title: A Fast, Accurate, Non-Projective, Semantically-Enriched Parser

abstract: Dependency parsers are critical components within many NLP systems. However, currently available dependency parsers each exhibit at least one of several weaknesses, including high running time, limited accuracy, vague dependency labels, and lack of non-projectivity support. Furthermore, no commonly used parser provides additional shallow semantic interpretation, such as preposition sense disambiguation and noun compound interpretation. In this paper, we present a new dependency-tree conversion of the Penn Treebank along with its associated fine-grain dependency labels and a fast, accurate parser trained on it. We explain how a non-projective extension to shift-reduce parsing can be incorporated into non-directional easy-first parsing. The parser performs well when evaluated on the standard test section of the Penn Treebank, outperforming several popular open source dependency parsers; it is, to the best of our knowledge, the first dependency parser capable of parsing more than 75 sentences per second at over 93% accuracy.


title: ViKY Robotic Scope Holder: Initial Clinical Experience and Preliminary Results Using Instrument Tracking

abstract: Robotic endoscope holders constitute an alternative to complete telesurgery systems, by offering a “third hand” to the surgeon during a laparoscopic procedure. ViKY robotic scope holder (Endocontrol, Grenoble, France) is a lightweight, sterilizable body-mounted robot with 3 DOF. In this paper, we present the specifics of this robot, the newly developed XL version dedicated to single-port surgery and the initial clinical experience. We have proposed a method to control the robot, based on the detection and tracking of surgical instrument from image analysis and shape priors, to enrich the interaction between the surgeon and the robot. We present here, our work in progress toward its integration into a probabilistic framework, with the aim of improving the method's speed and robustness. We also present the surgeons' viewpoints on the feasibility of its integration into the operating theater.


title: Non-pharmacological interventions and neuroplasticity in early stage Alzheimer's disease.

abstract: Non-pharmacological interventions have the potential to reduce cognitive decline and to improve psychosocial aspects in mild cognitive impairment and Alzheimer's dementia, and the absence of side effects makes them a favorable option also for preventive strategies. We provide an overview on recent studies involving cognitive training and reminiscence, stimulating and challenging experiences such as visual art and music, physical activities, and electromagnetic stimulation. We review findings on neuroplasticity in the aging brain and their relevance for cognitive improvement in patients with neurodegenerative diseases. We discuss cognitive reserve and possible mechanisms that drive neuroplasticity and new learning. Finally, we identify promising avenues for future intervention strategies and research, such as combinations of cognitive and pharmaceutical interventions, and individual strategies adapted to the disease stage and tailored to the needs, predispositions and preferences of patients.


title: Inductance calculation and layout optimization for planar spiral inductors

abstract: The inductance calculation and the layout optimization for spiral inductors still are research topics of actuality and very interesting especially in radio frequency integrated circuits. Our research work is fixed on the vast topics of this research area. In this effect we create a software program dedicate to dc inductance calculation and to layout optimization for spiral inductors. We use a wide range of inductors in the application made with our program; we compare our applications results with measurements results existing in the literature and with three-dimensional commercial field solver results in order to validate it. Our program is accurate enough, has a very friendly interface, is very easy to use and its running time is very sort compared with other similar programs. Since spiral inductors tolerance is generally on the order of several percent, a more accurate program is not needed in practice. The program is very useful for the spiral inductor design because it calculates the inductance of spiral inductors with a very good accuracy and also for the spiral inductor optimization, because it optimize the spiral inductor layouts in terms of technological restrictions and/or in terms of the designers' needs.


title: Voxel space automata: modeling with stochastic growth processes in voxel space

abstract: A novel stochastic modeling technique is described which operates on a voxel data base in which objects are represented as collections of voxel records. Models are "grown" from predefined geometric elements according to rules based on simple relationships like intersection, proximity, and occlusion which can be evaluated more quickly and easily in voxel space than with analytic geometry. Growth is probabilistic: multiple trials are attempted in which an element's position and orientation are randomly perturbed, and the trial which best fits a set of rules is selected. The term voxel space automata is introduced to describe growth processes that sense and react to a voxel environment.Applications include simulation of plant growth, for which voxel representation facilitates sensing the environment. Illumination can be efficiently estimated at each plant "node" at each growth iteration by casting rays into the voxel environment, allowing accurate simulation of reaction to light including heliotropism.


title: Robust Imitation of Diverse Behaviors

abstract: Deep generative models have recently shown great promise in imitation learning for motor control. Given enough data, even supervised approaches can do one-shot imitation learning; however, they are vulnerable to cascading failures when the agent trajectory diverges from the demonstrations. Compared to purely supervised methods, Generative Adversarial Imitation Learning (GAIL) can learn more robust controllers from fewer demonstrations, but is inherently mode-seeking and more difficult to train. In this paper, we show how to combine the favourable aspects of these two approaches. The base of our model is a new type of variational autoencoder on demonstration trajectories that learns semantic policy embeddings. We show that these embeddings can be learned on a 9 DoF Jaco robot arm in reaching tasks, and then smoothly interpolated with a resulting smooth interpolation of reaching behavior. Leveraging these policy representations, we develop a new version of GAIL that (1) is much more robust than the purely-supervised controller, especially with few demonstrations, and (2) avoids mode collapse, capturing many diverse behaviors when GAIL on its own does not. We demonstrate our approach on learning diverse gaits from demonstration on a 2D biped and a 62 DoF 3D humanoid in the MuJoCo physics environment.


title: Low-Cost CW-LFM Radar Sensor at 100 GHz

abstract: This paper presents a W -band high-resolution radar sensor for short-range applications. Low-cost technologies have been properly selected in order to implement a versatile and easily scalable radar system. A large operational bandwidth of 9 GHz, required for obtaining high-range resolution, is attained by means of a frequency multiplication-based architecture. The system characterization to identify the performance-limiting stages and the subsequent design optimization are presented. The assessment of system performance for several representative applications has been carried out.


title: A Technique for Measuring Data Persistence Using the Ext4 File System Journal

abstract: In this paper, we propose a method of measuring data persistence using the Ext4 journal. Digital Forensic tools and techniques are commonly used to extract data from media. A great deal of research has been dedicated to the recovery of deleted data, however, there is a lack of information on quantifying the chance that an investigator will be successful in this endeavor. To that end, we suggest the file system journal be used as a source to gather empirical evidence of data persistence, which can later be used to formulate the probability of recovering deleted data under various conditions. Knowing this probability can help investigators decide where to best invest their resources. We have implemented a proof of concept system that interrogates the Ext4 file system journal and logs relevant data. We then detail how this information can be used to track the reuse of data blocks from the examination of file system metadata structures. This preliminary design contributes a novel method of tracking deleted data persistence that can be used to generate the information necessary to formulate probability models regarding the full and/or partial recovery of deleted data.


title: Gamified educational website conversion optimization

abstract: In this paper, we proposed novel approach to e-learning website conversion optimization using gamification. Analysis of current website analytics has identified several gaps between expected and present state. We used traditional three-stage conversion optimization process to first set up new conversion goals, next to track realization, and further to perform optimization using gamified landing pages. Our expectations are focused on content download and student engagement optimization. Though our research focus was on education, findings from this paper may be applicable to other fields.


title: Characteristic Face: A Key Indicator for Direct Diagnosis of 22q11.2 Deletions in Chinese Velocardiofacial Syndrome Patients

abstract: Velocardiofacial syndrome (VCFS) is a disease in human with an expansive phenotypic spectrum and diverse genetic mechanisms mainly associated with copy number variations (CNVs) on 22q11.2 or other chromosomes. However, the correlations between CNVs and phenotypes remain ambiguous. This study aims to analyze the types and sizes of CNVs in VCFS patients, to define whether correlations exist between CNVs and clinical manifestations in Chinese VCFS patients. In total, 55 clinically suspected Chinese VCFS patients and 100 normal controls were detected by multiplex ligation-dependent probe amplification (MLPA). The data from MLPA and all the detailed clinical features of the objects were documented and analyzed. A total of 44 patients (80.0%) were diagnosed with CNVs on 22q11.2. Among them, 43 (78.2%) presented with 22q11.2 heterozygous deletions, of whom 40 (93.0%) had typical 3-Mb deletion, and 3 (7.0%) exhibited proximal 1.5-Mb deletion; no patient was found with atypical deletion on 22q11.2. One patient (1.8%) presented with a 3-Mb duplication mapping to the typical 3-Mb region on 22q11.2, while none of the chromosomal abnormalities in the MLPA kit were found in the other 11 patients and 100 normal controls. All the 43 patients with 22q11.2 deletions displayed characteristic face and palatal anomalies; 37 of them (86.0%) had cognitive or behavioral disorders, and 23 (53.5%) suffered from immune deficiencies; 10 patients (23.3%) manifested congenital heart diseases. Interestingly, all patients with the characteristic face had 22q11.2 heterozygous deletions, but no difference in phenotypic spectrum was observed between 3-Mb and 1.5-Mb deletions. Our data suggest that the characteristic face can be used as a key indicator for direct diagnosis of 22q11.2 deletions in Chinese VCFS patients.


title: MCDNN: An Execution Framework for Deep Neural Networks on Resource-Constrained Devices

abstract: Deep Neural Networks (DNNs) have become the computational tool of choice for many applications relevant to mobile devices. However, given their high memory and computational demands, running them on mobile devices has required expert optimization or custom hardware. We present a framework that, given an arbitrary DNN, compiles it down to a resource-efficient variant at modest loss in accuracy. Further, we introduce novel techniques to specialize DNNs to contexts and to share resources across multiple simultaneously executing DNNs. Using the challenging continuous mobile vision domain as a case study, we show that our techniques yield very significant reductions in DNN resource usage and perform effectively over a broad range of operating conditions.


title: Dialog state tracking with attention-based sequence-to-sequence learning

abstract: We present an advanced dialog state tracking system designed for the 5th Dialog State Tracking Challenge (DSTC5). The main task of DSTC5 is to track the dialog state in a human-human dialog. For each utterance, the tracker emits a frame of slot-value pairs considering the full history of the dialog up to the current turn. Our system includes an encoder-decoder architecture with an attention mechanism to map an input word sequence to a set of semantic labels, i.e., slot-value pairs. This handles the problem of the unknown alignment between the utterances and the labels. By combining the attention-based tracker with rule-based trackers elaborated for English and Chinese, the F-score for the development set improved from 0.475 to 0.507 compared to the rule-only trackers. Moreover, we achieved 0.517 F-score by refining the combination strategy based on the topic and slot level performance of each tracker. In this paper, we also validate the efficacy of each technique and report the test set results submitted to the challenge.


title: Voyant: generating structured feedback on visual designs using a crowd of non-experts

abstract: Feedback on designs is critical for helping users iterate toward effective solutions. This paper presents Voyant, a novel system giving users access to a non-expert crowd to receive perception-oriented feedback on their designs from a selected audience. Based on a formative study, the system generates the elements seen in a design, the order in which elements are noticed, impressions formed when the design is first viewed, and interpretation of the design relative to guidelines in the domain and the user's stated goals. An evaluation of the system was conducted with users and their designs. Users reported the feedback about impressions and interpretation of their goals was most helpful, though the other feedback types were also valued. Users found the coordinated views in Voyant useful for analyzing relations between the crowd's perception of a design and the visual elements within it. The cost of generating the feedback was considered a reasonable tradeoff for not having to organize critiques or interrupt peers.


Learning to Identify Metaphors from a Corpus of Proverbs


title: The Cross-National Invariance Properties of a New Scale to Measure 19 Basic Human Values A Test Across Eight Countries

abstract: Several studies that measured basic human values across countries with the Portrait Values Questionnaire (PVQ-21) reported violations of measurement invariance. Such violations may hinder meaningful cross-cultural research on human values because value scores may not be comparable. Schwartz et al. proposed a refined value theory and a new instrument (PVQ-5X) to measure 19 more narrowly defined values. We tested the measurement invariance of this instrument across eight countries. Configural and metric invariance were established for all values across almost all countries. Scalar invariance was supported across nearly all countries for 10 values. The analyses revealed that the cross-country invariance properties of the values measured with the PVQ-5X are substantially better than those measured with the PVQ-21.


Motion sickness: a special case of sensory rearrangement.


title: Causal Impact of Employee Work Perceptions on the Bottom Line of Organizations

abstract: Perceptions of work conditions have proven to be important to the well-being of workers. However, customer loyalty, employee retention, revenue, sales, and profit are essential to the success of any business. It is known that these outcomes are correlated with employee attitudes and perceptions of work conditions, but the research into direction of causality has been inconclusive. Using a massive longitudinal database that included 2,178 business units in 10 large organizations, we found evidence supporting the causal impact of employee perceptions on these bottom-line measures; reverse causality of bottom-line measures on employee perceptions existed but was weaker. Managerial actions and practices can impact employee work conditions and employee perceptions of these conditions, thereby improving key outcomes at the organizational level. Perceptions of specific work conditions that engage employees in their work provide practical guidance in how best to manage people to obtain desired results.


title: Poster: WiFi-based Device-Free Human Activity Recognition via Automatic Representation Learning

abstract: Existing human activity recognition approaches require either the deployment of extra infrastructure or the cooperation of occupants to carry dedicated devices, which are expensive, intrusive and inconvenient for pervasive implementation. In this paper, we propose SmartSense, a device-free human activity recognition system based on a novel machine learning algorithm with existing commercial off-the-shelf (COTS) WiFi routers. By exploiting the prevalence of existing WiFi infrastructure in buildings, we developed a novel OpenWrt based firmware for COTS WiFi routers to collect the CSI measurements from regular data frames. To identify different human activities, an automatic kernel representation learning method, namely auto-HSRL, is established to selection informative Hilbert space patterns from time, frequency, wavelet, and shape domains. A new information fusion tool based on multi-view kernel learning is proposed to combine the representations extracted from diverse perspectives and build up a robust and comprehensive activity classifier. Extensive experiments were conducted in an office and the experimental results demonstrate that SmartSense outperforms existing methods and achieves a 98% activity recognition accuracy.


title: UNDERSTANDING EMERGENCE AND OUTCOMES OF INFORMATION PRIVACY CONCERNS: A CASE OF FACEBOOK

abstract: Drawing on content analysis of user responses to the revisions in the Facebook Privacy Policy, this study develops a process model to explain emergence and outcome processes of users’ information privacy concerns in an online social networking context. The first phase of the model proposes three broad categories of informational practices – collection and storage; processing and use; and dissemination of personal data—associated with users’ information privacy concerns. This phase also identifies the conditions under which proposed practices are attributed as privacy issues. The second phase of the model describes outcomes of perceived privacy issues by proposing users’ affective and behavioral responses. The findings provide evidence for, (1) the important role of trigger conditions in emergence of users’ information privacy concerns, (2) the gap between privacy issues that are perceived by users and identified by domain experts, (3) the uniqueness of online social networking context in providing distinct privacy challenges.


title: Automated reference resolution in legal texts

abstract: This paper investigates the task of reference resolution in the legal domain. This is a new interesting task in Legal Engineering research. The goal is to create a system which can automatically detect references and then extracts their referents. Previous work limits itself to detect and resolve references at the document targets. In this paper, we go a step further in trying to resolve references to sub-document targets. Referents extracted are the smallest fragments of texts in documents, rather than the entire documents that contain the referenced texts. Based on analyzing the characteristics of reference phenomena in legal texts, we propose a four-step framework to deal with the task: mention detection, contextual information extraction, antecedent candidate extraction, and antecedent determination. We also show how machine learning methods can be exploited in each step. The final system achieves 80.06 % in the F1 score for detecting references, 85.61 % accuracy for resolving them, and 67.02 % in the F1 score for the end-to-end setting task on the Japanese National Pension Law corpus.


Chapter 19 3 D Face Reconstruction from Single Two-Tone and Color Images


title: Recommending scientific articles using bi-relational graph-based iterative RWR

abstract: The overabundance of scientific article information has created much inconvenience to researchers seeking interesting articles online. In this paper, we provide a Bi-Relational graph to represent the heterogenous information of scientific article recommendation system, which includes three parts: the article content similarity, researcher interest correlation, and researcher-article readership. Meanwhile, an iterative random walk with restarts learning method is proposed on the Bi-Relational graph to recommend a researcher rating for each article by making use of the known information. The proposed method has ability to perform both old and new article recommendation. A series of experiments on CiteULike dataset have shown that our method is more effective than other testing methods in the paper.


title: Robust Optimal Power Management System for a Hybrid AC/DC Micro-Grid

abstract: Hybrid ac/dc micro-grid is a new concept decoupling dc sources with dc loads and ac sources with ac loads, while power is exchanged between both sides using a bidirectional converter/inverter. This necessitates a supervisory control system to split power between its different resources, which has sparked attention on the development of power management systems (PMSs). In this paper, a robust optimal PMS (ROPMS) is developed for a hybrid ac/dc micro-grid, where the power flow in the micro-grid is supervised based on solving an optimization problem. Satisfying demanded power with maximum utilization of renewable resources, minimum usage of fuel-based generator, extending batteries lifetime, and limited utilization of the main power converter between the ac and dc micro-grids are important factors that are considered in this approach. Uncertainties in the resources output power and generation forecast errors, along with static and dynamic constraints of the resources, are taken into account. Furthermore, since uncertainties in the resources output power may result in fluctuations in the dc bus voltage, a two-level controller is used to regulate charge/discharge power of the battery banks. Effectiveness of the proposed supervisory system is evaluated through extensive simulation runs based on dynamical models of the power resources.


title: Phosphorylation of Chromosome Core Components May Serve as Axis Marks for the Status of Chromosomal Events during Mammalian Meiosis

abstract: Meiotic recombination and chromosome synapsis between homologous chromosomes are essential for proper chromosome segregation at the first meiotic division. While recombination and synapsis, as well as checkpoints that monitor these two events, take place in the context of a prophase I-specific axial chromosome structure, it remains unclear how chromosome axis components contribute to these processes. We show here that many protein components of the meiotic chromosome axis, including SYCP2, SYCP3, HORMAD1, HORMAD2, SMC3, STAG3, and REC8, become post-translationally modified by phosphorylation during the prophase I stage. We found that HORMAD1 and SMC3 are phosphorylated at a consensus site for the ATM/ATR checkpoint kinase and that the phosphorylated forms of HORMAD1 and SMC3 localize preferentially to unsynapsed chromosomal regions where synapsis has not yet occurred, but not to synapsed or desynapsed regions. We investigated the genetic requirements for the phosphorylation events and revealed that the phosphorylation levels of HORMAD1, HORMAD2, and SMC3 are dramatically reduced in the absence of initiation of meiotic recombination, whereas BRCA1 and SYCP3 are required for normal levels of phosphorylation of HORMAD1 and HORMAD2, but not of SMC3. Interestingly, reduced HORMAD1 and HORMAD2 phosphorylation is associated with impaired targeting of the MSUC (meiotic silencing of unsynapsed chromatin) machinery to unsynapsed chromosomes, suggesting that these post-translational events contribute to the regulation of the synapsis surveillance system. We propose that modifications of chromosome axis components serve as signals that facilitate chromosomal events including recombination, checkpoint control, transcription, and synapsis regulation.


title: Strategies for Control of Space Robots: A Review and Research Agenda

abstract: M odelling and control of space robots is not an easy task to perform, because the equations of motion that govern phenomenon are highly nonlinear. Furthermore, unlike fixed base manipulators a free- floating space robot exhibits non-holonomic behavior as a result of the non-integrability of the angular momentum conservation law. In recent days space robots are extensively used to play a significant role in space applications like, scheduled servicing of satellites and spacecrafts including refuelling tasks, inspection of remote sites or verification of structures, retrieval of tumbling tools or astronauts, and assembly or welding of space structures. In a large number of these applications, the manipulator endeffector is required to interact with the environment. Due to the interaction between the endeffector and the environment, the interaction torques act on the endeffector which gets transmitted through links to the base of the vehicle and the orientation of the vehicle changes. Hence, precise control of the manipulator's trajectory, attitude and impedance are critically important. This paper addressed the current state-oftheart in key areas of the space robotics by reviewing recently available literatures particularly on free flying and free floating space robots which help in summarizing various research outcomes in a structured manner. K eywords : s pace robots, free floating space robots, attitude control, impedance control, trajectory control.


title: Transfer Learning Based Cross-lingual Knowledge Extraction for Wikipedia

abstract: Wikipedia infoboxes are a valuable source of structured knowledge for global knowledge sharing. However, infobox information is very incomplete and imbalanced among the Wikipedias in different languages. It is a promising but challenging problem to utilize the rich structured knowledge from a source language Wikipedia to help complete the missing infoboxes for a target language. In this paper, we formulate the problem of cross-lingual knowledge extraction from multilingual Wikipedia sources, and present a novel framework, called WikiCiKE, to solve this problem. An instancebased transfer learning method is utilized to overcome the problems of topic drift and translation errors. Our experimental results demonstrate that WikiCiKE outperforms the monolingual knowledge extraction method and the translation-based method.


title: A digital channelizer design approach for broadband satellite communications based on frequency domain filter theory

abstract: Based on the frequency domain filtering (or FFT filtering) theory, a novel digital channelizer design approach is proposed in this paper, where the downlink broadband signal is formed after performing extracting, switching and synthesizing processing for the uplink broadband sub-signals. Our proposed digital channelizers apply for not only the scenarios with equal-bandwidth sub-signals, but also the scenarios with unequal-bandwidth sub-signals. Numerical simulation showed that, compared with the existing methods, our digital channelizers have advantage in performance and flexibility, and significantly reduces the computational complexity.


title: Unsupervised detection of surface defects: A two-step approach

abstract: In this paper, we focus on the problem of finding anomalies in surface images. Despite enormous research efforts and advances, it still remains a big challenge to be solved. This paper proposes a unified approach for defect detection. Our proposed method consists of two phases: (1) global estimation and (2) local refinement. First, we roughly estimate defects by applying a spectral-based approach in a global manner. We then locally refine the estimated region based on the distributions of pixel intensities derived from defect and defect-free regions. Experimental results show that the proposed method outperforms the previous defect detection methods and gives robust results even in noisy surface defect images.


Classification of Mammographic Breast Density Using a Combined Classifier Paradigm


title: Black Americans' Implicit Racial Associations And Their Implications for Intergroup Judgment

abstract: Abstract Many—though not all—black Americans exhibit an implicit evaluative preference for whites relative to blacks (e.g., Livingston, 2002). Are such biases meaningfully related to blacks' explicit attitudes and actual intergroup judgments? In the present study, 83 black participants who believed they would complete an intellectually challenging task with a partner rated their preferences for (fictitious) black and white potential partners. The less strongly participants implicitly preferred their ingroup, the lower their preference for a black vs. a white work partner. The magnitude of this relationship held even when controlling for explicit attitudes that were related to partner preference. Implicit biases were associated with explicit attitudes regarding black, but not white, persons and with system-justifying ideology (Jost & Banaji, 1994).


title: Dividends and taxes

abstract: Abstract We present sufficient conditions for taxable investors to be indifferent to dividends despite tax differentials in favor of capital gains (Strong Invariance Proposition). The conditions include two ‘seemingly unrelated’ provisions of the Internal Revenue Code: (1) the limitation of interest deductions to investment income received and (2) the tax-free accumulation of wealth at the before-tax interest rate on investments in life insurance. Although we use insurance for simplicity in the proof, many tax-equivalent investment vehicles now exist, notably pension funds. Our analysis suggests that the personal income tax is approaching a consumption tax with further drift likely.


title: Object Detection with Mask-based Feature Encoding

abstract: Region-based Convolutional Neural Networks (R-CNNs) have achieved great success in the field of object detection. The existing R-CNNs usually divide a Region-of-Interest (ROI) into grids, and then localize objects by utilizing the spatial information reflected by the relative position of each grid in the ROI. In this paper, we propose a novel feature-encoding approach, where spatial information is represented through the spatial distributions of visual patterns. In particular, we design a Mask Weight Network (MWN) to learn a set of masks and then apply channel-wise masking operations to ROI feature map, followed by a global pooling and a cheap fully-connected layer. We integrate the newly designed feature encoder into the Faster R-CNN architecture. The resulting new Faster R-CNNs can preserve the object-detection accuracy of the standard Faster R-CNNs by using substantially fewer parameters. Compared to R-FCNs using state-of-art PS ROI pooling and deformable PS ROI pooling, the new Faster R-CNNs can produce higher object-detection accuracy with good run-time efficiency. We also show that a specifically designed and learned MWN can capture global contextual information and further improve the object-detection accuracy. Validation experiments are conducted on both PASCAL VOC and MS COCO datasets.


title: On the Feasibility of Generic Deep Disaggregation for Single-Load Extraction

abstract: Recently, and with the growing development of big energy datasets, data-driven learning techniques began to represent a potential solution to the energy disaggregation problem outperforming engineered and hand-crafted models. However, most proposed deep disaggregation models are load-dependent in the sense that either expert knowledge or a hyper-parameter optimization stage is required prior to training and deployment (normally for each load category) even upon acquisition and cleansing of aggregate and sub-metered data. In this paper, we present a feasibility study on the development of a generic disaggregation model based on data-driven learning. Specifically, we present a generic deep disaggregation model capable of achieving state-of-art performance in load monitoring for a variety of load categories. The developed model is evaluated on the publicly available UK-DALE dataset with a moderately low sampling frequency and various domestic loads.


title: Comparative Analysis of Master-Slave Latches and Flip-Flops for High-Performance and Low-Power Systems

abstract: In this paper, we propose a set of rules for consistent estimation of the real performance and power features of the flip-flop and master-slave latch structures. A new simulation and optimization approach is presented, targeting both high-performance and power budget issues. The analysis approach reveals the sources of performance and power-consumption bottlenecks in different design styles. Certain misleading parameters have been properly modified and weighted to reflect the real properties of the compared structures. Furthermore, the results of the comparison of representative master-slave latches and flip-flops illustrate the advantages of our approach and the suitability of different design styles for high-performance and low-power applications.


title: Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding

abstract: Computer vision has a great potential to help our daily lives by searching for lost keys, watering flowers or reminding us to take a pill. To succeed with such tasks, computer vision methods need to be trained from real and diverse examples of our daily dynamic scenes. While most of such scenes are not particularly exciting, they typically do not appear on YouTube, in movies or TV broadcasts. So how do we collect sufficiently many diverse but boring samples representing our lives? We propose a novel Hollywood in Homes approach to collect such data. Instead of shooting videos in the lab, we ensure diversity by distributing and crowdsourcing the whole process of video creation from script writing to video recording and annotation. Following this procedure we collect a new dataset, Charades, with hundreds of people recording videos in their own homes, acting out casual everyday activities. The dataset is composed of 9,848 annotated videos with an average length of 30 s, showing activities of 267 people from three continents. Each video is annotated by multiple free-text descriptions, action labels, action intervals and classes of interacted objects. In total, Charades provides 27,847 video descriptions, 66,500 temporally localized intervals for 157 action classes and 41,104 labels for 46 object classes. Using this rich data, we evaluate and provide baseline results for several tasks including action recognition and automatic description generation. We believe that the realism, diversity, and casual nature of this dataset will present unique challenges and new opportunities for computer vision community.


title: Vistradas: Visual Analytics for Urban Trajectory Data

abstract: In the past few years a growing number of cities have started monitoring the position of public transportation vehicles using GPS devices. Most of these trajectory data are released in raw format and usually have issues, such as measurement errors. Providing insights from these valuable (and noisy) data is a major challenge in larger cities. In this paper we present a system, called Vistradas, for visual analytics of urban trajectory data. Vistradas allows users to analyze use cases related to trajectories of public buses such as: analysis of bus uniformity, verification of bus route, and the impact of events in bus traffic. Our proposed Vistradas system helps user to get insights into various aspects of public transportation.


title: An Analysis of Telecommunication Fraud using Outlier Detection Model based on Similar Coefficient Sum

abstract: In modern day, new technologies have a great impact on people's life. This has resulted in the increase of frauds in today's technological environment. Service providing companies such as telecommunication companies, banking companies etc. suffer from financial losses due to the customers' fraudulent behaviors. Telecommunication fraud not only financially effects the company but also effects the individual customers as well. So detection of fraud is very important in today's life. In this paper, we have identified the customer's fraudulent behaviour using outlier detection model based on similar coefficient sum. In this approach, frauds are detected by computing coefficient sum of every two objects. This method has also been implemented in Mat Lab R2010a and the obtained results show the feasibility and validity of the method.


title: A SELF-CALIBRATION METHOD FOR ARTICULATED ARM COORDINATE MEASURING MACHINES

abstract: To improve the accuracy of articulated arm coordinate measuring machines (AACMM) and simplify the calibration process, an improved self-calibration method was proposed. Unlike the traditional calibration methods, which need external expensive precision instruments and elaborate setups, the proposed self-calibration method only requires a gauge to assist the data acquisition operation. By designing a movement trajectory of the AACMM, a series of joint angles can be obtained to form overdetermined equations based on the kinematic model of the AACMM. Therefore, the structural parameters of the AACMM can be obtained by solving the equations. Consequently, the calibration can be achieved by solving these equations. The coefficient matrix of the equations was further analyzed to simplify the equations, and a constructive method was presented to identify the structural parameters by solving the simplified equations with a modified simulated annealing algorithm, in which an optimized search strategy was applied t...


title: Analysing fun as a candidate software requirement

abstract: Analysis of concepts related to “fun” is used to suggest the main areas where fun may be important to software design. The issue is important as many analyses and design methods fail to allow for designs aimed at user enjoyment, even though computer games are now a major industry. The main cases seem to be either where enjoyment is the chief aim (requirement) of the design, or where learning is important. The phenomena of “flow” experiences are also important here, and raise the issue of designing for desirable cognitive modes of interaction, as well as for desired end results. However the relationship of learning and fun, while clearly important, is complicated.


title: Wideband Dual-Polarized Cross-Shaped Vivaldi Antenna

abstract: This communication presents a wideband, dual-polarized Vivaldi antenna or tapered slot antenna with over a decade (10.7:1) of bandwidth. The dual-polarized antenna structure is achieved by inserting two orthogonal Vivaldi antennas in a cross-shaped form without a galvanic contact. The measured $- 10\,\hbox{dB}$ impedance bandwidth ( ${S_{11}}$ ) is approximately from 0.7 up to 7.30 GHz, corresponding to a 166% relative frequency bandwidth. The isolation ( ${S_{21}}$ ) between the antenna ports is better than 30 dB, and the measured maximum gain is 3.8–11.2 dB at the aforementioned frequency bandwidth. Orthogonal polarizations have the same maximum gain within the 0.7–3.6 GHz band, and a slight variation up from 3.6 GHz. The cross-polarization discrimination (XPD) is better than 19 dB across the measured 0.7–6.0 GHz frequency bandwidth, and better than 25 dB up to 4.5 GHz. The measured results are compared with the numerical ones in terms of $S$ -parameters, maximum gain, and XPD.


Bending the axial line: smoothly continuous road centre-line segments as a basis for road network analysis


title: Beyond Factoid QA: Effective Methods for Non-factoid Answer Sentence Retrieval

abstract: Retrieving finer grained text units such as passages or sentences as answers for non-factoid Web queries is becoming increasingly important for applications such as mobile Web search. In this work, we introduce the answer sentence retrieval task for non-factoid Web queries, and investigate how this task can be effectively solved under a learning to rank framework. We design two types of features, namely semantic and context features, beyond traditional text matching features. We compare learning to rank methods with multiple baseline methods including query likelihood and the state-of-the-art convolutional neural network based method, using an answer-annotated version of the TREC GOV2 collection. Results show that features used previously to retrieve topical sentences and factoid answer sentences are not sufficient for retrieving answer sentences for non-factoid queries, but with semantic and context features, we can significantly outperform the baseline methods.


title: Mission-level space situational awareness

abstract: Complex space systems typically provide the operator a means to understand the current state of system components. The operator often has to manually determine whether the system is able to perform a given set of high level objectives based on this information. The operations team needs a way for the system to quantify its capability to successfully complete a mission objective and convey that information in a clear, concise way.


title: Fast Normalized Cross-Correlation

abstract: Although it is well known that cross correlation can be efficiently implemented in the transform domain, the normalized form of cross correlation preferred for feature matching applications does not have a simple frequency domain expression. Normalized cross correlation has been computed in the spatial domain for this reason. This short paper shows that unnormalized cross correlation can be efficiently normalized using precomputing integrals of the image and image over the search window.


title: An AOI algorithm for PCB based on feature extraction

abstract: With the development of the micro-electronic industry, electronical components assembled on the printed circuit board (PCB) become more and more microsize and its mounted density is increasing. It is out of date to depend on manual inspection to assure the joints quality. Instead, automated optical inspection (AOI) for solder joints based on the machine vision has become more and more important. In this paper, based on the image acquired from a 3-CCD color camera and a 3-color hemispherical LED arrays light resource (red, green, and blue), the place, shape, and logical features of solder joints of chip components are extracted. The place features are related to solder joint place. As for shape features, we divide solder joint into several regions and extract its shape features by its color, occupancy ratio of area, center of gravity and continuous pixels. The logical features come from their close relationships of different regions of shape features, color distributing and place features. On the basis of the features, an AOI algorithm is developed. The defects of lacking solder, surplus solder, no solder, pseudo joints, wrong component, damaged component, component absent, shift, tomb stone, wrong polarity, etc. can be identified properly by using the proposed algorithm. Finally, some experiment results are presented to show the validity of the algorithm.


THE FIVE GREATEST APPLICATIONS OF MARKOV CHAINS


Putting Theories Together to Make Specifications


title: Symbolic Boolean manipulation with ordered binary-decision diagrams

abstract: Ordered Binary-Decision Diagrams (OBDDs) represent Boolean functions as directed acyclic graphs. They form a canonical representation, making testing of functional properties such as satisfiability and equivalence straightforward. A number of operations on Boolean functions can be implemented as graph algorithms on OBDD data structures. Using OBDDs, a wide variety of problems can be solved through symbolic analysis . First, the possible variations in system parameters and operating conditions are encoded with Boolean variables. Then the system is evaluated for all variations by a sequence of OBDD operations. Researchers have thus solved a number of problems in digital-system design, finite-state system analysis, artificial intelligence, and mathematical logic. This paper describes the OBDD data structure and surveys a number of applications that have been solved by OBDD-based symbolic analysis.


Deep Learning as an Opportunity in Virtual Screening


Validation of in vitro tests in neurotoxicology


title: Mapping the U.S. Political Blogosphere: Are Conservative Bloggers More Prominent?

abstract: Weblogs are now a key part of online culture, and social scientists are interested in characterising the networks formed by bloggers and measuring their extent and impact in areas such as politics. However, researchers wishing to conduct quantitative social science analysis of the blogging phenomenon are faced with the challenge of using new methods of data collection and analysis largely derived from fields outside of the social sciences, such as the information sciences. This paper presents an overview of one new approach for collecting and analysing weblog data, and illustrates this approach in the context of a preliminary quantitative analysis of online networks formed by a sample of North-American “A-list” political bloggers. There are two aims to this paper. First is to assess (using different data and methods) the conclusion of Adamic and Glance (2005) that there are significant differences in the behaviour of liberal and conservative bloggers, with the latter forming more dense patterns of linkages. We find broad support for this conclusion, and empirically assess the implications of differences in conservative/liberal linking behaviour for the online visibility of different political messages or ideologies. The second aim is to highlight the role of web mining and data visualisation in the analysis of weblogs, and the opportunities and challenges inherent in this new field of research.


title: Virtual Prototyping for Clean Industrial Processes

abstract: According to projections of UNO, by 2050, the urban population will account for 75% of the world population. This human hyperconcentration will have significant repercussions in terms of environmental and social impact. Vis-a-vis this substantial change caused by the population growth, technological development, and environmental emergency, the clean technology becomes the dominant model for tomorrow's urban planning. The design of a new product involves the realization of several prototypes. The need for duplicating the prototypes has consequently the over-exploitation of natural resources, the energy resources and the accumulation of waste. To decrease these problems, the industry increasingly oriented towards virtual prototyping (VP). The paper will start by presenting virtual prototyping like a new model of design that takes account of the economy and the protection of the resources. A discussion is proposed concerning, in particular, the characteristics, the requirements and the challenges of VP.


title: Health dialog systems for patients and consumers

abstract: There is a growing need for automated systems that can interview patients and consumers about their health and provide health education and behavior change interventions using natural language dialog. A number of these health dialog systems have been developed over the last two decades, many of which have been formally evaluated in clinical trials and shown to be effective. This article provides an overview of the theories, technologies and methodologies that are used in the construction and evaluation of these systems, along with a description of many of the systems developed and tested to date. The strengths and weaknesses of these approaches are also discussed, and the needs for future work in the field are delineated.


Duration prediction using multi-level model for GPR-based speech synthesis.


title: Privacy Risk Assessment on Online Photos

abstract: With the rising popularity of cameras and people's increasing desire to share photos, an overwhelming number of photos have been posted all over the Web. A digital photo usually contains much information in its metadata. Once published online, a photo could disclose much more information beyond what is visually depicted in the photo and what the owner expects to share. The metadata contained in digital photos could pose significant privacy threats to their owners. Our work aims to raise public awareness of privacy risks resulting from sharing photos online and subsequent photo handling conducted by contemporary media sites. To this end, we investigated the prevalence of metadata information among digital photos and assessed the potential privacy risks arising from the metadata information. We also studied the policies adopted by online media sites on handling the metadata information embedded in the photos they host. We examined nearly 100,000 photos collected from over 600 top-ranked websites in seven categories and found that the photo handling policy adopted by a site largely varies depending on the category of the site. We demonstrated that some trivial looking metadata information suffices to mount real-world attacks against photo owners.


title: Dual-process theory and signal-detection theory of recognition memory

abstract: Two influential models of recognition memory, the unequal-variance signal-detection model and a dual-process threshold/detection model, accurately describe the receiver operating characteristic, but only the latter model can provide estimates of recollection and familiarity. Such estimates often accord with those provided by the remember–know procedure, and both methods are now widely used in the neuroscience literature to identify the brain correlates of recollection and familiarity. However, in recent years, a substantial literature has accumulated directly contrasting the signal-detection model against the threshold/detection model, and that literature is almost unanimous in its endorsement of signal-detection theory. A dual-process version of signal-detection theory implies that individual recognition decisions are not process pure, and it suggests new ways to investigate the brain correlates of recognition memory.


title: Driver classification and driving style recognition using inertial sensors

abstract: Currently there are many research focused on using smartphone as a data collection device. Many have shown its sensors ability to replace a lab test bed. These inertial sensors can be used to segment and classify driving events fairly accurately. In this research we explore the possibility of using the vehicle's inertial sensors from the CAN bus to build a profile of the driver to ultimately provide proper feedback to reduce the number of dangerous car maneuver. Braking and turning events are better at characterizing an individual compared to acceleration events. Histogramming the time-series values of the sensor data does not help performance. Furthermore, combining turning and braking events helps better differentiate between two similar drivers when using supervised learning techniques compared to separate events alone, albeit with anemic performance.


title: FC^4: Fully Convolutional Color Constancy with Confidence-Weighted Pooling

abstract: Improvements in color constancy have arisen from the use of convolutional neural networks (CNNs). However, the patch-based CNNs that exist for this problem are faced with the issue of estimation ambiguity, where a patch may contain insufficient information to establish a unique or even a limited possible range of illumination colors. Image patches with estimation ambiguity not only appear with great frequency in photographs, but also significantly degrade the quality of network training and inference. To overcome this problem, we present a fully convolutional network architecture in which patches throughout an image can carry different confidence weights according to the value they provide for color constancy estimation. These confidence weights are learned and applied within a novel pooling layer where the local estimates are merged into a global solution. With this formulation, the network is able to determine what to learn and how to pool automatically from color constancy datasets without additional supervision. The proposed network also allows for end-to-end training, and achieves higher efficiency and accuracy. On standard benchmarks, our network outperforms the previous state-of-the-art while achieving 120x greater efficiency.


title: A Solid-State Marx Generator With a Novel Configuration

abstract: The new configuration proposed in this paper for Marx Generator (MG.) aims to generate high voltage for pulsed power applications through reduced number of semiconductor components with a more efficient load supplying process. The main idea is to charge two groups of capacitors in parallel through an inductor and take the advantage of resonant phenomenon in charging each capacitor up to a double input voltage level. In each resonant half a cycle, one of those capacitor groups are charged, and eventually the charged capacitors will be connected in series and the summation of the capacitor voltages can be appeared at the output of the topology. This topology can be considered as a modified Marx generator which works based on the resonant concept. Simulated models of this converter have been investigated in Matlab/SIMULINK platform and the acquired results fully satisfy the anticipations in proper operation of the converter.


title: High-Performance Unsupervised Anomaly Detection for Cyber-Physical System Networks

abstract: While the ever-increasing connectivity of cyber-physical systems enlarges their attack surface, existing anomaly detection frameworks often do not incorporate the rising heterogeneity of involved systems. Existing frameworks focus on a single fieldbus protocol or require more detailed knowledge of the cyber-physical system itself. Thus, we introduce a uniform method and framework for applying anomaly detection to a variety of fieldbus protocols. We use stacked denoising autoencoders to derive a feature learning and packet classification method in one step. As the approach is based on the raw byte stream of the network traffic, neither specific protocols nor detailed knowledge of the application is needed. Additionally, we pay attention on creating an efficient framework which can also handle the increased amount of communication in cyber-physical systems. Our evaluation on a Secure Water Treatment dataset using EtherNet/IP and a Modbus dataset shows that we can acquire network packets up to 100 times faster than packet parsing based methods. However, we still achieve precision and recall metrics for longer lasting attacks of over 99%.


title: The Real Relationship and Its Role in Psychotherapy Outcome: A Meta-Analysis

abstract: Although writing about the real relationship has existed from the beginnings of the "talking cure," it is only in recent years that empirical research has focused on this phenomenon. The real relationship is the personal relationship between patient and therapist marked by the extent to which each is genuine with the other and perceives/experiences the other in ways that are realistic. The strength of the real relationship is determined by both the extent to which it exists and the degree to which it is positive or favorable. In this article, a meta-analysis is presented on the association between the strength of the real relationship and the outcome of psychotherapy. Summed across 16 studies, this meta-analysis revealed a moderate association with outcome (r = .38, 95% confidence interval [.30, .44], p < .001, d = 0.80, N = 1.502). This real relationship-outcome association was independent of the type of outcome studied (treatment outcome, treatment progress, and session outcome) and of the source of the measure (whether the client or the therapist rated the real relationship and/or treatment outcome). We also present commonly used measures of the real relationship, limitations of the research, and patient contributions. The article concludes with diversity considerations and practice recommendations for developing and strengthening the real relationship. (PsycINFO Database Record (c) 2018 APA, all rights reserved).


title: IOT based Energy Meter Monitoring using ARM Cortex M4 with Android Application

abstract: Internet of Things (IOT) aims at interfacing different gadgets to the internet web – encouraging human-machine and machine-machine connections offering superior security, console and effectiveness. The concept of IOT is utilized in this model, remote monitoring of energy meter which is intended to overcome the issues in existing Automatic Meter Reading (AMR) system. It spares tremendous human work. A controller integrated with electronic energy meter assist in distant correspondence from the developed android application. This application enables monitoring of bill generation at consumer premises without human intervention and also in visualizing live data consumption and sight energy expended points of interest on daily/monthly basis. In addition, it gives authority to power organizations to seize lenient customers who have extraordinary dues for remote disconnection of the power supply. So IOT based remote AMR framework is more viable methodology than tradition of billing framework. General Terms Central office


title: Automating data extraction in systematic reviews: a systematic review

abstract: BACKGROUND ::: Automation of the parts of systematic review process, specifically the data extraction step, may be an important strategy to reduce the time necessary to complete a systematic review. However, the state of the science of automatically extracting data elements from full texts has not been well described. This paper performs a systematic review of published and unpublished methods to automate data extraction for systematic reviews. ::: ::: ::: METHODS ::: We systematically searched PubMed, IEEEXplore, and ACM Digital Library to identify potentially relevant articles. We included reports that met the following criteria: 1) methods or results section described what entities were or need to be extracted, and 2) at least one entity was automatically extracted with evaluation results that were presented for that entity. We also reviewed the citations from included reports. ::: ::: ::: RESULTS ::: Out of a total of 1190 unique citations that met our search criteria, we found 26 published reports describing automatic extraction of at least one of more than 52 potential data elements used in systematic reviews. For 25 (48 %) of the data elements used in systematic reviews, there were attempts from various researchers to extract information automatically from the publication text. Out of these, 14 (27 %) data elements were completely extracted, but the highest number of data elements extracted automatically by a single study was 7. Most of the data elements were extracted with F-scores (a mean of sensitivity and positive predictive value) of over 70 %. ::: ::: ::: CONCLUSIONS ::: We found no unified information extraction framework tailored to the systematic review process, and published reports focused on a limited (1-7) number of data elements. Biomedical natural language processing techniques have not been fully utilized to fully or even partially automate the data extraction step of systematic reviews.


title: A Survey of Data Provenance Techniques

abstract: Data management is growing in complexity as large-scale applications take advantage of the loosely coupled resources brought together by grid middleware and by abundant storage capacity. Metadata describing the data products used in and generated by these applications is essential to disambiguate the data and enable reuse. Data provenance, one kind of metadata, pertains to the derivation history of a data product starting from its original sources. The provenance of data products generated by complex transformations such as workflows is of considerable value to scientists. From it, one can ascertain the quality of the data based on its ancestral data and derivations, track back sources of errors, allow automated re-enactment of derivations to update a data, and provide attribution of data sources. Provenance is also essential to the business domain where it can be used to drill down to the source of data in a data warehouse, track the creation of intellectual property, and provide an audit trail for regulatory purposes. In this paper we create a taxonomy of data provenance techniques, and apply the classification to current research efforts in the field. The main aspect of our taxonomy categorizes provenance systems based on why they record provenance, what they describe, how they represent and store provenance, and ways to disseminate it. Our synthesis can help those building scientific and business metadata-management systems to understand existing provenance system designs. The survey culminates with an identification of open research problems in the field.


title: 8-Bit Approximations for Parallelism in Deep Learning

abstract: The creation of practical deep learning data-products often requires parallelization across processors and computers to make deep learning feasible on large data sets, but bottlenecks in communication bandwidth make it difficult to attain good speedups through parallelism. Here we develop and test 8-bit approximation algorithms which make better use of the available bandwidth by compressing 32-bit gradients and nonlinear activations to 8-bit approximations. We show that these approximations do not decrease predictive performance on MNIST, CIFAR10, and ImageNet for both model and data parallelism and provide a data transfer speedup of 2x relative to 32-bit parallelism. We build a predictive model for speedups based on our experimental data, verify its validity on known speedup data, and show that we can obtain a speedup of 50x and more on a system of 96 GPUs compared to a speedup of 23x for 32-bit. We compare our data types with other methods and show that 8-bit approximations achieve state-of-the-art speedups for model parallelism. Thus 8-bit approximation is an efficient method to parallelize convolutional networks on very large systems of GPUs.


title: Using Emotions as Intrinsic Motivation to Accelerate Classic Reinforcement Learning

abstract: Aiming at the need for autonomous learning in reinforcement learning (RL), a quantitative emotion-based motivation model is proposed by introducing psychological emotional factors as the intrinsic motivation. The curiosity is used to promote or hold back agents' exploration of unknown states, the happiness index is used to determine the current state-action's happiness level, the control power is used to indicate agents' control ability over its surrounding environment, and together to adjust agents' learning preferences and behavioral patterns. To combine intrinsic emotional motivations with classic RL, two methods are proposed. The first method is to use the intrinsic emotional motivations to explore unknown environment and learn the environment transitioning model ahead of time, while the second method is to combine intrinsic emotional motivations with external rewards as the ultimate joint reward function, directly to drive agents' learning. As the result shows, in the simulation experiments in the rat foraging in maze scenario, both methods have achieved relatively good performance, compared with classic RL purely driven by external rewards.


title: Computerized cognitive remediation improves verbal learning and processing speed in schizophrenia

abstract: Computerized cognitive remediation has resulted in improved executive function in schizophrenia, whereas results with regard to verbal memory were inconsistent. In the present study, 42 inpatients with schizophrenia were randomly assigned to a computerized cognitive remediation group or to a treatment-as-usual (TAU) control group. The remediation group received 15 sessions of computerized cognitive training (Cogpack) over a 3-week period. Neurocognitive functions were assessed at the beginning and end of this period. Compared to the control condition, remediation training resulted in improvements in verbal learning, processing speed and executive function (verbal fluency). The results indicate that cognitive remediation may lead to improvements beyond those of executive function.


title: A 1-MHz, 12-V ZVS Nonisolated Full-Bridge VRM With Gate Energy Recovery

abstract: In this paper, a new self-driven zero-voltage-switching (ZVS) nonisolated full-bridge converter is presented for 12-V input VRM applications. The advantages of the new circuit are: 1) duty cycle extension; 2) ZVS of all the control MOSFETs; 3) lower voltage stress and reduced reverse recovery loss of the synchronous rectifier (SR) MOSFETs; 4) high-drive voltage to reduce R DS( ON) and the conduction loss of the SRs due to gate energy recovery capability; and 5) reduced body-diode conduction and no external drive IC chips with dead time control needed for SRs. Existing multiphase buck controllers and buck drivers can be directly used in the proposed converter. The experimental results verify the principle of operation and significant efficiency improvement. At 12 V input, 1.3 V output voltage, and 1 MHz switching frequency, the proposed converter improves the efficiency, using the buck converter from 80.7% to 83.6% at 50 A, and from 77.9% to 80.5% at 60 A. With two parallel SRs, the efficiency is further improved from 83.6% (single SR) to 84.7% (two SRs) and at 60 A, the efficiency is improved from 80.5% (single SR) to 83.2% (two SRs).


title: Differences and Impact Factors between Customer Expected Value and Customer Perceived Value

abstract: In recent years, as the concept of customer value has attracted raising attention among marketing researchers and practitioners, more enterprises start to take customer value strategies in order to increase profits and ensure sustainable development. Basically, the variety of customer value has been brought by the differences between customer expected value (CEV) and customer perceived value (CPV). This paper analyzes systematically the discrepancy between CEV and CPV in three aspects, including characteristics, structure and influence factors, combined with theories of customer satisfaction and customer behaviors, constructs a measurement framework on the differences between CEV and CPV, which are composed of differentiated model and impact factors model. These models are tested using data from a survey among 220 college students in mobile phone market. The findings of this article are directions for future research and managerial implications.


title: Hardware for machine learning: Challenges and opportunities

abstract: Machine learning plays a critical role in extracting meaningful information out of the zetabytes of sensor data collected every day. For some applications, the goal is to analyze and understand the data to identify trends (e.g., surveillance, portable/wearable electronics); in other applications, the goal is to take immediate action based the data (e.g., robotics/drones, self-driving cars, smart Internet of Things). For many of these applications, local embedded processing near the sensor is preferred over the cloud due to privacy or latency concerns, or limitations in the communication bandwidth. However, at the sensor there are often stringent constraints on energy consumption and cost in addition to throughput and accuracy requirements. Furthermore, flexibility is often required such that the processing can be adapted for different applications or environments (e.g., update the weights and model in the classifier). In many applications, machine learning often involves transforming the input data into a higher dimensional space, which, along with programmable weights, increases data movement and consequently energy consumption. In this paper, we will discuss how these challenges can be addressed at various levels of hardware design ranging from architecture, hardware-friendly algorithms, mixed-signal circuits, and advanced technologies (including memories and sensors).


title: VeriSolid: Correct-by-Design Smart Contracts for Ethereum

abstract: The adoption of blockchain based distributed ledgers is growing fast due to their ability to provide reliability, integrity, and auditability without trusted entities. One of the key capabilities of these emerging platforms is the ability to create self-enforcing smart contracts. However, the development of smart contracts has proven to be error-prone in practice, and as a result, contracts deployed on public platforms are often riddled with security vulnerabilities. This issue is exacerbated by the design of these platforms, which forbids updating contract code and rolling back malicious transactions. In light of this, it is crucial to ensure that a smart contract is secure before deploying it and trusting it with significant amounts of cryptocurrency. To this end, we introduce the VeriSolid framework for the formal verification of contracts that are specified using a transition-system based model with rigorous operational semantics. Our model-based approach allows developers to reason about and verify contract behavior at a high level of abstraction. VeriSolid allows the generation of Solidity code from the verified models, which enables the correct-by-design development of smart contracts.


title: Transparent Top-Emitting Organic Light-Emitting Diodes Using a Cs/Al/Ag/ITO Semi-Transparent Cathode

abstract: A top-emitting organic light-emitting diode (TEOLED) was fabricated with a semi-transparent multilayer cathode composed of Cs/Al/Ag and an ITO capping layer. The optical properties of Cs (0.5 nm)/Al (2.0 nm)/Ag (20 nm) at a wavelength of 533 nm exhibited a transmittance of 50 %. The light-output of the TEOELD with the Cs/Al/Ag/ITO showed a maximum luminance of 51,000 cd/m at a current density of 2.32 A/m (corresponding to the forward bias voltage of 11.2 V); the device structure consisted of glass/Ag (100 nm)/ITO (125 nm)/2-TNATA (30 nm)/NPB (15 nm)/Alq3 (55 nm)/Cs (1 nm)/Al (2 nm)/Ag (20 nm)/ITO (100 nm). Also, the external quantum efficiency and the power efficiency at a luminance of 1000 cd/m (corresponding to the forward bias voltage of 6.8 V) were 1.7 % and 2.0 lm/W, respectively. This TEOLED exhibited similar electrical properties, but a little lower light output, compared with a reference TEOLED of the same structure using Alq3 instead of ITO as the capping layer. These characteristics are strongly governed by the thickness, the electrical properties, and optical properties of the semi-transparent multilayer cathode composed of Cs/Al/Ag and by the optical properties of the top capping layer.


title: Deep learning for magnification independent breast cancer histopathology image classification

abstract: Microscopic analysis of breast tissues is necessary for a definitive diagnosis of breast cancer which is the most common cancer among women. Pathology examination requires time consuming scanning through tissue images under different magnification levels to find clinical assessment clues to produce correct diagnoses. Advances in digital imaging techniques offers assessment of pathology images using computer vision and machine learning methods which could automate some of the tasks in the diagnostic pathology workflow. Such automation could be beneficial to obtain fast and precise quantification, reduce observer variability, and increase objectivity. In this work, we propose to classify breast cancer histopathology images independent of their magnifications using convolutional neural networks (CNNs). We propose two different architectures; single task CNN is used to predict malignancy and multi-task CNN is used to predict both malignancy and image magnification level simultaneously. Evaluations and comparisons with previous results are carried out on BreaKHis dataset. Experimental results show that our magnification independent CNN approach improved the performance of magnification specific model. Our results in this limited set of training data are comparable with previous state-of-the-art results obtained by hand-crafted features. However, unlike previous methods, our approach has potential to directly benefit from additional training data, and such additional data could be captured with same or different magnification levels than previous data.


title: Review: Data mining techniques and applications - A decade review from 2000 to 2011

abstract: In order to determine how data mining techniques (DMT) and their applications have developed, during the past decade, this paper reviews data mining techniques and their applications and development, through a survey of literature and the classification of articles, from 2000 to 2011. Keyword indices and article abstracts were used to identify 216 articles concerning DMT applications, from 159 academic journals (retrieved from five online databases), this paper surveys and classifies DMT, with respect to the following three areas: knowledge types, analysis types, and architecture types, together with their applications in different research and practical domains. A discussion deals with the direction of any future developments in DMT methodologies and applications: (1) DMT is finding increasing applications in expertise orientation and the development of applications for DMT is a problem-oriented domain. (2) It is suggested that different social science methodologies, such as psychology, cognitive science and human behavior might implement DMT, as an alternative to the methodologies already on offer. (3) The ability to continually change and acquire new understanding is a driving force for the application of DMT and this will allow many new future applications.


title: Dynamics of uniaxial tension of a viscoelastic strain-hardening body in a system with one degree of freedom. Part 3. Hydraulic loading of the system

abstract: We analyze the motion of an open mechanical system with one degree of freedom used as a model of uniaxial tension of a viscoelastic strain-hardening body in a machine with hydraulic drive creating a variable force and, at the same time, playing the role of a damping element. It is shown that a nonautonomous fourth-order dynamical system with stable periodic or aperiodic solutions (depending on its control parameters) may serve as a model of the transient processes of motion in the analyzed system. Due to a significant decrease in the viscous resistance of the system caused by the presence of the hydraulic drive, the aperiodic attenuation is typical of the transient process. It is shown that systems with hydraulic elements differ fundamentally from mechanical systems without elements of this sort.


title: Understanding customer experience.

abstract: Companies that systematically monitor customer experience can take important steps to improve it-and their bottom line.


title: Artificial Intelligence for Interstellar Travel

abstract: The large distances involved in interstellar travel require a high degree of spacecraft autonomy, realized by artificial intelligence. The breadth of tasks artificial intelligence could perform on such spacecraft involves maintenance, data collection, designing and constructing an infrastructure using in-situ resources. Despite its importance, existing publications on artificial intelligence and interstellar travel are limited to cursory descriptions where little detail is given about the nature of the artificial intelligence. This article explores the role of artificial intelligence for interstellar travel by compiling use cases, exploring capabilities, and proposing typologies, system and mission architectures. Estimations for the required intelligence level for specific types of interstellar probes are given, along with potential system and mission architectures, covering those proposed in the literature but also presenting novel ones. Finally, a generic design for interstellar probes with an AI payload is proposed. Given current levels of increase in computational power, a spacecraft with a similar computational power as the human brain would have a mass from dozens to hundreds of tons in a 2050-2060 timeframe. Given that the advent of the first interstellar missions and artificial general intelligence are estimated to be by the mid-21st century, a more in-depth exploration of the relationship between the two should be attempted, focusing on neglected areas such as protecting the artificial intelligence payload from radiation in interstellar space and the role of artificial intelligence in self-replication.


title: CDnet 2014: An Expanded Change Detection Benchmark Dataset

abstract: Change detection is one of the most important lowlevel tasks in video analytics. In 2012, we introduced the changedetection.net (CDnet) benchmark, a video dataset devoted to the evalaution of change and motion detection approaches. Here, we present the latest release of the CDnet dataset, which includes 22 additional videos (70; 000 pixel-wise annotated frames) spanning 5 new categories that incorporate challenges encountered in many surveillance settings. We describe these categories in detail and provide an overview of the results of more than a dozen methods submitted to the IEEE Change DetectionWorkshop 2014. We highlight strengths and weaknesses of these methods and identify remaining issues in change detection.


title: A dynamic over-sampling procedure based on sensitivity for multi-class problems

abstract: Classification with imbalanced datasets supposes a new challenge for researches in the framework of machine learning. This problem appears when the number of patterns that represents one of the classes of the dataset (usually the concept of interest) is much lower than in the remaining classes. Thus, the learning model must be adapted to this situation, which is very common in real applications. In this paper, a dynamic over-sampling procedure is proposed for improving the classification of imbalanced datasets with more than two classes. This procedure is incorporated into a memetic algorithm (MA) that optimizes radial basis functions neural networks (RBFNNs). To handle class imbalance, the training data are resampled in two stages. In the first stage, an over-sampling procedure is applied to the minority class to balance in part the size of the classes. Then, the MA is run and the data are over-sampled in different generations of the evolution, generating new patterns of the minimum sensitivity class (the class with the worst accuracy for the best RBFNN of the population). The methodology proposed is tested using 13 imbalanced benchmark classification datasets from well-known machine learning problems and one complex problem of microbial growth. It is compared to other neural network methods specifically designed for handling imbalanced data. These methods include different over-sampling procedures in the preprocessing stage, a threshold-moving method where the output threshold is moved toward inexpensive classes and ensembles approaches combining the models obtained with these techniques. The results show that our proposal is able to improve the sensitivity in the generalization set and obtains both a high accuracy level and a good classification level for each class.


title: Bullying among school-age children in the greater Beirut area: Risk and protective factors

abstract: This study investigated the prevalence of bullying at schools in the Greater Beirut Area and the extent to which differences in children's sociodemographics, family and school environment, and coping strategies could account for variation in academic achievement, PTSD and emotional and behavioral disorders. Participants were 665 male and female children of mean age 13.8 years. RESULTS indicated that a high proportion of children had been involved in bullying on a regular basis with victims having a higher prevalence ratio than bullies and bully/victims. Verbal bullying including spreading rumors was the most common type of victimization, followed by being rejected from a group. Being bullied about one's religion or sect comprised one of the most common bullying behaviors in schools. Students rarely tried to stop a student from being bullied and teachers were reported to have done relatively little or nothing to counteract bullying. Prevalence of bullying was more among boys than girls. School bullying was not associated with academic achievement or with having difficulties in reading and math. Children who were identified as bully/victim, victim, and bully suffered from PTSD compared to those who were not identified as being involved in bullying The study showed that both bullies and victims are at-risk for short term and long-term adjustment difficulties including hyperactivity, emotional symptoms, conduct problems and peer problems. Child's gender and emotion-focused coping stood out as risk factors for the development of bullying behaviors whereas age, problem-focused coping, family environment, and school environment were significant protective factors. Language: en


title: Latent entity space: a novel retrieval approach for entity-bearing queries

abstract: Analysis on Web search query logs has revealed that there is a large portion of entity-bearing queries, reflecting the increasing demand of users on retrieving relevant information about entities such as persons, organizations, products, etc. In the meantime, significant progress has been made in Web-scale information extraction, which enables efficient entity extraction from free text. Since an entity is expected to capture the semantic content of documents and queries more accurately than a term, it would be interesting to study whether leveraging the information about entities can improve the retrieval accuracy for entity-bearing queries. In this paper, we propose a novel retrieval approach, i.e., latent entity space (LES), which models the relevance by leveraging entity profiles to represent semantic content of documents and queries. In the LES, each entity corresponds to one dimension, representing one semantic relevance aspect. We propose a formal probabilistic framework to model the relevance in the high-dimensional entity space. Experimental results over TREC collections show that the proposed LES approach is effective in capturing latent semantic content and can significantly improve the search accuracy of several state-of-the-art retrieval models for entity-bearing queries.


title: Evidence-based approach to the treatment of hidradenitis suppurativa/acne inversa, based on the European guidelines for hidradenitis suppurativa

abstract: Hidradenitis suppurativa/acne inversa (HS) is a chronic inflammatory skin disease characterized by painful, recurrent nodules and abscesses that rupture and lead to sinus tracts and scarring. To date, an evidence-based therapeutic approach has not been the standard of care and this is likely due to the lack of evidence based treatment guidelines. The purpose of this study was to promote a holistic evidence-based approach which implemented Level of Evidence and Strength of Recommendation for the treatment of HS. Based upon the European Dermatology Forumguidelines for the management of HS, evidence-based approach was explored for the treatment of HS. The diagnosis of HS should be made by a dermatologist or other healthcare professional with expert knowledge in HS. All patients should be offered adjuvant therapy as needed (pain management, weight loss, tobacco cessation, treatment of super infections, and application of appropriate dressings). The treating physician should be familiar with disease severity scores, especially Hurley staging, physician global assessment and others. The routine use of patient’reported outcomesincluding DLQI, itch and pain assessment (Visual Analogue Scale) is strongly recommended. The need for surgical intervention should be assessed in all patients depending upon type and extent of scarring, and an evidence-based surgical approach should be implemented. Evidence-based medical treatment of mild disease consists of topical Clindamycin 1 % solution/gel b.i.d. for 12 weeks or Tetracycline 500 p.o. b.i.d. for 4 months (LOE IIb, SOR B), for more widespread disease. If patient fails to exhibit response to treatment or for a PGA of moderate-to-severe disease, Clindamycin 300 p.o. b.i.d. with Rifampicin 600 p.o. o.d. for 10 weeks (LOE III, SOR C) should be considered. If patient is not improved, then Adalimumab 160 mg at week 0, 80 mg at week 2; then 40 mg subcutaneously weekly should be administered (LOE Ib, SOR A). If improvement occurs then therapy should be maintained as long as HS lesions are present. If the patient fails to exhibit response, then consideration of second or third line therapy is required. A growing body of evidence is being published to guide the treatment of HS. HS therapy should be based upon the evaluation of the inflammatory components as well as the scarring and should be directed by evidence-based guidelines. Treatment should include surgery as well as medical treatment. Future studies should include benefit risk ratio analysis and long term assessment of efficacy and safety, in order to facilitate long term evidence based treatment and rational pharmacotherapy.


title: Deep Sets

abstract: We study the problem of designing models for machine learning tasks defined on sets. In contrast to the traditional approach of operating on fixed dimensional vectors, we consider objective functions defined on sets and are invariant to permutations. Such problems are widespread, ranging from the estimation of population statistics, to anomaly detection in piezometer data of embankment dams, to cosmology. Our main theorem characterizes the permutation invariant objective functions and provides a family of functions to which any permutation invariant objective function must belong. This family of functions has a special structure which enables us to design a deep network architecture that can operate on sets and which can be deployed on a variety of scenarios including both unsupervised and supervised learning tasks. We demonstrate the applicability of our method on population statistic estimation, point cloud classification, set expansion, and outlier detection.


title: FSONet: A Wireless Backhaul for Multi-Gigabit Picocells Using Steerable Free Space Optics

abstract: Expected increase in cellular demand has pushed recent interest in picocell networks which have reduced cell sizes (100-200m or less). For ease of deployment of such networks, a wireless backhaul network is highly desired. Since RF-based technologies are unlikely to provide the desired multi-gigabit data rates, we motivate and explore use of free space optics (FSO) for picocell backhaul. In particular, we present a novel network architecture based on steerable links and sufficiently many robust short-range links, to help circumvent the key challenge of outdoor effects in reliable operation of outdoor FSO links. Our architecture is motivated by the fact that, due to the high density of picocells, many short-range links will occur naturally in a picocell backhaul. Moreover, use of steerable FSO links facilitates networks with sufficient redundancy while using only a small number of interfaces per node. We address the key problems that arise in the context of such a backhaul architecture, viz., an FSO link design with desired characteristics, and related network design and management problems. We develop and evaluate a robust 100m FSO link prototype, and simulate the proposed architecture in many metro US cities while show its viability via evaluation of key performance metrics.


title: Comparative Assessment of Scoring Functions on a Diverse Test Set

abstract: Scoring functions are widely applied to the evaluation of protein−ligand binding in structure-based drug design. We have conducted a comparative assessment of 16 popular scoring functions implemented in main-stream commercial software or released by academic research groups. A set of 195 diverse protein−ligand complexes with high-resolution crystal structures and reliable binding constants were selected through a systematic nonredundant sampling of the PDBbind database and used as the primary test set in our study. All scoring functions were evaluated in three aspects, that is, “docking power”, “ranking power”, and “scoring power”, and all evaluations were independent from the context of molecular docking or virtual screening. As for “docking power”, six scoring functions, including GOLD::ASP, DS::PLP1, DrugScorePDB, GlideScore-SP, DS::LigScore, and GOLD::ChemScore, achieved success rates over 70% when the acceptance cutoff was root-mean-square deviation < 2.0 A. Combining these scoring functions into con...


title: Electrical stimulation of a small brain area reversibly disrupts consciousness

abstract: article i nfo The neuralmechanismsthat underlie consciousness are not fully understood.Wedescribe a regioninthe human brain where electrical stimulation reproducibly disrupted consciousness. A 54-year-old woman with intractable epilepsy underwentdepth electrodeimplantationandelectrical stimulationmapping.The electrode whosestim- ulation disrupted consciousness was between the left claustrum and anterior-dorsal insula. Stimulation of elec- trodes within 5 mm did not affect consciousness. We studied the interdependencies among depth recording signalsasafunctionoftimebynonlinearregressionanalysis(h 2 coefficient)duringstimulationsthatalteredcon- sciousness and stimulations of the same electrode at lower current intensities that were asymptomatic. Stimula- tion of the claustralelectrodereproduciblyresultedina completearrestofvolitional behavior,unresponsiveness, and amnesia withoutnegative motor symptomsormereaphasia. The disruption of consciousness did not outlast the stimulation and occurred without any epileptiform discharges. We found a significant increase in correlation for interactions affecting medial parietal and posterior frontal channels during stimulations that disrupted con- sciousness compared with those that did not. Our findings suggest that the left claustrum/anterior insula is an important part of a network that subserves consciousness and that disruption of consciousness is related to in- creased EEG signal synchrony within frontal-parietal networks.


title: Bootstrap, Review, Decode: Using Out-of-Domain Textual Data to Improve Image Captioning

abstract: We propose a novel way of using out-of-domain textual data to enhance the performance of existing image captioning systems. We evaluate this learning approach on a newly designed model that uses - and improves upon - building blocks from state-of-the-art methods. This model starts from detecting visual concepts present in an image which are then fed to a reviewer-decoder architecture with an attention mechanism. Unlike previous approaches that encode visual concepts using word embeddings, we instead suggest using regional image features which capture more intrinsic information. The main benefit of this architecture is that it synthesizes meaningful thought vectors that capture salient image properties and then applies a soft attentive decoder to decode the thought vectors and generate image captions. We evaluate our model on both Microsoft COCO and Flickr30K datasets and demonstrate that this model combined with our bootstrap learning method can largely improve performance and help the model to generate more accurate and diverse captions.


title: A fair method for resetting the target in interrupted one-day cricket matches

abstract: A method is described for setting revised target scores for the team batting second when a limited-overs cricket match has been forcibly shortened after it has commenced. It is designed so that neither team benefits or suffers from the shortening of the game and so is totally fair to both. It is easy to apply, requring nothing more than a single table of numbers and a pocket calculator, and is capable of dealing with any number of interruptions at any stage of either or both innings.


title: Freeze-Thaw Bayesian Optimization

abstract: In this paper we develop a dynamic form of Bayesian optimization for machine learning models with the goal of rapidly finding good hyperparameter settings. Our method uses the partial information gained during the training of a machine learning model in order to decide whether to pause training and start a new model, or resume the training of a previously-considered model. We specifically tailor our method to machine learning problems by developing a novel positive-definite covariance kernel to capture a variety of training curves. Furthermore, we develop a Gaussian process prior that scales gracefully with additional temporal observations. Finally, we provide an information-theoretic framework to automate the decision process. Experiments on several common machine learning models show that our approach is extremely effective in practice.


title: JavaScript instrumentation for browser security

abstract: It is well recognized that JavaScript can be exploited to launch browser-based security attacks. We propose to battle such attacks using program instrumentation. Untrusted JavaScript code goes through a rewriting process which identifies relevant operations, modifies questionable behaviors, and prompts the user (a web page viewer) for decisions on how to proceed when appropriate. Our solution is parametric with respect to the security policy-the policy is implemented separately from the rewriting, and the same rewriting process is carried out regardless of which policy is in use. Be-sides providing a rigorous account of the correctness of our solution, we also discuss practical issues including policy management and prototype experiments. A useful by-product of our work is an operational semantics of a core subset of JavaScript, where code embedded in (HTML) documents may generate further document pieces (with new code embedded) at runtime, yielding a form of self-modifying code.


title: Neurological bases of behavioral development in infancy

abstract: This article presents selected psychological competences that emerge in children during the first 2 years, together with correlated structural, biochemical and physiological changes in the brain. Major behavioral events of the 1st year are the disappearance of the neonatal reflexes, the improvement of recognition and working memory and the appearance of the universal fears of strangers and of separation from the caretaker. These behaviors are correlated in time with changes in the brain that allow the increased ability of the cortex to inhibit brainstem reflexes, with processes in the prefrontal cortex and hippocampus that facilitate the formation, storage and retrieval of memories, and with strengthened connections between the cortex and limbic system. Behavioral events of the 2nd year include the explosion of language and the emergence of self-awareness, both of which depend on the capacity for inference. The emergence of these capabilities is correlated with the intensified connectivity of the two hemispheres, the maturation of the prefrontal cortex and cortical-subcortical network.


title: Point-of-Interest Recommendation in Location Based Social Networks with Topic and Location Awareness.

abstract: The wide spread use of location based social networks (LBSNs) has enabled the opportunities for better location based services through Point-of-Interest (POI) recommendation. Indeed, the problem of POI recommendation is to provide personalized recommendations of places of interest. Unlike traditional recommendation tasks, POI recommendation is personalized, locationaware, and context depended. In light of this difference, this paper proposes a topic and location aware POI recommender system by exploiting associated textual and context information. Specifically, we first exploit an aggregated latent Dirichlet allocation (LDA) model to learn the interest topics of users and to infer the interest POIs by mining textual information associated with POIs. Then, a Topic and Location-aware probabilistic matrix factorization (TL-PMF) method is proposed for POI recommendation. A unique perspective of TL-PMF is to consider both the extent to which a user interest matches the POI in terms of topic distribution and the word-of-mouth opinions of the POIs. Finally, experiments on real-world LBSNs data show that the proposed recommendation method outperforms state-of-the-art probabilistic latent factor models with a significant margin. Also, we have studied the impact of personalized interest topics and word-of-mouth opinions on POI recommendations.


title: An ultra-wideband vertical transition from microstrip to stripline in PCB technology

abstract: An ultra-wideband transition from microstrip to stripline in PCB technology is presented applying only through via holes for simple fabrication. The design is optimized using full-wave EM simulations. A prototype is manufactured and measured achieving a return loss better than 8.7dB and an insertion loss better than 1.2 dB in the FCC frequency range. A meander-shaped delay line in stripline technique is presented as an example of application.


title: Metrics and benchmarking for parallel job scheduling

abstract: The evaluation of parallel job schedulers hinges on two things: the use of appropriate metrics, and the use of appropriate workloads on which the scheduler can operate. We argue that the focus should be on on-line open systems, and propose that a standard workload should be used as a benchmark for schedulers. This benchmark will specify distributions of parallelism and runtime, as found by analyzing accounting traces, and also internal structures that create different speedup and synchronization characteristics. As for metrics, we present some problems with slowdown and bounded slowdown that have been proposed recently.


Walter Thiel's Embalming Method: Review of Solutions and Applications in Different Fields of Biomedical Research


Decision Tree Based Algorithm for Intrusion Detection