title: Anti-vaccination movements and their interpretations.

abstract: Over the last two or three decades, growing numbers of parents in the industrialized world are choosing not to have their children vaccinated. In trying to explain why this is occurring, public health commentators refer to the activities of an anti-vaccination 'movement'. In the light of three decades of research on (new) social movements, what sense does it make to attribute decline in vaccination rates to the actions of an influential anti-vaccination movement? Two sorts of empirical data, drawn largely from UK and the Netherlands, are reviewed. These relate to the claims, actions and discourse of anti-vaccination groups on the one hand, and to the way parents of young children think about vaccines and vaccination on the other. How much theoretical sense it makes to view anti-vaccination groups as (new) social movement organizations (as distinct from pressure groups or self-help organizations) is as yet unclear. In any event there is no simple and unambiguous demarcation criterion. From a public health perspective, however, to focus attention on organized opponents of vaccination is appealing because it unites health professionals behind a banner of reason. At the same time it diverts attention from a potentially disruptive critique of vaccination practices; the critique in fact articulated by many parents. In the light of current theoretical discussion of 'scientific citizenship' this paper argues that identifying anti-vaccination groups with other social movements may ultimately have the opposite effect to that intended.


title: Ability-Based Design: Concept, Principles and Examples

abstract: Current approaches to accessible computing share a common goal of making technology accessible to users with disabilities. Perhaps because of this goal, they may also share a tendency to centralize disability rather than ability. We present a refinement to these approaches called ability-based design that consists of focusing on ability throughout the design process in an effort to create systems that leverage the full range of human potential. Just as user-centered design shifted the focus of interactive system design from systems to users, ability-based design attempts to shift the focus of accessible design from disability to ability. Although prior approaches to accessible computing may consider users’ abilities to some extent, ability-based design makes ability its central focus. We offer seven ability-based design principles and describe the projects that inspired their formulation. We also present a research agenda for ability-based design.


title: PrIter: A Distributed Framework for Prioritizing Iterative Computations

abstract: Iterative computations are pervasive among data analysis applications, including web search, online social network analysis, recommendation systems, and so on. These applications typically involve data sets of massive scale. Fast convergence of the iterative computations on the massive data set is essential for these applications. In this paper, we explore the opportunity for accelerating iterative computations by prioritization. Instead of performing computations on all data points without discrimination, we prioritize the computations that help convergence the most, so that the convergence speed of iterative process is significantly improved. We develop a distributed computing framework, PrIter, which supports the prioritized execution of iterative computations. PrIter either stores intermediate data in memory for fast convergence or stores intermediate data in files for scaling to larger data sets. We evaluate PrIter on a local cluster of machines as well as on Amazon EC2 Cloud. The results show that PrIter achieves up to 50 × speedup over Hadoop for a series of iterative algorithms. In addition, PrIter is shown better performance for iterative computations than other state-of-the-art distributed frameworks such as Spark and Piccolo.


title: Learning Logical Definitions from Relations

abstract: This paper describes FOIL, a system that learns Horn clauses from data expressed as relations. FOIL is based on ideas that have proved effective in attribute-value learning systems, but extends them to a first-order formalism. This new system has been applied successfully to several tasks taken from the machine learning literature.


title: Colour appearance descriptors for image browsing and retrieval

abstract: In this paper, we focus on the development of whole-scene colour appearance descriptors for classification to be used in browsing applications. The descriptors can classify a whole-scene image into various categories of semantically-based colour appearance. Colour appearance is an important feature and has been extensively used in image-analysis, retrieval and classification. By using pre-existing global CIELAB colour histograms, firstly, we try to develop metrics for wholescene colour appearance: “colour strength”, “high/low lightness” and “multicoloured”. Secondly we propose methods using these metrics either alone or combined to classify whole-scene images into five categories of appearance: strong, pastel, dark, pale and multicoloured. Experiments show positive results and that the global colour histogram is actually useful and can be used for whole-scene colour appearance classification. We have also conducted a small-scale human evaluation test on whole-scene colour appearance. The results show, with suitable threshold settings, the proposed methods can describe the whole-scene colour appearance of images close to human classification. The descriptors were tested on thousands of images from various scenes: paintings, natural scenes, objects, photographs and documents. The colour appearance classifications are being integrated into an image browsing system which allows them to also be used to refine browsing.


title: Game-Theoretic Methods for Robustness, Security, and Resilience of Cyberphysical Control Systems: Games-in-Games Principle for Optimal Cross-Layer Resilient Control Systems

abstract: Critical infrastructures, such as power grids and transportation systems, are increasingly using open networks for operation. The use of open networks poses many challenges for control systems. The classical design of control systems takes into account modeling uncertainties as well as physical disturbances, providing a multitude of control design methods such as robust control, adaptive control, and stochastic control. With the growing level of integration of control systems with new information technologies, modern control systems face uncertainties not only from the physical world but also from the cybercomponents of the system. The vulnerabilities of the software deployed in the new control system infrastructure will expose the control system to many potential risks and threats from attackers. Exploitation of these vulnerabilities can lead to severe damage as has been reported in various news outlets [1], [2]. More recently, it has been reported in [3] and [4] that a computer worm, Stuxnet, was spread to target Siemens supervisory control and data acquisition (SCADA) systems that are configured to control and monitor specific industrial processes.


title: Building 3D anatomical model of coiling of the internal carotid artery derived from CT angiographic data

abstract: The purpose of this study is to recreate live patient arterial anomalies using new recent application of three-dimensional (3D) printed anatomical models. Another purpose of building such models is to evaluate the effectiveness of angiographic data. With the help of the DICOM files from computed tomographic angiography (CT-A), we were able to build a printed model of variant course of the internal carotid artery (ICA). Images of coiling of the ICA taken by CT-A, were then converted into 3D images using Google SketchUp free software, and the images were saved in stereolithography format. Imaging helped us conduct the examination in details with reference to geometrical features of ICA, degree of curve, its extension, location and presence of loop. Challenging vascular anatomy was exposed with models of adverse curve of carotid anatomy, including highly angulated necks, conical necks, short necks, tortuous carotid arteries, and narrowed carotid lumens. It assisted us to comprehend spatial anatomy configuration of life-like models. 3D model can be very effective in cases when anatomical difficulties are detected through the CT-A, and therefore, a tactile approach is demanded preoperatively. 3D life-like models serve as an essential office-based tool in vascular surgery as they assist surgeons in preoperative planning, develop intraoperative guidance, teach both the patients and the surgical trainees, and simulate to show patient-specific procedures in medical field.


title: Diagonalwise Refactorization: An Efficient Training Method for Depthwise Convolutions

abstract: Depthwise convolutions provide significant performance benefits owing to the reduction in both parameters and mult-adds. However, training depthwise convolution layers with GPUs is slow in current deep learning frameworks because their implementations cannot fully utilize the GPU capacity. To address this problem, in this paper we present an efficient method (called diagonalwise refactorization) for accelerating the training of depthwise convolution layers. Our key idea is to rearrange the weight vectors of a depthwise convolution into a large diagonal weight matrix so as to convert the depthwise convolution into one single standard convolution, which is well supported by the cuDNN library that is highly-optimized for GPU computations. We have implemented our training method in five popular deep learning frameworks. Evaluation results show that our proposed method gains $15.4\times$ training speedup on Darknet, $8.4\times$ on Caffe, $5.4\times$ on PyTorch, $3.5\times$ on MXNet, and $1.4\times$ on TensorFlow, compared to their original implementations of depthwise convolutions.


title: Training deep neural networks with non-uniform frame-level cost function for automatic speech recognition

abstract: The aim of this paper is to exhibit two new variations of the frame-level cost function for training a deep neural network in order to achieve better word error rates in speech recognition. Optimization methods and their minimization functions are underlying aspects to consider when someone is working on neural nets, and hence their improvement is one of the salient objectives of researchers, and this paper deals in part with such a situation. The first proposed framework is based on the concept of extropy, the complementary dual function of an uncertainty measure. The conventional cross-entropy function can be mapped to a non-uniform loss function based on its corresponding extropy, enhancing the frames that have ambiguity in their belonging to specific senones. The second proposal makes a fusion of the presented mapped cross-entropy function and the idea of boosted cross-entropy, which emphasizes those frames with low target posterior probability. The proposed approaches have been performed by using a personalized mid-vocabulary speaker-independent voice corpus. This dataset is employed for recognition of digit strings and personal name lists in Spanish from the northern central part of Mexico on a connected-words phone dialing task. A relative word error rate improvement of 12.3% $12.3\%$ and 10.7% $10.7\%$ is obtained with the two proposed approaches, respectively, with regard to the conventional well-established cross-entropy objective function.


title: Object recognition during foveating eye movements

abstract: We studied how saccadic and smooth pursuit eye movements affect the recognition of briefly presented letters appearing within the eye movement target. First we compared the recognition performance during steady-state pursuit and during fixation. Single letters were presented for seven different durations ranging from 10 to 400 ms and four contrast levels ranging from 5% to 40%. For both types of eye movements the recognition rates increased with duration and contrast, but they were on average 11% lower during pursuit. In daily life humans use a combination of saccadic and smooth pursuit eye movements to foveate a peripheral moving object. To investigate this more natural situation, we presented a peripheral target that was either stationary or moving horizontally, above or below the fixation spot. Participants were asked to saccade to the target and to keep it foveated. The letters were presented at different times relative to the first target directed saccade. As would be expected from retinal masking and motion blur during saccades, the discrimination performance increased with increasing post-saccadic delay. If the target moved and the saccade was followed by pursuit, letter recognition performance was on average 16% lower than if the target was stationary and the saccade was followed by fixation.


title: Overview Applications of Data Mining In Health Care: The Case Study of Arusha Region

abstract: Data mining as one of many constituents of health care has been used intensively and extensively in many organizations around the globe as an efficient technique of finding correlations or patterns among dozens of fields in large relational databases to results into more useful health information. In healthcare, data mining is becoming increasingly popular and essential. Data mining applications can greatly benefits all parties involved in health care industry. The huge amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform huge amount of data into useful information for decision making. This paper explores data mining applications in healthcare in Arusha region of Tanzania more particularly; it discusses data mining and its applications in major areas such as evaluation of treatment effectiveness, management of healthcare itself and lowering medical costs.


title: Deep Collaborative Learning for Visual Recognition

abstract: Deep neural networks are playing an important role in state-of-the-art visual recognition. To represent high-level visual concepts, modern networks are equipped with large convolutional layers, which use a large number of filters and contribute significantly to model complexity. For example, more than half of the weights of AlexNet are stored in the first fully-connected layer (4,096 filters). ::: We formulate the function of a convolutional layer as learning a large visual vocabulary, and propose an alternative way, namely Deep Collaborative Learning (DCL), to reduce the computational complexity. We replace a convolutional layer with a two-stage DCL module, in which we first construct a couple of smaller convolutional layers individually, and then fuse them at each spatial position to consider feature co-occurrence. In mathematics, DCL can be explained as an efficient way of learning compositional visual concepts, in which the vocabulary size increases exponentially while the model complexity only increases linearly. We evaluate DCL on a wide range of visual recognition tasks, including a series of multi-digit number classification datasets, and some generic image classification datasets such as SVHN, CIFAR and ILSVRC2012. We apply DCL to several state-of-the-art network structures, improving the recognition accuracy meanwhile reducing the number of parameters (16.82% fewer in AlexNet).


Audio Retrieval by Rhythmic Similarity


title: Face recognition based on convolutional neural network and support vector machine

abstract: Face recognition is an important embodiment of human-computer interaction, which has been widely used in access control system, monitoring system and identity verification. However, since face images vary with expressions, ages, as well as poses of people and illumination conditions, the face images of the same sample might be different, which makes face recognition difficult. There are two main requirements in face recognition, the high recognition rate and less training time. In this paper, we combine Convolutional Neural Network (CNN) and Support Vector Machine (SVM) to recognize face images. CNN is used as a feature extractor to acquire remarkable features automatically. We first pre-train our CNN by ancillary data to get the updated weights, and then train the CNN by the target dataset to extract more hidden facial features. Finally we use SVM as our classifier instead of CNN to recognize all the classes. With the input of facial features extracted from CNN, SVM will recognize face images more accurately. In our experiments, some face images in the Casia-Webfaces database are used for pre-training, and FERET database is used for training and testing. The results in experiments demonstrate the efficiency with high recognition rate and less training time.


title: Countdown Regression: Sharp and Calibrated Survival Predictions

abstract: Personalized probabilistic forecasts of time to event (such as mortality) can be crucial in decision making, especially in the clinical setting. Inspired by ideas from the meteorology literature, we approach this problem through the paradigm of maximizing sharpness of prediction distributions, subject to calibration. In regression problems, it has been shown that optimizing the continuous ranked probability score (CRPS) instead of maximum likelihood leads to sharper prediction distributions while maintaining calibration. We introduce the Survival-CRPS, a generalization of the CRPS to the time to event setting, and present right-censored and interval-censored variants. To holistically evaluate the quality of predicted distributions over time to event, we present the Survival-AUPRC evaluation metric, an analog to area under the precision-recall curve. We apply these ideas by building a recurrent neural network for mortality prediction, using an Electronic Health Record dataset covering millions of patients. We demonstrate significant benefits in models trained by the Survival-CRPS objective instead of maximum likelihood.


title: Modeling Others using Oneself in Multi-Agent Reinforcement Learning

abstract: We consider the multi-agent reinforcement learning setting with imperfect information in which each agent is trying to maximize its own utility. The reward function depends on the hidden state (or goal) of both agents, so the agents must infer the other players' hidden goals from their observed behavior in order to solve the tasks. We propose a new approach for learning in these domains: Self Other-Modeling (SOM), in which an agent uses its own policy to predict the other agent's actions and update its belief of their hidden state in an online manner. We evaluate this approach on three different tasks and show that the agents are able to learn better policies using their estimate of the other players' hidden states, in both cooperative and adversarial settings.


title: Co-Training for Domain Adaptation

abstract: Domain adaptation algorithms seek to generalize a model trained in a source domain to a new target domain. In many practical cases, the source and target distributions can differ substantially, and in some cases crucial target features may not have support in the source domain. In this paper we introduce an algorithm that bridges the gap between source and target domains by slowly adding to the training set both the target features and instances in which the current algorithm is the most confident. Our algorithm is a variant of co-training [7], and we name it CODA (Co-training for domain adaptation). Unlike the original co-training work, we do not assume a particular feature split. Instead, for each iteration of co-training, we formulate a single optimization problem which simultaneously learns a target predictor, a split of the feature space into views, and a subset of source and target features to include in the predictor. CODA significantly out-performs the state-of-the-art on the 12-domain benchmark data set of Blitzer et al. [4]. Indeed, over a wide range (65 of 84 comparisons) of target supervision CODA achieves the best performance.


title: 4G/LTE technology for smart grid communication infrastructure

abstract: The smart grid concept has emerged as a new trend to operate and manage the electric power systems more effective and efficient in recent years as different from centralized and distributed power systems. In this concept, the electric energy consumption is optimized by using an advanced automation system which needs more information about the power system elements from generation units to end-users and controls them in light of this information. Undoubtedly, receiving and transmitting of this information requires two-way, high speed, reliable and secure communication infrastructure. In this study, Long Term Evolution (LTE) technology and its usage in smart grid infrastructure are investigated. Additionally, the challenges for using 4G/LTE in smart grid applications are also discussed.


title: Introduction to computer animation and its possible educational applications

abstract: Animation, which is basically a form of pictorial presentation, has become the most prominent feature of technology-based learning environments. It refers to simulated motion pictures showing movement of drawn objects. Recently, educational computer animation has turned out to be one of the most elegant tools for presenting multimedia materials for learners, and its significance in helping to understand and remember information has greatly increased since the advent of powerful graphics-oriented computers. In this book chapter we introduce and discuss the history of computer animation, its well-known fundamental principles and some educational applications. It is however still debatable if truly educational computer animations help in learning, as the research on whether animation aids learners' understanding of dynamic phenomena has come up with positive, negative and neutral results. We have tried to provide as much detailed information on computer animation as we could, and we hope that this book chapter will be useful for students who study computer science, computer-assisted education or some other courses connected with contemporary education, as well as researchers who conduct their research in the field of computer animation.


title: Modified T-shaped planar monopole antennas for multiband operation

abstract: In this paper, we propose a novel modified T-shaped planar monopole antenna in that two asymmetric horizontal strips are used as additional resonators to produce the lower and upper resonant modes. As a result, a dual-band antenna for covering 2.4- and 5-GHz wireless local area network (WLAN) bands is implemented. In order to expand the lower band, a multiband antenna for covering the digital communications systems, personal communications systems, Universal Mobile Telecommunications Systems, and 2.4/5-GHz WLAN bands is also developed. Prototypes of the multiband antenna have been successfully implemented. Good omnidirectional radiation in the desired frequency bands has been achieved. The proposed multiband antenna with relatively low profile is very suitable for multiband mobile communication systems


title: Using Learning Analytics to Predict (and Improve) Student Success: A Faculty Perspective

abstract: Abstract Learning analytics is receiving increased attention, in part because it offers to assist educational institutions in increasing student retention, improving student success, and easing the burden of accountability. Although these large-scale issues are worthy of consideration, faculty might also be interested in how they can use learning analytics in their own courses to help their students succeed. In this paper, we define learning analytics, how it has been used in educational institutions, what learning analytics tools are available, and how faculty can make use of data in their courses to monitor and predict student performance. Finally, we discuss several issues and concerns with the use of learning analytics in higher education.


title: Automated view and path planning for scalable multi-object 3D scanning

abstract: Demand for high-volume 3D scanning of real objects is rapidly growing in a wide range of applications, including online retailing, quality-control for manufacturing, stop motion capture for 3D animation, and archaeological documentation and reconstruction. Although mature technologies exist for high-fidelity 3D model acquisition, deploying them at scale continues to require non-trivial manual labor. We describe a system that allows non-expert users to scan large numbers of physical objects within a reasonable amount of time, and with greater ease. Our system uses novel view- and path-planning algorithms to control a structured-light scanner mounted on a calibrated motorized positioning system. We demonstrate the ability of our prototype to safely, robustly, and automatically acquire 3D models for large collections of small objects.


title: Whats in a Country Name - Twitter Hashtag Analysis of #singapore

abstract: Twitter as a micro-blogging platform rose to instant fame mainly due to its minimalist features that allow seamless communication between users. As the conversations grew thick and faster, a placeholder feature called as Hashtags became important as it captured the themes behind the tweets. Prior studies have investigated the conversation dynamics, inter-play with other media platforms and communication patterns between users for specific event-based hashtags such as the #Occupy movement. Commonplace hashtags which are used on a daily basis have been largely ignored due to their seemingly innocuous presence in tweets and also due to the lack of connection with real-world events. However, it can be postulated that utility of these hashtags is the main reason behind their continued usage. This study is aimed at understanding the rationale behind the usage of a particular type of commonplace hashtags - location hashtags such as country and city name hashtags. Tweets with the hashtag #singapore were extracted for one week duration. Manual and automatic tweet classification was performed along with social network analysis, to identify the underlying themes. Seven themes were identified. Findings indicate that the hashtag is prominent in tweets about local events, local news, users current location and landmark related information sharing. Users who share content from social media sites such as Instagram make use of the hashtag in a more prominent way when compared to users who post textual content. News agencies, commercial bodies and celebrities make use of the hashtag more than common individuals. Overall, the results show the non-conversational nature of the hashtag. The findings are to be validated with other country names and cross-validated with hashtag data from other social media platforms.


title: Mood and Emotions in Small Groups and Work Teams

abstract: Affective influences abound in groups. In this article we propose an organizing model for understanding these affective influences and their effects on group life. We begin with individual-level affective characteristics that members bring to their groups: moods, emotions, sentiments, and emotional intelligence. These affective characteristics then combine to form a group's affective composition. We discuss explicit and implicit processes through which this affective combination occurs by examining the research on emotional contagion, entrainment, modeling, and the manipulation of affect. We also explore how elements of the affective context, such as organizationwide emotion norms and the group's particular emotional history, may serve to constrain or amplify group members' emotions. The outcome, group emotion, results from the combination of the group's affective composition and the affective context in which the group is behaving. Last, we focus on the important interaction between nonaffective factors and affective factors in group life and suggest a possible agenda for future research.


Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.


Automated Brain Tumor Detection and Brain MRI Classification Using Artificial Neural Network-A Review


title: Fuzzy adaptive control for a class of nonlinear systems with unknown control gain

abstract: This paper deals with direct adaptive control using fuzzy systems of a class of uncertain SISO nonlinear systems with unknown control gain sign. Within this scheme, a fuzzy system is used to generate directly the control input signal without dynamic system estimation, and the Nussbaum-type function is used to deal with the unknown control gain sign. The stability of the closed-loop system is performed using a Lyapunov approach. Simulation results are provided to verify the effectiveness of the proposed design.


title: Semantic audio content-based music recommendation and visualization based on user preference examples

abstract: Preference elicitation is a challenging fundamental problem when designing recommender systems. In the present work we propose a content-based technique to automatically generate a semantic representation of the user's musical preferences directly from audio. Starting from an explicit set of music tracks provided by the user as evidence of his/her preferences, we infer high-level semantic descriptors for each track obtaining a user model. To prove the benefits of our proposal, we present two applications of our technique. In the first one, we consider three approaches to music recommendation, two of them based on a semantic music similarity measure, and one based on a semantic probabilistic model. In the second application, we address the visualization of the user's musical preferences by creating a humanoid cartoon-like character - the Musical Avatar - automatically inferred from the semantic representation. We conducted a preliminary evaluation of the proposed technique in the context of these applications with 12 subjects. The results are promising: the recommendations were positively evaluated and close to those coming from state-of-the-art metadata-based systems, and the subjects judged the generated visualizations to capture their core preferences. Finally, we highlight the advantages of the proposed semantic user model for enhancing the user interfaces of information filtering systems.


title: Customer Satisfaction in Online Shopping: a study into the reasons for motivations and inhibitions

abstract: This study endeavours to understand customer satisfaction in online shopping while investigating the major reasons that motivated customers' decision-making processes as well as inhibitions of online shopping. The Kotler and Killers (2009) Five Stage Buying Process Model was chosen as the basis of framework of this study to explain customer satisfaction through their motivations to buy products online. The existing literature was reviewed to discover reasons that would influence customers positively or negatively towards shopping online. Surveys were conducted by distributing questionnaires in the Wrexham area (North Wales) to gather data for this research. SPSS software package was used to present research data graphically and to test research hypothesis. From the findings, it was discovered that respondents use internet to purchase products through online because they believe it is convenience to them and the term convenient includes elements such as time saving, information availability, opening time, ease of use, websites navigation, less shopping stress, less expensive and shopping fun. In contrast, along with respondents' mind-sets, online payment security, personal privacy and trust, unclear warranties and returns policies and lack of personal customer service are the foremost barriers of online shopping. Furthermore, the result of hypotheses established that even though online shopping is convenient to all consumers, online payment system and privacy or security anxieties have significant impact on online shopping. Finally, some recommendations have been offered for online retailers to take initiatives for making online shopping more admired and trustworthy.


title: Twitter spam account detection based on clustering and classification methods

abstract: Twitter social network has gained more popularity due to the increase in social activities of registered users. Twitter performs dual functions of online social network (OSN), acting as a microblogging OSN, and at the same time as a news update platform. Recently, the growth in Twitter social interactions has attracted the attention of cybercriminals. Spammers have used Twitter to spread malicious messages, post phishing links, flood the network with fake accounts, and engage in other malicious activities. The process of detecting the network of spammers who engage in these activities is an important step toward identifying individual spam account. Researchers have proposed a number of approaches to identify a group of spammers. However, each of these approaches addressed a specific category of spammer. This paper proposes a different approach to detect spammers on Twitter based on the similarities that exist among spam accounts. A number of features were introduced to improve the performance of the three classification algorithms selected in this study. The proposed approach applied principal component analysis and tuned K-means algorithm to cluster over 200,000 accounts, randomly selected from more than 2 million tweets to detect the clusters of spammers. Experimental results show that Random Forest achieved the highest accuracy of 96.30%. This result is followed by multilayer perceptron with 96.00% and support vector machine, which achieved 95.60%. The performance of the selected classifiers based on class imbalance also revealed that Random Forest achieved the highest accuracy, precision, recall, and F-measure.


title: Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation

abstract: In this paper, we propose a novel joint Task-Recursive Learning (TRL) framework for the closing-loop semantic segmentation and monocular depth estimation tasks. TRL can recursively refine the results of both tasks through serialized task-level interactions. In order to mutually-boost for each other, we encapsulate the interaction into a specific Task-Attentional Module (TAM) to adaptively enhance some counterpart patterns of both tasks. Further, to make the inference more credible, we propagate previous learning experiences on both tasks into the next network evolution by explicitly concatenating previous responses. The sequence of task-level interactions are finally evolved along a coarse-to-fine scale space such that the required details may be reconstructed progressively. Extensive experiments on NYU-Depth v2 and SUN RGB-D datasets demonstrate that our method achieves state-of-the-art results for monocular depth estimation and semantic segmentation.


ELMoLex : Connecting ELMo and Lexicon features for Dependency Parsing


title: Airborne LiDAR for DEM generation: some critical issues

abstract: Airborne LiDAR is one of the most effective and reliable means of terrain data collection. Using LiDAR data for digital elevation model (DEM) generation is becoming a standard practice in spatial related areas. However, the effective processing of the raw LiDAR data and the generation of an efficient and high-quality DEM remain big challenges. This paper reviews the recent advances of airborne LiDAR systems and the use of LiDAR data for DEM generation, with special focus on LiDAR data filters, interpolation methods, DEM resolution, and LiDAR data reduction. Separating LiDAR points into ground and non-ground is the most critical and difficult step for DEM generation from LiDAR data. Commonly used and most recently developed LiDAR filtering methods are presented. Interpolation methods and choices of suitable interpolator and DEM resolution for LiDAR DEM generation are discussed in detail. In order to reduce the data redundancy and increase the efficiency in terms of storage and manipulation, LiDAR data redu...


title: Evaluating an IoT Application Using Software Measures

abstract: Internet of Things (IoT) allows daily objects, with computing and communication capabilities, to connect to the Internet. In this scenario, an application called GREatRoom runs in an IoT environment, which has distributed wireless labels in places and objects, to detect the presence of nearby users, providing services intuitively and efficiently. Considering that IoT systems have quality characteristics of human-computer interaction similar to those of ubiquitous systems, this paper investigates the applicability of software measures from ubiquitous to IoT systems and presents the positive results achieved in this evaluation.


title: EV/PHEV Bidirectional Charger Assessment for V2G Reactive Power Operation

abstract: This paper presents a summary of the available single-phase ac-dc topologies used for EV/PHEV, level-1 and -2 on-board charging and for providing reactive power support to the utility grid. It presents the design motives of single-phase on-board chargers in detail and makes a classification of the chargers based on their future vehicle-to-grid usage. The pros and cons of each different ac-dc topology are discussed to shed light on their suitability for reactive power support. This paper also presents and analyzes the differences between charging-only operation and capacitive reactive power operation that results in increased demand from the dc-link capacitor (more charge/discharge cycles and increased second harmonic ripple current). Moreover, battery state of charge is spared from losses during reactive power operation, but converter output power must be limited below its rated power rating to have the same stress on the dc-link capacitor.


title: Designing cell function: assembly of synthetic gene circuits for cell biology applications

abstract: Synthetic biology is the discipline of engineering application-driven biological functionalities that were not evolved by nature. Early breakthroughs of cell engineering, which were based on ectopic (over)expression of single sets of transgenes, have already had a revolutionary impact on the biotechnology industry, regenerative medicine and blood transfusion therapies. Now, we require larger-scale, rationally assembled genetic circuits engineered to programme and control various human cell functions with high spatiotemporal precision in order to solve more complex problems in applied life sciences, biomedicine and environmental sciences. This will open new possibilities for employing synthetic biology to advance personalized medicine by converting cells into living therapeutics to combat hitherto intractable diseases. Taking advantage of genetic engineering, synthetic biology allows control and design of new cell functions. Recent advances in the development of genetic tools and the assembly of progressively more sophisticated gene circuits have made ‘designer cells’ a reality, with applications ranging from industry and biotechnology to medicine.


title: Introduction to Cyberdeception

abstract: This book is an introduction to both offensive and defensive techniques of cyberdeception. Unlike most books on cyberdeception, this book focuses on methods rather than detection. It treats cyberdeception techniques that are current, novel, and practical, and that go well beyond traditional honeypots. It contains features friendly for classroom use: (1) minimal use of programming details and mathematics, (2) modular chapters that can be covered in many orders, (3) exercises with each chapter, and (4) an extensive reference list. Cyberattacks have grown serious enough that understanding and using deception is essential to safe operation in cyberspace. The deception techniques covered are impersonation, delays, fakes, camouflage, false excuses, and social engineering. Special attention is devoted to cyberdeception in industrial control systems and within operating systems. This material is supported by a detailed discussion of how to plan deceptions and calculate their detectability and effectiveness. Some of the chapters provide further technical details of specific deception techniques and their application. Cyberdeception can be conducted ethically and efficiently when necessary by following a few basic principles. This book is intended for advanced undergraduate students and graduate students, as well as computer professionals learning on their own. It will be especially useful for anyone who helps run important and essential computer systems such as critical-infrastructure and military systems.


title: Improved carrier mobility in few-layer MoS2 field-effect transistors with ionic-liquid gating

abstract: We report the fabrication of ionic liquid (IL)-gated field-effect transistors (FETs) consisting of bilayer and few-layer MoS2. Our transport measurements indicate that the electron mobility μ ≈ 60 cm2 V–1 s–1 at 250 K in IL-gated devices exceeds significantly that of comparable back-gated devices. IL-FETs display a mobility increase from ≈100 cm2 V–1 s–1 at 180 K to ≈220 cm2 V–1 s–1 at 77 K in good agreement with the true channel mobility determined from four-terminal measurements, ambipolar behavior with a high ON/OFF ratio >107 (104) for electrons (holes), and a near ideal subthreshold swing of ≈50 mV/dec at 250 K. We attribute the observed performance enhancement, specifically the increased carrier mobility that is limited by phonons, to the reduction of the Schottky barrier at the source and drain electrode by band bending caused by the ultrathin IL dielectric layer.


title: Graphene oxide: a nonspecific enhancer of cellular growth.

abstract: There have been multiple conflicting reports about the biocompatibility and antimicrobial activity of graphene oxide. To address this, we conducted a study to characterize the antimicrobial properties of graphene oxide (GO) and its biocompatibility with mammalian cells. When GO was added to a bacterial culture at 25 μg/mL, the results showed that bacteria grew faster and to a higher optical density than cultures without GO. Scanning electron microscopy indicated that bacteria formed dense biofilms in the presence of GO. This was shown by a large mass of aggregated cells and extracellular polymeric material. Bacterial growth on filters coated with 25 and 75 μg of GO grew 2 and 3 times better than on filters without GO. Closer analysis showed that bacteria were able to attach and proliferate preferentially in areas containing the highest GO levels. Graphene oxide films failed to produce growth inhibition zones around them, indicating a lack of antibacterial properties. Also, bacteria were able to grow on GO films to 9.5 × 10(9) cells from an initial inoculation of 1.0 × 10(6), indicating that it also lacks bacteriostatic activity. Thus, silver-coated GO films were able to produce clearing zones and cell death. Also, graphene oxide was shown to greatly enhance the attachment and proliferation of mammalian cells. This study conclusively demonstrates that graphene oxide does not have intrinsic antibacterial, bacteriostatic, and cytotoxic properties in both bacteria and mammalian cells. Furthermore, graphene oxide acts as a general enhancer of cellular growth by increasing cell attachment and proliferation.


title: Benchmarking Big Data Systems: State-of-the-Art and Future Directions

abstract: The great prosperity of big data systems such as Hadoop in recent years makes the benchmarking of these systems become crucial for both research and industry communities. The complexity, diversity, and rapid evolution of big data systems gives rise to various new challenges about how we design generators to produce data with the 4V properties (i.e. volume, velocity, variety and veracity), as well as implement application-specific but still comprehensive workloads. However, most of the existing big data benchmarks can be described as attempts to solve specific problems in benchmarking systems. This article investigates the state-of-the-art in benchmarking big data systems along with the future challenges to be addressed to realize a successful and efficient benchmark.


title: Indoor Scene Layout Estimation from a Single Image

abstract: With the popularity of the hand devices and intelligent agents, many aimed to explore machine's potential in interacting with reality. Scene understanding, among the many facets of reality interaction, has gained much attention for its relevance in applications such as augmented reality (AR). Scene understanding can be partitioned into several sub tasks (i.e., layout estimation, scene classification, saliency prediction, etc). In this paper, we propose a deep learning-based approach for estimating the layout of a given indoor image in real-time. Our method consists of a deep fully convolutional network, a novel layout-degeneration augmentation method, and a new training pipeline which integrate an adaptive edge penalty and smoothness terms into the training process. Unlike previous deep learning-based methods that depend on post-processing refinement (e.g., proposal ranking and optimization), our method motivates the generalization ability of the network and the smoothness of estimated layout edges without deploying postprocessing techniques. Moreover, the proposed approach is time-efficient since it only takes the model one forward pass to render accurate layouts. We evaluate our method on LSUN Room Layout and Hedau dataset and obtain estimation results comparable with the state-of-the-art methods.


title: BlockSee: Blockchain for IoT Video Surveillance in Smart Cities

abstract: The growing demand for safety in urban environments is supported by monitoring using video surveillance. The need to analyze multiple video-flows from different cameras deployed around the city by heterogeneous owners introduces vulnerabilities and privacy issues. Video frames, timestamps, and camera settings can be digitally manipulated by malicious users; the positions of cameras, their orientation and their mechanical settings can be physically manipulated. Digital and physical manipulations may have several effects, including the change of the observed scene and the potential violation of neighbors' privacy. To face these risks, we introduce BlockSee, a blockchain-based video surveillance system that jointly provides validation and immutability to camera settings and surveillance videos, making them readily available to authorized users in case of events. The encouraging results obtained with BlockSee pave the way to new distributed city-wide monitoring systems.


title: A 3D printed Two DoF Soft Robotic Finger With Variable Stiffness

abstract: Grippers based on fluid elastomer actuators have shown their ability to manipulate fragile and delicate objects. However, they cannot resist large external force due to their soft material and compliant structure, leading to an unstable or even failed grasp. Low stiffness has limited their applicability. In this paper, we propose a soft robotic finger based on multimaterial 3D printing and layer jamming to achieve varied finger bending motion and variable stiffness. The proposed finger has two DoF, including two pneumatic actuators (top actuator and bottom actuator) and each with an integrated layer jamming unit. A prototype of the proposed finger is directly printed out by an Object350Connex 3D printer without requiring an additional casting process. The finger’s bending angle and stiffness can be controlled due to the novel actuator design. Experiments were carried out to exam the finger’s bending angles. Four finger motion can be achieved and the stiffness of the finger is increased with the aid of the layer jamming mechanism. Multi-material 3D printing technique can provide flexibility to the design of soft robotics, shorten the fabrication process and offer a range of materials to achieve desired mechanical properties.


title: A New Technique for Tracking the Global Maximum Power Point of PV Arrays Operating Under Partial-Shading Conditions

abstract: The power-voltage characteristic of photovoltaic (PV) arrays operating under partial-shading conditions exhibits multiple local maximum power points (MPPs). In this paper, a new method to track the global MPP is presented, which is based on controlling a dc/dc converter connected at the PV array output, such that it behaves as a constant input-power load. The proposed method has the advantage that it can be applied in either stand-alone or grid-connected PV systems comprising PV arrays with unknown electrical characteristics and does not require knowledge about the PV modules configuration within the PV array. The experimental results verify that the proposed global MPP method guarantees convergence to the global MPP under any partial-shading conditions. Compared with past-proposed methods, the global MPP tracking process is accomplished after far fewer PV array power perturbation steps.


title: Neural Networks and Wavelet De-Noising for Stock Trading and Prediction

abstract: In this chapter, neural networks are used to predict the future stock prices and develop a suitable trading system. Wavelet analysis is used to de-noise the time series and the results are compared with the raw time series prediction without wavelet de-noising. Standard and Poor 500 (S&P 500) is used in experiments. We use a gradual data sub-sampling technique, i.e., training the network mostly with recent data, but without neglecting past data. In addition, effects of NASDAQ 100 are studied on prediction of S&P 500. A daily trading strategy is employed to buy/sell according to the predicted prices and to calculate the directional efficiency and the rate of returns for different periods. There are numerous exchange traded funds (ETF’s), which attempt to replicate the performance of S&P 500 by holding the same stocks in the same proportions as the index, and therefore, giving the same percentage returns as S&P 500. Therefore, this study can be used to help invest in any of the various ETFs, which replicates the performance of S&P 500. The experimental results show that neural networks, with appropriate training and input data, can be used to achieve high profits by investing in ETFs based on S&P 500.


title: Alpha-beta pruning for games with simultaneous moves

abstract: Alpha-Beta pruning is one of the most powerful and fundamental MiniMax search improvements. It was designed for sequential two-player zero-sum perfect information games. Inthis paper we introduce an Alpha-Beta-like sound pruning method for the more general class of "stacked matrix games" that allow for simultaneous moves by both players. This is accomplished by maintaining upper and lower bounds for achievable payoffs in states with simultaneous actions and dominated action pruning based on the feasibility of certain linear programs. Empirical data shows considerable savings in terms of expanded nodes compared to naive depth-first move computation without pruning.


title: Altitude Estimation and Control of an Insect-Scale Robot with an Onboard Proximity Sensor

abstract: Insect-scale micro-air vehicles (MAVs) require careful consideration of the size, weight and power for each component. The inherent instability of the system, exacerbated by the faster dynamics that result from increasing angular accelerations with decreasing scale, requires high bandwidth sensing to maintain stable flight. The Harvard RoboBee is the first MAV under 100 mg to demonstrate controlled flight using external motion capture cameras to measure the position and orientation of the vehicle during flight. Prior research into onboard sensing has demonstrated several sensors that provide sufficiently high-bandwidth and low-latency feedback to stabilize the attitude of the robot. To achieve autonomous flight, the vehicle needs to sense its attitude, altitude, and either lateral position or velocity. Here we build on previous work by incorporating a sensor that is size- and power-compatible with insect-scale flight, capable of estimating distance by measuring the time-of-flight of an infrared laser pulse. This sensor has sufficiently low latency to allow the robot to maintain constant altitude over multiple flight experiments. This work on onboard altitude control represents the latest results in achieving autonomous control and visually-guided flight.


title: Knowledge Sharing Behavior of Physicians in Hospitals

abstract: Abstract Recently, there has been much interest for knowledge sharing within professional group, especially physicians in hospital. This study investigates the factors affecting physician's knowledge sharing behavior within a hospital department by employing existing theories. The research models under investigation are the theory of reasoned action (TRA) and the theory of planned behavior (TPB). These models are empirically examined and compared based on the survey results on physicians' knowledge sharing behavior collected from 286 physicians practicing in 28 types of subunits in 13 tertiary hospitals in Korea. The TPB model exhibited good fit with the data and appeared to be superior to the TRA in explaining physicians' intention to share knowledge. In the modified TPB model, subjective norms were found to have the strongest total effects on behavioral intentions to share knowledge of physicians through direct and indirect path by attitude. Attitude was found to be the second important factor influencing physicians' intentions. Perceived behavioral control was also found to affect the intention to share knowledge, though in a lesser degree than subjective norms or attitudes. Implications are also discussed for physician's knowledge sharing activities.


title: Can Deep Learning Revolutionize Mobile Sensing?

abstract: Sensor-equipped smartphones and wearables are transforming a variety of mobile apps ranging from health monitoring to digital assistants. However, reliably inferring user behavior and context from noisy and complex sensor data collected under mobile device constraints remains an open problem, and a key bottleneck to sensor app development. In recent years, advances in the field of deep learning have resulted in nearly unprecedented gains in related inference tasks such as speech and object recognition. However, although mobile sensing shares many of the same data modeling challenges, we have yet to see deep learning be systematically studied within the sensing domain. If deep learning could lead to significantly more robust and efficient mobile sensor inference it would revolutionize the field by rapidly expanding the number of sensor apps ready for mainstream usage. In this paper, we provide preliminary answers to this potentially game-changing question by prototyping a low-power Deep Neural Network (DNN) inference engine that exploits both the CPU and DSP of a mobile device SoC. We use this engine to study typical mobile sensing tasks (e.g., activity recognition) using DNNs, and compare results to learning techniques in more common usage. Our early findings provide illustrative examples of DNN usage that do not overburden modern mobile hardware, while also indicating how they can improve inference accuracy. Moreover, we show DNNs can gracefully scale to larger numbers of inference classes and can be flexibly partitioned across mobile and remote resources. Collectively, these results highlight the critical need for further exploration as to how the field of mobile sensing can best make use of advances in deep learning towards robust and efficient sensor inference.


title: Efficiency evaluation of single-phase solutions for AC-DC PFC boost converters for plug-in-hybrid electric vehicle battery chargers

abstract: In this paper, the bridgeless interleaved boost topology is proposed for plug-in hybrid electric vehicle and electric vehicle battery chargers to achieve high efficiency, which is critical to minimize the charger size, charging time and the amount and cost of electricity drawn from the utility. An analytical model for this topology is developed, enabling the calculation of power losses and efficiency. Experimental and simulation results of prototype units converting the universal AC input voltage to 400 V DC at 3.4 kW are given to verify the proof of concept, and analytical work reported in this paper.


title: Prediction of Patient-Reported Physical Activity Scores from Wearable Accelerometer Data: A Feasibility Study

abstract: Many diseases are characterized by limitations in mobility, including a wide range of musculoskeletal and neurological conditions. Reduced mobility impacts a patients ability to perform activities of daily living, which in turn reduces health-related quality of life. Mobility can be assessed by collecting patient-reported outcome scores from standardized questionnaires and by directly measuring physical activity parameters from wearable accelerometer data. In this work, we explored the relationship between subjectively and objectively measured mobility by training machine learning models to predict patient responses based on features derived from real-world acceleration data. Our method achieved up to 82% accuracy using a random forest classifier and set the basis to develop novel data-driven digital biomarkers for objective, quantitative and more frequent evaluation of patients’ mobility.


title: Review: Plant species identification using digital morphometrics: A review

abstract: Plants are of fundamental importance to life on Earth. The shapes of leaves, petals and whole plants are of great significance to plant science, as they can help to distinguish between different species, to measure plant health, and even to model climate change. The growing interest in biodiversity and the increasing availability of digital images combine to make this topic timely. The global shortage of expert taxonomists further increases the demand for software tools that can recognize and characterize plants from images. A robust automated species identification system would allow people with only limited botanical training and expertise to carry out valuable field work. We review the main computational, morphometric and image processing methods that have been used in recent years to analyze images of plants, introducing readers to relevant botanical concepts along the way. We discuss the measurement of leaf outlines, flower shape, vein structures and leaf textures, and describe a wide range of analytical methods in use. We also discuss a number of systems that apply this research, including prototypes of hand-held digital field guides and various robotic systems used in agriculture. We conclude with a discussion of ongoing work and outstanding problems in the area.


title: Exploiting Rateless Codes in Cloud Storage Systems

abstract: Block-level cloud storage (BLCS) offers to users and applications the access to persistent block storage devices (virtual disks) that can be directly accessed and used as if they were raw physical disks. In this paper we devise ENIGMA, an architecture for the back-end of BLCS systems able to provide adequate levels of access and transfer performance, availability, integrity, and confidentiality, for the data it stores. ENIGMA exploits LT rateless codes to store fragments of sectors on storage nodes organized in clusters. We quantitatively evaluate how the various ENIGMA system parameters affect the performance, availability, integrity, and confidentiality of virtual disks. These evaluations are carried out by using both analytical modeling (for availability, integrity, and confidentiality) and discrete event simulation (for performance), and by considering a set of realistic operational scenarios. Our results indicate that it is possible to simultaneously achieve all the objectives set forth for BLCS systems by using ENIGMA, and that a careful choice of the various system parameters is crucial to achieve a good compromise among them. Moreover, they also show that LT coding-based BLCS systems outperform traditional BLCS systems in all the aspects mentioned before.


title: ViBE: A powerful random technique to estimate the background in video sequences

abstract: Background subtraction is a crucial step in many automatic video content analysis applications. While numerous acceptable techniques have been proposed so far for background extraction, there is still a need to produce more efficient algorithms in terms of adaptability to multiple environments, noise resilience, and computation efficiency. In this paper, we present a powerful method for background extraction that improves in accuracy and reduces the computational load. The main innovation concerns the use of a random policy to select values to build a samples-based estimation of the background. To our knowledge, it is the first time that a random aggregation is used in the field of background extraction. In addition we propose a novel policy that propagates information between neighboring pixels of an image. Experiment detailed in this paper show how our method improves on other widely used techniques, and how it outperforms these techniques for noisy images.


title: End-to-End Multimodal Emotion Recognition using Deep Neural Networks

abstract: Automatic affect recognition is a challenging task due to the various modalities emotions can be expressed with. Applications can be found in many domains including multimedia retrieval and human–computer interaction. In recent years, deep neural networks have been used with great success in determining emotional states. Inspired by this success, we propose an emotion recognition system using auditory and visual modalities. To capture the emotional content for various styles of speaking, robust features need to be extracted. To this purpose, we utilize a convolutional neural network (CNN) to extract features from the speech, while for the visual modality a deep residual network of 50 layers is used. In addition to the importance of feature extraction, a machine learning algorithm needs also to be insensitive to outliers while being able to model the context. To tackle this problem, long short-term memory networks are utilized. The system is then trained in an end-to-end fashion where—by also taking advantage of the correlations of each of the streams—we manage to significantly outperform, in terms of concordance correlation coefficient, traditional approaches based on auditory and visual handcrafted features for the prediction of spontaneous and natural emotions on the RECOLA database of the AVEC 2016 research challenge on emotion recognition.


title: Automated Parsing of Geographical Addresses: A Multilayer Feedforward Neural Network Based Approach

abstract: Delivering mails and parcels is an age-old problem. With the advent of E-Commerce, one can now get almost everything delivered. The core piece of this delivery puzzle is the delivery address. Though there has been a lot of technological advancement in supply chain management yet, delivery address verification remains manually intensive or at the best, dependent on some hardcoded rule-based process. Addresses are found in a large number of patterns and variations. In order to understand any address, we need to extract essential components of the address. They are majorly influenced by the syntax and semantics of an address. Traditionally, component extraction is done using hard-coded rules based on the expert knowledge of how addresses are written in a particular geography. This paper proposes a machine learning based approach for address parsing using a neural network. The proposed approach is compared to the existing methods and its advantages are depicted in terms of accuracy, scalability, and support for variations in addresses.


title: Venlafaxine compared with fluoxetine in outpatients with depression and concomitant anxiety

abstract: The aim of this double-blind study was to compare the efficacy and safety of venlafaxine vs. fluoxetine in the treatment of patients with depression and anxiety. A total of 146 moderately depressed patients with associated anxiety were randomized to receive 75 mg/d venlafaxine or 20 mg/d fluoxetine for 12 wk. Dose increases were permitted after 2 wk of treatment, to 150 mg/d venlafaxine and 40 mg/d fluoxetine, to optimize response. At the final visit, a statistically significantly greater efficacy of venlafaxine over fluoxetine was observed on depressive symptoms and concomitant anxiety, and 75.0 and 50.7% of patients administered venlafaxine and fluoxetine, respectively, showed an overall response. A sustained response (for at least 2 wk), present at the end of the study was achieved in 57.8 and 43.3% of patients in the venlafaxine and fluoxetine groups, respectively, and at the final visit, 59.4 and 40.3% of patients, respectively, were in remission (virtually asymptomatic). Dose increases were required by a greater percentage of patients in the fluoxetine group (52.9%), than in the venlafaxine group (37.1%), and in those patients whose dose was increased, a higher efficacy was again observed with venlafaxine. Venlafaxine and fluoxetine were well tolerated, with the most frequently experienced adverse events being nausea and headache. Fewer patients in the venlafaxine group than in the fluoxetine group reported at least one adverse event (55.7 and 67.1% patients, respectively). Venlafaxine therefore proved to be significantly more effective than fluoxetine in improving depressive symptoms and concomitant anxiety.


title: A self oscillating class D audio amplifier with 0.0012% THD+N and 116.5 dB dynamic range

abstract: A low distortion third-order self-oscillating class D audio amplifier is integrated in a 0.7-µm CMOS process. It can deliver 1.4 W into an 8 Ω load with 5 V power supply. The presented amplifier eliminates the requirement for a high quality carrier. It achieves a dynamic range (DR) of 116.5 dB, and a peak THD+N of 0.0012% for a 1 kHz sinusoidal input. The efficiency is 84.5%. The area of the amplifier is 6 mm2.


title: Design and Optimization of High-Power UWB Combined Antenna Based on Klopfenstein Impedance Taper

abstract: In order to improve the operating bandwidth and achieve a compact design of the combined antenna, the Klopfenstein impedance taper method is adopted to implement the structure of the combined antenna in this paper. The effects of Klopfenstein taper with different parameters are compared and analyzed with simulation method. Also, the quantity of the magnetic moment of the combined antenna is optimized to improve the low-frequency performance. The relationship between the performance and the structural parameters for the combined antenna is concluded based on the research results. Finally, an optimized combined antenna with length of 30 cm is designed and measured. The impedance bandwidth [voltage standing wave ratio (VSWR) $\lambda $ , is reduced to 0.18. The weighted radiation efficiency within 0–7 GHz is 93.7%. Both numerical and experimental results show that the antenna exhibits uni-directional patterns within the frequency range of 0.5–7 GHz. With a short ultra-wideband pulse excitation, gain of the antenna is measured and derived within the spectrum of the excitation, i.e., 0–3 GHz. The corner frequency of the antenna gain is about 200 MHz, which is close to that of VSWR. Moreover, the radiation efficiency is 76.1%, and the pulse fidelity factor is 0.82 with the applied excitation.


title: Inappropriate Medication Use and Medication Errors in the Elderly

abstract: Medications are commonly used by older adults. While medications can relieve symptoms and prevent further disease complications, they unfortunately can also cause adverse drug events (ADEs). An ADE can be defined as “an injury resulting from the use of a drug” (Aspden et al. 2007; Nebeker et al. 2004). The right side of Fig. 1 shows the three types of ADEs: (1) adverse drug reactions (ADRs) (i.e., a response to a drug that is noxious and unintended and occurs at doses normally used for the prophylaxis, diagnosis, or therapy of disease or for modification of physiological function); (2) therapeutic failures (TFs) (i.e., failure to accomplish the goals of treatment resulting from inadequate drug therapy and not related to the natural progression of disease); and (3) adverse drug withdrawal reactions (ADWEs) (i.e., a clinical set of symptoms or signs that are related to the removal of a drug) (Edwards and Aronson 2000; Hanlon et al. 2010). Besides death, which fortunately is rarely due to medications, one of the worst consequences of medication use in older adults is hospitalization. Studies have shown that up to 16 % of hospital admissions are due to ADRs, up to 11 % due to TFs, and approximately 1 % due to ADWEs (Beijer and de Blaey 2002; Kaiser et al. 2006; Marcum et al. 2011). Taken together, these medication-related problems are a significant cause of morbidity and mortality as well as unnecessary healthcare costs in older adults.


title: Instagram: Motives for its use and relationship to narcissism and contextual age

abstract: Abstract Instagram is the fastest growing social network site globally. This study investigates motives for its use, and its relationship to contextual age and narcissism. A survey of 239 college students revealed that the main reasons for Instagram use are “Surveillance/Knowledge about others,” “Documentation,” “Coolness,” and “Creativity.” The next significant finding was a positive relationship between those who scored high in interpersonal interaction and using Instagram for coolness, creative purposes, and surveillance. Another interesting finding shows that there is a positive relationship between high levels of social activity (traveling, going to sporting events, visiting friends, etc.) and being motivated to use Instagram as a means of documentation. In reference to narcissism, there was a positive relationship between using Instagram to be cool and for surveillance. Theoretical contributions of this study relate to our understanding of uses and gratifications theory. This study uncovers new motives for social media use not identified in previous literature.


title: Leucocyte classification for leukaemia detection using image processing techniques

abstract: Abstract Introduction The counting and classification of blood cells allow for the evaluation and diagnosis of a vast number of diseases. The analysis of white blood cells (WBCs) allows for the detection of acute lymphoblastic leukaemia (ALL), a blood cancer that can be fatal if left untreated. Currently, the morphological analysis of blood cells is performed manually by skilled operators. However, this method has numerous drawbacks, such as slow analysis, non-standard accuracy, and dependences on the operator's skill. Few examples of automated systems that can analyse and classify blood cells have been reported in the literature, and most of these systems are only partially developed. This paper presents a complete and fully automated method for WBC identification and classification using microscopic images. Methods In contrast to other approaches that identify the nuclei first, which are more prominent than other components, the proposed approach isolates the whole leucocyte and then separates the nucleus and cytoplasm. This approach is necessary to analyse each cell component in detail. From each cell component, different features, such as shape, colour and texture, are extracted using a new approach for background pixel removal. This feature set was used to train different classification models in order to determine which one is most suitable for the detection of leukaemia. Results Using our method, 245 of 267 total leucocytes were properly identified (92% accuracy) from 33 images taken with the same camera and under the same lighting conditions. Performing this evaluation using different classification models allowed us to establish that the support vector machine with a Gaussian radial basis kernel is the most suitable model for the identification of ALL, with an accuracy of 93% and a sensitivity of 98%. Furthermore, we evaluated the goodness of our new feature set, which displayed better performance with each evaluated classification model. Conclusions The proposed method permits the analysis of blood cells automatically via image processing techniques, and it represents a medical tool to avoid the numerous drawbacks associated with manual observation. This process could also be used for counting, as it provides excellent performance and allows for early diagnostic suspicion, which can then be confirmed by a haematologist through specialised techniques.


title: Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields

abstract: There is an increasing interest in agricultural robotics and precision farming. In such domains, relevant datasets are often hard to obtain, as dedicated fields need to be maintained and the timing...


title: Novel Common Vehicle Information Model (CVIM) for Future Automotive Vehicle Big Data Marketplaces

abstract: Even though connectivity services have been introduced in many of the most recent car models, access to vehicle data is currently limited due to its proprietary nature. The European project AutoMat has therefore developed an open Marketplace providing a single point of access for brand-independent vehicle data. Thereby, vehicle sensor data can be leveraged for the design and implementation of entirely new services even beyond traffic-related applications (such as hyper-local traffic forecasts). This paper presents the architecture for a Vehicle Big Data Marketplace as enabler of cross-sectorial and innovative vehicle data services. Therefore, the novel Common Vehicle Information Model (CVIM) is defined as an open and harmonized data model, allowing the aggregation of brand-independent and generic data sets. Within this work the realization of a prototype CVIM and Marketplace implementation is presented. The two use-cases of local weather prediction and road quality measurements are introduced to show the applicability of the AutoMat concept and prototype to non-automotive applications.


title: Camera Matrix Calibration Using Circular Control Points and Separate Correction of the Geometric Distortion Field

abstract: We achieve a precise camera calibration using circular control points by, first, separation of the lens distortion parameters from other camera parameters and computation of the distortion field in advance by using a calibration harp. Second, in order to compensate for perspective bias, which is prone to occur when using a circled pattern, we incorporate conic affine transformation into the minimization error when estimating the homography, and leave all the other calibration steps as they are used in the literature. Such an error function allows to compensate for the perspective bias. Combined with precise key point detection, the approach is shown to be more stable than current state-of-the-art global calibration method.


title: DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network

abstract: BACKGROUND ::: Medical practitioners use survival models to explore and understand the relationships between patients' covariates (e.g. clinical and genetic features) and the effectiveness of various treatment options. Standard survival models like the linear Cox proportional hazards model require extensive feature engineering or prior medical knowledge to model treatment interaction at an individual level. While nonlinear survival methods, such as neural networks and survival forests, can inherently model these high-level interaction terms, they have yet to be shown as effective treatment recommender systems. ::: ::: ::: METHODS ::: We introduce DeepSurv, a Cox proportional hazards deep neural network and state-of-the-art survival method for modeling interactions between a patient's covariates and treatment effectiveness in order to provide personalized treatment recommendations. ::: ::: ::: RESULTS ::: We perform a number of experiments training DeepSurv on simulated and real survival data. We demonstrate that DeepSurv performs as well as or better than other state-of-the-art survival models and validate that DeepSurv successfully models increasingly complex relationships between a patient's covariates and their risk of failure. We then show how DeepSurv models the relationship between a patient's features and effectiveness of different treatment options to show how DeepSurv can be used to provide individual treatment recommendations. Finally, we train DeepSurv on real clinical studies to demonstrate how it's personalized treatment recommendations would increase the survival time of a set of patients. ::: ::: ::: CONCLUSIONS ::: The predictive and modeling capabilities of DeepSurv will enable medical researchers to use deep neural networks as a tool in their exploration, understanding, and prediction of the effects of a patient's characteristics on their risk of failure.


title: A Robotic Skin for Collision Avoidance and Affective Touch Recognition

abstract: We describe a flexible robotic skin module that can measure proximity, contact and force, as well as algorithms for detecting obstacles, human hands, and affective touch gestures. The computational requirements of the proposed architecture are in line with the capabilities of a small microcontroller, allowing for a majority of the data to be processed colocated with the skin. The resulting system only communicates low-bandwidth information of interest, thereby solving challenges in routing high-bandwidth information and reducing the burden on a robot's central processing unit. We describe design and manufacturing of a 10.8 $\times$ 10.8 cm $^2$ skin patch containing 64 sensors, classification results for six different affective touch gestures, and a method that allows the skin to differentiate between an approaching human hand and various obstacles.


title: Relationship Between Compliance and Quality of Life of Senior Survivors of Stroke in the Community

abstract: Objective: To explore the relationship between compliance and quality of life after stroke in community.Methods: 241 stroke survivors were successfully investigated with Stroke Behavior Change Inventory(SBCI) and Short Form 36 Health Survey Questionnaire(SF-36).Results: ①Stroke survivors dramatically showed to have changed all the modifiable stroke risk factors,but the level of compliance was to be enhanced;②All correlations of life style,drug compliance and quality of life reached positive significance;③Life styles could account for 5.6% of variance of patients’ physical health summary when the effect of age and hemiplegy were controlled,but drug compliance had no significant effect.And when the effect of hemiplegy was controlled,life styles could predict 3.2% of variance of patients’ mental health summary,and drug compliance as well had a positive prediction towards mental health summary,and could give a 7.5% account for it.Conclusion: The compliance can significantly influence the quality of life of senior survivors of stroke in community.


title: 10x10: A Case Study in Highly-Programmable and Energy-Efficient Heterogeneous Federated Architecture

abstract: Customized architecture is widely recognized as an important approach for improved performance and energyefficiency. To balance generality and customization benefit, researchers have proposed to federate heterogeneous micro-engines. Using the 10x10 architecture and an integrated image and vision benchmark as a case study, we explore the performance and energy benefits achievable. Results for current 32nm technology and DDR3 memory show 10x10 architecture benefits of 140x performance and 72x energy overall. Adding 3D-stacked DRAM increase benefits to 171x (performance) and 100x (energy). Finally, considering future 7nm transistor process, benefits as large as 597x (performance) and 137x energy are observed.


title: Pilot Interaction With Cockpit Automation II: An Experimental Study of Pilots' Model and Awareness of the Flight Management System

abstract: Technological developments have made it possible to automate more and more functions on the commercial aviation flight deck and in other dynamic high-consequence domains. This increase in the degrees of freedom in design has shifted questions away from narrow technological feasibility. Many concerned groups, from designers and operators to regulators and researchers, have begun to ask questions about how we should use the possibilities afforded by technology skillfully to support and expand human performance. In this article, we report on an experimental study that addressed these questions by examining pilot interaction with the current generation of flight deck automation. Previous results on pilot-automation interaction derived from pilot surveys, incident reports, and training observations have produced a corpus of features and contexts in which human-machine coordination is likely to break down (e.g., automation surprises). We used these data to design a simulated flight scenario that contained a var...


title: Three-Dimensional Assembly Tolerance Analysis Based on the Jacobian-Torsor Statistical Model

abstract: The unified Jacobian-Torsor model has been developed for deterministic (worst case) tolerance analysis. This paper presents a comprehensive model for performing statistical tolerance analysis by integrating the unified Jacobian-Torsor model and Monte Carlo simulation. In this model, an assembly is sub-divided into surfaces, the Small Displacements Torsor (SDT) parameters are used to express the relative position between any two surfaces of the assembly. Then, 3D dimension-chain can be created by using a surface graph of the assembly and the unified Jacobian-Torsor model is developed based on the effect of each functional element on the whole functional requirements of products. Finally, Monte Carlo simulation is implemented for the statistical tolerance analysis. A numerical example is given to demonstrate the capability of the proposed method in handling three-dimensional assembly tolerance analysis.


title: An AI readability'' Formula for French as a Foreign Language

abstract: This paper present a new readability formula for French as a foreign language (FFL), which relies on 46 textual features representative of the lexical, syntactic, and semantic levels as well as some of the specificities of the FFL context. We report comparisons between several techniques for feature selection and various learning algorithms. Our best model, based on support vector machines (SVM), significantly outperforms previous FFL formulas. We also found that semantic features behave poorly in our case, in contrast with some previous readability studies on English as a first language.


title: A Cultural Perspective on Gender Diversity in Computing 1

abstract: This chapter presents a cultural perspective for thinking about, and acting on, issues concerning gender and computer science and related fi elds. We posit and demonstrate that the notion of a gender divide in how men and women relate to computing, traditionally attributed to gender differences, is largely a result of cultural and environmental conditions. Indeed, the reasons for women entering—or not entering—the fi eld of computer science have little to do with gender and a lot to do with environment and culture as well as the perception of the fi eld. Appropriate outreach, education, and interventions in the microculture can have broad impact, increasing participation in computing and creating environments where both men and women can fl ourish. Thus, we refute the popular notion that focusing on gender differences will enhance greater participation in computing, and we propose an alternative, more constructive approach that focuses on culture. We illustrate the cultural perspective using specifi c case studies based in different geographical and cultural regions.


title: A 0.47–0.66 pJ/bit, 4.8–8 Gb/s I/O Transceiver in 65 nm CMOS

abstract: A low-power forwarded-clock I/O transceiver architecture is presented that employs a high degree of output/input multiplexing, supply-voltage scaling with data rate, and low-voltage circuit techniques to enable low-power operation. The transmitter utilizes a 4:1 output multiplexing voltage-mode driver along with 4-phase clocking that is efficiently generated from a passive poly-phase filter. The output driver voltage swing is accurately controlled from 100–200 ${\rm mV}_{\rm ppd}$ using a low-voltage pseudo-differential regulator that employs a partial negative-resistance load for improved low frequency gain. 1:8 input de-multiplexing is performed at the receiver equalizer output with 8 parallel input samplers clocked from an 8-phase injection-locked oscillator that provides more than 1UI de-skew range. In the transmitter clocking circuitry, per-phase duty-cycle and phase-spacing adjustment is implemented to allow adequate timing margins at low operating voltages. Fabricated in a general purpose 65 nm CMOS process, the transceiver achieves 4.8–8 Gb/s at 0.47–0.66 pJ/b energy efficiency for ${\rm V}_{\rm DD}=0.6$ –0.8 V.


title: ACMG position statement on prenatal/preconception expanded carrier screening

abstract: For years, clinicians have offered gene-by-gene carrier screening to patients and couples considering future pregnancy or those with an ongoing pregnancy early in gestation. Examples include ethnic-specific screening offered to Ashkenazi Jewish patients and panethnic screening for cystic fibrosis and spinal muscular atrophy. Next-generation sequencing methods now available permit screening for many more disorders with high fidelity, quick turnaround time, and lower costs. However, instituting these technologies carries with it perils that must be addressed. The basis for the selection of disorders on expanded carrier screening panels should be disclosed. The information provided about disorders with mild phenotypes, variable expression, low penetrance, and/or characterized by an adult onset should be complete and transparent, allowing patients to opt out of receiving these test results. Patients also must be made aware of the concept of residual risk following negative test results. Laboratories have a duty to participate in and facilitate this information transfer.


A Plagiarism Detection Approach Based on SVM for Persian Texts


title: No bad local minima: Data independent training error guarantees for multilayer neural networks

abstract: We use smoothed analysis techniques to provide guarantees on the training loss of Multilayer Neural Networks (MNNs) at differentiable local minima. Specifically, we examine MNNs with piecewise linear activation functions, quadratic loss and a single output, under mild over-parametrization. We prove that for a MNN with one hidden layer, the training error is zero at every differentiable local minimum, for almost every dataset and dropout-like noise realization. We then extend these results to the case of more than one hidden layer. Our theoretical guarantees assume essentially nothing on the training data, and are verified numerically. These results suggest why the highly non-convex loss of such MNNs can be easily optimized using local updates (e.g., stochastic gradient descent), as observed empirically.


title: Heart rate spectrum analysis for sleep quality detection

abstract: To evaluate the quality of sleep, it is important to determine how much time was spent in each sleep stage during the night. The gold standard in this domain is an overnight polysomnography (PSG). But the recording of the necessary electrophysiological signals is extensive and complex and the environment of the sleep laboratory, which is unfamiliar to the patient, might lead to distorted results. In this paper, a sleep stage detection algorithm is proposed that uses only the heart rate signal, derived from electrocardiogram (ECG), as a discriminator. This would make it possible for sleep analysis to be performed at home, saving a lot of effort and money. From the heart rate, using the fast Fourier transformation (FFT), three parameters were calculated in order to distinguish between the different sleep stages. ECG data along with a hypnogram scored by professionals was used from Physionet database, making it easy to compare the results. With an agreement rate of 41.3%, this approach is a good foundation for future research.


title: A Framework for Estimating Driver Decisions Near Intersections

abstract: We present a framework for the estimation of driver behavior at intersections, with applications to autonomous driving and vehicle safety. The framework is based on modeling the driver behavior and vehicle dynamics as a hybrid-state system (HSS), with driver decisions being modeled as a discrete-state system and the vehicle dynamics modeled as a continuous-state system. The proposed estimation method uses observable parameters to track the instantaneous continuous state and estimates the most likely behavior of a driver given these observations. This paper describes a framework that encompasses the hybrid structure of vehicle-driver coupling and uses hidden Markov models (HMMs) to estimate driver behavior from filtered continuous observations. Such a method is suitable for scenarios that involve unknown decisions of other vehicles, such as lane changes or intersection access. Such a framework requires extensive data collection, and the authors describe the procedure used in collecting and analyzing vehicle driving data. For illustration, the proposed hybrid architecture and driver behavior estimation techniques are trained and tested near intersections with exemplary results provided. Comparison is made between the proposed framework, simple classifiers, and naturalistic driver estimation. Obtained results show promise for using the HSS-HMM framework.


title: The International Debt Situation and North-South Relations

abstract: Abstract The paper analyses the debt problem and possible solutions from the perspective of the major interests involved. It is shown that the emergence of a negative basic transfer — with resources flowing from South to North — is an inevitable consequence of heavy borrowing and high interest rates. In such a situation, borrowing countries are likely to consider a variety of default options. The banks, and major financial centres, have a strong interest in avoiding 100% default. Consequently, each of the powerful interests needs an alternative solution. Any lasting solution would need to reverse the negative basic transfer, and reduce the net present value of debt. Proposed solutions are discussed in the light of these requirements. Finally, the paper provides some statistical evidence on the bargaining position of different borrowing countries.


title: Adaptive Commutation Error Compensation Strategy Based on a Flux Linkage Function for Sensorless Brushless DC Motor Drives in a Wide Speed Range

abstract: This paper presents a novel adaptive commutation error compensation strategy for the sensorless brushless DC (BLDC) motor based on the flux linkage function. It addresses the two key challenge problems on the sensorless control of the BLDC motor. The one is presenting a novel detection method for the BLDC motor rotor position signal to filter out the interference and the noise by the high frequency. The other one is proposing a commutation error compensation strategy to improve the system efficiency. In traditional sensorless control strategy, a deep low-pass filter is usually used to obtain the BLDC motor rotor position signal from the high-frequency interference. However, the delayed angle caused by the low-pass filter may be more than 90 electrical degrees in the high speed range. Therefore, a novel sensorless control strategy based on the speed-independent flux linkage function was proposed in this paper. The interference of the diode freewheeling was filtered by the software filter, and a closed-loop compensation algorithm based on the deviation of line-to-line voltages was illustrated to correct the commutation error in the high speed range. The stability and accuracy of the method was confirmed by a series of experiments.


title: Social networking: a matter of character?

abstract: Over the last couple of years, online social networks such as Facebook have tremendously grown in popularity, especially among students. The technological advancements proceed faster than the understanding of the psychological factors behind this development. While motivations and gratifications related to Facebook usage have been investigated thoroughly, this study additionally focuses on the influence of underlying personality factors. However, instead of the commonly used Big Five, the characteristics narcissism, shyness and loneliness are investigated among undergraduate and graduate students. The results indicate that a high score on narcissism causes a higher Facebook usage and that the sub construct uniqueness/entitlement is the most important. The results also indicate a negative influence of loneliness on Facebook usage, while shyness seems irrelevant.


title: Finger Vein Recognition Using Local Line Binary Pattern

abstract: In this paper, a personal verification method using finger vein is presented. Finger vein can be considered more secured compared to other hands based biometric traits such as fingerprint and palm print because the features are inside the human body. In the proposed method, a new texture descriptor called local line binary pattern (LLBP) is utilized as feature extraction technique. The neighbourhood shape in LLBP is a straight line, unlike in local binary pattern (LBP) which is a square shape. Experimental results show that the proposed method using LLBP has better performance than the previous methods using LBP and local derivative pattern (LDP).


title: Infrastructure for the quantum internet

abstract: A team of researchers from the Massachusetts Institute of Technology (MIT) and Northwestern University (NU) is developing a system for long-distance, high- delity qubit tele-portation. Such a system will be required if future quantum computers are to be linked together into a quantum Internet. This paper presents recent progress that the MIT/NU team has made, beginning with a review of the teleportation architecture and its loss-limited performance analysis.


title: Unrealistic Optimism and the Health Belief Model

abstract: Why do people fail to engage in positive behaviors which will promote their health and well-being? Researchers addressing this question adopt primarily one of two perspectives, drawing either on theories of health behavior, such as the Health Belief Model (HBM), or on theories of risk perception, such as unrealistic optimism. To overcome this compartmentalization, two studies of cancer screening behavior assessed the extent to which unrealistic optimism occurred in relation to each of the elements of the HBM: severity and curability of cancer and the benefits of, and barriers to, having a screening test. Data were collected using telephone interviews, dialing numbers randomly selected from the telephone directory. In the first study 164 women aged 50 to 70 years responded to questions about breast cancer and screening mammography, while in the second study 200 men aged 45 to 60 years responded to questions about prostate cancer and screening using the prostate specific antigen test. Women had an optimistic bias in relation to breast cancer risk and severity and barriers to having a screening mammogram but not in relation to the benefits of screening. For prostate cancer, there was an optimistic bias for all HBM variables: risk and severity of prostate cancer and barriers to and benefits of screening. It was concluded that unrealistic optimism is broader than perceived risk, being evident for all elements of the HBM.


title: Mobile fronthaul over latency-optimized time division multiplexed passive optical networks

abstract: Transport of mobile fronthaul traffic has a tight latency requirement of about 250us one-way. The best solutions today are based on expensive point-to-point (PtP) fiber links and PtP wavelength division multiplexing. A time division multiplexed passive optical network (TDM-PON) is less expensive, but has a poor latency of about 1ms. Guidelines are suggested to design an optimized TDM-PON with minimal latency for transport of constant bit rate fronthaul traffic. The total latency is mathematically analyzed using queuing theory, and closed-form formulas are derived which allow study of the trade-offs enabled by the different parameters. Feasibility of acceptable levels of latency of 250us or less is established in several application scenarios.


title: LH: Linear Hashing for distributed files

abstract: LH* generalizes Linear Hashing to parallel or distributed RAM and disk files. An LH* file can be created from objects provided by any number of distributed and autonomous clients. It can grow gracefully, one bucket at a time, to virtually any number of servers. The number of messages per insertion is one in general, and three in the worst case. The number of messages per retrieval is two in general, and four in the worst case. The load factor can be about constant, 65-95%, depending on the file parameters. The file can also support parallel operations. An LH* file can be much faster than a single site disk file, and/or can hold a much larger number of objects. It can be more efficient than any file with a centralized directory, or a static parallel or distributed hash file.