title: The completion effect in charitable crowdfunding

abstract: Abstract We analyze data from two charitable crowdfunding platforms and find that donors make significantly larger donations, more frequently, and at a faster pace, in order to personally reach fundraising targets. This ‘completion effect’ occurs even when the target is of no consequence for provision, and even within donors who make multiple contributions on a platform. While the majority of donors follow suggested gift amounts at other points of the campaign, they deviate upwards in order to personally reach targets. We provide evidence that the effect is not driven by uncertainty about the recipient's ability to reach the fundraising goal; rather, it appears to be driven by a private benefit to the donor from personally completing campaigns.


Facebook sentiment: Reactions and Emojis


title: Enhancing Daily Well-Being at Work Through Lunchtime Park Walks and Relaxation Exercises: Recovery Experiences as Mediators

abstract: Only few studies so far have examined recovery from work during workday breaks. In this intervention study, based on the effort-recovery model and the conservation of resources theory, we examined how to enhance recovery during lunch breaks. More specifically, we examined the within-person effects of lunchtime park walks and relaxation exercises on employees' levels of concentration, strain, and fatigue experienced at the end of a working day. We moreover tested whether detachment from work and enjoyment experienced during lunch breaks transmitted the effects of these activities to well-being outcomes. Participants in the park walk (n = 51) and relaxation (n = 46) groups were asked to complete a 15-min exercise during their lunch break on 10 consecutive working days. Afternoon well-being, lunchtime detachment, and lunchtime enjoyment were assessed twice a week before, during, and after the intervention, altogether for 5 weeks. Multilevel analysis results showed that park walks at lunchtime were related to better concentration and less fatigue in the afternoon through enjoyment. Relaxation exercises were related to better concentration in the afternoon via detachment. In addition, relaxation exercises were directly linked to lower levels of strain and fatigue in the afternoon. Our study suggests that on days on which employees engage in recovering activities during lunch breaks, they experience higher levels of well-being at the end of a working day. These results add to the theory-based knowledge on recovery during workday breaks and highlight the importance of breaks for organizational practices. (PsycINFO Database Record


title: Guidelines for critique of a research report.

abstract: Before findings reported in a research article are used to change clinical practice, the research must be evaluated for evidence of credibility, integrity, and potential for replication. This paper provides guidelines for use in evaluating deductive, empirical research. The standards presented in this paper are derived from the tenets of the scientific method, measurement theory, statistical principles, and research ethics. The guidelines may be used to evaluate each section of a research report, from the title to the interpretation of findings.


title: Generating Natural Language Adversarial Examples

abstract: Deep neural networks (DNNs) are vulnerable to adversarial examples, perturbations to correctly classified examples which can cause the model to misclassify. In the image domain, these perturbations are often virtually indistinguishable to human perception, causing humans and state-of-the-art models to disagree. However, in the natural language domain, small perturbations are clearly perceptible, and the replacement of a single word can drastically alter the semantics of the document. Given these challenges, we use a black-box population-based optimization algorithm to generate semantically and syntactically similar adversarial examples that fool well-trained sentiment analysis and textual entailment models with success rates of 97% and 70%, respectively. We additionally demonstrate that 92.3% of the successful sentiment analysis adversarial examples are classified to their original label by 20 human annotators, and that the examples are perceptibly quite similar. Finally, we discuss an attempt to use adversarial training as a defense, but fail to yield improvement, demonstrating the strength and diversity of our adversarial examples. We hope our findings encourage researchers to pursue improving the robustness of DNNs in the natural language domain.


title: Highly Efficient Secondary-Resonant Active Clamp Flyback Converter

abstract: Both high switching frequency and high efficiency are critical in reducing power adapter size. The active clamp flyback (ACF) topology allows zero voltage soft switching (ZVS) under all line and load conditions, eliminates leakage inductance and snubber losses, and enables high frequency and high power density power conversion. Traditional ACF ZVS operation relies on the resonance between leakage inductance and a small primary-side clamping capacitor, which leads to increased rms current and high conduction loss. This also causes oscillatory output rectifier current and impedes the implementation of synchronous rectification. This paper proposes a secondary-side resonance scheme to shape the primary current waveform in a way that significantly improves synchronous rectifier operation and reduces primary rms current. The concept is verified with a ${\mathbf{25}}\hbox{--}{\text{W/in}}^{3}$ high-density 45-W adapter prototype using a monolithic gallium nitride power IC. Over 93% full-load efficiency was demonstrated at the worst case 90-V ac input and maximum full-load efficiency was 94.5%.


title: Sexual differentiation of the human brain: relevance for gender identity, transsexualism and sexual orientation

abstract: Male sexual differentiation of the brain and behavior are thought, on the basis of experiments in rodents, to be caused by androgens, following conversion to estrogens. However, observations in human subjects with genetic and other disorders show that direct effects of testosterone on the developing fetal brain are of major importance for the development of male gender identity and male heterosexual orientation. Solid evidence for the importance of postnatal social factors is lacking. In the human brain, structural diferences have been described that seem to be related to gender identity and sexual orientation.


title: Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone

abstract: Research on damage detection of road surfaces using image processing techniques has been actively conducted, achieving considerably high detection accuracies. Many studies only focus on the detection of the presence or absence of damage. However, in a real-world scenario, when the road managers from a governing body need to repair such damage, they need to clearly understand the type of damage in order to take effective action. In addition, in many of these previous studies, the researchers acquire their own data using different methods. Hence, there is no uniform road damage dataset available openly, leading to the absence of a benchmark for road damage detection. This study makes three contributions to address these issues. First, to the best of our knowledge, for the first time, a large-scale road damage dataset is prepared. This dataset is composed of 9,053 road damage images captured with a smartphone installed on a car, with 15,435 instances of road surface damage included in these road images. In order to generate this dataset, we cooperated with 7 municipalities in Japan and acquired road images for more than 40 hours. These images were captured in a wide variety of weather and illuminance conditions. In each image, we annotated the bounding box representing the location and type of damage. Next, we used a state-of-the-art object detection method using convolutional neural networks to train the damage detection model with our dataset, and compared the accuracy and runtime speed on both, using a GPU server and a smartphone. Finally, we demonstrate that the type of damage can be classified into eight types with high accuracy by applying the proposed object detection method. The road damage dataset, our experimental results, and the developed smartphone application used in this study are publicly available (https://github.com/sekilab/RoadDamageDetector/).


title: The Foreign Language Effect on Moral Judgment: The Role of Emotions and Norms

abstract: We investigated whether and why the use of a foreign language influences moral judgment. We studied the trolley and footbridge dilemmas, which propose an action that involves killing one individual to save five. In line with prior work, the use of a foreign language increased the endorsement of such consequentialist actions for the footbridge dilemma, but not for the trolley dilemma. But contrary to recent theorizing, this effect was not driven by an attenuation of emotions. An attenuation of emotions was found in both dilemmas, and it did not mediate the foreign language effect on moral judgment. An examination of additional scenarios revealed that foreign language influenced moral judgment when the proposed action involved a social or moral norm violation. We propose that foreign language influences moral judgment by reducing access to normative knowledge.


title: A fingerprint and timing-based snooping attack on residential sensor systems

abstract: We demonstrate a Fingerprint and Timing-based Snooping (FATS) attack in which private resident information is inferred from the wireless transmissions of sensors in a home, even when all of the transmissions are encrypted. This attack can already be carried out on millions of homes today, and will become more important as ubiquitous computing environments become more prevalent. We address this problem using a suite of privacy solutions and argue that privacy of monitored individuals must be addressed in all design layers in future cyber-physical systems.


title: Characterization of endomicroscopic images of the distal lung for computer-aided diagnosis

abstract: This paper presents a new approach for the classification of pathological vs. healthy endomicroscopic images of the alveoli. These images, never seen before, require an adequate description. We investigate two types of feature vector for discrimination: a high-level feature vector based on visual analysis of the images, and a pixel-based, generic feature vector, based on Local Binary Patterns (LBP). Both feature sets are evaluated on state-of-the-art classifiers and an intensive study of the LBP parameters is conducted. Indeed best results are obtained with the LBP-based approach, with correct classification rates reaching up to 91.73% and 97.85% for non-smoking and smoking groups, respectively. Even though tests on extended databases are needed, first results are very encouraging for this difficult task of classifying endomicroscopic images of the distal lung.


title: Fast and automatic body circular measurement based on a single kinect

abstract: The development of commodity depth camera such as Kinect has paved the way for low-cost human body measurement. The existing Kinect-based body measurement methods are too time-consuming since their purposes also include 3D reconstruction. In this paper, we present a Kinect-based simple but fast and effective solution that is tailored only for human body measurement. In particular, our method captures four views of a subject body and constructs a 3D point cloud for each view. The point clouds are then registered and merged to have a full body cloud on which the body measurements are estimated automatically. The experimental results show that our method requires much shorter execution time than the previous works while still producing measurements with comparable quality.


title: Six Sigma Applications: Enhancement Sigma Level towards Quality Improvement at Automative Company

abstract: Achieving high rate of performance is one of the aims in any organizations, especially under the pressure of competition. This paper presents the implementation of DMAIC Six Sigma Methodology in automotive industry. This project is focused on a critical product which is T605039-003. The objective of the project is to increase the sigma level of the process to 3.5σ. The project started with define phase which is problem identification. Pareto analysis was conducted to find out the critical defect for part T605039-003. As a result, defect of burr at thread was selected for further study. The existing process sigma level is 3.02σ. Measurement System Analysis (MSA) was carried out to validate the measurement system used. Next, the study continues with measure phase. GR&R study is conducted to verify the capability of the measurement equipment and from the result, most of the variation is due to the differences between parts and very little is due to measurement system error. This brings the project to the fourth phase which is the improve phase. A new process is developed to eliminate burr at thread from occurring. The percentage of reject is reduced to 1.59% whereas the sigma level is increased to 3.54σ.


title: Spectro-Temporal Analysis of Speech Using 2-D Gabor Filters

abstract: We present a 2-D spectro-temporal Gabor filterbank based on the 2-D Fast Fourier Transform, and show how it may be used to analyze localized patches of a spectrogram. We argue that the 2-D Gabor filterbank has the capacity to decompose a patch into its underlying dominant spectro-temporal components, and we illustrate the response of our filterbank to different speech phenomena such as harmonicity, formants, vertical onsets/offsets, noise, and overlapping simultaneous speakers.


Running Head : Letting Structure Emerge Letting Structure Emerge : Connectionist and Dynamical Systems Approaches to Understanding Cognition


title: Closed-Loop control of DC-DC buck converters based on exact feedback linearization

abstract: Exact feedback linearization technique is applied to standard DC-DC buck converter using precise model of the system. Considering parasitic resistances of the inductor, capacitor and switches, averaged state space method is used to develop converter model. Hence, developed controller is able to regulate output voltage of the converter in a wide range of operation. Using the optimal control theory, the coefficients of the developed controller are optimized. In order to evaluate the accuracy and effectiveness of the proposed method, designed controller is simulated using MATLAB/Simulink toolbox. Presented simulation results prove that the developed controller has acceptable dynamic and steady-state responses.


title: Intelligent criminal identification system

abstract: In many countries the amount of crime incidents that is reported per day is increasing dramatically. Concerning about Sri Lanka, The department of Police is the major organization of preventing crimes. In general, Sri Lankan police stations utilize paper-based information storing systems and they don't employ computer based applications up to a great extent. Due to this utilization of paper-based systems police officers have to spend a lot of time as well as man power to analyze existing crime information and to identify suspects for crime incidents. So the requirement of an efficient way for crime investigation has arisen. Data mining practices is one aspect of crime investigation, for which numerous technique are available. This paper highlights the use of data mining techniques, clustering and classification for effective investigation of crimes. Further the paper aims to identify suspects by analyzing existing evidences in situations where any witness or forensic clues are not present.


title: Low resolution vehicle re-identification based on appearance features for wide area motion imagery

abstract: The description of vehicle appearance in Wide Area Motion Imagery (WAMI) data is challenging due to low resolution and renunciation of color. However, appearance information can effectively support multiple object tracking or queries in a real-time vehicle database. In this paper, we present a systematic evaluation of existing appearance descriptors that are applicable to low resolution vehicle reidentification in WAMI data. The problem is formulated as a one-to-many re-identification problem in a closed-set, where a query vehicle has to be found in a list of candidates that is ranked w.r.t. their matching similarity. For our evaluation we use a subset of the WPAFB 2009 dataset. Most promising results are achieved by a combined descriptor of Local Binary Patterns (LBP) and Local Variance Measure (VAR) applied to local grid cells of the image. Our results can be used to improve appearance based multiple object tracking algorithms and real-time vehicle database search algorithms.


title: A wearable system for detecting eating activities with proximity sensors in the outer ear

abstract: This paper presents an approach for automatically detecting eating activities by measuring deformations in the ear canal walls due to mastication activity. These deformations are measured with three infrared proximity sensors encapsulated in an off-the-shelf earpiece. To evaluate our method, we conducted a user study in a lab setting where 20 participants were asked to perform eating and non-eating activities. A user dependent analysis demonstrated that eating could be detected with 95.3% accuracy. This result indicates that proximity sensing offers an alternative to acoustic and inertial sensing in eating detection while providing benefits in terms of privacy and robustness to noise.


title: Multimodal Densenet

abstract: Humans make accurate decisions by interpreting complex data from multiple sources. Medical diagnostics, in particular, often hinge on human interpretation of multi-modal information. In order for artificial intelligence to make progress in automated, objective, and accurate diagnosis and prognosis, methods to fuse information from multiple medical imaging modalities are required. However, combining information from multiple data sources has several challenges, as current deep learning architectures lack the ability to extract useful representations from multimodal information, and often simple concatenation is used to fuse such information. In this work, we propose Multimodal DenseNet, a novel architecture for fusing multimodal data. Instead of focusing on concatenation or early and late fusion, our proposed architectures fuses information over several layers and gives the model flexibility in how it combines information from multiple sources. We apply this architecture to the challenge of polyp characterization and landmark identification in endoscopy. Features from white light images are fused with features from narrow band imaging or depth maps. This study demonstrates that Multimodal DenseNet outperforms monomodal classification as well as other multimodal fusion techniques by a significant margin on two different datasets.


title: Anti-Respiratory Syncytial Virus Prenylated Dihydroquinolone Derivatives from the Gorgonian-Derived FungusAspergillussp. XS-20090B15

abstract: Two new prenylated dihydroquinolone derivatives, 22-O-(N-Me-l-valyl)aflaquinolone B (1) and 22-O-(N-Me-l-valyl)-21-epi-aflaquinolone B (2), and two known analogues, aflaquinolones A (3) and D (or a diastereomer of D, 4), were isolated from the mycelia of a gorgonian-derived Aspergillus sp. fungus. The structures of the new compounds were elucidated by spectroscopic methods, ECD spectra, Marfey’s method, and chemical conversion. Compounds 1 and 2 display an unusual esterification of N-Me-l-Val to the side-chain prenyl group. Compound 2 exhibited outstanding anti-RSV activity with an IC50 value of 42 nM, approximately 500-fold stronger than that of the positive control ribavirin (IC50 = 20 μM), and showed a comparatively higher therapeutic ratio (TC50/IC50 = 520).


title: EigenTrustp++: Attack resilient trust management

abstract: This paper argues that trust and reputation models should take into account not only direct experiences (local trust) and experiences from the circle of “friends”, but also be attack resilient by design in the presence of dishonest feedbacks and sparse network connectivity. We first revisit EigenTrust, one of the most popular reputation systems to date, and identify the inherent vulnerabilities of EigenTrust in terms of its local trust vector, its global aggregation of local trust values, and its eigenvector based reputation propagating model. Then we present EigenTrust++, an attack resilient trust management scheme. EigenTrust++ extends the eigenvector based reputation propagating model, the core of EigenTrust, and counters each of vulnerabilities identified with alternative methods that are by design more resilient to dishonest feedbacks and sparse network connectivity under four known attack models. We conduct extensive experimental evaluation on EigenTrust++, and show that EigenTrust++ can significantly outperform EigenTrust in terms of both performance and attack resilience in the presence of dishonest feedbacks and sparse network connectivity against four representative attack models.


title: FPMR : MapReduce Framework on FPGA A Case Study of RankBoost Acceleration

abstract: Machine learning and data mining are gaining increasing attentions of the computing society. FPGA provides a highly parallel, low power, and flexible hardware platform for this domain, while the difficulty of programming FPGA greatly limits its prevalence. MapReduce is a parallel programming framework that could easily utilize inherent parallelism in algorithms. In this paper, we describe FPMR, a MapReduce framework on FPGA, which provides programming abstraction, hardware architecture, and basic building blocks to developers. An on-chip processor scheduler is implemented to maximize the utilization of computation resources and achieve better load balancing. An efficient data access scheme is carefully designed to maximize data reuse and throughput. Meanwhile, the FPMR framework hides the task control, synchronization, and communication away from designers so that more attention can be paid to the application itself. A case study of RankBoost acceleration based on FPMR demonstrates that FPMR efficiently helps with the development productivity; and the speedup is 31.8x versus CPU-based implementation. This performance is comparable to a fully manually designed version, which achieves 33.5x speedup. Two other applications: SVM, PageRank are also discussed to show the generalization of the framework.


title: Dark silicon as a challenge for hardware/software co-design

abstract: Dark Silicon refers to the observation that in future technology nodes, it may only be possible to power-on a fraction of on-chip resources (processing cores, hardware accelerators, cache blocks and so on) in order to stay within the power budget and safe thermal limits, while the other resources will have to be kept powered-off or "dark". In other words, chips will have an abundance of transistors, i.e., more than the number that can be simultaneously powered-on. Heterogeneous computing has been proposed as one way to effectively leverage this abundance of transistors in order to increase performance, energy efficiency and even reliability within power and thermal constraints. However, several critical challenges remain to be addressed including design, automated synthesis, design space exploration and run-time management of heterogeneous dark silicon processors. The hardware/software co-design and synthesis community has potentially much to contribute in solving these new challenges introduced by dark silicon and, in particular, heterogeneous computing. In this paper, we identify and highlight some of these critical challenges, and outline some of our early research efforts in addressing them.


title: Accurate localization of four extreme corners for barcode images captured by mobile phones

abstract: In this paper, we propose a novel method to locate the four extreme corners of barcodes in the images captured by mobile phones. To achieve this goal, the two nearly-parallel outer boundary lines are firstly localized by utilizing the prior knowledge of the relative distances and angles between the lines, which are subsequently employed to obtain the initially localized corners. A novel post-localization process based on edge tracing is also proposed to further validate the initially localized corners. This is achieved by setting the constraints in maximum direction change when tracing from the current to the next candidate corners. The distinctive feature of the proposed algorithm lies in the capability of handling those curved barcode images taken by the mobile phones. Experiments conducted on a data set of 1410 images show the accuracy of the corner localization is about 92.3% and 90.5% for indoor and outdoor barcode images, respectively. The processing time taken is about 0.8 second using Matlab.


title: Software based modeling, simulation and fuzzy control of a Mecanum wheeled mobile robot

abstract: Mecanum wheeled omnidirectional mobile robots (MWORs) due to their special wheel structure, allow more maneuverability and agility in mobile manipulation. Because of their complex dynamics and kinematics it's difficult to provide a satisfactory model for them, analytically. Thus, instead of deriving mathematical equations, software based modeling and simulation of a three DOF Mecanum wheeled omnidirectional mobile robot are presented. In this way, a software package consists of Solidworks, MD.Adams, MATLAB and specially its module, SIMULINK, are used to provide a virtual numeric prototype which has minimal simplifying assumptions. The system is simulated using Co-Simulation technology between Adams and MATLAB, provided by Adams/Controls module in discrete mode. After defining fundamental movement modes for this class of robots, a novel control system is designed using three separate fuzzy controllers based on mental knowledge of the system in order to control position and orientation of the robot, simultaneously. Simulation results show good performance of the proposed controller without any need to tuning and optimization. Moreover the robot can turn around its center simultaneously when it is tracking a desired path.


title: Modeling stress effects on magnetic hysteresis and Barkhausen emission using a hysteretic-stochastic model

abstract: Summary form only given. An integrated magnetic hysteresis and Barkhausen effect (BE) model has been developed which provides a coherent description of the effects of stress on hysteresis loop and BE signals. To verify the model predictions hysteresis loops and BE signals were measured and simulated for steel under various tensile and compressive stresses within the elastic limit. BE signal was calculated based on the hysteretic-stochastic process model of domain wall dynamics, which has been recently developed for description of BE signal over the entire hysteresis cycle in which the permeability varies with applied field and stress. The BE signal voltage, which corresponds to the rate of irreversible changes in magnetization I/spl dot//sub irr/ is governed by d I/spl dot//sub 1rr//dt = /spl chi//sub 1rr///spl tau/ ((dH/sub a//dt) - (dH/sub o//dt)) /spl dot//sub 1rr///spl tau/, where H/sub a/ is the applied field, H/sub C/ is the local pinning field, /spl chi/'/sub 1rr/ is the irreversible susceptibility and /spl tau/ is proportional to /spl chi/'/sub 1rr/. An applied stress /spl sigma/ was treated as an effective field operating through the magnetostriction /spl lambda/. In this approach the stress effect on BE signal can be described via /spl chi/'/sub 1rr/ which is given by /spl chi/'/sub 1rr/ = /spl chi//sub 0/ (1 + (M/sub a/ - M/sub 1rr/)/(k/spl delta///spl mu//sub 0/) [/spl alpha/ + (3/spl sigma//2/spl mu//sub 0/)(/spl part//sup 2/ /spl lambda///spl part/M/sup 2/)](M/sub a/ - M/sub 1rr/)). The simulated hysteresis loop parameters such as the coercivity and remanence and the rms BE signal voltage were found to exhibit a stress dependence consistent with that of the experimental data, indicating that the integrated model can be used to describe the trends in the behavior of both hysteresis loop and BE signal as a function of stress.


title: The organization of the human striatum estimated by intrinsic functional connectivity

abstract: The striatum is connected to the cerebral cortex through multiple anatomical loops that process sensory, limbic, and heteromodal information. Tract-tracing studies in the monkey reveal that these corticostriatal connections form stereotyped patterns in the striatum. Here the organization of the striatum was explored in the human with resting-state functional connectivity MRI (fcMRI). Data from 1,000 subjects were registered with nonlinear deformation of the striatum in combination with surface-based alignment of the cerebral cortex. fcMRI maps derived from seed regions placed in the foot and tongue representations of the motor cortex yielded the expected inverted somatotopy in the putamen. fcMRI maps derived from the supplementary motor area were located medially to the primary motor representation, also consistent with anatomical studies. The topography of the complete striatum was estimated and replicated by assigning each voxel in the striatum to its most strongly correlated cortical network in two independent groups of 500 subjects. The results revealed at least five cortical zones in the striatum linked to sensorimotor, premotor, limbic, and two association networks with a topography globally consistent with monkey anatomical studies. The majority of the human striatum was coupled to cortical association networks. Examining these association networks further revealed details that fractionated the five major networks. The resulting estimates of striatal organization provide a reference for exploring how the striatum contributes to processing motor, limbic, and heteromodal information through multiple large-scale corticostriatal circuits.


THE IMPLICATION OF THE LEARNING THEORIES ON IMPLEMENTING E-LEARNING COURSES


title: Track “Coopetition Strategy. Towards a New Kind of Interfirm Dynamics? ” COOPETITION STRATEGY A NEW KIND OF INTERFIRM DYNAMICS FOR VALUE CREATION

abstract: The purpose of this paper is threefold. First, by proposing a first definition of coopetiton, it aims to move away from the mere recognition of the oversimplified conventional conception to a deeper understanding of the nature of coopetition. By suggesting that coopetition is a matter of incomplete interest (and goal) congruence concerning firms interdependence, the authors stress that coopetition does not simply emerge from coupling competition and cooperation issues, but rather it implies that cooperation and competition merge together to form a new kind of strategic interdependence between firms, giving rise to a coopetitive system of value creation. Second, the researchers advance a typology of coopetition based on the differing explanatory variables of this incomplete interest (and goal) congruence. Third, with the support of a number of coopetition microcases, especially referring to firms operating in the automobile industry, the contribution and the potential of coopetition strategy to the advancement of both strategic management, organization theory and managerial practice was clarified.


title: Simulation metamodels for modeling output distribution parameters

abstract: Metamodels are functions with calibrated parameters, used as abstractions and simplifications of the simulation model. A metamodel exposes the system's input-output relationship and can be used as an analysis tool for solving optimization problems or as a surrogate for building blocks in larger scale simulations. Our approach is to analyze statistically the response by modeling the normal distribution mean and variance functions, in order to better depict the problem and improve the knowledge about the system. The metamodel is checked using the confidence intervals of the estimated distribution parameters, and new design points are employed for predictive validation. An example is used to illustrate the development of analysis and surrogate metamodels.


title: On Valid Optimal Assignment Kernels and Applications to Graph Classification

abstract: The success of kernel methods has initiated the design of novel positive semidefinite functions, in particular for structured data. A leading design paradigm for this is the convolution kernel, which decomposes structured objects into their parts and sums over all pairs of parts. Assignment kernels, in contrast, are obtained from an optimal bijection between parts, which can provide a more valid notion of similarity. In general however, optimal assignments yield indefinite functions, which complicates their use in kernel methods. We characterize a class of base kernels used to compare parts that guarantees positive semidefinite optimal assignment kernels. These base kernels give rise to hierarchies from which the optimal assignment kernels are computed in linear time by histogram intersection. We apply these results by developing the Weisfeiler-Lehman optimal assignment kernel for graphs. It provides high classification accuracy on widely-used benchmark data sets improving over the original Weisfeiler-Lehman kernel.


title: 3D Orientation Estimation of Industrial Parts from 2D Images using Neural Networks

abstract: In this paper we propose a pose regression method employing a convolutional neural network (CNN) fed with single 2D images to estimate the 3D orientation of a specific industrial part. The network training dataset is generated by rendering pose-views from a textured CAD model to compensate for the lack of real images and their associated position label. Using several lighting conditions and material reflectances increases the robustness of the prediction and allows to anticipate challenging industrial situations. We show that using a geodesic loss function, the network is able to estimate a rendered view pose with a 5 degrees accuracy while inferring from real images gives visually convincing results suitable for any pose refinement processes.


title: Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension

abstract: This paper describes our system for SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. We use Three-way Attentive Networks (TriAN) to model interactions between the passage, question and answers. To incorporate commonsense knowledge, we augment input with relation embedding from the graph of general knowledge ConceptNet (Speer et al., 2017). As a result, our system achieves 2nd place with 83.95% accuracy on the official test data. Code is publicly available at this https URL


title: Physiological, kinematic, and electromyographic responses to kinesiology-type patella tape in elite cyclists

abstract: Abstract Kinesiology-type tape (KTT) has become popular in sports for injury prevention, rehabilitation, and performance enhancement. Many cyclists use patella KTT; however, its benefits remain unclear, especially in uninjured elite cyclists. We used an integrated approach to investigate acute physiological, kinematic, and electromyographic responses to patella KTT in twelve national-level male cyclists. Cyclists completed four, 4-minute submaximal efforts on an ergometer at 100 and 200 W with and without patella KTT. Economy, energy cost, oxygen cost, heart rate, efficiency, 3D kinematics, and lower-body electromyography signals were collected over the last minute of each effort. Comfort levels and perceived change in knee stability and performance with KTT were recorded. The effects of KTT were either unclear, non-significant, or clearly trivial on all collected physiological and kinematic measures. KTT significantly, clearly, and meaningfully enhanced vastus medialis peak, mean, and integrated electromyographic signals, and vastus medialis-to-lateralis activation. Electromyographic measures from biceps femoris and biceps-to-rectus femoris activation ratio decreased in either a significant or clinically meaningful manner. Despite most cyclists perceiving KTT as comfortable, increasing stability, and improving performance, the intervention exerted no considerable effects on all physiological and kinematic measures. KTT did alter neuromuscular recruitment, which has potential implications for injury prevention.


title: Incorporating Ethics into Artificial Intelligence

abstract: This article reviews the reasons scholars hold that driverless cars and many other AI equipped machines must be able to make ethical decisions, and the difficulties this approach faces. It then shows that cars have no moral agency, and that the term ‘autonomous’, commonly applied to these machines, is misleading, and leads to invalid conclusions about the ways these machines can be kept ethical. The article’s most important claim is that a significant part of the challenge posed by AI-equipped machines can be addressed by the kind of ethical choices made by human beings for millennia. Ergo, there is little need to teach machines ethics even if this could be done in the first place. Finally, the article points out that it is a grievous error to draw on extreme outlier scenarios—such as the Trolley narratives—as a basis for conceptualizing the ethical issues at hand.


title: Exploring design through wearable computing art(ifacts)

abstract: Usability is taken into account in design, however analysis of underlying technological values (such as trust, privacy, security) might become overlooked. In this paper, we illustrate how performance art can be used to elicit information about device design and usage. Wearable computing devices or art(ifacts) were used to spark behavior and debate. It was found that the degree of acceptability of the design was related to the perceived control the wearer had over the device. We suggest that what is learned from performance art can be incorporated into future design.


title: Depth Silhouettes Context: A New Robust Feature for Human Tracking and Activity Recognition based on Advanced Hidden Markov Model

abstract: In this paper, a depth camera-based novel approach for human activity recognition is presented using robust depth silhouettes context features and advanced Hidden Markov Models (HMMs). During HAR framework, at first, depth maps are processed to identify human silhouettes from noisy background by considering frame differentiation constraints of human body motion and compute depth silhouette area for each activity to track human movements in a scene. From the depth silhouettes context features, temporal frames information are computed for intensity differentiation measurements, depth history features are used to store gradient orientation change in overall activity sequence and motion difference features are extracted for regional motion identification. Then, these features are processed by Principal component analysis for dimension reduction and kmean clustering for code generation to make better activity representation. Finally, we proposed a new way to model, train and recognize different activities using advanced HMM. Each activity has been chosen with the highest likelihood value. Experimental results show superior recognition rate, resulting up to the mean recognition of 57.69% over the state of the art methods for fifteen daily routine activities using IM-Daily Depth Activity dataset. In addition, MSRAction3D dataset also showed some promising results.


title: Low-cost magnetic levitation project kits for teaching feedback system design

abstract: Low-cost magnetic levitation project kits are used in teaching system design in an introductory undergraduate control systems course. Students receive individual kits along with assembly instructions for building a magnetic levitation device that is marginally stable, and they are then challenged to modify the system to improve performance. The magnetic levitation kits provide an open-ended design problem, a class memento, and on-campus advertising for future students.


title: A Comparative Framework for Preconditioned Lasso Algorithms

abstract: The Lasso is a cornerstone of modern multivariate data analysis, yet its performance suffers in the common situation in which covariates are correlated. This limitation has led to a growing number of Preconditioned Lasso algorithms that pre-multiply X and y by matrices PX, Py prior to running the standard Lasso. A direct comparison of these and similar Lasso-style algorithms to the original Lasso is difficult because the performance of all of these methods depends critically on an auxiliary penalty parameter λ. In this paper we propose an agnostic framework for comparing Preconditioned Lasso algorithms to the Lasso without having to choose λ. We apply our framework to three Preconditioned Lasso instances and highlight cases when they will outperform the Lasso. Additionally, our theory reveals fragilities of these algorithms to which we provide partial solutions.


title: 3-D-MIMO With Massive Antennas Paves the Way to 5G Enhanced Mobile Broadband: From System Design to Field Trials

abstract: Three-dimensional (3D) multiple input and multiple output (3D-MIMO) with massive antennas is a key technology to achieve high spectral efficiency and user experienced data rate for the fifth generation (5G) mobile communication system. To implement 3D-MIMO in 5G system, practical constraints on the product design should be considered. This paper proposes a systematic design for the 3D-MIMO product by considering the restrictions of both base band and the hardware, including cost, size, weight, and heat dissipation. The design has been implemented for 2.6-GHz time-division duplex band, and field trials have been conducted for performance validation with practical intercell interference in commercial network. The trial results show that this 3D-MIMO design can meet the spectral efficiency requirement of the 5G enhanced mobile broadband services. The performance gain of 3D-MIMO varies with the traffic load. When the traffic load is heavy, 3D-MIMO can enhance the cell throughput by 4~6.7 times. When the traffic load is low, the performance gain of this 3D-MIMO design decreases. The results from field trial also show that the performance of 3D-MIMO degrades in mobility scenarios, where further enhancement on acquiring instant channel status information are necessary to improve the robustness of 3D-MIMO to mobility.


title: On cooperation in multi-agent systems

abstract: Cooperation is often presented as one of the key concepts which differentiates multi-agent systems from other related disciplines such as distributed computing, object-oriented systems, and expert systems. However, it is a concept whose precise usage in agent-based systems is at best unclear and at worst highly inconsistent. Given the centrality of the issue, and the different ideological viewpoints on the subject, this was a lively panel which dealt with the following main issues.


title: 3D detection of automobile scattering centers using UWB radar sensors at 24/77 GHz

abstract: Scattering centers of two different vehicles have been evaluated using SAR, and the combination of SAR and DBF. The results show that main scattering centers are located at the wheels, the regions around the license plate, door outer panel, windshield pillar, center pillar, rear pillar, and light units. Furthermore, it has been shown that multipath propagation enables an indirect detection of scattering centers. The analysis shows no significant differences between the scattering center locations at 24 GHz and 77 GHz. However, the increased bandwidth at 77 GHz allows resolving merged scattering centers and improves the contour determination. The comparison between the measurement results of the SAR processing and the mechanical scanning radar confirmed similar main scattering centers. Furthermore, the measurement results show that the contour and orientation of a vehicle can be determined at a distance of 10 m with a bandwidth of 2 GHz and an angular resolution of approximately 2°.


title: Why would Musical Training Benefit the Neural Encoding of Speech? The OPERA Hypothesis

abstract: Mounting evidence suggests that musical training benefits the neural encoding of speech. This paper offers a hypothesis specifying why such benefits occur. The “OPERA” hypothesis proposes that such benefits are driven by adaptive plasticity in speech-processing networks, and that this plasticity occurs when five conditions are met. These are: 1) Overlap: there is anatomical overlap in the brain networks that process an acoustic feature used in both music and speech (e.g., waveform periodicity, amplitude envelope), 2) Precision: music places higher demands on these shared networks than does speech, in terms of the precision of processing, 3) Emotion: the musical activities that engage this network elicit strong positive emotion, 4) Repetition: the musical activities that engage this network are frequently repeated, and 5) Attention: the musical activities that engage this network are associated with focused attention. According to the OPERA hypothesis, when these conditions are met neural plasticity drives the networks in question to function with higher precision than needed for ordinary speech communication. Yet since speech shares these networks with music, speech processing benefits. The OPERA hypothesis is used to account for the observed superior subcortical encoding of speech in musically trained individuals, and to suggest mechanisms by which musical training might improve linguistic reading abilities.


title: An Optimization Framework for Remapping and Reweighting Noisy Relevance Labels

abstract: Relevance labels is the essential part of any learning to rank framework. The rapid development of crowdsourcing platforms led to a significant reduction of the cost of manual labeling. This makes it possible to collect very large sets of labeled documents to train a ranking algorithm. However, relevance labels acquired via crowdsourcing are typically coarse and noisy, so certain consensus models are used to measure the quality of labels and to reduce the noise. This noise is likely to affect a ranker trained on such labels, and, since none of the existing consensus models directly optimizes ranking quality, one has to apply some heuristics to utilize the output of a consensus model in a ranking algorithm, e.g., to use majority voting among workers to get consensus labels. The major goal of this paper is to unify existing approaches to consensus modeling and noise reduction within a learning to rank framework. Namely, we present a machine learning algorithm aimed at improving the performance of a ranker trained on a crowdsourced dataset by proper remapping of labels and reweighting of samples. In the experimental part, we use several characteristics of workers/labels extracted via various consensus models in order to learn the remapping and reweighting functions. Our experiments on a large-scale dataset demonstrate that we can significantly improve state-of-the-art machine-learning algorithms by incorporating our framework.


title: Coherent risk measures under filtered historical simulation

abstract: Abstract Recent studies have strongly criticised conventional VaR models for not providing a coherent risk measure. Acerbi provides the intuition for an entire family of coherent measures of risk known as “spectral risk measures” [Spectral measures of risk: A coherent representation of subjective risk aversion. Journal of Banking and Finance 26 (7) (2002) 1505–1518]. In this study we illustrate how the Filtered Historical Simulation [Barone-Adesi, G., Bourgoin, F., Giannopoulos, K., 1998. Don’t look back. Risk 11, 100–104; Barone-Adesi, Giannopoulos, K., Vosper, L., 1999. VaR without correlations for non-linear portfolios. Journal of Futures Markets 19, 583–602], can provide an improved methodology for calculating the Expected Shortfall. Thereafter, we prove that these new risk measures are spectral and are coherent as well, following Acerbi. Furthermore, we provide the statistical error formula that allows to calculate the error for our model.


title: Y-MAC: An Energy-Efficient Multi-channel MAC Protocol for Dense Wireless Sensor Networks

abstract: As the use of wireless sensor networks (WSNs) becomes widespread, node density tends to increase. This poses a new challenge for Medium Access Control (MAC) protocol design. Although traditional MAC protocols achieve low-power operation, they use only a single channel which limits their performance. Several multi-channel MAC protocols for WSNs have been recently proposed. One of the key observations is that these protocols are less energy efficient than single-channel MAC protocols under light traffic conditions. In this paper, we propose an energy efficient multi-channel MAC protocol, Y-MAC, for WSNs. Our goal is to achieve both high performance and energy efficiency under diverse traffic conditions. In contrast to most of previous multi-channel MAC protocols for WSNs, we implemented Y-MAC on a real sensor node platform and conducted extensive experiments to evaluate its performance. Experimental results show that Y-MAC is energy efficient and maintains high performance under high-traffic conditions.


Clustering Algorithms In Intrusion Detection Systems


title: A Piezoelectric Energy Harvester for Rotary Motion Applications: Design and Experiments

abstract: This paper investigates the analysis and design of a vibration-based energy harvester for rotary motion applications. The energy harvester consists of a cantilever beam with a tip mass and a piezoelectric ceramic attached along the beam that is mounted on a rotating shaft. Using this system, mechanical vibration energy is induced in the flexible beam due to the gravitational force applied to the tip mass while the hub is rotating. The piezoelectric transducer is used to convert the induced mechanical vibration energy into electricity. The equations of motion of the flexible structure are utilized along with the physical characteristics of the piezoelectric transducer to derive expressions for the electrical power. Furthermore, expressions for the optimum load resistance and maximum output power are obtained and validated experimentally using PVDF and PZT transducers. The results indicate that a maximum power of 6.4 mW at a shaft speed of 138 rad/s can be extracted by using a PZT transducer with dimensions 50.8 mm × 38.1 mm × 0.13 mm. This amount of power is sufficient to provide power for typical wireless sensors such as accelerometers and strain gauges.


title: Decision Making in Multiagent Systems: A Survey

abstract: Intelligent transport systems, efficient electric grids, and sensor networks for data collection and analysis are some examples of the multiagent systems (MAS) that cooperate to achieve common goals. Decision making is an integral part of intelligent agents and MAS that will allow such systems to accomplish increasingly complex tasks. In this survey, we investigate state-of-the-art work within the past five years on cooperative MAS decision making models, including Markov decision processes, game theory, swarm intelligence, and graph theoretic models. We survey algorithms that result in optimal and suboptimal policies such as reinforcement learning, dynamic programming, evolutionary computing, and neural networks. We also discuss the application of these models to robotics, wireless sensor networks, cognitive radio networks, intelligent transport systems, and smart electric grids. In addition, we define key terms in the area and discuss remaining challenges that include incorporating big data advancements to decision making, developing autonomous, scalable and computationally efficient algorithms, tackling more complex tasks, and developing standardized evaluation metrics. While recent surveys have been published on this topic, we present a broader discussion of related models and applications. Note to Practitioners: Future smart cities will rely on cooperative MAS that make decisions about what actions to perform that will lead to the completion of their tasks. Decision making models and algorithms have been developed and reported in the literature to generate such sequences of actions. These models are based on a wide variety of principles including human decision making and social animal behavior. In this paper, we survey existing decision making models and algorithms that generate optimal and suboptimal sequences of actions. We also discuss some of the remaining challenges faced by the research community before more effective MAS deployment can be achieved in this age of Internet of Things, robotics, and mobile devices. These challenges include developing more scalable and efficient algorithms, utilizing the abundant sensory data available, tackling more complex tasks, and developing evaluation standards for decision making.


title: Closed-Book Training to Improve Summarization Encoder Memory

abstract: A good neural sequence-to-sequence summarization model should have a strong encoder that can distill and memorize the important information from long input texts so that the decoder can generate salient summaries based on the encoder's memory. In this paper, we aim to improve the memorization capabilities of the encoder of a pointer-generator model by adding an additional 'closed-book' decoder without attention and pointer mechanisms. Such a decoder forces the encoder to be more selective in the information encoded in its memory state because the decoder can't rely on the extra information provided by the attention and possibly copy modules, and hence improves the entire model. On the CNN/Daily Mail dataset, our 2-decoder model outperforms the baseline significantly in terms of ROUGE and METEOR metrics, for both cross-entropy and reinforced setups (and on human evaluation). Moreover, our model also achieves higher scores in a test-only DUC-2002 generalizability setup. We further present a memory ability test, two saliency metrics, as well as several sanity-check ablations (based on fixed-encoder, gradient-flow cut, and model capacity) to prove that the encoder of our 2-decoder model does in fact learn stronger memory representations than the baseline encoder.


title: HOLMES: an event-driven solution to monitor data centers through continuous queries and machine learning

abstract: Supervisory processes are fundamental when running data center operations striving for fault resilience: any downtime can directly affect the business's income and definitely its reputation. Current monitoring tools rely on experts to configure constant thresholds on single streams, which is not appropriated for dynamic systems and insufficient to capture complex patterns. We present HOLMES, built to support data center experts to anticipate failures with a solution that combines Event Driven Architecture, Complex Event Processing and an unsupervised machine learning algorithm. Based on rules created by the users, the system continuously checks for known problems. Meanwhile, for the unknown ones, we leverage the CEP engine for aggregating and joining streams of real-time data to feed normalized input to FRAHST, our machine learning algorithm that detects anomalous patterns across multivariate numerical streams. We describe how the UI module also operates within the publish/subscribe paradigm to enhance situational awareness. The system had very well acceptance and was successfully implemented at one of the largest Internet Service Providers in South America.


title: Cyberentity Security in the Internet of Things

abstract: A proposed Internet of Things system architecture offers a solution to the broad array of challenges researchers face in terms of general system security, network security, and application security.


title: #TwitterSearch: a comparison of microblog search and web search

abstract: Social networking Web sites are not just places to maintain relationships; they can also be valuable information sources. However, little is known about how and why people search socially-generated content. In this paper we explore search behavior on the popular microblogging/social networking site Twitter. Using analysis of large-scale query logs and supplemental qualitative data, we observe that people search Twitter to find temporally relevant information (e.g., breaking news, real-time content, and popular trends) and information related to people (e.g., content directed at the searcher, information about people of interest, and general sentiment and opinion). Twitter queries are shorter, more popular, and less likely to evolve as part of a session than Web queries. It appears people repeat Twitter queries to monitor the associated search results, while changing and developing Web queries to learn about a topic. The results returned from the different corpora support these different uses, with Twitter results including more social chatter and social events, and Web results containing more basic facts and navigational content. We discuss the implications of these findings for the design of next-generation Web search tools that incorporate social media.


A Survey of GPU-Based Large-Scale Volume Visualization


title: An Empirical Evaluation of Three Defect-Detection Techniques

abstract: We replicated a controlled experiment first run in the early 1980's to evaluate the effectiveness and efficiency of 50 student subjects who used three defect-detection techniques to observe failures and isolate faults in small C programs. The three techniques were code reading by stepwise abstraction, functional (black-box) testing, and structural (white-box) testing. Two internal replications showed that our relatively inexperienced subjects were similarly effective at observing failures and isolating faults with all three techniques. However, our subjects were most efficient at both tasks when they used functional testing. Some significant differences among the techniques in their effectiveness at isolating faults of different types were seen. These results suggest that inexperienced subjects can apply a formal verification technique (code reading) as effectively as an execution-based validation technique, but they are most efficient when using functional testing.


title: Options and considerations when selecting a quantitative proteomics strategy

abstract: The vast majority of proteomic studies to date have relied on mass spectrometric techniques to identify, and in some cases quantify, peptides that have been generated by proteolysis. Current approaches differ in the types of instrument used, their performance profiles, the manner in which they interface with biological research strategies, and their reliance on and use of prior information. Here, we consider the three main mass spectrometry (MS)-based proteomic approaches used today: shotgun (or discovery), directed and targeted strategies. We discuss the principles of each technique, their strengths and weaknesses and the dependence of their performance profiles on the composition of the biological sample. Our goal is to provide a rational framework for selecting strategies optimally suited to address the specific research issue under consideration.


title: Compressed double-array tries for string dictionaries supporting fast lookup

abstract: A string dictionary is a basic tool for storing a set of strings in many kinds of applications. Recently, many applications need space-efficient dictionaries to handle very large datasets. In this paper, we propose new compressed string dictionaries using improved double-array tries. The double-array trie is a data structure that can implement a string dictionary supporting extremely fast lookup of strings, but its space efficiency is low. We introduce approaches for improving the disadvantage. From experimental evaluations, our dictionaries can provide the fastest lookup compared to state-of-the-art compressed string dictionaries. Moreover, the space efficiency is competitive in many cases.


title: ERP software implementation: an integrative framework

abstract: ERP implementation is a socio-technical challenge that requires a fundamentally different outlook from technologically-driven innovation, and will depend on a balanced perspective where the organisation as a total system is considered. ERP implementation is considered to rely on behavioural processes and actions. It is a process that involves macro-implementation at the strategic level, and micro-implementation at the operational level. This therefore means that implementation in the context of ERP systems is not possible through an ON/OFF approach whereby deployment of the new systems will necessarily yield the desired and expected results. Understanding the implementation process through a balanced perspective will therefore prevent any unpleasant surprises, and will ensure and guide the change process to be embedded in a painless fashion. The balanced perspective means that socio-technical considerations must be borne in mind; the strategic, tactical and operational steps clearly defined; and the expected benefits evaluated and tracked through creating seamless and solid integration. This paper proposes an integrative framework for ERP implementation based on an extensive review of the factors and the essential elements that contribute to success in the context of ERP implementation.


title: Kinematic analysis of translational 3-DOF micro parallel mechanism using matrix method

abstract: We apply the matrix method to kinematic analysis of our translational 3-DOF micro parallel mechanism for an instance of general flexure mechanisms. The matrix method has been well developed in architecture to analyze a frame structure. We found that this method is well applicable to such a flexure mechanism with circular notched hinges as our micro parallel mechanism because it is approximate to the Rahmen structure. Our matrix method can calculate a compliance matrix with less nodes of matrix than conventional finite element method. First, the compliance matrices of a circular notched hinge and some other beams are defined and the coordinate transformations of compliance matrix are introduced. Next, an analysis of our micro parallel mechanism is demonstrated.


title: An OpenCL(TM) Deep Learning Accelerator on Arria 10

abstract: Convolutional neural nets (CNNs) have become a practical means to perform vision tasks, particularly in the area of image classification. FPGAs are well known to be able to perform convolutions efficiently, however, most recent efforts to run CNNs on FPGAs have shown limited advantages over other devices such as GPUs. Previous approaches on FPGAs have often been memory bound due to the limited external memory bandwidth on the FPGA device. We show a novel architecture written in OpenCL(TM), which we refer to as a Deep Learning Accelerator (DLA), that maximizes data reuse and minimizes external memory bandwidth. Furthermore, we show how we can use the Winograd transform to significantly boost the performance of the FPGA. As a result, when running our DLA on Intel's Arria 10 device we can achieve a performance of 1020 img/s, or 23 img/s/W when running the AlexNet CNN benchmark. This comes to 1382 GFLOPs and is 10x faster with 8.4x more GFLOPS and 5.8x better efficiency than the state-of-the-art on FPGAs. Additionally, 23 img/s/W is competitive against the best publicly known implementation of AlexNet on nVidia's TitanX GPU.


title: Audio-driven facial animation by joint end-to-end learning of pose and emotion

abstract: We present a machine learning technique for driving 3D facial animation by audio input in real time and with low latency. Our deep neural network learns a mapping from input waveforms to the 3D vertex coordinates of a face model, and simultaneously discovers a compact, latent code that disambiguates the variations in facial expression that cannot be explained by the audio alone. During inference, the latent code can be used as an intuitive control for the emotional state of the face puppet. We train our network with 3--5 minutes of high-quality animation data obtained using traditional, vision-based performance capture methods. Even though our primary goal is to model the speaking style of a single actor, our model yields reasonable results even when driven with audio from other speakers with different gender, accent, or language, as we demonstrate with a user study. The results are applicable to in-game dialogue, low-cost localization, virtual reality avatars, and telepresence.


title: The Internet Blockchain: A Distributed, Tamper-Resistant Transaction Framework for the Internet

abstract: Existing security mechanisms for managing the Internet infrastructural resources like IP addresses, AS numbers, BGP advertisements and DNS mappings rely on a Public Key Infrastructure (PKI) that can be potentially compromised by state actors and Advanced Persistent Threats (APTs). Ideally the Internet infrastructure needs a distributed and tamper-resistant resource management framework which cannot be subverted by any single entity. A secure, distributed ledger enables such a mechanism and the blockchain is the best known example of distributed ledgers. In this paper, we propose the use of a blockchain based mechanism to secure the Internet BGP and DNS infrastructure. While the blockchain has scaling issues to be overcome, the key advantages of such an approach include the elimination of any PKI-like root of trust, a verifiable and distributed transaction history log, multi-signature based authorizations for enhanced security, easy extensibility and scriptable programmability to secure new types of Internet resources and potential for a built in cryptocurrency. A tamper resistant DNS infrastructure also ensures that it is not possible for the application level PKI to spoof HTTPS traffic.


title: Autonomous driving for vehicular networks with nonlinear dynamics

abstract: In this paper, we introduce cooperative autonomous driving algorithms for vehicular networks with nonlinear mobile robot dynamics in urban environments that take human safety into account and are capable of performing vehicle-to-vehicle (V2V) and vehicle-to-pedestrian (V2P) collision avoidance. We argue that “flocks” are multi-agent models of vehicular traffic on roads and propose novel autonomous driving architectures and algorithms for cyber-physical vehicles capable of performing autonomous driving tasks such as lane-driving, lane-changing, braking, passing, and making turns. Our proposed autonomous driving algorithms are inspired by Olfati-Saber's flocking theory. Though, there are notable differences between autonomous driving on urban roads and flocking behavior — flocks have a single desired destination whereas most drivers on road do not share the same destination. We refer to this collective behavior (driving) as “multi-objective flocking.” The self-driving vehicles in our framework turn out to be hybrid systems with a finite number of discrete states that are related to the driving modes of vehicles. Complex driving maneuvers can be performed using a sequence of mode switchings. We use near-identity nonlinear transformations to extend the application of particle-based autonomous driving algorithms to multi-robot networks with nonlinear dynamics. The derivation of the mode switching conditions that preserve safety is non-trivial and an important part of the design of autonomous driving algorithms. We present several examples of driving tasks that can be effectively performed using our proposed driving algorithms.


title: Learning to generate chairs with convolutional neural networks

abstract: We train a generative convolutional neural network which is able to generate images of objects given object type, viewpoint, and color. We train the network in a supervised manner on a dataset of rendered 3D chair models. Our experiments show that the network does not merely learn all images by heart, but rather finds a meaningful representation of a 3D chair model allowing it to assess the similarity of different chairs, interpolate between given viewpoints to generate the missing ones, or invent new chair styles by interpolating between chairs from the training set. We show that the network can be used to find correspondences between different chairs from the dataset, outperforming existing approaches on this task.


title: An Improved Algorithm for Matching Large Graphs

abstract: In this paper an improved version of a graph matching algorithm is presented, which is able to efficiently solve the graph isomorphism and graph-subgraph isomorphism problems on Attributed Relational Graphs. This version is particularly suited to work with very large graphs, since its memory requirements are quite smaller than those of other algorithms of the same kind. After a detailed description of the algorithm, an experimental comparison is made against both the previous version (developed by the same authors) and the Ullmann’s algorithm.


title: Meta-analysis on the relationship between dimensions of organizational commitment with participation to decision making, direct service to the public and extra role behaviors

abstract: The aim of the present research was to conduct meta-analysis on the relationship between identification commitment, affiliation commitment and exchange commitment with participation to decision making, direct service to the public and exrarole behaviors. 15 studies from throughout of Iran involving 4129 participant were used in this meta-analysis. Data was analyzed using Schmit and Glass's method. Hence, the mean size of effect for the relations between dimensions of organizational commitment with participation to decision making, direct service to the public and extra role behaviors was computed. Results revealed that, the mean of effect size for participation in decision making is highest compared to the mean of effect sizes of direct service to public and extra role behaviors. Also results showed that participation in decision making has a mean effect size with identification commitment, exchange commitment, affiliation commitment (0.37, 0.32 and 0.67 respectively). Direct service to public with the three dimensions of organization commitment have mean of effect sizes of 0.33, 0.33 and 0.25 respectively. Finally, extra-role behaviors have mean of effect sizes of 0.19, 0.207 and 0.085 respectively.


title: Do you get tired of socializing? An empirical explanation of discontinuous usage behaviour in social network services

abstract: Stressor-strain-outcome framework is applied to examine discontinuous use of SNS.System feature, information and social overload induce social network fatigue.System feature overload and social network fatigue cause dissatisfaction.Social network fatigue and dissatisfaction influence discontinuous use intention. Based upon the stressor-strain-outcome framework, this study proposes that perceived system feature overload, information overload, and social overload are stressors which induce strain, in terms of social network fatigue and dissatisfaction. Both of social network fatigue and dissatisfaction further influence discontinuous use intention. The empirical results indicate that three types of perceived overload exert greater effects on social network fatigue than dissatisfaction, both of which further increase users' intention of discontinuance. The study also finds that demographic characteristics, such as gender and age, also have moderating effects on these relationships.


title: A novel unconditionally stable explicit integration method for finite element method

abstract: Physics-based deformation simulation demands much time in integration process for solving motion equations. To ameliorate, in this paper we resort to structural mechanics and mathematical analysis to develop a novel unconditionally stable explicit integration method for both linear and nonlinear FEM. First we advocate an explicit integration formula with three adjustable parameters. Then we analyze the spectral radius of both linear and nonlinear dynamic transfer function’s amplification matrix to obtain limitations for these three parameters to meet unconditional stability conditions. Finally, we theoretically analyze the accuracy property of the proposed method so as to optimize the computational errors. The experimental results indicate that our method is unconditionally stable for both linear and nonlinear systems and its accuracy property is superior to both common and recent explicit and implicit methods. In addition, the proposed method can efficiently solve the problem of huge computation cost in integration procedure for FEM.


title: Beyond Friendships and Followers: The Wikipedia Social Network

abstract: Most traditional social networks rely on explicitly given relations between users, their friends and followers. In this paper, we go beyond well structured data repositories and create a person-centric network from unstructured text -- the Wikipedia Social Network. To identify persons in Wikipedia, we make use of interwiki links, Wikipedia categories and person related information available in Wikidata. From the co-occurrences of persons on a Wikipedia page we construct a large-scale person-centric network and provide a weighting scheme for the relationship of two persons based on the distances of their mentions within the text. We extract key characteristics of the network such as centrality, clustering coefficient and component sizes for which we find values that are typical for social networks. Using state-of-the-art algorithms for community detection in massive networks, we identify interesting communities and evaluate them against Wikipedia categories. The Wikipedia social network developed this way provides an important source for future social analysis tasks.


title: Strategic Aspects of Cyber Risk Information Sharing

abstract: Cyber risk management largely reduces to a race for information between defenders of ICT systems and attackers. Defenders can gain advantage in this race by sharing cyber risk information with each other. Yet, they often exchange less information than is socially desirable, because sharing decisions are guided by selfish rather than altruistic reasons. A growing line of research studies these strategic aspects that drive defenders’ sharing decisions. The present survey systematizes these works in a novel framework. It provides a consolidated understanding of defenders’ strategies to privately or publicly share information and enables us to distill trends in the literature and identify future research directions. We reveal that many theoretical works assume cyber risk information sharing to be beneficial, while empirical validations are often missing.


title: Data mining for feature selection in gene expression autism data

abstract: We have developed the system of gene selection from microarray data in autism composed of an ensemble of methods.We have developed the system of recognizing the autism applying the classifiers cooperating with gene selection.We have performed successful experiments of recognizing autism from reference cases using our system. The paper presents application of data mining methods for recognizing the most significant genes and gene sequences (treated as features) stored in a dataset of gene expression microarray. The investigations are performed for autism data. Few chosen methods of feature selection have been applied and their results integrated in the final outcome. In this way we find the contents of small set of the most important genes associated with autism. They have been applied in the classification procedure aimed on recognition of autism from reference group members. The results of numerical experiments concerning selection of the most important genes and classification of the cases on the basis of the selected genes will be discussed. The main contribution of the paper is in developing the fusion system of the results of many selection approaches into the final set, most closely associated with autism. We have also proposed special procedure of estimating the number of highest rank genes used in classification procedure.


title: Design-oriented human-computer interaction

abstract: We argue that HCI has emerged as a design-oriented field of research, directed at large towards innovation, design, and construction of new kinds of information and interaction technology. But the understanding of such an attitude to research in terms of philosophical, theoretical, and methodological underpinnings seems however relatively poor within the field. This paper intends to specifically address what design 'is' and how it is related to HCI. First, three candidate accounts from design theory of what design 'is' are introduced; the conservative, the romantic, and the pragmatic. By examining the role of sketching in design, it is found that the designer becomes involved in a necessary dialogue, from which the design problem and its solution are worked out simultaneously as a closely coupled pair. In conclusion, it is proposed that we need to acknowledge, first, the role of design in HCI conduct, and second, the difference between the knowledge-generating Design-oriented Research and the artifact-generating conduct of Research-oriented Design.


title: Orienting in a defensive world: Mammalian modifications of our evolutionary heritage. A Polyvagal Theory

abstract: The vagus, the 10th cranial nerve, contains pathways that contribute to the regulation of the internal viscera, including the heart. Vagal efferent fibers do not originate in a common brainstem structure. The Polyvagal Theory is introduced to explain the different functions of the two primary medullary source nuclei of the vague: the nucleus ambiguus (NA) and the dorsal motor nucleus (DMNX). Although vagal pathways from both nuclei terminate on the sinoatrial node, it is argued that the fibers originating in NA are uniquely responsible for respiratory sinus arrhythmia (RSA). Divergent shifts in RSA and heart rate are explained by independent actions of DMNX and NA. The theory emphasizes a phylogenetic perspective and speculates that mammalian, but not reptilian, brainstem organization is characterized by a ventral vagal complex (including NA) related to processes associated with attention, motion, emotion, and communication. Various clinical disorders, such as sudden infant death syndrome and asthma, may be related to the competition between DMNX and NA. Descriptors: Vagus, Vagal tone, Nucleus ambiguus, Dorsal motor nucleus of the vagus, Respiratory sinus arrhythmia, Polyvagal Theory


title: Wi-Fi CSI-Based Behavior Recognition: From Signals and Actions to Activities

abstract: Human behavior recognition has been considered as a core technology that can facilitate variety of applications. However, accurate detection and recognition of human behavior is still a big challenge that attracts a lot of research efforts. Recent advances in the wireless technology (e.g., Wi-Fi Channel State Information, i.e., CSI) enable a new behavior recognition paradigm, which is able to recognize behaviors in a device-free and non-intrusive manner. In this article, we first provide an overview of the basics of Wi-Fi CSI based behavior recognition. Afterwards, we classify related applications into three-granularity: signals, actions and activities, and then provide some insights for designing new schemes. Finally, we conclude by discussing the challenges, possible solutions to these challenges and some open issues involved in CSI based behavior recognition.


title: Supersense Embeddings: A Unified Model for Supersense Interpretation, Prediction, and Utilization

abstract: Coarse-grained semantic categories such as supersenses have proven useful for a range of downstream tasks such as question answering or machine translation. To date, no effort has been put into integrating the supersenses into distributional word representations. We present a novel joint embedding model of words and supersenses, providing insights into the relationship between words and supersenses in the same vector space. Using these embeddings in a deep neural network model, we demonstrate that the supersense enrichment leads to a significant improvement in a range of downstream classification tasks.


title: Analysis of the impact of sampling on NetFlow traffic classification

abstract: The traffic classification problem has recently attracted the interest of both network operators and researchers. Several machine learning (ML) methods have been proposed in the literature as a promising solution to this problem. Surprisingly, very few works have studied the traffic classification problem with Sampled NetFlow data. However, Sampled NetFlow is a widely extended monitoring solution among network operators. In this paper we aim to fulfill this gap. First, we analyze the performance of current ML methods with NetFlow by adapting a popular ML-based technique. The results show that, although the adapted method is able to obtain similar accuracy than previous packet-based methods (~90%), its accuracy degrades drastically in the presence of sampling. In order to reduce this impact, we propose a solution to network operators that is able to operate with Sampled NetFlow data and achieve good accuracy in the presence of sampling.


Examining the Role of Business Analytics in Healthcare Value Chain


title: CNN-IETS: A CNN-based Probabilistic Approach for Information Extraction by Text Segmentation

abstract: Information Extraction by Text Segmentation (IETS) aims at segmenting text inputs to extract implicit data values contained in them.The state-of-art IETS approaches mainly rely on machine learning techniques, either supervised or unsupervised.However, while the supervised approaches require a large labelled training data, the performance of the unsupervised ones could be unstable on different data sets.To overcome their weaknesses, this paper introduces CNN-IETS, a novel unsupervised probabilistic approach that takes the advantages of pre-existing data and a Convolution Neural Network (CNN)-based probabilistic classification model. While using the CNN model can ease the burden of selecting high-quality features in associating text segments with attributes of a given domain, the pre-existing data as a domain knowledge base can provide training data with a comprehensive list of features for building the CNN model.Given an input text, we do initial segmentation (according to the occurrences of these words in the knowledge base) to generate text segments for CNN classification with probabilities. Then, based on the probabilistic CNN classification results, we work on finding the most probable labelling way to the whole input text.As a complementary, a bidirectional sequencing model learned on-demand from test data is finally deployed to do further adjustment to some problematic labelled segments.Our experimental study conducted on several real data collections shows that CNN-IETS improves the extraction quality of state-of-art approaches by more than 10%.


title: Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation

abstract: Modern robotics applications that involve human-robot interaction require robots to be able to communicate with humans seamlessly and effectively. Natural language provides a flexible and efficient medium through which robots can exchange information with their human partners. Significant advancements have been made in developing robots capable of interpreting free-form instructions, but less attention has been devoted to endowing robots with the ability to generate natural language. We propose a model that enables robots to generate natural language instructions that allow humans to navigate a priori unknown environments. We first decide which information to share with the user according to their preferences, using a policy trained from human demonstrations via inverse reinforcement learning. We then "translate" this information into a natural language instruction using a neural sequence-to-sequence model that learns to generate free-form instructions from natural language corpora. We evaluate our method on a benchmark route instruction dataset and achieve a BLEU score of 72.18% compared to human-generated reference instructions. We additionally conduct navigation experiments with human participants demonstrating that our method generates instructions that people follow as accurately and easily as those produced by humans.


title: Generalizing Case Frames Using A Thesaurus And The MDL Principle

abstract: A new method for automatically acquiring case frame patterns from large corpora is proposed. In particular, the problem of generalizing values of a case frame slot for a verb is viewed as that of estimating a conditional probability distribution over a partition of words, and a new generalization method based on the Minimum Description Length (MDL) principle is proposed. In order to assist with efficiency, the proposed method makes use of an existing thesaurus and restricts its attention to those partitions that are present as "cuts" in the thesaurus tree, thus reducing the generalization problem to that of estimating a "tree cut model" of the thesaurus tree. An efficient algorithm is given, which provably obtains the optimal tree cut model for the given frequency data of a case slot, in the sense of MDL. Case frame patterns obtained by the method were used to resolve PP-attachment ambiguity. Experimental results indicate that the proposed method improves upon or is at least comparable with existing methods.


title: Malware analysis using visualized images and entropy graphs

abstract: Today, along with the development of the Internet, the number of malicious software, or malware, distributed especially for monetary profits, is exponentially increasing, and malware authors are developing malware variants using various automated tools and methods. Automated tools and methods may reuse some modules to develop malware variants, so these reused modules can be used to classify malware or to identify malware families. Therefore, similarities may exist among malware variants can be analyzed and used for malware variant detections and the family classification. This paper proposes a new malware family classification method by converting binary files into images and entropy graphs. The experimental results show that the proposed method can effectively distinguish malware families.


title: ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing

abstract: With the aim of developing a fast yet accurate algorithm for compressive sensing (CS) reconstruction of natural images, we combine in this paper the merits of two existing categories of CS methods: the structure insights of traditional optimization-based methods and the speed of recent network-based ones. Specifically, we propose a novel structured deep network, dubbed ISTA-Net, which is inspired by the Iterative Shrinkage-Thresholding Algorithm (ISTA) for optimizing a general $$ norm CS reconstruction model. To cast ISTA into deep network form, we develop an effective strategy to solve the proximal mapping associated with the sparsity-inducing regularizer using nonlinear transforms. All the parameters in ISTA-Net (e.g. nonlinear transforms, shrinkage thresholds, step sizes, etc.) are learned end-to-end, rather than being hand-crafted. Moreover, considering that the residuals of natural images are more compressible, an enhanced version of ISTA-Net in the residual domain, dubbed ISTA-Net+, is derived to further improve CS reconstruction. Extensive CS experiments demonstrate that the proposed ISTA-Nets outperform existing state-of-the-art optimization-based and network-based CS methods by large margins, while maintaining fast computational speed. Our source codes are available: http://jianzhang.tech/projects/ISTA-Net.


title: The cyclooxygenase-2-prostaglandin E2 pathway maintains senescence of chronic obstructive pulmonary disease fibroblasts.

abstract: RATIONALE ::: Chronic obstructive pulmonary disease (COPD) is associated with lung fibroblast senescence, a process characterized by the irreversible loss of replicative capacity associated with the secretion of inflammatory mediators. However, the mechanisms of this phenomenon remain poorly defined. ::: ::: ::: OBJECTIVES ::: The aim of this study was to analyze the role of prostaglandin E2 (PGE2), a prostaglandin known to be increased in COPD lung fibroblasts, in inducing senescence and related inflammation in vitro in lung fibroblasts and in vivo in mice. ::: ::: ::: METHODS ::: Fibroblasts were isolated from patients with COPD and from smoker and nonsmoker control subjects. Senescence markers and inflammatory mediators were investigated in fibroblasts and in mice. ::: ::: ::: MEASUREMENTS AND MAIN RESULTS ::: Lung fibroblasts from patients with COPD exhibited higher expression of PGE2 receptors EP2 and EP4 as compared with nonsmoker and smoker control subjects. Compared with both nonsmoker and smoker control subjects, during long-term culture, COPD fibroblasts displayed increased senescent markers (increased senescence associated-β galactosidase activity, p16, and p53 expression and lower proliferative capacity), and an increased PGE2, IL-6, IL-8, growth-regulated oncogene (GRO), CX3CL1, and matrix metalloproteinase-2 protein and cyclooxygenase-2 and mPGES-1 mRNA expression. Using in vitro pharmacologic approaches and in vivo experiments in wild-type and p53(-/-) mice we demonstrated that PGE2 produced by senescent COPD fibroblasts is responsible for the increased senescence and related inflammation. PGE2 acts either in a paracrine or autocrine fashion by a pathway involving EP2 and EP4 prostaglandin receptors, cyclooxygenase-2-dependent reactive oxygen species production and signaling, and consecutive p53 activation. ::: ::: ::: CONCLUSIONS ::: PGE2 is a critical component of an amplifying and self-perpetuating circle inducing senescence and inflammation in COPD fibroblasts. Modulating the described PGE2 signaling pathway could provide a new basis to dampen senescence and senescence-associated inflammation in COPD.


Prevention of type 2 diabetes in British Bangladeshis: qualitative study of community, religious, and professional perspectives


title: Learning To Recognize Features Of Valid Textual Entailments

abstract: This paper advocates a new architecture for textual inference in which finding a good alignment is separated from evaluating entailment. Current approaches to semantic inference in question answering and textual entailment have approximated the entailment problem as that of computing the best alignment of the hypothesis to the text, using a locally decomposable matching score. We argue that there are significant weaknesses in this approach, including flawed assumptions of monotonicity and locality. Instead we propose a pipelined approach where alignment is followed by a classification step, in which we extract features representing high-level characteristics of the entailment problem, and pass the resulting feature vector to a statistical classifier trained on development data. We report results on data from the 2005 Pascal RTE Challenge which surpass previously reported results for alignment-based systems.


title: Time domain passivity control of haptic interfaces

abstract: An energy-based method is presented for controlling a haptic interface system to ensure stable contact under a wide variety of operating conditions. System stability is analyzed in terms of the time-domain definition of passivity. We define a "passivity observer" (PO) which measures energy flow in and out of one or more subsystems in real-time software. Active behavior is indicated by a negative value of the PO at any time. We also define the "passivity controller" (PC), an adaptive dissipative element which, at each time sample, absorbs exactly the net energy output (if any) measured by the PO. The method is tested with simulation and implementation in the "Excalibur" haptic interface system. Totally stable operation was achieved under conditions such as stiffness >100 N/mm or time delays of 15 ms. The PO/PC method requires very little additional computation and does not require a dynamical model to be identified.


title: A shared control method for online human-in-the-loop robot learning based on Locally Weighted Regression

abstract: We propose a novel method that arbitrates the control between the human and the robot actors in a teaching-by-demonstration setting to form synergy between the two and facilitate effective skill synthesis on the robot. We employed the human-in-the-loop teaching paradigm to teleoperate and demonstrate a complex task execution to the robot in real-time. As the human guides the robot to perform the task, the robot obtains the skill online during the demonstration. To encode the robotic skill we employed Locally Weighted Regression that fits local models to specific state region of the task based on the human demonstration. If the robot is in the state region where no local models exist, the control over the robotic mechanism is given to the human to perform the teaching. When local models are gradually obtained in that region, the control is given to the robot so that the human can examine its performance already during the demonstration stage, and take actions accordingly. This enables a co-adaptation between the agents and contributes to a faster and more efficient teaching. As a proof-of-concept, we realised the proposed robot teaching system on a haptic robot with the task of generation of a desired vertical force on a horizontal plane with unknown stiffness properties.


title: Mudra: User-friendly Fine-grained Gesture Recognition using WiFi Signals

abstract: There has been a great interest in recognizing gestures using wireless communication signals. We are motivated in detecting extremely fine, subtle finger gestures with WiFi signals. We envision this technology to find applications in finger-gesture control, disabled-friendly devices, physical therapy etc. The requirements of mm-level sensitivity and user-friendly feature using existing WiFi signals pose great challenges. Here, we present Mudra, a fine-grained finger gesture recognition system which leverages WiFi signals to enable a near-human-to-machine interaction with finger motion. Mudra uses a two-antenna receiver to detect and recognize finger gesture. It uses the signals received from one antenna to cancel the signal from the other. This "cancellation" is extremely sensitive to and enables us detect small variation in channel due to finger movements. Since Mudra decodes gestures with existing WiFi transmissions, Mudra enables gesture recognition without sacrificing WiFi transmission opportunities. Besides, Mudra is user-friendly with no need of user training. To demonstrate Mudra, we implement prototype on the NI-based SDR platform and use COTS WiFi adapter. We evaluate Mudra in a typical office environment. The results show that our system can achieve 96% accuracy.


title: Robot tendrils: Long, thin continuum robots for inspection in space operations

abstract: Traditional robots are constructed from rigid links which facilitate both stiffness and accuracy. However, these systems operate best in open spaces, and environments traversable by this technology are inherently restricted to scales and geometries which match the size and shape of the links. In this paper, we introduce a novel long, thin, continuously bending continuum robot tendril. Aimed toward NASA applications such as remote inspection and sensor mobility for improved sample acquisition, research with tendril agents could also have potential terrestrial applications and could be highly useful in areas of defense and security, search and rescue in hazardous environmental conditions, and as an innovative option for sensor placement in environmental monitoring. Using a prototype developed at Clemson University through NASA-funded research, we demonstrate the tendril's unique capabilities for adaptive sensor placement and remote inspection within an environment seeded with numerous disparate and slowly (over a matter of hours) time-varying sources, and discuss the potential for use of such robot tendrils in space applications.


title: Scene Segmentation with Conditional Random Fields Learned from Partially Labeled Images

abstract: Conditional Random Fields (CRFs) are an effective tool for a variety of different data segmentation and labeling tasks including visual scene interpretation, which seeks to partition images into their constituent semantic-level regions and assign appropriate class labels to each region. For accurate labeling it is important to capture the global context of the image as well as local information. We introduce a CRF based scene labeling model that incorporates both local features and features aggregated over the whole image or large sections of it. Secondly, traditional CRF learning requires fully labeled datasets which can be costly and troublesome to produce. We introduce a method for learning CRFs from datasets with many unlabeled nodes by marginalizing out the unknown labels so that the log-likelihood of the known ones can be maximized by gradient ascent. Loopy Belief Propagation is used to approximate the marginals needed for the gradient and log-likelihood calculations and the Bethe free-energy approximation to the log-likelihood is monitored to control the step size. Our experimental results show that effective models can be learned from fragmentary labelings and that incorporating top-down aggregate features significantly improves the segmentations. The resulting segmentations are compared to the state-of-the-art on three different image datasets.