title: Overview of the Stereo and Multiview Video Coding Extensions of the H.264/MPEG-4 AVC Standard

abstract: Significant improvements in video compression capability have been demonstrated with the introduction of the H.264/MPEG-4 advanced video coding (AVC) standard. Since developing this standard, the Joint Video Team of the ITU-T Video Coding Experts Group (VCEG) and the ISO/IEC Moving Picture Experts Group (MPEG) has also standardized an ex- tension of that technology that is referred to as multiview video coding (MVC). MVC provides a compact representation for multiple views of a video scene, such as multiple synchronized video cameras. Stereo-paired video for 3-D viewing is an im- portant special case of MVC. The standard enables inter-view prediction to improve compression capability, as well as sup- porting ordinary temporal and spatial prediction. It also sup- ports backward compatibility with existing legacy systems by structuring the MVC bitstream to include a compatible Bbase view.( Each other view is encoded at the same picture reso- lution as the base view. In recognition of its high-quality encod- ing capability and support for backward compatibility, the stereo high profile of the MVC extension was selected by the Blu-RayDiscAssociationas thecoding formatfor3-Dvideowith high-definition resolution. This paper provides an overview of the algorithmic design used for extending H.264/MPEG-4 AVC towards MVC. The basic approach of MVC for enabling inter- view prediction and view scalability in the context of H.264/ MPEG-4 AVC is reviewed. Related supplemental enhancement information (SEI) metadata is also described. Various Bframe compatible( approaches for support of stereo-view video as an alternative to MVC are also discussed. A summary of the coding performance achieved by MVC for both stereo- and multiview video is also provided. Future directions and challenges related to 3-D video are also briefly discussed.


title: Analysis of Bayesian classification-based approaches for Android malware detection

abstract: Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero-day Android malware. Hence, the authors develop and analyse proactive machine-learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification-based solutions for detecting unknown Android malware.


title: Grid Long Short-Term Memory

abstract: This paper introduces Grid Long Short-Term Memory, a network of LSTM cells arranged in a multidimensional grid that can be applied to vectors, sequences or higher dimensional data such as images. The network differs from existing deep LSTM architectures in that the cells are connected between network layers as well as along the spatiotemporal dimensions of the data. The network provides a unified way of using LSTM for both deep and sequential computation. We apply the model to algorithmic tasks such as 15-digit integer addition and sequence memorization, where it is able to significantly outperform the standard LSTM. We then give results for two empirical tasks. We find that 2D Grid LSTM achieves 1.47 bits per character on the Wikipedia character prediction benchmark, which is state-of-the-art among neural approaches. In addition, we use the Grid LSTM to define a novel two-dimensional translation model, the Reencoder, and show that it outperforms a phrase-based reference system on a Chinese-to-English translation task.


title: Modeling and execution of event stream processing in business processes

abstract: The Internet of Things and Cyber-physical Systems provide enormous amounts of real-time data in the form of streams of events. Businesses can benefit from the integration of these real-world data; new services can be provided to customers, or existing business processes can be improved. Events are a well-known concept in business processes. However, there is no appropriate abstraction mechanism to encapsulate event stream processing in units that represent business functions in a coherent manner across the process modeling, process execution, and IT infrastructure layer. In this paper we present Event Stream Processing Units (SPUs) as such an abstraction mechanism. SPUs encapsulate application logic for event stream processing and enable a seamless transition between process models, executable process representations, and components at the IT layer. We derive requirements for SPUs and introduce EPC and BPMN extensions to model SPUs at the abstract and at the technical process layer. We introduce a transformation from SPUs in EPCs to SPUs in BPMN and implement our modeling notation extensions in Software AG ARIS. We present a runtime infrastructure that executes SPUs and supports implicit invocation and completion semantics. We illustrate our approach using a logistics process as running example.


title: Comparison of Modern Methods for Analyzing Repeated Measures Data With Missing Values.

abstract: Missing data are a pervasive problem in many psychological applications in the real world. In this article we study the impact of dropout on the operational characteristics of several approaches that can be easily implemented with commercially available software. These approaches include the covariance pattern model based on an unstructured covariance matrix (CPM-U) and the true covariance matrix (CPM-T), multiple imputation-based generalized estimating equations (MI-GEE), and weighted generalized estimating equations (WGEE). Under the missing at random mechanism, the MI-GEE approach was always robust. The CPM-T and CPM-U methods were also able to control the error rates provided that certain minimum sample size requirements were met, whereas the WGEE was more prone to inflated error rates. In contrast, under the missing not at random mechanism, all evaluated approaches were generally invalid. Our results also indicate that the CPM methods were more powerful than the MI-GEE and WGEE methods and their supe...


NOTE 3 D-Printed Shepp-Logan Phantom as a Real-World Benchmark for MRI


title: Charcoal Burning and Maternal Filicide-Suicide Trends in Taiwan: The Impact of Accessibility of Lethal Methods

abstract: Charcoal burning has emerged as a novel suicide method in Taiwan and its impact on maternal filicide-suicide (MFS) remains unexplored. Using official national mortality data and reports of MFS cases from electronic newspaper archives, the authors aimed to examine whether the newly available charcoal burning was associated with an increase in MFS incidents during the period from 1999 to 2006. The trends for changes in age/gender/method-specific suicide rates and MFS incidence were analyzed and then correlated with each other. The results indicated that charcoal burning was the leading method of filicide in reported MFS incidents. The increase in MFS incidents paralleled that of charcoal burning-specific suicide rates in females aged 25–44 years, while suicide rates by other methods did not change significantly. Easy accessibility and perceived painlessness as conveyed by the media might account for the choices of charcoal burning for MFS. Restricting access to charcoal burning should therefore be prioritized for further prevention strategies.


title: Blockchain-based decentralized content trust for docker images

abstract: It is feasible to deploy Docker containers in IoT (Internet of Things) devices because their runtime overhead is almost zero. Default Docker installation does not verify an image authenticity. Authentication is vital for users to trust that the image is not malicious or tampered with. As Docker is currently a popular choice for developers, tightening its security is a priority for system administrators and DevOps engineers. Docker recently deployed Notary as a solution to verify authenticity of their images. Notary is a viable solution, but it has some potential threats. This paper specifically addresses its vulnerability towards Denial-of-Service (DoS) attacks, and propose a potential solution: blockchain-based Decentralized Docker Trust (DDT). The proposed solution involves decentralizing the trust via a blockchain. The solution greatly reduces the risk of DoS and at the same time provides a signature verification service for Docker images. We demonstrate the proposed blockchain-based solution’s scalability and efficiency by conducting performance evaluation. At the same time, we also implemented a system prototype of Decentralized Docker Trust (DDT), and conducted performance evaluation for it on Amazon Web Services (AWS) across multiple data centers.


title: Effects of Cadmium Exposure on Growth and Metabolic Profile of Bermudagrass [Cynodon dactylon (L.) Pers.]

abstract: Metabolic responses to cadmium (Cd) may be associated with variations in Cd tolerance in plants. The objectives of this study were to examine changes in metabolic profiles in bermudagrass in response to Cd stress and to identify predominant metabolites associated with differential Cd tolerance using gas chromatography-mass spectrometry. Two genotypes of bermudagrass with contrasting Cd tolerance were exposed to 0 and 1.5 mM CdSO4 for 14 days in hydroponics. Physiological responses to Cd were evaluated by determining turf quality, growth rate, chlorophyll content and normalized relative transpiration. All these parameters exhibited higher tolerance in WB242 than in WB144. Cd treated WB144 transported more Cd to the shoot than in WB242. The metabolite analysis of leaf polar extracts revealed 39 Cd responsive metabolites in both genotypes, mainly consisting of amino acids, organic acids, sugars, fatty acids and others. A difference in the metabolic profiles was observed between the two bermudagrass genotypes exposed to Cd stress. Seven amino acids (norvaline, glycine, proline, serine, threonine, glutamic acid and gulonic acid), four organic acids (glyceric acid, oxoglutaric acid, citric acid and malic acid,) and three sugars (xylulose, galactose and talose) accumulated more in WB242 than WB144. However, compared to the control, WB144 accumulated higher quantities of sugars than WB242 in the Cd regime. The differential accumulation of these metabolites could be associated with the differential Cd tolerance in bermudagrass.


title: Human-robot co-navigation using anticipatory indicators of human walking motion

abstract: Mobile, interactive robots that operate in human-centric environments need the capability to safely and efficiently navigate around humans. This requires the ability to sense and predict human motion trajectories and to plan around them. In this paper, we present a study that supports the existence of statistically significant biomechanical turn indicators of human walking motions. Further, we demonstrate the effectiveness of these turn indicators as features in the prediction of human motion trajectories. Human motion capture data is collected with predefined goals to train and test a prediction algorithm. Use of anticipatory features results in improved performance of the prediction algorithm. Lastly, we demonstrate the closed-loop performance of the prediction algorithm using an existing algorithm for motion planning within dynamic environments. The anticipatory indicators of human walking motion can be used with different prediction and/or planning algorithms for robotics; the chosen planning and prediction algorithm demonstrates one such implementation for human-robot co-navigation.


title: Extroverts tweet differently from introverts in Weibo

abstract: As dominant factors driving human actions, personalities can be excellent indicators to predict the offline and online behavior of individuals. However, because of the great expense and inevitable subjectivity in questionnaires and surveys, it is challenging for conventional studies to explore the connection between personality and behavior and to gain insight in the context of a large number of individuals. Considering the increasingly important role of online social media in daily communications, we argue that the footprints of massive numbers of individuals, such as tweets on Weibo, can be used as a proxy to infer personality and further understand its function in shaping online human behavior. In this study, a map from self-reports of personalities to online profiles of 293 active users on Weibo is established to train a competent machine learning model, which then successfully identifies more than 7000 users as extroverts or introverts. Systematic comparison from the perspectives of tempo-spatial patterns, online activities, emotional expressions and attitudes to virtual honors show that extroverts indeed behave differently from introverts on Weibo. Our findings provide solid evidence to justify the methodology of employing machine learning to objectively study the personalities of a massive number of individuals and shed light on applications of probing personalities and corresponding behaviors solely through online profiles.


title: Dialogue on the Principle of Mass-Energy Equivalence

abstract: This paper is directed to the readers who are familiar with the earlier papers by the author on the topic of mass-energy equivalence. A number of important questions about the total energy equation H = mv 2 and its implications are answered qualitatively. The relationship between the equation H = mv 2 and the 4-vector (Minkowski) representation of Special Relativity is discussed in detail. Other issues, such as de Broglie’s original formulation of wave mechanics, are also discussed.


title: Feasibility of Imaging Photoplethysmography

abstract: Contact and spot measurement have limited the application of photoplethysmography (PPG), thus an imaging PPG system comprising a digital CMOS camera and three wavelength light-emitting diodes (LEDs) is developed to detect the blood perfusion in tissue. With the means of the imaging PPG system, the ideally contactless monitoring with larger field of view and the different depth of tissue by applying multi- wavelength illumination can be achieved to understand the blood perfusion change. Corresponding to the individual wavelength LED illumination, the PPG signals can be derived in the both transmission and reflection modes, respectively. The outcome explicitly reveals the imaging PPG is able to detect blood perfusion in a illuminated tissue and indicates the vascular distribution and the blood cell response to individual wavelength LED. The functionality investigation leads to the engineering model for 3-D visualized blood perfusion of tissue and the development of imaging PPG tomography.


title: Change Distilling:Tree Differencing for Fine-Grained Source Code Change Extraction

abstract: A key issue in software evolution analysis is the identification of particular changes that occur across several versions of a program. We present change distilling, a tree differencing algorithm for fine-grained source code change extraction. For that, we have improved the existing algorithm by Chawathe et al. for extracting changes in hierarchically structured data. Our algorithm extracts changes by finding both a match between the nodes of the compared two abstract syntax trees and a minimum edit script that can transform one tree into the other given the computed matching. As a result, we can identify fine-grained change types between program versions according to our taxonomy of source code changes. We evaluated our change distilling algorithm with a benchmark that we developed, which consists of 1,064 manually classified changes in 219 revisions of eight methods from three different open source projects. We achieved significant improvements in extracting types of source code changes: Our algorithm approximates the minimum edit script 45 percent better than the original change extraction approach by Chawathe et al. We are able to find all occurring changes and almost reach the minimum conforming edit script, that is, we reach a mean absolute percentage error of 34 percent, compared to the 79 percent reached by the original algorithm. The paper describes both our change distilling algorithm and the results of our evolution.


title: Octagonal Ring Antenna (ORA) array design for mobile base stations

abstract: The original work on Octagonal Ring Antenna (ORA) was proposed as part of the Square Kilometer Array (SKA) project and there are opportunities to develop the technology for use in mobile communications. This paper provides a preliminary technical assessment of the ORA application to mobile base station. This involves modeling of the ORA antenna array in simulation software package Computer Simulation Technology (CST) Microwave Studio, prototyping and testing of the proposed ORA array in anechoic chamber. The good agreement between the simulation and measurement has validated the prototyped ORA array. The proposed preliminary ORA array has demonstrated its potential use for mobile industry.


title: VI-Tennis: a vibrotactile/audio exergame for players who are visually impaired

abstract: Lack of physical activity is a serious health concern for individuals who are visually impaired as they have fewer opportunities and incentives to engage in physical activities that provide the amounts and kinds of stimulation sufficient to maintain adequate fitness and to support a healthy standard of living. Exergames are video games that use physical activity as input and which have the potential to change sedentary lifestyles and associated health problems such as obesity. We identify that exergames have a number properties that could overcome the barriers to physical activity that individuals with visual impairments face. However, exergames rely upon being able to perceive visual cues that indicate to the player what input to provide. This paper presents VI Tennis, a modified version of a popular motion sensing exergame that explores the use of vibrotactile and audio cues. The effectiveness of providing multimodal (tactile/audio) versus unimodal (audio) cues was evaluated with a user study with 13 children who are blind. Children achieved moderate to vigorous levels of physical activity- the amount required to yield health benefits. No significant difference in active energy expenditure was found between both versions, though children scored significantly better with the tactile/audio version and also enjoyed playing this version more, which emphasizes the potential of tactile/audio feedback for engaging players for longer periods of time.


title: Fine-graind Image Classification via Combining Vision and Language

abstract: Fine-grained image classification is a challenging task due to the large intra-class variance and small inter-class variance, aiming at recognizing hundreds of sub-categories belonging to the same basic-level category. Most existing fine-grained image classification methods generally learn part detection models to obtain the semantic parts for better classification accuracy. Despite achieving promising results, these methods mainly have two limitations: (1) not all the parts which obtained through the part detection models are beneficial and indispensable for classification, and (2) fine-grained image classification requires more detailed visual descriptions which could not be provided by the part locations or attribute annotations. For addressing the above two limitations, this paper proposes the two-stream model combing vision and language (CVL) for learning latent semantic representations. The vision stream learns deep representations from the original visual information via deep convolutional neural network. The language stream utilizes the natural language descriptions which could point out the discriminative parts or characteristics for each image, and provides a flexible and compact way of encoding the salient visual aspects for distinguishing sub-categories. Since the two streams are complementary, combing the two streams can further achieves better classification accuracy. Comparing with 12 state-of-the-art methods on the widely used CUB-200-2011 dataset for fine-grained image classification, the experimental results demonstrate our CVL approach achieves the best performance.


title: 3D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single Image

abstract: 3D reconstruction from single view images is an ill-posed problem. Inferring the hidden regions from self-occluded images is both challenging and ambiguous. We propose a two-pronged approach to address these issues. To better incorporate the data prior and generate meaningful reconstructions, we propose 3D-LMNet, a latent embedding matching approach for 3D reconstruction. We first train a 3D point cloud auto-encoder and then learn a mapping from the 2D image to the corresponding learnt embedding. To tackle the issue of uncertainty in the reconstruction, we predict multiple reconstructions that are consistent with the input view. This is achieved by learning a probablistic latent space with a novel view-specific diversity loss. Thorough quantitative and qualitative analysis is performed to highlight the significance of the proposed approach. We outperform state-of-the-art approaches on the task of single-view 3D reconstruction on both real and synthetic datasets while generating multiple plausible reconstructions, demonstrating the generalizability and utility of our approach.


title: Zero-shot keyword spotting for visual speech recognition in-the-wild

abstract: Visual keyword spotting (KWS) is the problem of estimating whether a text query occurs in a given recording using only video information. This paper focuses on visual KWS for words unseen during training, a real-world, practical setting which so far has received no attention by the community. To this end, we devise an end-to-end architecture comprising (a) a state-of-the-art visual feature extractor based on spatiotemporal Residual Networks, (b) a grapheme-to-phoneme model based on sequence-to-sequence neural networks, and (c) a stack of recurrent neural networks which learn how to correlate visual features with the keyword representation. Different to prior works on KWS, which try to learn word representations merely from sequences of graphemes (i.e. letters), we propose the use of a grapheme-to-phoneme encoder-decoder model which learns how to map words to their pronunciation. We demonstrate that our system obtains very promising visual-only KWS results on the challenging LRS2 database, for keywords unseen during training. We also show that our system outperforms a baseline which addresses KWS via automatic speech recognition (ASR), while it drastically improves over other recently proposed ASR-free KWS methods.


title: Multiprocessor

abstract: Presents the case for billion-transistor processor architectures that will consist of chip multiprocessors (CMPs): multiple (four to 16) simple, fast processors on one chip. In their proposal, each processor is tightly coupled to a small, fast, level-one cache, and all processors share a larger level-two cache. The processors may collaborate on a parallel job or run independent tasks (as in the SMT proposal). The CMP architecture lends itself to simpler design, faster validation, cleaner functional partitioning, and higher theoretical peak performance. However for this architecture to realize its performance potential, either programmers or compilers will have to make code explicitly parallel. Old ISAs will be incompatible with this architecture (although they could run slowly on one of the small processors).


A Computational Approach to Grammatical Coding of English Words


Data Mining with Decision Trees: Theory and Applications


title: A Particle-and-Density Based Evolutionary Clustering Method for Dynamic Networks

abstract: Recently, dynamic networks are attracting increasing interest due to their high potential in capturing natural and social phenomena over time. Discovery of evolutionary communities in dynamic networks has become a critical task. The previous evolutionary clustering methods usually adopt the temporal smoothness framework, which has a desirable feature of controlling the balance between temporal noise and true concept drift of communities. They, however, have some major drawbacks: (1) assuming only a fixed number of communities over time; and (2) not allowing arbitrary start/stop of community over time. The forming of new communities and dissolving of existing communities are very common phenomena in real dynamic networks. In this paper, we propose a new particle-and-density based evolutionary clustering method that efficiently discovers a variable number of communities of arbitrary forming and dissolving. We first model a dynamic network as a collection of lots of particles called nano-communities, and a community as a densely connected subset of particles, called a quasi l-clique-by-clique (shortly, l-KK). Each particle contains a small amount of information about the evolution of data or patterns, and the quasi l-KKs inherent in a given dynamic network provide us with guidance on how to find a variable number of communities of arbitrary forming and dissolving. We propose a density-based clustering method that efficiently finds temporally smoothed local clusters of high quality by using a cost embedding technique and optimal modularity. We also propose a mapping method based on information theory that makes sequences of smoothed local clusters as close as possible to data-inherent quasi l-KKs. The result of the mapping method allows us to easily identify the stage of each community among the three stages: evolving, forming, and dissolving. Experimental studies, by using various data sets, demonstrate that our method improves the clustering accuracy, and at the same time, the time performance by an order of magnitude compared with the current state-of-the art method.


Are we witnessing the spectre of an HPC meltdown?


title: Measuring the Reading Abilities of Dyslexic Children through a Visual Game

abstract: In modern society about 10% of children suffer from a neuro-developmental disorder called dyslexia, which implies difficulties in learning to read. According to recent research, playing action videogames can be a valid teaching tool to improve the reading skills of dyslexic children through forms of engagement and interaction. This paper describes the design, implementation, validation and experimentation of an educational action game oriented to promote phonological and visuo-spatial attention training in dyslexic subjects aged between 7 and 9.


title: Personalizing EEG-based affective models with transfer learning

abstract: Individual differences across subjects and nonstationary characteristic of electroencephalography (EEG) limit the generalization of affective brain-computer interfaces in real-world applications. On the other hand, it is very time consuming and expensive to acquire a large number of subject-specific labeled data for learning subject-specific models. In this paper, we propose to build personalized EEG-based affective models without labeled target data using transfer learning techniques. We mainly explore two types of subject-to-subject transfer approaches. One is to exploit shared structure underlying source domain (source subject) and target domain (target subject). The other is to train multiple individual classifiers on source subjects and transfer knowledge about classifier parameters to target subjects, and its aim is to learn a regression function that maps the relationship between feature distribution and classifier parameters. We compare the performance of five different approaches on an EEG dataset for constructing an affective model with three affective states: positive, neutral, and negative. The experimental results demonstrate that our proposed subject transfer framework achieves the mean accuracy of 76.31% in comparison with a conventional generic classifier with 56.73% in average.


title: The ARTEMIS European driving cycles for measuring car pollutant emissions.

abstract: In the past 10 years, various work has been undertaken to collect data on the actual driving of European cars and to derive representative real-world driving cycles. A compilation and synthesis of this work is provided in this paper. In the frame of the European research project: ARTEMIS, this work has been considered to derive a set of reference driving cycles. The main objectives were as follows: to derive a common set of reference real-world driving cycles to be used in the frame of the ARTEMIS project but also in the frame of on-going national campaigns of pollutant emission measurements, to ensure the compatibility and integration of all the resulting emission data in the European systems of emission inventory; to ensure and validate the representativity of the database and driving cycles by comparing and taking into account all the available data regarding driving conditions; to include in three real-world driving cycles (urban, rural road and motorway) the diversity of the observed driving conditions, within sub-cycles allowing a disaggregation of the emissions according to more specific driving conditions (congested and free-flow urban). Such driving cycles present a real advantage as they are derived from a large database, using a methodology that was widely discussed and approved. In the main, these ARTEMIS driving cycles were designed using the available data, and the method of analysis was based to some extent on previous work. Specific steps were implemented. The study includes characterisation of driving conditions and vehicle uses. Starting conditions and gearbox use are also taken into account.


title: Microplastic pollution in China's inland water systems: A review of findings, methods, characteristics, effects, and management.

abstract: The pollution of marine environments and inland waters by plastic debris has raised increasing concerns worldwide in recent years. China is the world's largest developing country and the largest plastic producer. In this review, we gather available information on microplastic pollution in China's inland water systems. The results show that microplastics are ubiquitous in the investigated inland water systems, and high microplastic abundances were observed in developed areas. Although similar sampling and analytical methods were used for microplastic research in inland water and marine systems, methods of investigation should be standardized in the future. The characteristics of the detected microplastics suggest secondary sources as their major sources. The biological and ecological effects of microplastics have been demonstrated, but their risks are difficult to determine at this stage due to the discrepancy between the field-collected microplastics and microplastics used in ecotoxicological studies. Although many laws and regulations have already been established to manage and control plastic waste in China, the implementation of these laws and regulations has been ineffective and sometimes difficult. Several research priorities are identified, and we suggest that the Chinese government should be more proactive in tackling plastic pollution problems to protect the environment and fulfill international responsibilities.


title: Space-time sketching of character animation

abstract: We present a space-time abstraction for the sketch-based design of character animation. It allows animators to draft a full coordinated motion using a single stroke called the space-time curve (STC). From the STC we compute a dynamic line of action (DLOA) that drives the motion of a 3D character through projective constraints. Our dynamic models for the line's motion are entirely geometric, require no pre-existing data, and allow full artistic control. The resulting DLOA can be refined by over-sketching strokes along the space-time curve, or by composing another DLOA on top leading to control over complex motions with few strokes. Additionally, the resulting dynamic line of action can be applied to arbitrary body parts or characters. To match a 3D character to the 2D line over time, we introduce a robust matching algorithm based on closed-form solutions, yielding a tight match while allowing squash and stretch of the character's skeleton. Our experiments show that space-time sketching has the potential of bringing animation design within the reach of beginners while saving time for skilled artists.


title: Successful ERP implementation: an integrative model

abstract: Purpose – The paper aims to present a conceptual model that better defines critical success factors to ERP implementation organized with the technology, organization and environment (TOE) framework. The paper also adds to current literature the critical success factor of trust with the vendor, system and consultant which has largely been ignored in the past.Design/methodology/approach – The paper uses past literature and theoretical and conceptual framework development to illustrate a new conceptual model that incorporates critical success factors that have both been empirically tied to ERP implementation success in the past and new insights into how trust impacts ERP implementation success.Findings – The paper finds a lack of research depicted in how trust impacts ERP implementation success and likewise a lack of a greater conceptual model organized to provide insight into ERP implementation success.Originality/value – The paper proposes a holistic conceptual framework for ERP implementation success and ...


Automatic Annotation of Semantic Term Types in the Complete ACL Anthology Reference Corpus.


title: Do Online Reviews Reflect a Product's True Perceived Quality? - An Investigation of Online Movie Reviews Across Cultures

abstract: When does the reported average of online ratings match perceived average assessment? We apply behavioral theory to capture intentions in rating online movie reviews in two dissimilar countries - China and the United States. We argue that consumers' rating behaviors are affected by cultural influences. Based on data collected from imdb.com and douban.com, we found significant differences across raters from these two different cultures. Additionally, we examined how cultural elements influence rating behavior for a hybrid culture - Singapore. Under-reporting bias occurs when consumers with extreme opinions are more likely to report their opinions than consumers with moderate reviews, resulting in reviews that may be biased estimators of quality and certainly have higher variance. An experimental study shows that under-reporting is more prevalent among U.S. online social network, and thus that online reviews are more reflective of a movie's true perceived quality in Chinese and Singapore than in the U.S.


title: Description of shape information for 2-D and 3-D objects

abstract: The description of the spatial characteristics of two- and three-dimensional objects, in the framework of MPEG-7, is considered. The shape of an object is one of its fundamental properties, and this paper describes an efficient way to represent the coarse shape, scale and composition properties of an object. This representation is invariant to resolution, translation and rotation, and may be used for both two-dimensional (2-D) and three-dimensional (3-D) objects. This coarse shape descriptor will be included in the eXperimentation Model (XM) of MPEG-7. Applications of such a description to search object databases, in particular the CAESAR anthropometric database are discussed.


title: Combination of long term and short term forecasts, with application to tourism demand forecasting

abstract: Forecast combination is a well-established and well-tested approach for improving the forecasting accuracy. One beneficial strategy is to use constituent forecasts that have diverse information. In this paper we consider the idea of diversity being accomplished by using different time aggregations. For example, we could create a yearly time series from a monthly time series and produce forecasts for both, then combine the forecasts. These forecasts would each be tracking the dynamics of different time scales, and would therefore add diverse types of information. A comparison of several forecast combination methods, performed in the context of this setup, shows that this is indeed a beneficial strategy and generally provides a forecasting performance that is better than the performances of the individual forecasts that are combined.


title: Machine Learning-Based Antenna Selection in Wireless Communications

abstract: This letter is the first attempt to conflate a machine learning technique with wireless communications. Through interpreting the antenna selection (AS) in wireless communications (i.e., an optimization-driven decision) to multiclass-classification learning (i.e., data-driven prediction), and through comparing the learning-based AS using $k$ -nearest neighbors and support vector machine algorithms with conventional optimization-driven AS methods in terms of communications performance, computational complexity, and feedback overhead, we provide insight into the potential of fusion of machine learning and wireless communications.


title: Accelerated inexact soft-impute for fast large-scale matrix completion

abstract: Matrix factorization tries to recover a low-rank matrix from limited observations. A state-of-theart algorithm is the Soft-Impute, which exploits a special "sparse plus low-rank" structure of the matrix iterates to allow efficient SVD in each iteration. Though Soft-Impute is also a proximal gradient algorithm, it is generally believed that acceleration techniques are not useful and will destroy the special structure. In this paper, we show that Soft-Impute can indeed be accelerated without compromising the "sparse plus low-rank" structure. To further reduce the per-iteration time complexity, we propose an approximate singular value thresholding scheme based on the power method. Theoretical analysis shows that the proposed algorithm enjoys the fast O(1=T2) convergence rate of accelerated proximal gradient algorithms. Extensive experiments on both synthetic and large recommendation data sets show that the proposed algorithm is much faster than Soft-Impute and other state-of-the-art matrix completion algorithms.


Students as a Teaching Resource in Preparing Educational Leaders: An International Masters Programme


title: Deep Learning for Health Informatics

abstract: With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on machine learning in health informatics. Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful tool for machine learning, promising to reshape the future of artificial intelligence. Rapid improvements in computational power, fast data storage, and parallelization have also contributed to the rapid uptake of the technology in addition to its predictive power and ability to generate automatically optimized high-level features and semantic interpretation from the input data. This article presents a comprehensive up-to-date review of research employing deep learning in health informatics, providing a critical analysis of the relative merit, and potential pitfalls of the technique as well as its future outlook. The paper mainly focuses on key applications of deep learning in the fields of translational bioinformatics, medical imaging, pervasive sensing, medical informatics, and public health.


title: A hybrid ontology-based information extraction system

abstract: Information Extraction is the process of automatically obtaining knowledge from plain text. Because of the ambiguity of written natural language, Information Extraction is a difficult task. Ontology-based Information Extraction OBIE reduces this complexity by including contextual information in the form of a domain ontology. The ontology provides guidance to the extraction process by providing concepts and relationships about the domain. However, OBIE systems have not been widely adopted because of the difficulties in deployment and maintenance. The Ontology-based Components for Information Extraction OBCIE architecture has been proposed as a form to encourage the adoption of OBIE by promoting reusability through modularity. In this paper, we propose two orthogonal extensions to OBCIE that allow the construction of hybrid OBIE systems with higher extraction accuracy and a new functionality. The first extension utilizes OBCIE modularity to integrate different types of implementation into one extraction system, producing a more accurate extraction. For each concept or relationship in the ontology, we can select the best implementation for extraction, or we can combine both implementations under an ensemble learning schema. The second extension is a novel ontology-based error detection mechanism. Following a heuristic approach, we can identify sentences that are logically inconsistent with the domain ontology. Because the implementation strategy for the extraction of a concept is independent of the functionality of the extraction, we can design a hybrid OBIE system with concepts utilizing different implementation strategies for extracting correct or incorrect sentences. Our evaluation shows that, in the implementation extension, our proposed method is more accurate in terms of correctness and completeness of the extraction. Moreover, our error detection method can identify incorrect statements with a high accuracy.


title: Support Vector Data Description

abstract: Data domain description concerns the characterization of a data set. A good description covers all target data but includes no superfluous space. The boundary of a dataset can be used to detect novel data or outliers. We will present the Support Vector Data Description (SVDD) which is inspired by the Support Vector Classifier. It obtains a spherically shaped boundary around a dataset and analogous to the Support Vector Classifier it can be made flexible by using other kernel functions. The method is made robust against outliers in the training set and is capable of tightening the description by using negative examples. We show characteristics of the Support Vector Data Descriptions using artificial and real data.


title: Energy-balance studies reveal associations between gut microbes, caloric load, and nutrient absorption in humans

abstract: Background: Studies in mice indicate that the gut microbiome influences both sides of the energy-balance equation by contributing to nutrient absorption and regulating host genes that affect adiposity. However, it remains uncertain as to what extent gut microbiota are an important regulator of nutrient absorption in humans. Objective: With the use of a carefully monitored inpatient study cohort, we tested how gut bacterial community structure is affected by altering the nutrient load in lean and obese individuals and whether their microbiota are correlated with the efficiency of dietary energy harvest. Design: We investigated dynamic changes of gut microbiota during diets that varied in caloric content (2400 compared with 3400 kcal/d) by pyrosequencing bacterial 16S ribosomal RNA (rRNA) genes present in the feces of 12 lean and 9 obese individuals and by measuring ingested and stool calories with the use of bomb calorimetry. Results: The alteration of the nutrient load induced rapid changes in the gut microbiota. These changes were directly correlated with stool energy loss in lean individuals such that a 20% increase in Firmicutes and a corresponding decrease in Bacteroidetes were associated with an increased energy harvest of ’150 kcal. A high degree of overfeeding in lean individuals was accompanied by a greater fractional decrease in stool energy loss. Conclusions: These results show that the nutrient load is a key variable that can influence the gut (fecal) bacterial community structure over short time scales. Furthermore, the observed associations between gut microbes and nutrient absorption indicate a possible role of the human gut microbiota in the regulation of the nutrient harvest. This trial was registered at clinicaltrials.gov as NCT00414063. Am J Clin Nutr doi: 10.3945/ajcn.110.010132.


title: The Singularity Isn’t Simple! (However We Look at It) A Random Walk between Science Fiction and Science Fact

abstract: It seems to be accepted that intelligence—artificial or otherwise—and ‘the singularity’ are inseparable concepts: ‘The singularity’ will apparently arise from AI reaching a, supposedly particular, but actually poorly-defined, level of sophistication; and an empowered combination of hardware and software will take it from there (and take over from us). However, such wisdom and debate are simplistic in a number of ways: firstly, this is a poor definition of the singularity; secondly, it muddles various notions of intelligence; thirdly, competing arguments are rarely based on shared axioms, so are frequently pointless; fourthly, our models for trying to discuss these concepts at all are often inconsistent; and finally, our attempts at describing any ‘post-singularity’ world are almost always limited by anthropomorphism. In all of these respects, professional ‘futurists’ often appear as confused as storytellers who, through freer licence, may conceivably have the clearer view: perhaps then, that becomes a reasonable place to start. There is no attempt in this paper to propose, or evaluate, any research hypothesis; rather simply to challenge conventions. Using examples from science fiction to illustrate various assumptions behind the AI/singularity debate, this essay seeks to encourage discussion on a number of possible futures based on different underlying metaphysical philosophies. Although properly grounded in science, it eventually looks beyond the technology for answers and, ultimately, beyond the Earth itself.


title: Implementation of adaptive and synthetic-aperture processing schemes in integrated active-passive sonar systems

abstract: Progress in the implementation of state-of-the-art signal processing schemes in sonar systems is limited mainly by the moderate advance made in sonar computing architectures and the lack of operational evaluation of the advanced processing schemes. Until recently, matrix-based processing techniques, such as adaptive and synthetic-aperture processing, could not be efficiently implemented in the current type of sonar systems, even though it is widely believed that they have advantages that can address the requirements associated with the difficult operational problems that next-generation sonars will have to solve. Interestingly, adaptive and synthetic-aperture techniques may be viewed by other disciplines as conventional schemes. For the sonar technology discipline, however, they are considered as advanced schemes because of the very limited progress that has been made in their implementation in sonar systems. This paper is intended to address issues of implementation of advanced processing schemes in sonar systems and also to serve as a brief overview to the principles and applications of advanced sonar signal processing. The main development reported in this paper deals with the definition of a generic beam-forming structure that allows the implementation of nonconventional signal-processing techniques in integrated active-passive sonar systems. These schemes are adaptive and synthetic-aperture beam formers that have been shown experimentally to provide improvements in array gain for signals embedded in partially correlated noise fields. Using target tracking and localization results as performance criteria, the impact and merits of these techniques are contrasted with those obtained using the conventional beam former.


title: Logic and Databases: A Deductive Approach

abstract: The purpose of this paper is to show that logic provides a convenient formalism for studying classical database problems. There are two main parts to the paper, devoted respectively to conventional databases and deductive databases. In the first part, we focus on query languages, integrity modeling and maintenance, query optimization, and data dependencies. The second part deals mainly with the representation and manipulation of deduced facts and incomplete information. Categories and Subject Descriptors: H.2.1 [Database Management]: Logical Design— data models; H.2.3 [Database Management]: Languages— query languages; H.2.4 [Database Management]: Systems— query processing General Terms: Deductive Databases, Indefinite Data, Logic and Databases, Null Values, Relational Databases


title: A Compact Multiband Antenna for 4G/LTE and WLAN Mobile Phone Applications

abstract: A very simple multiband monopole antenna system is designed and developed. The proposed antenna system comprises of two alphabetic letter shaped elements: one is C-shaped and another is inverted L-shaped — all together can combinedly operate the wideband frequency range of LTE-1,2,3,7,13,14,22, 40, 43 and WLAN bands. The C-shaped is responsible for leading the lower bands and L-shaped for higher bands. The reflection coefficient is achieved 6 dB in case of LTE-13 and LTE-14 bands, whereas for all other bands is greater than 10 dB. This multiband antenna element has a compact volume of 20 × 24 × 0.8mm3 with total system size of 20 × 70 × 0.8mm3, which is suitable for 4G-LTE ultra slim mobile phones and wireless devices.


title: Blockchain and Artificial Intelligence

abstract: It is undeniable that artificial intelligence (AI) and blockchain concepts are spreading at a phenomenal rate. Both technologies have distinct degree of technological complexity and multi-dimensional business implications. However, a common misunderstanding about blockchain concept, in particular, is that blockchain is decentralized and is not controlled by anyone. But the underlying development of a blockchain system is still attributed to a cluster of core developers. Take smart contract as an example, it is essentially a collection of codes (or functions) and data (or states) that are programmed and deployed on a blockchain (say, Ethereum) by different human programmers. It is thus, unfortunately, less likely to be free of loopholes and flaws. In this article, through a brief overview about how artificial intelligence could be used to deliver bug-free smart contract so as to achieve the goal of blockchain 2.0, we to emphasize that the blockchain implementation can be assisted or enhanced via various AI techniques. The alliance of AI and blockchain is expected to create numerous possibilities.


title: The use and usefulness of the ISO/IEC 9126 quality standard

abstract: This paper reports an evaluation the utility of ISO/IEC 9126. ISO/IEC 9126 is an international standard intended to ensure the quality of all software-intensive products including safety-critical systems where lives are at risk if software components fail. Our evaluation exercise arose from an experiment that required a quality assessment of outputs of the design process. Although ISO/IEC 9126 is intended to support evaluation of intermediate software products, both the experimental subjects (158 final year computer science and engineering student) and experimenters found the standard was ambiguous in meaning, incomplete with respect to quality characteristics and overlapping with respect to measured properties. We conclude that ISO/IEC 9126 is not suitable for measuring design quality of software products. This casts serious doubts as to the validity of the standard as a whole.


title: Evaluation of solid-state accelerometer for positioning of vehicle

abstract: An evaluation of a low-cost, small sized solid state accelerometer in the absence of absolute positioning information is performed in this paper. Acceleration signal outputted by the sensor is doubly integrated with time that yields the travel distance. Due to bias and Thermal noise of the accelerometer which is accumulate in time, Kalman filter is need to be design to increase the accuracy of position estimation system. A modeling for bias of solid state accelerometer ADXL250 QCG without aiding sensor evaluated in this paper. During the preceding time instants, the position information becomes increasingly untrustworthy and the validity of the position updates decays. So, we used a smoother algorithm based Kalman filter for better estimation accuracy in position estimation. Result show smoothing algorithm increase the accuracy of position estimate for short time duration


title: Query-Oriented Data Cleaning with Oracles

abstract: As key decisions are often made based on information contained in a database, it is important for the database to be as complete and correct as possible. For this reason, many data cleaning tools have been developed to automatically resolve inconsistencies in databases. However, data cleaning tools provide only best-effort results and usually cannot eradicate all errors that may exist in a database. Even more importantly, existing data cleaning tools do not typically address the problem of determining what information is missing from a database. To overcome the limitations of existing data cleaning techniques, we present QOCO, a novel query-oriented system for cleaning data with oracles. Under this framework, incorrect (resp. missing) tuples are removed from (added to) the result of a query through edits that are applied to the underlying database, where the edits are derived by interacting with domain experts which we model as oracle crowds. We show that the problem of determining minimal interactions with oracle crowds to derive database edits for removing (adding) incorrect (missing) tuples to the result of a query is NP-hard in general and present heuristic algorithms that interact with oracle crowds. Finally, we implement our algorithms in our prototype system QOCO and show that it is effective and efficient through a comprehensive suite of experiments.


title: Effect of Ultrasonic Treatment on the Grain Refinement and Mechanical Properties of AZ91 Magnesium Alloy

abstract: The present study examines the microstructural evolution and mechanical properties of AZ91 magnesium alloy solidified under high intensity ultrasonic treatment (UST). High intensity ultrasonic vibrations were introduced isothermally below the liquidus temperature into the solidifying AZ91 alloy for the refinement of primary α-Mg grains and β-Mg17Al12 intermetallic phase. Various microstructures were produced using different intensity of ultrasonic vibration at a constant temperature and fixed duration of UST. Without any ultrasonic treatment, the structure contains dendrites of primary α-Mg phase which are coarser and non-uniform in size. Nearly uniform, equiaxed grains with continuous and uniform network of intermetallic phase segregated along the grain boundaries were obtained in alloy subjected to high intensity ultrasonic vibration. The average grain size in this case decreased drastically from 300 μm for (without UST) to 17 μm (with UST at intensity of 4 kW/cm2). Vickers hardness also increased steadily with increase in ultrasonic intensity. The mechanisms for microstructural refinement are discussed and it is concluded that the fine uniform grain structure achieved under ultrasonic vibrations is attributed to the cavitation and the acoustic flow induced by ultrasonic vibrations.


title: A deep multi-level network for saliency prediction

abstract: This paper presents a novel deep architecture for saliency prediction. Current state of the art models for saliency prediction employ Fully Convolutional networks that perform a non-linear combination of features extracted from the last convolutional layer to predict saliency maps. We propose an architecture which, instead, combines features extracted at different levels of a Convolutional Neural Network (CNN). Our model is composed of three main blocks: a feature extraction CNN, a feature encoding network, that weights low and high level feature maps, and a prior learning network. We compare our solution with state of the art saliency models on two public benchmarks datasets. Results show that our model outperforms under all evaluation metrics on the SALICON dataset, which is currently the largest public dataset for saliency prediction, and achieves competitive results on the MIT300 benchmark.


title: The bubble rebound obstacle avoidance algorithm for mobile robots

abstract: This paper proposes a simple, reactive algorithm for real time obstacle avoidance, aimed to be implemented on low cost, low power microcontrollers. According to this algorithm, the robot reacts to obstacles detected within an area called “sensitivity bubble”, whose shape and size are dynamically adjusted, depending on the kinematics of the robot. Upon detection of an obstacle, the robot “rebounds” in a direction having the lowest density of obstacles, and continues its motion in this direction until the goal becomes visible, or a new obstacle is encountered. Based on experimental results with simulators and real robots, the paper describes the performances and drawbacks of the algorithm, in comparison with other, simple obstacle avoidance algorithms.


title: An Efficient Algorithm for Calculating the Exact Hausdorff Distance

abstract: The Hausdorff distance (HD) between two point sets is a commonly used dissimilarity measure for comparing point sets and image segmentations. Especially when very large point sets are compared using the HD, for example when evaluating magnetic resonance volume segmentations, or when the underlying applications are based on time critical tasks, like motion detection, then the computational complexity of HD algorithms becomes an important issue. In this paper we propose a novel efficient algorithm for computing the exact Hausdorff distance. In a runtime analysis, the proposed algorithm is demonstrated to have nearly-linear complexity. Furthermore, it has efficient performance for large point set sizes as well as for large grid size; performs equally for sparse and dense point sets; and finally it is general without restrictions on the characteristics of the point set. The proposed algorithm is tested against the HD algorithm of the widely used national library of medicine insight segmentation and registration toolkit (ITK) using magnetic resonance volumes with extremely large size. The proposed algorithm outperforms the ITK HD algorithm both in speed and memory required. In an experiment using trajectories from a road network, the proposed algorithm significantly outperforms an HD algorithm based on R-Trees.


title: Inability of selected lactobacilli to inhibit the heat-labile or heat-stable enterotoxin effects of Escherichia coli B7A.

abstract: 1 ::: No evidence could be found of an ability among five strains of Lactobacillus to neutralize the effects of heat-labile or heat-stable toxin from human enterotoxigenic Escherichia coli B7A.


title: Fuzzy decision support system for risk analysis in e-commerce development

abstract: This paper describes the development of a fuzzy decision support system (FDSS) for the assessment of risk in e-commerce (EC) development. A Web-based prototype FDSS is designed and developed to assist EC project managers in identifying potential EC risk factors and the corresponding project risks. A risk analysis model for EC development using a fuzzy set approach is proposed and incorporated into the FDSS. The results of an evaluation indicate that the prototype performs to expectations.


title: Social networks, communication styles, and learning performance in a CSCL community

abstract: The aim of this study is to empirically investigate the relationships between communication styles, social networks, and learning performance in a computer-supported collaborative learning (CSCL) community. Using social network analysis (SNA) and longitudinal survey data, we analyzed how 31 distributed learners developed collaborative learning social networks, when they had work together on the design of aerospace systems using online collaboration tools. The results showed that both individual and structural factors (i.e., communication styles and a pre-existing friendship network) significantly affected the way the learners developed collaborative learning social networks. More specifically, learners who possessed high willingness to communicate (WTC) or occupied initially peripheral network positions were more likely to explore new network linkages. We also found that the resultant social network properties significantly influenced learners' performance to the extent that central actors in the emergent collaborative social network tended to get higher final grades. The study suggests that communication and social networks should be central elements in a distributed learning environment. We also propose that the addition of personality theory (operationalized here as communication styles) to structural analysis (SNA) contributes to an enhanced picture of how distributed learners build their social and intellectual capital in the context of CSCL.


title: Morphological, molecular and statistical tools to identify Castanea species and their hybrids

abstract: The aim of this study was to investigate, in relation to reference samples, the genetic structure of several natural Castanea populations of unknown origin and of hybrids from the clone collection of the Lourizan Forest Research Center. A total of 115 individuals sampled from four Castanea sativa stands located in the northwest of Spain, 61 Castanea crenata individuals and 27 Castanea mollissima individuals were classified on the basis of four morphological traits, and genotyped with 11 microsatellite loci to define a set of reference samples. The data analyzed with the program STRUCTURE detected four clusters: the two Asiatic species and two clusters in C. sativa. From these reference samples, pure individuals and hybrids of known genealogy were simulated to determine the efficiency with which STRUCTURE and NEWHYBRIDS assigned them to pure or hybrid groups and to a specific genealogical class, respectively. As expected, the discrimination and assignment of the simulated individuals improved with increasing F st value. The two clusters identified within C. sativa may correspond to gene pools with different adaptive characteristics previously identified in provenance tests; pure and admixed populations of both C. sativa gene pools were identified.


title: Fixed Point and Bregman Iterative Methods for Matrix Rank Minimization

abstract: The linearly constrained matrix rank minimization problem is widely applicable in many fields such as control, signal processing and system identification. The tightest convex relaxation of this problem is the linearly constrained nuclear norm minimization. Although the latter can be cast as a semidefinite programming problem, such an approach is computationally expensive to solve when the matrices are large. In this paper, we propose fixed point and Bregman iterative algorithms for solving the nuclear norm minimization problem and prove convergence of the first of these algorithms. By using a homotopy approach together with an approximate singular value decomposition procedure, we get a very fast, robust and powerful algorithm, which we call FPCA (Fixed Point Continuation with Approximate SVD), that can solve very large matrix rank minimization problems. Our numerical results on randomly generated and real matrix completion problems demonstrate that this algorithm is much faster and provides much better recoverability than semidefinite programming solvers such as SDPT3. For example, our algorithm can recover 1000 x 1000 matrices of rank 50 with a relative error of 1e-5 in about 3 minutes by sampling only 20 percent of the elements. We know of no other method that achieves as good recoverability. Numerical experiments on online recommendation, DNA microarray data set and image inpainting problems demonstrate the effectiveness of our algorithms.


title: Building a Dynamic Reputation System for DNS

abstract: The Domain Name System (DNS) is an essential protocol used by both legitimate Internet applications and cyber attacks. For example, botnets rely on DNS to support agile command and control infrastructures. An effective way to disrupt these attacks is to place malicious domains on a "blocklist" (or "blacklist") or to add a filtering rule in a firewall or network intrusion detection system. To evade such security countermeasures, attackers have used DNS agility, e.g., by using new domains daily to evade static blacklists and firewalls. In this paper we propose Notos, a dynamic reputation system for DNS. The premise of this system is that malicious, agile use of DNS has unique characteristics and can be distinguished from legitimate, professionally provisioned DNS services. Notos uses passive DNS query data and analyzes the network and zone features of domains. It builds models of known legitimate domains and malicious domains, and uses these models to compute a reputation score for a new domain indicative of whether the domain is malicious or legitimate. We have evaluated Notos in a large ISP's network with DNS traffic from 1.4 million users. Our results show that Notos can identify malicious domains with high accuracy (true positive rate of 96.8%) and low false positive rate (0.38%), and can identify these domains weeks or even months before they appear in public blacklists.


title: Difficult vaginal hysterectomy.

abstract: A difficult vaginal hysterectomy can challenge the most accomplished pelvic surgeon. Large uterine size or prior pelvic surgery is commonly thought to make a vaginal hysterectomy more difficult, but more common issues such as large body habitus or extensive prolapse may make performing a vaginal hysterectomy even more technically challenging. The knowledge and expertise needed to perform a routine vaginal hysterectomy are prerequisites for managing more difficult cases. As techniques in pelvic surgery continue to evolve, it is critical that this minimally invasive procedure continue to be taught and performed.


title: A Case Study on Attribute Recognition of Heated Metal Mark Image Using Deep Convolutional Neural Networks

abstract: Heated metal mark is an important trace to identify the cause of fire. However, traditional methods mainly focus on the knowledge of physics and chemistry for qualitative analysis and make it still a challenging problem. This paper presents a case study on attribute recognition of the heated metal mark image using computer vision and machine learning technologies. The proposed work is composed of three parts. Material is first generated. According to national standards, actual needs and feasibility, seven attributes are selected for research. Data generation and organization are conducted, and a small size benchmark dataset is constructed. A recognition model is then implemented. Feature representation and classifier construction methods are introduced based on deep convolutional neural networks. Finally, the experimental evaluation is carried out. Multi-aspect testings are performed with various model structures, data augments, training modes, optimization methods and batch sizes. The influence of parameters, recognitio efficiency and execution time are also analyzed. The results show that with a fine-tuned model, the recognition rate of attributes metal type, heating mode, heating temperature, heating duration, cooling mode, placing duration and relative humidity are 0.925, 0.908, 0.835, 0.917, 0.928, 0.805 and 0.92, respectively. The proposed method recognizes the attribute of heated metal mark with preferable effect, and it can be used in practical application.


title: Depression, Anxiety and Symptoms of Stress among Baccalaureate Nursing Students in Hong Kong: A Cross-Sectional Study

abstract: This study examines the prevalence of depression, anxiety and symptoms of stress among baccalaureate nursing students in Hong Kong. Recent epidemiological data suggest that the prevalence of mild to severe depression, anxiety and stress among qualified nurses in Hong Kong stands at 35.8%, 37.3% and 41.1%, respectively. A total of 661 nursing students were recruited to participate in our cross-sectional mental health survey using the Depression, Anxiety and Stress Scale 21. Multiple logistic regression was used to determine significant relationships between variables. Working in general medicine, being in financial difficulty, having sleep problems, not having leisure activity and perceiving oneself in poor mental health were significant correlates of past-week depression, anxiety and stress. Year of study, physical inactivity and family crisis in the past year correlated significantly with depression. Imbalanced diets significantly correlated with anxiety. Stress was significantly associated with a lack of alone time. This is the first study to confirm empirically that clinical specialty, financial difficulties and lifestyle factors can increase nursing students’ levels of depression and anxiety and symptoms of stress. Prevention, including the early detection and treatment of mental disorder, promises to reduce the prevalence of these indicators among this group.


title: Numerical Recipes in FORTRAN - The Art of Scientific Computing - Second Edition

abstract: Keywords: informatique ; numerical recipes Note: contient un CDRom Reference Record created on 2004-09-07, modified on 2016-08-08


DESC: enabling secure data exchange based on smart contracts


title: T-splines and T-NURCCs

abstract: This paper presents a generalization of non-uniform B-spline surfaces called T-splines. T-spline control grids permit T-junctions, so lines of control points need not traverse the entire control grid. T-splines support many valuable operations within a consistent framework, such as local refinement, and the merging of several B-spline surfaces that have different knot vectors into a single gap-free model. The paper focuses on T-splines of degree three, which are C2 (in the absence of multiple knots). T-NURCCs (Non-Uniform Rational Catmull-Clark Surfaces with T-junctions) are a superset of both T-splines and Catmull-Clark surfaces. Thus, a modeling program for T-NURCCs can handle any NURBS or Catmull-Clark model as special cases. T-NURCCs enable true local refinement of a Catmull-Clark-type control grid: individual control points can be inserted only where they are needed to provide additional control, or to create a smoother tessellation, and such insertions do not alter the limit surface. T-NURCCs use stationary refinement rules and are C2 except at extraordinary points and features.


title: Common phase error due to phase noise in OFDM-estimation and suppression

abstract: Orthogonal frequency division multiplexing (OFDM) has already become a very attractive modulation scheme for many applications. Unfortunately OFDM is very sensitive to synchronization errors, one of them being phase noise, which is of great importance in modern WLAN systems which target high data rates and tend to use higher frequency bands because of the spectrum availability. In this paper we propose a linear Kalman filter as a means for tracking phase noise and its suppression. The algorithm is pilot based. The performance of the proposed method is investigated and compared with the performance of other known algorithms.


title: On the Calculation of Optimality Ranges for Relational Query Execution Plans

abstract: Cardinality estimation is a crucial task in query optimization and typically relies on heuristics and basic statistical approximations. At execution time, estimation errors might result in situations where intermediate result sizes may differ from the estimated ones, so that the originally chosen plan is not the optimal plan anymore. In this paper we analyze the deviation from the estimate, and denote the cardinality range of an intermediate result, where the optimal plan remains optimal as the optimality range. While previous work used simple heuristics to calculate similar ranges, we generate the precise bounds for the optimality range considering all relevant plan alternatives. Our experimental results show that the fixed optimality ranges used in previous work fail to characterize the range of cardinalities where a plan is optimal. We derive theoretical worst case bounds for the number of enumerated plans required to compute the precise optimality range, and experimentally show that in real queries this number is significantly smaller. Our experiments also show the benefit for applications like Mid-Query Re-Optimization in terms of significant execution time improvement.


title: CoolSpots: reducing the power consumption of wireless mobile devices with multiple radio interfaces

abstract: CoolSpots enable a wireless mobile device to automatically switch between multiple radio interfaces, such as WiFi and Bluetooth, in order to increase battery lifetime. The main contribution of this work is an exploration of the policies that enable a system to switch among these interfaces, each with diverse radio characteristics and different ranges, in order to save power - supported by detailed quantitative measurements. The system and policies do not require any changes to the mobile applications themselves, and changes required to existing infrastructure are minimal. Results are reported for a suite of commonly used applications, such as file transfer, web browsing, and streaming media, across a range of operating conditions. Experimental validation of the CoolSpot system on a mobile research platform shows substantial energy savings: more than a 50% reduction in energy consumption of the wireless subsystem is possible, with an associated increase in the effective battery lifetime.


title: Optical-Flow based Self-Supervised Learning of Obstacle Appearance applied to MAV Landing

abstract: Monocular motion cues have been widely used by Micro Air Vehicles (MAVs) to detect obstacles during visual navigation. However, this approach requires significant movement, which reduces the efficiency of navigation and may even introduce risks in narrow spaces. In this paper, we introduce a novel setup of self-supervised learning (SSL), in which motion cues serve as a scaffold to learn the visual appearance of obstacles in the environment. We apply it to a landing task, in which initially 'surface roughness' is estimated from the optical flow field in order to detect obstacles. Subsequently, a regression function is learned that maps appearance features represented by texton distributions to a surface roughness estimate. After learning, the MAV can detect obstacles by just analyzing a still image. This allows the MAV to search for a landing spot without moving. We first demonstrate this principle to work with offline tests involving images captured from an on-board camera, and then demonstrate the principle in flight. Although surface roughness is a property of the entire flow field in the global image, the appearance learning even allows for the pixel-wise segmentation of obstacles.


title: A new optimization model for market basket analysis with allocation considerations: A genetic algorithm solution approach

abstract: Nowadays market basket analysis is one of the interested research areas of the data mining that has received more attention by researchers. But, most of the related research focused on the traditional and heuristic algorithms with limited factors that are not the only influential factors of the basket market analysis. In this paper to efficient modeling and analysis of the market basket data, the optimization model is proposed with considering allocation parameter as one of the important and effectual factors of the selling rate. The genetic algorithm approach is applied to solve the formulated non-linear binary programming problem and a numerical example is used to illustrate the presented model. The provided results reveal that the obtained solutions seem to be more realistic and applicable.


title: Sustainability in Fashion Business Operations

abstract: Under the global trend of sustainability, many companies selling fashion products have to reshape their operational strategies. Over the past few years, we have witnessed many fashion companies going green by re-engineering their business processes and establishing their formal sustainability programs. Many important topics, such as closed-loop supply chain management, corporate social responsibility, and economic sustainability, are all related to sustainable fashion business operations management. This paper provides a brief review of these critical topics, introduces the special issue, and proposes future research areas to achieve sustainable operations management in the fashion business.


Knet : beginning deep learning with 100 lines of


ANALYSIS AND DESIGN OF AN ULTRA WIDEBAND DIRECTIONAL COUPLER


title: Compact microstrip diplexers based on a dual-passband filter

abstract: Novel microstrip diplexer structures composed of a dual-passband filter together with two matching circuits are proposed. By using only a single dual-passband filter, the proposed diplexer may be made compact when compared with the conventional diplexer configuration which has two single- passband filters. In this study, the dual-passband filter is realized using the parallel-coupled microstrip structures, and the matching circuits are developed based on the stepped- impedance transmission-line sections along with suitable open stubs. By suitably incorporating the open stubs into the matching circuits, a 3 rd-order Chebyshev microstrip diplexer with dual center frequencies at 2.36 GHz and 5.17 GHz and an acceptable isolation is implemented.


title: The Tara 24 mva magnet system

abstract: Power transmission, conversion, control, and regulation aspects of the Tara tandem mirror 24 mva magnet system are discussed in detail. The magnet coil set, magnetic field configuration and support vacuum chamber are described. It is shown that a complete turn key pulse magnet power system has been built for under $21/kW.


title: Crisis decision theory: Decisions in the face of negative events.

abstract: How do people respond to negative life events? Crisis decision theory combines the strengths of coping theories with research on decision making to predict the responses people choose under negative circumstances. The theory integrates literatures on coping, health behavior, and decision making, among others, into 3 stages that describe the process of responding to negative events: (a) assessing the severity of the negative event, (b) determining response options, and (c) evaluating response options. The author reviews and organizes the relevant research on factors that shape information processing at each stage and that ultimately predict decisions in the face of negative events. Finally, the author presents a critique of crisis decision theory and discusses areas for future research.


title: Guiding Reinforcement Learning Exploration Using Natural Language

abstract: In this work we present a technique to use natural language to help reinforcement learning generalize to unseen environments. This technique uses neural machine translation, specifically the use of encoder-decoder networks, to learn associations between natural language behavior descriptions and state-action information. We then use this learned model to guide agent exploration using a modified version of policy shaping to make it more effective at learning in unseen environments. We evaluate this technique using the popular arcade game, Frogger, under ideal and non-ideal conditions. This evaluation shows that our modified policy shaping algorithm improves over a Q-learning agent as well as a baseline version of policy shaping.


title: On measuring social friend interest similarities in recommender systems

abstract: Social recommender system has become an emerging research topic due to the prevalence of online social networking services during the past few years. In this paper, aiming at providing fundamental support to the research of social recommendation problem, we conduct an in-depth analysis on the correlations between social friend relations and user interest similarities. When evaluating interest similarities without distinguishing different friends a user has, we surprisingly observe that social friend relations generally cannot represent user interest similarities. A user's average similarity on all his/her friends is even correlated with the average similarity on some other randomly selected users. However, when measuring interest similarities using a finer granularity, we find that the similarities between a user and his/her friends are actually controlled by the network structure in the friend network. Factors that affect the interest similarities include subgraph topology, connected components, number of co-friends, etc. We believe our analysis provides substantial impact for social recommendation research and will benefit ongoing research in both recommender systems and other social applications.


title: An implementation of data encryption for Internet of Things using blowfish algorithm on FPGA

abstract: Information security has become an important issue in data communications. One method to ensure the security of data is to use cryptographic method. Cryptography is a method to encode the information to keep the information from being hacked by the other party. The implementation of cryptography is used a significant amount of computer resources. Various range application of blowfish algorithm can be implemented for data encryption sent from an Internet of Things physical network which have IP-based data. In this research, blowfish algorithm is implemented on FPGA using VHDL programming language, and monitored the number of FPGA resource that is used. The blowfish algorithm is analyzed by computing certain metrics performances such as security, encryption time, avalanche effect, and throughput from multiple testing scenarios for system reliability. The testing showed that blowfish algorithm gave a good performance when implemented in FPGA and show a good alternative to proposed as network security on Internet of Things.


title: Robust and Dense Depth Estimation for Light Field Images

abstract: We propose a depth estimation method for light field images. Light field images can be considered as a collection of 2D images taken from different viewpoints arranged in a regular grid. We exploit this configuration and compute the disparity maps between specific pairs of views. This computation is carried out by a state of the art two-view stereo method providing a non dense disparity estimation. We propose a disparity interpolation method increasing the density and improving the accuracy of this initial estimate. Disparities obtained from several pairs of views are fused to obtain a unique and robust estimation. Finally, different experiments on synthetic and real images show how the proposed method outperforms state of the art results.


title: Robust Principal Component Analysis with Complex Noise

abstract: The research on robust principal component analysis (RPCA) has been attracting much attention recently. The original RPCA model assumes sparse noise, and use the L1-norm to characterize the error term. In practice, however, the noise is much more complex and it is not appropriate to simply use a certain Lp-norm for noise modeling. We propose a generative RPCA model under the Bayesian framework by modeling data noise as a mixture of Gaussians (MoG). The MoG is a universal approximator to continuous distributions and thus our model is able to fit a wide range of noises such as Laplacian, Gaussian, sparse noises and any combinations of them. A variational Bayes algorithm is presented to infer the posterior of the proposed model. All involved parameters can be recursively updated in closed form. The advantage of our method is demonstrated by extensive experiments on synthetic data, face modeling and background subtraction.


title: Full On-Chip CMOS Low-Dropout Voltage Regulator

abstract: The time required to make test measurements across a large diameter wafer, such as a 300 mm wafer, is reduced by using a wafer measuring system that employs theta ( theta ) and radial (r) control instead of X-Y control. In one embodiment, a measurement arm (14) having a measurement head (16) is positioned over a wafer chuck (18) and a wafer (12) is placed onto the wafer chuck (18). The wafer (12) is then moved to one or more measuring points below the measurement head (16) via theta rotation ( theta ) and radial positioning. A measurement is then taken at the selected location.


title: Folding and crumpling adaptive sheets

abstract: We present a technique for simulating plastic deformation in sheets of thin materials, such as crumpled paper, dented metal, and wrinkled cloth. Our simulation uses a framework of adaptive mesh refinement to dynamically align mesh edges with folds and creases. This framework allows efficient modeling of sharp features and avoids bend locking that would be otherwise caused by stiff in-plane behavior. By using an explicit plastic embedding space we prevent remeshing from causing shape diffusion. We include several examples demonstrating that the resulting method realistically simulates the behavior of thin sheets as they fold and crumple.


title: Multi-User Precoding and Channel Estimation for Hybrid Millimeter Wave Systems

abstract: In this paper, we develop a low-complexity channel estimation for hybrid millimeter wave (mmWave) systems, where the number of radio frequency (RF) chains is much less than the number of antennas equipped at each transceiver. The proposed mmWave channel estimation algorithm first exploits multiple frequency tones to estimate the strongest angle-of-arrivals (AoAs) at both base station (BS) and user sides for the design of analog beamforming matrices. Then, all the users transmit orthogonal pilot symbols to the BS along the directions of the estimated strongest AoAs in order to estimate the channel. The estimated channel will be adopted to design the digital zero-forcing (ZF) precoder at the BS for the multi-user downlink transmission. The proposed channel estimation algorithm is applicable to both the non-sparse and sparse mmWave channel environments. Furthermore, we derive a tight achievable rate upper bound of the digital ZF precoding with the proposed channel estimation algorithm scheme. Our analytical and simulation results show that the proposed scheme obtains a considerable achievable rate of fully digital systems, where the number of RF chains equipped at each transceiver is equal to the number of antennas. Besides, considering the effect of various types of errors, i.e., random phase errors, transceiver analog beamforming errors, and equivalent channel estimation errors, we derive a closed-form approximation for the achievable rate of the considered scheme. We illustrate the robustness of the proposed channel estimation and multi-user downlink precoding scheme against the system imperfection.


title: Comparison of Hybrid Localization Schemes using RSSI, TOA, and TDOA

abstract: This paper presents a simulation study of nonhybrid and hybrid localization techniques using RSSI, TOA, and TDOA location dependent parameters. Maximum likelihood and weighted least squares are considered and developed for both non-hybrid and hybrid cases. Monte-Carlo simulations using realistic radio parameters extracted from an ultra wide band measurement campaign are carried out in order to assess the performances of different techniques and to show the importance of hybrid data fusion for localization.


title: An SIW Cavity-Backed Self-Diplexing Antenna

abstract: A novel and simple design of a substrate-integrated waveguide (SIW)-based self-diplexing antenna is presented in this letter. The SIW cavity is fed by two individual microstrip feed lines. The two transverse slots of different lengths, which radiate at two distinct resonant frequencies, are etched on the top surface of the cavity. A high isolation (better than 27.9 dB) is achieved between two ports, which leads to obtaining the self-diplexing characteristic of the antenna. The proposed antenna offers a gain of 3.56 and 5.24 dBi at 8.26 and 10.46 GHz, respectively. The antenna exhibits independent frequency tunability at both the operating bands.


title: Decision Support with Belief Functions Theory for Seabed Characterization

abstract: The seabed characterization from sonar images is a very hard task because of the produced data and the unknown environment, even for an human expert. In this work we propose an original approach in order to combine binary classifiers arising from different kinds of strategies such as one-versus-one or one-versus-rest, usually used in the SVM-classification. The decision functions coming from these binary classifiers are interpreted in terms of belief functions in order to combine these functions with one of the numerous operators of the belief functions theory. Moreover, this interpretation of the decision function allows us to propose a process of decisions by taking into account the rejected observations too far removed from the learning data, and the imprecise decisions given in unions of classes. This new approach is illustrated and evaluated with a SVM in order to classify the different kinds of sediment on image sonar.


title: Hybrid Mode Wideband Patch Antenna Loaded With a Planar Metamaterial Unit Cell

abstract: A wideband patch antenna loaded with a planar metamaterial unit cell is proposed. The metamaterial unit cell is composed of an interdigital capacitor and a complementary split-ring resonator (CSRR) slot. A dispersion analysis of the metamaterial unit cell reveals that an increase in series capacitance can decrease the half-wavelength resonance frequency, thus reducing the electrical size of the proposed antenna. In addition, circulating current distributions around the CSRR slot with increased interdigital finger length bring about the TM01 mode radiation, while the normal radiation mode is the TM10 mode. Furthermore, the TM01 mode can be combined with the TM10 mode without a pattern distortion. The hybridization of the two modes yields a wideband property (6.8%) and a unique radiation pattern that is comparable with two independent dipole antennas positioned orthogonally. Also, the proposed antenna achieves high efficiency (96%) and reasonable gain (3.85 dBi), even though the electrical size of the antenna is only 0.24λ0×0.24λ0×0.02λ0.


title: A broadband 480-GHz passive frequency doubler in 65-nm bulk CMOS with 0.23mW output power

abstract: A passive 480-GHz frequency doubler based on accumulation-mode MOS varactor in CMOS process is reported. Using a compact partially-coupled ring structure, the doubler achieves a simultaneous broadband matching for fundamental and 2nd-harmonic signals. With optimized gate length and oxide thickness, the MOS varactor achieves a dynamic cutoff frequency of 870GHz while sustaining large voltage swing for high power generation. At 480-GHz output frequency, the doubler has a measured minimum conversion loss of 14.3dB and an unsaturated output power of 0.23mW. The simulated 3-dB output bandwidth is 70GHz (14.6%). The doubler is fabricated using 65-nm low power bulk CMOS technology and consumes zero DC power.


title: Using Nighttime Satellite Imagery as a Proxy Measure of Human Well-Being

abstract: Improving human well-being is increasingly recognized as essential for movement toward a sustainable and desirable future. Estimates of different aspects of human well-being, such as Gross Domestic Product, or percentage of population with access to electric power, or measuring the distribution of income in society are often fraught with problems. There are few standardized methods of data collection; in addition, the required data is not obtained in a reliable manner and on a repetitive basis in many parts of the world. Consequently, inter-comparability of the data that does exist becomes problematic. Data derived from nighttime satellite imagery has helped develop various globally consistent proxy measures of human well-being at the gridded, sub-national, and national level. We review several ways in which nighttime satellite imagery has been used to measure the human well-being within nations.


title: A vector-contraction inequality for Rademacher complexities

abstract: The contraction inequality for Rademacher averages is extended to Lipschitz functions with vector-valued domains, and it is also shown that in the bounding expression the Rademacher variables can be replaced by arbitrary iid symmetric and sub-gaussian variables. Example applications are given for multi-category learning, K-means clustering and learning-to-learn.


title: MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search

abstract: The MalaCards human disease database (http://www.malacards.org/) is an integrated compendium of annotated diseases mined from 68 data sources. MalaCards has a web card for each of ∼20 000 disease entries, in six global categories. It portrays a broad array of annotation topics in 15 sections, including Summaries, Symptoms, Anatomical Context, Drugs, Genetic Tests, Variations and Publications. The Aliases and Classifications section reflects an algorithm for disease name integration across often-conflicting sources, providing effective annotation consolidation. A central feature is a balanced Genes section, with scores reflecting the strength of disease-gene associations. This is accompanied by other gene-related disease information such as pathways, mouse phenotypes and GO-terms, stemming from MalaCards’ affiliation with the GeneCards Suite of databases. MalaCards’ capacity to inter-link information from complementary sources, along with its elaborate search function, relational database infrastructure and convenient data dumps, allows it to tackle its rich disease annotation landscape, and facilitates systems analyses and genome sequence interpretation. MalaCards adopts a ‘flat’ disease-card approach, but each card is mapped to popular hierarchical ontologies (e.g. International Classification of Diseases, Human Phenotype Ontology and Unified Medical Language System) and also contains information about multi-level relations among diseases, thereby providing an optimal tool for disease representation and scrutiny.


title: Industrie 4.0: Enabling technologies

abstract: With the development of industries, we have realized the third industrial revolution. Following the development of Cyber-Physical Systems (CPS), industrial wireless network and some other enabling technologies, the fourth industrial revolution is being gradually rolled out. This paper presents an overview of the background, concept, basic methods, major technologies and application scenarios for industrie 4.0. In our view, industrie 4.0 as an abstract concept can closely integrate the physical world with virtual world. This strategy of industrie 4.0 will lead to more and more people coming to participate in the manufacturing process and further popularize our products through CPS technology. The typical approach for industrie 4.0 is the social manufacturing. In fact, the social manufacturing can directly link our customers' need and our industries, but it must be based on the enabling technologies, such as embedded systems, wireless sensor network, industrial robots, 3D printing, cloud computing, and big data. Therefore, this paper in detail explains these concepts, advantages and the relations to industries. We can foresee that our life will be changed to be more efficient, fast, safe and convenient due to the development of industrie 4.0 in the near future.


title: Arc Flash Calculations for Exposures to DC Systems

abstract: Electrical systems with AC voltage have been shown to exhibit arc flash incident energy during faults. Are there arc flash hazards related to dc systems, such as battery banks for UPS or drives, or dc buses used in chemical processes? Methods are available to estimate arc flash energy of ac exposures, but not dc. This paper will show the basic equations for maximum power from a dc arc and the resulting estimated arc flash incident energy. Research is required to find accurate values, but until research is completed, these equations can help the engineer provide a preliminary estimate of the thermal energy values for applying protective clothing for these exposures.


title: A new and efficient stemming technique for Arabic Text Categorization

abstract: Text preprocessing of Arabic Language is a challenge and crucial stage in Text Categorization (TC) particularly and Text Mining (TM) generally. Stemming algorithms can be used in Arabic text preprocessing to reduce multiple forms of the word to one form (root or stem). Arabic stemming algorithms can be classified, according to the desired level of analysis, as root-based approach (exp Khoja); stem-based approach (Larkey); and statistical approach (n- garm). Yet no a complete stemmer for this language is available: The existing stemmers not have a high performance. In this paper, in order to improve the accuracy of stemming and therefore the accuracy of our proposed TC system, an efficient hybrid method is proposed for stemming Arabic text. The effectiveness of the aforementioned four methods was evaluated and compared in term of the accuracy of the Naive Bayesian classifier used in our TC system. The proposed stemming algorithm was found to supersede the other stemming ones: The obtained results illustrate that using the proposed stemmer enhances the performance of Arabic Text Categorization: the averages accuracies are: 74.41% for khoja, 59.71% for light stemming, 48.17% for n-grams, and 82.33% for our stemmer