title: Classification and synthesis of quality approaches in e‐government services

abstract: Purpose – The purpose of this paper is to present a multi‐faceted summary and classification of the existing literature in the field of quality of service for e‐government and outline the main components of a quality model for e‐government services.Design/methodology/approach – Starting with fundamental quality principles the paper examines and analyzes 36 different quality approaches concerning public sector services, e‐services in general and more specifically e‐government services. Based on the dimensions measured by each approach the paper classifies the approaches and concludes on the basic factors needed for the development of a complete quality model of e‐government services.Findings – Based on the classification of literature approaches, the paper provides information about the main components of a quality model that may be used for the continuous monitoring and measuring of public e‐services' quality. The classification forms the basis for answering questions that must be addressed by the quality...


title: Pressure Ulcer Prevention and Management

abstract: This chapter reviews 218 published and unpublished research reports of pressure ulcer prevention and management by nurse researchers and researchers from other disciplines. The electronic databases MEDLINE (1966-July 2001), CINAHL (1982-June 2001), AMED (1985-July 2001), and EI Compedex Plus (1980-June 2001) were selected for the searches because of their focus on health and applied research. Moreover, evaluations of previous review articles and seminal studies that were published before 1966 are also included. Research conducted worldwide and published in English between 1930 and 2001 was included for review. Studies using descriptive, correlational, longitudinal, and randomized control trials were included. This review found that numerous gaps remain in our understanding of effective pressure ulcer prevention and management. Moreover, the majority of pressure ulcer care is derived from expert opinion rather than empirical evidence. Thus, additional research is needed to investigate pressure ulcer risk factors of ethnic minorities. Further studies are needed that examine the impact of specific preventive interventions (e.g., turning intervals based on risk stratification) and the cost-effectiveness of comprehensive prevention programs to prevent pressure ulcers. Finally, an evaluation is needed of various aspects of pressure ulcer management (e.g., use of support surfaces, use of adjunctive therapies) and healing of pressure ulcers.


title: The Beta Reputation System

abstract: Reputation systems can be used to foster good behaviour and to encourage adherence to contracts in e-commerce. Several reputation systems have been deployed in practical applications or proposed in the literature. This paper describes a new system called the beta reputation system which is based on using beta probability density functions to combine feedback and derive reputation ratings. The advantage of the beta reputation system is flexibility and simplicity as well as its foundation on the theory of statistics.


title: Learning analytics: drivers, developments and challenges

abstract: Learning analytics is a significant area of technology-enhanced learning that has emerged during the last decade. This review of the field begins with an examination of the technological, educational and political factors that have driven the development of analytics in educational settings. It goes on to chart the emergence of learning analytics, including their origins in the 20th century, the development of data-driven analytics, the rise of learning-focused perspectives and the influence of national economic concerns. It next focuses on the relationships between learning analytics, educational data mining and academic analytics. Finally, it examines developing areas of learning analytics research, and identifies a series of future challenges.


title: Fatal positional asphyxia

abstract: A 64-year-old man was found dead in an unusual prone positition on the floor close to the bed in his apartment. The upper part of the body was leaning against a bedside cabinet (approximately 40 cm in height) with the head, the left arm and the right hand lying on the top of the cabinet (Fig. 1). The neck was considerably hyperextended and rotated to the left, so that the right side of the neck was pressed firmly against the upper front edge of the cabinet. The man was not wedged in a confined narrow space, for example, a space between the bed and the cabinet. Only a duvet was spread over the lower part of his body, and there were no objects that could have fixed his body position. Police investigations revealed that the man was not known to abuse illegal substances, but to consume alcoholic beverages on a daily basis. He had no previous medical history and was last seen alive 5 days earlier.


title: Cloud, fog and edge: Cooperation for the future?

abstract: Traditional cloud-based infrastructures are not enough for the current demands of Internet of Things (IoT) applications. Two major issues are the limitations in terms of latency and network bandwidth. In recent years, the concepts of fog computing and edge computing were proposed to alleviate these limitations by moving data processing capabilities closer to the network edge. Considering IoT growth and development forecasts, we believe the full potential of IoT can, in many cases, only be unlocked by combining cloud, fog and edge computing. This paper discusses four possible approaches for distributing workload among these levels. We also highlight developments and possibilities as well as consider challenges for implementation in the areas of hardware, machine learning, security, privacy and communication.


title: A state-space partitioned-block adaptive filter for echo cancellation using inter-band correlations in the Kalman gain computation

abstract: A partitioned-block-based architecture for a model-based acoustic echo canceller in the frequency domain was recently presented. Partitioned-block-based frequency domain adaptive filters provide a lower algorithmic delay compared to the non-partitioned formulations, which is achieved by partitioning the acoustic echo path and hence shortening the time-frequency transforms. Under these circumstances, avoiding the linearisation of the involved circular convolutions can significantly impair the performance. This paper extends the already proposed diagonalization of the Kalman gain matrix by taking into account the neglected inter-band correlations to improve the performance of the acoustic echo canceller. This comes at the cost of a moderate increase in the algorithmic complexity.


title: Social Media: A Review and Tutorial of Applications in Medicine and Health Care

abstract: BACKGROUND ::: Social media are dynamic and interactive computer-mediated communication tools that have high penetration rates in the general population in high-income and middle-income countries. However, in medicine and health care, a large number of stakeholders (eg, clinicians, administrators, professional colleges, academic institutions, ministries of health, among others) are unaware of social media's relevance, potential applications in their day-to-day activities, as well as the inherent risks and how these may be attenuated and mitigated. ::: ::: ::: OBJECTIVE ::: We conducted a narrative review with the aim to present case studies that illustrate how, where, and why social media are being used in the medical and health care sectors. ::: ::: ::: METHODS ::: Using a critical-interpretivist framework, we used qualitative methods to synthesize the impact and illustrate, explain, and provide contextual knowledge of the applications and potential implementations of social media in medicine and health care. Both traditional (eg, peer-reviewed) and nontraditional (eg, policies, case studies, and social media content) sources were used, in addition to an environmental scan (using Google and Bing Web searches) of resources. ::: ::: ::: RESULTS ::: We reviewed, evaluated, and synthesized 76 articles, 44 websites, and 11 policies/reports. Results and case studies are presented according to 10 different categories of social media: (1) blogs (eg, WordPress), (2) microblogs (eg, Twitter), (3) social networking sites (eg, Facebook), (4) professional networking sites (eg, LinkedIn, Sermo), (5) thematic networking sites (eg, 23andMe), (6) wikis (eg, Wikipedia), (7) mashups (eg, HealthMap), (8) collaborative filtering sites (eg, Digg), (9) media sharing sites (eg, YouTube, Slideshare), and others (eg, SecondLife). Four recommendations are provided and explained for stakeholders wishing to engage with social media while attenuating risk: (1) maintain professionalism at all times, (2) be authentic, have fun, and do not be afraid, (3) ask for help, and (4) focus, grab attention, and engage. ::: ::: ::: CONCLUSIONS ::: The role of social media in the medical and health care sectors is far reaching, and many questions in terms of governance, ethics, professionalism, privacy, confidentiality, and information quality remain unanswered. By following the guidelines presented, professionals have a starting point to engage with social media in a safe and ethical manner. Future research will be required to understand the synergies between social media and evidence-based practice, as well as develop institutional policies that benefit patients, clinicians, public health practitioners, and industry alike.


title: Lens-based Ka-band antenna system using planar feed

abstract: This paper presents a simple, low-cost and compact mobile ground terminal antenna for Ka-band satellite communications that operates in the downlink band (19.7–20.2 GHz). The antenna is composed of a shaped dielectric lens which tilts in front of a planar feed to direct the beam. The planar feed is a circularly polarized patch antenna placed inside a cavity. The lens allows a mechanical beam steering from 0° to 57° in relation to zenith with a scan loss of 4.5 dB. In order to show the potential of a planar antenna as a feeder for this application, the proposed system is compared with a previous solution composed of the same dielectric lens and a horn antenna as feeder.


title: Service quality models: a review

abstract: Purpose – The main objective of this paper is to critically appraise various service quality models and identify issues for future research based on the critical analysis of literature.Design/methodology/approach – The paper critically examines 19 different service quality models reported in the literature. The critical review of the different service quality models is intended to derive linkage between them, and highlight the area for further research.Findings – The review of various service quality model revealed that the service quality outcome and measurement is dependent on type of service setting, situation, time, need etc factors. In addition to this even the customer's expectations towards particular services are also changing with respect to factors like time, increase in the number of encounters with a particular service, competitive environment, etc. This paper provides a rich agenda for future research in the subject.Research limitations/implications – This research developed a linkage between...


Gamification of Authoring Interactive E-Books for Children: The Q-Tales Ecosystem


title: The reviewing of object files: Object specific integration of information

abstract: A series of experiments explored a form of object-specific priming. In all experiments a preview field containing two or more letters is followed by a target letter that is to be named. The displays are designed to produce a perceptual interpretation of the target as a new state of an object that previously contained one of the primes. The link is produced in different experiments by a shared location, by a shared relative position in a moving pattern, or by successive appearance in the same moving frame. An object-specific advantage is consistently observed: naming is facilitated by a preview of the target, if (and in some cases only if) the two appearances are linked to the same object. The amount and the object specificity of the preview benefit are not affected by extending the preview duration to 1 s, or by extending the temporal gap between fields to 590 ms. The results are interpreted in terms of a reviewing process, which is triggered by the appearance of the target and retrieves just one of the previewed items. In the absence of an object link, the reviewing item is selected at random. We develop the concept of an object file as a temporary episodic representation, within which successive states of an object are linked and integrated.


title: A study of grid-connected PV-AC module with active power decoupling and ESS

abstract: Because AC module system is directly connected to grid source, power pulsation is appeared at AC module input capacitor. So, it can adversely affect maximum power point tracking (MPPT) control. Furthermore, photovoltaic (PV) module power can be fluctuated with varying irradiation while constant load demand. To reduce power fluctuation for MPPT control and stabilize the PV system with varying conditions, conventional flyback converter has auxiliary circuits for active decoupling and energy storage system (ESS) is proposed for energy efficiency. Proposed AC module system can solve the above problem (i.e. power pulsation, varying irradiation and load demand) by auxiliary circuit operation. But when the proposed AC module system is operating, it has various modes according to power flowing. In this paper, analyzes proposed AC module of operating modes, which show perform the overall system operation. To confirming proposed AC module system, verified by PSIM simulation result.


title: Ka band substrate integrated waveguide slot array antenna with high efficiency

abstract: An efficient K a band substrate integrated waveguide (SIW) slot array antenna is presented. Proposed antenna consists of an arrangement of linear SIW arrays that are fed with a parallel feeding circuit implemented by using both metallic waveguide and SIW power dividers. By using this hybrid feeding scheme, feed line losses are minimized. Measured antenna parameters such as gain, beam width and side lobe levels are compared with electromagnetic simulations and results are reported.


NDR2 promotes the antiviral immune response via facilitating TRIM25-mediated RIG-I activation in macrophages


title: A 0.5 V 1.28-MS/s 4.68-fJ/Conversion-Step SAR ADC With Energy-Efficient DAC and Trilevel Switching Scheme

abstract: This paper describes a 10-bit successive approximation register (SAR) analog-to-digital converter (ADC) with an energy-efficient trilevel alternate switching capacitive digital-to-analog converter (CDAC). The switching scheme of this CDAC preserves the features of the asymmetric-switching CDAC. By narrowing and smoothing the dynamic variation of DAC voltage, the switching scheme diminishes the dynamic offset effect induced by the asymmetric-switching CDAC. The CDAC reduces the capacitor requirement by almost fourfold and improves the average switching energy efficiency by almost 86.5% when compared with the conventional switching CDACs. This SAR ADC was implemented using the 90-nm CMOS technology, and its measured performances were as follows: 1) spurious free dynamic range of 56.98 dB; 2) signal-to-noise-and-distortion ratio of 68.79 dB; and 3) power dissipation of 3.45 $\mu \text{W}$ at an operation of 0.5 V and 1.28 MS/s. The ADC achieves a figure-of-merit of 4.68-fJ/conversion-step.


title: Seasonal influence in exacerbations of inflammatory bowel disease.

abstract: Several epidemiological studies have suggested a possible seasonality in the presentation and course of inflammatory bowel disease. Our aim was to investigate whether in our area there is a seasonality in the presentation and evolution of inflammatory bowel disease. In this retrospective evaluation 255 patients were included, of whom 141 patients were diagnosed with ulcerative colitis and 114 with Crohn's disease according to conventional criteria, with histological confirmation in 225 patients. Relapse was defined as the development of symptoms that required an increase or change in the usual treatment. Periods of activity were related to the month and grouped within one of four seasonal periods: December to February, March to May, June to August and September to November. Bouts of ulcerative colitis and Crohn's disease were most frequently diagnosed in the period June to August (p < 0.05). Both diseases showed similar seasonality, and analysis of the 305 attacks did not show any significant trend. In conclusion, inflammatory bowel disease begins more frequently in the period June to August, but its course does not show consistent seasonal variations.


title: On hierarchical palmprint coding with multiple features for personal identification in large databases

abstract: Automatic personal identification is a significant component of security systems with many challenges and practical applications. The advances in biometric technology have led to the very rapid growth in identity authentication. This paper presents a new approach to personal identification using palmprints. To tackle the key issues such as feature extraction, representation, indexing, similarity measurement, and fast search for the best match, we propose a hierarchical multifeature coding scheme to facilitate coarse-to-fine matching for efficient and effective palmprint verification and identification in a large database. In our approach, four-level features are defined: global geometry-based key point distance (Level-1 feature), global texture energy (Level-2 feature), fuzzy "interest" line (Level-3 feature), and local directional texture energy (Level-4 feature). In contrast to the existing systems that employ a fixed mechanism for feature extraction and similarity measurement, we extract multiple features and adopt different matching criteria at different levels to achieve high performance by a coarse-to-fine guided search. The proposed method has been tested in a database with 7752 palmprint images from 386 different palms. The use of Level-1, Level-2, and Level-3 features can remove candidates from the database by 9.6%, 7.8%, and 60.6%, respectively. For a system embedded with an Intel Pentium III processor (500 MHz), the execution time of the simulation of our hierarchical coding scheme for a large database with 10/sup 6/ palmprint samples is 2.8 s while the traditional sequential approach requires 6.7 s with 4.5% verification equal error rate. Our experimental results demonstrate the feasibility and effectiveness of the proposed method.


title: Web Service Composition: A Survey of Techniques and Tools

abstract: Web services are a consolidated reality of the modern Web with tremendous, increasing impact on everyday computing tasks. They turned the Web into the largest, most accepted, and most vivid distributed computing platform ever. Yet, the use and integration of Web services into composite services or applications, which is a highly sensible and conceptually non-trivial task, is still not unleashing its full magnitude of power. A consolidated analysis framework that advances the fundamental understanding of Web service composition building blocks in terms of concepts, models, languages, productivity support techniques, and tools is required. This framework is necessary to enable effective exploration, understanding, assessing, comparing, and selecting service composition models, languages, techniques, platforms, and tools. This article establishes such a framework and reviews the state of the art in service composition from an unprecedented, holistic perspective.


title: Deep-Spying: Spying using Smartwatch and Deep Learning

abstract: Wearable technologies are today on the rise, becoming more common and broadly available to mainstream users. In fact, wristband and armband devices such as smartwatches and fitness trackers already took an important place in the consumer electronics market and are becoming ubiquitous. By their very nature of being wearable, these devices, however, provide a new pervasive attack surface threatening users privacy, among others. ::: In the meantime, advances in machine learning are providing unprecedented possibilities to process complex data efficiently. Allowing patterns to emerge from high dimensional unavoidably noisy data. ::: The goal of this work is to raise awareness about the potential risks related to motion sensors built-in wearable devices and to demonstrate abuse opportunities leveraged by advanced neural network architectures. ::: The LSTM-based implementation presented in this research can perform touchlogging and keylogging on 12-keys keypads with above-average accuracy even when confronted with raw unprocessed data. Thus demonstrating that deep neural networks are capable of making keystroke inference attacks based on motion sensors easier to achieve by removing the need for non-trivial pre-processing pipelines and carefully engineered feature extraction strategies. Our results suggest that the complete technological ecosystem of a user can be compromised when a wearable wristband device is worn.


title: Importance effects on performance in event‐based prospective memory tasks

abstract: In the present study we manipulated the importance of performing two event‐based prospective memory tasks. In Experiment 1, the event‐based task was assumed to rely on relatively automatic processes, whereas in Experiment 2 the event‐based task was assumed to rely on a more demanding monitoring process. In contrast to the first experiment, the second experiment showed that importance had a positive effect on prospective memory performance. In addition, the occurrence of an importance effect on prospective memory performance seemed to be mainly due to the features of the prospective memory task itself, and not to the characteristics of the ongoing tasks that only influenced the size of the importance effect. The results suggest that importance instructions may improve prospective memory if the prospective task requires the strategic allocation of attentional monitoring resources.


title: Human-machine interaction: A case study on fake news detection using a backtracking based on a cognitive system

abstract: Abstract Although the Internet provides a variety of news, it also can give confusion caused by personal subjective thoughts such as personal TV, blogs, and unproven news. The unproven news is written in a subjective direction with added personal opinions rather than objective content, so readers may acquire knowledge with the wrong outlook. In addition, fake news is being produced and the problem of social polarization is becoming serious. In the end, it is necessary to detect the fake news, but it is not easy to distinguish the truth of published news because of the lack of fake news distinction time compared to the speed of information sharing on the Internet and the diversity and strong subjectivity of news. Therefore, in this paper, the possibility of fake news is defined by using the reverse-tracking method of the articles which are posted on the Cognitive System. Finally, as the result, the detection rate is average 85%.


title: Greybox fuzzing as a contextual bandits problem

abstract: Greybox fuzzing is one of the most useful and effective techniques for the bug detection in large scale application programs. It uses minimal amount of instrumentation. American Fuzzy Lop (AFL) is a popular coverage based evolutionary greybox fuzzing tool. AFL performs extremely well in fuzz testing large applications and finding critical vulnerabilities, but AFL involves a lot of heuristics while deciding the favored test case(s), skipping test cases during fuzzing, assigning fuzzing iterations to test case(s). In this work, we aim at replacing the heuristics the AFL uses while assigning the fuzzing iterations to a test case during the random fuzzing. We formalize this problem as a `contextual bandit problem' and we propose an algorithm to solve this problem. We have implemented our approach on top of the AFL. We modify the AFL's heuristics with our learned model through the policy gradient method. Our learning algorithm selects the multiplier of the number of fuzzing iterations to be assigned to a test case during random fuzzing, given a fixed length substring of the test case to be fuzzed. We fuzz the substring with this new energy value and continuously updates the policy based upon the interesting test cases it produces on fuzzing.


title: Imagining Artificial Intelligence Applications with People with Visual Disabilities using Tactile Ideation

abstract: There has been a surge in artificial intelligence (AI) technologies co-opted by or designed for people with visual disabilities. Researchers and engineers have pushed technical boundaries in areas such as computer vision, natural language processing, location inference, and wearable computing. But what do people with visual disabilities imagine as their own technological future? To explore this question, we developed and carried out tactile ideation workshops with participants in the UK and India. Our participants generated a large and diverse set of ideas, most focusing on ways to meet needs related to social interaction. In some cases, this was a matter of recognizing people. In other cases, they wanted to be able to participate in social situations without foregrounding their disability. It was striking that this finding was consistent across UK and India despite substantial cultural and infrastructural differences. In this paper, we describe a new technique for working with people with visual disabilities to imagine new technologies that are tuned to their needs and aspirations. Based on our experience with these workshops, we provide a set of social dimensions to consider in the design of new AI technologies: social participation, social navigation, social maintenance, and social independence. We offer these social dimensions as a starting point to forefront users' social needs and desires as a more deliberate consideration for assistive technology design.


Choosing between optical flow algorithms for UAV position change measurement


title: High-performance code generation for stencil computations on GPU architectures

abstract: Stencil computations arise in many scientific computing domains, and often represent time-critical portions of applications. There is significant interest in offloading these computations to high-performance devices such as GPU accelerators, but these architectures offer challenges for developers and compilers alike. Stencil computations in particular require careful attention to off-chip memory access and the balancing of work among compute units in GPU devices. In this paper, we present a code generation scheme for stencil computations on GPU accelerators, which optimizes the code by trading an increase in the computational workload for a decrease in the required global memory bandwidth. We develop compiler algorithms for automatic generation of efficient, time-tiled stencil code for GPU accelerators from a high-level description of the stencil operation. We show that the code generation scheme can achieve high performance on a range of GPU architectures, including both nVidia and AMD devices.


title: An auditory brain–computer interface (BCI)

abstract: Abstract Brain–computer interfaces (BCIs) translate brain activity into signals controlling external devices. BCIs based on visual stimuli can maintain communication in severely paralyzed patients, but only if intact vision is available. Debilitating neurological disorders however, may lead to loss of intact vision. The current study explores the feasibility of an auditory BCI. Sixteen healthy volunteers participated in three training sessions consisting of 30 2–3 min runs in which they learned to increase or decrease the amplitude of sensorimotor rhythms (SMR) of the EEG. Half of the participants were presented with visual and half with auditory feedback. Mood and motivation were assessed prior to each session. Although BCI performance in the visual feedback group was superior to the auditory feedback group there was no difference in performance at the end of the third session. Participants in the auditory feedback group learned slower, but four out of eight reached an accuracy of over 70% correct in the last session comparable to the visual feedback group. Decreasing performance of some participants in the visual feedback group is related to mood and motivation. We conclude that with sufficient training time an auditory BCI may be as efficient as a visual BCI. Mood and motivation play a role in learning to use a BCI.


title: Integrated virtual commissioning an essential activity in the automation engineering process: From virtual commissioning to simulation supported engineering

abstract: Production plants within manufacturing and process industries are becoming more and more complex systems. For a safe and demand sufficient production the automation system of a production plant is mission critical. Therefore the correct functioning and engineering of the automation system is essential. Nowadays the use of a virtual commissioning step before deploying the automation software on the real production plant is used more often. Within the virtual commissioning the automation software is tested against a virtual plant model, sufficient to check out the correct functioning of the automation system. Usually virtual commissioning is used as a step at the end of the automation engineering, as proposed by VDI 4499. Within this paper an integrated virtual commissioning is proposed, where the automation software is continuously tested against a virtual plant model during the overall engineering phase. The virtual plant is growing together with the automation software and thus enables simulation supported automation engineering. Benefits of the proposed workflow are that errors can be detected immediately after the implementation and the use of the virtual plant model could be extended to various stages of the engineering workflow.


title: Bi-directional LSTM Recurrent Neural Network for Chinese Word Segmentation

abstract: Recurrent neural network (RNN) has been broadly applied to natural language process (NLP) problems. This kind of neural network is designed for modeling sequential data and has been testified to be quite efficient in sequential tagging tasks. In this paper, we propose to use bi-directional RNN with long short-term memory (LSTM) units for Chinese word segmentation, which is a crucial task for modeling Chinese sentences and articles. Classical methods focus on designing and combining hand-craft features from context, whereas bi-directional LSTM network (BLSTM) does not need any prior knowledge or pre-designing, and is expert in creating hierarchical feature representation of contextual information from both directions. Experiment result shows that our approach gets state-of-the-art performance in word segmentation on both traditional Chinese datasets and simplified Chinese datasets.


title: Positioning of high-speed trains using 5G new radio synchronization signals

abstract: We study positioning of high-speed trains in 5G new radio (NR) networks by utilizing specific NR synchronization signals. The studies are based on simulations with 3GPP-specified radio channel models including path loss, shadowing and fast fading effects. The considered positioning approach exploits measurement of Time-Of-Arrival (TOA) and Angle-Of-Departure (AOD), which are estimated from beamformed NR synchronization signals. Based on the given measurements and the assumed train movement model, the train position is tracked by using an Extended Kalman Filter (EKF), which is able to handle the non-linear relationship between the TOA and AOD measurements, and the estimated train position parameters. It is shown that in the considered scenario the TOA measurements are able to achieve better accuracy compared to the AOD measurements. However, as shown by the results, the best tracking performance is achieved, when both of the measurements are considered. In this case, a very high, sub-meter, tracking accuracy can be achieved for most (>75%) of the tracking time, thus achieving the positioning accuracy requirements envisioned for the 5G NR. The pursued high-accuracy and high-availability positioning technology is considered to be in a key role in several envisioned HST use cases, such as mission-critical autonomous train systems.


title: Efficient financial time series forecasting model using DWT decomposition

abstract: This paper proposes an efficient time series fore- casting model for exchange rates. Previous literature reveals that Functional Link Artificial Neural Network (FLANN) is very effective in financial time series forecasting involving less computational load and fast forecasting capability. Autoregressive Integrated Moving Average (ARIMA) models are well known for their remarkable forecasting accuracy. In this literature, we have used Discrete Wavelet Transform (DWT) to decompose the in-sample training data into linear (detailed) and nonlinear (approximate) components, then applied ARIMA and FLANN model to forecast the respective components. The proposed method amalgamate the unique strengths of ARIMA, FLANN and DWT to improve the forecasting accuracy of a financial time series data. Simulation results show superiority of the proposed method.


title: Steady-state somatosensory evoked potentials: suitable brain signals for brain-computer interfaces?

abstract: One of the main issues in designing a brain-computer interface (BCI) is to find brain patterns, which could easily be detected. One of these pattern is the steady-state evoked potential (SSEP). SSEPs induced through the visual sense have already been used for brain-computer communication. In this work, a BCI system is introduced based on steady-state somatosensory evoked potentials (SSSEPs). Transducers have been used for the stimulation of both index fingers using tactile stimulation in the "resonance"-like frequency range of the somatosensory system. Four subjects participated in the experiments and were trained to modulate induced SSSEPs. Two of them learned to modify the patterns in order to set up a BCI with an accuracy of between 70% and 80%. Results presented in this work give evidence that it is possible to set up a BCI which is based on SSSEPs.


title: Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework †

abstract: The design of multiple human activity recognition applications in areas such as healthcare, sports and safety relies on wearable sensor technologies. However, when making decisions based on the data acquired by such sensors in practical situations, several factors related to sensor data alignment, data losses, and noise, among other experimental constraints, deteriorate data quality and model accuracy. To tackle these issues, this paper presents a data-driven iterative learning framework to classify human locomotion activities such as walk, stand, lie, and sit, extracted from the Opportunity dataset. Data acquired by twelve 3-axial acceleration sensors and seven inertial measurement units are initially de-noised using a two-stage consecutive filtering approach combining a band-pass Finite Impulse Response (FIR) and a wavelet filter. A series of statistical parameters are extracted from the kinematical features, including the principal components and singular value decomposition of roll, pitch, yaw and the norm of the axial components. The novel interactive learning procedure is then applied in order to minimize the number of samples required to classify human locomotion activities. Only those samples that are most distant from the centroids of data clusters, according to a measure presented in the paper, are selected as candidates for the training dataset. The newly built dataset is then used to train an SVM multi-class classifier. The latter will produce the lowest prediction error. The proposed learning framework ensures a high level of robustness to variations in the quality of input data, while only using a much lower number of training samples and therefore a much shorter training time, which is an important consideration given the large size of the dataset.


title: Deep contextual description of superpixels for aerial urban scenes classification

abstract: This paper proposes a new approach for contextual feature extraction from superpixels in aerial urban scenes. Our method extracts features with many levels of context from superpixels by exploiting different layers of a pre-trained convolutional neural network. Experimental results show the effectiveness of the proposed approach, which outperforms traditional methods based on handcrafted feature extraction algorithms.


title: An Axiomatic Approach to Community Detection

abstract: Inspired by social choice theory in voting and other contexts, we provide the first axiomatic approach to community identification in a social network. We start from an abstract framework, called preference networks, which, for each member, gives their ranking of all the other members of the network. This complete-information preference model enables us to focus on the fundamental conceptual question: What constitutes a community in a social network? Within this framework, we axiomatically study the formation and structures of communities in two different ways. First, we apply social choice theory and define communities indirectly by postulating that they are fixed points of a preference aggregation function obeying certain desirable axioms. Second, we directly postulate six desirable axioms for communities to satisfy, without reference to preference aggregation. For the second approach, we prove a taxonomy theorem that provides a beautiful structural characterization of the family of axiom-conforming community rules as a lattice. We complement this structural theorem with a complexity result, showing that, while for some rules in the lattice, community characterization is straightforward, it is coNP-complete to characterize subsets according to others. Our study also sheds light on the limitations of defining community rules solely based on preference aggregation, namely that many aggregation functions lead to communities which violate at least one of our community axioms. These include any aggregation function satisfying Arrow's "independence of irrelevant alternatives" axiom, as well as commonly used aggregation schemes like the Borda count or generalizations thereof. Finally, we give a polynomial-time rule consistent with five axioms and weakly satisfying the sixth axiom.


title: Simulation optimization in Discrete Event Logistics Systems: The challenge of operational control

abstract: Simulation optimization tools have the potential to provide an unprecedented level of support for the design and execution of operational control in Discrete Event Logistics Systems (DELS). While much of the simulation optimization literature has focused on developing and exploiting integration and syntactical interoperability between simulation and optimization tools, maximizing the effectiveness of these tools to support the design and execution of control behavior requires an even greater degree of interoperability than the current state of the art. In this paper, we propose a modeling methodology for operational control decision-making that can improve the interoperability between these two analysis methods and their associated tools in the context of DELS control. This methodology establishes a standard definition of operational control for both simulation and optimization methods and defines a mapping between decision variables (optimization) and execution mechanisms (simulation / base system). The goal is a standard for creating conforming simulation and optimization tools that are capable of meeting the functional needs of operational control decision making in DELS.


title: Application of the Theory of Planned Behaviour to weight control in an overweight cohort. Results from a pan-European dietary intervention trial (DiOGenes)

abstract: Abstract Using the Theory of Planned Behaviour (TPB), this study investigates weight control in overweight and obese participants (27 kg/m2 ⩽ BMI


title: A Dynamic Interplay within the Frontoparietal Network Underlies Rhythmic Spatial Attention

abstract: Classic studies of spatial attention assumed that its neural and behavioral effects were continuous over time. Recent behavioral studies have instead revealed that spatial attention leads to alternating periods of heightened or diminished perceptual sensitivity. Yet, the neural basis of these rhythmic fluctuations has remained largely unknown. We show that a dynamic interplay within the macaque frontoparietal network accounts for the rhythmic properties of spatial attention. Neural oscillations characterize functional interactions between the frontal eye fields (FEF) and the lateral intraparietal area (LIP), with theta phase (3-8 Hz) coordinating two rhythmically alternating states. The first is defined by FEF-dominated beta-band activity, associated with suppressed attentional shifts, and LIP-dominated gamma-band activity, associated with enhanced visual processing and better behavioral performance. The second is defined by LIP-specific alpha-band activity, associated with attenuated visual processing and worse behavioral performance. Our findings reveal how network-level interactions organize environmental sampling into rhythmic cycles.


title: Stochastic Geometric Analysis of User Mobility in Heterogeneous Wireless Networks

abstract: Horizontal and vertical handoffs are important ramifications of user mobility in multitier heterogeneous wireless networks. They directly impact the signaling overhead and quality of calls. However, they are difficult to analyze due to the irregularly shaped network topologies introduced by multiple tiers of cells. In this paper, a stochastic geometric analysis framework on user mobility is proposed, to capture the spatial randomness and various scales of cell sizes in different tiers. We derive theoretical expressions for the rates of all handoff types experienced by an active user with arbitrary movement trajectory. Furthermore, noting that the data rate of a user depends on the set of cell tiers that it is willing to use, we provide guidelines for optimal tier selection under various user velocities, taking both the handoff rates and the data rate into consideration. Empirical studies using user mobility trace data and extensive simulation are conducted, demonstrating the correctness and usefulness of our analysis.


title: 60 GHz on-chip patch antenna integrated in a 0.13-µm CMOS technology

abstract: This paper presents a design method of on-chip patch antenna integration in a standard CMOS technology without post processing. A 60 GHz on-chip patch antenna is designed utilizing the top metal layer and an intermediate metal layer as the patch and ground plane, respectively. Interference between the patch and digital baseband circuits located beneath the ground plane is analyzed. The 60 GHz on-chip antenna occupies an area of 1220 µm by 1580 µm with carefully placed fillers and slots to meet the design rules of the CMOS process. The antenna is centered at 60.51 GHz with 810 MHz bandwidth. The peak gain and radiation efficiency are −3.32 dBi and 15.87%, respectively. Analysis for mutual signal coupling between the antenna and the clock H-tree beneath the ground plane is reported, showing a −61 dB coupling from the antenna to the H-tree and a −95 dB coupling of 2 GHz clock signal from the H-tree to the antenna.


title: Multiuser Resource Allocation for Mobile-Edge Computation Offloading

abstract: Mobile-edge computation offloading (MECO) offloads intensive mobile computation to clouds located at the edges of cellular networks. Thereby, MECO is envisioned as a promising technique for prolonging the battery lives and enhancing the computation capacities of mobiles. In this paper, we consider resource allocation in a MECO system comprising multiple users that time share a single edge cloud and have different computation loads. The optimal resource allocation is formulated as a convex optimization problem for minimizing the weighted sum mobile energy consumption under constraint on computation latency and for both the cases of infinite and finite edge cloud computation capacities. The optimal policy is proved to have a threshold-based structure with respect to a derived offloading priority function, which yields priorities for users according to their channel gains and local computing energy consumption. As a result, users with priorities above and below a given threshold perform complete and minimum offloading, respectively. Computing the threshold requires iterative computation. To reduce the complexity, a sub-optimal resource-allocation algorithm is proposed and shown by simulation to have close-to-optimal performance.


On the Measurement of Intangibles. A Principal Eigenvector Approach to Relative Measurement Derived from Paired Comparisons


title: An Improved Morphological Algorithm for Filtering Airborne LiDAR Point Cloud Based on Multi-Level Kriging Interpolation

abstract: Filtering is one of the core post-processing steps for airborne LiDAR point cloud. In recent years, the morphology-based filtering algorithms have proven to be a powerful and efficient tool for filtering airborne LiDAR point cloud. However, most traditional morphology-based algorithms have difficulties in preserving abrupt terrain features, especially when using larger filtering windows. In order to suppress the omission error caused by protruding terrain features, this paper proposes an improved morphological algorithm based on multi-level kriging interpolation. This algorithm is essentially a combination of progressive morphological filtering algorithm and multi-level interpolation filtering algorithm. The morphological opening operation is performed with filtering window gradually downsizing, while kriging interpolation is conducted at different levels according to the different filtering windows. This process is iterative in a top to down fashion until the filtering window is no longer greater than the preset minimum filtering window. Fifteen samples provided by the ISPRS commission were chosen to test the performance of the proposed algorithm. Experimental results show that the proposed method can achieve promising results not only in flat urban areas but also in rural areas. Comparing with other eight classical filtering methods, the proposed method obtained the lowest omission error, and preserved protruding terrain features better.


title: Evolving Ensemble Fuzzy Classifier

abstract: The concept of ensemble learning offers a promising avenue in learning from data streams under complex environments because it better addresses the bias and variance dilemma than its single-model counterpart and features a reconfigurable structure, which is well suited to the given context. While various extensions of ensemble learning for mining nonstationary data streams can be found in the literature, most of them are crafted under static base-classifier and revisit preceding samples in the sliding window for a retraining step. This feature causes computationally prohibitive complexity and is not flexible enough to cope with rapidly changing environments. Their complexities are often demanding because they involve a large collection of offline classifiers due to the absence of structural complexities reduction mechanisms and lack of an online feature selection mechanism. A novel evolving ensemble classifier, namely Parsimonious Ensemble (pENsemble), is proposed in this paper. pENsemble differs from existing architectures in the fact that it is built upon an evolving classifier from data streams, termed Parsimonious Classifier. pENsemble is equipped by an ensemble pruning mechanism, which estimates a localized generalization error of a base classifier. A dynamic online feature selection scenario is integrated into the pENsemble. This method allows for dynamic selection and deselection of input features on the fly. pENsemble adopts a dynamic ensemble structure to output a final classification decision where it features a novel drift detection scenario to grow the ensemble's structure. The efficacy of the pENsemble has been numerically demonstrated through rigorous numerical studies with dynamic and evolving data streams, where it delivers the most encouraging performance in attaining a tradeoff between accuracy and complexity.


title: ?) 1992 The Review of Economic Studies Limited Social Norms and Community

abstract: The present paper extends the theory of self-enforcing agreements in a long-term relationship (the Folk Theorem in repeated games) to the situation where agents change their partners over time. Cooperation is sustained because defection against one agent causes sanction by others, and the paper shows how such a "social norm" is sustained by self-interested agents under various degrees of observability. Two main results are presented. The first one is an example where a community can sustain cooperation even when each agent knows nothing more than his personal experience. The second shows a Folk Theorem that the community can realize any mutually beneficial outcomes when each agent carries a label such as reputation, membership, or licence, which are revised in a systematic way.


title: Aerial-Image Denoising Based on Convolutional Neural Network with Multi-Scale Residual Learning Approach

abstract: Aerial images are subject to various types of noise, which restricts the recognition and analysis of images, target monitoring, and search services. At present, deep learning is successful in image recognition. However, traditional convolutional neural networks (CNNs) extract the main features of an image to predict directly and are limited by the requirements of the training sample size (i.e., a small data size is not successful enough). In this paper, using a small sample size, we propose an aerial-image denoising recognition model based on CNNs with a multi-scale residual learning approach. The proposed model has the following three advantages: (1) Instead of directly learning latent clean images, the proposed model learns the noise from noisy images and then subtracts the learned residual from the noisy images to obtain reconstructed (denoised) images; (2) The developed image denoising recognition model is beneficial to small training datasets; (3) We use multi-scale residual learning as a learning approach, and dropout is introduced into the model architecture to force the network to learn to generalize well enough. Our experimental results on aerial-image denoising recognition reveal that the proposed approach is highly superior to the other state-of-the-art methods.


title: Get real in individual participant data (IPD) meta‐analysis: a review of the methodology

abstract: Individual participant data (IPD) meta-analysis is an increasingly used approach for synthesizing and investigating treatment effect estimates. Over the past few years, numerous methods for conducting an IPD meta-analysis (IPD-MA) have been proposed, often making different assumptions and modeling choices while addressing a similar research question. We conducted a literature review to provide an overview of methods for performing an IPD-MA using evidence from clinical trials or non-randomized studies when investigating treatment efficacy. With this review, we aim to assist researchers in choosing the appropriate methods and provide recommendations on their implementation when planning and conducting an IPD-MA. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.


title: Online Anomaly Prediction for Robust Cluster Systems

abstract: In this paper, we present a stream-based mining algorithm for online anomaly prediction. Many real-world applications such as data stream analysis requires continuous cluster operation. Unfortunately, today's large-scale cluster systems are still vulnerable to various software and hardware problems. System administrators are often overwhelmed by the tasks of correcting various system anomalies such as processing bottlenecks (i.e., full stream buffers), resource hot spots, and service level objective (SLO) violations. Our anomaly prediction scheme raises early alerts for impending system anomalies and suggests possible anomaly causes. Specifically, we employ Bayesian classification methods to capture different anomaly symptoms and infer anomaly causes. Markov models are introduced to capture the changing patterns of different measurement metrics. More importantly, our scheme combines Markov models and Bayesian classification methods to predict when a system anomaly will appear in the foreseeable future and what are the possible anomaly causes. To the best of our knowledge, our work provides the first stream-based mining algorithm for predicting system anomalies. We have implemented our approach within the IBM System S distributed stream processing cluster, and conducted case study experiments using fully implemented distributed data analysis applications processing real application workloads. Our experiments show that our approach efficiently predicts and diagnoses several bottleneck anomalies with high accuracy while imposing low overhead to the cluster system.


title: Extracting Logical Hierarchical Structure of HTML Documents Based on Headings

abstract: We propose a method for extracting logical hierarchical structure of HTML documents. Because mark-up structure in HTML documents does not necessarily coincide with logical hierarchical structure, it is not trivial how to extract logical structure of HTML documents. Human readers, however, easily understand their logical structure. The key information used by them is headings in the documents. Human readers exploit the following properties of headings: (1) headings appear at the beginning of the corresponding blocks, (2) headings are given prominent visual styles, (3) headings of the same level share the same visual style, and (4) headings of higher levels are given more prominent visual styles. Our method also exploits these properties for extracting hierarchical headings and their associated blocks. Our experiment shows that our method outperforms existing methods. In addition, our method extracts not only hierarchical blocks but also their associated headings.


title: Harnessing Twitter "Big Data" for Automatic Emotion Identification

abstract: User generated content on Twitter (produced at an enormous rate of 340 million tweets per day) provides a rich source for gleaning people's emotions, which is necessary for deeper understanding of people's behaviors and actions. Extant studies on emotion identification lack comprehensive coverage of "emotional situations" because they use relatively small training datasets. To overcome this bottleneck, we have automatically created a large emotion-labeled dataset (of about 2.5 million tweets) by harnessing emotion-related hash tags available in the tweets. We have applied two different machine learning algorithms for emotion identification, to study the effectiveness of various feature combinations as well as the effect of the size of the training data on the emotion identification task. Our experiments demonstrate that a combination of unigrams, big rams, sentiment/emotion-bearing words, and parts-of-speech information is most effective for gleaning emotions. The highest accuracy (65.57%) is achieved with a training data containing about 2 million tweets.


title: A Qualitative Framework for Collecting and Analyzing Data in Focus Group Research

abstract: Despite the abundance of published material on conducting focus groups, scant specific information exists on how to analyze focus group data in social science research. Thus, the authors provide a new qualitative framework for collecting and analyzing focus group data. First, they identify types of data that can be collected during focus groups. Second, they identify the qualitative data analysis techniques best suited for analyzing these data. Third, they introduce what they term as a micro-interlocutor analysis, wherein meticulous information about which participant responds to each question, the order in which each participant responds, response characteristics, the nonverbal communication used, and the like is collected, analyzed, and interpreted. They conceptualize how conversation analysis offers great potential for analyzing focus group data. They believe that their framework goes far beyond analyzing only the verbal communication of focus group participants, thereby increasing the rigor of focus g...


title: Demand Characteristics in Assessing Motion Sickness in a Virtual Environment: Or Does Taking a Motion Sickness Questionnaire Make You Sick?

abstract: The experience of motion sickness in a virtual environment may be measured through pre and postexperiment self-reported questionnaires such as the Simulator Sickness Questionnaire (SSQ). Although research provides converging evidence that users of virtual environments can experience motion sickness, there have been no controlled studies to determine to what extent the user's subjective response is a demand characteristic resulting from pre and posttest measures. In this study, subjects were given either SSQ's both pre and postvirtual environment immersion, or only postimmersion. This technique tested for contrast effects due to demand characteristics in which administration of the questionnaire itself suggested to the participant that the virtual environment may produce motion sickness. Results indicate that reports of motion sickness after immersion in a virtual environment are much greater when both pre and postquestionnaires are given than when only a posttest questionnaire is used. The implications for assessments of motion sickness in virtual environments are discussed.


title: Usability Meanings and Interpretations in ISO Standards

abstract: The usability of a software product has recently become a key software quality factor. The International Organization for Standardization (ISO) has developed a variety of models to specify and measure software usability but these individual models do not support all usability aspects. Furthermore, they are not yet well integrated into current software engineering practices and lack tool support. The aim of this research is to survey the actual representation (meanings and interpretations) of usability in ISO standards, indicate some of existing limitations and address them by proposing an enhanced, normative model for the evaluation of software usability.


Influence of Tensioner Friction on Accessory Drive Dynamics


title: The Influence of Afrocentric Facial Features in Criminal Sentencing

abstract: Prior research has shown that within a racial category, people with more Afrocentric facial features are presumed more likely to have traits that are stereotypic of Black Americans compared with people with less Afrocentric features. The present study investigated whether this form of feature-based stereotyping might be observed in criminal-sentencing decisions. Analysis of a random sample of inmate records showed that Black and White inmates, given equivalent criminal histories, received roughly equivalent sentences. However, within each race, inmates with more Afrocentric features received harsher sentences than those with less Afrocentric features. These results are consistent with laboratory findings, and they suggest that although racial stereotyping as a function of racial category has been successfully removed from sentencing decisions, racial stereotyping based on the facial features of the offender is a form of bias that is largely overlooked.


title: A semi-synchronous label propagation algorithm with constraints for community detection in complex networks

abstract: Community structure is an important feature of a complex network, where detection of the community structure can shed some light on the properties of such a complex network. Amongst the proposed community detection methods, the label propagation algorithm (LPA) emerges as an effective detection method due to its time efficiency. Despite this advantage in computational time, the performance of LPA is affected by randomness in the algorithm. A modified LPA, called CLPA-GNR, was proposed recently and it succeeded in handling the randomness issues in the LPA. However, it did not remove the tendency for trivial detection in networks with a weak community structure. In this paper, an improved CLPA-GNR is therefore proposed. In the new algorithm, the unassigned and assigned nodes are updated synchronously while the assigned nodes are updated asynchronously. A similarity score, based on the Sorensen-Dice index, is implemented to detect the initial communities and for breaking ties during the propagation process. Constraints are utilised during the label propagation and community merging processes. The performance of the proposed algorithm is evaluated on various benchmark and real-world networks. We find that it is able to avoid trivial detection while showing substantial improvement in the quality of detection.


title: Unsupervised Random Forest Manifold Alignment for Lipreading

abstract: Lip reading from visual channels remains a challenging topic considering the various speaking characteristics. In this paper, we address an efficient lip reading approach by investigating the unsupervised random forest manifold alignment (RFMA). The density random forest is employed to estimate affinity of patch trajectories in speaking facial videos. We propose novel criteria for node splitting to avoid the rank-deficiency in learning density forests. By virtue of the hierarchical structure of random forests, the trajectory affinities are measured efficiently, which are used to find embeddings of the speaking video clips by a graph-based algorithm. Lip reading is formulated as matching between manifolds of query and reference video clips. We employ the manifold alignment technique for matching, where the L∞-norm-based manifold-to-manifold distance is proposed to find the matching pairs. We apply this random forest manifold alignment technique to various video data sets captured by consumer cameras. The experiments demonstrate that lip reading can be performed effectively, and outperform state-of-the-arts.


title: A Study of Hashtag Activism for Raising Awareness about Riverbank Erosion in Bangladesh

abstract: Millions of vulnerable people around the world are suffering from intensifying climate-related disruptions that could be construed as limits problems. Because those who suffer the most are often the most marginalized, these communities are largely neglected by governments and national media. In rich countries, social media has enabled ordinary citizens to add their voices to the public discourse. In the Global South, access to Internet and social media technologies is extremely constrained. In this paper, we study how, despite these barriers, communities in Bangladesh that are chronically affected by riverbank erosion are managing to use Facebook to participate in "hashtag activism." This activism makes some progress toward filling the information gap regarding the impacts of riverbank erosion in Bangladesh, and can inform the national media and government for taking action.


title: Combining Verbal and Nonverbal Features to Overcome the “Information Gap” in Task-Oriented Dialogue

abstract: Dialogue act modeling in task-oriented dialogue poses significant challenges. It is particularly challenging for corpora consisting of two interleaved communication streams: a dialogue stream and a task stream. In such corpora, information can be conveyed implicitly by the task stream, yielding a dialogue stream with seemingly missing information. A promising approach leverages rich resources from both the dialog and the task streams, combining verbal and non-verbal features. This paper presents work on dialogue act modeling that leverages body posture, which may be indicative of particular dialogue acts. Combining three information sources (dialogue exchanges, task context, and users' posture), three types of machine learning frameworks were compared. The results indicate that some models better preserve the structure of task-oriented dialogue than others, and that automatically recognized postural features may help to disambiguate user dialogue moves.


title: OntoSakai: On the optimization of a Learning Management System using semantics and user profiling

abstract: Abstract This paper proposes recommendation services and user profiling features in Learning Management Systems (LMS) by means of a semantic intelligent system combining context information and expert knowledge. LMS users’ context is represented through an ontology model called OntoSakai. It consists of four ontologies parceling different areas of the learning process: competences, users’ profiles, learning tools and semantic classification of the elements in an LMS. Thus, we provide a standardized common vocabulary about LMS elements and academic tasks developed within these platforms. This model also enables inference processes about the behavior of LMS users. Indeed, our system incorporates an extensible set of expert rules to offer recommendation and user profiling services. This combination of context information and expert knowledge could be easily integrated with other systems in the academic world in order to promote the interoperability between them. Specifically, in this paper we integrate our proposal into Sakai, a well-known LMS for university-level. As a result of this integration, OntoSakai is able to generate users’ profiles aimed at personalizing the use of LMS tools and to recommend resources to reach the optimum benefit in both lecturing and learning. As a proof of concept, a real case often detected in on-line students is shown as a running scenario where the services offered by OntoSakai could help them to improve their experiences and academic results.


Application of SIMUS method for assessment alternative transport policies for container carriage


title: Imbalanced sentiment classification

abstract: Sentiment classification has undergone significant development in recent years. However, most existing studies assume the balance between negative and positive samples, which may not be true in reality. In this paper, we investigate imbalanced sentiment classification instead. In particular, a novel clustering-based stratified under-sampling framework and a centroid-directed smoothing strategy are proposed to address the imbalanced class and feature distribution problems respectively. Evaluation across different datasets shows the effectiveness of both the under-sampling framework and the smoothing strategy in handling the imbalanced problems in real sentiment classification applications.


title: The DET curve in assessment of detection task performance

abstract: Abstract : We introduce the DET Curve as a means of representing performance on detection tasks that involve a tradeoff of error types. We discuss why we prefer it to the traditional ROC Curve and offer several examples of its use in speaker recognition and language recognition. We explain why it is likely to produce approximately linear curves. We also note special points that may be included on these curves, how they are used with multiple targets, and possible further applications.


title: A new remote user authentication scheme using smart cards

abstract: We propose a new remote user authentication scheme using smart cards. The scheme is based on the ElGamal's (1985) public key cryptosystem. Our scheme does not require a system to maintain a password table for verifying the legitimacy of the login users. In addition, our scheme can withstand message replaying attack.


title: Anomaly Detection Framework Using Rule Extraction for Efficient Intrusion Detection

abstract: Huge datasets in cyber security, such as network trac logs, can be analyzed using machine learning and data mining methods. However, the amount of collected data is increasing, which makes analysis more dicult. Many machine learning methods have not been designed for big datasets, and consequently are slow and dicult to understand. We address the issue of ecient network trac classication by creating an intrusion detection framework that applies dimensionality reduction and conjunctive rule extraction. The system can perform unsupervised anomaly detection and use this information to create conjunctive rules that classify huge amounts of trac in real time. We test the implemented system with the widely used KDD Cup 99 dataset and real-world network logs to conrm that the performance is satisfactory. This system is transparent and does not work like a black box, making it intuitive for domain experts, such as network administrators.


title: Abstract Surface ¯attening based on energy model

abstract: Abstract This paper presents a method for three-dimensional surface flattening, which can be efficiently used in three-dimensional computer aided garment design. First, a facet model is used to present a complex surface. Then, a spring–mass model based on energy function is used to flatten the 3D mesh surfaces into 2D patterns. The surface elastic deformation energy distribution is depicted by a color graph, which determines a surface cutting line. The method presented here can efficiently solve flattening problems for complex surfaces. The accuracy of a developed surface can easily be controlled locally. Thus, compared to earlier methods, this method provides more flexibility for solving CAD and CAM problems.


title: Safe, strong and tractable relevance analysis for planning

abstract: In large and complex planning problems, there will almost inevitably be aspects that are not relevant to a specific problem instance. Thus, identifying and removing irrelevant parts from an instance is one of the most important techniques for scaling up automated planning. We examine the path-based relevance analysis method, which is safe (preserves plan existence and cost) and powerful but has exponential time complexity, and show how to make it run in polynomial time with only a minimal loss of pruning power.


title: Cache-efficient dynamic programming algorithms for multicores

abstract: We present cache-efficient chip multiprocessor (CMP) algorithms with good speed-up for some widely used dynamic programming algorithms. We consider three types of caching systems for CMPs: D-CMP with a private cache for each core, S-CMP with a single cache shared by all cores, and Multicore, which has private L1 caches and a shared L2 cache. We derive results for three classes of problems: local dependency dynamic programming (LDDP), Gaussian Elimination Paradigm (GEP), and parenthesis problem. For each class of problems, we develop a generic CMP algorithm with an associated tiling sequence. We then tailor this tiling sequence to each caching model and provide a parallel schedule that results in a cache-efficient parallel execution up to the critical path length of the underlying dynamic programming algorithm. We present experimental results on an 8-core Opteron for two sequence alignment problems that are important examples of LDDP. Our experimental results show good speed-ups for simple versions of our algorithms.


title: Diffusion mapping of drug targets on disease signaling network elements reveals drug combination strategies

abstract: The emergence of drug resistance to traditional chemotherapy and newer targeted therapies in cancer patients is a major clinical challenge. Reactivation of the same or compensatory signaling pathways is a common class of drug resistance mechanisms. Employing drug combinations that inhibit multiple modules of reactivated signaling pathways is a promising strategy to overcome and prevent the onset of drug resistance. However, with thousands of available FDA-approved and investigational compounds, it is infeasible to experimentally screen millions of possible drug combinations with limited resources. Therefore, computational approaches are needed to constrain the search space and prioritize synergistic drug combinations for preclinical studies. In this study, we propose a novel approach for predicting drug combinations through investigating potential effects of drug targets on disease signaling network. We first construct a disease signaling network by integrating gene expression data with disease-associated driver genes. Individual drugs that can partially perturb the disease signaling network are then selected based on a drug-disease network "impact matrix", which is calculated using network diffusion distance from drug targets to signaling network elements. The selected drugs are subsequently clustered into communities (subgroups), which are proposed to share similar mechanisms of action. Finally, drug combinations are ranked according to maximal impact on signaling sub-networks from distinct mechanism-based communities. Our method is advantageous compared to other approaches in that it does not require large amounts drug dose response data, drug-induced "omics" profiles or clinical efficacy data, which are not often readily available. We validate our approach using a BRAF-mutant melanoma signaling network and combinatorial in vitro drug screening data, and report drug combinations with diverse mechanisms of action and opportunities for drug repositioning.


title: Composite Subscriptions in Content-Based Publish/Subscribe Systems

abstract: Distributed publish/subscribe systems are naturally suited for processing events in distributed systems. However, support for expressing patterns about distributed events and algorithms for detecting correlations among these events are still largely unexplored. Inspired from the requirements of decentralized, event-driven workflow processing, we design a subscription language for expressing correlations among distributed events. We illustrate the potential of our approach with a workflow management case study. The language is validated and implemented in PADRES. In this paper we present an overview of PADRES, highlighting some of its novel features, including the composite subscription language, the coordination patterns, the composite event detection algorithms, the rule-based router design, and a detailed case study illustrating the decentralized processing of workflows. Our experimental evaluation shows that rule-based brokers are a viable and powerful alternative to existing, special-purpose, content-based routing algorithms. The experiments also show that the use of composite subscriptions in PADRES significantly reduces the load on the network. Complex workflows can be processed in a decentralized fashion with a gain of 40% in message dissemination cost. All processing is realized entirely in the publish/subscribe paradigm.


title: Methods and Tools for Ontology-Based Configuration Processes of AAL Environments

abstract: In the last few years, technologies with a semantic middleware were established under the AAL platforms. These technologies simplify the reaction to various and rapidly changing needs of assisted elderly. Building on this established semantic basis, the set-up and configuration of individual use cases can be simplified. Up to now it is hardly possible to set up an AAL environment without technical knowledge. This paper presents how the process of set-up and configuration of AAL environments based on ontologies could proceed. The support starts with the developers of the use cases, continues with the integration in the middleware and ends up in the maintenance during operation. At the end, different configuration support tools based on the semantic middleware universAAL will be described.


THE ROLE OF IMPUTATION IN DETECTING FRAUDULENT FINANCIAL REPORTING


title: Broadcasting brake lights with MIMO-OFDM based vehicular VLC

abstract: Inter-vehicular connectivity to enhance road safety and enable highly autonomous driving is increasingly becoming popular. Despite the prevalent works on radio-frequency (RF) based vehicular communication schemes, visible light communication (VLC) is considered to be a promising candidate for vehicular communications due to its low complexity and RF interference-free nature. This paper investigates applicability of VLC to enhance road safety based on real world measurements. Deployment of multiple light emitting diodes (LEDs) enables multiple-input multiple-output (MIMO) transmission in the context of vehicular VLC. We consider direct current biased optical orthogonal frequency division multiplexing (DCO-OFDM) based MIMO transmission scheme and evaluate the performances of different MIMO modes including repetition code (RC) and spatial multiplexing (SM), different modulation orders and different transmitter-receiver selection. The results reveal that selection of the closest transmitters to the receivers, provide better performance due to high signal-to-noise-ratio (SNR) requirements for RC mode. However, usage of all possible transmitters does not always yield better performance due to power division at the transmitter side. Moreover, SM suffers from channel correlation whereas the performance of RC shows more degradation on higher-order modulations that are required to yield the same throughput with SM.


title: A 128-Channel, 710 M Samples/Second, and Less Than 10 ps RMS Resolution Time-to-Digital Converter Implemented in a Kintex-7 FPGA

abstract: Recent investigations of Field Programmable Gate Array (FPGA)-based time-to-digital converters (TDCs) have predominantly focused on improving the time resolution of the device. However, the monolithic integration of multi-channel TDCs and the achievement of high measurement throughput remain challenging issues for certain applications. In this paper, the potential of the resources provided by the Kintex-7 Xilinx FPGA is fully explored, and a new design is proposed for the implementation of a high performance multi-channel TDC system on this FPGA. Using the tapped-delay-line wave union TDC architecture, in which a negative pulse is triggered by the hit signal propagating along the carry chain, two time measurements are performed in a single carry chain within one clock cycle. The differential non-linearity and time resolution can be significantly improved by realigning the bins. The on-line calibration and on-line updating of the calibration table reduce the influence of variations of environmental conditions. The logic resources of the 6-input look-up tables in the FPGA are employed for hit signal edge detection and bubble-proof encoding, thereby allowing the TDC system to operate at the maximum allowable clock rate of the FPGA and to achieve the maximum possible measurement throughput. This resource-efficient design, in combination with a modular implementation, makes the integration of multiple channels in one FPGA practicable. Using our design, a 128-channel TDC with a dead time of 1.47 ns, a dynamic range of 360 ns, and a root-mean-square resolution of less than 10 ps was implemented in a single Kintex-7 device.


title: A highly integrated RF frontend module including Doherty PA, LNA and switch for high SHF wide-band massive MIMO in 5G

abstract: A highly integrated RF frontend module including a three-stage power amplifier (PA), a two-stage low noise amplifier (LNA) and a switch (SW) is presented for high SHF wide-band massive MIMO in 5G. In order to achieve efficient operation over wide-band frequency, Doherty PA configuration using a parasitic output capacitance neutralization technique is proposed for final stage PA. To improve LNA's gain flatness over wide frequency band, two R-LC stabilization circuits with different resonance frequencies are proposed. They are fabricated with 0.15-μm GaAs process, and integrated into a 5 × 5 mm2 QFN package. The state-of-the-art measured results show that final stage PA and three-stage PA achieve a drain efficiency (DE) at 8dB back-off of more than 22 % and 12 %, respectively, and a two-stage LNA achieves a noise figure (NF) of less than 1.4dB over 14.5–15.0 GHz.


Bartók's style : as reflected in Sonata for two pianos and percussion and Music for strings, percussion and celesta


title: Mastering the game of Go without human knowledge

abstract: A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo's own move selections and also the winner of AlphaGo's games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100-0 against the previously published, champion-defeating AlphaGo.


title: CDL-A compiler implementation language

abstract: The Compiler Description Language CDL has evolved from a more or less classical compiler compiler system to an implementation language, which is well suited for the design and realization of compilers. In this paper it is discussed how those properties that make the language oriented towards compiler writing, have been enhanced in the revised version of CDL, and the advantages to be gained from such specialization.


title: Low order dynamical system for fluid-rigid body interaction problem using POD method

abstract: This paper describes the Reduced Order Modeling (ROM) for fluid rigid body interaction problem and discusses Proper Orthogonal Decomposition (POD) utilisation. The principal difficulty for using POD being the moving domains, a referenced fixed domain is introduced. The POD is applied for the velocity field obtained on the fixed domain. Then a method to reduce dynamical system in rigid body fluid interaction is developed. This method uses the fictitious domain method approach, which consists in treating the entire fluid-solid domain as a fluid. The rigid body is considered as a fluid, by using a high viscosity which can play the role of a penalisation factor of the rigidity constraint, and by adding a distributed Lagrange multiplied associated to this constraint in the weak formulation. The fluid flow problem is then formulated on the reference domain and POD modes are used in the weak formulation. The results are compared with computational solution and discussed.


title: VizDSL: Towards a Graphical Visualisation Language for Enterprise Systems Interoperability

abstract: In response to the rise of Big Data, modern enterprise architecture has become significantly more complex. Model driven engineering (MDE) has been proposed as a methodology for developing software to deal with complex integration and interoperability. Domain specific languages (DSLs) play a crucial role in MDE and represent languages for a specific purpose that are highly abstract and easy to use. In this paper we propose a new language VizDSL for creating interactive visualisations that facilitate the understanding of complex data and information structures for enterprise systems interoperability. In comparison to existing visualisation languages VizDSL provides the benefits of visualising the semantics of data using a graphical notation. VizDSL is based on the Interaction Flow Modelling Language (IFML) and Agile Visualisation and has been implemented in a prototype. The prototype has been applied on an open data set and results show that interactive visualisation can be implemented quickly using the VizDSL language without writing code which makes it easier to design for non- programmers.


Generation of Combinational Hazard Identification Functions


title: A Novel Graph-based Mechanism for Identifying Traffic Vulnerabilities in Smart Home IoT

abstract: Smart home IoT devices have been more prevalent than ever before but the relevant security considerations fail to keep up with due to device and technology heterogeneity and resource constraints, making IoT systems susceptible to various attacks. In this paper, we propose a novel graph-based mechanism to identify the vulnerabilities in communication of IoT devices for smart home systems. Our approach takes one or more packet capture files as inputs to construct a traffic graph by passing the captured messages, identify the correlated subgraphs by examining the attribute-value pairs associated with each message, and then quantify their vulnerabilities based on the sensitivity levels of different keywords. To test the effectiveness of our approach, we setup a smart home system that can control a smart bulb LB100 via either the smartphone APP for LB100 or the Google Home speaker. We collected and analyzed 58,714 messages and exploited 6 vulnerable correlated sub graphs, based on which we implemented 6 attack cases that can be easily reproduced by attackers with little knowledge of IoT. This study is novel as our approach takes only the collected traffic files as inputs without requiring the knowledge of the device firmware while being able to identify new vulnerabilities. With this approach, we won the third prize out of 20 teams in a hacking competition.


title: Adversarial Attack? Don't Panic

abstract: Deep learning is playing a more and more important role in our daily life and scientific research such as autonomous systems, intelligent life and data mining. However, numerous studies have showed that deep learning with superior performance on many tasks may suffer from subtle perturbations constructed by attacker purposely, called adversarial perturbations, which are imperceptible to human observers but completely effect deep neural network models. The emergence of adversarial attacks has led to questions about neural networks. Therefore, machine learning security and privacy are becoming an increasingly active research area. In this paper, we summarize the prevalent methods for the generating adversarial attacks according to three groups. We elaborated on their ideas and principles of generation. We further analyze the common limitations of these methods and implement statistical experiments of the last layer output on CleverHans to reveal that the detection of adversarial samples is not as difficult as it seems and can be achieved in some relatively simple manners.


title: FaSST: fast, scalable and simple distributed transactions with two-sided (RDMA) datagram RPCs

abstract: FaSST is an RDMA-based system that provides distributed in-memory transactions with serializability and durability. Existing RDMA-based transaction processing systems use one-sided RDMA primitives for their ability to bypass the remote CPU. This design choice brings several drawbacks. First, the limited flexibility of one-sided RDMA reduces performance and increases software complexity when designing distributed data stores. Second, deep-rooted technical limitations of RDMA hardware limit scalability in large clusters. FaSST eschews one-sided RDMA for fast RPCs using two-sided unreliable datagrams, which we show drop packets extremely rarely on modern RDMA networks. This approach provides better performance, scalability, and simplicity, without requiring expensive reliability mechanisms in software. In comparison with published numbers, FaSST outperforms FaRM on the TATP benchmark by almost 2x while using close to half the hardware resources, and it outperforms DrTM+R on the SmallBank benchmark by around 1.7x without making data locality assumptions.


title: Lares: An Architecture for Secure Active Monitoring Using Virtualization

abstract: Host-based security tools such as anti-virus and intrusion detection systems are not adequately protected on today's computers. Malware is often designed to immediately disable any security tools upon installation, rendering them useless. While current research has focused on moving these vulnerable security tools into an isolated virtual machine, this approach cripples security tools by preventing them from doing active monitoring. This paper describes an architecture that takes a hybrid approach, giving security tools the ability to do active monitoring while still benefiting from the increased security of an isolated virtual machine. We discuss the architecture and a prototype implementation that can process hooks from a virtual machine running Windows XP on Xen. We conclude with a security analysis and show the performance of a single hook to be 28 musecs in the best case.


title: Block segmentation and text extraction in mixed text/image documents

abstract: Abstract The segmentation and classification of digitized printed documents into regions of text and images is a necessary first processing step in document analysis systems. It is shown that a constrained run length algorithm is well suited to partition most documents into areas of text lines, solid black lines, and rectangular ☐es enclosing graphics and halftone images. During the processing these areas are labeled and meaningful features are calculated. By making use of the regular appearance of text lines as textured stripes, a linear adaptive classification scheme is constructed to discriminate text regions from others.


title: Direct Trajectory Optimization and Costate Estimation of General Optimal Control Problems Using a Radau Pseudospectral Method

abstract: A method is presented for direct trajectory optimization and costate estimation using global collocation at Legendre-Gauss-Radau (LGR) points. The method is formulated first by casting the dynamics in integral form and computing the integralfrom the initialpoint to the interior LGR points and the terminal point. The resulting integration matrix is nonsingular and thus can be inverted so as to express the dynamics in inverse integral form. Then, by appropriate choice of the approximation for the state, a pseudospectral (i.e., differential) form that is equivalent to the inverse integral form is derived. As a result, the method presented in this paper can be thought of as either a global implicit integration method or a pseudospectral method. Moreover, the formulation derived in this paper enables solving general finite-horizon problems using global collocation at the LGR points. A key feature of the method is that it provides an accurate way to map the KKT multipliers of the nonlinear programming problem (NLP) to the costates of the optimal control problem. Finally,


title: Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets

abstract: Automatic and accurate whole-heart and great vessel segmentation from 3D cardiac magnetic resonance (MR) images plays an important role in the computer-assisted diagnosis and treatment of cardiovascular disease. However, this task is very challenging due to ambiguous cardiac borders and large anatomical variations among different subjects. In this paper, we propose a novel densely-connected volumetric convolutional neural network, referred as DenseVoxNet, to automatically segment the cardiac and vascular structures from 3D cardiac MR images. The DenseVoxNet adopts the 3D fully convolutional architecture for effective volume-to-volume prediction. From the learning perspective, our DenseVoxNet has three compelling advantages. First, it preserves the maximum information flow between layers by a densely-connected mechanism and hence eases the network training. Second, it avoids learning redundant feature maps by encouraging feature reuse and hence requires fewer parameters to achieve high performance, which is essential for medical applications with limited training data. Third, we add auxiliary side paths to strengthen the gradient propagation and stabilize the learning process. We demonstrate the effectiveness of DenseVoxNet by comparing it with the state-of-the-art approaches from HVSMR 2016 challenge in conjunction with MICCAI, and our network achieves the best dice coefficient. We also show that our network can achieve better performance than other 3D ConvNets but with fewer parameters.


title: A Voxel-based Octree Construction approach for Procedural Cave Generation

abstract: Procedural content generation is becoming an increasingly popular research area as a means of algorithmically generating scene content for virtual environments. The automated generation of such content avoids the manual labour typically associated with creating scene content, and is extremely useful in application areas such as computer graphics, movie production and video games. While virtual 3D caves are commonly featured in these virtual environment applications, procedural cave generation is not an area that has received much attention among researchers to date. This paper presents a procedural approach to generating 3D cave structures. Other than the development of a method to effectively automate the generation of visually believable 3D cave models, this paper also investigates how to efficiently construct and store this spatial information using a voxel-based octree data structure. In addition, the proposed approach demonstrates that caves with different characteristics can be generated by adjusting certain parameters in order to facilitate the creation of diverse cave structures.


title: User Activity Patterns During Information Search

abstract: Personalization of support for information seeking depends crucially on the information retrieval system's knowledge of the task that led the person to engage in information seeking. Users work during information search sessions to satisfy their task goals, and their activity is not random. To what degree are there patterns in the user activity during information search sessionsq Do activity patterns reflect the user's situation as the user moves through the search task under the influence of his or her task goalq Do these patterns reflect aspects of different types of information-seeking tasksq Could such activity patterns identify contexts within which information seeking takes placeq To investigate these questions, we model sequences of user behaviors in two independent user studies of information search sessions (N e 32 users, 128 sessions, and N e 40 users, 160 sessions). Two representations of user activity patterns are used. One is based on the sequences of page use; the other is based on a cognitive representation of information acquisition derived from eye movement patterns in service of the reading process. One of the user studies considered journalism work tasks; the other concerned background research in genomics using search tasks taken from the TREC Genomics Track. The search tasks differed in basic dimensions of complexity, specificity, and the type of information product (intellectual or factual) needed to achieve the overall task goal. The results show that similar patterns of user activity are observed at both the cognitive and page use levels. The activity patterns at both representation layers are able to distinguish between task types in similar ways and, to some degree, between tasks of different levels of difficulty. We explore relationships between the results and task difficulty and discuss the use of activity patterns to explore events within a search session. User activity patterns can be at least partially observed in server-side search logs. A focus on patterns of user activity sequences may contribute to the development of information systems that better personalize the user's search experience.


title: Altered level of consciousness: evidence-based management in the emergency department [digest].

abstract: A child who presents to the emergency department with an altered level of consciousness can be clinically unstable and can pose a great diagnostic challenge. The emergency clinician must quickly develop a wide differential of possible etiologies in order to administer potentially life-saving medications or interventions. The history, physical examination, and appropriate diagnostic tests can aid greatly in rapidly narrowing the differential diagnosis. Once initial stabilization, workup, and first-line interventions are completed, most patients who present with unresolved or unidentified altered level of consciousness should be admitted for further evaluation and close monitoring. This issue provides a review of the etiologies of altered level of consciousness as well as guidance for the management and disposition of patients with this condition. [Points & Pearls is a digest of Pediatric Emergency Medicine Practice].


title: Region Covariance : A Fast Descriptor for Detection and Classification

abstract: We describe a new region descriptor and apply it to two problems, object detection and texture classification. The covariance of d-features, e.g., the three-dimensional color vector, the norm of first and second derivatives of intensity with respect to x and y, etc., characterizes a region of interest. We describe a fast method for computation of covariances based on integral images. The idea presented here is more general than the image sums or histograms, which were already published before, and with a series of integral images the covariances are obtained by a few arithmetic operations. Covariance matrices do not lie on Euclidean space, therefore we use a distance metric involving generalized eigenvalues which also follows from the Lie group structure of positive definite matrices. Feature matching is a simple nearest neighbor search under the distance metric and performed extremely rapidly using the integral images. The performance of the covariance features is superior to other methods, as it is shown, and large rotations and illumination changes are also absorbed by the covariance matrix.