title: A survey and comparison of peer-to-peer overlay network schemes

abstract: Over the Internet today, computing and communications environments are significantly more complex and chaotic than classical distributed systems, lacking any centralized organization or hierarchical control. There has been much interest in emerging Peer-to-Peer (P2P) network overlays because they provide a good substrate for creating large-scale data sharing, content distribution, and application-level multicast applications. These P2P overlay networks attempt to provide a long list of features, such as: selection of nearby peers, redundant storage, efficient search/location of data items, data permanence or guarantees, hierarchical naming, trust and authentication, and anonymity. P2P networks potentially offer an efficient routing architecture that is self-organizing, massively scalable, and robust in the wide-area, combining fault tolerance, load balancing, and explicit notion of locality. In this article we present a survey and comparison of various Structured and Unstructured P2P overlay networks. We categorize the various schemes into these two groups in the design spectrum, and discuss the application-level network performance of each group.


title: Agricultural plant Leaf Disease DetectionUsing Image Processing

abstract: The detection of plant leaf is an very important factor to prevent serious outbreak. Automatic detection of plant disease is essential research topic. Most plant diseases are caused by fungi, bacteria, and viruses. Fungi are identified primarily from their morphology, with emphasis placed on their reproductive structures. Bacteria are considered more primitive than fungi and generally have simpler life cycles. With few exceptions, bacteria exist as single cells and increase in numbersby dividing into two cells during a process called binary fission Viruses are extremely tiny particles consisting of protein and genetic material with no associated protein. The term disease is usually used only for the destruction of live plants. The developed processing scheme consists of four main steps, first a color transformation structure for the input RGB image is created, this RGB is converted to HSI because RGB is for color generation and his for color descriptor. Then green pixels are masked and removed using specific threshold value, then the image is segmented and the useful segments are extracted, finally the texture statistics is computed. from SGDM matrices. Finally the presence of diseases on the plant leaf is evaluated.


title: The relationship between nature connectedness and happiness: a meta-analysis

abstract: Research suggests that contact with nature can be beneficial, for example leading to improvements in mood, cognition, and health. A distinct but related idea is the personality construct of subjective nature connectedness, a stable individual difference in cognitive, affective, and experiential connection with the natural environment. Subjective nature connectedness is a strong predictor of pro-environmental attitudes and behaviours that may also be positively associated with subjective well-being. This meta-analysis was conducted to examine the relationship between nature connectedness and happiness. Based on 30 samples (n = 8,523), a fixed-effect meta-analysis found a small but significant effect size (r = .19). Those who are more connected to nature tended to experience more positive affect, vitality, and life satisfaction compared to those less connected to nature. Publication status, year, average age, and percentage of females in the sample were not significant moderators. Vitality had the strongest relationship with nature connectedness (r = .24), followed by positive affect (r = .22) and life satisfaction (r = .17). In terms of specific nature connectedness measures, associations were the strongest between happiness and inclusion of nature in self (r = .27), compared to nature relatedness (r = .18) and connectedness to nature (r = .18). This research highlights the importance of considering personality when examining the psychological benefits of nature. The results suggest that closer human-nature relationships do not have to come at the expense of happiness. Rather, this meta-analysis shows that being connected to nature and feeling happy are, in fact, connected.


title: German NER with a Multilingual Rule Based Information Extraction System: Analysis and Issues.

abstract: This paper presents a rule-based approach to Named Entity Recognition for the German language. The approach rests upon deep linguistic parsing and has already been applied to English and Russian. In this paper we present the first results of our system, ABBYY InfoExtractor, on GermEval 2014 Shared Task corpus. We focus on the main challenges of German NER that we have encountered when adapting our system to German and possible solutions for them.


title: Multi-criteria evaluation of alternative-fuel vehicles via a hierarchical hesitant fuzzy linguistic model

abstract: We concern with a fleet operator considering to adopt an alternative-fuel vehicle.The problem is a multi-expert and multicriteria problem with conflicting criteria.1st major contribution is identification and classification of evaluation criteria.2nd major contribution is a hierarchical hesitant fuzzy linguistic model.The paper presents a real world application with sensitivity and scenario analyzes. Decision on alternative-fuel vehicles is one of the most important problems for fleet operations. In this paper we propose a hierarchical hesitant fuzzy linguistic model that captures hesitant linguistic evaluations of multiple experts on multiple criteria for alternative-fuel vehicles. We apply the proposed model on the alternative-fuel vehicle selection problem of a home health care service provider in the USA. The results show that an electric vehicle is the best fit for the application in today's conditions. We also show robustness of the decision through a sensitivity analysis as well as analyze three scenarios representing possible changes in conditions.


title: Security-as-a-Service for Microservices-Based Cloud Applications

abstract: Microservice architecture allows different parts of an application to be developed, deployed and scaled independently, therefore becoming a trend for developing cloud applications. However, it comes with challenging security issues. First, the network complexity introduced by the large number of microservices greatly increases the difficulty in monitoring the security of the entire application. Second, microservices are often designed to completely trust each other, therefore compromise of a single microservice may bring down the entire application. The problems are only exacerbated by the cloud, since applications no longer have complete control over their networks. In this paper, we propose a design for security-as-a-service for microservices-based cloud applications. By adding a new API primitive FlowTap for the network hypervisor, we build a flexible monitoring and policy enforcement infrastructure for network traffic to secure cloud applications. We demonstrate the effectiveness of our solution by deploying the Bro network monitor using FlowTap. Results show that our solution is flexible enough to support various kinds of monitoring scenarios and policies and it incurs minimal overhead (~6%) for real world usage. As a result, cloud applications can leverage our solution to deploy network security monitors to flexibly detect and block threats both external and internal to their network.


Analysis, Design, and Optimization of Spiral Inductors and Transformers for Si RF IC's


title: Baseball Throwing Mechanics as They Relate to Pathology and Performance - A Review

abstract: It is a commonly held perception amongst biomechanists, sports medicine practitioners, baseball coaches and players, that an individual baseball player's style of throwing or pitching influences their performance and susceptibility to injury. With the results of a series of focus groups with baseball managers and pitching coaches in mind, the available scientific literature was reviewed regarding the contribution of individual aspects of pitching and throwing mechanics to potential for injury and performance. After a discussion of the limitations of kinematic and kinetic analyses, the individual aspects of pitching mechanics are discussed under arbitrary headings: Foot position at stride foot contact; Elbow flexion; Arm rotation; Arm horizontal abduction; Arm abduction; Lead knee position; Pelvic orientation; Deceleration-phase related issues; Curveballs; and Teaching throwing mechanics. In general, popular opinion of baseball coaching staff was found to be largely in concordance with the scientific investigations of biomechanists with several notable exceptions. Some difficulties are identified with the practical implementation of analyzing throwing mechanics in the field by pitching coaches, and with some unquantified aspects of scientific analyses. ::: ::: ::: ::: Key points ::: ::: ::: Biomechanical analyses including kinematic and kinetic analyses allow for estimation of pitching performance and potential for injury. ::: ::: ::: Some difficulties both theoretic and practical exist for the implementation and interpretation of such analyses. ::: ::: ::: Commonly held opinions of baseball pitching authorities are largely held to concur with biomechanical analyses. ::: ::: ::: Recommendations can be made regarding appropriate pitching and throwing technique in light of these investigations.


title: Recruitment of the human TREX complex to mRNA during splicing.

abstract: In yeast, the TREX complex contains the THO transcription elongation complex, which functions in direct cotranscriptional recruitment of the mRNA export proteins Sub2 and Yra1 to nascent transcripts. Here we report the identification of the human THO complex and show that it associates with spliced mRNA, but not with unspliced pre-mRNA in vitro. Transcription is not required for this recruitment. We also show that the human THO complex colocalizes with splicing factors in nuclear speckle domains in vivo. Considering that splicing occurs cotranscriptionally in humans, our data indicate that recruitment of the human TREX complex to spliced mRNA is not directly coupled to transcription, but is instead coupled to transcription indirectly through splicing.


title: Perceptions of Traditional Information Sources and Use of the World Wide Web to Seek Health Information: Findings From the Health Information National Trends Survey

abstract: As medical information becomes increasingly available and individuals take a more active role in managing their personal health, it is essential for scholars to better understand the general public's information-seeking behavior. The study reported here explores the use of the World Wide Web to seek health information in a contemporary information-media environment. Drawing from uses and gratifications theory and the comprehensive model of health information seeking, perceptions of traditional information sources (e.g., mass media, one's health care provider, etc.) are posited to predict use of the Web to seek health information and perceptions of information acquired from searches. Data from the Health Information National Trends Survey (HINTS; N = 3982) were analyzed to test study hypotheses. Trust in information-oriented media, entertainment-oriented media, and one's health care provider all predicted Web use behavior and perceptions. The implications of the findings for research on information seeking...


Healthcare recommender system based on Smart–health Routes


title: Social media as a shield: Facebook buffers acute stress

abstract: Abstract Facebook remains the most widely used social media platform. Research suggests that Facebook may both enhance and undermine psychosocial constructs related to well-being, and that it may impair physiological stress recovery. However, little is known about its influence on stress reactivity. Using novel experimental methods, this study examined how Facebook influences reactivity to an acute social stressor. Facebook users (n = 104, 53 males, mean age 19.50, SD = 1.73) were randomly assigned to use their own Facebook account or sit quietly with the option of reading electronic magazines before experiencing an acute social stressor. All participants showed significant changes in subjective and physiological stress markers in response to the stressor. However, participants who used Facebook experienced lower levels of psychosocial stress, physiological stress, and rated the stressor as less threatening (p's


title: A Test Automation Framework Based on WEB

abstract: Many software applications today are written as web-based applications to be run in an Internet browser. Selenium is a set of powerful different software tools working with many browsers, operating systems, programming languages, and testing frameworks each with a different approach to supporting automation test for testing web-based applications. JMeter is used to simulate a heavy load on a server, network or object to test its strength or to analyze overall performance under different load types. JMeter operates at the protocol-level, on the other hand, Selenium works at the user-level.In this paper, authors have designed an automatic software testing framework for web applications based on the Selenium and JMeter. With the use of the software framework, we efficiently improve the extensibility and reusability of automated test. The results show that the new software framework improves software products quality and develop efficiency. This paper also illustrates how to design web-based test automation framework in details.


title: Penile length, digit length, and anogenital distance according to birth weight in newborn male infants

abstract: Purpose ::: Anogential distance (AGD) and the 2:4 digit length ratio appear to provide a reliable guide to fetal androgen exposure. We intended to investigate the current status of penile size and the relationship between penile length and AGD or digit length according to birth weight in Korean newborn infants.


title: Design and prototype of monolithic compliant grippers for adaptive grasping

abstract: This study presents the designs of monolithic compliant grippers made by silicon rubber for adaptive grasping applications. The proposed grippers are symmetric two-finger designs actuated by one linear actuator. An optimal design procedure including topology and size optimization methods is used to synthesize the rubber grippers with the objective to maximize mechanical advantage, which is defined as the ratio of output force to input force. Three grippers synthesized with different design parameters are prototyped. The best design is identified through the adaptability test, and a robotic gripper assembly is developed to demonstrate the effectiveness of the design through the test of grasping irregular objects. The maximum payload for the gripper assembly is identified as 2.5kg.


title: A review on recent Mppt techniques for photovoltaic system

abstract: Solar energy is an abundant renewable energy source (RES) which is available without any price from the Sun to the earth. It can be a good alternative of energy source in place of non-renewable sources (NRES) of energy like as fossil fuels and petroleum articles. Sun light can be utilized through solar cells which fulfills the need of energy of the utilizer instead of energy generation by NRES. The development of solar cells has crossed by a number of modifications from one age to another. The cost and efficiency of solar cells are the obstacles in the advancement. In order to select suitable solar photovoltaic (PV) cells for a particular area, operators are needed to sense the basic mechanisms and topologies of diverse solar PV with maximum power point tracking (MPPT) methodologies that are checked to a great degree. In this article, authors reviewed and analyzed a successive growth in the solar PV cell research from one decade to other, and explained about their coming fashions and behaviors. This article also attempts to emphasize on many experiments and technologies to contribute the perks of solar energy.


title: Design and evaluation of a multi-modulation retrodirective RFID tag

abstract: Movement towards mm-wave backscatter communications in radio-frequency identification (RFID) systems necessitates seeking designs that compensate for the path loss introduced by the radio channel. A viable, simple, and power-efficient solution is to equip RFID tags with retrodirective arrays, which guarantee reflection with maximal gain in the direction of incidence. In this paper, we design and implement an original retrodirective tag in which its feeding network is a properly-terminated rat-race coupler. We start by deriving the required terminations that ensure retrodirectivity. Then, we build a retrodirective tag and experimentally compare its radar cross section to that of a single-antenna tag. The measurements reveal that the radar cross section of the proposed retrodirective tag is, on average, approximately 6 dB more than that of a single-antenna tag while both tags have the same field-of-view. From the promising results in this paper, the proposed design is a potential candidate for next-generation microwave and mm-wave RFID tags because it is compatible with low-power reflection amplifiers (e.g., tunnel diodes), it can implement multiple modulation schemes without changing the circuit layout, and it can be implemented using only a single RF switch.


title: Evaluating connectivity between marine protected areas using CODAR high-frequency radar

abstract: To investigate the connectivity between central California marine protected areas (MPAs), back-projections were calculated using the network of high-frequency (HF) radar ocean surface current mapping stations operated along the California coast by the member institutions of the Coastal Ocean Currents Monitoring Program with funding provided by California voters through Propositions 40 & 50 and administered by the State Coastal Conservancy. Trajectories of 1 km resolution grids of water particles were back-projected from ten MPAs each hour, out through 40 days in the past, from each day in 2008, producing a map of where surface waters travel over a 40-day period to reach the MPAs — and visualizations of the length of time the waters travel along these paths. By comparing the travel times of those back-projected track-points that crossed between MPA regions, the connection time between MPAs along the State's central coast was assessed. Repeating these calculations resulted in a connectivity matrix between the MPAs in the region, and may be useful for assessing connectivity for the important invertebrate and fish larvae that are restricted to the surface ocean during a fraction of their lifecycle.


title: Face recognition using Principal Component Analysis

abstract: The strategy of face recognition involves the examination of facial features in a picture, recognizing those features and matching them to 1 of the many faces in the database. There are lots of algorithms effective at performing face recognition, such as for instance: Principal Component Analysis, Discrete Cosine Transform, 3D acceptance methods, Gabor Wavelets method etc. This work has centered on Principal Component Analysis (PCA) method for face recognition in an efficient manner. There are numerous issues to take into account whenever choosing a face recognition method. The main element is: Accuracy, Time limitations, Process speed and Availiability. With one of these in minds PCA way of face recognition is selected because it is really a simplest and easiest approach to implement, extremely fast computation time. PCA (Principal Component Analysis) is an activity that extracts the absolute most relevant information within a face and then tries to construct a computational model that best describes it.


title: Real-Time Rendering of Rough Refraction

abstract: We present an algorithm to render objects made of transparent materials with rough surfaces in real-time, under all-frequency distant illumination. Rough surfaces cause wide scattering as light enters and exits objects, which significantly complicates the rendering of such materials. We present two contributions to approximate the successive scattering events at interfaces, due to rough refraction: First, an approximation of the Bidirectional Transmittance Distribution Function (BTDF), using spherical Gaussians, suitable for real-time estimation of environment lighting using preconvolution; second, a combination of cone tracing and macrogeometry filtering to efficiently integrate the scattered rays at the exiting interface of the object. We demonstrate the quality of our approximation by comparison against stochastic ray tracing. Furthermore we propose two extensions to our method for supporting spatially varying roughness on object surfaces and local lighting for thin objects.


title: Design and Implementation of a 40-kV, 20-kJ/s Capacitor Charger for Pulsed-Power Application

abstract: This paper presents the design and implementation of a 40-kV, 20-kJ/s high-voltage capacitor charger based on a series/parallel resonant converter. The inclusion of the parallel resonant capacitor component in this circuit results in the production of a trapezoidal resonant current, which reduces conduction loss. This capacitor is practically realized as a part of the balancing network of the high-voltage rectifiers in the secondary side of the circuit. Particular attention in this paper is paid to the high-voltage transformer, which must be carefully designed to provide the required functionality without negative impact on the resonant circuit. A PSpice simulation has been used to prove that the proposed control method for the circuit is valid. This is supported with the experimental results, which verify that the operation of the circuit is as expected and that the converter is able to meet all the design criteria with an efficiency of up to 96%.


abstract: A local density-based approach for outlier detection is proposed.The theoretical properties of the proposed outlierness score are derived.Three types of nearest neighbors are presented. This paper presents a simple and effective density-based outlier detection approach with local kernel density estimation (KDE). A Relative Density-based Outlier Score (RDOS) is introduced to measure local outlierness of objects, in which the density distribution at the location of an object is estimated with a local KDE method based on extended nearest neighbors of the object. Instead of using only k nearest neighbors, we further consider reverse nearest neighbors and shared nearest neighbors of an object for density distribution estimation. Some theoretical properties of the proposed RDOS including its expected value and false alarm probability are derived. A comprehensive experimental study on both synthetic and real-life data sets demonstrates that our approach is more effective than state-of-the-art outlier detection methods.


title: Estimation of Symptom Severity During Chemotherapy From Passively Sensed Data: Exploratory Study

abstract: Background: Physical and psychological symptoms are common during chemotherapy in cancer patients, and real-time monitoring of these symptoms can improve patient outcomes. Sensors embedded in mobile phones and wearable activity trackers could be potentially useful in monitoring symptoms passively, with minimal patient burden. Objective: The aim of this study was to explore whether passively sensed mobile phone and Fitbit data could be used to estimate daily symptom burden during chemotherapy. Methods: A total of 14 patients undergoing chemotherapy for gastrointestinal cancer participated in the 4-week study. Participants carried an Android phone and wore a Fitbit device for the duration of the study and also completed daily severity ratings of 12 common symptoms. Symptom severity ratings were summed to create a total symptom burden score for each day, and ratings were centered on individual patient means and categorized into low, average, and high symptom burden days. Day-level features were extracted from raw mobile phone sensor and Fitbit data and included features reflecting mobility and activity, sleep, phone usage (eg, duration of interaction with phone and apps), and communication (eg, number of incoming and outgoing calls and messages). We used a rotation random forests classifier with cross-validation and resampling with replacement to evaluate population and individual model performance and correlation-based feature subset selection to select nonredundant features with the best predictive ability. Results: Across 295 days of data with both symptom and sensor data, a number of mobile phone and Fitbit features were correlated with patient-reported symptom burden scores. We achieved an accuracy of 88.1% for our population model. The subset of features with the best accuracy included sedentary behavior as the most frequent activity, fewer minutes in light physical activity, less variable and average acceleration of the phone, and longer screen-on time and interactions with apps on the phone. Mobile phone features had better predictive ability than Fitbit features. Accuracy of individual models ranged from 78.1% to 100% (mean 88.4%), and subsets of relevant features varied across participants. Conclusions: Passive sensor data, including mobile phone accelerometer and usage and Fitbit-assessed activity and sleep, were related to daily symptom burden during chemotherapy. These findings highlight opportunities for long-term monitoring of cancer patients during chemotherapy with minimal patient burden as well as real-time adaptive interventions aimed at early management of worsening or severe symptoms. [J Med Internet Res 2017;19(12):e420]


title: Beacons for indoor positioning

abstract: This work aims to develop a system for the tracking and control of elderly or handicapped people in an indoor environment. We have developed both a special box using an Arduino board, and a mobile application on Android to determine the location of the target based on the Bluetooth Low Energy signals transmitted by special Beacons which are placed in the area of interest. Both systems are used to determine the position of the person and to monitor any event that would cause an alert. Those events can be a long stay in a fixed position, or the entry into predefined forbidden zones. The location data will be stored in a database for further processing. Alerts can trigger messages to be sent to local authorities and/or relatives of the people. Measurements and test results are discussed regarding their performance. It is clear that more research and development is necessary for obtaining reliable products.


title: Stack-Captioning: Coarse-to-Fine Learning for Image Captioning

abstract: The existing image captioning approaches typically train a one-stage sentence decoder, which is difficult to generate rich fine-grained descriptions. On the other hand, multi-stage image caption model is hard to train due to the vanishing gradient problem. In this paper, we propose a coarse-to-fine multi-stage prediction framework for image captioning, composed of multiple decoders each of which operates on the output of the previous stage, producing increasingly refined image descriptions. Our proposed learning approach addresses the difficulty of vanishing gradients during training by providing a learning objective function that enforces intermediate supervisions. Particularly, we optimize our model with a reinforcement learning approach which utilizes the output of each intermediate decoder's test-time inference algorithm as well as the output of its preceding decoder to normalize the rewards, which simultaneously solves the well-known exposure bias problem and the loss-evaluation mismatch problem. We extensively evaluate the proposed approach on MSCOCO and show that our approach can achieve the state-of-the-art performance.


title: How does social software change knowledge management? Toward a strategic research agenda

abstract: Knowledge management is commonly understood as IS implementations that enable processes of knowledge creation, sharing, and capture. Knowledge management at the firm level is changing rapidly. Previous approaches included centrally managed, proprietary knowledge repositories, often involving structured and controlled search and access. Today the trend is toward knowledge management by social software, which provides open and inexpensive alternatives to traditional implementations. While social software carries great promise for knowledge management, this also raises fundamental questions about the very essence and value of firm knowledge, the possibility for knowledge protection, firm boundaries, and the sources of competitive advantage. I draft a strategic research agenda consisting of five fundamental issues that should reinvigorate research in knowledge management.


title: Molding process development for high density I/Os Fan-Out Wafer Level Package (FOWLP) with fine pitch RDL

abstract: With the perpetual demand for greater functionalities, better performance and greater energy efficiency at cheaper manufacturing cost and smaller form factor, Fan-Out Wafer Level Packaging (FOWLP) technology has emerged as one of the most promising technology in fulfilling the demands from electronic devices for mobile and network applications. In our FOWLP mold-first approach development work, we developed the 300mm compression molding process for high density input/output (IO)s reconstituted FOWLP mold wafer fabricated with fine pitch redistributed layer (RDL) of line width / line space (LW/LS) ≤5/5μm using the conventional mold-first approach. Our compression molding process development aims to achieve a chip-to-mold non planarity ≤3μm, warpage of reconstituted FOWLP mold wafer ≤1mm and wafer level die shift ≤10μm. In this paper, we will discuss on the 300mm compression molding development work like materials selection, pick-and-place (PnP) process parameters and die shift compensation via constant and dynamic pre-shift methodologies in achieving our targeted specifications for the fabrication of fine pitch RDL FOWLP.


title: Multiple view semantic segmentation for street view images

abstract: We propose a simple but powerful multi-view semantic segmentation framework for images captured by a camera mounted on a car driving along streets. In our approach, a pair-wise Markov Random Field (MRF) is laid out across multiple views. Both 2D and 3D features are extracted at a super-pixel level to train classifiers for the unary data terms of MRF. For smoothness terms, our approach makes use of color differences in the same image to identify accurate segmentation boundaries, and dense pixel-to-pixel correspondences to enforce consistency across different views. To speed up training and to improve the recognition quality, our approach adaptively selects the most similar training data for each scene from the label pool. Furthermore, we also propose a powerful approach within the same framework to enable large-scale labeling in both the 3D space and 2D images. We demonstrate our approach on more than 10,000 images from Google Maps Street View.


title: On the xorshift random number generators

abstract: G. Marsaglia recently introduced a class of very fast xorshift random number generators, whose implementation uses three “xorshift” operations. They belong to a large family of generators based on linear recurrences modulo 2, which also includes shift-register generators, the Mersenne twister, and several others. In this article, we analyze the theoretical properties of xorshift generators, search for the best ones with respect to the equidistribution criterion, and test them empirically. We find that the vast majority of xorshift generators with only three xorshift operations, including those having good equidistribution, fail several simple statistical tests. We also discuss generators with more than three xorshifts.


title: Image Aesthetic Evaluation Using Parallel Deep Convolution Neural Network

abstract: Traditional image aesthetic evaluation method usually involves the extraction of a set of relevant image aesthetic features and classification by a classifier trained on the set of features. The system's performance greatly depends on the effectiveness of the features. However, most of these features are carefully hand-crafted for specific datasets and assumed strong prior knowledge. Therefore, these features would not be optimal for general image aesthetic evaluation. The deep convolution neural network (DCNN) has the ability to automatically learn aesthetic features, and network structure of different complexity can learn aesthetic features at different scales and different point of views. Moreover, traditional image features, such as edge and saliency map, can be used as auxiliary information for the DCNN. Therefore, a Network-Paralleled and Data-Paralleled DCNN (NP-DP-DCNN) structure is proposed. The Network-Paralleled DCNN fuses networks of different complexity and the Data-Paralleled DCNN fuses original image data and derived feature maps to learn the aesthetic features from different scales and different point of views. Experimental results show that the proposed NP-DP-DCNN structure is able to achieve better classification performance than many existing methods.


Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model.


title: Access Control Model Based on Role and Attribute and Its Implementation

abstract: Combining the role-based access control (RBAC) model with the attribute-based access control (ABAC) model is a popular direction of current research on access control models. At present, many RABAC (RBAC + ABAC) models have been proposed. On the basis of RBAC model, these models dynamically apply ABAC rules to user-role mapping, role-permission mapping and user-permission mapping, thus realizing the usability and flexibility of access control models to some extent. But these models still have some insufficiencies in access control granularity and flexibility. This paper analyzes the defects of existing RABAC models and their causes, proposes a more fine-grained, flexible and efficient RABAC model, and realizes an access control system based on this model. Through the well-designed ABAC rules and their application modes, the system has achieved the goal of the RABAC model proposed by this paper and facilitated the administrator's management of access control rules.


title: A 2.4 GHz SlW-based circular resonator for bandpass filter application

abstract: A design of 2.4 GHz bandpass filter is developed based on Substrate Integrated Waveguide (SIW) using printed circuit board technology. The circular cavity structure using TM010 mode is employed in the design of this bandpass filter. To prove the concept, the filter structure is fabricated using Rogers RT5880 with dielectric constant, er = 2.2 and height of substrate 1.57 mm. The design was simulated using high frequency circuit simulator and was measured using vector network analyzer. The measured results are in good agreement with simulated result. Slight frequency shift and losses are still in acceptable range.


title: Global Maximum Power Point Tracking Method for Photovoltaic Arrays Under Partial Shading Conditions

abstract: The power–voltage characteristic of photovoltaic (PV) arrays displays multiple local maximum power points when all the modules do not receive uniform solar irradiance, i.e., under partial shading conditions (PSCs). Conventional maximum power point tracking (MPPT) methods are shown to be effective under uniform solar irradiance conditions. However, they may fail to track the global peak under PSCs. This paper proposes a new method for MPPT of PV arrays under both PSCs and uniform conditions. By analyzing the solar irradiance pattern and using the popular Hill Climbing method, the proposed method tracks all local maximum power points. The performance of the proposed method is evaluated through simulations in MATLAB/SIMULINK environment. Besides, the accuracy of the proposed method is proved using experimental results.


title: PPRank: Economically Selecting Initial Users for Influence Maximization in Social Networks

abstract: This paper focuses on seeking a new heuristic scheme for an influence maximization problem in social networks: how to economically select a subset of individuals (so-called seeds) to trigger a large cascade of further adoptions of a new behavior based on a contagion process. Most existing works on selection of seeds assumed that the constant number $k$ seeds could be selected, irrespective of the intrinsic property of each individual's different susceptibility of being influenced (e.g., it may be costly to persuade some seeds to adopt a new behavior). In this paper, a price-performance-ratio inspired heuristic scheme, PPRank, is proposed, which investigates how to economically select seeds within a given budget and meanwhile try to maximize the diffusion process. Our paper's contributions are threefold. First, we explicitly characterize each user with two distinct factors: the susceptibility of being influenced (SI) and influential power (IP) representing the ability to actively influence others and formulate users' SIs and IPs according to their social relations, and then, a convex price-demand curve-based model is utilized to properly convert each user's SI into persuasion cost (PC) representing the cost used to successfully make the individual adopt a new behavior. Furthermore, a novel cost-effective selection scheme is proposed, which adopts both the price performance ratio (PC-IP ratio) and user's IP as an integrated selection criterion and meanwhile explicitly takes into account the overlapping effect; finally, simulations using both artificially generated and real-trace network data illustrate that, under the same budgets, PPRank can achieve larger diffusion range than other heuristic and brute-force greedy schemes without taking users' persuasion costs into account.


Animated Exploration of Graphs with Radial Layout


title: Case-based reasoning: A review

abstract: Case-Based Reasoning (CBR) is a relatively recent problem solving technique that is attracting increasing attention. However, the number of people with first-hand theoretical or practical experience of CBR is still small. The main objective of this review is to provide a comprehensive overview of the subject to people new to CBR. The paper outlines the development of CBR in the US in the 1980s. It describes the fundamental techniques of CBR and contrasts its approach to that of model-based reasoning systems. 1 A critical review of currently available CBR software tools is followed by descriptions of CBR applications both from academic research and, in more detail, three CBR systems that are presently being used commercially. Each of the three commercial case studies highlights features that made CBR particularly suitable for the application. Moreover, the last case study describes a development methodology for implementing CBR systems. The paper concludes with a research agenda for CBR. A detailed categorized bibliography of CBR research is provided in a companion paper (Marir & Watson, 1994).


title: Text Coherence Analysis Based on Deep Neural Network

abstract: In this paper, we propose a novel deep coherence model (DCM) using a convolutional neural network architecture to capture the text coherence. The text coherence problem is investigated with a new perspective of learning sentence distributional representation and text coherence modeling simultaneously. In particular, the model captures the interactions between sentences by computing the similarities of their distributional representations. Further, it can be easily trained in an end-to-end fashion. The proposed model is evaluated on a standard Sentence Ordering task. The experimental results demonstrate its effectiveness and promise in coherence assessment showing a significant improvement over the state-of-the-art by a wide margin.


title: Tree-to-tree Neural Networks for Program Translation

abstract: Program translation is an important tool to migrate legacy code in one language into an ecosystem built in a different language. In this work, we are the first to consider employing deep neural networks toward tackling this problem. We observe that program translation is a modular procedure, in which a sub-tree of the source tree is translated into the corresponding target sub-tree at each step. To capture this intuition, we design a tree-to-tree neural network as an encoder-decoder architecture to translate a source tree into a target one. Meanwhile, we develop an attention mechanism for the tree-to-tree model, so that when the decoder expands one non-terminal in the target tree, the attention mechanism locates the corresponding sub-tree in the source tree to guide the expansion of the decoder. We evaluate the program translation capability of our tree-to-tree model against several state-of-the-art approaches. Compared against other neural translation models, we observe that our approach is consistently better than the baselines with a margin of up to 15 points. Further, our approach can improve the previous state-of-the-art program translation approaches by a margin of 20 points on the translation of real-world projects.


title: Approximate 32-bit floating-point unit design with 53% power-area product reduction

abstract: The floating-point unit is one of the most common building block in any computing system and is used for a huge number of applications. By combining two state-of-the-art techniques of imprecise hardware, namely Gate-Level Pruning and Inexact Speculative Adder, and by introducing a novel Inexact Speculative Multiplier architecture, three different approximate FPUs and one reference IEEE-754 compliant FPU have been integrated in a 65 nm CMOS process within a low-power multi-core processor. Silicon measurements show up to 27% power, 36% area and 53 % power-area product savings compared to the IEEE-754 single-precision FPU. Accuracy loss has been evaluated with a high-dynamic-range image tone-mapping algorithm, resulting in small but non-visible errors with image PSNR value of 90 dB.


title: Style Imitation and Chord Invention in Polyphonic Music with Exponential Families

abstract: Modeling polyphonic music is a particularly challenging task because of the intricate interplay between melody and harmony. A good model should satisfy three requirements: statistical accuracy (capturing faithfully the statistics of correlations at various ranges, horizontally and vertically), flexibility (coping with arbitrary user constraints), and generalization capacity (inventing new material, while staying in the style of the training corpus). Models proposed so far fail on at least one of these requirements. We propose a statistical model of polyphonic music, based on the maximum entropy principle. This model is able to learn and reproduce pairwise statistics between neighboring note events in a given corpus. The model is also able to invent new chords and to harmonize unknown melodies. We evaluate the invention capacity of the model by assessing the amount of cited, re-discovered, and invented chords on a corpus of Bach chorales. We discuss how the model enables the user to specify and enforce user-defined constraints, which makes it useful for style-based, interactive music generation.


title: Cell Cycle Dynamics in Glioma Cancer Stem Cells.

abstract: Cancer stem cells, sometimes referred to as tumor initiating cells, play pivotal roles in tumor initiation, progression, metastasis, resistance to therapy, and relapse. Understanding how these populations of cells expand in response to a host of conditions is critical in determining effective cancer therapeutics. A defining feature of cancer stem cells is the ability to switch between modes of quiescence and symmetric/asymmetric division to protect and conserve the population, this feature is traditionally reserved for normal adult stem cell populations. Understanding how the core cell cycle machinery responds to external cues to drive symmetric/asymmetric division vs. quiescence will reveal fundamental information about how cancer stem cell populations survive and expand. This chapter will describe methods to study the cell cycle dynamics in brain cancer stem cell populations and how they compare to the other populations in a tumor.


title: Symbolic PathFinder: integrating symbolic execution with model checking for Java bytecode analysis

abstract: Symbolic PathFinder (SPF) is a software analysis tool that combines symbolic execution with model checking for automated test case generation and error detection in Java bytecode programs. In SPF, programs are executed on symbolic inputs representing multiple concrete inputs and the values of program variables are represented by expressions over those symbolic inputs. Constraints over these expressions are generated from the analysis of different paths through the program. The constraints are solved with off-the-shelf solvers to determine path feasibility and to generate test inputs. Model checking is used to explore different symbolic program executions, to systematically handle aliasing in the input data structures, and to analyze the multithreading present in the code. SPF incorporates techniques for handling input data structures, strings, and native calls to external libraries, as well as for solving complex mathematical constraints. We describe the tool and its application at NASA, in academia, and in industry.


What is Phenotypic Plasticity and Why is it Important?


title: Sensor Correction of a 6-Band Multispectral Imaging Sensor for UAV Remote Sensing

abstract: Unmanned aerial vehicles (UAVs) represent a quickly evolving technology, broadening the availability of remote sensing tools to small-scale research groups across a variety of scientific fields. Development of UAV platforms requires broad technical skills covering platform development, data post-processing, and image analysis. UAV development is constrained by a need to balance technological accessibility, flexibility in application and quality in image data. In this study, the quality of UAV imagery acquired by a miniature 6-band multispectral imaging sensor was improved through the application of practical image-based sensor correction techniques. Three major components of sensor correction were focused upon: noise reduction, sensor-based modification of incoming radiance, and lens distortion. Sensor noise was reduced through the use of dark offset imagery. Sensor modifications through the effects of filter transmission rates, the relative monochromatic efficiency of the sensor and the effects of vignetting were removed through a combination of spatially/spectrally dependent correction factors. Lens distortion was reduced through the implementation of the Brown–Conrady model. Data post-processing serves dual roles in data quality improvement, and the identification of platform limitations and sensor idiosyncrasies. The proposed corrections improve the quality of the raw multispectral imagery, facilitating subsequent quantitative image analysis.


title: Momentum, business cycle and time-varying expected returns

abstract: A growing number of researchers argue that time-series patterns in returns are due to investor irrationality and thus can be translated into abnormal profits. Continuation of short-term returns or momentum is one such pattern that has defied any rational explanation and is at odds with market efficiency. This paper shows that profits to momentum strategies can be explained by a set of lagged macroeconomic variables and payoffs to momentum strategies disappear once stock returns are adjusted for their predictability based on these macroeconomic variables. Our results provide a possible role for time-varying expected returns as an explanation for momentum payoffs. Copyright The American Finance Association 2002.


title: Anatomy of the long head of biceps femoris: An ultrasound study

abstract: Hamstring strains, particularly involving the long head of biceps femoris (BFlh) at the proximal musculotendinous junction (MTJ), are commonly experienced by athletes. With the use of diagnostic ultrasound increasing, an in-depth knowledge of normal ultrasonographic anatomy is fundamental to better understanding hamstring strain. The aim of this study was to describe the architecture of BFlh, using ultrasonography, in young men and cadaver specimens. BFlh morphology was examined in 19 healthy male participants (mean age 21.6 years) using ultrasound. Muscle, tendon and MTJ lengths were recorded and architectural parameters assessed at four standardised points along the muscle. Measurement accuracy was validated by ultrasound and dissection of BFlh in six male cadaver lower limbs (mean age 76 years). Intra-rater reliability of architectural parameters was examined for repeat scans, image analysis and dissection measurements. Distally the BFlh muscle had significantly (P < 0.05) shorter fascicles and larger pennation angles than proximal sites. Agreement between ultrasound and dissection (cadaver study) was excellent for all architectural parameters, except pennation angle (PA), and MTJ length. All other measures demonstrated good-excellent repeatability. BFlh is not uniform in architecture when imaged using ultrasound. It is likely that its distal-most segment is better suited for force production in comparison to the more proximal segments, which show excursive potential, traits which possibly contribute to the high rate of injury at the proximal MTJ. The data presented in this study provide specific knowledge of the normal ultrasonographic anatomy of BFlh, which should be of assistance in analysing BFlh injury via imaging. Clin. Anat. 29:738-745, 2016. © 2016 Wiley Periodicals, Inc.


title: Characterizing and Supporting Question Answering in Human-to-Human Communication

abstract: Email continues to be one of the most important means of online communication. People spend a significant amount of time sending, reading, searching and responding to email in order to manage tasks, exchange information, etc. In this paper, we focus on information exchange over enterprise email in the form of questions and answers. We study a large scale publicly available email dataset to characterize information exchange via questions and answers in enterprise email. We augment our analysis with a survey to gain insights on the types of questions exchanged, when and how do people get back to them and whether this behavior is adequately supported by existing email management and search functionality. We leverage this understanding to define the task of extracting question/answer pairs from threaded email conversations. We propose a neural network based approach that matches the question to the answer considering comparisons at different levels of granularity. We also show that we can improve the performance by leveraging external data of question and answer pairs. We test our approach using a manually labeled email data collected using a crowd-sourcing annotation study. Our findings have implications for designing email clients and intelligent agents that support question answering and information lookup in email.


title: Recommendations for e-commerce systems in the tourism industry of sub-Saharan Africa

abstract: The recommendations described in this paper are a continuation of research previously reported in the Telematics and Informatics journal. This paper explains how the tourism organisations from sub-Saharan Africa can evolve their websites into marketing tools and how they can overcome the impediments to e-commerce adoption and usage. The recommendations also explain how the other major players within the economies of these countries can make the environment conducive for e-commerce development and growth so that the tourism organisations from this region can break into the lucrative international tourism market. The recommendations were tested by sending them to the African organisations and experts in e-commerce and tourism who have worked in, or are currently based in Africa, south of the Sahara. The results showed most organisations and experts who responded think that these recommendations will help African tourism organisations adopt and use e-commerce. African tourism organisations that intend to implement or are in the process of implementing e-commerce systems should follow the recommendations outlined in this paper to help sub-Saharan Africa reach its tourism potential.


title: Improvement of C2W Collective Bonding Reliability and UPH through Innovations in Machine, Materials and Methods

abstract: We report detailed observations for temperature deviation (dT) on stacked dice and temperature for satellite dice (Ts) on several base materials with different thermal resistances to inspire new concepts for innovative collective bonding under a Thermal-compression Bonding (TCB) process. We also developed an enhanced Non-conductive Film (NCF) and flip-chip bonder, in order to solve current problems facing collective bonding for the C2W process. Various improvement measures for machine, material and method are presented to address the dT, Ts and units per hour (UPH) improvement challenges. We conclude that, when the proposed improvements are implemented, C2W collective bonding with high reliability and high-speed over 20,000 UPH is possible. As a further investigation, this paper is focusing on the productivity bottleneck to improve UPH for the pre-bonding step on a 3D stacked IC process. Finally, we propose a new 4-head flip chip bonder to increase UPH for C2W collective bonding drastically.


DESIGN OF SMART ROBOT FOR WRIST REHABILITATION


title: Deriving Semantic Sensor Metadata from Raw Measurements

abstract: Sensor network deployments have become a primary source of big data about the real world that surrounds us, measuring a wide range of physical properties in real time. With such large amounts of heterogeneous data, a key challenge is to describe and annotate sensor data with high-level metadata, using and extending models, for instance with ontologies. However, to automate this task there is a need for enriching the sensor metadata using the actual observed measurements and extracting useful meta-information from them. ::: ::: This paper proposes a novel approach of characterization and extraction of semantic metadata through the analysis of sensor data raw observations. This approach consists in using approximations to represent the raw sensor measurements, based on distributions of the observation slopes, building a classification scheme to automatically infer sensor metadata like the type of observed property, integrating the semantic analysis results with existing sensor networks metadata.


title: How to build an optimal broadband channel sounder

abstract: Spatio-temporal channel information obtained via channel sounding is invaluable for implementing equalizers, multi-antenna systems, and dynamic modulation schemes in next-generation wireless systems. The most straightforward means of performing channel measurements is in the frequency domain using a vector network analyzer (VNA). However, the high cost of VNAs often leads engineers to seek more economical solutions by measuring the wireless channel in the time domain. The bandwidth compression of the sliding correlator channel sounder makes it the preferred means of performing time-domain channel measurements.


title: Harmonics suppression of microstrip patch antenna using defected ground structure

abstract: Active micro strip patch antennas radiate harmonic frequencies and Suppression of Harmonics is an important factor for MPA. In this paper suppression of higher order modes upto third harmonics of the fundamental operating frequency of microstrip patch antenna has been discussed. The design will reduce higher order harmonics without affecting fundamental frequency. A stub with defected ground structure (DGS) is integrated to a microstrip transmission line of patch antenna to reject the harmonics. The DGS pattern used here is simple and can easily be etched on ground plane. Here compact low pass filter (LPF) with circular head dumbell shaped DGS and circular headed open stub have been designed. Wide stop band and high filter selectivity have been achieved by this design. The designed low pass filter is integrated with microstrip line feed of the square patch antenna. The addition of the circular headed stub with feed line improves the suppression of higher order harmonics of fundamental operating frequency. The equivalent circuit is also discussed here. The fundamental operating frequency passes through the pass band and the higher order harmonics got rejected by the stop band of the low pass filter.


title: Multi-class imagery EEG recognition based on adaptive subject-based feature extraction and SVM-BP classifier

abstract: In brain-computer interface (BCI), the classification accuracy significantly drops when the system has multiple motor imagery tasks for different subjects. To improve the classification accuracy and individual adaptability of the system, a new method of feature extraction and classification is presented in this paper for recognizing the four different motor imagery tasks (right hand, left hand, foots and tongue movements). Wavelet packet basis is selected for extracting the frequency bands in which the feature can be classified easily. Then, the EEG feature is extracted from the frequency bands information with One Versus the Rest Common Spatial Patterns (OVR-CSP) algorithm. Furthermore, a hybrid classification model of Support Vector Machines combining with the Back Propagation neural network (SVM-BP) is built to classify the multi-class EEG feature. Experiment results show that the proposed approach achieves better performance than other methods and it has adaptability for different subjects to some extent.


title: Carbon emission and mitigation cost comparisons between fossil fuel, nuclear and renewable energy resources for electricity generation

abstract: A study was conducted to compare the electricity generation costs of a number of current commercial technologies with technologies expected to become commercially available within the coming decade or so. The amount of greenhouse gas emissions resulting per kWh of electricity generated were evaluated. A range of fossil fuel alternatives (with and without physical carbon sequestration),were compared with the baseline case of a pulverised coal,steam cycle power plant. Nuclear,hydro,wind,bioenergy and solar generating plants were also evaluated. The objectives were to assess the comparative costs of mitigation per tonne of carbon emissions avoided,and to estimate the total amount of carbon mitigation that could result from the global electricity sector by 2010 and 2020 as a result of fuel switching,carbon dioxide sequestration and the greater uptake of renewable energy. Most technologies showed potential to reduce both generating costs and carbon emission avoidance by 2020 with the exception of solar power and carbon dioxide sequestration. The global electricity industry has potential to reduce its carbon emissions by over 15% by 2020 together with cost saving benefits compared with existing generation. r 2002 Elsevier Science Ltd. All rights reserved.


title: MapReduce programming with apache Hadoop

abstract: Apache Hadoop has become the platform of choice for developing large-scale dataintensive applications. In this tutorial, we will discuss design philosophy of Hadoop, describe how to design and develop Hadoop applications and higher-level application frameworks to crunch several terabytes of data, using anywhere from four to 4,000 computers. We will discuss solutions to common problems encountered in maximizing Hadoop application performance. We will also describe several frameworks and utilities developed using Hadoop that increase programmer-productivity and application-performance.


title: Minerva: A Scalable and Highly Efficient Training Platform for Deep Learning

abstract: The tooling landscape of deep learning is fragmented by a growing gap between the generic and productivity-oriented tools that optimize for algorithm development and the task-specific ones that optimize for speed and scale. This creates an artificial barrier to bring new innovations into real-world applications. Minerva addresses this issue with a layered design that provides language flexibility and execution efficiency simultaneously within one coherent framework. It proposes a matrix-based API, resulting in compact codes and the Matlab-like, imperative and procedural coding style. The code is dynamically translated into an internal dataflow representation, which is then efficiently executed against different hardware. The same user code runs on modern laptop and workstation, high-end multi-core server, or server clusters, with and without GPU acceleration, delivering performance and scalability better than or competitive with existing tools on different platforms.


title: An Embarrassingly Simple Approach to Visual Domain Adaptation

abstract: We show that it is possible to achieve high-quality domain adaptation without explicit adaptation. The nature of the classification problem means that when samples from the same class in different domains are sufficiently close, and samples from differing classes are separated by large enough margins, there is a high probability that each will be classified correctly. Inspired by this, we propose an embarrassingly simple yet effective approach to domain adaptation—only the class mean is used to learn class-specific linear projections. Learning these projections is naturally cast into a linear-discriminant-analysis-like framework, which gives an efficient, closed form solution. Furthermore, to enable to application of this approach to unsupervised learning, an iterative validation strategy is developed to infer target labels. Extensive experiments on cross-domain visual recognition demonstrate that, even with the simplest formulation, our approach outperforms existing non-deep adaptation methods and exhibits classification performance comparable with that of modern deep adaptation methods. An analysis of potential issues effecting the practical application of the method is also described, including robustness, convergence, and the impact of small sample sizes.


title: Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems

abstract: In this work, we present a hybrid learning method for training task-oriented dialogue systems through online user interactions. Popular methods for learning task-oriented dialogues include applying reinforcement learning with user feedback on supervised pre-training models. Efficiency of such learning method may suffer from the mismatch of dialogue state distribution between offline training and online interactive learning stages. To address this challenge, we propose a hybrid imitation and reinforcement learning method, with which a dialogue agent can effectively learn from its interaction with users by learning from human teaching and feedback. We design a neural network based task-oriented dialogue agent that can be optimized end-to-end with the proposed learning method. Experimental results show that our end-to-end dialogue agent can learn effectively from the mistake it makes via imitation learning from user teaching. Applying reinforcement learning with user feedback after the imitation learning stage further improves the agent's capability in successfully completing a task.


title: Mining Permission Request Patterns from Android and Facebook Applications

abstract: Android and Facebook provide third-party applications with access to users’ private data and the ability to perform potentially sensitive operations (e.g., post to a user’s wall or place phone calls). As a security measure, these platforms restrict applications’ privileges with permission systems: users must approve the permissions requested by applications before the applications can make privacy- or security-relevant API calls. However, recent studies have shown that users often do not understand permission requests and lack a notion of typicality of requests. As a rst step towards simplifying


title: Fractional-slot concentrated-winding axial-flux permanent-magnet machine with tooth-wound coils

abstract: This paper presents the design, finite-element analysis (FEA), and experimental verification of a single-stator double-rotor fractional-slot concentrated-winding axial-flux permanent-magnet machine with core-wound coils. The advantages that are obtained by adopting such coil construction in terms of shorter end-winding connections higher fill factor, and mechanical robustness of the magnetic structure are highlighted. Machine performances both at no load and at rated load are analyzed through static FEA, and a simple index is introduced to compare induced losses in the permanent magnets for various machine configurations. Time-stepped transient FEA is used to validate the index. The construction and experimental testing of a full-scale prototype is described to confirm the expected performances and to characterize the prototype in terms of losses (both at no-load and in loaded conditions), cogging torque, and main machine parameters.


title: Middle School Start Times: The Importance of a Good Night's Sleep for Young Adolescents

abstract: With the onset of adolescence, teenagers require 9.2 hr of sleep and experience a delay in the timing of sleep. In the “real world” with early school start times, however, they report less sleep, striking differences between their school-weekend sleep schedules, and significant daytime sleepiness. Prior studies demonstrated that high schoolers with later school starts do not further delay bedtimes but obtain more sleep due to later wake times. This study examined sleep–wake patterns of young adolescents attending urban, public middle schools with early (7:15 a.m.) versus late (8:37 a.m.) start times. Students (N = 205) were assessed at 2 time periods. Students at the late-starting school reported waking up over 1 hr later on school mornings and obtaining 50 min more sleep each night, less sleepiness, and fewer tardies than students at the early school. All students reported similar school-night bedtimes, sleep hygiene practices, and weekend sleep schedules.


title: Beyond Turing: Intelligent Agents Centered on the User

abstract: Most research on intelligent agents centers on the agent and not on the user. We look at the origins of agent-centric research for slot-filling, gaming and chatbot agents. We then argue that it is important to concentrate more on the user. After reviewing relevant literature, some approaches for creating and assessing user-centric systems are proposed.


title: PhenoLines: Phenotype Comparison Visualizations for Disease Subtyping via Topic Models

abstract: PhenoLines is a visual analysis tool for the interpretation of disease subtypes, derived from the application of topic models to clinical data. Topic models enable one to mine cross-sectional patient comorbidity data (e.g., electronic health records) and construct disease subtypes—each with its own temporally evolving prevalence and co-occurrence of phenotypes—without requiring aligned longitudinal phenotype data for all patients. However, the dimensionality of topic models makes interpretation challenging, and de facto analyses provide little intuition regarding phenotype relevance or phenotype interrelationships. PhenoLines enables one to compare phenotype prevalence within and across disease subtype topics, thus supporting subtype characterization, a task that involves identifying a proposed subtype's dominant phenotypes, ages of effect, and clinical validity. We contribute a data transformation workflow that employs the Human Phenotype Ontology to hierarchically organize phenotypes and aggregate the evolving probabilities produced by topic models. We introduce a novel measure of phenotype relevance that can be used to simplify the resulting topology. The design of PhenoLines was motivated by formative interviews with machine learning and clinical experts. We describe the collaborative design process, distill high-level tasks, and report on initial evaluations with machine learning experts and a medical domain expert. These results suggest that PhenoLines demonstrates promising approaches to support the characterization and optimization of topic models.


title: Traditional Indian Medicine (TIM) based Humor Identification – A recurrence based Approach

abstract: Objective analysis and interpretation of radial pulse signal has been attempted. Capacitance variation based sensing and instrumentation mechanism has been developed for pulse sensing and logging. Traditional Indian Medicine (TIM) perspective for humor identification has been attempted. Pulse signal of healthy individual analysed in dominant humor interval, display prominent changes in recurrence quantification measures for pulse amplitude variations. These marker results are observed to correlate strongly with those obtained for a group of healthy individuals in a dominant humor interval period. Recurrence quantification analysis can possibly help in arriving at inferences apparent to sense perception for a TIM physician. General Terms Biomedical Signal Processing, Traditional Indian Medicine(TIM) – Ayurveda.


Software Cost Estimation Methods: A Review 1


title: Suitability Analysis of FPGAs for Heterogeneous Platforms in HPC

abstract: High performance computing (HPC) systems currently integrate several resources such as multi-cores (CPUs), graphic processing units (GPUs) and reconfigurable logic devices, like field programmable gate arrays (FPGAs). The role of the latter two has traditionally being confined to act as secondary accelerators rather than as main execution units. We perform a deep survey around state of the art research and implementation of HPC algorithms; we extract features relevant to each family and list them as key factors to obtain higher performance. Due to the broad spectra of the survey we only include the most complete references found. We provide a general classification of the 13 HPC families with respect to their needs and suitability for hardware implementation. In addition, we present an analysis based on current and future technology availability as well as in particular aspects identified in the survey. Finally we list general guidelines and opportunities to be accounted for in future heterogeneous designs that employ FPGAs for HPC.


title: Design of UWB filter -antenna with notched band at 5.8GHz

abstract: In this paper, an UWB integrated filter-antenna system with notched band at 5.8 GHz has been presented. The design of the proposed integrated filter-antenna is based on cascading an UWB notched filter with an UWB antenna. The two parts of the system have already been matched to each others. The antenna-filter has a small size (68 × 27mm2). The system bandwidth meets the UWB requirements.


title: Discovery of Collocation Patterns: from Visual Words to Visual Phrases

abstract: A visual word lexicon can be constructed by clustering primitive visual features, and a visual object can be described by a set of visual words. Such a "bag-of-words" representation has led to many significant results in various vision tasks including object recognition and categorization. However, in practice, the clustering of primitive visual features tends to result in synonymous visual words that over-represent visual patterns, as well as polysemous visual words that bring large uncertainties and ambiguities in the representation. This paper aims at generating a higher-level lexicon, i.e. visual phrase lexicon, where a visual phrase is a meaningful spatially co-occurrent pattern of visual words. This higher-level lexicon is much less ambiguous than the lower-level one. The contributions of this paper include: (1) a fast and principled solution to the discovery of significant spatial co-occurrent patterns using frequent itemset mining; (2) a pattern summarization method that deals with the compositional uncertainties in visual phrases; and (3) a top-down refinement scheme of the visual word lexicon by feeding back discovered phrases to tune the similarity measure through metric learning.


title: Electrophysiological consequences of exposure of hippocampal slices to dihydroxyfumarate, a generator of superoxide radicals

abstract: In an effort to understand the damaging actions of free radicals to neuronal electrophysiology, the superoxide generator, dihydroxyfumarate (DHF), was evaluated in slices of guinea pig hippocampus. Using field potential recording techniques, population spikes and population synaptic potentials were recorded in field CA1. Slices were exposed to 3 mM DHF either alone or in the presence of a protectant. DHF did not alter the ability of the afferent volley to generate a synaptic potential, but it did impair the ability of the synaptic potential to elicit a population spike. In addition, DHF induced lipid peroxidation as measured by the thiobarbituric acid assay. Superoxide dismutase (SOD) provided no protection. Instead, SOD treatment promoted DHF damage to synaptic potentials. Catalase alone mitigated the actions of DHF, but only in SOD plus catalase was the DHF-induced electrophysiological deficit and lipid peroxidation completely antagonized. The iron chelator, Desferal, did not protect but promoted synaptic damage. Desferal may be ineffective because of the nitroxide radical formed upon its reaction with DHF. The hydroxyl radical scavenger, dimethylsulfoxide, prevented lipid peroxidation and reduced the DHF-induced deficit but did not completely prevent the impairment of spike generation. These data suggest that DHF exerts its actions through generation of hydrogen peroxide which would further react with tissue iron to produce hydroxyl radicals.


title: Occurrence of partenites in mollusks and the influence that metacercaria of Apophallus muehlingi (Jagerskiold, 1898) and Posthodiplostomum cuticola (Nordmann, 1832) has on some biochemical parameters in fish

abstract: A heavy parasitic load from trematode partenites of Apophallus muehlingi (Jagerskiold, 1898) was observed in the mollusk population of Lithoglyphus naticoides (Pfeiffer) after its natural invasion into the Rybinsk Reservoir. An elevated concentration of glycogen was detected in the muscles of infected fish, both in the natural populations and under experimental conditions. Interspecific differences were found for the protein concentrations when elevated concentrations were observed in the muscles of the infected roach, and no differences were discovered for the goldfish (both infected and parasite-free). The ties between the biochemical parameters and motor activity are discussed for the infected fish.


title: A new variable stiffness design: Matching requirements of the next robot generation

abstract: Facing new tasks, the conventional rigid design of robotic joints has come to its limits. Operating in unknown environments current robots are prone to failure when hitting unforeseen rigid obstacles. Moreover, safety constraints are a major aspect for robots interacting with humans. In order to operate safely, existing robotic systems in this field are slow and have a lack of performance. To circumvent these limitations, a new robot joint with a variable stiffness approach (VS-Joint) is presented. It combines a compact and highly integrated design with high performance actuation. The VS- Joint features a highly dynamic stiffness adjustment along with a mechanically programmable system behavior. This allows an easy adaption to a big variety of tasks. A benefit of the joint is its intrinsic robustness against impacts and hard contacts, which permits faster trajectories and handling. Thus, it provides excellent attributes for the use in shoulder and elbow joints of an anthropomorphic robot arm.


title: Conducted Electrical Weapon Deployed Probe Wounds

abstract: Deployment of probes is a common method of use for some handheld conducted electrical weapons (CEWs). Probe deployment allows for greater spread between the probes that favors induction of neuromuscular incapacitation. The probes can embed in clothing or tissue and leave wounds from kinetic energy impact, penetration trauma, and the conducted electrical current. Factors such as the angle and depth of penetration, the duration of exposure, and the length of time passage since the exposure can create wounds of different appearance.


The local behavior of the quadratic Hermite-Pad 'e approximation


title: A data-centric profiler for parallel programs

abstract: It is difficult to manually identify opportunities for enhancing data locality. To address this problem, we extended the HPCToolkit performance tools to support data-centric profiling of scalable parallel programs. Our tool uses hardware counters to directly measure memory access latency and attributes latency metrics to both variables and instructions. Different hardware counters provide insight into different aspects of data locality (or lack thereof). Unlike prior tools for data-centric analysis, our tool employs scalable measurement, analysis, and presentation methods that enable it to analyze the memory access behavior of scalable parallel programs with low runtime and space overhead. We demonstrate the utility of HPCToolkit's new data-centric analysis capabilities with case studies of five well-known benchmarks. In each benchmark, we identify performance bottlenecks caused by poor data locality and demonstrate non-trivial performance optimizations enabled by this guidance.


title: Novel Bidirectional Single-phase Single-Stage Isolated AC–DC Converter With PFC for Charging of Electric Vehicles

abstract: This paper proposes a novel bidirectional single-phase single-stage ac–dc converter for Electric Vehicle (EV) charging application. AC side of the proposed converter has current-fed half-bridge converter that is connected to the full-bridge converter on secondary side of a high-frequency transformer. Current at the input of the ac side can be regulated to realize power factor correction. The proposed converter achieves Zero Current Switching of primary side switches and zero current turn-ON for secondary side devices throughout the operation without any additional components. This paper also presents novel modulation technique and control algorithm to ensure soft switching throughout the operation range of the converter in both directions of power flow. Design equations are derived to help suitable selection of components for a given specification. Design of the converter, control strategy, and the results are presented for a 1.5-kVA converter for EV charger.


title: Pareto frontier for job execution and data transfer time in hybrid clouds

abstract: a b s t r a c t This paper proposes a solution to calculate the Pareto frontier for the execution of a batch of jobs versus data transfer time for hybrid clouds. Based on the nature of the cloud application, jobs are assumed to require a number of data-files from either public or private clouds. For example, gene probes can be used to identify various infection agents such as bacteria, viruses, etc. The heavy computational task of aligning probes of a patient's DNA (private-data) with normal sequences (public-data) with various data sizes is the key to this process. Such files have different characteristics - depends on their nature - and could be either allowed for replication or not in the cloud. Files could be too big to replicate (big data), others might be small enough to be replicated but they cannot be replicated as they contain sensitive information (private data). To show the relationship between the execution time of a batch of jobs and the transfer time needed for their required data in hybrid cloud, we first model this problem as a bi-objective optimization problem, and then propose a Particle Swarm Optimization (PSO)-based approach, called here PSO-ParFnt, to find the relevant Pareto frontier. The results are promising and provide new insights into this complex problem. © 2013 Elsevier B.V. All rights reserved.


title: Implementation of Association Rule Mining for Network Intrusion Detection

abstract: Many modern intrusion detection systems are based on data mining and database-centric architecture, where a number of data mining techniques have been found. Among the most popular techniques, association rule mining is one of the important topics in data mining research. This approach determines interesting relationships between large sets of data items. This technique was initially applied to the so-called market basket analysis, which aims at finding regularities in shopping behaviour of customers of supermarkets. In contrast to dataset for market basket analysis, which takes usually hundreds of attributes, network audit databases face tens of attributes. So the typical Apriori algorithm of association rule mining, which needs so many database scans, can be improved, dealing with such characteristics of transaction database. In this paper we propose an impoved Apriori algorithm, very useful in practice, using scan of network audit database only once by transaction cutting and hashing.


title: Analysis of Time and Frequency Domain Features of Accelerometer Measurements

abstract: This paper addresses the signal processing aspect of wireless sensor networks. It analyzes several time and frequency domain features of measurements that are taken from 3D accelerometer sensors. The measurements represent various types of movements related to humans and cars. The aim is to obtain quantitative as well as qualitative comparisons concerning the expression power of these features in the presence of various sources of uncertainties (calibration, placement of sensors, and time synchronization). For the qualitative analysis, we define fuzzy sets and fuzzy membership functions for all the features. Particular attention is given to the analysis of the existence of correlation between measurements of different sensor nodes. We will demonstrate that correlation coefficients of both time and frequency domain features exhibit high degrees of uncertainties. On the other hand, short time Fourier transformations (STFT) of all types of movements prove to be agnostic of various forms of measurement and calibration errors. I. INTRODUCTION


title: The Permo-Carboniferous genus Sagenodus and the beginning of modern lungfish

abstract: were present in North America ( The lungfish Sagenodus is a widespread Permo-Carboniferous genus found in Europe and North America. Important localities in the U.S.A. include Middle Pennsylvanian coals near Linton, Ohio, Upper Pennsylvanian deposits near Robinson and Hamilton, Kansas, and Peoria, Illinois; Lower Permian sediments near Cameron, Ohio; and Lower Permian “Red Beds” of Texas and Oklahoma. At least three species of Sagenodus S. copeanus, S. periprion, S. serratus). S. ohiensis is represented solely by one skull. Knowledge of the osteology of Sagenodus is enhanced by the study of well-preserved but disassociated elements from Robinson, Kansas ( S. copeanus) and Little Bitter Creek, Texas (S. serratus). The orbital series is now known to be comprised of six elements and the sensory canal system is more complex than previously realized. The only known articulated skeleton of this genus, from Hamilton Quarry, Kansas, permits a restoration of the entire animal including the median fins. The dorsal and anal fins are not separate; there is instead, a continuous fin around the caudal end of the body, as found in other post-Devonian lungfishes. Sagenodus is structurally intermediate between more primitive Devonian dipnoans and post-Paleozoic lungfishes. Evident trends can be seen in the reduction of bone (both number of bones and degree of ossification), the loss of cosmine, the nature of the scales, the structure and histology of tooth plates, and the configuration of the median fins. Sagenodus is a member of a euryhaline faunal assemblage that can be found from shallow marine to freshwater deposits.


title: Joint Modeling of User Check-in Behaviors for Real-time Point-of-Interest Recommendation

abstract: Point-of-Interest (POI) recommendation has become an important means to help people discover attractive and interesting places, especially when users travel out of town. However, the extreme sparsity of a user-POI matrix creates a severe challenge. To cope with this challenge, we propose a unified probabilistic generative model, the Topic-Region Model (TRM), to simultaneously discover the semantic, temporal, and spatial patterns of users’ check-in activities, and to model their joint effect on users’ decision making for selection of POIs to visit. To demonstrate the applicability and flexibility of TRM, we investigate how it supports two recommendation scenarios in a unified way, that is, hometown recommendation and out-of-town recommendation. TRM effectively overcomes data sparsity by the complementarity and mutual enhancement of the diverse information associated with users’ check-in activities (e.g., check-in content, time, and location) in the processes of discovering heterogeneous patterns and producing recommendations. To support real-time POI recommendations, we further extend the TRM model to an online learning model, TRM-Online, to track changing user interests and speed up the model training. In addition, based on the learned model, we propose a clustering-based branch and bound algorithm (CBB) to prune the POI search space and facilitate fast retrieval of the top-k recommendations. ::: We conduct extensive experiments to evaluate the performance of our proposals on two real-world datasets, including recommendation effectiveness, overcoming the cold-start problem, recommendation efficiency, and model-training efficiency. The experimental results demonstrate the superiority of our TRM models, especially TRM-Online, compared with state-of-the-art competitive methods, by making more effective and efficient mobile recommendations. In addition, we study the importance of each type of pattern in the two recommendation scenarios, respectively, and find that exploiting temporal patterns is most important for the hometown recommendation scenario, while the semantic patterns play a dominant role in improving the recommendation effectiveness for out-of-town users.


Network Forensics Models for Converged Architectures


title: The Mind and Brain of Short-Term Memory

abstract: The past 10 years have brought near-revolutionary changes in psychological theories about short-term memory, with similarly great advances in the neurosciences. Here, we critically examine the major psychological theories (the “mind”) of short-term memory and how they relate to evidence about underlying brain mechanisms. We focus on three features that must be addressed by any satisfactory theory of short-term memory. First, we examine the evidence for the architecture of short-term memory, with special attention to questions of capacity and how—or whether—short-term memory can be separated from long-term memory. Second, we ask how the components of that architecture enact processes of encoding, maintenance, and retrieval. Third, we describe the debate over the reason about forgetting from short-term memory, whether interference or decay is the cause. We close with a conceptual model tracing the representation of a single item through a short-term memory task, describing the biological mechanisms that might support psychological processes on a moment-by-moment basis as an item is encoded, maintained over a delay with some forgetting, and ultimately retrieved.


title: Robust Deep Auto-encoder for Occluded Face Recognition

abstract: Occlusions by sunglasses, scarf, hats, beard, shadow etc, can significantly reduce the performance of face recognition systems. Although there exists a rich literature of researches focusing on face recognition with illuminations, poses and facial expression variations, there is very limited work reported for occlusion robust face recognition. In this paper, we present a method to restore occluded facial regions using deep learning technique to improve face recognition performance. Inspired by SSDA for facial occlusion removal with known occlusion type and explicit occlusion location detection from a preprocessing step, this paper further introduces Double Channel SSDA (DC-SSDA) which requires no prior knowledge of the types and the locations of occlusions. Experimental results based on CMU-PIE face database have showed that, the proposed method is robust to a variety of occlusion types and locations, and the restored faces could yield significant recognition performance improvements over occluded ones.


The Smart Car Parking System Based on Iot Commanded by Android Application


title: Improved detection of submaximum effort in upper extremity strength and strength-endurance performance testing

abstract: This study was a continuation of 2 investigations into methods used to help discriminate between maximum and submaximum effort in maximal power grip (strength), and repeated maximal power grip (strength-endurance). The purpose was to identify valid discriminator variables, which in combination would improve the detection of submaximum effort over single discriminator variables. Three discriminator variables were identified for power grip strength, and also for power grip strength-endurance. A criterion test was developed where the criterion for intentional submaximum effort was ≥ two positive discriminator variables, and the criterion for a maximum effort was ≤ one positive discriminator variable in both power grip strength and strength-endurance. The criterion test correctly identified 100% of healthy control workers (n=26) applying maximum effort in power grip strength and strength-endurance; and correctly identified a total of 20 out of 24 (83.3%) submaximum tests for workers purposefully restricting their performances. The criterion test was also applied across the power grip data for a retrospective sample of 51 Workers Compensation patients with unilateral upper extremity injuries, classifying 17 patients (33.3%) as submaximal when using their unaffected limbs. Cominations of multiple discriminator variables were found to improve detection rates for submaximum effort when compared to using single discriminator variables.


title: How to build a human: practical physics-based character animation

abstract: We present state-of-the-art character animation techniques for generating realistic anatomical motion of muscles, fat, and skin. Physics-based character animation uses computational resources in lieu of exhaustive artist effort to produce physically realistic images and animations. This principle has already seen widespread adoption in rendering, fluids, and cloth simulation. We believe that the savings in manpower and improved realism of results provided by a physics- and anatomy-based approach to characters cannot be matched by other techniques. Over the past year we have developed a physics-based character toolkit at Ziva Dynamics and used it to create a photo-realistic human character named Adrienne. We give an overview of the workflow used to create Adrienne, from modeling of anatomical bodies to their simulation via the Finite Element Method. We also discuss practical considerations necessary for effective physics-based character animation.


Lossless data compression


THE IMPORTANCE OF SUBWORD EMBEDDINGS IN SENTENCE PAIR MODELING


title: Electron transfer through organic molecular wires: A theoretical study

abstract: Abstract Density Functional Theory (DFT) and Time Dependent DFT (TDDFT) calculations have been carried out on a series of Donor-Bridge-Acceptor (DBA) systems, where the bridges are π-conjugated molecular wires. In these systems a lack of strong dependence of the charge-transfer (CT) rate on large variations in the D-A distance has been observed. The results show that a suitably defined length, R eff , shows significantly smaller variation over the different bridges which have widely varying geometrical lengths. The distance R eff is determined on the basis of application of a modified Mulliken’s formula for the CT energies and comparison to those calculated by TDDFT.


title: Stochastic Variational Deep Kernel Learning

abstract: Deep kernel learning combines the non-parametric flexibility of kernel methods with the inductive biases of deep learning architectures. We propose a novel deep kernel learning model and stochastic variational inference procedure which generalizes deep kernel learning approaches to enable classification, multi-task learning, additive covariance structures, and stochastic gradient training. Specifically, we apply additive base kernels to subsets of output features from deep neural architectures, and jointly learn the parameters of the base kernels and deep network through a Gaussian process marginal likelihood objective. Within this framework, we derive an efficient form of stochastic variational inference which leverages local kernel interpolation, inducing points, and structure exploiting algebra. We show improved performance over stand alone deep networks, SVMs, and state of the art scalable Gaussian processes on several classification benchmarks, including an airline delay dataset containing 6 million training points, CIFAR, and ImageNet.


Generating and Adapting Game Mechanics


title: Sex Similarities and Differences in Preferences for Short-Term Mates

abstract: Are there sex differences in criteria for sexual relationships? The answer depends on what question a researcher asks. Data suggest that, whereas the sexes differ in whether they will enter short-term sexual relationships, they are more similar in what they prioritize in partners for such relationships. However, additional data and context of other findings and theory suggest different underlying reasons. In Studies 1 and 2, men and women were given varying “mate budgets” to design short-term mates and were asked whether they would actually mate with constructed partners. Study 3 used a mate-screening paradigm. Whereas women have been found to prioritize status in long-term mates, they instead (like men) prioritize physical attractiveness much like an economic necessity in short-term mates. Both sexes also show evidence of favoring well-rounded long- and short-term mates when given the chance. In Studies 4 and 5, participants report reasons for having casual sex and what they find physically attractive. For women, results generally support a good genes account of short-term mating, as per strategic pluralism theory (S. W. Gangestad & J. A. Simpson, 2000). Discussion addresses broader theoretical implications for mate preference, and the link between method and theory in examining social decision processes.


title: Active learning in multimedia annotation and retrieval: A survey

abstract: Active learning is a machine learning technique that selects the most informative samples for labeling and uses them as training data. It has been widely explored in multimedia research community for its capability of reducing human annotation effort. In this article, we provide a survey on the efforts of leveraging active learning in multimedia annotation and retrieval. We mainly focus on two application domains: image/video annotation and content-based image retrieval. We first briefly introduce the principle of active learning and then we analyze the sample selection criteria. We categorize the existing sample selection strategies used in multimedia annotation and retrieval into five criteria: risk reduction, uncertainty, diversity, density and relevance. We then introduce several classification models used in active learning-based multimedia annotation and retrieval, including semi-supervised learning, multilabel learning and multiple instance learning. We also provide a discussion on several future trends in this research direction. In particular, we discuss cost analysis of human annotation and large-scale interactive multimedia annotation.


A Type-Theoretical system for the FraCaS test suite: Grammatical Framework meets Coq.


title: vSlicer: latency-aware virtual machine scheduling via differentiated-frequency CPU slicing

abstract: Recent advances in virtualization technologies have made it feasible to host multiple virtual machines (VMs) in the same physical host and even the same CPU core, with fair share of the physical resources among the VMs. However, as more VMs share the same core/CPU, the CPU access latency experienced by each VM increases substantially, which translates into longer I/O processing latency perceived by I/O-bound applications. To mitigate such impact while retaining the benefit of CPU sharing, we introduce a new class of VMs called latency-sensitive VMs (LSVMs), which achieve better performance for I/O-bound applications while maintaining the same resource share (and thus cost) as other CPU-sharing VMs. LSVMs are enabled by vSlicer, a hypervisor-level technique that schedules each LSVM more frequently but with a smaller micro time slice. vSlicer enables more timely processing of I/O events by LSVMs, without violating the CPU share fairness among all sharing VMs. Our evaluation of a vSlicer prototype in Xen shows that vSlicer substantially reduces network packet round-trip times and jitter and improves application-level performance. For example, vSlicer doubles both the connection rate and request processing throughput of an Apache web server; reduces a VoIP server's upstream jitter by 62%; and shortens the execution times of Intel MPI benchmark programs by half or more.


title: Stimulating Innovation in ASEAN Institutional Support, R&D Activity and Intellectual Property Rights

abstract: Using a stylized framework of technological capability development through pursuing Keynesian-Kaleckian style demand management strategies, this paper discusses initiatives that poorer member governments should take to stimulate technological upgrading of firms at the bottom with a focus on innovation, as well as, discussed the governance framework of intellectual property rights (IPRs) in ASEAN. Typologies of taxonomies and trajectories were used to evolve a policy framework to coordinate the relationship between macroinstitutions, meso-organizations and micro-agents (firms) for ASEAN members upgrade to transform from developing nations to join Singapore as developed nations. Recognizing the varying capacities of ASEAN members, the paper recommends that a common platform of IPRs be developed with the more developed members assisting the LDC members to quicken the development of a technologically more egalitarian region.