title: Predicting Motion of Vulnerable Road Users using High-Definition Maps and Efficient ConvNets

abstract: Following detection and tracking of traffic actors, prediction of their future motion is the next critical component of a self-driving vehicle (SDV) technology, allowing the SDV to operate safely and efficiently in its environment. This is particularly important when it comes to vulnerable road users (VRUs), such as pedestrians and bicyclists. These actors need to be handled with special care due to an increased risk of injury, as well as the fact that their behavior is less predictable than that of motorized actors. To address this issue, in this paper we present a deep learning-based method for predicting VRU movement, where we rasterize high-definition maps and actor's surroundings into bird's-eye view image used as an input to deep convolutional networks. In addition, we propose a fast architecture suitable for real-time inference, and present a detailed ablation study of various rasterization choices. The results strongly indicate benefits of using the proposed approach for motion prediction of VRUs, both in terms of accuracy and latency.


title: Incorporating Copying Mechanism in Sequence-to-Sequence Learning

abstract: We address an important problem in sequence-to-sequence (Seq2Seq) learning referred to as copying, in which certain segments in the input sequence are selectively replicated in the output sequence. A similar phenomenon is observable in human language communication. For example, humans tend to repeat entity names or even long phrases in conversation. The challenge with regard to copying in Seq2Seq is that new machinery is needed to decide when to perform the operation. In this paper, we incorporate copying into neural network-based Seq2Seq learning and propose a new model called CopyNet with encoder-decoder structure. CopyNet can nicely integrate the regular way of word generation in the decoder with the new copying mechanism which can choose sub-sequences in the input sequence and put them at proper places in the output sequence. Our empirical study on both synthetic data sets and real world data sets demonstrates the efficacy of CopyNet. For example, CopyNet can outperform regular RNN-based model with remarkable margins on text summarization tasks.


title: Finding the Adaptive Sweet Spot: Balancing Compliance and Achievement in Automated Stress Reduction

abstract: Automated coaching systems offer a convenient, cost-effective way to reduce stress, which can be a serious health issue. However, one concern with such systems is compliance; users fail to achieve daily stress reduction goals because goals are too easy or too difficult. To address this, we built DStress (Design for Stress), a theoretically grounded system that sets adaptive goals in three coaching dimensions: Exercise, Meditation and Accessibility. DStress modifies goal-difficulty based on the individual's immediately previous performance. In a 28-day deployment with 65 users, DStress reduced scores on one direct measure of stress almost in half, significantly more than two other non-adaptive coaching strategies. However, on a second direct stress measure, no improvement was found. There were also no improvements on other indirect stress measures. Analysis of 2842 user-generated reports suggests our findings were the result of DStress balancing compliance against the degree of challenge of the goals it would set.


title: BreathVR: Leveraging Breathing as a Directly Controlled Interface for Virtual Reality Games

abstract: With virtual reality head-mounted displays rapidly becoming accessible to mass audiences, there is growing interest in new forms of natural input techniques to enhance immersion and engagement for players. Research has explored physiological input for enhancing immersion in single player games through indirectly controlled signals like heart rate or galvanic skin response. In this paper, we propose breathing as a directly controlled physiological signal that can facilitate unique and engaging play experiences through natural interaction in single and multiplayer virtual reality games. Our study (N = 16) shows that participants report a higher sense of presence and find the gameplay more fun and challenging when using our breathing actions. From study observations and analysis we present five design strategies that can aid virtual reality game designers interested in using directly controlled forms of physiological input.


title: To Put That in Perspective: Generating Analogies that Make Numbers Easier to Understand

abstract: Laypeople are frequently exposed to unfamiliar numbers published by journalists, social media users, and algorithms. These figures can be difficult for readers to comprehend, especially when they are extreme in magnitude or contain unfamiliar units. Prior work has shown that adding "perspective sentences" that employ ratios, ranks, and unit changes to such measurements can improve people's ability to understand unfamiliar numbers (e.g., "695,000 square kilometers is about the size of Texas"). However, there are many ways to provide context for a measurement. In this paper we systematically test what factors influence the quality of perspective sentences through randomized experiments involving over 1,000 participants. We develop a statistical model for generating perspectives and test it against several alternatives, finding beneficial effects of perspectives on comprehension that persist for six weeks. We conclude by discussing future work in deploying and testing perspectives at scale.


title: Mapping CMMI Level 2 to Scrum Practices: An Experience Report

abstract: CMMI has been adopted advantageously in large companies for improvements in software quality, budget fulfilling, and customer satisfaction. However SPI strategies based on CMMI-DEV require heavy software development processes and large investments in terms of cost and time that medium/small companies do not deal with. The so-called light software development processes, such as Agile Software Development (ASD), deal with these challenges. ASD welcomes changing requirements and stresses the importance of adaptive planning, simplicity and continuous delivery of valuable software by short time-framed iterations. ASD is becoming convenient in a more and more global, and changing software market. It would be greatly useful to be able to introduce agile methods such as Scrum in compliance with CMMI process model. This paper intends to increase the understanding of the relationship between ASD and CMMI-DEV reporting empirical results that confirm theoretical comparisons between ASD practices and CMMI level2.


title: A Hierarchical Cluster Synchronization Framework of Energy Internet

abstract: The concept of “Energy Internet” has been recently proposed to improve the efficiency of renewable energy resources. Accordingly, this paper investigates the synchronization of the Energy Internet system. Specifically, an appropriate hierarchical cluster synchronization framework is firstly developed for the Energy Internet, which includes primary synchronization, secondary synchronization, and tertiary synchronization. Furthermore, a secondary synchronization strategy (cluster synchronization) is proposed for the synchronization of multiple microgrid clusters in the Energy Internet, which is based on the consensus algorithm of multiagent networks. The detailed synchronization framework and control scheme are presented in this paper. Finally, some simulation results are provided and discussed to validate the performance and effectiveness of the proposed strategy.


title: An ESPRIT-Based Approach for 2-D Localization of Incoherently Distributed Sources in Massive MIMO Systems

abstract: In this paper, an approach of estimating signal parameters via rotational invariance technique (ESPRIT) is proposed for two-dimensional (2-D) localization of incoherently distributed (ID) sources in large-scale/massive multiple-input multiple-output (MIMO) systems. The traditional ESPRIT-based methods are valid only for one-dimensional (1-D) localization of the ID sources. By contrast, in the proposed approach the signal subspace is constructed for estimating the nominal azimuth and elevation direction-of-arrivals and the angular spreads. The proposed estimator has closed-form expressions and hence it does not have to search over the entire feasible field. Therefore, it imposes significantly lower computational complexity than the conventional 2-D estimation approaches. Our analysis shows that the estimation performance of the proposed approach improves when the large-scale/massive MIMO systems are employed. The approximate Cramer-Rao bound of the proposed estimator for the 2-D localization is also derived. Numerical results demonstrate that albeit the proposed estimation method is comparable with the traditional 2-D estimators in terms of performance, it benefits from a remarkably lower computational complexity


title: Efficient Online Classification using an Ensemble of Bayesian Linear Logistic Regressors

abstract: We present a novel ensemble of logistic linear regressors that combines the robustness of online Bayesian learning with the flexibility of ensembles. The ensemble of classifiers are built on top of a Randomly Varying Coefficient model designed for online regression with the fusion of classifiers done at the level of regression before converting it into a class label using a logistic link function. The locally weighted logistic regressor is compared against the state-of-the-art methods to reveal its excellent generalization performance with low time and space complexities.


Fully Convolutional Network for Depth Estimation and Semantic Segmentation


title: Implementation and Analysis of Distributed Relaxed Concurrent Queues in Remote Memory Access Model

abstract: Abstract Remote memory access (RMA) technique is a very attractive way for improving efficiency of high-performance computations (HPC) and simplifying parallel program development. However, coming up with efficient concurrent data structures for distributed environments with deep hierarchy, such as computer clusters and data centres, is challenging. We propose a novel approach to design distributed concurrent data structures. The core idea behind the approach is relaxation of the order of executed operations. For example, a relaxed priority queues only requires the elements returned by delete-min operation to be sufficiently close to the minimum. Similarly, in a relaxed queue or stack, the removed elements are close to the first or the last one, respectively. There are multiple evidences that, on most workloads, relaxed data structures outperform data structures with strict semantics and ensure acceptable degrees of operation reordering. In this work we approve our approach on the example of a distributed queue, evaluate its efficiency and compare it with the other implementations of distributed lists.


title: Core Design and Optimization for Better Misalignment Tolerance and Higher Range of Wireless Charging of PHEV

abstract: In this paper, a design and optimization method of ferrite core is proposed for wireless charging system of plug-in hybrid electric vehicle to improve its misalignment tolerance and minimize the core loss. While optimizing for higher misalignment tolerance, the primary focus is given to maintain uniform flux density in the core to minimize the core loss. An excessive use of core increases the weight and loss of the system, which need to be minimized through optimal design. Moreover, the flux density changes with misalignment, and the misalignment probability for car parking could be different in different direction. Hence the directional probability of misalignment needs to be considered during core design. In this paper, a core structure is proposed for a circular and bar-shaped core to achieve uniform flux density and an optimization method is proposed based on sequential nonlinear programming algorithm in FEA. Compared to typical uniform thickness core structure, in an optimized core, the core loss per unit volume is reduced by 30% for circular core and by 39% for bar-shaped core and the core volume per unit core loss is reduced by 17.5% in optimized circular shaped core.


title: Factors Associated with Participation in Employment for High School Leavers with Autism

abstract: This study aimed to identify the factors associated with participation in employment for high school leavers with autism. A secondary data analysis of the National Longitudinal Transition Study 2 (NLTS2) data was performed. Potential factors were assessed using a weighted multivariate logistic regression. This study found that annual household income, parental education, gender, social skills, whether the child had intellectual disability, whether the child graduated from high school, whether the child received career counseling during high school, and whether the child’s school contacted postsecondary vocational training programs or potential employers were the significant factors associated with participation in employment. These findings may have implications for professionals who provide transition services and post-secondary programs for individuals with autism.


title: Discovery of blog communities based on mutual awareness

abstract: Blogs have many fast growing communities on the Internet. Discovering such communities in the blogosphere is important for sustaining and encouraging new blogger participation. We focus on extracting communities based on two key insights – (a) communities form due to individual blogger actions that are mutually observable; (b) semantics of the hyperlink structure are different from traditional web analysis problems. Our approach involves developing computational models for mutual awareness that incorporates the specific action type, frequency and time of occurrence. We use the mutual awareness feature with a rankingbased community extraction algorithm to discover communities. To validate our approach, four performance measures are used on the WWW2006 Blog Workshop dataset and the NEC focused blog dataset with excellent quantitative results. The extracted communities also demonstrate to be semantically cohesive with respect to their topics of interest.


title: Deep Structured Energy Based Models for Anomaly Detection

abstract: In this paper, we attack the anomaly detection problem by directly modeling the data distribution with deep architectures. We propose deep structured energy based models (DSEBMs), where the energy function is the output of a deterministic deep neural network with structure. We develop novel model architectures to integrate EBMs with different types of data such as static data, sequential data, and spatial data, and apply appropriate model architectures to adapt to the data structure. Our training algorithm is built upon the recent development of score matching \cite{sm}, which connects an EBM with a regularized autoencoder, eliminating the need for complicated sampling method. Statistically sound decision criterion can be derived for anomaly detection purpose from the perspective of the energy landscape of the data distribution. We investigate two decision criteria for performing anomaly detection: the energy score and the reconstruction error. Extensive empirical studies on benchmark tasks demonstrate that our proposed model consistently matches or outperforms all the competing methods.


SYSTEM APPROACH TO ENSURE PERFORMANCE OF MARINE AND COASTAL ELECTRICAL SYSTEMS DURING OPERATION


Stiff person syndrome , startle and other immune-mediated movement disorders – new insights


title: A bias correction function for classification performance assessment in two-class imbalanced problems

abstract: This paper introduces a framework that allows to mitigate the impact of class imbalance on most scalar performance measures when used to evaluate the behavior of classifiers. Formally, a correction function is defined with the aim of highlighting those classification results that present moderately higher prediction rates on the minority class. Besides, this function punishes those scenarios that are biased towards the majority class, but also those that are strongly biased to favor the minority class. This strategy assumes a typical imbalance task, in which the minority class contains the most relevant samples to the research purposes. A novel experimental framework is designed to show the advantages of our approach when compared to the standard use of well-established measures, demonstrating its consistency and validity.


Human Activity Recognition from Sensor Data


title: Gamified In-Home Rehabilitation for Stroke Survivors: Analytical Review

abstract: A stroke is a life-changing event that may end up as a disability, with repercussions on the patient’s quality of life. Stroke rehabilitation therapies are helpful to regain some of the patient’s lost functionality. However, in practice stroke patients may suffer from a gradual loss of motivation. Gamified systems are used to increase user motivation, hence, gamified elements have been implemented into stroke rehabilitation therapies in order to improve patients’ engagement and adherence. This review work focuses on selecting and analyzing developed and validated gamified stroke rehabilitation systems published between 2009 and 2017 to identify the most important features of these systems. After extensive research, 32 articles have met the selection criteria, resulting in a total of 28 unique works. The works were analyzed and a total of 20 features were identified. The features are explained, making emphasis on the works that implement them extensively. Finally, a classification of features based on objectives is proposed, which was used to identify the relationships between features and implementation gaps. It was found that there is a tendency to develop low-cost solutions as in-home therapy systems and provide a variety of games. This review allowed the definition of the opportunities for future research direction such as systems addressing the three rehabilitation areas; data analytics to make decisions; motivational content identification based on automatic engagement detection and emotion recognition; and alert systems for patient´s safety.


title: Combining Model Checking and Testing

abstract: Model checking and testing have a lot in common. Over the last two decades, significant progress has been made on how to broaden the scope of model checking from finite-state abstractions to actual software implementations. One way to do this consists of adapting model checking into a form of systematic testing that is applicable to industrial-size software. This chapter presents an overview of this strand of software model checking.


title: Design and Implementation of High Frequency Inverter Operation for Induction Heating Cooking Application Using PSIM Software

abstract: An inverter circuit which generates a output high frequency AC. A low frequency AC (LFAC) to high frequency AC (HFAC) power conversion system without a DC smoothing Capacitor filter is introduced. The operating principle of a utility frequency AC-high frequency AC power frequency conversion circuit defined as a high frequency inverter is described. This synthesized converter can be considered as an output extension of a full-bridge topology where the AC to DC conversion is done by a bridge converter and again the required output AC is obtained with the help of series- resonant inverter circuit. It allows the control of simultaneously and independently, up to their rated power reducing the component requirement along with the converter solution. To verify the theoretical predictions, the conversion system is designed and simulated using the PSIM Simulink with an induction-heating prototype. The advantage of such a circuit is that the AC side line current harmonic components and power factor characteristics are improved. The performance of the output high frequency inverter simulated with switching frequency 66 KHz and RMS input voltage 240 V. I. Introduction In general, the electromagnetic Induction Heating (IH) is concerned with noncontact, high efficiency, and cleans electric heating method due to the energy conversion heated device by induction eddy current based on Faraday's law of electromagnetic induction principle in addition to Joule's heating principle. Attractive IH technology used for industrial and consumer applications is roughly classified as low frequency IH and high frequency IH (over 66 kHz). The full bridge inverters are based on the use of the quasi square wave control technique; the harmonic effects of output voltage and current are not considered, while the output voltage and current are contain harmonic with this technique. The equivalent circuit modeling of the electromagnetic induction heating load is discussed. On the other hand, the latest technology developments of IH application products emphasized on the environment have attracted special interest in the consumer application fields as follows: IH cooking device, rice cooker and warmer, boiler, hot-water producer, floor and wall fluid heater, fryer, dryer, wastes treatment and soil sterilization equipment using cleaning, sterilization and drying of atmospheric pressure super-heated steamer. A typical arrangement of an induction cooker is shown in Fig. 1. Induction cookers take the energy from the main voltage, which is rectified by a bridge of diodes. Then the inverter topology supplies the high-frequency current to the induction coil. The low frequency AC (LFAC)-high frequency AC (HFAC) power conversion circuit, defined as a high frequency inverter or converter, with an output series resonance inverter and does not include the DC smoothing filter stage. Also, the paper gives the circuit operation of the inverter with the quasi square wave control technique for the induction heating cooking appliances. Then, the circuit operation verification of the high frequency inverter is carried out using simulation. Finally, the power regulation characteristics and power conversion efficiency characteristics of the high frequency inverter are evaluated and discussed, concerning the active filtering characteristics of the harmonic line current components, and the total harmonic power factor on the AC power grid utility side. II. Operating Principal Of Full Bridge Induction System A typical arrangement of an induction cooker is shown in fig. 1. Induction cookers take the energy from the main voltage, which is rectified by a bridge of diodes. Then the inverter topology supplies the high-frequency current to the induction coil.


title: Technical Dependency Challenges in Large-Scale Agile Software Development

abstract: This qualitative study investigates challenges associated with technical dependencies and their communication. Such challenges frequently occur when agile practices are scaled to large-scale software development. The use of thematic analysis on semi-structured interviews revealed five challenges: planning, task prioritization, knowledge sharing, code quality, and integration. More importantly, these challenges interact with one another and can lead to a domino effect or vicious circle. If an organization struggles with one challenge, it is likely that the other challenges become problematic as well. This situation can have a significant impact on process and product quality. Our recommendations focus on improving planning and knowledge sharing (with practices such as scrum-of-scrums, continuous integration, open space technology) to break the vicious circle, and to reestablish effective communication across teams, which will then enable large-scale companies to achieve the benefits of large-scale agility.


Incentivizing the use of bike trailers for dynamic repositioning in bike sharing systems


title: Routing Strategies for Wireless Sensor Networks

abstract: This paper evaluates three routing strategies for wireless sensor networks: source, shortest path, and hierarchical-geographical, which are the three most commonly employed by wireless ad-hoc and sensor networks algorithms. Source routing was selected because it does not require costly topology maintenance, while shortest path routing was chosen because of its simple discovery routing approach and hierarchical-geographical routing was elected because it uses location information via Global Positioning System (GPS). The performance of these three routing strategies is evaluated by simulation using OPNET, in terms of latency, End to End Delay (EED), packet delivery ratio, routing overhead, overhead and routing load.


title: Reinforcement Learning of Coordination in Cooperative Multi-Agent Systems

abstract: We report on an investigation of reinforcement learning techniques for the learning of coordination in cooperative multi-agent systems. Specifically, we focus on a novel action selection strategy for Q-learning (Watkins 1989). The new technique is applicable to scenarios where mutual observation of actions is not possible.To date, reinforcement learning approaches for such independent agents did not guarantee convergence to the optimal joint action in scenarios with high miscoordination costs. We improve on previous results (Claus & Boutilier 1998) by demonstrating empirically that our extension causes the agents to converge almost always to the optimal joint action even in these difficult cases.


title: Implications of rejection sensitivity for intimate relationships.

abstract: People who are sensitive to social rejection tend to anxiously expect, readily perceive, and overreact to it. This article shows that this cognitive-affective processing disposition undermines intimate relationships. Study 1 describes a measure that operationalizes the anxious-expectations component of rejection sensitivity. Study 2 provides experimental evidence that people who anxiously expect rejection readily perceive intentional rejection in the ambiguous behavior of others. Study 3 shows that people who enter romantic relationships with anxious expectations of rejection readily perceive intentional rejection in the insensitive behavior of their new partners. Study 4 demonstrates that rejection-sensitive people and their romantic partners are dissatisfied with their relationships. Rejection-sensitive men's jealousy and rejection-sensitive women's hostility and diminished supportiveness help explain their partners' dissatisfaction.


title: Immersive real-time large-scale network simulation: A research summary

abstract: Immersive real-time large-scale network simulation is a technique that supports simulation of large-scale networks to interact with real implementations of network protocols, network services, and distributed applications. Traffic generated by real network applications is carried by the virtual network simulated in real time where delays and losses are calculated according to the simulated network conditions. We emphasize network immersion so that the virtual network is indistinguishable from a physical testbed in terms of network behavior, yet allows the flexibility of simulation. In this paper we provide a summary of current research in immersive real-time large-scale network simulation, particularly in areas of hybrid network traffic modeling and scalable emulation infrastructure design.


title: An Ant Approach to Membership Overlay Design

abstract: Designing an optimal overlay communication network for a set of processes on the Internet is a central problem of peer-to-peer (P2P) computing. Such a network defines membership and allows for members to disseminate information within the group. The network has to be robust and the available bandwidth has to be utilized in an optimal manner to allow for maximally efficient communication. This problem can be formulated as a dynamic optimization problem where classical combinatorial optimization techniques must face the further challenge of time-varying input data. ACO systems appear to be particularly fit for this class of problems, being able to construct an internal model of the instance to face and to exploit it for fast adaptation to modified contexts.


title: Design of prognostic circuit for hot carrier injection failure of integrated circuit

abstract: Prognostics and health management has become a preferred approach to achieve warning or respond to impending failure of electronic systems. A prognostic circuit for hot carrier injection (HCI) failure of integrated circuit was proposed in the paper, and it was simulated on the base of the SMIC 0.18 um mixed-signal CMOS process model. The prognostic circuit consists of stress voltage generation module, HCI failure detecting module, two-stage comparator, offset voltage cancellation module, non-overlapping clock generation module, and output module. When the increase amount of threshold voltage for the MOSFET exceeds a preset value due to hot carrier injection, the output of the prognostic circuit designed to fail faster will jump from low voltage to high voltage. It indicates the impending failure of hosted circuit because of that the prognostic circuit experiences the same manufacturing process and operational environment as the hosted circuit. The research results are useful for the prediction of HCI degradation and failure of integrated circuit.


title: Audio-visual feature selection and reduction for emotion classification

abstract: Recognition of expressed emotion from speech and facial gestures was investigated in experiments on an audio-visual emotional database. A total of 106 audio and 240 visual features were extracted and then features were selected with Plus l-Take Away r algorithm based on Bhattacharyya distance criterion. In the second step, linear transformation methods, principal component analysis (PCA) and linear discriminant analysis (LDA), were applied to the selected features and Gaussian classifiers were used for classification of emotions. The performance was higher for LDA features compared to PCA features. The visual features performed better than audio features, for both PCA and LDA. Across a range of fusion schemes, the audio-visual feature results were close to that of visual features. A highest recognition rate of 53% was achieved with audio features, 98% with visual features, and 98% with audio-visual features selected by Bhattacharyya distance and transformed by LDA.


title: Model predictive trajectory tracking control for hydraulic excavator on digging operation

abstract: In order to increase work efficiency, alleviating burden of operators is important. An autonomous hydraulic excavator is expected to improve it. In this paper, an automatic control of a digging operation for the hydraulic excavator is studied. We propose a method for the trajectory tracking control using model predictive control (MPC) which incorporates servo mechanism. MPC can optimize motion and avoids rapid change of velocity using constraints. However, it is difficult to cope with unknown reaction forces caused by contacting with underground objects. Servo mechanism suppresses the disturbance by the integration of the tracking error. However, the error may be accumulated in the integration. Hence, the trajectory tracking may result in rapid response when the objects are removed. By combining MPC and servo mechanism, we can expect that servo mechanism works against the disturbance and the tracking performance is improved. We show effectiveness of the proposed method through simulations under the presence of the disturbance.


title: Evaluating Health Information Systems Using Ontologies

abstract: Background: There are several frameworks that attempt to address the challenges of evaluation of health information systems by offering models, methods, and guidelines about what to evaluate, how t ...


title: Accessory Axillary Breast Excision with Liposuction Using Minimal Incision: A Preliminary Report

abstract: BACKGROUND ::: Accessory breasts have received little attention in the surgical fields, although the condition is quite common in the female population, with 2-6% of women suffering from it. Its convexity and cyclic pain make women feel embarrassed and uncomfortable, so patients often desire surgical excision to improve their appearances and to remove the pain. ::: ::: ::: METHODS ::: A total of 967 patients who had been treated by an excision of accessory breast tissue with liposuction using minimal incision from September 2013 to Dec 2015 at the Damsoyu Hospital were analyzed for clinical factors retrospectively. ::: ::: ::: RESULTS ::: All 967 patients were female. There were 514 (53.2%) unmarried patients and 453 (46.8%) married patients. The major clinical manifestation was the problem in the appearance with cyclic pain in both unmarried and married groups (82.7 vs. 87.9%). Three types of accessory breasts were observed: 779 (80.6%) breast tissue only in axillae, 182 (18.8%) breast tissue with accessory nipple, and 6 (0.6%) breast tissue with accessory nipple-areolar complex. The mean operation time was 58 min. All cyclic axillar pain in our cases was resolved after the operation. Postoperative complications developed in 160 patients (16.55%). Among them, seroma after operation was the most common (11.27%). In our study, 95.65% of the patients were satisfied with the cosmetic outcomes. ::: ::: ::: CONCLUSIONS ::: The surgical excision of accessory breasts with liposuction through the minimal incision is a safe and effective method to make women feel comfortable in clinical manifestations and be satisfied with their cosmetic axillar line. ::: ::: ::: LEVEL OF EVIDENCE V ::: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .


title: A computational account of conceptual blending in basic mathematics Action editor : Cleotilde Gonzalez

abstract: We present an account of a process by which different conceptualisations of number can be blended together to form new conceptualisations via recognition of common features, and judicious combination of their distinctive features. The accounts of number are based on Lakoff and Nunez's cognitively-based grounding metaphors for arithmetic. The approach incorporates elements of analogical inference into a generalised framework of conceptual blending, using some ideas from the work of Goguen. The ideas are worked out using Heuristic-Driven Theory Projection (HDTP, a method based on higher-order anti-unification). HDTP provides generalisations between domains, giving a crucial step in the process of finding commonalities between theories. In addition to generalisations, HDTP can also transfer concepts from one domain to another, allowing the construction of new conceptual blends. Alongside the methods by which conceptual blends may be constructed, we provide heuristics to guide this process.


Learning variable impedance control On behalf of: Multimedia Archives can be found at: The International Journal of Robotics Research Additional services and information fornav Permissions: at UNIV OF SOUTHERN CALIFORNIA on Learning variable impedance control


title: Chitin-mediated changes in bacterial communities of the soil , rhizosphere and within roots of cotton in relation to nematode control

abstract: Abstract Changes in microbial communities associated with nematode control were studied by comparing population numbers of fungi and bacteria in the soil and in internal root tissues (endorhiza) in non-amended and chitin-amended soils. Addition of chitin to soil at 1% (w/w) eliminated plant-parasitic nematodes in a first planting of cotton cv. `Rowden' and significantly reduced Meloidogyne incognita infestation in a second planting, confirming long-term nematode suppressiveness induced by this organic amendment. The chitin amendment was associated with an increase in fungal and bacterial populations, especially those with chitinolytic activity. The bacterial communities of soil, rhizosphere and endorhiza were assessed by examining the taxonomic diversity of recoverable bacteria based on identification with fatty acid analysis of sample sizes of 35 soil and rhizosphere bacteria and 25 endophytic bacteria. All major bacterial species which formed at least 2% of the total population in non-amended soils and rhizospheres also occurred with chitin amendment. In contrast, chitin-amended soils and rhizospheres yielded several species which were not found without chitin amendment, including Aureobacterium testaceum, Corynebacterium aquaticum and Rathayibacter tritici. Burkholderia cepacia was recovered from both amended and non-amended soils and rhizospheres, but it was most abundant with chitin amendment at the end of the first cotton planting. Soil was probably the major source for bacterial endophytes of cotton roots, since nearly all endophytic bacteria were also found in the soil or rhizosphere. However, two dominant genera in the soil and rhizosphere, Bacillus and Arthrobacter, were not detected as endophytes. Chitin amendment exhibited a further specific influence on the endophytic bacterial community; Phyllobacterium rubiacearum was not a common endophyte following chitin amendment, even though chitin amendment stimulated its populations in non-planted soil. Burkholderia cepacia, found in similar numbers in the soil of both treatments, was the dominant endophyte in plants grown in chitin-amended soil but rarely colonized cotton roots grown in non-amended soil. These results indicate that application of an organic amendment can lead to modifications of the bacterial communities of the soil, rhizosphere and endorhiza.


title: A 4-bit 1 GHz sub-half micrometer CMOS/SOS flash analog-to-digital converter using focused ion beam implants

abstract: A description is given of a novel 4-bit, ultrafast, low-power flash analog-to-digital converter (ADC) chip that was fabricated using digital CMOS/SOS SPEAR technology with sub-half-micrometer (0.3- mu m) gates and focused-ion-beam (FIB) channel implants. The FIB technology allows localized implants to be used to customize the threshold voltages of the MOSFETs used in each comparator. The maximum sample rate of the circuit exceed 1 GHz at a supply voltage of 5.0 V and decreased slightly to 900 MHz when the supply voltage was lowered to 3.3 V. the power dissipation, apart from the output buffers, at 3.3 V and 900 MHz was only 80 mW. Because of the very low power dissipation and small size ( >


title: A Polymer-Based Capacitive Sensing Array for Normal and Shear Force Measurement

abstract: In this work, we present the development of a polymer-based capacitive sensing array. The proposed device is capable of measuring normal and shear forces, and can be easily realized by using micromachining techniques and flexible printed circuit board (FPCB) technologies. The sensing array consists of a polydimethlysiloxane (PDMS) structure and a FPCB. Each shear sensing element comprises four capacitive sensing cells arranged in a 2 × 2 array, and each capacitive sensing cell has two sensing electrodes and a common floating electrode. The sensing electrodes as well as the metal interconnect for signal scanning are implemented on the FPCB, while the floating electrodes are patterned on the PDMS structure. This design can effectively reduce the complexity of the capacitive structures, and thus makes the device highly manufacturable. The characteristics of the devices with different dimensions were measured and discussed. A scanning circuit was also designed and implemented. The measured maximum sensitivity is 1.67%/mN. The minimum resolvable force is 26 mN measured by the scanning circuit. The capacitance distributions induced by normal and shear forces were also successfully captured by the sensing array.


title: Assessment of stresses in the cervical spine caused by posture and position of the head.

abstract: Preamble. Billions of people are using cell phone devices on the planet, essentially in poor posture. The purpose of this study is to assess the forces incrementally seen by the cervical spine as the head is tilted forward, into worsening posture. This data is also necessary for cervical spine surgeons to understand in the reconstruction of the neck.


Natural Language Processing using NLTK and WordNet


title: Chiron: Privacy-preserving Machine Learning as a Service

abstract: Major cloud operators offer machine learning (ML) as a service, enabling customers who have the data but not ML expertise or infrastructure to train predictive models on this data. Existing ML-as-a-service platforms require users to reveal all training data to the service operator. We design, implement, and evaluate Chiron, a system for privacy-preserving machine learning as a service. First, Chiron conceals the training data from the service operator. Second, in keeping with how many existing ML-as-a-service platforms work, Chiron reveals neither the training algorithm nor the model structure to the user, providing only black-box access to the trained model. Chiron is implemented using SGX enclaves, but SGX alone does not achieve the dual goals of data privacy and model confidentiality. Chiron runs the standard ML training toolchain (including the popular Theano framework and C compiler) in an enclave, but the untrusted model-creation code from the service operator is further confined in a Ryoan sandbox to prevent it from leaking the training data outside the enclave. To support distributed training, Chiron executes multiple concurrent enclaves that exchange model parameters via a parameter server. We evaluate Chiron on popular deep learning models, focusing on benchmark image classification tasks such as CIFAR and ImageNet, and show that its training performance and accuracy of the resulting models are practical for common uses of ML-as-a-service.


title: Adversarial Logit Pairing

abstract: In this paper, we develop improved techniques for defending against adversarial examples at scale. First, we implement the state of the art version of adversarial training at unprecedented scale on ImageNet and investigate whether it remains effective in this setting - an important open scientific question (Athalye et al., 2018). Next, we introduce enhanced defenses using a technique we call logit pairing, a method that encourages logits for pairs of examples to be similar. When applied to clean examples and their adversarial counterparts, logit pairing improves accuracy on adversarial examples over vanilla adversarial training; we also find that logit pairing on clean examples only is competitive with adversarial training in terms of accuracy on two datasets. Finally, we show that adversarial logit pairing achieves the state of the art defense on ImageNet against PGD white box attacks, with an accuracy improvement from 1.5% to 27.9%. Adversarial logit pairing also successfully damages the current state of the art defense against black box attacks on ImageNet (Tramer et al., 2018), dropping its accuracy from 66.6% to 47.1%. With this new accuracy drop, adversarial logit pairing ties with Tramer et al.(2018) for the state of the art on black box attacks on ImageNet.


title: Assessing naturalness and emotional intensity: a perceptual study of animated facial motion

abstract: Animated characters appear in applications for entertainment, education, and therapy. When these characters display appropriate emotions for their context, they can be particularly effective. Characters can display emotions by accurately mimicking the facial expressions and vocal cues that people display or by damping or exaggerating the emotionality of the expressions. In this work, we explored which of these strategies would be most effective for animated characters. We investigated the effects of altering the auditory and facial levels of expressiveness on emotion recognition accuracy and ratings of perceived emotional intensity and naturalness. We ran an experiment with emotion (angry, happy, sad), auditory emotion level (low, high), and facial motion magnitude (damped, unaltered, exaggerated) as within-subjects factors. Participants evaluated animations of a character whose facial motion matched that of an actress we tracked using an active appearance model. This method of tracking and animation can capture subtle facial motions in real-time, a necessity for many interactive animated characters. We manipulated auditory emotion level by asking the actress to speak sentences at varying levels, and we manipulated facial motion magnitude by exaggerating and damping the actress's spatial motion. We found that the magnitude of auditory expressiveness was positively related to emotion recognition accuracy and ratings of emotional intensity. The magnitude of facial motion was positively related to ratings of emotional intensity but negatively related to ratings of naturalness.


title: AI for Global Disease Surveillance

abstract: Disease surveillance has been practiced for decades and continues to be an indispensable approach for detecting emerging disease outbreaks and epidemics. Early knowledge of a disease outbreak plays an important role in improving response effectiveness. In this article we presents the application of AI for the global disease surveillance systems. This also provides in depth survey that analyzes and evaluates existing syndromic surveillance system and related outbreak modelling and detection work under a unified framework. Many intelligent global disease surveillance systems have begun to emerge. To illustrate, we present the following study.


title: Learning a Deep Model for Human Action Recognition from Novel Viewpoints

abstract: Recognizing human actions from unknown and unseen (novel) views is a challenging problem. We propose a Robust Non-Linear Knowledge Transfer Model (R-NKTM) for human action recognition from novel views. The proposed R-NKTM is a deep fully-connected neural network that transfers knowledge of human actions from any unknown view to a shared high-level virtual view by finding a set of non-linear transformations that connects the views. The R-NKTM is learned from 2D projections of dense trajectories of synthetic 3D human models fitted to real motion capture data and generalizes to real videos of human actions. The strength of our technique is that we learn a single R-NKTM for all actions and all viewpoints for knowledge transfer of any real human action video without the need for re-training or fine-tuning the model. Thus, R-NKTM can efficiently scale to incorporate new action classes. R-NKTM is learned with dummy labels and does not require knowledge of the camera viewpoint at any stage. Experiments on three benchmark cross-view human action datasets show that our method outperforms existing state-of-the-art.


title: Adopting A Systemic Approach to The Instructional Integration of Mobile Devices: The Case Study of a Primary School in Cyprus

abstract: The research discussed here comes from an ongoing multifaceted two-year program designed to explore the potential of tablet technologies for enhancing mathematics teaching and learning at the primary school level. The program is taking place within a public primary school in Cyprus, and aims at the effective integration of tablet technologies within the mathematics school curriculum. It has adopted a systemic approach to the introduction of mobile devices in the school setting that focuses on the broad preparation and on-going engagement of all key stakeholders involved in the educational process. In the article, we report on the main experiences gained from the first year of the program. We describe the content and structure of the program, and discuss its impact on teachers' knowledge, skills, and confidence in incorporating tablet technologies within the mathematics curriculum.


title: Initial study on in vivo conductivity mapping of breast cancer using MRI

abstract: PURPOSE ::: To develop and apply a method to measure in vivo electrical conductivity values using magnetic resonance imaging (MRI) in subjects with breast cancer. ::: ::: ::: MATERIALS AND METHODS ::: A recently developed technique named MREPT (MR electrical properties tomography) together with a novel coil combination process was used to quantify the conductivity values. The overall technique was validated using a phantom study. In addition, 90 subjects were imaged (50 subjects with previously biopsy-confirmed breast tumor and 40 normal subjects), which was approved by our institutional review board (IRB). A routine clinical protocol, specifically a T2 -weighted FSE (fast spin echo) imaging data, was used for reconstruction of conductivity. ::: ::: ::: RESULTS ::: By employing the coil combination, the relative error in the conductivity map was reduced from ~70% to 10%. The average conductivity values in breast cancers regions (0.89 ± 0.33S/m) was higher compared to parenchymal tissue (0.43 S/m, P < 0.0001) and fat (0.07 S/m, P < 0.00005) regions. Malignant cases (0.89 S/m, n = 30) showed increased conductivity compared to benign cases (0.56 S/m, n = 5) (P < 0.05). In addition, invasive cancers (0.96 S/m) showed higher mean conductivity compared to in situ cancers (0.57 S/m) (P < 0.0005). ::: ::: ::: CONCLUSION ::: This study shows that conductivity mapping of breast cancers is feasible using a noninvasive in vivo MREPT technique combined with a coil combination process. The method may provide a tool in the MR diagnosis of breast cancer.


title: A review on feature selection and feature extraction for text classification

abstract: Day by day the number of text documents in digital form is increasing. Text classification is used to organize these text documents. However, text classification has the problem of high dimensionality of feature space. This high dimensionality of feature space is solved by feature selection and feature extraction methods and improves the performance of text categorization. The feature selection and feature extraction techniques remove the irrelevant features from the text documents and reduce the dimensionality of feature space. This paper presents the various feature selection and feature extraction methods. This paper also discusses various classifiers to classify the documents.


title: subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs

abstract: In this paper, we present subgraph2vec, a novel approach for learning latent representations of rooted subgraphs from large graphs inspired by recent advancements in Deep Learning and Graph Kernels. These latent representations encode semantic substructure dependencies in a continuous vector space, which is easily exploited by statistical models for tasks such as graph classification, clustering, link prediction and community detection. subgraph2vec leverages on local information obtained from neighbourhoods of nodes to learn their latent representations in an unsupervised fashion. We demonstrate that subgraph vectors learnt by our approach could be used in conjunction with classifiers such as CNNs, SVMs and relational data clustering algorithms to achieve significantly superior accuracies. Also, we show that the subgraph vectors could be used for building a deep learning variant of Weisfeiler-Lehman graph kernel. Our experiments on several benchmark and large-scale real-world datasets reveal that subgraph2vec achieves significant improvements in accuracies over existing graph kernels on both supervised and unsupervised learning tasks. Specifically, on two realworld program analysis tasks, namely, code clone and malware detection, subgraph2vec outperforms state-of-the-art kernels by more than 17% and 4%, respectively.


title: Heavy resistance training is safe and improves bone, function, and stature in postmenopausal women with low to very low bone mass: novel early findings from the LIFTMOR trial

abstract: Summary ::: The aim of the LIFTMOR (Lifting Intervention For Training Muscle and Osteoporosis Rehabilitation) trial is to determine the safety and efficacy of brief, bone-targeted, high-intensity progressive resistance training (HiPRT) with impact loading for postmenopausal women with low bone mass. Preliminary findings indicate the LIFTMOR program is safe and effective.


title: TherML: Thermodynamics of Machine Learning

abstract: In this work we offer a framework for reasoning about a wide class of existing objectives in machine learning. We develop a formal correspondence between this work and thermodynamics and discuss its implications.


title: Effect of urea on the solubility of bovine beta-lactoglobulins.

abstract: The effect of exposure of bovine ,B-Iactoglobulin to 7M urea at 25°C on its solubility in 2M ammonium sulphate-O· 5M sodium acetate-O· 22M acetic acid (PH 5·2) is examined. The solubility of the B and C variants decreases with increasing reaction time at pH 5·2, the change for B being greater than that for C.


Subgroup analysis via recursive partitioning


title: Virtual Reality Treatment versus exposure in vivo: A Comparative Evaluation in Acrophobia

abstract: Abstract The aim of the present study was to evaluate the effectiveness of low-budget virtual reality (VR) exposure versus exposure in vivo in a between-group design in 33 patients suffering from acrophobia. The virtual environments used in treatment were exactly copied from the real environments used in the exposure in vivo program. VR exposure was found to be as effective as exposure in vivo on anxiety and avoidance as measured with the Acrophobia Questionnaire (AQ), the Attitude Towards Heights Questionnaire (ATHQ) and the Behavioral Avoidance Test (BAT). Results were maintained up to six months follow-up. The present study shows that VR exposure can be effective with relatively cheap hardware and software on stand-alone computers currently on the market. Further studies into the effectiveness of VR exposure are recommended in other clinical groups as agoraphobics and social phobics and studies in which VR exposure is compared with more emerging virtual worlds as presented in CAVE-type systems.


title: A New Class of Waveguide Dual-Mode Filters Using TM and Nonresonating Modes

abstract: An innovative class of very compact and selective waveguide dual-mode filters is presented in this paper. The basic structure is the TM dual-mode cavity. Such a cavity employs both resonant and nonresonating modes so as to provide two reflection and two transmission zeros. The high design flexibility in terms of transmission zero positioning and response bandwidth has been demonstrated by means of several single cavity designs. The design of Nth-order multiple cavity filters with N transmission zeros is presented and discussed. Different filter topologies are obtained depending on the waveguide structure used to connect adjacent cavities. An efficient mode-matching analysis method is proposed and verified for fast filter optimization. An eighth-order filter with eight transmission zeros has been designed, manufactured, and tested to demonstrate the potentialities of the filter class proposed.


title: Detecting Controversies in Online News Media

abstract: This paper sets out to detect controversial news reports using online discussions as a source of information. We define controversy as a public discussion that divides society and demonstrate that a content and stylometric analysis of these debates yields useful signals for extracting disputed news items. Moreover, we argue that a debate-based approach could produce more generic models, since the discussion architectures we exploit to measure controversy occur on many different platforms.


title: PU-LP: A novel approach for positive and unlabeled learning by label propagation

abstract: For the positive and unlabeled learning algorithms, when there is only small amount of labeled positive examples available, the algorithms can hardly extract reliable negative examples from the unlabeled examples in step one, which makes it hard to build the classifier with good performance in step two. Based on the same label assumption from graph based semi-supervised learning, we propose a novel graph-based PU learning algorithm, PU-LP, which takes Katz index to measure the similarities between vertices. After enlarging labeled positive set and extracting reliable negative examples, PU-LP build the classifier by label propagation algorithm. Experiments on UCI datasets shows that PU-LP has excellent performance when there is only small amount of labeled positive examples available, and it outperforms than PNB algorithm.


title: Structural XML Query Processing

abstract: Since the boom in new proposals on techniques for efficient querying of XML data is now over and the research world has shifted its attention toward new types of data formats, we believe that it is crucial to review what has been done in the area to help users choose an appropriate strategy and scientists exploit the contributions in new areas of data processing. The aim of this work is to provide a comprehensive study of the state-of-the-art of approaches for the structural querying of XML data. In particular, we start with a description of labeling schemas to capture the structure of the data and the respective storage strategies. Then we deal with the key part of every XML query processing: a twig query join, XML query algebras, optimizations of query plans, and selectivity estimation of XML queries. To the best of our knowledge, this is the first work that provides such a detailed description of XML query processing techniques that are related to structural aspects and that contains information about their theoretical and practical features as well as about their mutual compatibility and general usability.


title: Neurobiology of craving, conditioned reward and relapse.

abstract: Chronic vulnerability to relapse is a formidable challenge for the treatment of drug addiction. The neurobiological basis of relapse and its prevention has, therefore, attracted major attention in addiction research. Current conceptualizations of addiction recognize craving as a central driving force for ongoing drug use, as well as for relapse following abstinence. Progress has been made in understanding experiential factors, neurocircuitry components and signaling mechanisms that mediate conditioned drug-seeking behaviour, craving and long-lasting susceptibility to relapse. Importantly, stress contributes to drug craving, and there is evidence for overlap between the neural and neuroendocrine mechanisms implicated in drug desire evoked by drug cues and stress. Recent research has substantially advanced our understanding of the neurobiological factors responsible for drug craving and relapse, with promising therapeutic implications.


title: Enabling on-body transmissions with commodity devices

abstract: We show for the first time that commodity devices can be used to generate wireless data transmissions that are confined to the human body. Specifically, we show that commodity input devices such as fingerprint sensors and touchpads can be used to transmit information to only wireless receivers that are in contact with the body. We characterize the propagation of the resulting transmissions across the whole body and run experiments with ten subjects to demonstrate that our approach generalizes across different body types and postures. We also evaluate our communication system in the presence of interference from other wearable devices such as smartwatches and nearby metallic surfaces. Finally, by modulating the operations of these input devices, we demonstrate bit rates of up to 50 bits per second over the human body.


title: MACHINE LEARNING METHODS FOR SPAM E-MAIL CLASSIFICATION

abstract: The increasing volume of unsolicited bulk e-mail (also known as spam) has generated a need for reliable anti-spam filters. Machine learning techniques now days used to automatically filter the spam e-mail in a very successful rate. In this paper we review some of the most popular machine learning methods (Bayesian classification, k-NN, ANNs, SVMs, Artificial immune system and Rough sets) and of their applicability to the problem of spam Email classification. Descriptions of the algorithms are presented, and the comparison of their performance on the SpamAssassin spam corpus is presented.


title: Data mining in education

abstract: Applying data mining DM in education is an emerging interdisciplinary research field also known as educational data mining EDM. It is concerned with developing methods for exploring the unique types of data that come from educational environments. Its goal is to better understand how students learn and identify the settings in which they learn to improve educational outcomes and to gain insights into and explain educational phenomena. Educational information systems can store a huge amount of potential data from multiple sources coming in different formats and at different granularity levels. Each particular educational problem has a specific objective with special characteristics that require a different treatment of the mining problem. The issues mean that traditional DM techniques cannot be applied directly to these types of data and problems. As a consequence, the knowledge discovery process has to be adapted and some specific DM techniques are needed. This paper introduces and reviews key milestones and the current state of affairs in the field of EDM, together with specific applications, tools, and future insights. © 2012 Wiley Periodicals, Inc.


title: A robust and easy to implement method for IMU calibration without external equipments

abstract: Motion sensors as inertial measurement units (IMU) are widely used in robotics, for instance in the navigation and mapping tasks. Nowadays, many low cost micro electro mechanical systems (MEMS) based IMU are available off the shelf, while smartphones and similar devices are almost always equipped with low-cost embedded IMU sensors. Nevertheless, low cost IMUs are affected by systematic error given by imprecise scaling factors and axes misalignments that decrease accuracy in the position and attitudes estimation. In this paper, we propose a robust and easy to implement method to calibrate an IMU without any external equipment. The procedure is based on a multi-position scheme, providing scale and misalignments factors for both the accelerometers and gyroscopes triads, while estimating the sensor biases. Our method only requires the sensor to be moved by hand and placed in a set of different, static positions (attitudes). We describe a robust and quick calibration protocol that exploits an effective parameterless static filter to reliably detect the static intervals in the sensor measurements, where we assume local stability of the gravity's magnitude and stable temperature. We first calibrate the accelerometers triad taking measurement samples in the static intervals. We then exploit these results to calibrate the gyroscopes, employing a robust numerical integration technique. The performances of the proposed calibration technique has been successfully evaluated via extensive simulations and real experiments with a commercial IMU provided with a calibration certificate as reference data.


title: Database principles in information extraction

abstract: Information Extraction commonly refers to the task of populating a relational schema, having predefined underlying semantics, from textual content. This task is pervasive in contemporary computational challenges associated with Big Data. This tutorial gives an overview of the algorithmic concepts and techniques used for performing Information Extraction tasks, and describes some of the declarative frameworks that provide abstractions and infrastructure for programming extractors. In addition, the tutorial highlights opportunities for research impact through principles of data management, illustrates these opportunities through recent work, and proposes directions for future research.


title: Active Inference, Curiosity and Insight

abstract: This article offers a formal account of curiosity and insight in terms of active (Bayesian) inference. It deals with the dual problem of inferring states of the world and learning its statistical structure. In contrast to current trends in machine learning (e.g., deep learning), we focus on how people attain insight and understanding using just a handful of observations, which are solicited through curious behavior. We use simulations of abstract rule learning and approximate Bayesian inference to show that minimizing (expected) variational free energy leads to active sampling of novel contingencies. This epistemic behavior closes explanatory gaps in generative models of the world, thereby reducing uncertainty and satisfying curiosity. We then move from epistemic learning to model selection or structure learning to show how abductive processes emerge when agents test plausible hypotheses about symmetries (i.e., invariances or rules) in their generative models. The ensuing Bayesian model reduction evinces ...


title: Motion history images for online speaker/signer diarization

abstract: We present a solution to the problem of online speaker/signer diarization - the task of determining who spoke/signed when?. Our solution is based on the idea that gestural activity (hands and body movement) is highly correlated with uttering activity. This correlation is necessarily true for sign languages and mostly true for spoken languages. The novel part of our solution is the use of motion history images (MHI) as a likelihood measure for probabilistically detecting uttering activities. MHI is an efficient representation of where and how motion occurred for a fixed period of time. We conducted experiments on 4.9 hours of the AMI meeting data and 1.4 hours of sign language dataset (Kata Kolok data). The best performance obtained is 15.70% for sign language and 31.90% for spoken language (measurements are in DER). These results show that our solution is applicable in real-world applications like video conferences and information retrieval.


title: Modularity clustering is force-directed layout

abstract: Two natural and widely used representations for the community structure of networks are clusterings, which partition the vertex set into disjoint subsets, and layouts, which assign the vertices to positions in a metric space. This paper unifies prominent characterizations of layout quality and clustering quality, by showing that energy models of pairwise attraction and repulsion subsume Newman and Girvan's modularity measure. Layouts with optimal energy are relaxations of, and are thus consistent with, clusterings with optimal modularity, which is of practical relevance because the two representations are complementary and often used together.


title: Unsupervised adaptation of deep neural networks for sound source localization using entropy minimization

abstract: This paper describes an unsupervised method of adapting deep neural networks (DNNs) for sound source localization (SSL). DNNs-based SSL achieves high localization accuracy for sound data that are similar to training data. However, the accuracy deteriorates if a sound source is at an unknown position in unknown reverberant environments. We solve the problem by using unsupervised adaption of the DNNs' parameters to the observed sound signals. Entropy is used as the objective function and minimized to optimize the parameters on the basis of the gradient method. Adaptation without overfitting is achieved by using 1) a parameter adaptation layer, such as linear transform network, and 2) early stopping of the parameter updates. Experimental results indicated that our method improved localization accuracy by a maximum of 20 points for unknown positions and reverberant data.


title: Microperforate hymen at 27 years of age diagnosed with withdrawal bleeding and hysterofiberscopy

abstract: Microperforate hymen is a congenital obstructive vaginal membrane with a tiny opening. It is usually detected long before menarche because of recurring vulvovaginitis and urinary tract infection. We report a case of microperforate hymen in a 27-year-old women presenting oligomenorrhea, in whom the location of the tiny opening on the hymenal membrane was detected by withdrawal bleeding without anesthesia, and definitively diagnosed preoperatively by using hysterofiberscopy.


Cross-national comparison of humor categories: France and Germany


title: Word embedding revisited: a new representation learning and explicit matrix factorization perspective

abstract: Recently significant advances have been witnessed in the area of distributed word representations based on neural networks, which are also known as word embeddings. Among the new word embedding models, skip-gram negative sampling (SGNS) in the word2vec toolbox has attracted much attention due to its simplicity and effectiveness. However, the principles of SGNS remain not well understood, except for a recent work that explains SGNS as an implicit matrix factorization of the pointwise mutual information (PMI) matrix. In this paper, we provide a new perspective for further understanding SGNS. We point out that SGNS is essentially a representation learning method, which learns to represent the co-occurrence vector for a word. Based on the representation learning view, SGNS is in fact an explicit matrix factorization (EMF) of the words' co-occurrence matrix. Furthermore, extended supervised word embedding can be established based on our proposed representation learning view.


title: Two-Dimensional Active Learning for image classification

abstract: In this paper, we propose a two-dimensional active learning scheme and show its application in image classification. Traditional active learning methods select samples only along the sample dimension. While this is the right strategy in binary classification, it is sub-optimal for multi-label classification. In multi-label classification, we argue that, for each selected sample, only a part of more effective labels are necessary to be annotated while others can be inferred by exploring the correlations among the labels. The reason is that the contributions of different labels to minimizing the classification error are different due to the inherent label correlations. To this end, we propose to select sample-label pairs, rather than only samples, to minimize a multi-label Bayesian classification error bound. This new active learning strategy not only considers the sample dimension but also the label dimension, and we call it Two-Dimensional Active Learning (2DAL). We also show that the traditional active learning formulation is a special case of 2DAL when there is only one label. Extensive experiments conducted on two real-world applications show that the 2DAL significantly outperforms the best existing approaches which did not take label correlation into account.


title: The Complexity of Near-Optimal Graph Coloring

abstract: Graph coloring problems, in which one would like to color the vertices of a given graph with a small number of colors so that no two adjacent vertices receive the same color, arise in many applications, including various scheduling and partitioning problems. In this paper the complexity and performance of algorithms which construct such colorings are investigated. For a graph <italic>G</italic>, let &khgr;(<italic>G</italic>) denote the minimum possible number of colors required to color <italic>G</italic> and, for any graph coloring algorithm <italic>A</italic>, let <italic>A</italic>(<italic>G</italic>) denote the number of colors used by <italic>A</italic> when applied to <italic>G</italic>. Since the graph coloring problem is known to be “NP-complete,” it is considered unlikely that any efficient algorithm can guarantee <italic>A</italic>(<italic>G</italic>) = &khgr;(<italic>G</italic>) for all input graphs. In this paper it is proved that even coming close to khgr;(<italic>G</italic>) with a fast algorithm is hard. Specifically, it is shown that if for some constant <italic>r</italic> < 2 and constant <italic>d</italic> there exists a polynomial-time algorithm <italic>A</italic> which guarantees <italic>A</italic>(<italic>G</italic>) ≤ <italic>r</italic>·&khgr;(<italic>G</italic>) + <italic>d</italic>, then there also exists a polynomial-time algorithm <italic>A</italic> which guarantees <italic>A</italic>(<italic>G</italic>) = &khgr;(<italic>G</italic>).


title: Improving security decisions with polymorphic and audited dialogs

abstract: Context-sensitive guidance (CSG) can help users make better security decisions. Applications with CSG ask the user to provide relevant context information. Based on such information, these applications then decide or suggest an appropriate course of action. However, users often deem security dialogs irrelevant to the tasks they are performing and try to evade them. This paper contributes two new techniques for hardening CSG against automatic and false user answers. Polymorphic dialogs continuously change the form of required user inputs and intentionally delay the latter, forcing users to pay attention to security decisions. Audited dialogs thwart false user answers by (1) warning users that their answers will be forwarded to auditors, and (2) allowing auditors to quarantine users who provide unjustified answers. We implemented CSG against email-borne viruses on the Thunderbird email agent. One version, CSG-PD, includes CSG and polymorphic dialogs. Another version, CSG-PAD, includes CSG and both polymorphic and audited dialogs. In user studies, we found that untrained users accept significantly less unjustified risks with CSG-PD than with conventional dialogs. Moreover, they accept significantly less unjustified risks with CSG-PAD than with CSG-PD. CSG-PD and CSG-PAD have insignificant effect on acceptance of justified risks.


title: Optimal control approach for pneumatic artificial muscle with using pressure-force conversion model

abstract: In this paper, we propose an optimal control framework for pneumatic actuators. In particular, we consider using Pneumatic Artificial Muscle (PAM) as a part of Pneumatic-Electric (PE) hybrid actuation system. An optimal control framework can be useful for PE hybrid system to properly distribute desired torque outputs to the actuators that have different characteristics. In the optimal control framework, the standard choice to represent control cost is squared force or torque outputs. However, since the control input for PAM is pressure rather than the force or the torque, we should explicitly consider the pressure of PAM as the control cost in an objective function of the optimal control method. We show that we are able to use pressure input as the control cost for PAM by explicitly considering the model which represents a relationship between the pressure input and the force output of PAM. We demonstrate that one-DOF robot with the PE hybrid actuation system can generate pressure-optimized ball throwing movements by using the optimal control method.


title: 3D Hand Gesture Recognition Based on Sensor Fusion of Commodity Hardware

abstract: With the advent of various video game consoles and tablet devices gesture recognition got quite popular to control computer systems. E.g. touch screens allow for an intuitive control of small 2d user interfaces with finger gestures. For interactive manipulation of 3d objects in a large 3d projection environment a similar intuitive 3d interaction method is required. In this paper, we present a dynamic 3d hand and arm gesture recognition system using commodity hardware. The input data is captured with low cost depth sensors (e.g. Microsoft Kinect) and HD color sensors (e.g. Logitech C910). Our method combines dynamic hand and arm gesture recognition based on the depth sensor with static hand gesture recognition based on the HD color sensor.


title: Detecting Malicious URLs: A Semi-Supervised Machine Learning System Approach

abstract: As malware industry grows, so does the means of infecting a computer or device evolve. One of the most common infection vector is to use the Internet as an entry point. Not only that this method is easy to use, but due to the fact that URLs come in different forms and shapes, it is really difficult to distinguish a malicious URL from a benign one. Furthermore, every system that tries to classify or detect URLs must work on a real time stream and needs to provide a fast response for every URL that is submitted for analysis (in our context a fast response means less than 300-400 milliseconds/URL). From a malware creator point of view, it is really easy to change such URLs multiple times in one day. As a general observation, malicious URLs tend to have a short life (they appear, serve malicious content for several hours and then they are shut down usually by the ISP where they reside in). This paper aims to present a system that analyzes URLs in network traffic that is also capable of adjusting its detection models to adapt to new malicious content. Every correctly classified URL is reused as part of a new dataset that acts as the backbone for new detection models. The system also uses different clustering techniques in order to identify the lack of features on malicious URLs, thus creating a way to improve detection for this kind of threats.


title: Real-time human motion tracking using multiple depth cameras

abstract: In this paper, we consider the problem of tracking human motion with a 22-DOF kinematic model from depth images. In contrast to existing approaches, our system naturally scales to multiple sensors. The motivation behind our approach, termed Multiple Depth Camera Approach (MDCA), is that by using several cameras, we can significantly improve the tracking quality and reduce ambiguities as for example caused by occlusions. By fusing the depth images of all available cameras into one joint point cloud, we can seamlessly incorporate the available information from multiple sensors into the pose estimation. To track the high-dimensional human pose, we employ state-of-the-art annealed particle filtering and partition sampling. We compute the particle likelihood based on the truncated signed distance of each observed point to a parameterized human shape model. We apply a coarse-to-fine scheme to recognize a wide range of poses to initialize the tracker. In our experiments, we demonstrate that our approach can accurately track human motion in real-time (15Hz) on a GPGPU. In direct comparison to two existing trackers (OpenNI, Microsoft Kinect SDK), we found that our approach is significantly more robust for unconstrained motions and under (partial) occlusions.


Let the Comments Fly: The Effects of Flying Commentary Presentation on Consumer Judgment


CASNSC: A context-based approach for accurate pedestrian motion prediction at intersections


title: MPLS based hybridization in SDN

abstract: The new paradigm of Software Defined Networking (SDN) although has great potential to address the complex problems presented by enterprise networks, it has its own deployment and scalability issues. Further, a full SDN deployment has its own business and economic challenges. A smooth transition from legacy networks to SDN (disruption free, accommodating budget constraints, with progressive improvement in network management) requires a hybrid networking model as an inevitable intermediate step; that allows heterogeneous paradigms to function together while the full transition is realized in phases. Therefore, the need of the hour is to develop an incremental deployment strategy that caters to the needs of the organization. We present here a class-based hybrid SDN model for Multi Protocol Label Switching (MPLS) networks. We discuss the model, design, components, their interactions, advantages and drawbacks. We also present a n implementation and evaluation of a prototype. In legacy networks, MPLS architecture closely resembles SDN paradigm in terms of separation of control and data planes, flow-abstraction etc. Moreover, ISPs have preferred MPLS over the years due to benefits of virtual private networks and traffic engineering. The central idea is to partition traffic using forwarding equivalence classes at the ingress router, the rules of which can be updated via a centralized controller using OpenFlow. Therefore, we aim to use the standard MPLS data-plane together with a control-plane based on OpenFlow to come up with a systematic incremental deployment methodology as well as a hybrid operation model


title: Knowledge management: a key determinant in advancing technological innovation?

abstract: Purpose – This paper purports to analyze the relationship between knowledge management (KM) and technological innovation in the Malaysian manufacturing sector. Furthermore, the interrelationships between the KM dimensions will also be investigated. Design/methodology/approach – Survey data from 162 manufacturing firms were obtained. Multiple linear regression and neural network analysis were performed in this study to examine the relationships between KM and technological innovation; as well as the interrelationships between KM practices themselves. Findings – This research study provides empirical evidence and confirms the results of past researchers that KM practices (i.e. knowledge sharing, knowledge application and knowledge storage) are positively and significantly related to technological innovation (i.e. product and process innovation). Moreover, it is also discovered that the interrelationships between the KM dimensions are positive and significant. Research limitations/implications – This researc...


The compromised gut: what do we really know and what is clinically relevant?


title: Impossibility Theorems for Domain Adaptation

abstract: The domain adaptation problem in machine learning occurs when the test data generating distribution differs from the one that generates the training data. It is clear that the success of learning under such circumstances depends on similarities between the two data distributions. We study assumptions about the relationship between the two distributions that one needed for domain adaptation learning to succeed. We analyze the assumptions in an agnostic PAC-style learning model for a the setting in which the learner can access a labeled training data sample and an unlabeled sample generated by the test data distribution. We focus on three assumptions: (i) similarity between the unlabeled distributions, (ii) existence of a classifier in the hypothesis class with low error on both training and testing distributions, and (iii) the covariate shift assumption. I.e., the assumption that the conditioned label distribution (for each data point) is the same for both the training and test distributions. We show that without either assumption (i) or (ii), the combination of the remaining assumptions is not sufficient to guarantee successful learning. Our negative results hold with respect to any domain adaptation learning algorithm, as long as it does not have access to target labeled examples. In particular, we provide formal proofs that the popular covariate shift assumption is rather weak and does not relieve the necessity of the other assumptions.


title: Decoding IEEE 802.11a/g/p OFDM in software using GNU radio

abstract: We just released an Open Source receiver that is able to decode IEEE 802.11a/g/p Orthogonal Frequency Division Multiplexing (OFDM) frames in software. This is the first Software Defined Radio (SDR) based OFDM receiver supporting channel bandwidths up to 20MHz that is not relying on additional FPGA code. Our receiver comprises all layers from the physical up to decoding the MAC packet and extracting the payload of IEEE 802.11a/g/p frames. In our demonstration, visitors can interact live with the receiver while it is decoding frames that are sent over the air. The impact of moving the antennas and changing the settings are displayed live in time and frequency domain. Furthermore, the decoded frames are fed to Wireshark where the WiFi traffic can be further investigated. It is possible to access and visualize the data in every decoding step from the raw samples, the autocorrelation used for frame detection, the subcarriers before and after equalization, up to the decoded MAC packets. The receiver is completely Open Source and represents one step towards experimental research with SDR.


title: Thinking Inside the Box: System-Level Failures of Tamper Proofing

abstract: PIN entry devices (PEDs) are critical security components in EMV smartcard payment systems as they receive a customer's card and PIN. Their approval is subject to an extensive suite of evaluation and certification procedures. In this paper, we demonstrate that the tamper proofing of PEDs is unsatisfactory, as is the certification process. We have implemented practical low-cost attacks on two certified, widely-deployed PEDs - the Ingenico 13300 and the Dione Xtreme. By tapping inadequately protected smartcard communications, an attacker with basic technical skills can expose card details and PINs, leaving cardholders open to fraud. We analyze the anti-tampering mechanisms of the two PEDs and show that, while the specific protection measures mostly work as intended, critical vulnerabilities arise because of the poor integration of cryptographic, physical and procedural protection. As these vulnerabilities illustrate a systematic failure in the design process, we propose a methodology for doing it better in the future. These failures also demonstrate a serious problem with the Common Criteria. So we discuss the incentive structures of the certification process, and show how they can lead to problems of the kind we identified. Finally we recommend changes to the Common Criteria framework in light of the lessons learned.


title: Photorealistic Style Transfer with Screened Poisson Equation

abstract: Recent work has shown impressive success in transferring painterly style to images. These approaches, however, fall short of photorealistic style transfer. Even when both the input and reference images are photographs, the output still exhibits distortions reminiscent of a painting. In this paper we propose an approach that takes as input a stylized image and makes it more photorealistic. It relies on the Screened Poisson Equation, maintaining the fidelity of the stylized image while constraining the gradients to those of the original input image. Our method is fast, simple, fully automatic and shows positive progress in making a stylized image photorealistic. Our results exhibit finer details and are less prone to artifacts than the state-of-the-art.


title: On the Security of Cognitive Radio Networks

abstract: Cognitive radio has emerged as an essential recipe for future high-capacity, high-coverage multitier hierarchical networks. Securing data transmission in these networks is of the utmost importance. In this paper, we consider the cognitive wiretap channel and propose multiple antennas to secure the transmission at the physical layer, where the eavesdropper overhears the transmission from the secondary transmitter to the secondary receiver. The secondary receiver and the eavesdropper are equipped with multiple antennas, and passive eavesdropping is considered where the channel state information (CSI) of the eavesdropper's channel is not available at the secondary transmitter. We present new closed-form expressions for the exact and asymptotic secrecy outage probability. Our results reveal the impact of the primary network on the secondary network in the presence of a multiantenna wiretap channel.


title: CRED: A Deep Residual Network of Convolutional and Recurrent Units for Earthquake Signal Detection

abstract: Earthquake signal detection is at the core of observational seismology. A good detection algorithm should be sensitive to small and weak events with a variety of waveform shapes, robust to background noise and non-earthquake signals, and efficient for processing large data volumes. Here, we introduce the Cnn-Rnn Earthquake Detector (CRED), a detector based on deep neural networks. CRED uses a combination of convolutional layers and bi-directional long-short-term memory units in a residual structure. It learns the time-frequency characteristics of the dominant phases in an earthquake signal from three component data recorded on individual stations. We train the network using 500,000 seismograms (250k associated with tectonic earthquakes and 250k identified as noise) recorded in Northern California. The robustness of the trained model with respect to the noise level and non-earthquake signals is shown by applying it to a set of semi-synthetic signals. We also apply the model to one month of continuous data recorded at Central Arkansas to demonstrate its efficiency, generalization, and sensitivity. Our model is able to detect more than 800 microearthquakes as small as −1.3 ML induced during hydraulic fracturing far away than the training region. We compare the performance of the model with the STA/LTA, template matching, and FAST algorithms. Our results indicate an efficient and reliable performance of CRED. This framework holds great promise for lowering the detection threshold while minimizing false positive detection rates.


title: Dual Convolutional Neural Network for Graph of Graphs Link Prediction

abstract: Graphs are general and powerful data representations which can model complex real-world phenomena, ranging from chemical compounds to social networks; however, effective feature extraction from graphs is not a trivial task, and much work has been done in the field of machine learning and data mining. The recent advances in graph neural networks have made automatic and flexible feature extraction from graphs possible and have improved the predictive performance significantly. In this paper, we go further with this line of research and address a more general problem of learning with a graph of graphs (GoG) consisting of an external graph and internal graphs, where each node in the external graph has an internal graph structure. We propose a dual convolutional neural network that extracts node representations by combining the external and internal graph structures in an end-to-end manner. Experiments on link prediction tasks using several chemical network datasets demonstrate the effectiveness of the proposed method.


title: What is the difference between the purpose of usability and user experience evaluation methods

abstract: There are different interpretations of user experience that lead to different scopes of measure. The ISO definition suggests measures of user experience are similar to measures of satisfaction in usability. A survey at Nokia showed that user experience was interpreted in a similar way to usability, but with the addition of anticipation and hedonic responses. CHI 2009 SIG participants identified not just measurement methods, but methods that help understanding of how and why people use products. A distinction can be made between usability methods that have the objective of improving human performance, and user experience methods that have the objective of improving user satisfaction with achieving both pragmatic and hedonic goals. Sometimes the term “user experience” is used to refer to both approaches. DEFINITIONS OF USABILITY AND USER EXPERIENCE There has been a lot of recent debate about the scope of user experience, and how it should be defined [5]. The definition of user experience in ISO FDIS 9241-210 is:


Expert System Applications : Agriculture


title: Understanding the Performance and Potential of Cloud Computing for Scientific Applications

abstract: Commercial clouds bring a great opportunity to the scientific computing area. Scientific applications usually require significant resources, however not all scientists have access to sufficient high-end computing systems. Cloud computing has gained the attention of scientists as a competitive resource to run HPC applications at a potentially lower cost. But as a different infrastructure, it is unclear whether clouds are capable of running scientific applications with a reasonable performance per money spent. This work provides a comprehensive evaluation of EC2 cloud in different aspects. We first analyze the potentials of the cloud by evaluating the raw performance of different services of AWS such as compute, memory, network and I/O. Based on the findings on the raw performance, we then evaluate the performance of the scientific applications running in the cloud. Finally, we compare the performance of AWS with a private cloud, in order to find the root cause of its limitations while running scientific applications. This paper aims to assess the ability of the cloud to perform well, as well as to evaluate the cost of the cloud in terms of both raw performance and scientific applications performance. Furthermore, we evaluate other services including S3, EBS and DynamoDB among many AWS services in order to assess the abilities of those to be used by scientific applications and frameworks. We also evaluate a real scientific computing application through the Swift parallel scripting system at scale. Armed with both detailed benchmarks to gauge expected performance and a detailed monetary cost analysis, we expect this paper will be a recipe cookbook for scientists to help them decide where to deploy and run their scientific applications between public clouds, private clouds, or hybrid clouds.


title: Linguistic Matrix Theory

abstract: Recent research in computational linguistics has developed algorithms which associate matrices with adjectives and verbs, based on the distribution of words in a corpus of text. These matrices are linear operators on a vector space of context words. They are used to construct the meaning of composite expressions from that of the elementary constituents, forming part of a compositional distributional approach to semantics. We propose a Matrix Theory approach to this data, based on permutation symmetry along with Gaussian weights and their perturbations. A simple Gaussian model is tested against word matrices created from a large corpus of text. We characterize the cubic and quartic departures from the model, which we propose, alongside the Gaussian parameters, as signatures for comparison of linguistic corpora. We propose that perturbed Gaussian models with permutation symmetry provide a promising framework for characterizing the nature of universality in the statistical properties of word matrices. The matrix theory framework developed here exploits the view of statistics as zero dimensional perturbative quantum field theory. It perceives language as a physical system realizing a universality class of matrix statistics characterized by permutation symmetry.


title: The Table Mystery: An Augmented Reality Collaborative Game for Chemistry Education

abstract: Educational games constitute a major field inside the serious games ecosystem, attempting to educate the players, while entertaining them. Augmented Reality (AR) has found application in educational games, introducing properties that improve gameplay and that potentially produce unique educational affordances. In this study, we present the “Table Mystery” game, an under-development mystery-adventure game utilising Augmented Reality to provide an exciting and engaging educational experience related to chemistry and, more specifically, to the elements of the periodic table. The game is developed for the Science Centre in Oppland county, Norway (Vitensenteret Innlandet). The long-term study’s purpose is to examine the effect of Augmented Reality on providing engaging and exciting, short-term educational experiences.


title: Artificial intelligence in 2027

abstract: Every day we read in the scientific and popular press about advances in AI and how AI is changing our lives. Things are moving at a fast pace, with no obvious end in sight.


title: Heuristic Event Filtering Methodology for Interval based Temporal Semantics

abstract: this paper, a novel heuristic event filtering methodology that exploits the temporal characteristics in the Complex Event query is presented. Complex Event query is processed in Complex Event Processing(CEP). CEP involves inferring complex events from primitive events in real time. Massive amount of primitive events arrive in real time from multiple distributed sources in real time applications like E-business applications, Business Intelligence systems, Stock Monitoring Systems and Hazard Monitoring Systems. Removal of irrelevant events or filtering will result in effective processing. Thus, there is a need for effective filtering mechanism to filter out the irrelevant events. Therefore, Event Filtering (EF) is to be performed ahead of the event processing. A heuristic event filtering methodology over Sliding Window to increase the throughput of the system is proposed. The proposed system has been validated using a prototype.


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title: Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework.

abstract: The atypical face scanning patterns in individuals with Autism Spectrum Disorder (ASD) has been repeatedly discovered by previous research. The present study examined whether their face scanning patterns could be potentially useful to identify children with ASD by adopting the machine learning algorithm for the classification purpose. Particularly, we applied the machine learning method to analyze an eye movement dataset from a face recognition task [Yi et al., 2016], to classify children with and without ASD. We evaluated the performance of our model in terms of its accuracy, sensitivity, and specificity of classifying ASD. Results indicated promising evidence for applying the machine learning algorithm based on the face scanning patterns to identify children with ASD, with a maximum classification accuracy of 88.51%. Nevertheless, our study is still preliminary with some constraints that may apply in the clinical practice. Future research should shed light on further valuation of our method and contribute to the development of a multitask and multimodel approach to aid the process of early detection and diagnosis of ASD. Autism Res 2016, 9: 888-898. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.


Transfer Learning from Alternative Mechanisms to Optimize Differentially Private GAN Release


Overview of the ImageCLEF 2017 Tuberculosis Task - Predicting Tuberculosis Type and Drug Resistances