title: The dark side of food addiction

abstract: In drug addiction, the transition from casual drug use to dependence has been linked to a shift away from positive reinforcement and towards negative reinforcement. That is, drugs ultimately are relied on to prevent or relieve negative states that otherwise result from abstinence (e.g., withdrawal) or from adverse environmental circumstances (e.g., stress). Recent work has suggested that this “dark side” shift also is key in the development of food addiction. Initially, palatable food consumption has both positive reinforcing, pleasurable effects and negative reinforcing, “comforting” effects that can acutely normalize organism responses to stress. Repeated, intermittent intake of palatable food may instead amplify brain stress circuitry and downregulate brain reward pathways such that continued intake becomes obligatory to prevent negative emotional states via negative reinforcement. Stress, anxiety and depressed mood have shown high comorbidity with and the potential to trigger bouts of addiction-like eating behavior in humans. Animal models indicate that repeated, intermittent access to palatable foods can lead to emotional and somatic signs of withdrawal when the food is no longer available, tolerance and dampening of brain reward circuitry, compulsive seeking of palatable food despite potentially aversive consequences, and relapse to palatable food-seeking in response to anxiogenic-like stimuli. The neurocircuitry identified to date in the “dark” side of food addiction qualitatively resembles that associated with drug and alcohol dependence. The present review summarizes Bart Hoebel’s groundbreaking conceptual and empirical contributions to understanding the role of the “dark side” in food addiction along with related work of those that have followed him.


A Roadmap for Natural Language Processing Research in Information Systems


title: Object recognition and 3D reconstruction of occluded objects using binocular stereo

abstract: Object recognition is one of the key areas in computer vision which comprises of object detection, recognition and reconstruction. The image of the object to be recognized is captured using camera and matched with pre-stored templates of the model object. Recognizing 3D view of the object is difficult in the presence of object occlusion and view-point invariants. This paper focuses on the problem of occlusion and provides a solution for handling self and inter-object occlusion. Self-occlusion has been addressed by the suitable calibration of the cameras and a novel algorithm has been proposed to address inter-object occlusion. A modified geometric mapping technique has been proposed for the 3D reconstruction of the recognized object. Real-time setup has been used to test the proposed solutions to identify objects of multiple shapes and sizes. The results show that the performance of the algorithm was superior and enabled recognition of objects with 80% occlusion or less.


title: Wnt signaling and osteoarthritis.

abstract: Osteoarthritis is a common disease, clinically manifested by joint pain, swelling and progressive loss of function. The severity of disease manifestations can vary but most of the patients only need intermittent symptom relief without major interventions. However, there is a group of patients that shows fast progression of the disease process leading to disability and ultimately joint replacement. Apart from symptom relief, no treatments have been identified that arrest or reverse the disease process. Therefore, there has been increasing attention devoted to the understanding of the mechanisms that are driving the disease process. Among these mechanisms, the biology of the cartilage-subchondral bone unit has been highlighted as key in osteoarthritis, and pathways that involve both cartilage and bone formation and turnover have become prime targets for modulation, and thus therapeutic intervention. Studies in developmental, genetic and joint disease models indicate that Wnt signaling is critically involved in these processes. Consequently, targeting Wnt signaling in a selective and tissue specific manner is an exciting opportunity for the development of disease modifying drugs for osteoarthritis.


title: A parallel Smith-Waterman algorithm based on divide and conquer

abstract: Biological sequence comparison is an important tool for researchers in molecular biology. There are several algorithms for sequence comparison. The Smith-Waterman algorithm, based on dynamic programming, is one of the most fundamental algorithms in bioinformatics. However, the existing parallel Smith-Waterman algorithm needs large memory space. As the data of biological sequences expand rapidly, the memory requirement of the existing parallel Smith-Waterman algorithm has becoming a critical problem. For resolving this problem, we develop a new parallel Smith-Waterman algorithm using the method of divide and conquer, named PSW-DC. Memory space required in the new parallel algorithm is reduced significantly in comparison with existing ones. A key technique, named the C&E method, is developed for implementation of the new parallel Smith-Waterman algorithm.


title: Evaluating the Quality of Discovered Process Models

abstract: In the domain of process mining the evaluation of models (i.e., \How can we measure the quality of a mined process model?") is still subject to ongoing research. Because the types of models used in process mining are typically on a higher level of abstraction (they, for example, allow to capture concurrency), the problem of model evaluation is challenging. In this paper, we elaborate on the problem of process model evaluation, and we evaluate both new and existing fitness metrics for different levels of noise. The new metrics and the noise generation are based on Hidden Markov Models (HMMs).


DETECTING ICE LAYERS IN RADAR IMAGES WITH DEEP LEARNING


DeepVQ: A Deep Network Architecture for Vector Quantization


Influence of the SiO 2 interlayer thickness on the density and polarity of charges in Si / SiO 2 / Al 2 O 3 stacks as studied by optical second-harmonic generation


title: Coming to Terms with Intramural Differences Over Policy Frameworks: How Policy Researchers Accommodate Rival Claims Among their Peers

abstract: Although we readily scrutinize conflicts among political stakeholders, similar attention is seldom paid to how we deal with contestable understandings within our own field of inquiry. Debates over competing scholarly perspectives and contested models are rarely subject to any systematic postmortem or attempts to account for differential survival. Given the indeterminacy of many of our conceptual schemes, empirical data seldom carry the day to a resolution all can accept. Accordingly, there are eventually many different versions of any given dispute, each offering a different path to resolution or equanimity. Disputants retire and conflicts fade, providing a demographic resolution of sorts, but not a scientific or a conceptual one. The work presented here claims that there is much to be gained from systematic scrutiny of our conceptual disputes, especially as a means to access the different perspectives we assume to handle them. We argue that there is an internal logic to the different perspectives on any given dispute. It is not the dispute per se that draws our interest; but rather, how any given dispute generates multiple interpretations and reconstructed versions. We propose and illustrate an approach to analyzing disputes that makes their internal logic more transparent and attends to the pathways that emerge for resolution. We will find, in the process, that there are some reliable routes to conciliation and some fault lines that remain unstable.


title: Interaction of sedentary behaviour, sports participation and fitness with weight status in elementary school children

abstract: AbstractEven though the effect of single components contributing to weight gain in children have been addressed only limited research is available on the combined association of sports participation, physical fitness and time spent watching TV with body weight in children. Baseline data from 1594 children (809 male; 785 female), 7.1±0.6 years of age participating in a large school-based intervention in southern Germany was used. Height and weight was measured and body mass index (BMI) percentiles (BMIPCT) were determined accordingly. Sports participation and time spent watching TV was assessed via parent questionnaire while fitness was determined via a composite fitness test. Combined and single associations of sports participation, TV time and fitness with BMIPCT and weight status were assessed via ANCOVA as well as logistic regression analysis, controlling for age and sex. A significant interaction of TV time, sports participation and fitness on BMIPCT occurred, despite low correlations among the three ...


title: 'Viral' Turing Machines, Computation from Noise and Combinatorial Hierarchies

abstract: The interactive computation paradigm is reviewed and a particular example is extended to form the stochastic analog of a computational process via a transcription of a minimal Turing Machine into an equivalent asynchronous Cellular Automaton with an exponential waiting times distribution of effective transitions. Furthermore, a special toolbox for analytic derivation of recursive relations of important statistical and other quantities is introduced in the form of an Inductive Combinatorial Hierarchy.


title: Cognitive Control Deficits in Schizophrenia: Mechanisms and Meaning

abstract: Although schizophrenia is an illness that has been historically characterized by the presence of positive symptomatology, decades of research highlight the importance of cognitive deficits in this disorder. This review proposes that the theoretical model of cognitive control, which is based on contemporary cognitive neuroscience, provides a unifying theory for the cognitive and neural abnormalities underlying higher cognitive dysfunction in schizophrenia. To support this model, we outline converging evidence from multiple modalities (eg, structural and functional neuroimaging, pharmacological data, and animal models) and samples (eg, clinical high risk, genetic high risk, first episode, and chronic subjects) to emphasize how dysfunction in cognitive control mechanisms supported by the prefrontal cortex contribute to the pathophysiology of higher cognitive deficits in schizophrenia. Our model provides a theoretical link between cellular abnormalities (eg, reductions in dentritic spines, interneuronal dysfunction), functional disturbances in local circuit function (eg, gamma abnormalities), altered inter-regional cortical connectivity, a range of higher cognitive deficits, and symptom presentation (eg, disorganization) in the disorder. Finally, we discuss recent advances in the neuropharmacology of cognition and how they can inform a targeted approach to the development of effective therapies for this disabling aspect of schizophrenia.


title: Continuous Reasoning: Scaling the impact of formal methods

abstract: This paper describes work in continuous reasoning, where formal reasoning about a (changing) codebase is done in a fashion which mirrors the iterative, continuous model of software development that is increasingly practiced in industry. We suggest that advances in continuous reasoning will allow formal reasoning to scale to more programs, and more programmers. The paper describes the rationale for continuous reasoning, outlines some success cases from within industry, and proposes directions for work by the scientific community.


title: Three-Port Bidirectional Converter for Hybrid Fuel Cell Systems

abstract: The implementation of a hybrid fuel cell/battery system is proposed to improve the slow transient response of a fuel cell stack. This system can be used for an autonomous device with quick load variations. A suitable three-port, galvanic isolated, bidirectional power converter is proposed to control the power flow. An energy management method for the proposed three-port circuit is analyzed and implemented. Measurements from a 500-W laboratory prototype are presented to demonstrate the validity of the approach


title: Volume of signaling traffic reaching cellular networks from mobile phones

abstract: Cellular networks are facing a big challenge emerging from excessive increase in signaling traffic due to background activities on smartphones. The excessive signaling if not properly managed contributes to signaling storms causing outages in mobile networks and poor quality of service, especially during peak times. For smartphone users, device-specific excessive signaling traffic caused by mobile apps can lead to poor device performance. Additionally, the cost of supporting this traffic is increasing and becoming unsustainable compared to the revenues generated. This paper aims to provide an understanding of the magnitude, scale, and the sources of signaling traffic, which is important for researchers and network operators if they are to devise effective solutions to manage the excessive signaling. We provide an analysis of characteristics of traffic resulting from background activity on smartphones and cellular networks based on datasets collected using passive (traces from a leading mobile operator) and active experiments (over 29 million logs from selected mobile apps). From the findings we deduce that the volume of signaling traffic is determined by the number of mobile devices, the kind of technologies supported by the devices, and roaming of mobile devices between different networks. Apps on the mobile devices generate background traffic without the active intervention of the device user(s). The traffic is generated in small quantities as the devices connect and tear down connections to the network periodically. Though, the average signaling traffic is tolerable, high peaks of this traffic can cause signaling floods, when some elements in the network become unbearable; this can render the network unavailable. Our findings provide network operators with a quantified understanding of the magnitude of the signaling challenge. For smartphone owners, our results provide an awareness of and insights into the impact of signaling traffic caused by individual mobile apps.


title: Predicting disease risks from highly imbalanced data using random forest

abstract: BackgroundWe present a method utilizing Healthcare Cost and Utilization Project (HCUP) dataset for predicting disease risk of individuals based on their medical diagnosis history. The presented methodology may be incorporated in a variety of applications such as risk management, tailored health communication and decision support systems in healthcare.MethodsWe employed the National Inpatient Sample (NIS) data, which is publicly available through Healthcare Cost and Utilization Project (HCUP), to train random forest classifiers for disease prediction. Since the HCUP data is highly imbalanced, we employed an ensemble learning approach based on repeated random sub-sampling. This technique divides the training data into multiple sub-samples, while ensuring that each sub-sample is fully balanced. We compared the performance of support vector machine (SVM), bagging, boosting and RF to predict the risk of eight chronic diseases.ResultsWe predicted eight disease categories. Overall, the RF ensemble learning method outperformed SVM, bagging and boosting in terms of the area under the receiver operating characteristic (ROC) curve (AUC). In addition, RF has the advantage of computing the importance of each variable in the classification process.ConclusionsIn combining repeated random sub-sampling with RF, we were able to overcome the class imbalance problem and achieve promising results. Using the national HCUP data set, we predicted eight disease categories with an average AUC of 88.79%.


title: Delay Analysis of Hybrid WiFi-LiFi System

abstract: Heterogeneous wireless networks are capable of effectively leveraging different access technologies to provide a wide variety of coverage areas. In this paper, the coexistence of WiFi and visible light communication (VLC) is investigated as a paradigm. The delay of two configurations of such heterogeneous system has been evaluated. In the first configuration, the non-aggregated system, any request is either allocated to WiFi or VLC. While in the second configuration, the aggregated system, each request is split into two pieces, one is forwarded to WiFi and the other is forwarded to VLC. Under the assumptions of Poisson arrival process of requests and the exponential distribution of requests size, it is mathematically proved that the aggregated system provides lower minimum average system delay than that of the non-aggregated system. For the non-aggregated system, the optimal traffic allocation ratio is derived. For the aggregated system, an efficient solution for the splitting ratio is proposed. Empirical results show that the solution proposed here incurs a delay penalty (less than 3\%) over the optimal result.


title: Hand-free head mouse control based on mouth tracking

abstract: Hand-free control for human-computer interface has been very important nowadays. For hand-free solution, to use human body movement tracking in video input and utilize the tracking is an important kind of solution. In this paper, we have presented a hand-free head mouse control based on mouth tracking. Our system firstly nonlinearly transformed the input video frame of human head into YCbCr color space, then use the visible chrominance feature of face in this color space to detect human face region. And then for face candidate, use the nearly reversed relationship information between Cb and Cr cluster of face feature to detect mouth position. We use mouth position interval information between front-frame and the next-frame in video frames of head motion to confirm if a mouth tracking is movement information or command information, once a movement information has been determined, we use a mapping function which is set up by relationship between MMA and screen to map mouth position, then the real mouse position can be got, by this way we can operate mouse cursor in OS. On the other ways, if mouse click command information is determined, we can capture the mouth movement tracking, and then use recognition machine to recognize the tracking to determine which command it is. We have made experiments on Windows XP System to evaluate that our algorithm can represent a good effect for human-computer interface.


title: The Perspective Matters! Multisensory Integration in Ego-Centric Reference Frames Determines Full-Body Ownership

abstract: Recent advances in experimental science have made it possible to investigate the perceptual processes involved in generating a sense of owning an entire body. This is achieved by full-body ownership illusions which make use of specific patterns of visual and somatic stimuli integration. Here we investigate the fundamental question of the reference frames used in the process of attributing an entire body to the self. We quantified the strength of the body-swap illusion in conditions where the participants were observing this artificial body from the perspective of the first or third person. Consistent results from subjective reports and physiological recordings show that the first person visual perspective is critical for the induction of this full-body ownership illusion. This demonstrates that the multisensory integration processes producing the sense of corporeal self operates in an ego-centric reference frame.


title: Accurate Scene Text Recognition Based on Recurrent Neural Network

abstract: Scene text recognition is a useful but very challenging task due to uncontrolled condition of text in natural scenes. This paper presents a novel approach to recognize text in scene images. In the proposed technique, a word image is first converted into a sequential column vectors based on Histogram of Oriented Gradient (HOG). The Recurrent Neural Network (RNN) is then adapted to classify the sequential feature vectors into the corresponding word. Compared with most of the existing methods that follow a bottom-up approach to form words by grouping the recognized characters, our proposed method is able to recognize the whole word images without character-level segmentation and recognition. Experiments on a number of publicly available datasets show that the proposed method outperforms the state-of-the-art techniques significantly. In addition, the recognition results on publicly available datasets provide a good benchmark for the future research in this area.


title: Web Application Vulnerability Prediction Using Hybrid Program Analysis and Machine Learning

abstract: Due to limited time and resources, web software engineers need support in identifying vulnerable code. A practical approach to predicting vulnerable code would enable them to prioritize security auditing efforts. In this paper, we propose using a set of hybrid (static+dynamic) code attributes that characterize input validation and input sanitization code patterns and are expected to be significant indicators of web application vulnerabilities. Because static and dynamic program analyses complement each other, both techniques are used to extract the proposed attributes in an accurate and scalable way. Current vulnerability prediction techniques rely on the availability of data labeled with vulnerability information for training. For many real world applications, past vulnerability data is often not available or at least not complete. Hence, to address both situations where labeled past data is fully available or not, we apply both supervised and semi-supervised learning when building vulnerability predictors based on hybrid code attributes. Given that semi-supervised learning is entirely unexplored in this domain, we describe how to use this learning scheme effectively for vulnerability prediction. We performed empirical case studies on seven open source projects where we built and evaluated supervised and semi-supervised models. When cross validated with fully available labeled data, the supervised models achieve an average of 77 percent recall and 5 percent probability of false alarm for predicting SQL injection, cross site scripting, remote code execution and file inclusion vulnerabilities. With a low amount of labeled data, when compared to the supervised model, the semi-supervised model showed an average improvement of 24 percent higher recall and 3 percent lower probability of false alarm, thus suggesting semi-supervised learning may be a preferable solution for many real world applications where vulnerability data is missing.


title: An algorithm for constructing and solving imperfect recall abstractions of large extensive-form games

abstract: We solve large two-player zero-sum extensive-form games with perfect recall. We propose a new algorithm based on fictitious play that significantly reduces memory requirements for storing average strategies. The key feature is exploiting imperfect recall abstractions while preserving the convergence rate and guarantees of fictitious play applied directly to the perfect recall game. The algorithm creates a coarse imperfect recall abstraction of the perfect recall game and automatically refines its information set structure only where the imperfect recall might cause problems. Experimental evaluation shows that our novel algorithm is able to solve a simplified poker game with 7 ċ 105 information sets using an abstracted game with only 1:8% of information sets of the original game. Additional experiments on poker and randomly generated games suggest that the relative size of the abstraction decreases as the size of the solved games increases.


title: Efficient Genome Editing in Apple Using a CRISPR/Cas9 system

abstract: Genome editing is a powerful technique for genome modification in molecular research and crop breeding, and has the great advantage of imparting novel desired traits to genetic resources. However, the genome editing of fruit tree plantlets remains to be established. In this study, we describe induction of a targeted gene mutation in the endogenous apple phytoene desaturase (PDS) gene using the CRISPR/Cas9 system. Four guide RNAs (gRNAs) were designed and stably transformed with Cas9 separately in apple. Clear and partial albino phenotypes were observed in 31.8% of regenerated plantlets for one gRNA, and bi-allelic mutations in apple PDS were confirmed by DNA sequencing. In addition, an 18-bp gRNA also induced a targeted mutation. These CRIPSR/Cas9 induced-mutations in the apple genome suggest activation of the NHEJ pathway, but with some involvement also of the HR pathway. Our results demonstrate that genome editing can be practically applied to modify the apple genome.


title: DEVELOPMENT OF WEARABLE HUMAN FALL DETECTION SYSTEM USING MULTILAYER PERCEPTRON NEURAL NETWORK

abstract: Abstract This paper presents an accurate wearable fall detection system which can identify the occurrence of falls among elderly population. A waist worn tri-axial accelerometer was used to capture the movement signals of human body. A set of laboratory-based falls and activities of daily living (ADL) were performed by volunteers with different physical characteristics. The collected acceleration patterns were classified precisely to fall and ADL using multilayer perceptron (MLP) neural network. This work was resulted to a high accuracy wearable fall-detection system with the accuracy of 91.6%.


title: Perceiving Groups: The People Perception of Diversity and Hierarchy

abstract: The visual perception of individuals has received considerable attention (visual person perception), but little social psychological work has examined the processes underlying the visual perception of groups of people (visual people perception). Ensemble-coding is a visual mechanism that automatically extracts summary statistics (e.g., average size) of lower-level sets of stimuli (e.g., geometric figures), and also extends to the visual perception of groups of faces. Here, we consider whether ensemble-coding supports people perception, allowing individuals to form rapid, accurate impressions about groups of people. Across nine studies, we demonstrate that people visually extract high-level properties (e.g., diversity, hierarchy) that are unique to social groups, as opposed to individual persons. Observers rapidly and accurately perceived group diversity and hierarchy, or variance across race, gender, and dominance (Studies 1–3). Further, results persist when observers are given very short display times, backward pattern masks, color- and contrast-controlled stimuli, and absolute versus relative response options (Studies 4a–7b), suggesting robust effects supported specifically by ensemble-coding mechanisms. Together, we show that humans can rapidly and accurately perceive not only individual persons, but also emergent social information unique to groups of people. These people perception findings demonstrate the importance of visual processes for enabling people to perceive social groups and behave effectively in group-based social interactions. (PsycINFO Database Record (c) 2018 APA, all rights reserved)


title: Automatic segmentation of dermoscopy images using saliency combined with Otsu threshold

abstract: Abstract Segmentation is one of the crucial steps for the computer-aided diagnosis (CAD) of skin cancer with dermoscopy images. To accurately extract lesion borders from dermoscopy images, a novel automatic segmentation algorithm using saliency combined with Otsu threshold is proposed in this paper, which includes enhancement and segmentation stages. In the enhancement stage, prior information on healthy skin is extracted, and the color saliency map and brightness saliency map are constructed respectively. By fusing the two saliency maps, the final enhanced image is obtained. In the segmentation stage, according to the histogram distribution of the enhanced image, an optimization function is designed to adjust the traditional Otsu threshold method to obtain more accurate lesion borders. The proposed model is validated from enhancement effectiveness and segmentation accuracy. Experimental results demonstrate that our method is robust and performs better than other state-of-the-art methods.


title: Packer identification based on metadata signature

abstract: Malware applies lots of obfuscation techniques, which are often automatically generated by the use of packers. This paper presents a packer identification of packed code based on metadata signature, which is a frequency vector of occurrences of classified obfuscation techniques. First, BE-PUM (Binary Emulator for PUshdown Model generation) disassembles and generates the control flow graph of malware in an on-the-fly manner, using concolic testing. Second, obfuscation techniques in the generated control flow graph are detected based on the formal criteria of each obfuscation technique. Last, the used packer is identified with the chisquare test on the metadata signature of a packed code. The precision is evaluated with experiments on 12814 malware from VX heaven and Virusshare, in which 608 examples are detected inconsistent with commercial packer identification at PEiD, CFF Explore, and VirusTotal. We manually confirm that, except for 1 example, BE-PUM is correct. The only case that BE-PUM misunderstands is between MEW and FSG, which are quite similar packers and current BE-PUM extension does not support MEW.


title: Third-Person Imitation Learning

abstract: Reinforcement learning (RL) makes it possible to train agents capable of achieving sophisticated goals in complex and uncertain environments. A key difficulty in reinforcement learning is specifying a reward function for the agent to optimize. Traditionally, imitation learning in RL has been used to overcome this problem. Unfortunately, hitherto imitation learning methods tend to require that demonstrations are supplied in the first-person: the agent is provided with a sequence of states and a specification of the actions that it should have taken. While powerful, this kind of imitation learning is limited by the relatively hard problem of collecting first-person demonstrations. Humans address this problem by learning from third-person demonstrations: they observe other humans perform tasks, infer the task, and accomplish the same task themselves. ::: In this paper, we present a method for unsupervised third-person imitation learning. Here third-person refers to training an agent to correctly achieve a simple goal in a simple environment when it is provided a demonstration of a teacher achieving the same goal but from a different viewpoint; and unsupervised refers to the fact that the agent receives only these third-person demonstrations, and is not provided a correspondence between teacher states and student states. Our methods primary insight is that recent advances from domain confusion can be utilized to yield domain agnostic features which are crucial during the training process. To validate our approach, we report successful experiments on learning from third-person demonstrations in a pointmass domain, a reacher domain, and inverted pendulum.


title: Modeling of Through-Silicon Via (TSV) Interposer Considering Depletion Capacitance and Substrate Layer Thickness Effects

abstract: To support the recent progress in 3-D integration based on through-silicon via (TSV) technology, an improved electromagnetic modeling method for TSVs is presented. In the framework of the mixed-potential integral equations combined with cylindrical modal basis functions, the proposed method can extract the effects of depletion capacitances and a finite substrate. To include the effects of depletion region generated by an external dc bias voltage, an additional capacitive cell is employed around a TSV. The proposed method also considers the effect from the finite silicon substrate accurately by employing the multilayered Green’s functions. To reduce the computational cost for calculations involving Green’s functions, a method to approximate Green’s functions over localized intervals when computing partial potential coefficients is presented. The proposed method is validated for simple TSV examples and shows an improved accuracy with the acceptable usage of memory and simulation time. In addition, a $10 \times 10$ TSV array is modeled using different design parameters, showing the capability for dealing with larger size problems using this method.


title: Open-Set Classification for Automated Genre Identification

abstract: Automated Genre Identification (AGI) of web pages is a problem of increasing importance since web genre (e.g. blog, news, e-shops, etc.) information can enhance modern Information Retrieval (IR) systems. The state-of-the-art in this field considers AGI as a closed-set classification problem where a variety of web page representation and machine learning models have intensively studied. In this paper, we study AGI as an open-set classification problem which better formulates the real world conditions of exploiting AGI in practice. Focusing on the use of content information, different text representation methods (words and character n-grams) are tested. Moreover, two classification methods are examined, one-class SVM learners, used as a baseline, and an ensemble of classifiers based on random feature subspacing, originally proposed for author identification. It is demonstrated that very high precision can be achieved in open-set AGI while recall remains relatively high.


title: The social and ethical issues of post-genomic human biobanks

abstract: Biobanking — the organized collection of biological samples and associated data — ranges in scope from small collections of samples in academic or hospital settings to large-scale national repositories. Biobanks raise many ethical concerns, to which authorities are responding by introducing specific regulations. Genomics research, which thrives on the sharing of samples and information, is affected by two prominent ethical questions: do ethical principles prevent or promote the sharing of stored biological resources? How does the advent of large-scale biobanking alter the way in which ethical issues are addressed?


title: Benchtop testing and comparisons among three types of through-the-scope endoscopic clipping devices

abstract: Background ::: Through-the-scope (TTS) endoscopic clipping devices are widely used. No benchtop testing or direct comparisons of these endoclips have been performed to show their rotational ability and inherent mechanical strengths during closure and after deployment. This study aimed to provide benchtop data that can be used to guide clinical applications and to promote future device research and development.


title: The use of terrestrial LiDAR technology in forest science: application fields, benefits and challenges

abstract: • Introduction ::: The use of terrestrial LiDAR (light detection and ranging) scanners in forest environments is being studied extensively at present due to the high potential of this technology to acquire three-dimensional data on standing trees rapidly and accurately. This article aims to establish the state-of-the-art in this emerging area.


title: An overview of continuous improvement: from the past to the present

abstract: Purpose – To provide an overview of the history, evolution, and existing research on continuous improvement.Design/methodology/approach – Extensive review of the literature.Findings – This paper provides an overview of continuous improvement, its inception, how it evolved into sophisticated methodologies used in organizations today, and existing research in this field in the literature.Research limitations/implications – It does not provide an exhaustive review of the existing literature, or an exhaustive list of all continuous improvement programs, only the most well known.Originality/value – This paper traces how organizations have used various tools and techniques to address the need for improvement on various levels. The paper also presents research conducted in this field. It should be of value to practitioners of continuous improvement programs and to academics who are interested in how continuous improvement has evolved, and where it is today. To the authors’ knowledge, no recent papers have provid...


title: Deeply Improved Sparse Coding for Image Super-Resolution

abstract: Deep learning techniques have been successfully applied in many areas of computer vision, including low-level image restoration problems. For image super-resolution, several models based on deep neural networks have been recently proposed and attained superior performance that overshadows all previous handcrafted models. The question then arises whether large-capacity and data-driven models have become the dominant solution to the ill-posed super-resolution problem. In this paper, we argue that domain expertise represented by the conventional sparse coding model is still valuable, and it can be combined with the key ingredients of deep learning to achieve further improved results. We show that a sparse coding model particularly designed for super-resolution can be incarnated as a neural network, and trained in a cascaded structure from end to end. The interpretation of the network based on sparse coding leads to much more efficient and effective training, as well as a reduced model size. Our model is evaluated on a wide range of images, and shows clear advantage over existing state-of-the-art methods in terms of both restoration accuracy and human subjective quality.


title: Engagement vs. deceit: Virtual humans with human autobiographies

abstract: We discuss the ethical and practical issues involved in developing virtual humans that relate personal, fictitious, human autobiographical stories ("back stories") to their users. We describe a virtual human exercise counselor that interacts with users daily to promote exercise, and the integration of a dynamic social storytelling engine used to maintain user engagement with the agent and retention in the intervention. A longitudinal randomized controlled experiment tested user attitudes towards the agent when it presented the stories in first person (as its own history) compared to third person (as happening to humans that it knew). Participants in the first person condition reported enjoying their interactions with the agent significantly more and completed more conversations with the agent, compared to participants in the third person condition, while ratings of agent dishonesty were not significantly different between the groups.


title: The AMI System for the Transcription of Speech in Meetings

abstract: The automatic processing of speech collected in conference style meetings has attracted considerable interest with several large scale projects devoted to this area. This paper describes the development of a baseline automatic speech transcription system for meetings in the context of the AMI (Augmented Multiparty Interaction) project. We present several techniques important to processing of this data and show the performance in terms of word error rates (WERs). An important aspect of transcription of this data is the necessary flexibility in terms of audio pre-processing. Real world systems have to deal with flexible input, for example by using microphone arrays or randomly placed microphones in a room. Automatic segmentation and microphone array processing techniques are described and the effect on WERs is discussed. The system and its components presented in this paper yield competitive performance and form a baseline for future research in this domain.


Persuasive Technology for Smartphone Fitness Apps


title: A Lithium-Ion Battery-in-the-Loop Approach to Test and Validate Multiscale Dual H Infinity Filters for State-of-Charge and Capacity Estimation

abstract: An accurate battery capacity and state estimation method is one of the most significant and difficult techniques to ensure efficient and safe operation of the batteries for electric vehicles (EVs). Since capacity and state of charge (SoC) are strongly correlated, the SoC is hardly to be accurately estimated without knowing accurate battery capacity. Thus, a multiscale dual H infinity filter (HIF) has been proposed to estimate battery SoC and capacity in real time with dual timescales in response to slow-varying battery parameters and fast-varying battery state. The proposed method is first evaluated and verified using off-line experimental data and then compared with the single/multiscale dual Kalman filters (KFs). The results show that the proposed multiscale dual HIFs has better robustness and higher estimation accuracy than the single/multiscale dual KFs. To further validate the feasibility of the proposed method for EV applications, a lithium-ion battery-in-the-loop approach is applied to verify the stability and accuracy of the SoC estimation, and it is found that the SoC estimated from the proposed method can converge to the reference value gradually and be stabilized within 2%.


title: Online Convex Programming and Generalized Infinitesimal Gradient Ascent

abstract: Convex programming involves a convex set F ⊆ Rn and a convex cost function c : F → R. The goal of convex programming is to find a point in F which minimizes c. In online convex programming, the convex set is known in advance, but in each step of some repeated optimization problem, one must select a point in F before seeing the cost function for that step. This can be used to model factory production, farm production, and many other industrial optimization problems where one is unaware of the value of the items produced until they have already been constructed. We introduce an algorithm for this domain. We also apply this algorithm to repeated games, and show that it is really a generalization of infinitesimal gradient ascent, and the results here imply that generalized infinitesimal gradient ascent (GIGA) is universally consistent.


title: LGND: a new method for multi-class novelty detection

abstract: Multi-class novelty detection is a crucial yet challenging aspect for recognition systems. Several methods have been presented, which either concatenate multiple classes into a large artificial super-class, or combine several independent classifiers of each known class, or utilize the results of multi-class classifiers. However, these methods ignore the correlation within each class, or cannot be elegantly formulated in a joint model. To overcome these limitations, we propose a new local and global novelty detection model (LGND). Different from the previous approaches, LGND incorporates the local correlation with the global regularization in a unified framework. This new optimization model boils down to a convex quadratic programming with guaranteed global optimum solution. Furthermore, comprehensive discussions, including the relationship between locality and globality, the discussion on the parameters in LGND and the connections to multi-class classification, are also presented. LGND opens up a new way for multi-class novelty detection from both local and global perspectives. Experimental results on Corel5k, Caltech-256, and five UCI data sets confirm that LGND outperforms or is at least comparable to state-of-the-art methods.


title: Strategic facility location: A review

abstract: Abstract Facility location decisions are a critical element in strategic planning for a wide range of private and public firms. The ramifications of siting facilities are broadly based and long-lasting, impacting numerous operational and logistical decisions. High costs associated with property acquisition and facility construction make facility location or relocation projects long-term investments. To make such undertakings profitable, firms plan for new facilities to remain in place and in operation for an extended time period. Thus, decision makers must select sites that will not simply perform well according to the current system state, but that will continue to be profitable for the facility's lifetime, even as environmental factors change, populations shift, and market trends evolve. Finding robust facility locations is thus a difficult task, demanding that decision makers account for uncertain future events. The complexity of this problem has limited much of the facility location literature to simplified static and deterministic models. Although a few researchers initiated the study of stochastic and dynamic aspects of facility location many years ago, most of the research dedicated to these issues has been published in recent years. In this review, we report on literature which explicitly addresses the strategic nature of facility location problems by considering either stochastic or dynamic problem characteristics. Dynamic formulations focus on the difficult timing issues involved in locating a facility (or facilities) over an extended horizon. Stochastic formulations attempt to capture the uncertainty in problem input parameters such as forecast demand or distance values. The stochastic literature is divided into two classes: that which explicitly considers the probability distribution of uncertain parameters, and that which captures uncertainty through scenario planning. A wide range of model formulations and solution approaches are discussed, with applications ranging across numerous industries.


title: Suitability of Google Scholar as a source of scientific information and as a source of data for scientific evaluation—Review of the Literature

abstract: As Google Scholar (GS) gains more ground as free scholarly literature retrieval source it’s becoming important to understand its quality and reliability in terms of scope and content. Studies comparing GS to controlled databases such as Scopus, Web of Science (WOS) and others have been published almost since GS inception. These studies focus on its coverage, quality and ability to replace controlled databases as a source of reliable scientific literature. In addition, GS introduction of citations tracking and journal metrics have spurred a body of literature focusing on its ability to produce reliable metrics. In this article we aimed to review some studies in these areas in an effort to provide insights into GS ability to replace controlled databases in various subject areas. We reviewed 91 comparative articles from 2005 until 2016 which compared GS to various databases and especially Web of Science (WOS) and Scopus in an effort to determine whether GS can be used as a suitable source of scientific information and as a source of data for scientific evaluation. Our results show that GS has significantly expanded its coverage through the years which makes it a powerful database of scholarly literature. However, the quality of resources indexed and overall policy still remains known. Caution should be exercised when relying on GS for citations and metrics mainly because it can be easily manipulated and its indexing quality still remains a challenge.


title: A novel HU-shaped common-mode filter for GHz differential signals

abstract: A novel low-cost filter design for common-mode noise suppression in high-speed differential signals is proposed. It is realized by etching a HU-shaped defected-ground structure (DGS) to perturb the return current of the common-mode noise. A simple LC resonator model for the proposed structure is also developed with good agreement to the full-wave simulation and measurement result. It is found that over 15 dB of common-mode noise suppression can be achieved over a wide frequency ranges from 3.6 to 9.1 GHz, while the differential signals still keep good signal integrity in eye-pattern observation.


Deep Aligned Representations


title: Dental implant with contact nano-sensors for the treatment of xerostomia

abstract: Salivation is a biologically controlled process that self-regulates and has a crucial purpose in digestion, taste, hygiene and comfort in humans. However, this process can become disrupted, temporary or permanently, leading to a condition called xerostomia. Previous research regarding xerostomia successfully utilized electro-stimulation of the nerves in the oral cavity to treat the symptoms and manage the discomfort created by the condition. This paper describes a revolutionary way of treating xerostomia, by using a permanent micro-electronic implant which can measure and stimulate the salivary secretion in the oral cavity.


title: The Attack of the Clones: A Study of the Impact of Shared Code on Vulnerability Patching

abstract: Vulnerability exploits remain an important mechanism for malware delivery, despite efforts to speed up the creation of patches and improvements in software updating mechanisms. Vulnerabilities in client applications (e.g., Browsers, multimedia players, document readers and editors) are often exploited in spear phishing attacks and are difficult to characterize using network vulnerability scanners. Analyzing their lifecycle requires observing the deployment of patches on hosts around the world. Using data collected over 5 years on 8.4 million hosts, available through Symantec's WINE platform, we present the first systematic study of patch deployment in client-side vulnerabilities. We analyze the patch deployment process of 1,593 vulnerabilities from 10 popular client applications, and we identify several new threats presented by multiple installations of the same program and by shared libraries distributed with several applications. For the 80 vulnerabilities in our dataset that affect code shared by two applications, the time between patch releases in the different applications is up to 118 days (with a median of 11 days). Furthermore, as the patching rates differ considerably among applications, many hosts patch the vulnerability in one application but not in the other one. We demonstrate two novel attacks that enable exploitation by invoking old versions of applications that are used infrequently, but remain installed. We also find that the median fraction of vulnerable hosts patched when exploits are released is at most 14%. Finally, we show that the patching rate is affected by user-specific and application-specific factors, for example, hosts belonging to security analysts and applications with an automated updating mechanism have significantly lower median times to patch.


title: Critical Analysis of Learning Algorithms in Random Neural Network Based Cognitive Engine for LTE Systems

abstract: In this paper, we critically analyze the performance of an intelligent Long-Term Evolution-Uplink (LTE-UL) system having a cognitive engine (CE) embedded in e-NodeB. Performance characterization, optimal radio parameters prediction, and inter-cell-interference coordination (ICIC) are studied. The embedded CE allocates the optimal radio parameters to serving users and suggests the acceptable transmit power to users served by adjacent cells for ICIC. The desired cognition has been achieved with a novel random neural network (RNN) based CE architecture. To achieve the best learning performance, we critically analyzed three learning algorithms, gradient descent (GD), adaptive inertia weight particle swarm optimization (AIW-PSO) and differential evolution (DE). The analysis showed that AIW-PSO was 10.57% better than GD and 8.012% better than DE in terms of learning accuracy (based on MSE), but with considerable compromise on computational time as compared to GD. Moreover, in terms of convergence time (to achieve the MSE less than 1.04E-03), AIW-PSO took 60% less iterations than GD and 50% less than DE.


title: Crowdcloud: a crowdsourced system for cloud infrastructure

abstract: The widespread adoption of truly portable, smart devices and Do-It-Yourself computing platforms by the general public has enabled the rise of new network and system paradigms. This abundance of well-connected, well-equipped, affordable devices, when combined with crowdsourcing methods, enables the development of systems with the aid of the crowd. In this work, we introduce the paradigm of Crowdsourced Systems, systems whose constituent infrastructure, or a significant part of it, is pooled from the general public by following crowdsourcing methodologies. We discuss the particular distinctive characteristics they carry and also provide their “canonical” architecture. We exemplify the paradigm by also introducing Crowdcloud, a crowdsourced cloud infrastructure where crowd members can act both as cloud service providers and cloud service clients. We discuss its characteristic properties and also provide its functional architecture. The concepts introduced in this work underpin recent advances in the areas of mobile edge/fog computing and co-designed/co-created systems.


title: Intelligent mobile satellite antenna tracking system design

abstract: This research applied both the traditional and the fuzzy control methods for mobile satellite antenna tracking system design. The antenna tracking and the stabilization loops were designed firstly according to the bandwidth and phase margin requirements. However, the performance would be degraded if the tracking loop gain is reduced due to parameter variation. On the other hand a PD type of fuzzy controller was also applied for tracking loop design. It can be seen that the system performance obtained by the fuzzy controller was better for low antenna tracking gain. Thus this research proposed an adaptive law by taking either traditional or fuzzy controllers for antenna tracking system depending on the tracking loop gain, then the tracking gain parameter variation effect can be reduced.


title: Automatic Acquisition of Lexical Formality

abstract: There has been relatively little work focused on determining the formality level of individual lexical items. This study applies information from large mixed-genre corpora, demonstrating that significant improvement is possible over simple word-length metrics, particularly when multiple sources of information, i.e. word length, word counts, and word association, are integrated. Our best hybrid system reaches 86% accuracy on an English near-synonym formality identification task, and near perfect accuracy when comparing words with extreme formality differences. We also test our word association method in Chinese, a language where word length is not an appropriate metric for formality.


title: Social network-based service recommendation with trust enhancement

abstract: Abstract Given the increasing applications of service computing and cloud computing, a large number of Web services are deployed on the Internet, triggering the research of Web service recommendation. Despite of service QoS, the use of user feedback is becoming the current trend in service recommendation. Likewise in traditional recommender systems, sparsity, cold-start and trustworthiness are major issues challenging service recommendation in adopting similarity-based approaches. Meanwhile, with the prevalence of social networks, nowadays people become active in interacting with various computers and users, resulting in a huge volume of data available, such as service information, user-service ratings, interaction logs, and user relationships. Therefore, how to incorporate the trust relationship in social networks with user feedback for service recommendation motivates this work. In this paper, we propose a social network-based service recommendation method with trust enhancement known as RelevantTrustWalker. First, a matrix factorization method is utilized to assess the degree of trust between users in social network. Next, an extended random walk algorithm is proposed to obtain recommendation results. To evaluate the accuracy of the algorithm, experiments on a real-world dataset are conducted and experimental results indicate that the quality of the recommendation and the speed of the method are improved compared with existing algorithms.


THE RELATIONSHIP BETWEEN MORAL REASONING AND PEDAGOGICAL PREFERENCE IN KENYAN AND AMERICAN COLLEGE STUDENTS


title: Equation Embeddings

abstract: We present an unsupervised approach for discovering semantic representations of mathematical equations. Equations are challenging to analyze because each is unique, or nearly unique. Our method, which we call equation embeddings, finds good representations of equations by using the representations of their surrounding words. We used equation embeddings to analyze four collections of scientific articles from the arXiv, covering four computer science domains (NLP, IR, AI, and ML) and $\sim$98.5k equations. Quantitatively, we found that equation embeddings provide better models when compared to existing word embedding approaches. Qualitatively, we found that equation embeddings provide coherent semantic representations of equations and can capture semantic similarity to other equations and to words.


Object Detection Featuring 3D Audio Localization for Microsoft HoloLens - A Deep Learning based Sensor Substitution Approach for the Blind


title: Customer Journey a method to investigate user experience.

abstract: Workplaces are stages for work experiences. There is a need to understand the experience as a definitive factor in workplace management. However the ways to investigate the experience are many and there are many perspectives to approach the methods. This paper aims to answer the question how to asses the work environments from the user perspective, as part of user experience? The methods for user orientated workplace management are presented. The conclusions indicate the different objects of different methods, which all together provide rich data for workplace management, user organisation and other stakeholders – for the ones creating user experiences.


title: Carrier aggregation for LTE-advanced mobile communication systems

abstract: In order to achieve up to 1 Gb/s peak data rate in future IMT-Advanced mobile systems, carrier aggregation technology is introduced by the 3GPP to support very-high-data-rate transmissions over wide frequency bandwidths (e.g., up to 100 MHz) in its new LTE-Advanced standards. This article first gives a brief review of continuous and non-continuous CA techniques, followed by two data aggregation schemes in physical and medium access control layers. Some technical challenges for implementing CA technique in LTE-Advanced systems, with the requirements of backward compatibility to LTE systems, are highlighted and discussed. Possible technical solutions for the asymmetric CA problem, control signaling design, handover control, and guard band setting are reviewed. Simulation results show Doppler frequency shift has only limited impact on data transmission performance over wide frequency bands in a high-speed mobile environment when the component carriers are time synchronized. The frequency aliasing will generate much more interference between adjacent component carriers and therefore greatly degrades the bit error rate performance of downlink data transmissions.


title: Adaptive posicast controller for time-delay systems with relative degree n*<=2

abstract: In this paper, we present an Adaptive Posicast Controller that deals with parametric uncertainties in linear systems with delays. It is assumed that the plant has no right half plane zeros and the delay is known. The adaptive controller is based on the Smith Predictor and Finite Spectrum Assignment with time-varying parameters adjusted online. A novel Lyapunov-Krasovskii functional is used to show semi-global stability of the closed-loop error equations. The controller is applied to engine fuel-to-air ratio control. The implementation results show that the Adaptive Posicast Controller significantly improves the closed-loop performance when compared to the case with the existing baseline controller.


title: Enhancing Person Re-identification in a Self-Trained Subspace

abstract: Despite the promising progress made in recent years, person re-identification (re-ID) remains a challenging task due to the complex variations in human appearances from different camera views. For this challenging problem, a large variety of algorithms have been developed in the fully supervised setting, requiring access to a large amount of labeled training data. However, the main bottleneck for fully supervised re-ID is the limited availability of labeled training samples. To address this problem, we propose a self-trained subspace learning paradigm for person re-ID that effectively utilizes both labeled and unlabeled data to learn a discriminative subspace where person images across disjoint camera views can be easily matched. The proposed approach first constructs pseudo-pairwise relationships among unlabeled persons using the k-nearest neighbors algorithm. Then, with the pseudo-pairwise relationships, the unlabeled samples can be easily combined with the labeled samples to learn a discriminative projection by solving an eigenvalue problem. In addition, we refine the pseudo-pairwise relationships iteratively, which further improves learning performance. A multi-kernel embedding strategy is also incorporated into the proposed approach to cope with the non-linearity in a person’s appearance and explore the complementation of multiple kernels. In this way, the performance of person re-ID can be greatly enhanced when training data are insufficient. Experimental results on six widely used datasets demonstrate the effectiveness of our approach, and its performance can be comparable to the reported results of most state-of-the-art fully supervised methods while using much fewer labeled data.


title: Effects of rest intervals and training loads on metabolic stress and muscle hypertrophy

abstract: We investigated the effects of volume-matched resistance training (RT) with different training loads and rest intervals on acute responses and long-term muscle and strength gains. Ten subjects trained with short rest (30 s) combined with low load (20 RM) (SL) and ten subjects performed the same protocol with long rest (3 min) and high load (8 RM) (LH). Cross-sectional area (CSA) of the upper arm was measured by magnetic resonance imaging before and after 8 weeks of training. Acute stress markers such as growth hormone (GH) and muscle thickness (MT) changes have been assessed pre and post a single RT session. Only the SL group demonstrated significant increases in GH (7704·20 ± 11833·49%, P<0·05) and MT (35·2 ± 16·9%, P<0·05) immediately after training. After 8 weeks, the arm CSA s in both groups significantly increased [SL: 9·93 ± 4·86% (P<0·001), LH: 4·73 ± 3·01% (P<0·05)]. No significant correlation between acute GH elevations and CSA increases could be observed. We conclude that short rest combined with low-load training might induce a high amount of metabolic stress ultimately leading to improved muscle hypertrophy while long rest with high-load training might lead to superior strength increases. Acute GH increases seem not to be directly correlated with muscle hypertrophy.


title: Multi-modal Learning from Unpaired Images: Application to Multi-organ Segmentation in CT and MRI

abstract: Convolutional neural networks have been widely used in medical image segmentation. The amount of training data strongly determines the overall performance. Most approaches are applied for a single imaging modality, e.g., brain MRI. In practice, it is often difficult to acquire sufficient training data of a certain imaging modality. The same anatomical structures, however, may be visible in different modalities such as major organs on abdominal CT and MRI. In this work, we investigate the effectiveness of learning from multiple modalities to improve the segmentation accuracy on each individual modality. We study the feasibility of using a dual-stream encoder-decoder architecture to learn modality-independent, and thus, generalisable and robust features. All of our MRI and CT data are unpaired, which means they are obtained from different subjects and not registered to each other. Experiments show that multi-modal learning can improve overall accuracy over modality-specific training. Results demonstrate that information across modalities can in particular improve performance on varying structures such as the spleen.


title: Interactive Colormapping: Enabling Multiple Data Range and Detailed Views of Ocean Salinity

abstract: Ocean salinity is a critical component to understanding climate change. Salinity concentrations and temperature drive large ocean currents which in turn drive global weather patterns. Melting ice caps lower salinity at the poles while river deltas bring fresh water into the ocean worldwide. These processes slow ocean currents, changing weather patterns and producing extreme climate events which disproportionally affect those living in poverty. Analysis of salinity presents a unique visualization challenge. Important data are found in narrow data ranges, varying with global location. Changing values of salinity are important in understanding ocean currents, but are difficult to map to colors using traditional tools. Commonly used colormaps may not provide sufficient detail for this data. Current editing tools do not easily enable a scientist to explore the subtleties of salinity. We present a workflow, enabled by an interactive colormap tool that allows a scientist to interactively apply sophisticated colormaps to scalar data. The intuitive and immediate interaction of the scientist with the data is a critical contribution of this work.


title: Cluster-Based Certificate Revocation with Vindication Capability for Mobile Ad Hoc Networks

abstract: Mobile ad hoc networks (MANETs) have attracted much attention due to their mobility and ease of deployment. However, the wireless and dynamic natures render them more vulnerable to various types of security attacks than the wired networks. The major challenge is to guarantee secure network services. To meet this challenge, certificate revocation is an important integral component to secure network communications. In this paper, we focus on the issue of certificate revocation to isolate attackers from further participating in network activities. For quick and accurate certificate revocation, we propose the Cluster-based Certificate Revocation with Vindication Capability (CCRVC) scheme. In particular, to improve the reliability of the scheme, we recover the warned nodes to take part in the certificate revocation process; to enhance the accuracy, we propose the threshold-based mechanism to assess and vindicate warned nodes as legitimate nodes or not, before recovering them. The performances of our scheme are evaluated by both numerical and simulation analysis. Extensive results demonstrate that the proposed certificate revocation scheme is effective and efficient to guarantee secure communications in mobile ad hoc networks.


title: Caching memcached at reconfigurable network interface

abstract: Memcached is a technology that improves response speed of web servers by caching data on DRAMs in distributed servers. In order to achieve higher performance, memcached has been evaluated on various platforms. Among them, FPGA seems to be the most efficient platform to run memcached, and several research groups are trying to achieve higher throughput with it. However, it is difficult to utilize a large amount of memory (several dozen gigabytes) with an FPGA. Some groups are trying to solve this problem by using an embedded CPU for memory allocation and another group is employing an SSD. Unlike other approaches that try to replace memcached itself on FPGAs, our approach augments the software memcached running on the host CPU by caching its data and some operations at the FPGA-equipped network interface card (NIC) mounted on the server. The locality of memcached data enables the FPGA NIC to have a fairly high hit rate with a smaller memory. We first explore the cache parameters by software simulations and estimate the effectiveness of our approach, and then prototype a system to prove its effectiveness. Through our evaluation with YCSB, a standard key-value store (KVS) benchmarking tool, we estimate that the latency improved by an order of magnitude over software memcached running on a high performance CPU.


title: A Nonlinear Approach to Modeling of Software Aging in a Web Server

abstract: Software aging refers to the phenomenon that the performance degradation, increasing failure rate or massive resource usage demonstrated in long-running software systems such as web servers. It was reported that software aging has a significant relationship with resource usage. To study the software aging phenomenon in web servers, we built an experimental platform with a server and multiple clients to collect the data of system resource usage on the server. In this paper, we present several experiments based on the platform and study software aging phenomenon of a widely used web server software system - the Apache HTTP server. Some repeatable software aging phenomena are observed in our experiments. The process of software aging is shown to be nonlinear and chaotic. In order to better present the nonlinear interplay of various resource parameters of web server and forecast resource exhaustion, a nonlinear dynamic model of software aging is constructed to formulate software aging process.


title: Moving Objects Detection and Tracking Framework for UAV-based Surveillance

abstract: Automated motion detection and tracking of ground moving objects using aerial platforms is challenging due to the small object size in comparison with objects such as buildings, as well as the fact that flying cameras can undergo rapid translations and rotations. As such, our objectives are to develop a system for gathering useful information from aerial images by mapping visited areas through image mosaic king and to detect moving objects in the captured video. To do so, the Moving Objects Detection and Tracking (MODAT) framework has been developed to facilitate the application and combination of various relevant computer vision and image processing techniques in order to achieve our objectives.


title: The effectiveness of the combined use of VIX and Support Vector Machines on the prediction of S&P 500

abstract: The aim of this research is to analyse the effectiveness of the Chicago Board Options Exchange Market Volatility Index (VIX) when used with Support Vector Machines (SVMs) in order to forecast the weekly change in the S&P 500 index. The data provided cover the period between 3 January 2000 and 30 December 2011. A trading simulation is implemented so that statistical efficiency is complemented by measures of economic performance. The inputs retained are traditional technical trading rules commonly used in the analysis of equity markets such as Relative Strength Index, Moving Average Convergence Divergence, VIX and the daily return of the S&P 500. The SVM identifies the best situations in which to buy or sell in the market. The two outputs of the SVM are the movement of the market and the degree of set membership. The obtained results show that SVM using VIX produce better results than the Buy and Hold strategy or SVM without VIX. The influence of VIX in the trading system is particularly significant when bearish periods appear. Moreover, the SVM allows the reduction in the Maximum Drawdown and the annualised standard deviation.


Malware Classification using Naïve Bayes Classifier for Android


title: Image Segmentation UsingHigher-Order Correlation Clustering

abstract: In this paper, a hypergraph-based image segmentation framework is formulated in a supervised manner for many high-level computer vision tasks. To consider short- and long-range dependency among various regions of an image and also to incorporate wider selection of features, a higher-order correlation clustering (HO-CC) is incorporated in the framework. Correlation clustering (CC), which is a graph-partitioning algorithm, was recently shown to be effective in a number of applications such as natural language processing, document clustering, and image segmentation. It derives its partitioning result from a pairwise graph by optimizing a global objective function such that it simultaneously maximizes both intra-cluster similarity and inter-cluster dissimilarity. In the HO-CC, the pairwise graph which is used in the CC is generalized to a hypergraph which can alleviate local boundary ambiguities that can occur in the CC. Fast inference is possible by linear programming relaxation, and effective parameter learning by structured support vector machine is also possible by incorporating a decomposable structured loss function. Experimental results on various data sets show that the proposed HO-CC outperforms other state-of-the-art image segmentation algorithms. The HO-CC framework is therefore an efficient and flexible image segmentation framework.


Scalpel: A Frugal, High Performance File Carver


title: Detection of smooth pursuits using eye movement shape features

abstract: Smooth pursuit eye movements hold information about the health, activity and situation of people, but to date there has been no efficient method for their automated detection. In this work we present a method to tackle the problem, based on machine learning. At the core of our method is a novel set of shape features that capture the characteristic shape of smooth pursuit movements over time. The features individually represent incomplete information about smooth pursuits but are combined in a machine learning approach. In an evaluation with eye movements collected from 18 participants, we show that our method can detect smooth pursuit movements with an accuracy of up to 92%, depending on the size of the feature set used for their prediction. Our results have twofold significance. First, they demonstrate a method for smooth pursuit detection in mainstream eye tracking, and secondly they highlight the utility of machine learning for eye movement analysis.


title: CD33 Alzheimer's Risk-Altering Polymorphism, CD33 Expression, and Exon 2 Splicing

abstract: Genome-wide association studies are identifying novel Alzheimer's disease (AD) risk factors. Elucidating the mechanism underlying these polymorphisms is critical to the validation process and, by identifying rate-limiting steps in AD risk, may yield novel therapeutic targets. Here, we elucidate the mechanism of action of the AD-associated polymorphism rs3865444 in the promoter of CD33, a member of the sialic acid-binding Ig-superfamily of lectins (SIGLECs). Immunostaining established that CD33 is expressed in microglia in human brain. Consistent with this finding, CD33 mRNA expression correlated well with expression of the microglial genes CD11b and AIF-1 and was modestly increased with AD status and the rs3865444C AD-risk allele. Analysis of CD33 isoforms identified a common isoform lacking exon 2 (D2-CD33). The proportion of CD33 expressed as D2-CD33 correlated robustly with rs3865444 genotype. Because rs3865444 is in the CD33 promoter region, we sought the functional polymorphism by sequencing CD33 from the promoter through exon 4. We identified a single polymorphism that is coinherited with rs3865444, i.e., rs12459419 in exon 2. Minigene RNA splicing studies in BV2 microglial cells established that rs12459419 is a functional single nucleotide polymorphism (SNP) that modulates exon 2 splicing efficiency. Thus, our primary findings are that CD33 is a microglial mRNA and that rs3865444 is a proxy SNP for rs12459419 that modulates CD33 exon 2 splicing. Exon 2 encodes the CD33 IgV domain that typically mediates sialic acid binding in SIGLEC family members. In summary, these results suggest a novel model wherein SNP-modulated RNA splicing modulates CD33 function and, thereby, AD risk.


title: Unsupervised Learning of Task-Specific Tree Structures with Tree-LSTMs

abstract: For years, recursive neural networks (RvNNs) have shown to be suitable for representing text into fixed-length vectors and achieved good performance on several natural language processing tasks. However, the main drawback of RvNN is that it requires explicit tree structure (e.g. parse tree), which makes data preparation and model implementation hard. In this paper, we propose a novel tree-structured long short-term memory (Tree-LSTM) architecture that efficiently learns how to compose task-specific tree structures only from plain text data. To achieve this property, our model uses Straight-Through (ST) Gumbel-Softmax estimator to decide the parent node among candidates and to calculate gradients of the discrete decision. We evaluate the proposed model on natural language interface and sentiment analysis and show that our model outperforms or at least comparable to previous Tree-LSTM-based works. Especially in the natural language interface task, our model establishes the new state-of-the-art accuracy of 85.4%. We also find that our model converges significantly faster and needs less memory than other models of complex structures.


title: Multilayer CPW-Fed Patch Antenna on New AMC Ground Plane for 60 GHz Millimeter-Wave Communications

abstract: This paper presents a 60 GHz CPW-fed patch antenna on multilayer LTCC- and LCP-based materials with new and compact AMC. The proposed antenna structure consists of top three layers of antenna and bottom three layers of AMC ground plane. First, a new and compact three- layer AMC is designed on both LTCC- and LCP-based substrates. The designed AMC is more compact and provides 37.5% higher in-phase bandwidth as compared to a state-of-the-art design. Then, a three-layer CPW- fed patch antenna is designed and optimized on LTCC- and LCP-based substrates on top of the three-layer AMC ground plane of the same material. We perform a gain- bandwidth comparative study of antennas on multilayer LTCC- and LCP-based substrates with AMC/PEC ground planes for 60 GHz. We observe that antennas on LCP- based substrate outperform those on LTCC-based substrate in terms of input impedance, bandwidth, and gains on both AMC and PEC ground planes, while antennas on LTCC-based substrate have smaller antenna and AMC dimensions.


title: Mutual calibration of sensors for multispectral 3D scanning of surface

abstract: This paper presents procedure for calibrating several sensors used for multispectral 3D surface scanning. Besides intrinsic parameters evaluates also the mutual position of sensor relatively to each other. Such parameters can be used for further data fusion, e.g. using ray tracing method. The method is deeply described to be able to reconstruct it and its functioning is experimentally verified.


title: Speech emotion recognition combining acoustic features and linguistic information in a hybrid support vector machine-belief network architecture

abstract: In this paper we introduce a novel approach to the combination of acoustic features and language information for a most robust automatic recognition of a speaker's emotion. Seven discrete emotional states are classified throughout the work. Firstly a model for the recognition of emotion by acoustic features is presented. The derived features of the signal-, pitch-, energy, and spectral contours are ranked by their quantitative contribution to the estimation of an emotion. Several different classification methods including linear classifiers, Gaussian mixture models, neural nets, and support vector machines are compared by their performance within this task. Secondly an approach to emotion recognition by the spoken content is introduced applying belief network based spotting for emotional key-phrases. Finally the two information sources are integrated in a soft decision fusion by using a neural net. The gain is evaluated and compared to other advances. Two emotional speech corpora used for training and evaluation are described in detail and the results achieved applying the propagated novel advance to speaker emotion recognition are presented and discussed.


title: Disease Prediction from Electronic Health Records Using Generative Adversarial Networks

abstract: Electronic health records (EHRs) have contributed to the computerization of patient records so that they can be used not only for efficient and systematic medical services, but also for research on data science. In this paper, we compared the disease prediction performance of generative adversarial networks (GANs) and conventional learning algorithms in combination with missing value prediction methods. As a result, the highest accuracy of 98.05% was obtained using a stacked autoencoder as the missing value prediction method and an auxiliary classifier GANs (AC-GANs) as the disease predicting method. Our results show that the combination of the stacked autoencoder and the AC-GANs significantly outperforms existing algorithms for the problem of disease prediction in which missing values and class imbalance exist.


title: An always-on 3.8μJ/86% CIFAR-10 mixed-signal binary CNN processor with all memory on chip in 28nm CMOS

abstract: The trend of pushing deep learning from cloud to edge due to concerns of latency, bandwidth, and privacy has created demand for low-energy deep convolutional neural networks (CNNs). The single-layer classifier in [1] achieves sub-nJ operation, but is limited to moderate accuracy on low-complexity tasks (90% on MNIST). Larger CNN chips provide dataflow computing for high-complexity tasks (AlexNet) at mJ energy [2], but edge deployment remains a challenge due to off-chip DRAM access energy. This paper describes a mixed-signal binary CNN processor that performs image classification of moderate complexity (86% on CIFAR-10) and employs near-memory computing to achieve a classification energy of 3.8μJ, a 40x improvement over TrueNorth [3]. We accomplish this using (1) the BinaryNet algorithm for CNNs with weights and activations constrained to +1/−1 [4], which drastically simplifies multiplications (XNOR) and allows integrating all memory on-chip; (2) an energy-efficient switched-capacitor (SC) neuron that addresses BinaryNet's challenge of wide vector summation; (3) architectural parallelism, parameter reuse, and locality.


title: Catch Me If You Can: Detecting Compromised Users Through Partial Observation on Networks

abstract: People are suffering from a range of risks in the ubiquitous networks of current world, such as rumours spreading in social networks, computer viruses propagating throughout the Internet and unexpected failures happened in Smart grids. We usually monitor only a few users of detecting various risks due to the resource constraints and privacy protection. This leads to a critical problem to detect compromised users who are out of surveillance. In this paper, we propose a risk assessment method to address this problem. The aim is to assess the security status of unmonitored users according to the limited information collected from monitored users in networks. There are two innovative techniques developed: First, we identify the source of risk propagation by inversely disseminating risks from the influenced (by rumours) or infected (by viruses) monitored users. We show a new finding that the ones who synchronously receive the risk copies from all monitored users are most likely to be the sources. Second, we propose a microscopic mathematical model to present the risk propagation from the exposed sources. This model forms a discriminant to classify the compromised users from others. For evaluations, we collect three real networks on which we launch simulated risk propagation and then sample the status of monitored users. The experiment results show that our method is effective and the result of risk assessment well matches the real status of the unmonitored users.


title: Local Deep Descriptors in Bag-of-Words for Image Retrieval

abstract: The Bag-of-Words (BoW) models using the SIFT descriptors have achieved great success in content-based image retrieval over the past decade. Recent studies show that the neuron activations of the convolutional neural networks (CNN) can be viewed as local descriptors, which can be aggregated into effective global descriptors for image retrieval. However, little work has been done on using these local deep descriptors in BoW models, especially in the case of large visual vocabularies. In this paper, we provide the key ingredients to build an effective BoW model using deep descriptors. Specifically, we show how to use the CNN as a combination of local feature detector and extractor, without the need of feeding multiple image patches to the network. Moreover, we revisit the classic issues of BoW - including the burstiness and quantization error - in our scenario and improve the retrieval accuracy by addressing these problems. Lastly, we demonstrate that our model can scale up to large visual vocabularies, enjoying the advantages of both the sparseness of visual word histogram and the discriminative power of deep descriptor. Experiments show that our model achieves state-of-the-art performance on different datasets without re-ranking.


Vibration Suppression Control Method for Trochoidal Reduction Gears under Load Conditions


title: Machine Learning: The High Interest Credit Card of Technical Debt

abstract: Machine learning offers a fantastically powerful toolkit for building complex systems quickly. This paper argues that it is dangerous to think of these quick wins as coming for free. Using the framework of technical debt, we note that it is remarkably easy to incur massive ongoing maintenance costs at the system level when applying machine learning. The goal of this paper is highlight several machine learning specific risk factors and design patterns to be avoided or refactored where possible. These include boundary erosion, entanglement, hidden feedback loops, undeclared consumers, data dependencies, changes in the external world, and a variety of system-level anti-patterns. 1 Machine Learning and Complex Systems Real world software engineers are often faced with the challenge of moving quickly to ship new products or services, which can lead to a dilemma between speed of execution and quality of engineering. The concept of technical debt was first introduced by Ward Cunningham in 1992 as a way to help quantify the cost of such decisions. Like incurring fiscal debt, there are often sound strategic reasons to take on technical debt. Not all debt is necessarily bad, but technical debt does tend to compound. Deferring the work to pay it off results in increasing costs, system brittleness, and reduced rates of innovation. Traditional methods of paying off technical debt include refactoring, increasing coverage of unit tests, deleting dead code, reducing dependencies, tightening APIs, and improving documentation [4]. The goal of these activities is not to add new functionality, but to make it easier to add future improvements, be cheaper to maintain, and reduce the likelihood of bugs. One of the basic arguments in this paper is that machine learning packages have all the basic code complexity issues as normal code, but also have a larger system-level complexity that can create hidden debt. Thus, refactoring these libraries, adding better unit tests, and associated activity is time well spent but does not necessarily address debt at a systems level. In this paper, we focus on the system-level interaction between machine learning code and larger systems as an area where hidden technical debt may rapidly accumulate. At a system-level, a machine learning model may subtly erode abstraction boundaries. It may be tempting to re-use input signals in ways that create unintended tight coupling of otherwise disjoint systems. Machine learning packages may often be treated as black boxes, resulting in large masses of “glue code” or calibration layers that can lock in assumptions. Changes in the external world may make models or input signals change behavior in unintended ways, ratcheting up maintenance cost and the burden of any debt. Even monitoring that the system as a whole is operating as intended may be difficult without careful design.


title: 3D smart city simulator

abstract: The aim of this research work is to design a smart city simulator. The proposed simulator contains the 3D model of several locations of the city with each location equipped with multiple types of virtual sensors node. The virtual sensors mainly include Noise Sensor, Air Pollution, Ultrasonic Sensor, Humidity Sensor, Temperature Sensor and Video Camera. These sensors continuously monitor and collect the data from the environment and upload it to a public cloud for further analysis through virtual gateway node. The virtual gateway node provides communication of sensors node with virtual cloud hosted server. In addition, the proposed simulator also provides a mapping interface that aids to integrate the physical sensors with the virtual sensors of the 3D model. This will benefit the visualization of physical level environment on the 3D model of the smart city simulator at the same time.


title: Distributed Representation for Traditional Chinese Medicine Herb via Deep Learning Models

abstract: Traditional Chinese Medicine (TCM) has accumulated a big amount of precious resource in the long history of development. TCM prescriptions that consist of TCM herbs are an important form of TCM treatment, which are similar to natural language documents, but in a weakly ordered fashion. Directly adapting language modeling style methods to learn the embeddings of the herbs can be problematic as the herbs are not strictly in order, the herbs in the front of the prescription can be connected to the very last ones. In this paper, we propose to represent TCM herbs with distributed representations via Prescription Level Language Modeling (PLLM). In one of our experiments, the correlation between our calculated similarity between medicines and the judgment of professionals achieves a Spearman score of 55.35 indicating a strong correlation, which surpasses human beginners (TCM related field bachelor student) by a big margin (over 10%).


title: Wireless Power Transmission With Self-Regulated Output Voltage for Biomedical Implant

abstract: This paper presents a wireless power transfer (WPT) system for powering implantable biomedical devices; the system is configured to achieve high efficiency even with CMOS switches and printed-circuit-board pattern coils and to maintain constant output voltage against coupling and loading variations without any additional blocks. It is shown that the parallel-resonant transmitter (TX) and receiver (RX) topology is advantageous for high efficiency even with lossy but compact components. In addition, the output voltage of the topology is insensitive to coupling and/or loading variations if the operating frequency is automatically adjusted according to coupling variations. A parallel-resonant class-D oscillator TX is developed to track the optimum operating frequency for the constant output voltage. The operating distance for the constant output voltage is also extended using a novel resonator structure, which contains two resonating coils. These proposed schemes allow a compact, efficient, and robust wireless power system. Maximum power of 174 mW can be transmitted with 63% overall efficiency.


title: Counting YouTube videos via random prefix sampling

abstract: Leveraging the characteristics of YouTube video id space and exploiting a unique property of YouTube search API, in this paper we develop a random prefix sampling method to estimate the total number of videos hosted by YouTube. Through theoretical modeling and analysis, we demonstrate that the estimator based on this method is unbiased, and provide bounds on its variance and confidence interval. These bounds enable us to judiciously select sample sizes to control estimation errors. We evaluate our sampling method and validate the sampling results using two distinct collections of YouTube video id's (namely, treating each collection as if it were the "true" collection of YouTube videos). We then apply our sampling method to the live YouTube system, and estimate that there are a total of roughly 500 millions YouTube videos by May, 2011. Finally, using an unbiased collection of YouTube videos sampled by our method, we show that YouTube video view count statistics collected by prior methods (e.g., through crawling of related video links) are highly skewed, significantly under-estimating the number of videos with very small view counts (


title: Pharmacological treatment of schizophrenia with comorbid substance use disorder.

abstract: INTRODUCTION ::: While antipsychotics remain the cornerstone of treatment for schizophrenic patients with comorbid substance use disorder (SUD), such treatment is nonetheless complicated by frequent medical comorbidity and poor adherence to medication. ::: ::: ::: AREAS COVERED ::: Randomised controlled trials (RCTs) on the efficacy of antipsychotics for the treatment of schizophrenic patients with comorbid SUD are reviewed and analysed on the basis of a systematic literature search (PubMed) ranging from 1985 to 2015. On the same basis, findings from RCTs on the efficacy of psychotropic and other medications used for primary SUD are summarised, and the main issues liable to influence treatment choice are discussed, including pharmacodynamic as well as pharmacokinetic interactions, adherence, medical comorbidity and the impact on brain structure. ::: ::: ::: EXPERT OPINION ::: As far as the treatment of schizophrenic patients with SUD is concerned, direct and indirect evidence tends to stand in favour of the use of second-generation antipsychotics (SGAs), and particularly those with lower metabolic, cardiovascular and extrapyramidal side effects, as well as those with a depot formulation. A few of the usual medications for the treatment of primary SUD, such as naltrexone and disulfiram for alcohol use and bupropion for tobacco cessation, can also be safely and efficiently administered to schizophrenic patients with SUD.


title: Database Meets Deep Learning: Challenges and Opportunities

abstract: Deep learning has recently become very popular on account of its incredible success in many complex datadriven applications, including image classification and speech recognition. The database community has worked on data-driven applications for many years, and therefore should be playing a lead role in supporting this new wave. However, databases and deep learning are different in terms of both techniques and applications. In this paper, we discuss research problems at the intersection of the two fields. In particular, we discuss possible improvements for deep learning systems from a database perspective, and analyze database applications that may benefit from deep learning techniques.


title: A Survey on Content-Aware Image and Video Retargeting

abstract: This survey introduces the current state of the art in image and video retargeting and describes important ideas and technologies that have influenced the recent work. Retargeting is the process of adapting an image or video from one screen resolution to another to fit different displays, for example, when watching a wide screen movie on a normal television screen or a mobile device. As there has been considerable work done in this field already, this survey provides an overview of the techniques. It is meant to be a starting point for new research in the field. We include explanations of basic terms and operators, as well as the basic workflow of the different methods.


title: Multiview Clustering via Adaptively Weighted Procrustes

abstract: In this paper, we make a multiview extension of the spectral rotation technique raised in single view spectral clustering research. Since spectral rotation is closely related to the Procrustes Analysis for points matching, we point out that classical Procrustes Average approach can be used for multiview clustering. Besides, we show that direct applying Procrustes Average (PA) in multiview tasks may not be optimal theoretically and empirically, since it does not take the clustering capacity differences of different views into consideration. Other than that, we propose an Adaptively Weighted Procrustes (AWP) approach to overcome the aforementioned deficiency. Our new AWP weights views with their clustering capacities and forms a weighted Procrustes Average problem accordingly. The optimization algorithm to solve the new model is computational complexity analyzed and convergence guaranteed. Experiments on five real-world datasets demonstrate the effectiveness and efficiency of the new models.


title: Analytical Design of Microstrip Short-Circuit Terminated Stepped-Impedance Resonator Dual-Band Filters

abstract: This paper proposes an analytical method to design a dual-band Alter using the short-circuit terminated half-wavelength stepped-impedance resonator (SIR). The SIR has an advantage to easily control the first and second resonances by adjusting its structural parameters. In the proposed method, the structural parameters of the SIR are obtained analytically according to the two passband center frequencies and bandwidths of the filter. As a result, the achievable specifications of the dual-band filter can be rapidly determined. The coupling between adjacent SIRs is realized by a short-circuited stub, which is characterized as a K-inverter network. The dual-frequency transformer incorporated with the tapped-line input/output structure is used for the external coupling. Applying the analytical equations in the design process, a dual-band filter can be easily and quickly realized. More importantly, compared to the published dual-band filters, the proposed method is easier to design, especially for a relatively high-order dual-band filter. Two fourth-order and one sixth-order dual-band filters are designed and fabricated to demonstrate the proposed method.


title: A cryptographic solution to implement access control in a hierarchy and more

abstract: The need for access control in a hierarchy arises in severaldifferent contexts. One such context is managing the information ofan organization where the users are divided into different securityclasses depending on who has access to what. Several cryptographicsolutions have been proposed to address this problem --- thesolutions are based on generating cryptographic keys for eachsecurity class such that the key for a lower level security classdepends on the key for the security class that is higher up in thehierarchy. Most solutions use complex cryptographic techniques:integrating these into existing systems may not be trivial. Othershave impractical requirement: if a user at a security level wantsto access data at lower levels, then all intermediate nodes must betraversed. Moreover, if there is an access control policy that doesnot conform to the hierarchical structure, such policy cannot behandled by existing solutions. We propose a new solution thatovercomes the above mentioned shortcomings. Our solution not onlyaddresses the problem of access control in a hierarchy but also canbe used for general cases. It is a scheme similar to the RSAcryptosystem and can be easily incorporated in existing systems.