title: Does Performance Measurement Improve Strategic Decision Making? Findings From a National Survey of Nonprofit Social Service Agencies

abstract: Nonprofits have encountered increased pressures for accountability and performance in recent years, both from their funding entities as well as the public. The adoption of performance measurement systems assumes that managers will use performance information to make better decisions. However, little research has focused on performance information use in the nonprofit sector. This study seeks to address this gap in the literature. Using survey data from several hundred nonprofit social service organizations in the United States, this article examines the extent to which reliance on various performance measures improves strategic decision making within nonprofit organizations. Authors find a positive relationship between the range of performance measures used by nonprofits and their level of effectiveness in strategic decision making. Other factors that also contribute to strategic decision making within nonprofits include effective governance, funding diversity, and education level of the executive director.


title: Research on the key technologies of IOT applied on Smart Grid

abstract: Internet of Things and Smart Grid are very popular topics recently. In such a context, this paper makes analysis on the application requirements of IoT in Smart Grid combined with basic functions of IoT. Under the framework of Smart Grid, the structure and basic functions of IoT in Smart Grid have been proposed tentatively. IoT in Smart Grid is an intelligent network based on network technology, database technology, and communication technology and so on. Its application will significantly improve the power system's stability and security, as well as protect the environment and meet the sustainable development requirements. Moreover, the level of the power system will be promoted.


title: Distinguishing Between Natural and Computer-Generated Images Using Convolutional Neural Networks

abstract: Distinguishing between natural images (NIs) and computer-generated (CG) images by naked human eyes is difficult. In this paper, we propose an effective method based on a convolutional neural network (CNN) for this fundamental image forensic problem. Having observed the rather limited performance of training existing CCNs from scratch or fine-tuning pre-trained network, we design and implement a new and appropriate network with two cascaded convolutional layers at the bottom of a CNN. Our network can be easily adjusted to accommodate different sizes of input image patches while maintaining a fixed depth, a stable structure of CNN, and a good forensic performance. Considering the complexity of training CNNs and the specific requirement of image forensics, we introduce the so-called local-to-global strategy in our proposed network. Our CNN derives a forensic decision on local patches, and a global decision on a full-sized image can be easily obtained via simple majority voting. This strategy can also be used to improve the performance of existing methods that are based on hand-crafted features. Experimental results show that our method outperforms existing methods, especially in a challenging forensic scenario with NIs and CG images of heterogeneous origins. Our method also has good robustness against typical post-processing operations, such as resizing and JPEG compression. Unlike previous attempts to use CNNs for image forensics, we try to understand what our CNN has learned about the differences between NIs and CG images with the aid of adequate and advanced visualization tools.


A Data-Science-as-a-Service Model


title: Identifying salient sounds using dual-task experiments

abstract: Auditory saliency refers to the characteristics of a sound that cause it to attract the attention of a listener. Pre-attentive or bottom-up saliency has to do with automatic processing in the human auditory system that does not require and often precedes attention. Unlike visual saliency, where eye-tracking is a commonly used evaluation method, with auditory saliency, there is no easily trackable physical correlate that can be used for evaluation. Other auditory saliency models [1, 2] have been evaluated using tests that did not specifically target bottom-up saliency. In this paper, we present a method to conclusively isolate bottom-up auditory saliency. There are also several important applications to bottom-up saliency in auditory scene analysis, auditory display design and analysis, and speech processing.


title: Effectiveness of occupation-based interventions to improve areas of occupation and social participation after stroke: an evidence-based review.

abstract: This evidence-based review examined the evidence supporting the use of occupation-based interventions to improve areas of occupation and social participation poststroke. A total of 39 studies met the inclusion criteria and were critically evaluated. Most of the literature targeted activity of daily living (ADL)-based interventions and collectively provided strong evidence for the use of occupation-based interventions to improve ADL performance. The evidence related to instrumental ADLs was much more disparate, with limited evidence to support the use of virtual reality interventions and emerging evidence to support driver education programs to improve occupational performance poststroke. Only 6 studies addressed leisure, social participation, or rest and sleep, with sufficient evidence to support only leisure-based interventions. The implications of this review for research, education, and practice in occupational therapy are also discussed.


title: FLASH assembly of TALENs for high-throughput genome editing

abstract: Transcription activator–like effector nucleases (TALENs) enable genetic modification at specific sites in a genome. Reyon et al. present a method for high-throughput generation of TALENs, facilitating large-scale genome engineering.


title: Hidradenitis Suppurativa and Squamous Cell Carcinoma

abstract: BACKGROUND ::: Hidradenitis suppurativa is a recurrent, chronic inflammatory disease with complications such as abscesses and sinus tract formations, fistulization, and scarring. Squamous cell carcinoma is rare, and is the most serious complication. ::: ::: ::: OBJECTIVE ::: To present the evolution of squamous cell carcinoma in long-term hidradenitis suppurativa. ::: ::: ::: METHODS ::: Case report and literature review. ::: ::: ::: CONCLUSION ::: Many conservative therapies have no or minimal effect in perineal hidradenitis suppurativa. Therefore surgical excision must be considered in the early stages of the disease at this region to prevent further complications.


title: Novel Variable-Stiffness Robotic Fingers with Built-In Position Feedback

abstract: Despite the increasing popularity of soft robotic research, the application of soft robots is hindered by their limited ability to change compliance and acquire force and position feedback. In this article, both the controllability of compliance and the acquisition of position feedback are achieved in soft robotic fingers in the novel design of a three-dimensional (3D)-printed multismart material substrate. The substrate is composed of shape memory polymer (SMP) and conductive elastomer thermoplastic polyurethane (TPU). The SMP material is utilized to modulate the finger stiffness through its elastic modulus change around glass transition temperature (Tg). The conductive TPU has two functions: one is to tune SMP temperature by producing Joule heat when electrical power is supplied and the other is to provide position feedback of the finger by utilizing the piezoresistive effect of the conductive TPU. Theoretical modeling of finger position feedback and stiffness modulation are conducted. The theoretical analysis has been experimentally validated by a prototype robotic finger built from the proposed concept.


title: Empirical Study of Artificial Fish Swarm Algorithm

abstract: Artificial fish swarm algorithm (AFSA) is one of the swarm intelligence optimization algorithms that works based on population and stochastic search. In order to achieve acceptable result, there are many parameters needs to be adjusted in AFSA. Among these parameters, visual and step are very significant in view of the fact that artificial fish basically move based on these parameters. In standard AFSA, these two parameters remain constant until the algorithm termination. Large values of these parameters increase the capability of algorithm in global search, while small values improve the local search ability of the algorithm. In this paper, we empirically study the performance of the AFSA and different approaches to balance between local and global exploration have been tested based on the adaptive modification of visual and step during algorithm execution. The proposed approaches have been evaluated based on the four well-known benchmark functions. Experimental results show considerable positive impact on the performance of AFSA.


title: The Chemopotential Effect of Annona muricata Leaves against Azoxymethane-Induced Colonic Aberrant Crypt Foci in Rats and the Apoptotic Effect of Acetogenin Annomuricin E in HT-29 Cells: A Bioassay-Guided Approach

abstract: Annona muricata has been used in folk medicine for the treatment of cancer and tumors. This study evaluated the chemopreventive properties of an ethyl acetate extract of A. muricata leaves (EEAML) on azoxymethane-induced colonic aberrant crypt foci (ACF) in rats. Moreover, the cytotoxic compound of EEAML (Annomuricin E) was isolated, and its apoptosis-inducing effect was investigated against HT-29 colon cancer cell line using a bioassay-guided approach. This experiment was performed on five groups of rats: negative control, cancer control, EEAML (250 mg/kg), EEAML (500 mg/kg) and positive control (5-fluorouracil). Methylene blue staining of colorectal specimens showed that application of EEAML at both doses significantly reduced the colonic ACF formation compared with the cancer control group. Immunohistochemistry analysis showed the down-regulation of PCNA and Bcl-2 proteins and the up-regulation of Bax protein after administration of EEAML compared with the cancer control group. In addition, an increase in the levels of enzymatic antioxidants and a decrease in the malondialdehyde level of the colon tissue homogenates were observed, suggesting the suppression of lipid peroxidation. Annomuricin E inhibited the growth of HT-29 cells with an IC50 value of 1.62 ± 0.24 μg/ml after 48 h. The cytotoxic effect of annomuricin E was further substantiated by G1 cell cycle arrest and early apoptosis induction in HT-29 cells. Annomuricin E triggered mitochondria-initiated events, including the dissipation of the mitochondrial membrane potential and the leakage of cytochrome c from the mitochondria. Prior to these events, annomuricin E activated caspase 3/7 and caspase 9. Upstream, annomuricin E induced a time-dependent upregulation of Bax and downregulation of Bcl-2 at the mRNA and protein levels. In conclusion, these findings substantiate the usage of A. muricata leaves in ethnomedicine against cancer and highlight annomuricin E as one of the contributing compounds in the anticancer activity of A. muricata leaves.


title: A Probabilistic Address Parser Using Conditional Random Fields and Stochastic Regular Grammar

abstract: Automatic semantic annotation of data from databases or the web is an important pre-process for data cleansing and record linkage. It can be used to resolve the problem of imperfect field alignment in a database or identify comparable fields for matching records from multiple sources. The annotation process is not trivial because data values may be noisy, such as abbreviations, variations or misspellings. In particular, overlapping features usually exist in a lexicon-based approach. In this work, we present a probabilistic address parser based on linear-chain conditional random fields (CRFs), which allow more expressive token-level features compared to hidden Markov models (HMMs). In additions, we also proposed two general enhancement techniques to improve the performance. One is taking original semi-structure of the data into account. Another is post-processing of the output sequences of the parser by combining its conditional probability and a score function, which is based on a learned stochastic regular grammar (SRG) that captures segment-level dependencies. Experiments were conducted by comparing the CRF parser to a HMM parser and a semi-Markov CRF parser in two real-world datasets. The CRF parser out-performed the HMM parser and the semi-Markov CRF in both datasets in terms of classification accuracy. Leveraging the structure of the data and combining the linear-chain CRF with the SRG further improved the parser to achieve an accuracy of 97% on a postal dataset and 96% on a company dataset.


title: Forecasting the onset and course of mental illness with Twitter data

abstract: We developed computational models to predict the emergence of depression and Post-Traumatic Stress Disorder in Twitter users. Twitter data and details of depression history were collected from 204 individuals (105 depressed, 99 healthy). We extracted predictive features measuring affect, linguistic style, and context from participant tweets (N = 279,951) and built models using these features with supervised learning algorithms. Resulting models successfully discriminated between depressed and healthy content, and compared favorably to general practitioners’ average success rates in diagnosing depression, albeit in a separate population. Results held even when the analysis was restricted to content posted before first depression diagnosis. State-space temporal analysis suggests that onset of depression may be detectable from Twitter data several months prior to diagnosis. Predictive results were replicated with a separate sample of individuals diagnosed with PTSD (Nusers = 174, Ntweets = 243,775). A state-space time series model revealed indicators of PTSD almost immediately post-trauma, often many months prior to clinical diagnosis. These methods suggest a data-driven, predictive approach for early screening and detection of mental illness.


IEEE 802.11i WLAN Security Protocol – A Software Engineer’s Model


title: A model of smooth pursuit in primates based on learning the target dynamics

abstract: While the predictive nature of the primate smooth pursuit system has been evident through several behavioural and neurophysiological experiments, few models have attempted to explain these results comprehensively. The model we propose in this paper in line with previous models employing optimal control theory; however, we hypothesize two new issues: (1) the medical superior temporal (MST) area in the cerebral cortex implements a recurrent neural network (RNN) in order to predict the current or future target velocity, and (2) a forward model of the target motion is acquired by on-line learning. We use stimulation studies to demonstrate how our new model supports these hypotheses.


title: Towards unifying diffusion and exemplar-based inpainting

abstract: A novel framework for image inpainting is proposed, relying on graph-based diffusion processes. Depending on the construction of the graph, both flow-based and exemplar-based inpainting methods can be implemented by the same equations, hence providing a unique framework for geometry and texture-based approaches to inpainting. Furthermore, the use of a variational framework allows to overcome the usual sensitivity of exemplar-based methods to the heuristic issues by providing an evolution criterion. The use of graphs also makes our framework more flexible than former nonlocal variational formulations, allowing for example to mix spatial and non-local constraints and to use a data term to provide smoother blending between the initial image and the result.


title: Critical Success Factors of Total Quality Management and their impact on Performance of Iranian Automotive Industry

abstract: Th is paper shows a model to conduct an empirical study in Iranian automotive industry in order to improve their performance. There are many factors which are effective factors in improving performance of Iranian automobile industry namely, leadership, customer focus, training, supplier quality management, product design, process management, and team work. The quality improvement plays a fundamental role in determining the performance in Iranian manufacturing industries. In this research, a model has been developed that includes Quality culture, Critical success factors of Total Quality Management and quality improvement to study their influence on the performance of Iranian automotive industry. It is hoped that this paper can provide an academic source for both academicians and managers due to investigate the relationship between Quality culture, critical success factors of Total Quality Management, Quality improvement, and Performance in a systematic manner to increase successful rate of Total Quality Management implementation.


What is Social Informatics and Why Does it Matter?


title: Societal impacts of big data: challenges and opportunities in Europe

abstract: This paper presents the risks and opportunities of big data and the potential social benefits it can bring. The research is based on an analysis of the societal impacts observed in a set of six case studies across different European sectors. These impacts are divided into economic, social and ethical, legal and political impacts, and affect areas such as improved efficiency, innovation and decision making, changing business models, dependency on public funding, participation, equality, discrimination and trust, data protection and intellectual property rights, private and public tensions and losing control to actors abroad. A special focus is given to the risks and opportunities coming from the legal framework and how to counter the negative impacts of big data. Recommendations are presented for four specific legal frameworks: copyright and database protection, protection of trade secrets, privacy and data protection and anti-discrimination. In addition, the potential social benefits of big data are exemplified in six domains: improved decision making and event detection; data-driven innovations and new business models; direct social, environmental and other citizen benefits; citizen participation, transparency and public trust; privacy-aware data practices; and big data for identifying discrimination. Several best practices are suggested to capture these benefits.


title: Indoor Motion Classification Using Passive RF Sensing Incorporating Deep Learning

abstract: This paper proposes a method of recognizing and classifying the basic activities such as forward and backward motions by applying a deep learning framework on passive radio frequency (RF) signals. The echoes from the moving body possess unique pattern which can be used to recognize and classify the activity. A passive RF sensing test- bed is set up with two channels where the first one is the reference channel providing the un- altered echoes of the transmitter signals and the other one is the surveillance channel providing the echoes of the transmitter signals reflecting from the moving body in the area of interest. The echoes of the transmitter signals are eliminated from the surveillance signals by performing adaptive filtering. The resultant time series signal is classified into different motions as predicted by proposed novel method of convolutional neural network (CNN). Extensive amount of training data has been collected to train the model, which serves as a reference benchmark for the later studies in this field.


title: Towards End-to-End Face Recognition through Alignment Learning

abstract: Plenty of effective methods have been proposed for face recognition during the past decade. Although these methods differ essentially in many aspects, a common practice of them is to specifically align the facial area based on the prior knowledge of human face structure before feature extraction. In most systems, the face alignment module is implemented independently. This has actually caused difficulties in the designing and training of end-to-end face recognition models. In this paper we study the possibility of alignment learning in end-to-end face recognition, in which neither prior knowledge on facial landmarks nor artificially defined geometric transformations are required. Specifically, spatial transformer layers are inserted in front of the feature extraction layers in a Convolutional Neural Network (CNN) for face recognition. Only human identity clues are used for driving the neural network to automatically learn the most suitable geometric transformation and the most appropriate facial area for the recognition task. To ensure reproducibility, our model is trained purely on the publicly available CASIA-WebFace dataset, and is tested on the Labeled Face in the Wild (LFW) dataset. We have achieved a verification accuracy of 99.08\% which is comparable to state-of-the-art single model based methods.


title: A Cascaded Microsecond-Pulse Generator for Discharge Applications

abstract: Gas discharges using pulsed power are a promising and efficient approach for producing low-temperature plasmas at atmospheric pressure. Pulsed power generators vary widely in performance and should be chosen according to the load and application requirements. In this paper, a microsecond-pulselength high voltage (HV) generator is developed for atmospheric-pressure plasma jets that use a cascade-type voltage circuit. The electrical parameters including voltage amplitude, pulse repetition frequency, and pulsewidth, are determined by the trigger system. The voltage amplitude can be up to 10 kV and the pulse repetition frequency varies from 1 Hz to 5 kHz. The unipolar output pulse can be either positive or negative, with either square or triangular wave profile that can also be controlled by the optical trigger system. When the output pulse is a square wave, the rising edge is about 100 ns, the falling edge is approximately \(2~\mu \) s, and the pulsewidth at the top varies from 0.5 to 30 \(\mu \) s. When the output pulse is a triangular wave, the base of the pulse is from 7 to 62 \(\mu \) s, and the stepped rising edge is from 5 to 30 \(\mu \) s. The HV pulse generator is used for producing helium plasma jets into open air. Preliminary experimental data show the effects of the pulse voltage amplitude, pulse repetition frequency, and pulsewidth on plasma jets, and confirm that the generator can provide a good performance for driving cold plasma jets.


title: Phonological Factors in Social Media Writing

abstract: Does phonological variation get transcribed into social media text? This paper investigates examples of the phonological variable of consonant cluster reduction in Twitter. Not only does this variable appear frequently, but it displays the same sensitivity to linguistic context as in spoken language. This suggests that when social media writing transcribes phonological properties of speech, it is not merely a case of inventing orthographic transcriptions. Rather, social media displays influence from structural properties of the phonological system.


title: A general divide and conquer approach for process mining

abstract: Operational processes leave trails in the information systems supporting them. Such event data are the starting point for process mining - an emerging scientific discipline relating modeled and observed behavior. The relevance of process mining is increasing as more and more event data become available. The increasing volume of such data (“Big Data”) provides both opportunities and challenges for process mining. In this paper we focus on two particular types of process mining: process discovery (learning a process model from example behavior recorded in an event log) and conformance checking (diagnosing and quantifying discrepancies between observed behavior and modeled behavior). These tasks become challenging when there are hundreds or even thousands of different activities and millions of cases. Typically, process mining algorithms are linear in the number of cases and exponential in the number of different activities. This paper proposes a very general divide-and-conquer approach that decomposes the event log based on a partitioning of activities. Unlike existing approaches, this paper does not assume a particular process representation (e.g., Petri nets or BPMN) and allows for various decomposition strategies (e.g., SESE- or passage-based decomposition). Moreover, the generic divide-and-conquer approach reveals the core requirements for decomposing process discovery and conformance checking problems.


title: Understanding determinants of cloud computing adoption using an integrated TAM-TOE model

abstract: Purpose – The purpose of this paper is to integrate TAM model and TOE framework for cloud computing adoption at organizational level. Design/methodology/approach – A conceptual framework was developed using technological and organizational variables of TOE framework as external variables of TAM model while environmental variables were proposed to have direct impact on cloud computing adoption. A questionnaire was used to collect the data from 280 companies in IT, manufacturing and finance sectors in India. The data were analyzed using exploratory and confirmatory factor analyses. Further, structural equation modeling was used to test the proposed model. Findings – The study identified relative advantage, compatibility, complexity, organizational readiness, top management commitment, and training and education as important variables for affecting cloud computing adoption using perceived ease of use (PEOU) and perceived usefulness (PU) as mediating variables. Also, competitive pressure and trading partner s...


RELWALK – A LATENT VARIABLE MODEL APPROACH


title: Cyberbullying and Primary-School Aged Children: The Psychological Literature and the Challenge for Sociology

abstract: Cyberbullying is an international issue for schools, young people and their families. Whilst many research domains have explored this phenomenon, and bullying more generally, the majority of reported studies appear in the psychological and educational literatures, where bullying, and more recently, cyberbullying has been examined primarily at the individual level: amongst adolescents and young people, with a focus on the definition, its prevalence, behaviours, and impact. There also is growing evidence that younger children are increasingly accessing technology and engaging with social media, yet there is limited research dedicated to this younger age group. The purpose of this paper is to report on a systematic literature review from the psychological and educational research domains related to this younger age group, to inform future research across the disciplines. Younger children require different methods of engagement. This review highlights the methodological challenges associated with this age group present in the psychological literature, and argues for a greater use of sociological, child-centred approaches to data collection. This review examined studies published in English, between 2009 and 2014, and conducted with children aged 5–12 years, about their experiences with cyberbullying. Searches were conducted on seven key databases using keywords associated with cyberbullying and age of children. A Google Scholar search also examined published and unpublished reports. A total of 966 articles and reports were retrieved. A random peer review process was employed to establish inter-rater reliability and veracity of the review. Findings revealed 38 studies reported specifically on children aged 5–12 years. The dominant focus of these articles was on prevalence of cyberbullying, established through survey methodology. Few studies noted impacts, understanding and behaviours or engaged children’s independent voice. This review highlights current gaps in our knowledge about younger children’s experiences with this form of bullying, and the importance of employing cross-disciplinary and developmentally appropriate methodologies to inform future research.


title: Show, Attend, and Translate: Unsupervised Image Translation With Self-Regularization and Attention

abstract: Image translation between two domains is a class of problems aiming to learn mapping from an input image in the source domain to an output image in the target domain. It has been applied to numerous applications, such as data augmentation, domain adaptation, and unsupervised training. When paired training data is not accessible, image translation becomes an ill-posed problem. We constrain the problem with the assumption that the translated image needs to be perceptually similar to the original image and also appears to be drawn from the new domain, and propose a simple yet effective image translation model consisting of a single generator trained with a self-regularization term and an adversarial term. We further notice that the existing image translation techniques are agnostic to the subjects of interest and often introduce unwanted changes or artifacts to the input. Thus, we propose to add an attention module to predict an attention map to guide the image translation process. The module learns to attend to key parts of the image while keeping everything else unaltered, essentially avoiding undesired artifacts or changes. Extensive experiments and evaluations show that our model while being simpler, achieves significantly better performance than existing image translation methods.


title: Short-Term Load Forecasting With Seasonal Decomposition Using Evolution for Parameter Tuning

abstract: This paper studies data-driven short-term load forecasting, where historic data are used to predict the expected load for the next 24 h. Our focus is to simplify and automate the estimation and analysis of various forecasting models. We propose a three-stage approach to load forecasting, consisting of preprocessing, forecasting, and postprocessing, where the forecasting stage uses evolution to automatically set the parameters for each model. In our implementation, the preprocessing stage includes removal of daily and weekly seasonality by a nonparametric method. This seasonal pattern is added in the postprocessing stage. The system allows for easy exploration of several forecasting models, without the need to have in-depth knowledge of how to obtain the best performance for each model. We apply the method to several forecasting algorithms and on three datasets: 1) distribution substation; 2) GEFCom 2012; and 3) a transmission level dataset. We find that the forecasting algorithms considered produce significantly more accurate forecasts when combined with our proposed preprocessing stage compared with applying the same algorithms directly on the raw data. We also find that the parameter values chosen by evolution often provide insights into the interplay between the different datasets and forecast models. Software is available online.


title: Scaling agile software development through lean governance

abstract: Agile project teams are potentially easy to govern than traditional project teams due to the greater visibility and opportunities to steer provided to stakeholders. Unfortunately, traditional IT governance strategies prove to be at odds to agile's collaborative, value-focused strategies. As a result a lean approach based on enablement, collaboration, and motivation is required to effectively govern agile teams.


title: KBGAN: Adversarial Learning for Knowledge Graph Embeddings

abstract: We introduce an adversarial learning framework, which we named KBGAN, to improve the performances of a wide range of existing knowledge graph embedding models. Because knowledge graph datasets typically only contain positive facts, sampling useful negative training examples is a non-trivial task. Replacing the head or tail entity of a fact with a uniformly randomly selected entity is a conventional method for generating negative facts used by many previous works, but the majority of negative facts generated in this way can be easily discriminated from positive facts, and will contribute little towards the training. Inspired by generative adversarial networks (GANs), we use one knowledge graph embedding model as a negative sample generator to assist the training of our desired model, which acts as the discriminator in GANs. The objective of the generator is to generate difficult negative samples that can maximize their likeliness determined by the discriminator, while the discriminator minimizes its training loss. This framework is independent of the concrete form of generator and discriminator, and therefore can utilize a wide variety of knowledge graph embedding models as its building blocks. In experiments, we adversarially train two translation-based models, TransE and TransD, each with assistance from one of the two probability-based models, DistMult and ComplEx. We evaluate the performances of KBGAN on the link prediction task, using three knowledge base completion datasets: FB15k-237, WN18 and WN18RR. Experimental results show that adversarial training substantially improves the performances of target embedding models under various settings.


title: Exploiting multi-channels deep convolutional neural networks for multivariate time series classification

abstract: Time series classification is related to many different domains, such as health informatics, finance, and bioinformatics. Due to its broad applications, researchers have developed many algorithms for this kind of tasks, e.g., multivariate time series classification. Among the classification algorithms, k-nearest neighbor (k-NN) classification (particularly 1-NN) combined with dynamic time warping (DTW) achieves the state of the art performance. The deficiency is that when the data set grows large, the time consumption of 1-NN with DTWwill be very expensive. In contrast to 1-NN with DTW, it is more efficient but less effective for feature-based classification methods since their performance usually depends on the quality of hand-crafted features. In this paper, we aim to improve the performance of traditional feature-based approaches through the feature learning techniques. Specifically, we propose a novel deep learning framework, multi-channels deep convolutional neural networks (MC-DCNN), for multivariate time series classification. This model first learns features from individual univariate time series in each channel, and combines information from all channels as feature representation at the final layer. Then, the learnt features are applied into a multilayer perceptron (MLP) for classification. Finally, the extensive experiments on real-world data sets show that our model is not only more efficient than the state of the art but also competitive in accuracy. This study implies that feature learning is worth to be investigated for the problem of time series classification.


title: Logically centralized?: state distribution trade-offs in software defined networks

abstract: Software Defined Networks (SDN) give network designers freedom to refactor the network control plane. One core benefit of SDN is that it enables the network control logic to be designed and operated on a global network view, as though it were a centralized application, rather than a distributed system - logically centralized. Regardless of this abstraction, control plane state and logic must inevitably be physically distributed to achieve responsiveness, reliability, and scalability goals. Consequently, we ask: "How does distributed SDN state impact the performance of a logically centralized control application?" Motivated by this question, we characterize the state exchange points in a distributed SDN control plane and identify two key state distribution trade-offs. We simulate these exchange points in the context of an existing SDN load balancer application. We evaluate the impact of inconsistent global network view on load balancer performance and compare different state management approaches. Our results suggest that SDN control state inconsistency significantly degrades performance of logically centralized control applications agnostic to the underlying state distribution.


title: Gray-level co-occurrence matrix bone fracture detection

abstract: Problem statement: Currently doctors in orthopedic wards inspect the bone x-ray images according to their experience and knowledge in bone fracture analysis. Manual examination of x-rays has multitude drawbacks. The process is time-consuming and subjective. Approach: Since detection of fractures is an important orthopedics and radiologic problem and therefore a Computer Aided Detection(CAD) system should be developed to improve the scenario. In this study, a fracture detection CAD based on GLCM recognition could improve the current manual inspection of x-ray images system. The GLCM for fracture and non-fracture bone is computed and analysis is made. Features of Homogeneity, contrast, energy, correlation are calculated to classify the fractured bone. Results: 30 images of femur fractures have been tested, the result shows that the CAD system can differentiate the x-ray bone into fractured and nonfractured femur. The accuracy obtained from the system is 86.67. Conclusion: The CAD system is proved to be effective in classifying the digital radiograph of bone fracture. However the accuracy rate is not perfect, the performance of this system can be further improved using multiple features of GLCM and future works can be done on classifying the bone into different degree of fracture specifically.


title: Social Entrepreneurship: Theoretical Antecedents and Empirical Analysis of Entrepreneurial Processes and Outcomes

abstract: Purpose ::: ::: ::: ::: – The purpose of this paper is to address the emerging practice of social entrepreneurship by exploring the historical and theoretical antecedents of social enterprise and its contemporary practice. By exploring key theoretical concepts, the paper draws comparisons between “for‐profit” and social entrepreneurs. The paper seeks to discuss the contemporary practice of social entrepreneurship. Discussion of the theory of entrepreneurship and contemporary practice of social entrepreneurs seeks to create a more nuanced view of social entrepreneurship and develop greater theoretical insights into this phenomenon and its recent expansion. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: – A phenomenological research approach was adopted, and 80 in‐depth interviews with social entrepreneurs from across the UK identified as the most appropriate data collection tool. Data analysis sought to identify and understand similarities between the more understood and studied behaviour of “profit‐seeking” entrepreneurs and those of an emerging group of social entrepreneurs. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: – In‐depth interviews revealed five key themes within which the practice of social entrepreneurship could be compared and contrasted with for‐profit entrepreneurship. These included: the entrepreneurial process, in particular, opportunity recognition; network embeddedness; the nature of financial risk and profit; the role of individual versus collective action in managing and structuring enterprises; and creativity and innovation. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: – Findings suggest that while the contemporary practices of social enterprises share many similarities with their for‐profit counterparts, significant differences can be found when comparing these practices with extant entrepreneurship research. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: – The paper addresses an emerging phenomenon within the practice and theory of entrepreneurship and offers insight into similarities and differences between entrepreneurship in the profit and not‐for‐profit sectors.


title: A Roadmap to the Programmable World: Software Challenges in the IoT Era

abstract: The Internet of Things (IoT) represents the next significant step in the evolution of the Internet and software development. Although most IoT research focuses on data acquisition, analytics, and visualization, a subtler but equally important transition is underway. Hardware advances are making it possible to embed fully fledged virtual machines and dynamic language runtimes virtually everywhere, leading to a Programmable World in which all our everyday things are connected and programmable dynamically. The emergence of millions of remotely programmable devices in our surroundings will pose significant software development challenges. A roadmap from today's cloud-centric, data-centric IoT systems to the Programmable World highlights the technical challenges that deserve to be part of developer education and deserve deeper investigation beyond those IoT topics that receive the most attention today.


title: Global R&D Alliances in China: Collaborations With Universities and Research Institutes

abstract: A learning perspective was applied to examining when multinational corporations select universities rather than local firms as partners in international R&D alliances. Data were collected on 327 international R&D alliances established over the 1995-2001 period in China, an emerging economy where intellectual property rights protection is still far from adequate, over the 1995-2001 period. The effects of factors such as the international investor's host country R&D experience and the venture's research objectives on the selection of universities or research institutes as local partners for R&D alliances were analyzed. Analysis using logistic regression models suggests that the contribution of local universities and research institutes to such R&D collaborations is likely to be high when foreign investors have had abundant prior R&D experience in the host country and when the alliance has been established primarily for research rather than development purposes. The implications for theory, practice, and policymaking are discussed.


title: Automatic video shot boundary detection of news stream using a high-level fuzzy Petri net

abstract: With the advent of digital era, the users have difficulty in using and absorbing overwhelming information brought out by technological advances in multimedia. Thus, the development of video summarization enables users to have a general idea about videos in a short time. This study focuses on the shot change, a part of the video summarization, to conduct an experimental sample on news programs. Moreover, a high-level fuzzy Petri net model is presented to describe boundary frames combination which indicates a shot boundary used for video frame sequence to detect both cut transitions and gradual transitions. The experimental results manifest it saves a lot of time and reduces the occurrence of improper shot change caused by the motions of objects and cameras when comparing this method with human labor.


title: Do today's trades affect tomorrow's IPO allocations?.

abstract: Underwriters using bookbuilding have discretionary power for allocating shares of initial public offerings (IPOs). Commissions paid to underwriters by investors are one of the determinants of IPO allocations. We test the hypothesis that investors trade liquid stocks in order to affect their IPO allocations. Consistent with this hypothesis, we find that money left on the table by IPOs affects the trading volume of the 50 most liquid stocks close to the offer date. For an IPO that leaves $1 billion on the table, in the six days ending on the day that trading commences there is abnormal volume in the 50 most liquid stocks of 2.7 to 4.1%, although only during the internet bubble period is this statistically significant.


title: Predictive Representations of State

abstract: We show that states of a dynamical system can be usefully represented by multi-step, action-conditional predictions of future observations. State representations that are grounded in data in this way may be easier to learn, generalize better, and be less dependent on accurate prior models than, for example, POMDP state representations. Building on prior work by Jaeger and by Rivest and Schapire, in this paper we compare and contrast a linear specialization of the predictive approach with the state representations used in POMDPs and in k-order Markov models. Ours is the first specific formulation of the predictive idea that includes both stochasticity and actions (controls). We show that any system has a linear predictive state representation with number of predictions no greater than the number of states in its minimal POMDP model.


title: MLbase: A Distributed Machine-learning System

abstract: Machine learning (ML) and statistical techniques are key to transforming big data into actionable knowledge. In spite of the modern primacy of data, the complexity of existing ML algorithms is often overwhelming|many users do not understand the trade-os and challenges of parameterizing and choosing between dierent learning techniques. Furthermore, existing scalable systems that support machine learning are typically not accessible to ML researchers without a strong background in distributed systems and low-level primitives. In this work, we present our vision for MLbase, a novel system harnessing the power of machine learning for both end-users and ML researchers. MLbase provides (1) a simple declarative way to specify ML tasks, (2) a novel optimizer to select and dynamically adapt the choice of learning algorithm, (3) a set of high-level operators to enable ML researchers to scalably implement a wide range of ML methods without deep systems knowledge, and (4) a new run-time optimized for the data-access patterns of these high-level operators.


title: The design of focus area maturity models

abstract: Maturity models are a well-known instrument to support the improvement of functional domains in IS, like software development or testing While maturity models may share a common structure, they have to be developed anew for each functional domain Focus area maturity models are distinguished from fixed-level maturity models, like CMM, in that they are especially suited to the incremental improvement of functional domains In this paper we present a generic method for developing focus area maturity models based on both extensive industrial experience and scientific investigation In doing so, we show two examples of focus area maturity models, one for enterprise architecture and one for software product management We used a design science research process to develop the method presented.


title: Digital planning of high tibial osteotomy. Interrater reliability by using two different software

abstract: PURPOSE ::: The purpose of the study was to determine the interrater reliability as well as the correlation of mediCAD(®) and PreOPlan(®) in deformity analysis and digital planning of osteotomies. ::: ::: ::: METHODS ::: Digital radiographs were obtained from 81 patients planned to undergo an open wedge high tibial osteotomy. The JPEG files of the radiographs were imported to landmark-based software. Deformity analysis and planning of correction were performed by 1 experienced and 2 unexperienced observers. Osteotomy planning was aimed at correction to the predefined mechanical tibiofemoral angle of 3° valgus leg alignment. The interrater reliability of measurements was assessed using intraclass correlation coefficients (ICCs) and the confidence interval. ::: ::: ::: RESULTS ::: The ICC of PreOPlan(®) was from 0.841 (mechanical lateral distal femur angle) to 0.993 (wedge-angle) and from 0.896 (joint line convergence angle) to 0.995 (mechanical tibiofemoral angle) of mediCAD(®). The ICC of height of wedge-base was 0.979 with PreOPlan(®) and 0.969 with mediCAD(®). Comparing PreOPlan(®) and mediCAD(®), the ICC of the height of wedge-base of the observers was 0.966, 0.956 and 0.969, respectively. ::: ::: ::: CONCLUSIONS ::: The results show a high interrater reliability of digital planning software. Experience of the observer had no influence on results. Furthermore, a high interrater reliability and correlation of digital planning specific parameters was found. Surgeons need to master limb geometry measurements and osteotomy planning on digital radiographs as digital planning reports are used for intercolleagual correspondence, teaching purposes and as medicolegal documents. The digital planning software tested agrees with the actual demands and could be recommended for deformity analysis and planning of osteotomies. ::: ::: ::: LEVEL OF EVIDENCE ::: Diagnostic studies, Level I.


title: CPU scheduling and memory management for interactive real-time applications

abstract: In this paper, we propose, design, implement, and evaluate a CPU scheduler and a memory management scheme for interactive soft real-time applications. Our CPU scheduler provides a new CPU reservation algorithm that is based on the well-known Constant Bandwidth Server (CBS) algorithm but is more flexible in allocating the CPU time to multiple concurrently-executing real-time applications. Our CPU scheduler also employs a new multicore scheduling algorithm, extending the Earliest Deadline First to yield Window-constraint Migrations (EDF-WM) algorithm, to improve the absolute CPU bandwidth available in reservation-based systems. Furthermore, we propose a memory reservation mechanism incorporating a new paging algorithm, called Private-Shared-Anonymous Paging (PSAP). This PSAP algorithm allows interactive real-time applications to be responsive under memory pressure without wasting and compromising the memory resource available for contending best-effort applications. Our evaluation demonstrates that our CPU scheduler enables the simultaneous playback of multiple movies to perform at the stable frame-rates more than existing real-time CPU schedulers, while also improves the ratio of hard-deadline guarantee for randomly-generated task sets. Furthermore, we show that our memory management scheme can protect the simultaneous playback of multiple movies from the interference introduced by memory pressure, whereas these movies can become unresponsive under existing memory management schemes.


title: Watermarking of Uncompressed and Compressed Video

abstract: Abstract In this paper, methods for embedding additive digital watermarks into uncompressed and compressed video sequences are presented. The basic principle borrows from spread spectrum communications. It consists of addition of an encrypted, pseudo-noise signal to the video that is invisible, statistically unobtrusive, and robust against manipulations. For practical applications, watermarking schemes operating on compressed video are desirable. A method for watermarking of MPEG-2 encoded video is presented. The scheme is a compatible extension of the scheme operating on uncompressed video. The watermark is generated exactly in the same manner as for uncompressed video, transformed using the discrete cosine transform (DCT) and embedded into the MPEG-2 bit-stream without increasing the bit-rate. The watermark can be retrieved from the decoded video and without knowledge of the original, unwatermarked video. Although an existing MPEG-2 bit-stream is partly altered, the scheme avoids visible artifacts by addition of a drift compensation signal. The proposed method is robust and of much lower complexity than a complete decoding process followed by watermarking in the pixel domain and re-encoding. Fast implementations exist which have a complexity comparable to a video decoder. Experimental results are given. The scheme is also applicable to other hybrid transform coding schemes like MPEG-1, MPEG-4, H.261, and H.263.


title: Splitting the hyperspectral-multispectral image fusion problem autonomously into weighted pan-sharpening tasks — The spectral grouping concept

abstract: Due to the trade-off between spatial and spectral resolution in optical imaging systems, hyperspectral instruments provide data of comparatively low spatial resolution. In order to develop higher resolution hyperspectral data via post-processing, image fusion algorithms can be applied to combine this data with imagery of lower spectral but higher spatial resolution. The similar but simpler pan-sharpening problem, which refers to the fusion of low resolution multispectral with higher resolution panchromatic imagery, has been extensively studied and solved in the past two decades. This paper aims at making the large number of sophisticated pan-sharpening methods usable to solving the more challenging hyperspectral-multispectral image fusion problem. We propose a generic algorithm — called Spectral Grouping — to devide the hyperspectral-multispectral image fusion problem autonomously into weighted pan-sharpening problems using the relative spectral responses of both instruments.


title: Model-based object pose in 25 lines of code

abstract: In this paper, we describe a method for finding the pose of an object from a single image. We assume that we can detect and match in the image four or more noncoplanar feature points of the object, and that we know their relative geometry on the object. The method combines two algorithms; the first algorithm,POS (Pose from Orthography and Scaling) approximates the perspective projection with a scaled orthographic projection and finds the rotation matrix and the translation vector of the object by solving a linear system; the second algorithm,POSIT (POS with ITerations), uses in its iteration loop the approximate pose found by POS in order to compute better scaled orthographic projections of the feature points, then applies POS to these projections instead of the original image projections. POSIT converges to accurate pose measurements in a few iterations. POSIT can be used with many feature points at once for added insensitivity to measurement errors and image noise. Compared to classic approaches making use of Newton's method, POSIT does not require starting from an initial guess, and computes the pose using an order of magnitude fewer floating point operations; it may therefore be a useful alternative for real-time operation. When speed is not an issue, POSIT can be written in 25 lines or less in Mathematica; the code is provided in an Appendix.


title: Shock Capturing, Level Sets and PDE Based Methods in Computer Vision and Image Processing: A Review of Osher's Contributions

abstract: In this paper we review the algorithm development and applications in high resolution shock capturing methods, level set methods, and PDE based methods in computer vision and image processing. The emphasis is on Stanley Osher's contribution in these areas and the impact of his work. We will start with shock capturing methods and will review the Engquist-Osher scheme, TVD schemes, entropy conditions, ENO and WENO schemes, and numerical schemes for Hamilton-Jacobi type equations. Among level set methods we will review level set calculus, numerical techniques, fluids and materials, variational approach, high codimension motion, geometric optics, and the computation of discontinuous solutions to Hamilton-Jacobi equations. Among computer vision and image processing we Will review the total variation model for image denoising, images on implicit surfaces, and the level set method in image processing and computer vision.


title: Anomaly based DDoS Attack Detection

abstract: Distributed denial-of-service (DDoS) attack poses a serious threat to network security. Several methods have been introduced to reduce the damage. However, most of the methods have been found unable to detect the attack in realtime with high detection accuracy. This paper presents a simple yet effective method to detect DDoS attack for all possible attack scenarios given by Mirkoviac [1] viz constant rate, pulsing rate, increasing rate and sub-group. The proposed method is validated using well known CAIDA dataset. General Terms Pattern Recognition, Security.


title: Low parasitics planar transformer for LLC resonant battery chargers

abstract: Recently, high efficiency LLC resonant converters have gained popularity to implement battery chargers and employ traditional wire-wound transformers. Due to the height of traditional cores, the form factor of LLC chargers is often plump and bulky. In order to implement slim profile chargers for EVs and portable electronic applications, planar transformers can be used featuring low height, reproducibility, and low thermal resistance. Despite those advantages, planar magnetics suffer from very high parasitic capacitance resulting in severe regulation problems for LLC chargers that require wide voltage regulation. This paper analyzes LLC regulation issues with planar magnetics and proposes a novel strategy to provide wide regulation by reducing the effects of intra-winding capacitance. The proposed strategy employs a simplified capacitance network and achieves low parasitics to successfully design LLC chargers with wide regulation. Experimental results of a 1.2kW LLC converter confirms that the proposed winding layout not only provides better output voltage regulation in light load conditions, but also increases the overall efficiency of the power converter.


title: A Trust-Based Intrusion Detection System for Mobile RPL Based Networks

abstract: Successful deployment of Low power and Lossy Networks (LLNs) requires self-organising, self-configuring, security, and mobility support. However, these characteristics can be exploited to perform security attacks against the Routing Protocol for Low-Power and Lossy Networks (RPL). In this paper, we address the lack of strong identity and security mechanisms in RPL. We first demonstrate by simulation the impact of Sybil-Mobile attack, namely SybM, on RPL with respect to control overhead, packet delivery and energy consumption. Then, we introduce a new Intrusion Detection System (IDS) scheme for RPL, named Trust-based IDS (T-IDS). T-IDS is a distributed, cooperative and hierarchical trust-based IDS, which can detect novel intrusions by comparing network behavior deviations. In T-IDS, each node is considered as monitoring node and collaborates with his peers to detect intrusions and report them to a 6LoWPAN Border Router (6BR). In our solution, we introduced a new timer and minor extensions to RPL messages format to deal with mobility, identity and multicast issues. In addition, each node is equipped with a Trusted Platform Module co-processor to handle identification and off-load security related computation and storage.


title: Link Prediction in Complex Networks: A Survey

abstract: Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labelled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.


title: Making Health Care Safe: A Critical Analysis of Patient Safety Practices

abstract: OBJECTIVES ::: Patient safety has received increased attention in recent years, but mostly with a focus on the epidemiology of errors and adverse events, rather than on practices that reduce such events. This project aimed to collect and critically review the existing evidence on practices relevant to improving patient safety.


title: A Continuous-Time Sturdy-MASH $\Delta\Sigma$ Modulator in 28 nm CMOS

abstract: This paper presents a practical way to achieve both wide signal bandwidth and high dynamic range in a continuous- time (CT) delta-sigma modulator. Quantization noise is suppressed aggressively by increasing the effective order of the noise transfer function based on a sturdy multi-stage noise-shaping (SMASH) architecture. The proposed CT SMASH architecture has a much wider signal bandwidth which was limited in the discrete-time (DT) SMASH architecture due to the inherent sampling frequency limitation of DT implementation. Furthermore, the proposed CT SMASH architecture provides better quantization noise suppression by more completely canceling the quantization noise from the 1st-loop. The CT SMASH architecture is implemented with several efficient circuit techniques suitable for high operation speed. As a result, the prototype fabricated in 28 nm CMOS achieves DR of 85 dB, peak SNDR of 74.9 dB, SFDR of 89.3 dBc, and Schreier FOM of 172.9 dB over a 50 MHz bandwidth at a 1.8 GHz sampling frequency.


title: The click modular router

abstract: Clicks is a new software architecture for building flexible and configurable routers. A Click router is assembled from packet processing modules called elements . Individual elements implement simple router functions like packet classification, queuing, scheduling, and interfacing with network devices. A router configurable is a directed graph with elements at the vertices; packets flow along the edges of the graph. Several features make individual elements more powerful and complex configurations easier to write, including pull connections, which model packet flow drivn by transmitting hardware devices, and flow-based router context, which helps an element locate other interesting elements. Click configurations are modular and easy to extend. A standards-compliant Click IP router has 16 elements on its forwarding path; some of its elements are also useful in Ethernet switches and IP tunnelling configurations. Extending the IP router to support dropping policies, fairness among flows, or Differentiated Services simply requires adding a couple of element at the right place. On conventional PC hardware, the Click IP router achieves a maximum loss-free forwarding rate of 333,000 64-byte packets per second, demonstrating that Click's modular and flexible architecture is compatible with good performance.


SystemT: Declarative Text Understanding for Enterprise


ISCAS_Sogou at TAC-KBP 2017


title: Under frequency load shedding (UFLS): Principles and implementation

abstract: Under frequency load shedding is implemented to restore power system frequency stability if system frequency drops below the operational set point during major disturbance such as lost of generation. Different countries/utility companies have their own philosophies in implementing the under frequency load shedding scheme. Generally, it is based on country/utility requirements, e.g. the overall power system network and the country's demographic. This paper presents the principles and implementation of the under frequency load shedding (UFLS) and presented using simulations of 56 test bus-system. The performance of the developed schemes under various conditions of disturbance were compared and analyzed. All the simulation works were performed using Siemens PTI software PSS®E.


title: A Neural Architecture for Preattentive Segmentation of Sewage Pipes Video Images

abstract: This article describes a neural architecture for real time preattentive segmentation of sewage pipes video images, whose mechanisms are based on the mammalian early visual system. The architecture corresponds to a modified and simplified version of the Boundary Contour System tuned to take advantage of the circular symmetric characteristics of the pipes images. Remarkable aspects of the proposed architecture are the application of a spatial complex logarithmic mapping stage, and the use of cooperative receptive fields with noncollinear branches.


title: Surveillance, Persuasion, and Panopticon

abstract: The surveillance in public and private places, both physically and digitally, is increasing for different reasons. In this paper we intend to discuss surveillance and persuasive technology in an ethical perspective with an eye to its historical and cultural context. In section 1, we present some different tendencies of surveillance in society. In section 2, we elaborate on some important historical ideas on surveillance. In section 3, we consider the use of persuasive technology for surveillance purposes. In section 4, we discuss the development towards increasing surveillance in society, at work, in public places etc. In section 5, we draw up some ethical concerns on surveillance, and finally. In section 6, we discuss the question of a possible need for a public and democratic control of the use of surveillance technology.


title: A Lobster-inspired Robotic Glove for Hand Rehabilitation

abstract: This paper presents preliminary results of the design, development, and evaluation of a hand rehabilitation glove fabricated using lobster-inspired hybrid design with rigid and soft components for actuation. Inspired by the bending abdomen of lobsters, hybrid actuators are built with serially jointed rigid shells actuated by pressurized soft chambers inside to generate bending motions. Such bio-inspiration absorbs features from the classical rigid-bodied robotics with precisely-defined motion generation, as well as the emerging soft robotics with light-weight, physically safe, and adaptive actuation. The fabrication procedure is described, followed by experiments to mechanically characterize these actuators. Finally, an open-palm glove design integrated with these hybrid actuators is presented for a qualitative case study. A hand rehabilitation system is developed by learning patterns of the sEMG signals from the user's forearm to train the assistive glove for hand rehabilitation exercises.


title: A qualitative study of CVD management and dietary changes: problems of ‘too much’ and ‘contradictory’ information

abstract: Nutrition education for cardiovascular disease (CVD) management is not effective for all population groups. There is little understanding of the factors that hinder patients from adhering to dietary recommendations. 37 interviews were conducted with people living with CVD in Adelaide, Australia. Recruitment occurred via General Practitioner (GP) clinics and hospital cardiac rehabilitation programs. Participants were either receiving preventive treatment or active treatment for established CVD. The volume and contradictory nature of dietary information were the most prominent barriers to making changes identified in interviews, especially by order participants. Patients will seek out, or come into contact with information which contradicts advice from their GPs. The volume of information may lead them to resort to old and familiar habits. GPs play a valuable role in highlighting key take-home messages and reliable external sources of information. The findings have implications for GP practice given that lifestyle changes are a cost- and clinically-effective means of managing CVD.


title: The Variational Gaussian Approximation Revisited

abstract: The variational approximation of posterior distributions by multivariate gaussians has been much less popular in the machine learning community compared to the corresponding approximation by factorizing distributions. This is for a good reason: the gaussian approximation is in general plagued by an number of variational parameters to be optimized, being the number of random variables. In this letter, we discuss the relationship between the Laplace and the variational approximation, and we show that for models with gaussian priors and factorizing likelihoods, the number of variational parameters is actually . The approach is applied to gaussian process regression with nongaussian likelihoods.


title: Midinfrared real-time polarization imaging with all-dielectric metasurfaces

abstract: Polarization is an inherent property of light that contains information that is usually ignored by intensity-based sensors. A polarization imaging system forms the images over an extended scene in different polarization basis sets, which is widely used in fields ranging from target detection to biosensing. Typically, conventional polarization imaging systems based on polarization filtering form images only for linearly polarized light and use bulky and complex elements. Here, we proposed an all-silicon metasurface for midinfrared (MIR) active-illumination polarization imaging. The metasurface operates at a wavelength of 10.6 μm and functions as a division of focal plane imaging mask. In experiment, the single-layered metasurface can simultaneously form images in two orthogonal polarization basis sets in a real-time condition, which realizes both linear polarization imaging and chiral imaging. This work unveils the potential of compact MIR polarization detection metasystems.Polarization is an inherent property of light that contains information that is usually ignored by intensity-based sensors. A polarization imaging system forms the images over an extended scene in different polarization basis sets, which is widely used in fields ranging from target detection to biosensing. Typically, conventional polarization imaging systems based on polarization filtering form images only for linearly polarized light and use bulky and complex elements. Here, we proposed an all-silicon metasurface for midinfrared (MIR) active-illumination polarization imaging. The metasurface operates at a wavelength of 10.6 μm and functions as a division of focal plane imaging mask. In experiment, the single-layered metasurface can simultaneously form images in two orthogonal polarization basis sets in a real-time condition, which realizes both linear polarization imaging and chiral imaging. This work unveils the potential of compact MIR polarization detection metasystems.


Efficient Design of FIR Filter using Modified Booth Multiplier


title: Visualization, Selection, and Analysis of Traffic Flows

abstract: Visualization of the trajectories of moving objects leads to dense and cluttered images, which hinders exploration and understanding. It also hinders adding additional visual information, such as direction, and makes it difficult to interactively extract traffic flows, i.e., subsets of trajectories. In this paper we present our approach to visualize traffic flows and provide interaction tools to support their exploration. We show an overview of the traffic using a density map. The directions of traffic flows are visualized using a particle system on top of the density map. The user can extract traffic flows using a novel selection widget that allows for the intuitive selection of an area, and filtering on a range of directions and any additional attributes. Using simple, visual set expressions, the user can construct more complicated selections. The dynamic behaviors of selected flows may then be shown in annotation windows in which they can be interactively explored and compared. We validate our approach through use cases where we explore and analyze the temporal behavior of aircraft and vessel trajectories, e.g., landing and takeoff sequences, or the evolution of flight route density. The aircraft use cases have been developed and validated in collaboration with domain experts.


title: Tackling the Redundancy and Sparsity in Crowd Sensing Applications

abstract: Driven by the proliferation of sensor-rich mobile devices, crowd sensing has emerged as a new paradigm of gathering information about the physical world. In crowd sensing applications, user observations are usually unevenly distributed across the monitored entities, and this gives rise to two major challenges -- redundancy and sparsity. On one hand, multiple users may observe the same entity, and their observations are sometimes conflicting with each other due to the unreliable nature of human-carried sensors. On the other hand, crowd sensing data are usually very sparse, and there may exist considerable number of entities that never receive any observations from users. Some existing work studies these two challenges separately. However, we can gain great benefits by dealing with them jointly. In this paper, we develop an integrated framework to estimate the true values of entities from redundant and sparse data in crowd sensing applications. In this framework, we propose an effective algorithm to infer the "missing" observations for each entity, and aggregate both user-contributed and inferred observations to discover the true values of entities. We conduct extensive experiments on real-world crowd sensing systems to demonstrate the advantages of the proposed framework on correctly inferring entity truths from redundant and sparse data.


title: Bridging the Gap Between Forensics and Biometric-Enabled Watchlists for e-Borders

abstract: A biometric-enabled watchlist (or a database of persons of interest) is commonly accepted by national and international security agencies. In particular, the facetrait has demonstrated promising performance in large-scale open-set tasks in forensics and law enforcement scenarios. However, their application in the security of mass-transit systems, such as e-borders, is very limited. We evaluate the risks of unwanted effects using Doddington's metric and various databases. The key contribution of this paper is a novel technique for traveler risk categorization using a biometric-enabled watchlist and an evidence-accumulation paradigm, as well as its impact on e-border performance.


title: An improved ID3 decision tree algorithm

abstract: Decision tree is an important method for both induction research and data mining, which is mainly used for model classification and prediction. ID3 algorithm is the most widely used algorithm in the decision tree so far. Through illustrating on the basic ideas of decision tree in data mining, in this paper, the shortcoming of ID3's inclining to choose attributes with many values is discussed, and then a new decision tree algorithm combining ID3 and Association Function(AF) is presented. The experiment results show that the proposed algorithm can overcome ID3's shortcoming effectively and get more reasonable and effective rules


title: www.elsevier.com/locate/humov Probabilistic models in human sensorimotor control

abstract: Sensory and motor uncertainty form a fundamental constraint on human sensorimotor control. Bayesian decision theory (BDT) has emerged as a unifying framework to understand how the central nervous system performs optimal estimation and control in the face of such uncertainty. BDT has two components: Bayesian statistics and decision theory. Here we review Bayesian statistics and show how it applies to estimating the state of the world and our own body. Recent results suggest that when learning novel tasks we are able to learn the statistical properties of both the world and our own sensory apparatus so as to perform estimation using Bayesian statistics. We review studies which suggest that humans can combine multiple sources of information to form maximum likelihood estimates, can incorporate prior beliefs about possible states of the world so as to generate maximum a posteriori estimates and can use Kalman filter-based processes to estimate time-varying states. Finally, we review Bayesian decision theory in motor control and how the central nervous system processes errors to determine loss functions and select optimal actions. We review results that suggest we plan movements based on statistics of our actions that result from signal-dependent noise on our motor outputs. Taken together these studies provide a statistical framework for how the motor system performs in the presence of uncertainty.


title: Convergence Analysis of MAP Based Blur Kernel Estimation

abstract: One popular approach for blind deconvolution is to formulate a maximum a posteriori (MAP) problem with sparsity priors on the gradients of the latent image, and then alternatingly estimate the blur kernel and the latent image. While several successful MAP based methods have been proposed, there has been much controversy and confusion about their convergence, because sparsity priors have been shown to prefer blurry images to sharp natural images. In this paper, we revisit this problem and provide an analysis on the convergence of MAP based approaches. We first introduce a slight modification to a conventional joint energy function for blind deconvolution. The reformulated energy function yields the same alternating estimation process, but more clearly reveals how blind deconvolution works. We then show the energy function can actually favor the right solution instead of the no-blur solution under certain conditions, which explains the success of previous MAP based approaches. The reformulated energy function and our conditions for the convergence also provide a way to compare the qualities of different blur kernels, and we demonstrate its applicability to automatic blur kernel size selection, blur kernel estimation using light streaks, and defocus estimation.


Side-Channel Attack Standard Evaluation Board SASEBO-W for Smartcard Testing


title: Applications of machine learning and rule induction

abstract: M achine learning is the study of computational methods for improving performance by mechanizing the acquisition of knowledge from experience. Expert performance requires much domain-specific knowledge, and knowledge engineering has produced hundreds of AI expert systems that are now used regularly in industry. Machine learning aims to provide increasing levels of automation in the knowledge engineering process, replacing much time-consuming human activity with automatic techniques that improve accuracy or efficiency by discovering and exploiting regularities in training data. The ultimate test of machine learning is its ability to produce systems that are used regularly in industry, education, and elsewhere.


title: Finite-State Transducers In Language And Speech Processing

abstract: Finite-machines have been used in various domains of natural language processing. We consider here the use of a type of transducer that supports very efficient programs: sequential transducers. We recall classical theorems and give new ones characterizing sequential string-to-string transducers. Transducers that outpur weights also play an important role in language and speech processing. We give a specific study of string-to-weight transducers, including algorithms for determinizing and minizizing these transducers very efficiently, and characterizations of the transducers admitting determinization and the corresponding algorithms. Some applications of these algorithms in speech recognition are described and illustrated.


title: Source Code Curation on StackOverflow: The Vesperin System

abstract: The past few years have witnessed the rise of software question and answer sites like StackOverflow, where developers can pose detailed coding questions and receive quality answers. Developers using these sites engage in a complex code foraging process of understanding and adapting the code snippets they encounter. We introduce the notion of source code curation to cover the act of discovering some source code of interest, cleaning and transforming (refining) it, and then presenting it in a meaningful and organized way. In this paper, we present Vesperin, a source code curation system geared towards curating Java code examples on StackOverflow.


title: Neural Networks for Open Domain Targeted Sentiment

abstract: Open domain targeted sentiment is the joint information extraction task that finds target mentions together with the sentiment towards each mention from a text corpus. The task is typically modeled as a sequence labeling problem, and solved using state-of-the-art labelers such as CRF. We empirically study the effect of word embeddings and automatic feature combinations on the task by extending a CRF baseline using neural networks, which have demonstrated large potentials for sentiment analysis. Results show that the neural model can give better results by significantly increasing the recall. In addition, we propose a novel integration of neural and discrete features, which combines their relative advantages, leading to significantly higher results compared to both baselines.


title: A framework for building a natural language interface for Bangla

abstract: Mobile Computing Devices are enabling connection between people and the Internet, largest source of information in the world. However to properly utilize this knowledge in these devices most people need a Natural Language Interface. Siri, Google Now, Cortana are examples of such interfaces. Because Bangla is a low resource language building such interface is very difficult and time consuming. However due to the increasing numbers of smart phone and smart device users in the Bangla speaking regions, application developers are facing the need for such interfaces to provide web services effectively. This paper addresses this issue and gives an empirical framework on how to build a feasible Natural Language Interface in Bangla and similar low resource languages.


title: The Fakability of Bar-On's Emotional Quotient Inventory Short Form: Catch Me if You Can

abstract: This study investigated the fakability of the Emotional Quotient Inventory Short Form (EQ-i:S), a mixed-model emotional intelligence test developed by Bar-On (2002). A sample of 229 undergraduate students from a southeastern university completed a battery of selection and assessment measures in both an honest and faking good condition. When responded to honestly, the EQ-i:Sis predicted by The Big Five with a multiple correlation of .79. Therefore, the EQ-i:S can be viewed as an aggregation of The Big Five constructs. When faking, respondents were able to improve scores on the EQ-i:S, each of its subtests, and each of The Big Five measures. Respondents improved scores on the EQ-i:S by .83 SD. Faking on the EQ-i:S was primarily predicted by cognitive ability and agreeableness. The relative ease with which respondents can substantially raise their scores limits the value of the EQ-i:S as an applicant screening tool. The substantial extent to which the EQ-i:S is predicted by The Big Five casts doubt on the construct of emotional intelligence as operationalized in the EQ-i:S.


title: On eye-gaze and turn-taking

abstract: In this paper we describe our eye-tracking data collection and preliminary experiments concerning the relation between eyegazing and turn-taking in natural human-human conversations, and how these observations can be extended to multimodal human-machine interactions. We confirm the earlier findings that eye-gaze is important in coordinating turn-taking and information flow in dialogues, but note that in multiparty dialogues also head movement seems to pay a crucial role in signalling the person's intention to take, hold, or yield the turn.


title: Cognition meets le Corbusier - Cognitive principles of architectural design

abstract: Research on human spatial memory and navigational ability has recently shown the strong influence of reference systems in spatial memory on the ways spatial information is accessed in navigation and other spatially oriented tasks. One of the main findings can be characterized as a large cognitive cost, both in terms of speed and accuracy that occurs whenever the reference system used to encode spatial information in memory is not aligned with the reference system required by a particular task. In this paper, the role of aligned and misaligned reference systems is discussed in the context of the built environment and modern architecture. The role of architectural design on the perception and mental representation of space by humans is investigated. The navigability and usability of built space is systematically analysed in the light of cognitive theories of spatial and navigational abilities of humans. It is concluded that a building's navigability and related wayfinding issues can benefit from architectural design that takes into account basic results of spatial cognition research.


Mechanism-Aware Neural Machine for Dialogue Response Generation.


title: Technology integration in the classroom: A case of students experiences in using Edmodo to support learning in a blended classroom in a Kenyan University

abstract: Edmodo is simply a controlled online networking application that can be used by teachers and students to communicate and remain connected. This paper explores the experiences from a group of students who were using Edmodo platform in their course work. It attempts to use the SAMR (Substitution, Augmentation, Modification and Redefinition) framework of technology integration in education to access and evaluate technology use in the classroom. The respondents were a group of 62 university students from a Kenyan University whose lecturer had created an Edmodo account and introduced the students to participate in their course work during the September to December 2015 semester. More than 82% of the students found that they had a personal stake in the quality of work presented through the platforms and that they were able to take on different subtopics and collaborate to create one final product. This underscores the importance of Edmodo as an environment with skills already in the hands of the students that we can use to integrate technology in the classroom.


title: Evaluating remote centers of motion for minimally invasive surgical robots by computer vision

abstract: This paper addresses the measurement of the location and precision of stationary points in the three dimensional space, commonly termed remote center of motion (RCM), of minimally invasive surgical robotic manipulators. Two-view computer vision is used for its versatility, portability, remote sensing, and cost effectiveness. Geometrical models of cylindrical tools are constructed from camera images. The RCM is computed from the vectors of the cylindrical tool center lines at multiple poses. To verify the approach, a gold standard spherical bearing is first used to measure the RCM location and precision. The same computer vision system and RCM method is then applied to evaluate the RCM of a commercially available laparoscopy surgical robot.


title: Modelling Symbolic Music: Beyond the Piano Roll

abstract: In this paper, we consider the problem of probabilistically modelling symbolic music data. We introduce a representation which reduces polyphonic music to a univariate categorical sequence. In this way, we are able to apply state of the art natural language processing techniques, namely the long short-term memory sequence model. The representation we employ permits arbitrary rhythmic structure, which we assume to be given. We show that our model is effective on four out of four piano roll based benchmark datasets. We further improve our model by augmenting our training data set with transpositions of the original pieces through all musical keys, thereby convincingly advancing the state of the art on these benchmark problems. We also fit models to music which is unconstrained in its rhythmic structure, discuss the properties of this model, and provide musical samples which are more sophisticated than previously possible with this class of recurrent neural network sequence models. We also provide our newly preprocessed data set of non piano-roll music data.


A Direct Search Approach to Nonlinear Programming Problems Using an Augmented Lagrangian Method with Explicit Treatment of Linear Constraints


title: Review of "Statistical language learning" by Eugene Charniak

abstract: From the Publisher: ::: Eugene Charniak breaks new ground in artificial intelligence research by presenting statistical language processing from an artificial intelligence point of view in a text for researchers and scientists with a traditional computer science background. ::: New, exacting empirical methods are needed to break the deadlock in such areas of artificial intelligence as robotics, knowledge representation, machine learning, machine translation, and natural language processing (NLP). It is time, Charniak observes, to switch paradigms. This text introduces statistical language processing techniques -- word tagging, parsing with probabilistic context free grammars, grammar induction, syntactic disambiguation, semantic word classes, word-sense disambiguation -- along with the underlying mathematics and chapter exercises. ::: Charniak points out that as a method of attacking NLP problems, the statistical approach has several advantages. It is grounded in real text and therefore promises to produce usable results, and it offers an obvious way to approach learning: "one simply gathers statistics." ::: Language, Speech, and Communication


title: An asynchronous dataflow FPGA architecture

abstract: We discuss the design of a high-performance field programmable gate array (FPGA) architecture that efficiently prototypes asynchronous (clockless) logic. In this FPGA architecture, low-level application logic is described using asynchronous dataflow functions that obey a token-based compute model. We implement these dataflow functions using finely pipelined asynchronous circuits that achieve high computation rates. This asynchronous dataflow FPGA architecture maintains most of the performance benefits of a custom asynchronous design, while also providing postfabrication logic reconfigurability. We report results for two asynchronous dataflow FPGA designs that operate at up to 400 MHz in a typical TSMC 0.25 /spl mu/m CMOS process.


title: Robust and Accurate Eye Contour Extraction

abstract: This paper describes a novel algorithm for exact eye contour detection in frontal face image. The exact eye shape is a useful piece of input information for applications like facial expression recognition, feature-based face recognition and face modelling. In contrast to well-known eye-segmentation methods, we do not rely on deformable models or image luminance gradient (edge) map. The eye windows (rough eye regions) are assumed to be known. The detection algorithm works in several steps. First, iris center and radius is estimated, then, exact upper eyelid contour is detected by searching for luminance valley points. Finally, lower eyelid is estimated from the eye corners coordinates and iris. The proposed technique has been tested on images of about fifty individuals taken under different lighting conditions with different cameras. It proved to be sufficiently robust and accurate for wide variety of images.


Adding Innovation Diffusion Theory to the Technology Acceptance Model: Supporting Employees' Intentions to use E-Learning Systems


title: A 1.1 V 2y-nm 4.35 Gb/s/pin 8 Gb LPDDR4 Mobile Device With Bandwidth Improvement Techniques

abstract: The demands on higher bandwidth with reduced power consumption in mobile market are driving mobile DRAM with advanced design techniques. Proposed LPDDR4 in this paper achieves over 39% improvement in power efficiency and over 4.3 Gbps data rate with 1.1 V supply voltage. These are challenging targets compared with those of LPDDR3. This work describes design schemes employed in LPDDR4 to satisfy these requirements, such as multi-channel-per-die architecture, multiple training modes, low-swing interface, DQS and clock frequency dividing, and internal reference for data and command-address signals. This chip was fabricated in a 3-metal 2y-nm DRAM CMOS process.


title: Fully Convolutional Network Ensembles for White Matter Hyperintensities Segmentation in MR Images

abstract: Abstract White matter hyperintensities (WMH) are commonly found in the brains of healthy elderly individuals and have been associated with various neurological and geriatric disorders. In this paper, we present a study using deep fully convolutional network and ensemble models to automatically detect such WMH using fluid attenuation inversion recovery (FLAIR) and T1 magnetic resonance (MR) scans. The algorithm was evaluated and ranked 1st in the WMH Segmentation Challenge at MICCAI 2017. In the evaluation stage, the implementation of the algorithm was submitted to the challenge organizers, who then independently tested it on a hidden set of 110 cases from 5 scanners. Averaged dice score, precision and robust Hausdorff distance obtained on held-out test datasets were 80%, 84% and 6.30 mm respectively. These were the highest achieved in the challenge, suggesting the proposed method is the state-of-the-art. Detailed descriptions and quantitative analysis on key components of the system were provided. Furthermore, a study of cross-scanner evaluation is presented to discuss how the combination of modalities affect the generalization capability of the system. The adaptability of the system to different scanners and protocols is also investigated. A quantitative study is further presented to show the effect of ensemble size and the effectiveness of the ensemble model. Additionally, software and models of our method are made publicly available. The effectiveness and generalization capability of the proposed system show its potential for real-world clinical practice.


title: Adaptive filtering for music/voice separation exploiting the repeating musical structure

abstract: The separation of the lead vocals from the background accompaniment in audio recordings is a challenging task. Recently, an efficient method called REPET (REpeating Pattern Extraction Technique) has been proposed to extract the repeating background from the non-repeating foreground. While effective on individual sections of a song, REPET does not allow for variations in the background (e.g. verse vs. chorus), and is thus limited to short excerpts only. We overcome this limitation and generalize REPET to permit the processing of complete musical tracks. The proposed algorithm tracks the period of the repeating structure and computes local estimates of the background pattern. Separation is performed by soft time-frequency masking, based on the deviation between the current observation and the estimated background pattern. Evaluation on a dataset of 14 complete tracks shows that this method can perform at least as well as a recent competitive music/voice separation method, while being computationally efficient.


title: Minimal “Self ” Peptides That Inhibit Phagocytic Clearance and Enhance Delivery of Nanoparticles

abstract: Foreign particles and cells are cleared from the body by phagocytes that must also recognize and avoid clearance of "self" cells. The membrane protein CD47 is reportedly a "marker of self" in mice that impedes phagocytosis of self by signaling through the phagocyte receptor CD172a. Minimal "Self" peptides were computationally designed from human CD47 and then synthesized and attached to virus-size particles for intravenous injection into mice that express a CD172a variant compatible with hCD47. Self peptides delay macrophage-mediated clearance of nanoparticles, which promotes persistent circulation that enhances dye and drug delivery to tumors. Self-peptide affinity for CD172a is near the optimum measured for human CD172a variants, and Self peptide also potently inhibits nanoparticle uptake mediated by the contractile cytoskeleton. The reductionist approach reveals the importance of human Self peptides and their utility in enhancing drug delivery and imaging.


title: Dual Dictionary Compression for the Last Level Cache

abstract: The performance of GPUs is rapidly improving as the top GPU vendors keep pushing the boundaries of process technologies. While larger die sizes help improve performance given the nature of parallel workloads, additional architectural improvements can also help by utilizing the available die real estate more efficiently. Introducing a compressed Last Level Cache (LLC) can make better use of die area, and can improve memory system performance. With widespread adoption of high-resolution displays, most modern game developers are trying to generate high quality graphics output leveraging state-of-the-art GPUs, all of which greatly increases amount of data that needs to be processed. These modern graphics workloads will need to rely on compression to help save memory bandwidth and improve the performance of the LLC. A compressed LLC can help by increasing the hit-rate due to logical cache expansion, as well as provide bandwidth savings due to compressed data on the memory bus. In this paper we propose a novel scheme to extend dynamic dictionary-based compression to store compressed data in memory. Current dictionary-based compression schemes need to decompress the data when a cache block gets evicted. This is because the dynamic dictionary entries are not guaranteed to stay the same and data consistency cannot be maintained. This results in bandwidth savings that is limited to the logical cache expansion. We propose a dual-dictionary scheme (DDC) that can help maintain data consistency, as well as improve bandwidth savings. Our scheme saves bandwidth by coupling logical cache expansion with compressed data on the memory bus. We achieve bandwidth savings of 18.55% for reads and 11.01% for writes, on average, for a diverse range of graphics workloads.


Modelling and Analysis of Single Diode Photovoltaic Module using MATLAB / Simulink