsky 2004). We have tested whether the Local Cloud turbulence has the following properties of turbulence in the solar corona or the solar wind: (a) velocity fluctuations mainly perpendicular to the average magnetic field, (b) a temperature anisotropy in the sense that the perpendicular temperature is larger than the parallel temperature (or at least enhanced relative to expectation), and (c) an ion temperature which is dependent on the ion Larmor radius, in the sense that more massive ions have higher temperatures. Our analysis of the data does not show compelling evidence for any of these properties in Local Cloud turbulence, indicating possible differences with heliospheric plasmas. In the case of anisotropy of velocity fluctuations, although the expected observational signature is not seen, we cannot exclude the possibility of relatively high degrees of anisotropy (anisotropy parameter $\epsilon \sim 0.50 - 0.70$), if some other process in the the Local Clouds is causing variations in the turbulent line width from one line of sight to another. We briefly consider possible reasons for differences between coronal and solar wind turbulence and that in the Local Clouds.


title: Developing understanding of hacker language through the use of lexical semantics

abstract: The need for more research scrutinizing online hacker communities is a common suggestion in recent years. However, researchers and practitioners face many challenges when attempting to do so. In particular, they may encounter hacking-specific terms, concepts, tools, and other items that are unfamiliar and may be challenging to understand. For these reasons, we are motivated to develop an automated method for developing understanding of hacker language. We utilize the latest advancements in recurrent neural network language models (RNNLMs) to develop an unsupervised machine learning technique for learning hacker language. The selected RNNLM produces state-of-the-art word embeddings that are useful for understanding the relations between different hacker terms and concepts. We evaluate our work by testing the RNNLMs ability to learn relevant relations between known hacker terms. Results suggest that the latest work in RNNLMs can aid in modeling hacker language, providing promising direction for future research.


title: Vibration control of a nonlinear quarter-car active suspension system by reinforcement learning

abstract: This article presents the investigation of performance of a nonlinear quarter-car active suspension system with a stochastic real-valued reinforcement learning control strategy. As an example, a model of a quarter car with a nonlinear suspension spring subjected to excitation from a road profile is considered. The excitation is realised by the roughness of the road. The quarter-car model to be considered here can be approximately described as a nonlinear two degrees of freedom system. The experimental results indicate that the proposed active suspension system suppresses the vibrations greatly. A simulation of a nonlinear quarter-car active suspension system is presented to demonstrate the effectiveness and examine the performance of the learning control algorithm.


title: How to scale distributed deep learning?

abstract: Training time on large datasets for deep neural networks is the principal workflow bottleneck in a number of important applications of deep learning, such as object classification and detection in automatic driver assistance systems (ADAS). To minimize training time, the training of a deep neural network must be scaled beyond a single machine to as many machines as possible by distributing the optimization method used for training. While a number of approaches have been proposed for distributed stochastic gradient descent (SGD), at the current time synchronous approaches to distributed SGD appear to be showing the greatest performance at large scale. Synchronous scaling of SGD suffers from the need to synchronize all processors on each gradient step and is not resilient in the face of failing or lagging processors. In asynchronous approaches using parameter servers, training is slowed by contention to the parameter server. In this paper we compare the convergence of synchronous and asynchronous SGD for training a modern ResNet network architecture on the ImageNet classification problem. We also propose an asynchronous method, gossiping SGD, that aims to retain the positive features of both systems by replacing the all-reduce collective operation of synchronous training with a gossip aggregation algorithm. We find, perhaps counterintuitively, that asynchronous SGD, including both elastic averaging and gossiping, converges faster at fewer nodes (up to about 32 nodes), whereas synchronous SGD scales better to more nodes (up to about 100 nodes).


title: Company Relation Extraction from Web News Articles for Analyzing Industry Structure

abstract: It is indispensable to understand and analyze industry structure and company relations from documents, such as news articles, in order to make management decisions concerning supply chains, selection of business partners, etc. Analysis of company relations from news articles requires both a macro-viewpoint, e.g., overviewing competitor groups, and a micro-viewpoint, e.g., grasping the descriptions of the relationship between a specific pair of companies collaborating. Research has typically focused on only the macro-viewpoint, classifying each company pair into a specific relation type. In this paper, to support company relation analysis from both macro-and micro-viewpoints, we propose a method that extracts collaborative/competitive company pairs from individual sentences in Web news articles by applying a Markov logic network and gather extracted relations from each company pair. By this method, we are able not only to perform clustering of company pairs into competitor groups based on the dominant relations of each pair (macro-viewpoint) but also to know how each company pair is described in individual sentences (micro-viewpoint). We empirically confirmed that the proposed method is feasible through analysis of 4,661 Web news articles on the semiconductor and related industries.


title: STM32-based vehicle data acquisition system for Internet-of-Vehicles

abstract: As the new era of the Internet of Things(IoT) is driving the evolution of conventional Vehicle Ad-hoc Networks into the Internet of Vehicles(IoV), vehicles are equipped with different kinds of sensor and become a sensing node themselves in IoV. Consequently, complexity of the automotive electronic system inside the vehicles are daily increasing. To guarantee the safe operation of vehicles, it is of great significance to acquire vehicle data in real-time to realize the on-line diagnosis, cyber-security attacking detection and et al. In this paper, we propose to realize a STM32-based data acquisition system(DAS), where the vehicle data transferred on CAN networks are acquired through the OBD2(On-Broad Diagnosis) interface. And then, the acquired vehicle data are parsed and analyzed preliminarily according to the OBD2 protocol, and then shown on a LED displayer. Through the implementation of a prototype system, the feasibility and effectiveness of the proposed design of DAS is verified.


title: Relationships among values, achievement orientations, and attitudes in youth sport

abstract: This research examines the value-expressive function of attitudes and achievement goal theory in predicting moral attitudes. In Study 1, the Youth Sport Values Questionnaire (YSVQ; Lee, Whitehead, & Balchin, 2000) was modified to measure moral, competence, and status values. In Study 2, structural equation modeling on data from 549 competitors (317 males, 232 females) aged 12–15 years showed that moral and competence values predicted prosocial attitudes, whereas moral (negatively) and status values (positively) predicted antisocial attitudes. Competence and status values predicted task and ego orientation, respectively, and task and ego orientation partially mediated the effect of competence values on prosocial attitudes and of status values on antisocial attitudes, respectively. The role of sport values is discussed, and new research directions are proposed.


A Systematic Study of Neural Discourse Models for Implicit Discourse Relation.


title: Fast Marginal Likelihood Maximisation for Sparse Bayesian Models

abstract: The ‘sparse Bayesian’ modelling approach, as exemplified by the ‘relevance vector machine’, enables sparse classification and regression functions to be obtained by linearly-weighting a small number of fixed basis functions from a large dictionary of potential candidates. Such a model conveys a number of advantages over the related and very popular ‘support vector machine’, but the necessary ‘training’ procedure — optimisation of the marginal likelihood function — is typically much slower. We describe a new and highly accelerated algorithm which exploits recently-elucidated properties of the marginal likelihood function to enable maximisation via a principled and efficient sequential addition and deletion of candidate basis functions.


title: Anti-Localization Anonymous Routing for Delay Tolerant Network

abstract: This paper focuses on the problem of how to allow a source to send a message without revealing its physical location and proposes an anti-localization routing protocol, ALAR, to achieve anonymous delivery in Delay/Disruption Tolerant Networks. The objectives of ALAR are to minimize the probability of a data source being localized and to maximize the destination's probability of receiving the message. ALAR can protect the sender's location privacy through message fragmentation and forwarding each segment to different receivers. ALAR is validated on two real-world human mobility datasets. This study indicates that ALAR increases the sender's anonymity performance by over 81% in different adversary densities with a 5% reduction in delivery ratio.


title: AI Programmer: Autonomously Creating Software Programs Using Genetic Algorithms

abstract: In this paper, we present the first-of-its-kind machine learning (ML) system, called AI Programmer, that can automatically generate full software programs requiring only minimal human guidance. At its core, AI Programmer uses genetic algorithms (GA) coupled with a tightly constrained programming language that minimizes the overhead of its ML search space. Part of AI Programmer's novelty stems from (i) its unique system design, including an embedded, hand-crafted interpreter for efficiency and security and (ii) its augmentation of GAs to include instruction-gene randomization bindings and programming language-specific genome construction and elimination techniques. We provide a detailed examination of AI Programmer's system design, several examples detailing how the system works, and experimental data demonstrating its software generation capabilities and performance using only mainstream CPUs.


title: Facebook Use and Social Capital - A Longitudinal Study.

abstract: How does the use of social networking sites (SNSs) affect social capital offline and online? The increasing popularity of SNSs such as Facebook (FB) implies a significant change in the way we interact with others. In an extensive longitudinal study (N = 311), carried out in Norway between 2008 2010, we examined the relationship between FB use and social capital, with a focus on interpersonal interaction, including face-to-face (F2F) interaction. Our findings suggest that overall FB use does not significantly affect offline capital, despite a significant increase in the use of FB from 2008 to 2010, after controlling for age, gender and education. However, different types of communication strategies on FB correlate with various kinds of F2F interaction. Importantly, FB users who focus on FB communication with their close friends, also interact more frequently with their close friends F2F, compared to those using FB to communicate with "online strangers”. The results also indicate that FB has become an important tool for keeping in touch with family members and existing friends rather than forming new connections.


title: Effectiveness of web-based social sensing in health information dissemination-A review

abstract: Role and impact of social media in healthcare has moved to a centered-stage.Social media has been proved to be effective in disseminating health information.To foresee the impact of web-based tools on the outcome of healthcare interventions.To what extent ontology has been helpful in extracting relevant healthcare data. Social media has moved ahead of just being a tool for adolescent users to share their personal lives (messages, pictures) but has also been taken up by healthcare stakeholders to disseminate health information worldwide. The role of social media in healthcare industry and impact of patient engagement has moved to centered-stage and is continuously propelled by patient demand, mobile technology and mounting influence of the native digital generation. The pace with which the social activity is growing, it is important to foresee what impact these web-based platforms are having on the deliverance and outcome of healthcare interventions. This survey looks at how far social media has been prove to be effective in disseminating health information and to what extent ontological advancing approaches have been helpful in extracting relevant knowledge from sheer amount of healthcare data.


Venture Philanthropy: A Case Study of Three Nonprofit Organizations


title: Co-factorization machines: modeling user interests and predicting individual decisions in Twitter

abstract: Users of popular services like Twitter and Facebook are often simultaneously overwhelmed with the amount of information delivered via their social connections and miss out on much content that they might have liked to see, even though it was distributed outside of their social circle. Both issues serve as difficulties to the users and drawbacks to the services. Social media service providers can benefit from understanding user interests and how they interact with the service, potentially predicting their behaviors in the future. In this paper, we address the problem of simultaneously predicting user decisions and modeling users' interests in social media by analyzing rich information gathered from Twitter. The task differs from conventional recommender systems as the cold-start problem is ubiquitous, and rich features, including textual content, need to be considered. We build predictive models for user decisions in Twitter by proposing Co-Factorization Machines (CoFM), an extension of a state-of-the-art recommendation model, to handle multiple aspects of the dataset at the same time. Additionally, we discuss and compare ranking-based loss functions in the context of recommender systems, providing the first view of how they vary from each other and perform in real tasks. We explore an extensive set of features and conduct experiments on a real-world dataset, concluding that CoFM with ranking-based loss functions is superior to state-of-the-art methods and yields interpretable latent factors.


title: Applying a flexible microactuator to robotic mechanisms

abstract: A flexible microactuator (FMA) driven by an electropneumatic (or electrohydraulic) system has been developed. It has three degrees of freedom-pitch, yaw, and stretch-making it suitable for robotic mechanisms such as fingers, arms, or legs. It is made of fiber-reinforced rubber, and the mechanism is very simple, enabling miniature robots without conventional link mechanisms to be designed. Serially connected FMAs act as a miniature robot manipulator. The kinematics and control algorithm for this type of robot are presented. FMAs combined in parallel act as a multifingered robot hand, with each FMA representing a finger. An algorithm for the cooperative control for such FMAs, the stable region for holding, and its performance have been developed. >


title: Hidden in plain sight: Automatically identifying security requirements from natural language artifacts

abstract: Natural language artifacts, such as requirements specifications, often explicitly state the security requirements for software systems. However, these artifacts may also imply additional security requirements that developers may overlook but should consider to strengthen the overall security of the system. The goal of this research is to aid requirements engineers in producing a more comprehensive and classified set of security requirements by (1) automatically identifying security-relevant sentences in natural language requirements artifacts, and (2) providing context-specific security requirements templates to help translate the security-relevant sentences into functional security requirements. Using machine learning techniques, we have developed a tool-assisted process that takes as input a set of natural language artifacts. Our process automatically identifies security-relevant sentences in the artifacts and classifies them according to the security objectives, either explicitly stated or implied by the sentences. We classified 10,963 sentences in six different documents from healthcare domain and extracted corresponding security objectives. Our manual analysis showed that 46% of the sentences were security-relevant. Of these, 28% explicitly mention security while 72% of the sentences are functional requirements with security implications. Using our tool, we correctly predict and classify 82% of the security objectives for all the sentences (precision). We identify 79% of all security objectives implied by the sentences within the documents (recall). Based on our analysis, we develop context-specific templates that can be instantiated into a set of functional security requirements by filling in key information from security-relevant sentences. Index Terms— Security, requirements, objectives, templates, access control, auditing, text classification, constraints, natural language parsing.


title: Velocity and Acceleration Estimation for Optical Incremental Encoders

abstract: Abstract Optical incremental encoders are extensively used for position measurements in motion systems. The position measurements suffer from quantization errors. Velocity and acceleration estimations obtained by numerical differentiation largely amplify the quantization errors. In this paper, the time stamping concept is used to obtain more accurate position, velocity and acceleration estimations. Time stamping makes use of stored events, consisting of the encoder counts and their time instants, captured at a high resolution clock. Encoder imperfections and the limited resolution of the capturing rate of the encoder events result in errors in the estimations. In this paper, we propose a method to extend the observation interval of the stored encoder events using a skip operation. Experiments on a motion system show that the velocity estimation is improved by 54% and the acceleration estimation by 92%.


Constructed Identities and Perpetuated Inequalities in App Dating


title: Latent Space Oddity: on the Curvature of Deep Generative Models

abstract: Deep generative models provide a systematic way to learn nonlinear data distributions, through a set of latent variables and a nonlinear "generator" function that maps latent points into the input space. The nonlinearity of the generator imply that the latent space gives a distorted view of the input space. Under mild conditions, we show that this distortion can be characterized by a stochastic Riemannian metric, and demonstrate that distances and interpolants are significantly improved under this metric. This in turn improves probability distributions, sampling algorithms and clustering in the latent space. Our geometric analysis further reveals that current generators provide poor variance estimates and we propose a new generator architecture with vastly improved variance estimates. Results are demonstrated on convolutional and fully connected variational autoencoders, but the formalism easily generalize to other deep generative models.


title: Big data and extreme-scale computing : Pathways to Convergence-Toward a shaping strategy for a future software and data ecosystem for scientific inquiry

abstract: Over the past four years, the Big Data and Exascale Computing (BDEC) project organized a series of five international workshops that aimed to explore the ways in which the new forms of data-centric...


title: Privacy Invasions in Ubiquitous Computing

abstract: While recent surveys often cite people who have experienced some form of privacy invasion, the exact nature of such invasions remains elusive. Yet in order to build ubiquitous computing systems that will respect the privacy of the individual, it is crucial to understand when it is exactly that people feel their privacy has been invaded. This paper motivates why privacy is necessary, describes an approach called privacy boundaries that tries to capture the various reasons a certain flow of personal information is perceived threatening, and looks briefly at how ubiquitous computing intensifies these problems.


title: Using participatory activities with seniors to critique, build, and evaluate mobile phones

abstract: Mobile phones can provide a number of benefits to older people. However, most mobile phone designs and form factors are targeted at younger people and middle-aged adults. To inform the design of mobile phones for seniors, we ran several participatory activities where seniors critiqued current mobile phones, chose important applications, and built their own imagined mobile phone system. We prototyped this system on a real mobile phone and evaluated the seniors' performance through user tests and a real-world deployment. We found that our participants wanted more than simple phone functions, and instead wanted a variety of application areas. While they were able to learn to use the software with little difficulty, hardware design made completing some tasks frustrating or difficult. Based on our experience with our participants, we offer considerations for the community about how to design mobile devices for seniors and how to engage them in participatory activities.


title: Paired Regions for Shadow Detection and Removal

abstract: In this paper, we address the problem of shadow detection and removal from single images of natural scenes. Differently from traditional methods that explore pixel or edge information, we employ a region-based approach. In addition to considering individual regions separately, we predict relative illumination conditions between segmented regions from their appearances and perform pairwise classification based on such information. Classification results are used to build a graph of segments, and graph-cut is used to solve the labeling of shadow and nonshadow regions. Detection results are later refined by image matting, and the shadow-free image is recovered by relighting each pixel based on our lighting model. We evaluate our method on the shadow detection dataset in Zhu et al. . In addition, we created a new dataset with shadow-free ground truth images, which provides a quantitative basis for evaluating shadow removal. We study the effectiveness of features for both unary and pairwise classification.


title: CRAFT Objects from Images

abstract: Object detection is a fundamental problem in image understanding. One popular solution is the R-CNN framework and its fast versions. They decompose the object detection problem into two cascaded easier tasks: 1) generating object proposals from images, 2) classifying proposals into various object categories. Despite that we are handling with two relatively easier tasks, they are not solved perfectly and there's still room for improvement. In this paper, we push the "divide and conquer" solution even further by dividing each task into two sub-tasks. We call the proposed method "CRAFT" (Cascade Region-proposal-network And FasT-rcnn), which tackles each task with a carefully designed network cascade. We show that the cascade structure helps in both tasks: in proposal generation, it provides more compact and better localized object proposals; in object classification, it reduces false positives (mainly between ambiguous categories) by capturing both inter- and intra-category variances. CRAFT achieves consistent and considerable improvement over the state-of-the-art on object detection benchmarks like PASCAL VOC 07/12 and ILSVRC.


title: Biomechanical Design of a Novel Flexible Exoskeleton for Lower Extremities

abstract: This paper presents a novel wearable walking assistance device for the elderly to provide physical gait assistance. The wearable device, to be worn invisibly underneath clothes, not only fits tightly to the wearer's lower body, but also comfortably transmits assistive torque to the wearer's hip/knee joint. To overcome deficiencies in the usability of conventional wearable walking assistance devices, two novel features are proposed: 1) a unique flexible support frame that withstands vertical loads while maintaining the natural curvature of the wearer's lower body; and 2) a kinematically similar anthropomorphic joint that minimizes the unintended resistance force during the wearer's usual motions. The developed wearable walking assistance device based on these features was verified by simulations and experiments whose results are described.


title: LiDAR-Camera Calibration using 3D-3D Point correspondences

abstract: With the advent of autonomous vehicles, LiDAR and cameras have become an indispensable combination of sensors. They both provide rich and complementary data which can be used by various algorithms and machine learning to sense and make vital inferences about the surroundings. We propose a novel pipeline and experimental setup to find accurate rigid-body transformation for extrinsically calibrating a LiDAR and a camera. The pipeling uses 3D-3D point correspondences in LiDAR and camera frame and gives a closed form solution. We further show the accuracy of the estimate by fusing point clouds from two stereo cameras which align perfectly with the rotation and translation estimated by our method, confirming the accuracy of our method's estimates both mathematically and visually. Taking our idea of extrinsic LiDAR-camera calibration forward, we demonstrate how two cameras with no overlapping field-of-view can also be calibrated extrinsically using 3D point correspondences. The code has been made available as open-source software in the form of a ROS package, more information about which can be sought here: https://github.com/ankitdhall/lidar_camera_calibration .


title: Savoring the Past: Positive Memories Evoke Value Representations in the Striatum

abstract: SUMMARY Reminders of happy memories can bring back pleasant feelings tied to the original experience, suggesting an intrinsic value in reminiscing about the positive past. However, the neural circuitry underlying the rewarding aspects of autobiographical memory is poorly understood. Using fMRI, we observed enhanced activity during the recall of positive relative to neutral autobiographical memories in corticostriatal circuits that also responded to monetary reward. Enhanced activity in the striatum and medial prefrontal cortex was associated with increases in positive emotion during recall, and striatal engagement further correlated with individual measures of resiliency. Striatal response to the recall of positive memories was greater in individuals whose mood improved after the task. Notably, participants were willing to sacrifice a more tangible reward, money, in order to reminisce about positive past experiences. Our findings suggest that recalling positive autobiographical memories is intrinsically valuable, which may be adaptive for regulating positive emotion and promoting better well-being.


title: A Wireless Sensor Network for Distribution Bus Protection

abstract: A wireless sensor network (WSN) for distribution bus protection is presented. Architecture of the WSN and bus protection principles are presented in this paper. Based on the traveling-wave generating in power system when a fault occurs, a novel time synchronization scheme is presented. Simulation results indicate that the scheme can acquire US synchronization time and reduce the energy consumption.


title: Interactive 3D modeling of indoor environments with a consumer depth camera

abstract: Detailed 3D visual models of indoor spaces, from walls and floors to objects and their configurations, can provide extensive knowledge about the environments as well as rich contextual information of people living therein. Vision-based 3D modeling has only seen limited success in applications, as it faces many technical challenges that only a few experts understand, let alone solve. In this work we utilize (Kinect style) consumer depth cameras to enable non-expert users to scan their personal spaces into 3D models. We build a prototype mobile system for 3D modeling that runs in real-time on a laptop, assisting and interacting with the user on-the-fly. Color and depth are jointly used to achieve robust 3D registration. The system offers online feedback and hints, tolerates human errors and alignment failures, and helps to obtain complete scene coverage. We show that our prototype system can both scan large environments (50 meters across) and at the same time preserve fine details (centimeter accuracy). The capability of detailed 3D modeling leads to many promising applications such as accurate 3D localization, measuring dimensions, and interactive visualization.


title: Surface lightness influences perceived room height

abstract: Surprisingly little scientific research has been conducted on the effects of colour and lightness on the perception of spaciousness. Practitioners and architects typically suggest that a room's ceiling appears higher when it is painted lighter than the walls, while darker ceilings appear lower. Employing a virtual reality setting, we studied the effects of the lightness of different room surfaces on perceived height in two psychophysical experiments. Observers judged the height of rooms varying in physical height as well as in the lightness of ceiling, floor, and walls. Experiment 1 showed the expected increase of perceived height with increases in ceiling lightness. Unexpectedly, the perceived height additionally increased with wall lightness, and the effects of wall lightness and ceiling lightness were roughly additive, incompatible with a simple effect of the lightness contrast between the ceiling and the walls. Experiment 2 demonstrated that the floor lightness has no significant effect on perceived h...


A Linked Data wrapper for CrunchBase


title: Bifidobacteria: the model human gut commensal.

abstract: This chapter discusses biology of bifidobacteria for probiotic and replacement therapy. Bifidobacteria were originally isolated from a breast-fed infant. Since then several species have been identified in the gastrointestinal tract (GIT) of mammals and insects. The availability of whole-genome sequences from bifidobacteria is extremely informative for the understanding of the processes underlying speciation and evolution in this genus as well as the adaptation to its specific habitat (e.g., the human intestine). Since bifidobacteria encounter a vast array of substrates in the GIT, information regarding their metabolic properties is crucial to formulate an optimal growth medium. Prebiotics are food substances which resist digestion in the proximal GIT and reach intact the distal region of the gut, where, besides their direct physiological effect, they also affect the GIT ecosystem by specifically stimulating the growth and activity of the probiotic components of the GIT microbiota (e.g., bifidobacteria), thereby eliciting a beneficial effect on the host’s health. Prebiotics are typically oligosaccharides that consist of a mixture of hexose oligomers with a variable extent of polymerization. The transmission of novel genes through horizontal gene transfer (HGT) is generally supported by vectors such as bacteriophages or plasmids which may become integrated into the bacterial chromosome. Generally, plasmids are rarely found in bifidobacteria. So far the majority of bifidobacterial plasmids have been identified within different B. longum biotype longum strains. The chapter ends with a discussion on taxonomy of bifidobacteria.


ProximiTEE: Hardened SGX Attestation and Trusted Path through Proximity Verification


title: Lotus-Eaters, Pilgrims, Seekers, and Accidental Tourists: How Different Travelers Consume the Sacred and the Profane

abstract: Travelers visit destinations that are associated with organized religion for a variety of reasons. Building on Cohen's (2003) work, we categorize visitors to “religious” travel destinations as: (1) seekers who intend to visit both religious and secular tourist sites, (2) lotus-eaters who intend to visit only secular tourist sites, (3) pilgrims who intend to visit only religious tourist sites, and (4) accidental tourists who intend to visit neither type of tourist site. We use these four types of tourists to accomplish three aims: (1) explain each type of traveler, (2) explain the interaction between religious and secular elements at travel sites, and (3) provide guidelines for attracting each type of traveler.


title: Robust Machine Learning Systems: Reliability and Security for Deep Neural Networks

abstract: Machine learning is commonly being used in almost all the areas that involve advanced data analytics and intelligent control. From applications like Natural Language Processing (NLP) to autonomous driving are based upon machine learning algorithms. An increasing trend is observed in the use of Deep Neural Networks (DNNs) for such applications. While the slight inaccuracy in applications like NLP does not have any severe consequences, it is not the same for other safety-critical applications, like autonomous driving and smart healthcare, where a small error can lead to catastrophic effects. Apart from high-accuracy DNN algorithms, there is a significant need for robust machine learning systems and hardware architectures that can generate reliable and trustworthy results in the presence of hardware-level faults while also preserving security and privacy. This paper provides an overview of the challenges being faced in ensuring reliable and secure execution of DNNs. To address the challenges, we present several techniques for analyzing and mitigating the reliability and security threats in machine learning systems.


title: 75–110-GHz $W$ -band High-Linearity Traveling-Wave T/R Switch by Using Negative Gate/Body-Biasing in 90-nm CMOS

abstract: In this letter, a 75–110-GHz W-band high-linearity traveling-wave single-pole-double-throw switch in 90-nm CMOS GUTM 1P9M is presented. Compared with the traditional traveling-wave switch with negative body-biasing technique, the proposed design applies the negative biasing to the gate terminals simultaneously. Therefore, the third-order intermodulation product is significantly reduced. The measured results show that the insertion loss is less than 3.4 dB, return loss is better than 10 dB, and Tx-to-Rx isolation is higher than 40 dB, respectively. For the linearity of the switch, the measured IP1dB is higher than 15 dBm and IIP3 is 37.1 dBm at 94 GHz, respectively. The proposed switch presents good performance of insertion loss, isolation, IP1dB, and IIP3, among the compared millimeter-wave CMOS switches.


Investigating the interaction between personalities and the benefit of gamification


LEADERSHIP STYLES OF NURSE MANAGERS AND THEIR EFFECTS ON NURSE AND ORGANISATIONAL PERFORMANCE , ISSUES AND PROBLEMS *


title: Vibrations in automotive radar systems

abstract: In the automotive advanced driver assistance systems market there is an increasing trend towards hidden installation of radar sensors. This rises new challenges concerning the sensor's mounting position, fixture and cover. In this paper several types of vibration, which can occur in an automotive environment are classified and their influence on the radar sensor's performance is discussed. A detailed analysis combined with measurement results is given to vibration-induced errors, caused by a relative movement between the radar sensor and its cover.


title: Integrating Environmental Management and Supply Chain Strategies

abstract: The decisions related to managing the supply chain and supply chain strategy are already considered important in many organizations. As more executives adopt environmental practices, supply chain strategies will only increase in importance. In this paper, we review how companies develop environmental supply chain strategies. Our interviews with companies from The United States, The United Kingdom, Japan and Korea, along with prior research, are used to develop a framework for environmental supply chain strategy decision-making. We then use this framework to suggest guidelines for how companies might change their current supply chain practices to successfully integrate environmental issues into their supply chain strategy. Copyright © 2005 John Wiley & Sons, Ltd and ERP Environment.


title: Do Risk Perceptions Influence Physician's Resistance to Use Electronic Medical Records? An Exploratory Research in German Hospitals

abstract: IT in health care can lower the cost of health care delivery, improve the quality of care for patients and reduce medical errors.Given these strong advantages, it is interesting that technology diffusion for process support in hospitals is somewhat slow.The major process of a hospital -delivering patient care- is still supported by traditional paper files in the vast majority ofGerman hospitals.In this paper, we ask what the barriers towards implementing and using an electronic medical record (EMR) -the electronicpatient file- might be. Technology resistance theories indicate that perceived risks are a major inhibitor towards systemsacceptance. In the absence of thorough empirical studies, we start our investigation by conducting exploratory research intothe risks hospital-based physicians associate with using an EMR. A list of possible risks was derived from the literature and10 physicians were interviewed to gather their assessment. Our findings show that, indeed, physicians associate several riskswith adopting EMRs, thereby suggesting these risks will need to be mitigated to enable proper user acceptance.


title: Etiological heterogeneity in autism spectrum disorders: More than 100 genetic and genomic disorders and still counting

abstract: There is increasing evidence that autism spectrum disorders (ASDs) can arise from rare highly penetrant mutations and genomic imbalances. The rare nature of these variants, and the often differing orbits of clinical and research geneticists, can make it difficult to fully appreciate the extent to which we have made progress in understanding the genetic etiology of autism. In fact, there is a persistent view in the autism research community that there are only a modest number of autism loci known. We carried out an exhaustive review of the clinical genetics and research genetics literature in an attempt to collate all genes and recurrent genomic imbalances that have been implicated in the etiology of ASD. We provide data on 103 disease genes and 44 genomic loci reported in subjects with ASD or autistic behavior. These genes and loci have all been causally implicated in intellectual disability, indicating that these two neurodevelopmental disorders share common genetic bases. A genetic overlap between ASD and epilepsy is also apparent in many cases. Taken together, these findings clearly show that autism is not a single clinical entity but a behavioral manifestation of tens or perhaps hundreds of genetic and genomic disorders. Increased recognition of the etiological heterogeneity of ASD will greatly expand the number of target genes for neurobiological investigations and thereby provide additional avenues for the development of pathway-based pharmacotherapy. Finally, the data provide strong support for high-resolution DNA microarrays as well as whole-exome and whole-genome sequencing as critical approaches for identifying the genetic causes of ASDs.