title: Deep Visuo-Tactile Learning: Estimation of Material Properties from Images

abstract: Estimation of tactile properties from vision, such as slipperiness or roughness, is important to effectively interact with the environment. These tactile properties help us decide which actions we should choose and how to perform them. E.g., we can drive slower if we see that we have bad traction or grasp tighter if an item looks slippery. We believe that this ability also helps robots to enhance their understanding of the environment, and thus enables them to tailor their actions to the situation at hand. We therefore propose a model to estimate the degree of tactile properties from visual perception alone (e.g., the level of slipperiness or roughness). Our method extends a encoder-decoder network, in which the latent variables are visual and tactile features. In contrast to previous works, our method does not require manual labeling, but only RGB images and the corresponding tactile sensor data. All our data is collected with a webcam and uSkin tactile sensor mounted on the end-effector of a Sawyer robot, which strokes the surfaces of 25 different materials. We show that our model generalizes to materials not included in the training data by evaluating the feature space, indicating that it has learned to associate important tactile properties with images.


title: Dynamic thermal modeling of distribution transformers

abstract: A comprehensive test program was performed on a 2500-kVA oil natural-air natural cooling mode (ONAN) transformer without external cooling. It is shown that the hot-spot to top-oil temperature gradient depends on the transformer construction. The top-oil time constant formula, which has already been defined and validated in the authors' previous work related to transformers with external cooling, is modified in order to take into account the basic design differences. The results are verified by thermocouple measurements and tests at varying loading current. The hot-spot and top-oil temperature responses predicted by the IEEE Loading Guide, Annex G, are also compared to the measured values.


title: Learning Human Identity From Motion Patterns

abstract: We present a large-scale study exploring the capability of temporal deep neural networks to interpret natural human kinematics and introduce the first method for active biometric authentication with mobile inertial sensors. At Google, we have created a first-of-its-kind dataset of human movements, passively collected by 1500 volunteers using their smartphones daily over several months. We (1) compare several neural architectures for efficient learning of temporal multi-modal data representations, (2) propose an optimized shift-invariant dense convolutional mechanism (DCWRNN), and (3) incorporate the discriminatively-trained dynamic features in a probabilistic generative framework taking into account temporal characteristics. Our results demonstrate that human kinematics convey important information about user identity and can serve as a valuable component of multi-modal authentication systems.


Author Attribution with CNN’s


title: Gateway Framework for In-Vehicle Networks Based on CAN, FlexRay, and Ethernet

abstract: This paper proposes a gateway framework for in-vehicle networks (IVNs) based on the controller area network (CAN), FlexRay, and Ethernet. The proposed gateway framework is designed to be easy to reuse and verify to reduce development costs and time. The gateway framework can be configured, and its verification environment is automatically generated by a program with a dedicated graphical user interface (GUI). The gateway framework provides state-of-the-art functionalities that include parallel reprogramming, diagnostic routing, network management (NM), dynamic routing update, multiple routing configuration, and security. The proposed gateway framework was developed, and its performance was analyzed and evaluated.


title: Comparing NoSQL MongoDB to an SQL DB

abstract: NoSQL database solutions are becoming more and more prevalent in a world currently dominated by SQL relational databases. NoSQL databases were designed to provide database solutions for large volumes of data that is not structured. However, the advantages (or disadvantages) of using a NoSQL database for data that is structured, and not necessarily "Big," is not clear. There are not many studies that compare the performance of processing a modest amount of structured data in a NoSQL database with a traditional relational database. In this paper, we compare one of the NoSQL solutions, MongoDB, to the standard SQL relational database, SQL Server. We compare the performance, in terms of runtime, of these two databases for a modest-sized structured database. Results show that MongoDB performs equally as well or better than the relational database, except when aggregate functions are utilized.


title: A Dual-PSoC based reconfigurable wearable computing framework for ECG monitoring

abstract: We propose a reconfigurable framework using Dual-PSoC (Programmable System-on-Chip) based wearable computing system capable of ECG monitoring. Keeping in view wearability constraints involving comfort, ease and usability, we chose to use vectorcardiographic leads for minimizing the number of electrodes. It also helped to choose proper parameters to capture entire cardiac activity instead of relying only on Heart Rate Variability (HRV). Hidden Markov Model (HMM) combined with Timed Automata (TA) was used for modeling the ECG signal and extracting the features. A combination of Viterbi algorithm and Time-recurrent decision trees were used for classification of the input ECG signal and alert generation in case of abnormality noticed beyond threshold level. In all eight class models of heart diseases have been defined. Physikalisch-Technische Bundesanstalt Database (PTBDB) available at physionet.org was used for modeling. Data specific to these HMM models as well as decision trees were stored in the form of lookup tables in PSoC memory. As a part of the proposal, a belt embedded data acquisition unit has been envisioned for providing signals acquired from electrodes to PSoC. PSoC with wireless module was used for signal processing and further analysis in terms of classification and possible alert generation. The scheme envisages use of two PSoCs such that while first PSoc plays a role in data acquisition and processing, second one shoulders the responsibility of classification and decision making.


title: Roadmap towards beyond 4G: Key technologies and challenges for 5G

abstract: 5G are being intensively studied in order to deliver systems by 2020. This paper presents technological challenges and perspectives beyond 4G systems. It addresses the research landscape of 5G systems and the requirements to be fullfilled. It also presents some key technologies driving 5G development in order to give form to the future generation mobile systems.


title: GDSW: A General Framework for Distributed Sliding Window over Data Streams

abstract: The big data era is characterized by the emergence of live data with high volume and fast arrival rate, it poses a new challenge to stream processing applications: how to process the unbounded live data in real time with high throughput. The sliding window technique is widely used to handle the unbounded live data by storing the most recent history of streams. However, existing centralized solutions cannot satisfy the requirements for high processing capacity and low latency due to the single-node bottleneck. Moreover, existing studies on distributed windows primarily focus on specific operators, while a general framework for processing various window-based operators is wanted. In this paper, we firstly classify the window-based operators to two categories: data-independent operators and data-dependent operators. Then, we propose GDSW, a general framework for distributed count-based sliding window, which can handle both of data-independent and data-dependent operators. Besides, in order to balance system load, we further propose a dynamic load balance algorithm called DAD based on buffer usage. Our framework is implemented on Apache Storm 0.10.0. Extensive evaluation shows that GDSW can achieve sub-second latency, and 10X improvement in throughput compared with centralized processing, when processing rapid data rate or big size window.


title: Endgame Analysis of Dou Shou Qi

abstract: Dou Shou Qi is a game in which two players control a number of pieces, each of them aiming to move one of their pieces onto a given square. We implemented an engine for analyzing the game. Moreover, we created a series of endgame tablebases containing all configurations with up to four pieces. These tablebases are the first steps towards theoretically solving the game. Finally, we constructed decision trees based on the endgame tablebases. In this note we report on some interesting patterns.


title: Building Community Resilience to Violent Extremism Through Genuine Partnerships

abstract: What is community resilience in relation to violent extremism, and how can we build it? This article explores strategies to harness community assets that may contribute to preventing youth from embracing violent extremism, drawing from models of community resilience as defined in relation to disaster preparedness. Research suggests that social connection is at the heart of resilient communities and any strategy to increase community resilience must both harness and enhance existing social connections, and endeavor to not damage or diminish them. First, the role of social connection within and between communities is explored. Specifically, the ways in which social bonding and social bridging can diminish risk for violence, including violent extremism, is examined. Second, research on the role of social connection between communities and institutions or governing bodies (termed social linking) is described. This research is discussed in terms of how the process of government partnering with community members can both provide systems for early intervention for violent extremism, as well as strengthen bonding and bridging social networks and in this way contribute broadly to building community resilience. Finally, community-based participatory research, a model of community engagement and partnership in research, is presented as a road map for building true partnerships and community engagement. (PsycINFO Database Record


Identifier Inference through Neural Networks


title: Managing professional intellect: making the most of the best.

abstract: In the postindustrial era, the success of a corporation lies more in its intellectual and systems capabilities than in its physical assets. The capacity to manage human intellect—and to convert it into useful products and services—is fast becoming the critical executive skill of the age. As a result, there has been a flurry of interest in intellectual capital, creativity, innovation, and the learning organization, but surprisingly little attention has been given to managing professional intellect.


title: Cross-national comparison of childhood obesity: the epidemic and the relationship between obesity and socioeconomic status

abstract: BACKGROUND ::: Obesity has become a worldwide epidemic. Recently WHO acknowledged an urgent need to examine child obesity across countries using a standardized international standard. Studies in adults find obesity and socioeconomic factors (SES) factors are correlated, but results are inconsistent for children. Using an international standard, we examined the prevalence of obesity and compared the associations between SES factors and obesity across countries. ::: ::: ::: METHODS ::: Data for children aged 6-18 from nationwide surveys in the US (NHANES III, 1988-1994), China (1993), and Russia (1992) were used. We used the recently updated US NCHS body mass index (BMI = wt/ht(2)) reference to define obesity (BMI > or =95th percentile) and overweight (85th< or =BMI<95th percentile). The WHO recommends an early version of the NCHS reference for international use. We conducted logistic analyses to examine the relationship between SES and obesity. ::: ::: ::: RESULTS ::: The prevalence of obesity and overweight was 11.1% and 14.3%, respectively, in the US, 6.0% and 10.0% in Russia, and 3.6% and 3.4% in China. The relationship between obesity and SES varied across countries. Higher SES subjects were more likely to be obese in China and Russia, but in the US low-SES groups were at a higher risk. Obesity was more prevalent in urban areas in China but in rural areas in Russia. ::: ::: ::: CONCLUSIONS ::: Child obesity is becoming a public health problem worldwide, but the prevalence of obesity varies remarkably across countries with different socioeconomic development levels. Different SES groups are at different risks, and the relationship between obesity and SES varies across countries.


title: Shape Memory Alloy-Based Soft Gripper with Variable Stiffness for Compliant and Effective Grasping.

abstract: Soft pneumatic actuators and motor-based mechanisms being concomitant with the cumbersome appendages have many challenges to making the independent robotic system with compact and lightweight configuration. Meanwhile, shape memory actuators have shown a promising alternative solution in many engineering applications ranging from artificial muscle to aerospace industry. However, one of the main limitations of such systems is their inherent softness resulting in a small actuation force, which prevents them from more effective applications. This issue can be solved by combining shape memory actuators and the mechanism of stiffness modulation. As a first, this study describes a shape memory alloy-based soft gripper composed of three identical fingers with variable stiffness for adaptive grasping in low stiffness state and effective holding in high stiffness state. Each finger with two hinges is fabricated through integrating soft composite actuator with stiffness changeable material where each hinge can approximately achieve a 55-fold changeable stiffness independently. Besides, each finger with two hinges can actively achieve multiple postures by both selectively changing the stiffness of hinges and actuating the relevant SMA wire. Based on these principles, the gripper is applicable for grasping objects with deformable shapes and varying shapes with a large range of weight where its maximum grasping force is increased to ∼10 times through integrating with the stiffness changeable mechanism. The final demonstration shows that the finger with desired shape-retained configurations enables the gripper to successfully pick up a frustum-shaped object.


title: The Independence of Fairness-Aware Classifiers

abstract: Due to the spread of data mining technologies, such technologies are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair in sensitive features, such as race, gender, religion, and so on. The goal of fairness-aware classifiers is to classify data while taking into account the potential issues of fairness, discrimination, neutrality, and/or independence. In this paper, after reviewing fairness-aware classification methods, we focus on one such method, Calders and Verwer's two-naive-Bayes method. This method has been shown superior to the other classifiers in terms of fairness, which is formalized as the statistical independence between a class and a sensitive feature. However, the cause of the superiority is unclear, because it utilizes a somewhat heuristic post-processing technique rather than an explicitly formalized model. We clarify the cause by comparing this method with an alternative naive Bayes classifier, which is modified by a modeling technique called "hypothetical fair-factorization." This investigation reveals the theoretical background of the two-naive-Bayes method and its connections with other methods. Based on these findings, we develop another naive Bayes method with an "actual fair-factorization technique" and empirically show that this new method can achieve an equal level of fairness as that of the two-naive-Bayes classifier.


title: Meditation (Vipassana) and the P3a Event-Related Brain Potential

abstract: A three-stimulus auditory oddball series was presented to experienced Vipassana meditators during meditation and a control thought period to elicit event-related brain potentials (ERPs) in the two different mental states. The stimuli consisted of a frequent standard tone (500 Hz), an infrequent oddball tone (1000 Hz), and an infrequent distracter (white noise), with all stimuli passively presented through headphones and no task imposed. The strongest meditation compared to control state effects occurred for the distracter stimuli: N1 amplitude from the distracter was reduced frontally during meditation; P2 amplitude from both the distracter and oddball stimuli were somewhat reduced during meditation; P3a amplitude from the distracter was reduced during meditation. The meditation-induced reduction in P3a amplitude was strongest in participants reporting more hours of daily meditation practice and was not evident in participants reporting drowsiness during their experimental meditative session. The findings suggest that meditation state can decrease the amplitude of neurophysiologic processes that subserve attentional engagement elicited by unexpected and distracting stimuli. Consistent with the aim of Vipassana meditation to reduce cognitive and emotional reactivity, the state effect of reduced P3a amplitude to distracting stimuli reflects decreased automated reactivity and evaluative processing of task irrelevant attention-demanding stimuli.


title: The Influence of the Sigmoid Function Parameters on the Speed of Backpropagation Learning

abstract: Sigmoid function is the most commonly known function used in feed forward neural networks because of its nonlinearity and the computational simplicity of its derivative. In this paper we discuss a variant sigmoid function with three parameters that denote the dynamic range, symmetry and slope of the function respectively. We illustrate how these parameters influence the speed of backpropagation learning and introduce a hybrid sigmoidal network with different parameter configuration in different layers. By regulating and modifying the sigmoid function parameter configuration in different layers the error signal problem, oscillation problem and asymmetrical input problem can be reduced. To compare the learning capabilities and the learning rate of the hybrid sigmoidal networks with the conventional networks we have tested the two-spirals benchmark that is known to be a very difficult task for backpropagation and their relatives.


title: Resilience and Sustainable Development: Building Adaptive Capacity in a World of Transformations

abstract: Emerging recognition of two fundamental errors underpinning past polices for natural resource issues heralds awareness of the need for a worldwide fundamental change in thinking and in practice of environmental management. The first error has been an implicit assumption that ecosystem responses to human use are linear, predictable and controllable. The second has been an assumption that human and natural systems can be treated independently. However, evidence that has been accumulating in diverse regions all over the world suggests that natural and social systems behave in nonlinear ways, exhibit marked thresholds in their dynamics, and that social-ecological systems act as strongly coupled, complex and evolving integrated systems. This article is a summary of a report prepared on behalf of the Environmental Advisory Council to the Swedish Government, as input to the process of the World Summit on Sustainable Development (WSSD) in Johannesburg, South Africa in 26 August 4 September 2002. We use the concept of resilience--the capacity to buffer change, learn and develop--as a framework for understanding how to sustain and enhance adaptive capacity in a complex world of rapid transformations. Two useful tools for resilience-building in social-ecological systems are structured scenarios and active adaptive management. These tools require and facilitate a social context with flexible and open institutions and multi-level governance systems that allow for learning and increase adaptive capacity without foreclosing future development options.


title: DeepCas: an End-to-end Predictor of Information Cascades

abstract: Information cascades, effectively facilitated by most social network platforms, are recognized as a major factor in almost every social success and disaster in these networks. Can cascades be predicted? While many believe that they are inherently unpredictable, recent work has shown that some key properties of information cascades, such as size, growth, and shape, can be predicted by a machine learning algorithm that combines many features. These predictors all depend on a bag of hand-crafting features to represent the cascade network and the global network structures. Such features, always carefully and sometimes mysteriously designed, are not easy to extend or to generalize to a different platform or domain. ::: ::: Inspired by the recent successes of deep learning in multiple data mining tasks, we investigate whether an end-to-end deep learning approach could effectively predict the future size of cascades. Such a method automatically learns the representation of individual cascade graphs in the context of the global network structure, without hand-crafted features or heuristics. We find that node embeddings fall short of predictive power, and it is critical to learn the representation of a cascade graph as a whole. We present algorithms that learn the representation of cascade graphs in an end-to-end manner, which significantly improve the performance of cascade prediction over strong baselines including feature based methods, node embedding methods, and graph kernel methods. Our results also provide interesting implications for cascade prediction in general.


title: Automatic Generation of Related Work Sections in Scientific Papers: An Optimization Approach

abstract: In this paper, we investigate a challenging task of automatic related work generation. Given multiple reference papers as input, the task aims to generate a related work section for a target paper. The generated related work section can be used as a draft for the author to complete his or her final related work section. We propose our Automatic Related Work Generation system called ARWG to address this task. It first exploits a PLSA model to split the sentence set of the given papers into different topic-biased parts, and then applies regression models to learn the importance of the sentences. At last it employs an optimization framework to generate the related work section. Our evaluation results on a test set of 150 target papers along with their reference papers show that our proposed ARWG system can generate related work sections with better quality. A user study is also performed to show ARWG can achieve an improvement over generic multi-document summarization baselines.


title: Exploring lexical patterns in text: Lexical cohesion analysis with WordNet

abstract: We present a system for the linguistic exploration and analysis of lexical cohesion in English texts. Using an electronic thesaurus-like resource, Princeton WordNet, and the Brown Corpus of English, we have implemented a process of annotating text with lexical chains and a graphical user interface for inspection of the annotated text. We describe the system and report on some sample linguistic analyses carried out using the combined thesaurus-corpus resource.


Semantically Grounded Learning from Unstructured Demonstrations


title: Misinterpretations of Significance: A Problem Students Share with Their Teachers?

abstract: The use of significance tests in science has been debated from the invention of these tests until the present time. Apart from theoretical critiques on their appropriateness for evaluating scientific hypotheses, significance tests also receive criticism for inviting misinterpretations. We presented six common misinterpretations to psychologists who work in German universities and found out that they are still surprisingly widespread - even among instructors who teach statistics to psychology students. Although these mi-sinterpretations are well documented among students, until now there has been little research on pedagogical methods to remove them. Rather, they are considered "hard facts" that are impervious to correction. We discuss the roots of these misinterpreta-tions and propose a pedagogical concept to teach significance tests, which involves ex-plaining the meaning of statistical significance in an appropriate way.


title: Surreptitious Deployment and Execution of Kernel Agents in Windows Guests

abstract: As more and more virtual machines (VM) are packed into a physical machine, refactoring common kernel components shared by the virtual machines running on the same physical machine significantly reduces the overall resource consumption. A refactored kernel component typically runs on a special VM called a virtual appliance. Because of the semantics gap in Hardware Abstraction Layer (HAL)-based virtualization, a physical machine's virtual appliance requires the support of per-VM in-guest agents to perform VM-specific operations such as kernel data structure access and modification. To simplify deployment, these agents must be injected into guest virtual machines without requiring any manual installation. Moreover, it is essential to protect the integrity of in-guest agents at run time, especially when the underlying refactored kernel service is security-related. This paper describes the design, implementation and evaluation of a surreptitious kernel agent deployment and execution mechanism called SADE that requires zero installation effort and effectively hides the execution of agent code. To demonstrate the efficacy of SADE, we describe a signature-based memory scanning virtual appliance that uses SADE to inject its in-guest kernel agents without any support from the injected virtual machine, and show that both the start-up overhead and the run-time performance penalty of SADE are quite modest in practice.


title: An optimized self-powered P-SSHI circuit for piezoelectric energy harvesting

abstract: Self powered energy management circuits make energy harvesting converters more efficient and more reliable. This paper presents an improvement of a self-powered parallel synchronized switch harvesting on inductor (P-SSHI) circuit for piezoelectric transducers. Two different switching techniques are evaluated, which are switching based on two MOSFETs controlled by two operational amplifiers and switching based on bipolar transistors. Simulation results show that the circuit using the switch based on bipolar transistors delivers a 19.2 times higher power comparing to the other switching technique. Optimization of this result is proposed by reducing losses in the circuit using less energy consuming components which increases the power output by more than 300% in comparison to formerly proposed solutions. The optimized circuit is designed and tested using a commercially available piezoelectric converter with and without a proof mass. Experimental results are compared to simulations made using the same excitation sources. A good agreement is found between the course of both simulation and experimental results.


title: Load Balancing of Nodes in Cloud Using Ant Colony Optimization

abstract: In this paper, we proposed an algorithm for load distribution of workloads among nodes of a cloud by the use of Ant Colony Optimization (ACO). This is a modified approach of ant colony optimization that has been applied from the perspective of cloud or grid network systems with the main aim of load balancing of nodes. This modified algorithm has an edge over the original approach in which each ant build their own individual result set and it is later on built into a complete solution. However, in our approach the ants continuously update a single result set rather than updating their own result set. Further, as we know that a cloud is the collection of many nodes, which can support various types of application that is used by the clients on a basis of pay per use. Therefore, the system, which is incurring a cost for the user should function smoothly and should have algorithms that can continue the proper system functioning even at peak usage hours.


title: Content of metals in flounder, Platichthys flesus L., and Baltic herring, Clupea harengus membras L., from the southern Baltic Sea

abstract: Selected metal concentrations in the muscles, liver, and kidney of flounder, Platichthys flesus L., and Baltic herring, Clupea harengus membras L., from the southern Baltic Sea were measured, and the relationships between the fish species and the metal concentrations in the organs and muscles were analyzed. The metal concentrations found in tissues varied as follows: Al - 0.73-5.78; Cd - 0.004-1.035; Cu - 0.1-18.5; Fe - 1.54-301.02; Hg - 0.015-0.084, Li - <0.001-0.044; Mn - 0.07-1.06; Ni - <0.01-0.31; Pb - <0.001-0.221; Zn - 3.19-44.60 mg kg -1 wet weight. This research showed that the metal concentrations in the muscles of the investigated fish species were lower than the maximum levels set by EU legislation.


title: On the performance of Time Varying Step-size Least Mean Squares(TVS-LMS) adaptive filter for MA reduction from PPG signals

abstract: Clinical Investigation of hypoxic status of the patients requires accurate information about the heart rate and oxygen saturation of arterial blood. Pulse oximeters are widely used for monitoring these parameters by recording the raw pulse oximeter signal, namely Photoplethysmogram (PPG). The recorded PPG Signal acquired using PPG sensors are usually corrupted with Motion Artifacts (MA) due to the voluntary or involuntary movements of patient. Reduction of MA has received much attention in the literature over recent years. In this paper, we present an efficient adaptive filtering technique based on Time Varying Step-size Least Mean Squares (TVS-LMS) algorithm for MA reduction. The novelty of the method lies in the fact that a synthetic noise reference signal for adaptive filtering, representing MA noise, is generated internally from the MA corrupted PPG signal itself instead of using any additional hardware such as accelerometer or source-detector pair for noise reference signal generation. Convergence analysis, SNR calculations and Statistical analysis revealed that the proposed TVS-LMS method has a clear edge over the Constant Step-size LMS (CS-LMS) based adaptive filtering technique. Test results, on the PPG data recorded with different MAs, demonstrated the efficacy of the proposed TVS-LMS algorithm in MA reduction and thus making it best suitable for real-time pulse oximetry applications.


title: IQ test performance of Black children adopted by White families.

abstract: The poor performance of black children on IQ tests and in school has been hypothesized to arise from (a) genetic racial differences or (6) cultural/ environmental disadvantages. To separate genetic fac- tors from rearing conditions, 130 black/interracial chil- dren adopted by advantaged white families were studied. The socially classified black adoptees, whose natural parents were educationally average, scored above the IQ and the school achievement mean of the white popu- lation. Biological children of the adoptive parents scored even higher. Genetic and environmental de- terminants of differences among the black/interracial adoptees were largely confounded. The high IQ scores of the socially classified black adoptees indicate malle- ability for IQ under rearing conditions that are relevant to the tests and the schools. It is well known that black children reared by their own families achieve IQ scores that average about a standard deviation (IS points) below whites (Jensen, 1973; Loehlin, Lindzey, & Spuhler, 197S). This finding is at the heart of a continuing contro- versy in the educational arena. Recent studies (Cleary, Humphreys, Kendrick, & Wesman, 1975) confirm the hypothesis that low IQ scores predict poor school performance, regardless of race. Thus, more black children than white children fail to achieve academically and to earn the credentials required by higher occupational status, with its


title: Post-traumatic stress disorder and chronic fatigue syndrome-like illness among Gulf War veterans: a population-based survey of 30,000 veterans.

abstract: The authors estimated the prevalence of post-traumatic stress disorder (PTSD) and illness resembling chronic fatigue syndrome (CFS) in the entire population of Gulf War and non-Gulf-War veterans. They also evaluated the relation between the extent of deployment-related stress and the risk of either PTSD or CFS. In 1995-1997, the authors conducted a health survey in which these two symptom-based medical diagnoses in a population-based sample of 15,000 Gulf War veterans representing four military branches and three unit components (active, reserve, and National Guard) were compared with those of 15,000 non-Gulf veteran controls. Gulf War veterans, compared with non-Gulf veteran controls, reported significantly higher rates of PTSD (adjusted odds ratio = 3.1, 95% confidence interval: 2.7, 3.4) and CFS (adjusted odds ratio = 4.8, 95% confidence interval: 3.9, 5.9). The prevalence of PTSD increased monotonically across six levels of deployment-related stress intensity (test for trend: p < 0.01), while the prevalence of CFS rose only at the low end of the stress spectrum. While deployment-related stress could account for the higher risks of both PTSD and CFS, additional factor(s) unique to the Gulf environment may have contributed to the risk of CFS among Gulf War veterans.


title: Effect of Slot-and-Pole Combination on the Leakage Inductance and the Performance of Tooth-Coil Permanent-Magnet Synchronous Machines

abstract: The influence of slot-and-pole-number combinations on the leakage inductance of double-layer fractional-slot concentrated nonoverlapping winding (i.e., tooth-coil winding) permanent-magnet synchronous machines is studied. A Fourier-analysis-based method for calculating the harmonic air-gap leakage inductance applied to the current-linkage waveform (winding function) in the air gap is suggested. Different slot-and-pole combinations are considered, and their influence, in particular, on the air-gap leakage inductance is indicated. In tooth-coil machines, the air-gap harmonic leakage can have a surprisingly large impact on the machine performance.


title: Social interactions: A first-person perspective

abstract: This paper presents a method for the detection and recognition of social interactions in a day-long first-person video of u social event, like a trip to an amusement park. The location and orientation of faces are estimated and used to compute the line of sight for each face. The context provided by all the faces in a frame is used to convert the lines of sight into locations in space to which individuals attend. Further, individuals are assigned roles based on their patterns of attention. The rotes and locations of individuals are analyzed over time to detect and recognize the types of social interactions. In addition to patterns of face locations and attention, the head movements of the first-person can provide additional useful cues as to their attentional focus. We demonstrate encouraging results on detection and recognition of social interactions in first-person videos captured from multiple days of experience in amusement parks.


Topic Segmentation: Algorithms and Applications


title: Discovering Trends in Large Datasets Using Neural Networks

abstract: A novel knowledge discovery technique using neural networks is presented. A neural network is trained to learn the correlations and relationships that exist in a dataset. The neural network is then pruned and modified to generalize the correlations and relationships. Finally, the neural network is used as a tool to discover all existing hidden trends in four different types of crimes (murder, rape, robbery, and auto theft) in US cities as well as to predict trends based on existing knowledge inherent in the network.


title: Random Erasing Data Augmentation

abstract: In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN). In training, Random Erasing randomly selects a rectangle region in an image and erases its pixels with random values. In this process, training images with various levels of occlusion are generated, which reduces the risk of over-fitting and makes the model robust to occlusion. Random Erasing is parameter learning free, easy to implement, and can be integrated with most of the CNN-based recognition models. Albeit simple, Random Erasing is complementary to commonly used data augmentation techniques such as random cropping and flipping, and yields consistent improvement over strong baselines in image classification, object detection and person re-identification. Code is available at: this https URL


title: Refreshing: A Minimal Executive Function

abstract: Executive functions include processes by which important information (e.g., words, objects, task goals, contextual information) generated via perception or thought can be foregrounded and thereby influence current and subsequent processing. One simple executive process that has the effect of foregrounding information is refreshing ‐ thinking briefly of a just-activated representation. Previous studies (e.g., Johnson et al., 2005) identified refresh-related activity in several areas of left prefrontal cortex (PFC). To further specify the respective functions of these PFC areas in refreshing, in Experiment 1, healthy young adult participants were randomly cued to think of a just previously seen word (refresh) or cued to press a button (act). Compared to simply reading a word, refresh and act conditions resulted in similar levels of activity in left lateral anterior PFC but only refreshing resulted in greater activity in left dorsolateral PFC. In Experiment 2, refreshing was contrasted with a minimal phonological rehearsal condition. Refreshing was associated with activity in left dorsolateral PFC and rehearsing with activity in left ventrolateral PFC. In both experiments, correlations of activity among brain areas suggest different functional connectivity for these processes. Together, these findings provide evidence that (1) left lateral anterior PFC is associated with initiating a non-automatic process, (2) left dorsolateral PFC is associated with foregrounding a specific mental representation, and (3) refreshing and rehearsing are neurally distinguishable processes.


title: Gödel's Theorem and Information

abstract: Godel's theorem may be demonstrated using arguments having an informationtheoretic flavor. In such an approach it is possible to argue that if a theorem contains more information than a given set of axioms, then it is impossible for the theorem to be derived from the axioms. In contrast with the traditional proof based on the paradox of the liar, this new viewpoint suggests that the incompleteness phenomenon discovered by Godel is natural and widespread rather than pathological and unusual.


title: RFID Tag and RF Structures on a Paper Substrate Using Inkjet-Printing Technology

abstract: In this paper, inkjet-printed UHF and microwave circuits fabricated on paper substrates are investigated for the first time as an approach that aims for a system-level solution for fast and ultra-low-cost mass production. First, the RF characteristics of the paper substrate are studied by using the microstrip ring resonator in order to characterize the relative permittivity (epsivr) and loss tangent (tan delta) of the substrate at the UHF band for the first time reported. A UHF RFID tag module is then developed with the inkjet-printing technology, proving this approach could function as an enabling technology for much simpler and faster fabrication on/in paper. Simulation and well-agreed measurement results, which show very good agreement, verify a good performance of the tag module. In addition, the possibility of multilayer RF structures on a paper substrate is explored, and a multilayer patch resonator bandpass filter demonstrates the feasibility of ultra-low-cost 3-D paper-on-paper RF/wireless structures.


title: A Cross-Sectional Study Assessing Dietary Intake and Physical Activity in Canadian Patients with Nonalcoholic Fatty Liver Disease vs Healthy Controls

abstract: Abstract Background Poor diet and a sedentary lifestyle can contribute to nonalcoholic fatty liver disease (NAFLD). Objective Our aim was to compare diet and physical activity of patients with NAFLD and healthy controls with current recommendations. Design This was a cross-sectional study. Participants/settings Seventy-four patients with biopsy-proven NAFLD (33 simple steatosis and 41 steatohepatitis [NASH]) and 27 healthy controls participated between 2003 and 2011. Main outcome measures Food records and activity logs were completed for 7 days. Results were compared with Dietary Reference Intakes and Canadian Physical Activity Guidelines. Plasma vitamin C was measured to assess food record accuracy. Statistical analyses performed Intake/activity for each participant was compared with the recommendations and proportion of subjects not meeting the requirements was calculated. Groups were compared by Kruskal-Wallis and Mann-Whitney U test or z -test with Bonferroni adjustment. Results More patients with NASH (58.5%) were obese compared with patients with simple steatosis (24.2%) and healthy controls (7.4%; P P ≤0.001). The proportion of subjects from each group exceeding acceptable energy intake from fat was as follows: simple steatosis: 27.3%; NASH: 46.3%; healthy controls: 63.0% (simple steatosis vs health controls; P P 80% of subjects did not consume enough linoleic or linolenic acid, vitamin D, and vitamin E, and >60% exceeded the upper intake level for sodium. Only 53.1% of patients with simple steatosis and 53.8% of patients with NASH, but 84.6% of healthy controls, met recommendations for physical activity ( P =0.020). Plasma vitamin C was normal, similar among groups, and correlated with vitamin C intakes. Conclusions All participants followed a similar Western diet with high fat and sodium intakes and suboptimal micronutrient intakes. However, physical activity was lower in NAFLD compared with healthy controls and was associated with higher body mass index and insulin resistance.


title: Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)

abstract: Given the potential risk of X-ray radiation to the patient, low-dose CT has attracted a considerable interest in the medical imaging field. Currently, the main stream low-dose CT methods include vendor-specific sinogram domain filtration and iterative reconstruction algorithms, but they need to access raw data, whose formats are not transparent to most users. Due to the difficulty of modeling the statistical characteristics in the image domain, the existing methods for directly processing reconstructed images cannot eliminate image noise very well while keeping structural details. Inspired by the idea of deep learning, here we combine the autoencoder, deconvolution network, and shortcut connections into the residual encoder–decoder convolutional neural network (RED-CNN) for low-dose CT imaging. After patch-based training, the proposed RED-CNN achieves a competitive performance relative to the-state-of-art methods in both simulated and clinical cases. Especially, our method has been favorably evaluated in terms of noise suppression, structural preservation, and lesion detection.


title: A neurochemical closed-loop controller for deep brain stimulation: toward individualized smart neuromodulation therapies

abstract: Current strategies for optimizing deep brain stimulation (DBS) therapy involve multiple postoperative visits. During each visit, stimulation parameters are adjusted until desired therapeutic effects are achieved and adverse effects are minimized. However, the efficacy of these therapeutic parameters may decline with time due at least in part to disease progression, interactions between the host environment and the electrode, and lead migration. As such, development of closed-loop control systems that can respond to changing neurochemical environments, tailoring DBS therapy to individual patients, is paramount for improving the therapeutic efficacy of DBS. Evidence obtained using electrophysiology and imaging techniques in both animals and humans suggests that DBS works by modulating neural network activity. Recently, animal studies have shown that stimulation-evoked changes in neurotransmitter release that mirror normal physiology are associated with the therapeutic benefits of DBS. Therefore, to fully understand the neurophysiology of DBS and optimize its efficacy, it may be necessary to look beyond conventional electrophysiological analyses and characterize the neurochemical effects of therapeutic and non-therapeutic stimulation. By combining electrochemical monitoring and mathematical modeling techniques, we can potentially replace the trial-and-error process used in clinical programming with deterministic approaches that help attain optimal and stable neurochemical profiles. In this manuscript, we summarize the current understanding of electrophysiological and electrochemical processing for control of neuromodulation therapies. Additionally, we describe a proof-of-principle closed-loop controller that characterizes DBS-evoked dopamine changes to adjust stimulation parameters in a rodent model of DBS. The work described herein represents the initial steps toward achieving a "smart" neuroprosthetic system for treatment of neurologic and psychiatric disorders.


title: Visual Tracking via Dynamic Graph Learning

abstract: Existing visual tracking methods usually localize a target object with a bounding box, in which the performance of the foreground object trackers or detectors is often affected by the inclusion of background clutter. To handle this problem, we learn a patch-based graph representation for visual tracking. The tracked object is modeled by with a graph by taking a set of non-overlapping image patches as nodes, in which the weight of each node indicates how likely it belongs to the foreground and edges are weighted for indicating the appearance compatibility of two neighboring nodes. This graph is dynamically learned and applied in object tracking and model updating. The proposed algorithm performs three main steps in each frame. First, the graph is initialized by assigning binary weights of some image patches to indicate the object and background patches according to the predicted bounding box. Second, the graph is optimized to refine the patch weights. Third, the object feature representation is updated by imposing the weights of patches on the extracted image features. The object location is predicted by maximizing the classification score in the structured support vector machine. Extensive experiments show that the proposed tracking algorithm performs well against the state-of-the-art methods.


title: DeepStyle: Learning User Preferences for Visual Recommendation

abstract: Visual information is an important factor in recommender systems. Some studies have been done to model user preferences for visual recommendation. Usually, an item consists of two fundamental components: style and category. Conventional methods model items in a common visual feature space. In these methods, visual representations always can only capture the categorical information but fail in capturing the styles of items. Style information indicates the preferences of users and has significant effect in visual recommendation. Accordingly, we propose a DeepStyle method for learning style features of items and sensing preferences of users. Experiments conducted on two real-world datasets illustrate the effectiveness of DeepStyle for visual recommendation.


title: Micro Hand Gesture Recognition System Using Ultrasonic Active Sensing

abstract: In this paper, we propose a micro hand gesture recognition system and methods using ultrasonic active sensing. This system uses micro dynamic hand gestures for recognition to achieve human–computer interaction (HCI). The implemented system, called hand-ultrasonic gesture (HUG), consists of ultrasonic active sensing, pulsed radar signal processing, and time-sequence pattern recognition by machine learning. We adopt lower frequency (300 kHz) ultrasonic active sensing to obtain high resolution range-Doppler image features. Using high quality sequential range-Doppler features, we propose a state-transition-based hidden Markov model for gesture classification. This method achieves a recognition accuracy of nearly 90% by using symbolized range-Doppler features and significantly reduces the computational complexity and power consumption. Furthermore, to achieve higher classification accuracy, we utilize an end-to-end neural network model and obtain a recognition accuracy of 96.32%. In addition to offline analysis, a real-time prototype is released to verify our method’s potential for application in the real world.


title: Limited Gradient Descent: Learning With Noisy Labels

abstract: Label noise may affect the generalization of classifiers, and the effective learning of main patterns from samples with noisy labels is an important challenge. Recent studies have shown that deep neural networks tend to prioritize the learning of simple patterns over the memorization of noise patterns. This suggests a possible method to search for the best generalization that learns the main pattern until the noise begins to be memorized. Traditional approaches often employ a clean validation set to find the best stop timing of learning, i.e., early stopping. However, the generalization performance of such methods relies on the quality of validation sets. Further, in practice, a clean validation set is sometimes difficult to obtain. To solve this problem, we propose a method that can estimate the optimal stopping timing without a clean validation set, called limited gradient descent. We modified the labels of a few samples in a noisy dataset to obtain false labels and to create a reverse pattern. By monitoring the learning progress of the noisy and reverse samples, we can determine the stop timing of learning. In this paper, we also theoretically provide some necessary conditions on learning with noisy labels. Experimental results on CIFAR-10 and CIFAR-100 datasets demonstrate that our approach has a comparable generalization performance to methods relying on a clean validation set. Thus, on the noisy Clothing-1M dataset, our approach surpasses methods that rely on a clean validation set.


title: Globally and locally consistent image completion

abstract: We present a novel approach for image completion that results in images that are both locally and globally consistent. With a fully-convolutional neural network, we can complete images of arbitrary resolutions by filling-in missing regions of any shape. To train this image completion network to be consistent, we use global and local context discriminators that are trained to distinguish real images from completed ones. The global discriminator looks at the entire image to assess if it is coherent as a whole, while the local discriminator looks only at a small area centered at the completed region to ensure the local consistency of the generated patches. The image completion network is then trained to fool the both context discriminator networks, which requires it to generate images that are indistinguishable from real ones with regard to overall consistency as well as in details. We show that our approach can be used to complete a wide variety of scenes. Furthermore, in contrast with the patch-based approaches such as PatchMatch, our approach can generate fragments that do not appear elsewhere in the image, which allows us to naturally complete the images of objects with familiar and highly specific structures, such as faces.


title: TmRPL++: Trust based smarter-HOP for optimized mobility in RPL

abstract: Ensuring security in routing protocols designed for Internet of Things (IoT) is more challenging owing to the fact that devices are resource constrained. The need for lightweight security mechanisms is apparent because cryptography based mechanisms have intense computation requirements. Although Routing Protocol for Low power and lossy networks (RPL) inherently comprises a few security mechanisms, it has been shown that those mechanisms are fragile, and additional measures are required to strengthen its security. In this paper, we propose a lightweight trust based security algorithm for RPL, called TmRPL++, to prevent the consequences of routing attacks. TmRPL++ has been implemented and integrated in the Contiki 6LoWPAN/RPL stack, and its functionality has been verified through extensive simulations.


title: A pilot study of yoga for breast cancer survivors: physical and psychological benefits.

abstract: BACKGROUND ::: Physical activity provides a number of physical and psychological benefits to cancer survivors, including lessening the impact of detrimental cancer-related symptoms and treatment side-effects (e.g. fatigue, nausea), and improving overall well-being and quality of life. The purpose of the present pilot study was to examine the physical and psychological benefits afforded by a 7-week yoga program for cancer survivors. ::: ::: ::: METHOD ::: Eligible participants (per-screened with PAR-Q/PAR-MED-X) were randomly assigned to either the intervention (n=20) or control group (n=18). All participants completed pre- and post-testing assessments immediately before and after the yoga program, respectively. ::: ::: ::: RESULTS ::: The yoga program participants (M age=51.18 (10.33); 92% female) included primarily breast cancer survivors, on average 55.95 (54.39) months post-diagnosis. Significant differences between the intervention and the control group at post-intervention were seen only in psychosocial (i.e. global quality of life, emotional function, and diarrhea) variables (all p's <0.05). There were also trends for group differences, in the hypothesized directions, for the psychosocial variables of emotional irritability, gastrointestinal symptoms, cognitive disorganization, mood disturbance, tension, depression, and confusion (all p's <0.10). Finally, there were also significant improvements in both the program participants and the controls from pre- to post-intervention on a number of physical fitness variables. ::: ::: ::: CONCLUSIONS ::: These initial findings suggest that yoga has significant potential and should be further explored as a beneficial physical activity option for cancer survivors. Future research might attempt to include a broader range of participants (e.g. other types of cancer diagnoses, male subjects), a larger sample size, and a longer program duration in an RCT.


title: Handwritten signature verification: Online verification using a fuzzy inference system

abstract: Biometric features posses the significant advantage of being difficult to lose, forget or duplicate. Hence, a FIS-based method is used for signature verification. FIS is well suited for this task due to the similarity between an individual signatures with subtle differences between each signature sample. Signature samples are collected using a tablet PC. The individuals draw their signatures usinga pressure sensitive pen on the tablet. Eight dynamic features are extracted from the signature data. These eight features are then fuzzified for training of a FIS. The system is then used to determine whether the signature is genuine or forged. A False Acceptance Rate (FAR) of 10.67% and a False Rejection Rate (FRR) of 8.0% demonstrate the promise of this system.


title: A Critical Evaluation of Website Fingerprinting Attacks

abstract: Recent studies on Website Fingerprinting (WF) claim to have found highly effective attacks on Tor. However, these studies make assumptions about user settings, adversary capabilities, and the nature of the Web that do not necessarily hold in practical scenarios. The following study critically evaluates these assumptions by conducting the attack where the assumptions do not hold. We show that certain variables, for example, user's browsing habits, differences in location and version of Tor Browser Bundle, that are usually omitted from the current WF model have a significant impact on the efficacy of the attack. We also empirically show how prior work succumbs to the base rate fallacy in the open-world scenario. We address this problem by augmenting our classification method with a verification step. We conclude that even though this approach reduces the number of false positives over 63\%, it does not completely solve the problem, which remains an open issue for WF attacks.


Measuring Respiration and Heart Rate using Two Acceleration Sensors on a Fully Embedded Platform


title: DBLSTM-based multi-scale fusion for dynamic emotion prediction in music

abstract: Dynamic Music Emotion Prediction is crucial to the emerging applications of music retrieval and recommendation. Considering the influence of temporal context and hierarchical structure on emotion in music, we propose a Deep Bidirectional Long Short-Term Memory (DBLSTM) based multi-scale regression method. In this method, a post-processing component is utilised for individual DBSLTM output to further enhance the ability of temporal context processing and a fusion component is to integrate the output of all DBLSTM models with different scales. In addition, we investigate how the difference of sequence length between the training and predicting phase affects the performance of DBLSTM. We conduct our experiments on a public database of Emotion in Music task at MediaEval 2015, and the result shows that our method achieves significant improvement when compared with the state-of-art methods.


title: Concolic testing

abstract: Concolic testing automates test input generation by combining the concrete and symbolic (concolic) execution of the code under test. Traditional test input generation techniques use either (1) concrete execution or (2) symbolic execution that builds constraints and is followed by a generation of concrete test inputs from these constraints. In contrast, concolic testing tightly couples both concrete and symbolic executions: they run simultaneously, and each gets feedback from the other. We have implemented concolic testing in tools for testing both C and Java programs. We have used the tools to find bugs in several real-world software systems including SGLIB, a popular C data structure library used in a commercial tool, a third-party implementation of the Needham-Schroeder protocol and the TMN protocol, the scheduler of Honeywell's DEOS real-time operating system, and the Sun Microsystems' JDK 1.4 collection framework. In this tutorial, we will describe concolic testing and some of its recent extensions


title: Dynamic Task Parallelism with a GPU Work-Stealing Runtime System

abstract: NVIDIA’s Compute Unified Device Architecture (CUDA) enabled GPUs become accessible to mainstream programming. Abundance of simple computational cores and high memory bandwidth make GPUs ideal candidates for data parallel applications. However, its potential for executing applications that combine task and data parallelism has not been explored in detail. CUDA does not provide a viable interface for creating dynamic tasks and handling load balancing issues. Any support for such has to be orchestrated entirely by the CUDA programmer today.


title: Weighted Task Regularization for Multitask Learning

abstract: Multitask Learning has been proven to be more effective than the traditional single task learning on many real-world problems by simultaneously transferring knowledge among different tasks which may suffer from limited labeled data. However, in order to build a reliable multitask learning model, nontrivial effort to construct the relatedness between different tasks is critical. When the number of tasks is not large, the learning outcome may suffer if there exists outlier tasks that inappropriately bias majority. Rather than identifying or discarding such outlier tasks, we present a weighted regularized multitask learning framework based on regularized multitask learning, which uses statistical metrics, such as Kullback-Leibler divergence, to assign weights prior to regularization process that robustly reduces the impact of outlier tasks and results in better learned models for all tasks. We then show that this formulation can be solved using dual form like optimizing a standard support vector machine with varied kernels. We perform experiments using both synthetic dataset and real-world dataset from petroleum industry which shows that our methodology outperforms existing methods.


Propagation Losses Through Common Building Materials 2 . 4 GHz vs 5 GHz Reflection and Transmission Losses Through Common Building Materials


title: Lung findings on thoracic high-resolution computed tomography in patients with ankylosing spondylitis. Correlations with disease duration, clinical findings and pulmonary function testing

abstract: The aim of this study was to identify the spectrum of abnormalities revealed on high-resolution computerized tomography (HRCT) in patients with ankylosing spondylitis (AS), to compare findings with those of plain radiography and pulmonary function testing (PFT), and to look for correlations between lung involvement and AS severity. We prospectively studied 55 consecutive patients with a diagnosis of AS according to the modified New York criteria who attended our department over a period of 2 years. All patients had a detailed rheumatological examination and underwent plain chest radiography, chest HRCT and PFT. HRCT revealed abnormalities in 29 patients (52.7%), whereas plain chest radiography was abnormal in only 2. Abnormalities consisted of interstitial lung disease (ILD) ( n=4), apical fibrosis ( n=5), emphysema ( n=5), bronchiectasis ( n=4), ground glass attenuation ( n=2), and non-specific interstitial abnormalities ( n=26). Only apical fibrosis and bronchiectasis were statistically more frequent with increasing disease duration (significant trend chi(2)test, p=0.0029 and 0.028, respectively). PFT showed a restrictive process in 19 patients (34.5%). No correlation was noted between HRCT and PFT, nor with AS symptomatic and structural severity parameters. However, there was a statistically significant correlation between PFT and AS symptomatic and structural severity parameters. In conclusion,: this study confirms that the chest HRCT of patients with AS showed a great number of abnormalities undetectable by standard X-rays. The high incidence of lung abnormalities emphasizes the importance of excluding such a diagnosis in patients with AS even without respiratory symptoms.


title: Epigenetic Signaling in Psychiatric Disorders

abstract: Psychiatric disorders are complex multifactorial illnesses involving chronic alterations in neural circuit structure and function. While genetic factors are important in the etiology of disorders such as depression and addiction, relatively high rates of discordance among identical twins clearly indicate the importance of additional mechanisms. Environmental factors such as stress or prior drug exposure are known to play a role in the onset of these illnesses. Such exposure to environmental insults induces stable changes in gene expression, neural circuit function, and ultimately behavior, and these maladaptations appear distinct between developmental and adult exposures. Increasing evidence indicates that these sustained abnormalities are maintained by epigenetic modifications in specific brain regions. Indeed, transcriptional dysregulation and associated aberrant epigenetic regulation is a unifying theme in psychiatric disorders. Aspects of depression and addiction can be modeled in animals by inducing disease-like states through environmental manipulations (e.g., chronic stress, drug administration). Understanding how environmental factors recruit the epigenetic machinery in animal models reveals new insight into disease mechanisms in humans.


title: A MATLAB-simulink-based solar photovoltaic array (SPVA) module with MPPT

abstract: The performance of a solar photovoltaic array (SPVA) is dependent upon the temperature and irradiance level and it is necessary to study the characteristics of photovoltaic (PV) array. To utilize PV power or extract maximum power from PV array, the maximum power point tracking (MPPT) technique is essential to study and implement. In this paper, an equivalent electrical circuit of PV system has been modelled and its characteristics are studied. Response of the PV array with different irradiance level is also obtained. Incremental conductance algorithm of MPPT is modelled and developed to extract maximum power from PV array. The design of dc-dc boost converter has been carried out. It is observed that maximum power from PV array is achieved at input and output side of the dc-dc converter. The simulation work is done in MATLAB/SIMULINK environment.


title: Analytical Formula for the Torque/Emf Constant of a Slotless BLDC Motor

abstract: An original analytical formula for the torque/emf constant of a slotless brushless DC (BLDC) motor is presented. It is obtained by solving and integrating the permanent magnet field. In addition, formulas for the both stator and rotor iron fields are derived, in order to estimate the saturation level and losses. The results are verified using a FEM simulation


title: Immunotherapy for hepatocellular carcinoma

abstract: abstract Despite significant research efforts, only a few treatment approaches have been developed for hepatocellular carcinoma (HCC). In recent years, immune checkpoint inhibitors (anti-PD-1, anti-PD-L1, and anti-CTLA-4 antibodies) have exhibited potential therapeutic effects for advanced HCC. With the development of gene-editing technologies, gene-sequencing technologies, big data strategies, and artificial intelligence algorithms, engineered immune cell infusion and personalized cancer vaccine therapy have emerged as important directions for anti-HCC treatment. Combining different immunotherapies or combining immunotherapies with conventional therapeutic approaches may provide synergistic effects and facilitate the development of personalized medicine. In this study, we provide an overview of the liver immunoanatomy, the potential immune mechanisms of HCC, and current (pre)clinical developments in this field.


title: Ultrafast imaging in biomedical ultrasound

abstract: Although the use of ultrasonic plane-wave transmissions rather than line-per-line focused beam transmissions has been long studied in research, clinical application of this technology was only recently made possible through developments in graphical processing unit (GPU)-based platforms. Far beyond a technological breakthrough, the use of plane or diverging wave transmissions enables attainment of ultrafast frame rates (typically faster than 1000 frames per second) over a large field of view. This concept has also inspired the emergence of completely novel imaging modes which are valuable for ultrasound-based screening, diagnosis, and therapeutic monitoring. In this review article, we present the basic principles and implementation of ultrafast imaging. In particular, present and future applications of ultrafast imaging in biomedical ultrasound are illustrated and discussed.


title: Solving Poisson's Equation using Deep Learning in Particle Simulation of PN Junction

abstract: Simulating the dynamic characteristics of a PN junction at the microscopic level requires solving the Poisson's equation at every time step. Solving at every time step is a necessary but time-consuming process when using the traditional finite difference (FDM) approach. Deep learning is a powerful technique to fit complex functions. In this work, deep learning is utilized to accelerate solving Poisson's equation in a PN junction. The role of the boundary condition is emphasized in the loss function to ensure a better fitting. The resulting I-V curve for the PN junction, using the deep learning solver presented in this work, shows a perfect match to the I-V curve obtained using the finite difference method, with the advantage of being 10 times faster at every time step.


title: Multi-Human Parsing Machines

abstract: Human parsing is an important task in human-centric analysis. Despite the remarkable progress in single-human parsing, the more realistic case of multi-human parsing remains challenging in terms of the data and the model. Compared with the considerable number of available single-human parsing datasets, the datasets for multi-human parsing are very limited in number mainly due to the huge annotation effort required. Besides the data challenge to multi-human parsing, the persons in real-world scenarios are often entangled with each other due to close interaction and body occlusion, making it difficult to distinguish body parts from different person instances. In this paper we propose the Multi-Human Parsing Machines (MHPM) system, which contains an MHP Montage model and an MHP Solver, to address both challenges in multi-human parsing. Specifically, the MHP Montage model in MHPM generates realistic images with multiple persons together with the parsing labels. It intelligently composes single persons onto background scene images while maintaining the structural information between persons and the scene. The generated images can be used to train better multi-human parsing algorithms. On the other hand, the MHP Solver in MHPM solves the bottleneck of distinguishing multiple entangled persons with close interaction. It employs a Group-Individual Push and Pull (GIPP) loss function, which can effectively separate persons with close interaction. We experimentally show that the proposed MHPM can achieve state-of-the-art performance on the multi-human parsing benchmark and the person individualization benchmark, which distinguishes closely entangled person instances.


title: Logical particle filtering

abstract: In this paper, we consider the problem of filtering in rela- tional hidden Markov models. We present a compact representation for such models and an associated logical particle filtering algorithm. Each particle contains a logical formula that describes a set of states. The algorithm updates the formulae as new observations are received. Since a single particle tracks many states, this filter can be more accurate than a traditional particle filter in high dimensional state spaces, as we demonstrate in experiments. Consider an agent operating in a complex environment, made up of an un- known, possibly infinite, number of objects. The agent can take actions and make observations of the state of the world, and it knows a probabilistic model of how the state changes over time as a result of its actions and of how the observations are generated from the states. How can it eciently estimate the underlying state of the environment? Filtering is the problem of predicting a distribution over the underlying environment state given a history of the agent's actions and observations. This problem is pervasive in AI: a dialogue system has to estimate the belief state of the user; an oce-assistant must track the states and relationships among people, meetings, and projects; a household robot has to track the locations of furniture, the state of the dishes in the dishwasher, and the desires of the humans in its house. At their root, these problems are controlled hidden Markov models (HMMs) or POMDPs. The standard techniques for filtering in such models require enu- meration of the individual states of the environment. This quickly becomes in- tractable, and is impossible in infinite worlds. Particle filtering methods (1) make approximations by representing a small set of likely states. They can be exe- cuted online with constant computation per time step and can be used to track arbitrary state spaces, but reliable estimates in large domains require a very large number of particles. This problem can often be ameliorated by using Rao- Blackwellization, in which the filtering distribution is decomposed into two fac- tors, one that is sampled and one that is computed exactly. Rao-Blackwellization has been eectively applied in both propositional (2) and relational (3) state rep- resentations. In both cases, however, a finite universe of objects must be known in advance. Quantified logical expressions are a powerful method for using short de- scriptions to name large (possibly infinite) sets of states. When the underlying model of uncertainty in the domain is nondeterministic rather than probabilistic


Boosting Information Extraction Systems with Character-level Neural Networks and Free Noisy Supervision


title: Nudging People at Work and Other Third-Party Locations

abstract: Nudging people towards positive behaviour change is an important issue recognised by academia, individuals, and even governments. Although much research has been published in this area, little has focused on non-domestic environments such as the workplace. It is widely reported that changing individual behaviour of employees can make a significant contribution to such reductions. This position paper focuses on the unique aspects that make nudging consumption behaviour in third-party environments like the workplace a very different problem to that of nudging in people’s domestic and private lives. Several studies are discussed to provide context as well as evidence towards our position.


title: A Scape Plot Representation for Visualizing Repetitive Structures of Music Recordings

abstract: The development of automated methods for revealing the repetitive structure of a given music recording is of central importance in music information retrieval. In this paper, we present a novel scape plot representation that allows for visualizing repetitive structures of the entire music recording in a hierarchical, compact, and intuitive way. In a scape plot, each point corresponds to an audio segment identified by its center and length. As our main contribution, we assign to each point a color value so that two segment properties become apparent. Firstly, we use the lightness component of the color to indicate the repetitiveness of the encoded segment, where we revert to a recently introduced fitness measure. Secondly, we use the hue component of the color to reveal the relations between different segments. To this end, we introduce a novel grouping procedure that automatically maps related segments to similar hue values. By discussing a number of popular and classical music examples, we illustrate the potential and visual appeal of our representation and also indicate limitations.


title: The Evolving Philosophers Problem: Dynamic Change Management.

abstract: A model for dynamic change management which separates structural concerns from component application concerns is presented. This separation of concerns permits the formulation of general structural rules for change at the configuration level without the need to consider application state, and the specification of application component actions without prior knowledge of the actual structural changes which may be introduced. In addition, the changes can be applied in such a way so as to leave the modified system in a consistent state, and cause no disturbance to the unaffected part of the operational system. The model is applied to an example problem, 'evolving philosophers'. The principles of this model have been implemented and tested in the Conic environment for distributed systems. >


title: Toward a Lightweight Authentication and Authorization Framework for Smart Objects

abstract: The Internet of Things (IoT) represents the current technology revolution that is intended to transform the current environment into a more pervasive and ubiquitous world. In this emerging ecosystem, the application of standard security technologies has to cope with the inherent nature of constrained physical devices, which are seamlessly integrated into the Internet infrastructure. This work proposes a set of lightweight authentication and authorization mechanisms in order to support smart objects during their life cycle. Furthermore, such mechanisms are framed within a proposed security framework, which is compliant with the Architectural Reference Model , recently presented by the EU FP7 IoT-A project. The resulting architecture is intended to provide a holistic security approach to be leveraged in the design of novel and lightweight security protocols for IoT constrained environments.


Privacy-Preserving Distributed Profile Matching in Proximity-based Mobile Social Networks


title: Designing a socially assistive robot for personalized number concepts learning in preschool children

abstract: Designing technological systems for personalized education is an iterative and interdisciplinary process that demands a deep understanding of the application domain, the limitations of current methods and technologies, and the computational methods and complexities behind user modeling and adaptation. We present our design process and the Socially Assistive Robot (SAR) tutoring system to support the efforts of educators in teaching number concepts to preschool children. We focus on the computational considerations of designing a SAR system for young children that may later be personalized along multiple dimensions. We conducted an initial data collection to validate that the system is at the proper challenge level for our target population, and discovered promising patterns in participants' learning styles, nonverbal behavior, and performance. We discuss our plans to leverage the data collected to learn and validate a computational, multidimensional model of number concepts learning.


title: Differentially private smart metering with battery recharging

abstract: The energy industry has recently begun using smart meters to take fine-grained readings of energy usage. These smart meters enable flexible time-of-use billing, forecasting, and demand response, but they also raise serious user privacy concerns. We propose a novel technique for provably hiding sensitive power consumption information in the overall power consumption stream. Our technique relies on a rechargeable battery that is connected to the household's power supply. This battery is used to modify the household's power consumption by adding or subtracting noise (i.e., increasing or decreasing power consumption), in order to establish strong privacy guarantees in the sense of differential privacy. To achieve these privacy guarantees in realistic settings, we first investigate the influence of, and the interplay between, capacity and throughput bounds that batteries face in reality. We then propose an integrated method based on noise cascading that allows for recharging the battery on-the-fly so that differential privacy is retained, while adhering to capacity and throughput constraints, and while keeping the additional consumption of energy induced by our technique to a minimum.


title: Appearance Modeling via Proxy-to-Image Alignment

abstract: We present a novel approach for quickly extracting a non-parametric appearance model from a single photograph of a reference object, allowing users to easily enrich detail-less 3D shapes with realistic geometric detail and surface texture.


title: Label-free classification of cultured cells through diffraction imaging

abstract: Automated classification of biological cells according to their 3D morphology is highly desired in a flow cytometer setting. We have investigated this possibility experimentally and numerically using a diffraction imaging approach. A fast image analysis software based on the gray level co-occurrence matrix (GLCM) algorithm has been developed to extract feature parameters from measured diffraction images. The results of GLCM analysis and subsequent classification demonstrate the potential for rapid classification among six types of cultured cells. Combined with numerical results we show that the method of diffraction imaging flow cytometry has the capacity as a platform for high-throughput and label-free classification of biological cells.


title: Using TF-IDF n-gram and Word Embedding Cluster Ensembles for Author Profiling

abstract: This paper presents our approach and results for the 2017 PAN Author Profiling Shared Task. Language-specific corpora were provided for four langauges: Spanish, English, Portuguese, and Arabic. Each corpus consisted of tweets authored by a number of Twitter users labeled with their gender and the specific variant of their language which was used in the documents (e.g. Brazilian or European Portuguese). The task was to develop a system to infer the same attributes for unseen Twitter users. Our system employs an ensemble of two probabilistic classifiers: a Logistic regression classifier trained on TF-IDF transformed n-grams and a Gaussian Process classifier trained on word embedding clusters derived for an additional, external corpus of tweets.


Clinical genetics evaluation in identifying the etiology of autism spectrum disorders: 2013 guideline revisions


title: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.


title: Applying Innovation Resistance Theory to Understand User Acceptance of Online Shopping: The Moderating Effect of Different Product Types

abstract: There exists a gap between the number of Internet and electronic commerce (EC) user. Previous researches attempt to explore the factors that will affect user acceptance of online shopping, in order to increase the EC market. However, this study insists that for those users who known but never (rarely) use EC services, it is important to understand the reasons of why they refuse to use. Innovation resistance theory is employed in this study to understand this issue. Besides, previous studies indicated that different product types will influence the relationship between factors and user intention in various degrees in the context of online shopping. Therefore, the purpose of this research attempts to integrate innovation resistance theory and product types to explore the moderating effect of different product types. The result indicates that value and image barriers are critical factors for user refused to shop online. The authors also find that "intangible and information products" have more strong moderating influence than "tangible and physical products" between usage barriers/value barriers and intention to use.


title: ChemInk: a natural real-time recognition system for chemical drawings

abstract: We describe a new sketch recognition framework for chemical structure drawings that combines multiple levels of visual features using a jointly trained conditional random field. This joint model of appearance at different levels of detail makes our framework less sensitive to noise and drawing variations, improving accuracy and robustness. In addition, we present a novel learning-based approach to corner detection that achieves nearly perfect accuracy in our domain. The result is a recognizer that is better able to handle the wide range of drawing styles found in messy freehand sketches. Our system handles both graphics and text, producing a complete molecular structure as output. It works in real time, providing visual feedback about the recognition progress. On a dataset of chemical drawings our system achieved an accuracy rate of 97.4%, an improvement over the best reported results in literature. A preliminary user study also showed that participants were on average over twice as fast using our sketch-based system compared to ChemDraw, a popular CAD-based tool for authoring chemical diagrams. This was the case even though most of the users had years of experience using ChemDraw and little or no experience using Tablet PCs.


title: Analysis and detection R-peak detection using Modified Pan-Tompkins algorithm

abstract: Cardiac diseases that gives rise to the death and possibly forms the immedicable danger in order to monitor the heart the proposed algorithm is used. An automatic detection of peaks in Electrocardiogram (ECG) signals is presented. The aim of the design work is to achieve higher performance in terms of reliability and better diagnosis of patients through Peak detection. The new method called Modified Pan-Tompkins algorithm based on the slope and amplitude of ECG signal. The QRS complex detection algorithm uses optimized Bandpass-filtering to reduce false detection. The purpose of prefiltering is to reduce various noise components in order to achieve improved detection reliability. The QRS detection reliability of an algorithm was tested with an noisy stress ECG signal. The usefulness of the proposed method is shown by applying the algorithm to signal from MIT- BIH Arrhythmia database to obtain the number of heart-beats per minute which helps to diagnose the heart disease.


title: A Projective Integration Method for Transient Stability Assessment of Power Systems With a High Penetration of Distributed Generation

abstract: The impact of numerous distributed generators (DGs) coupled with the implementation of virtual inertia on the transient stability of power systems has been studied extensively. Time-domain simulation is the most accurate and reliable approach to evaluate the dynamic behavior of power systems. However, the computational efficiency is restricted by their multi-time-scale property due to the combination of various DGs and synchronous generators. This paper presents a novel projective integration method (PIM) for the efficient transient stability simulation of power systems with high DG penetration. One procedure of the proposed PIM is decomposed into two stages, which adopt mixed explicit-implicit integration methods to achieve both efficiency and numerical stability. Moreover, the stability of the PIM is not affected by its parameter, which is related to the step size. Based on this property, an adaptive parameter scheme is developed based on error estimation to fit the time constants of the system dynamics and further increase the simulation speed. The presented approach is several times faster than the conventional integration methods with a similar level of accuracy. The proposed method is demonstrated using test systems with DGs and virtual synchronous generators, and the performance is verified against MATLAB/Simulink and DIgSILENT PowerFactory.


title: HDSKG: Harvesting domain specific knowledge graph from content of webpages

abstract: Knowledge graph is useful for many different domains like search result ranking, recommendation, exploratory search, etc. It integrates structural information of concepts across multiple information sources, and links these concepts together. The extraction of domain specific relation triples (subject, verb phrase, object) is one of the important techniques for domain specific knowledge graph construction. In this research, an automatic method named HDSKG is proposed to discover domain specific concepts and their relation triples from the content of webpages. We incorporate the dependency parser with rule-based method to chunk the relations triple candidates, then we extract advanced features of these candidate relation triples to estimate the domain relevance by a machine learning algorithm. For the evaluation of our method, we apply HDSKG to Stack Overflow (a Q&A website about computer programming). As a result, we construct a knowledge graph of software engineering domain with 35279 relation triples, 44800 concepts, and 9660 unique verb phrases. The experimental results show that both the precision and recall of HDSKG (0.78 and 0.7 respectively) is much higher than the openIE (0.11 and 0.6 respectively). The performance is particularly efficient in the case of complex sentences. Further more, with the self-training technique we used in the classifier, HDSKG can be applied to other domain easily with less training data.


title: A reconfigurable hybrid actuator with rigid and soft components

abstract: Classical rigid-bodied robotic systems are presented with proven success in theoretical development and industrial applications, are recently challenged by the emergence of soft robotics due to a growing need in physical human-robot interactions (pHRI), such as wearable devices, medical robots, personal robots, etc. In this paper, we present the design and fabrication of a robust, hybrid bending actuator build from both rigid and soft components inspired by crustaceans, where its bending radius and axis can be mechanically programmed through the selective activation of the rigid exterior joints, actuated by the soft actuators inside. The hybrid actuator was experimentally measured in terms of bending and force tests to demonstrate the utility of this design. Finally, a case study was presented to demonstrate its capacity to adapt to specific objects geometry, anticipating its potential application in situations where compliance is the priority.


title: Autonomous Crowdsourcing through Human-Machine Collaborative Learning

abstract: In this paper, we introduce a general iterative human-machine collaborative method for training crowdsource workers: the classifier (i.e., the machine) selects the highest quality examples for training the crowdsource workers (i.e., the humans). Then, the latter annotate the lower quality examples such that the classifier can be re-trained with more accurate examples. This process can be iterated several times. We tested our approach on two different tasks, Relation Extraction and Community Question Answering, which are also in two different languages, English and Arabic, respectively. Our experimental results show a significant improvement for creating Gold Standard data over distant supervision or just crowdsourcing without worker training. At the same time, our method approach the performance than state-of-the-art methods using expensive Gold Standard for training workers


title: Geometry of Miura-folded metamaterials

abstract: This paper describes two folded metamaterials based on the Miura-ori fold pattern. The structural mechanics of these metamaterials are dominated by the kinematics of the folding, which only depends on the geometry and therefore is scale-independent. First, a folded shell structure is introduced, where the fold pattern provides a negative Poisson’s ratio for in-plane deformations and a positive Poisson’s ratio for out-of-plane bending. Second, a cellular metamaterial is described based on a stacking of individual folded layers, where the folding kinematics are compatible between layers. Additional freedom in the design of the metamaterial can be achieved by varying the fold pattern within each layer.


title: Virtual communities in schools as tools to promote social capital with high schools students

abstract: The general aim of this study was to test the hypothesis that students belonging to a school online community would show higher levels of both offline bridging and bonding social capital than a control group of students, not using the online community. We further hypothesized that the more students used the online community the higher their level of their offline bonding and bridging social capital. Participants were 264 high school boys and girls, 126, (62 males and 64 females) who had joined the community online Spallanzani shout and 138, (62 males and 76 females) who did not. ANOVAs analysis showed that levels of bridging and bonding social capitals were significantly higher for members. Instead intensity of use was significantly related only to bridging but not to bonding social capital. Implications of the findings and directions for future research are discussed.


title: The Moderating Role of Organizational Culture in the Relationship between Power, Trust, and eSCMS Adoption Intention

abstract: Building on multiple theoretical perspectives, we examined how organizational culture moderates the relationship between power, trust, and a firm’s eSCMS adoption intention. We tested the hypotheses using survey data collected from senior executives in China. Our findings reveal that a target firm’s perceived mediated power would negatively impact its trust toward a dominant firm, while its perceived non-mediated power would positively affect its trust. Meanwhile, trust can positively influence the target firm’s eSCMS adoption intention. Further, an internally focused culture weakens the negative effects of mediated power on trust. Meanwhile, an externally focused culture weakens the positive relationships between non-mediated power and trust, and between trust and eSCMS adoption intention. The externally focused culture could weaken the negative relationship between mediated power and trust either. The theoretical contributions and managerial implications of the study are discussed.


title: A Heterogeneous Label Propagation Algorithm for Disease Gene Discovery

abstract: Label propagation is an effective and efficient technique to utilize local and global features in a network for semi-supervised learning. In the literature, one challenge is how to propagate information in heterogeneous networks comprising several subnetworks, each of which has its own cluster structures that need to be explored independently. In this paper, we introduce an intutitive algorithm MINProp (Mutual Interaction-based Network Propagation) and a simple regularization framework for propagating information between subnetworks in a heterogeneous network. MINProp sequentially performs label propagation on each individual subnetwork with the current label information derived from the other subnetworks and repeats this step until convergence to the global optimal solution to the convex objective function of the regularization framework. The independent label propagation on each subnetwork explores the cluster structure in the subnetwork. The label information from the other subnetworks is used to capture mutual interactions (bicluster structures) between the vertices in each pair of the subnetworks. MINProp algorithm is applied to disease gene discovery from a heterogeneus network of disease phenotypes and genes. In the experiments, MINProp significantly output-performed the original label propagation algorithm on a single network and the state-of-the-art methods for discovering disease genes. The results also suggest that MINProp is more effective in utilizing the modular structures in a heterogenous network. Finally, MINProp discovered new disease-gene associations that are only reported recently.


Klinefelter's syndrome.


title: Artificial intelligence scholars question wheter there is na overvaluation of machines without regard human intelligence: na invitation to reflection

abstract: We seek to raise questions about Artificial Intelligence starting from the ideas of researchers as Dennet, Crevier, Dreyfus, Minsk, Virilio, who aim to relate the implications of such technological movement and its variations in current days. The reports are disturbing for a society in which technology depends on other markets, therefore, being servant. Considering this, we are guided by the thoughts of those scholars the facts reported in the work lead us to a main question, which is both sensitive and disturbing, as well as potentially emblematic: In addition to agile allies, could Artificial Intelligence also take on contours that would make us our opponents? Such questioning is not new. Discussed at a time when knowledge was beginning to emerge from the pastoral moorings to evidence the reason and lift a mediator: the man. At that moment, a reconfiguration and appreciation of other knowledge arose. From dark days to less obscure ones? It seems to us that the question asked becomes legitimate given the power relation that is formed when a system composed of aluminum, steel and, supposedly, “mind”, which looms over our lives, is so called being.


title: An evaluation of indirect attacks and countermeasures in fingerprint verification systems

abstract: Biometric recognition systems are vulnerable to numerous security threats. These include direct attacks to the sensor or indirect attacks, which represent the ones aimed towards internal system modules. In this work, indirect attacks against fingerprint verification systems are analyzed in order to better understand how harmful they can be. Software attacks via hill climbing algorithms are implemented and their success rate is studied under different conditions. In a hill climbing attack, a randomly generated synthetic template is presented to the matcher, and is iteratively modified based on the score output until it is accepted as genuine. Countermeasures against such attacks are reviewed and analyzed, focusing on score quantization as a case study. It is found that hill climbing attacks are highly effective in the process of creating synthetic templates that are accepted by the matcher as genuine ones. We also find that score quantization drastically reduces the attack success rate. We analyze the hill climbing approach over two state-of-the-art fingerprint verification systems: the NIST Fingerprint Image Software 2, running on a PC and a prototype system fully embedded in a smart card (Match-on-Card). Results of both systems are obtained using a sub corpus of the publicly available MCYT database.