title: Dynamic smile visualization and quantification: Part 2. Smile analysis and treatment strategies.

abstract: The "art of the smile" lies in the clinician's ability to recognize the positive elements of beauty in each patient and then create a strategy to enhance the attributes that fall outside the parameters of the prevailing esthetic concept. New technologies have enhanced our ability to see our patients more dynamically and facilitated the quantification and communication of newer concepts of function and appearance. In a 2-part article, we present a comprehensive methodology for recording, assessing, and planning treatment of the smile in 4 dimensions. In part 1, we discussed the evolution of smile analysis and reviewed the dynamic records needed. In part 2, we review smile analysis and treatment strategies and present a brief case report.


title: Parameter Design of a Novel Series-Parallel-Resonant LCL Filter for Single-Phase Half-Bridge Active Power Filters

abstract: This paper proposes a novel high-order passive filter, i.e., series-parallel-resonant LCL (SPRLCL) filter, for single-phase half-bridge active power filters. The proposed SPRLCL filter consists of a series resonance introduced by adding a small inductor to the capacitor branch loop and a parallel resonance by paralleling a small capacitor with the gird-side inductor. Three design methods are proposed to fine tune the parameters of the SPRLCL filter. Design method I and method II enable the SPRLCL filter to attenuate more switching-frequency and double switching-frequency current harmonics than LCL or LLCL filters, while with design method III, the SPRLCL filter can be more robust against filter parameter variations. In order to achieve a better damping performance and facilitate the design of active damping control, the dominant resonance frequency of the proposed filter is set at one-third of the system sampling frequency. Based on this, a comprehensive parameter design process of the SPRLCL filter is presented, where the variation of source inductance is also considered. A proportional plus repetitive current-loop controller is designed to ensure system control stability and satisfactory harmonic compensation. Simulation and experimental results are finally presented to validate the feasibility of the theoretical analysis.


title: Attitudes of nursing staff towards computerisation: a case of two hospitals in Nairobi, Kenya

abstract: The health sector is faced with constant changes as new approaches to tackle illnesses are unveiled through research. Information, communication and technology have greatly transformed healthcare practice the world over. Nursing is continually exposed to a variety of changes. Variables including age, educational level, years worked in nursing, computer knowledge and experience have been found to influence the attitudes of nurses towards computerisation. The purpose of the study was to determine the attitudes of nurses towards the use of computers and the factors that influence these attitudes. This cross sectional descriptive study was conducted among staff nurses working at one public hospital (Kenyatta National Hospital, (KNH) and one private hospital (Aga Khan University Hospital (AKUH). A convenience sample of 200 nurses filled the questionnaires. Data was collected using the modified Nurses’ Attitudes Towards Computerisation (NATC) questionnaire. Nurses had a favorable attitude towards computerisation. Non-users had a significantly higher attitude score compared to the users (p = 0.0274). Statistically significant associations were observed with age (p = 0.039), level of education (p = 0.025), duration of exposure to computers (p = 0.025) and attitudes towards computerisation. Generally, nurses have positive attitudes towards computerisation. This information is important for the planning and implementation of computerisation in the hospital as suggested in other studies.


title: Distributed Learning in Multiuser OFDMA Femtocell Networks

abstract: This paper elaborates on self-organized and distributed interference management for femtocells that share the available radio resources with macrocells. A multi-agent learning approach is examined, based on distributed Q-learning, where femtocell base stations control their transmit power, such that the femtocell capacity is maximized, while the aggregated downlink interference generated at macro users' receivers is maintained within acceptable limits. The distributed Q-learning algorithm is carried out at the femto nodes, in the way that the interference is controlled at each resource block. The contribution of this work is to integrate multi-user scheduling in the operation of the macrocell network, so that instantaneous changes, with 1 ms granularity, are encountered in the perception that the femtocell agents get of the environment under observation. We demonstrate that, by relying on 3GPP Long Term Evolution (LTE) compliant signalling from the macro network on the intended macrocell scheduling policies, the proposed learning approach allows each femto node to react on these instantaneous changes in the environment, such that the femto-to-macro interference is appropriately controlled.


title: Building Functional Prototypes Using Conductive Inkjet Printing

abstract: The recently developed conductive inkjet printing process enables conductive circuits to be created quickly, cheaply, and easily using a consumer-grade inkjet printer. In its basic form, the technique supports a single layer of wiring on a flexible substrate. This can be a valuable tool for pervasive computing research because it allows simple electronic circuits and devices to be built and iterated quickly, in an analogous manner to the use of 3D printers for prototyping mechanical structures. It is possible to rapidly create touch- and proximity-sensitive surfaces, to cut and fold the printed conductive patterns, and to augment them with off-the-shelf electronic components and custom-made subcircuits. The authors present the possibilities enabled by conductive inkjet printing, bringing together their previously published results and presenting their latest insights and findings. They consider these printing and fabrication techniques as a suite of tools for researchers and practitioners who wish to fabricate a variety of functional device prototypes. They aim to enable others to understand the strengths, weaknesses, and applicability of conductive inkjet printing across a range of pervasive computing applications. This article is part of a special issue on printing and fabrication.


title: Vehicle color classification under different lighting conditions through color correction

abstract: This paper presents a novel vehicle color classification technique for classifying vehicles into seven categories under different lighting conditions via color correction. First, to reduce lighting effects, a mapping function is built to minimize the color distortions between frames. In addition to color distortions, the effect of specular highlights can also make the window of a vehicle appear white and degrade the accuracy of vehicle classification. To reduce this effect, a window-removal task is performed to make vehicle pixels with the same color more concentrated on the analyzed vehicle. Thus, a vehicle can be more accurately classified into its corresponding category even when it is shone by strong sunlight. One major problem in vehicle color classification is that there are many shade colors; for example, white versus silver and black versus navy. Traditional methods lack the ability to classify vehicles with shade colors because a wrong classifier is designed by putting vehicles with the same label together even though their chromatic attributes are different. To treat this problem, a novel tree-based classifier is designed for classifying vehicles into chromatic/nonchromatic classes with their nonchromatic strengths and then into detailed color classes with their color features. The separation can significantly improve the accuracy of vehicle color classification even that vehicles are with various shade colors and captured under different lighting conditions.


title: Assurable, transparent, and mutual restraining e-voting involving multiple conflicting parties

abstract: E-voting techniques and systems have not been widely accepted and deployed by society due to various concerns and problems. One particular issue associated with many existing e-voting techniques is the lack of transparency, leading to the failure to deliver voter assurance. In this work, we propose an assurable, transparent, and mutual restraining e-voting protocol that exploits the existing two-party political dynamics in the US. The proposed e-voting protocol consists of three original technical contributions - universal verifiable voting vector, for- ward and backward mutual lock voting, and in-process check and enforcement - that, in combination, resolves the apparent conflicts in voting such as anonymity vs. accountability and privacy vs. verifiability. Especially, the trust is split equally among tallying authorities who have conflicting interests and will technically restrain each other. The voting and tallying processes are transparent to voters and any third party, which allow any voter to verify that his vote is indeed counted and also allow any third party to audit the tally. Index Terms—E-voting, Verifiability, Anonymity, Privacy, As- surance, Transparency, Mutual restraining voting


title: Multitechnique paraphrase alignment: A contribution to pinpointing sub-sentential paraphrases

abstract: This work uses parallel monolingual corpora for a detailed study of the task of sub-sentential paraphrase acquisition. We argue that the scarcity of this type of resource is compensated by the fact that it is the most suited type for studies on paraphrasing. We propose a large exploration of this task with experiments on two languages with five different acquisition techniques, selected for their complementarity, their combinations, as well as four monolingual corpus types of varying comparability. We report, under all conditions, a significant improvement over all techniques by validating candidate paraphrases using a maximum entropy classifier. An important result of our study is the identification of difficult-to-acquire paraphrase pairs, which are classified and quantified in a bilingual typology.


title: Building Online Trust through Socially Rich Web Interfaces

abstract: This paper investigates how human warmth and sociability (social presence) can be integrated through the Web interface to positively impact online trust. An empirical study was undertaken to explore the impact of various levels of socially-rich text and picture design elements on the perception of online social presence and its subsequent effect on online trust and its antecedents. Higher levels of social presence are shown to positively impact the perceived usefulness, enjoyment and trust of commercial Websites. Implications of these finding for practitioners and future research are outlined.


QualityFlow : Provenance Generation from Data Quality


title: Operators' adaptation to imperfect automation - Impact of miss-prone alarm systems on attention allocation and performance

abstract: Operators in complex environments are often supported by alarm systems that indicate when to shift attention to certain tasks. As alarms are not perfectly reliable, operators have to select appropriate strategies of attention allocation to compensate for unreliability and to maintain overall performance. This study explores how humans adapt to differing alarm reliabilities. Within a multi-task simulation consisting of a monitoring task and two other concurrent tasks, participants were assigned to one of five groups. In the manual control group none of the tasks was supported by an alarm system, whereas the four experimental groups were supported in the monitoring task by a miss-prone alarm system differing in reliability, i.e. 68.75%, 75%, 87.5%, 93.75%. Compared to the manual control group, all experimental groups benefited from the support by alarms, with best performance for the highest reliability condition. However, for the lowest reliability group the benefit was associated with an increased attentional effort, a more demanding attention allocation strategy, and a declined relative performance in a concurrent task. Results are discussed in the context of recent automation research.


title: Era of Big Data Processing: A New Approach via Tensor Networks and Tensor Decompositions

abstract: Many problems in computational neuroscience, neuroinformatics, pattern/image recognition, signal processing and machine learning generate massive amounts of multidimensional data with multiple aspects and high dimensionality. Tensors (i.e., multi-way arrays) provide often a natural and compact representation for such massive multidimensional data via suitable low-rank approximations. Big data analytics require novel technologies to efficiently process huge datasets within tolerable elapsed times. Such a new emerging technology for multidimensional big data is a multiway analysis via tensor networks (TNs) and tensor decompositions (TDs) which represent tensors by sets of factor (component) matrices and lower-order (core) tensors. Dynamic tensor analysis allows us to discover meaningful hidden structures of complex data and to perform generalizations by capturing multi-linear and multi-aspect relationships. We will discuss some fundamental TN models, their mathematical and graphical descriptions and associated learning algorithms for large-scale TDs and TNs, with many potential applications including: Anomaly detection, feature extraction, classification, cluster analysis, data fusion and integration, pattern recognition, predictive modeling, regression, time series analysis and multiway component analysis. ::: Keywords: Large-scale HOSVD, Tensor decompositions, CPD, Tucker models, Hierarchical Tucker (HT) decomposition, low-rank tensor approximations (LRA), Tensorization/Quantization, tensor train (TT/QTT) - Matrix Product States (MPS), Matrix Product Operator (MPO), DMRG, Strong Kronecker Product (SKP).


title: Describing Visual Scenes using Transformed Dirichlet Processes

abstract: Motivated by the problem of learning to detect and recognize objects with minimal supervision, we develop a hierarchical probabilistic model for the spatial structure of visual scenes. In contrast with most existing models, our approach explicitly captures uncertainty in the number of object instances depicted in a given image. Our scene model is based on the transformed Dirichlet process (TDP), a novel extension of the hierarchical DP in which a set of stochastically transformed mixture components are shared between multiple groups of data. For visual scenes, mixture components describe the spatial structure of visual features in an object-centered coordinate frame, while transformations model the object positions in a particular image. Learning and inference in the TDP, which has many potential applications beyond computer vision, is based on an empirically effective Gibbs sampler. Applied to a dataset of partially labeled street scenes, we show that the TDP's inclusion of spatial structure improves detection performance, flexibly exploiting partially labeled training images.


title: A new representation for linear lists

abstract: We present a new data structure for maintaining a set of records in a linear list according to their key values. This data structure has the property that we can keep a number of fingers at points of interest in the key space (e.g., the beginning or the end of the list), so that access and modification in the neighborhood of a finger is very efficient. In the Section 2 we discuss the general structure of our B-tree. Since we propose to search the tree from a leaf upwards, additional links need to be introduced. In Section 3 we show how to obtain our result for the case of one finger. A key idea is the construction of a number representation behaving as described above, which we can use to model the propagation of modifications in the B-tree along the finger path. In Section 4 we generalize the structure so that several fingers in the key space can be maintained, with the advantage that access is cheap in the neighborhood of each finger. Finally in Section 5 we present some implementation notes and applications, mostly to sorting.


title: Random Forest with Learned Representations for Semantic Segmentation

abstract: We present a random forest framework that learns the weights, shapes, and sparsities of feature representations for real-time semantic segmentation. Typical filters (kernels) have predetermined shapes and sparsities and learn only weights. A few feature extraction methods fix weights and learn only shapes and sparsities. These predetermined constraints restrict learning and extracting optimal features. To overcome this limitation, we propose an unconstrained representation that is able to extract optimal features by learning weights, shapes, and sparsities. We then present the random forest framework that learns the flexible filters using an iterative optimization algorithm and segments input images using the learned representations. We demonstrate the effectiveness of the proposed method using a hand segmentation dataset for hand-object interaction and using two semantic segmentation datasets. The results show that the proposed method achieves real-time semantic segmentation using limited computational and memory resources.


title: A comparison of issues and advantages in agile and incremental development between state of the art and an industrial case

abstract: Recent empirical studies have been conducted identifying a number of issues and advantages of incremental and agile methods. However, the majority of studies focused on one model (Extreme Programming) and small projects. To draw more general conclusions we conduct a case study in large-scale development identifying issues and advantages, and compare the results with previous empirical studies on the topic. The principle results are that (1) the case study and literature agree on the benefits while new issues arise when using agile in large-scale and (2) an empirical research framework is needed to make agile studies comparable.


title: Software&Systems Engineering Process and Tools for the Development of Autonomous Driving Intelligence

abstract: When a large number of people with heterogeneous knowledge and skills run a project together, it is important to use a sensible engineering process. This especially holds for a project building an intelligent autonomously driving car to participate in the 2007 DARPA Urban Challenge. In this article, we present essential elements of a software and systems engineering process for the development of artificial intelligence capable of driving autonomously in complex urban situations. The process includes agile concepts, like test first approach, continuous integration of every software module and a reliable release and configuration management assisted by software tools in integrated development environments. However, the most important ingredients for an efficient and stringent development are the ability to efficiently test the behavior of the developed system in a flexible and modular simulator for urban situations. I. I ntroduction O CUSED research is often centered around interesting challenges and awards. The airplane industry started off with awards for the first flight over the British Channel as well as the Atlantic Ocean. The Human Genome Project, the Robo Cups and the series of DARPA Grand Challenges for autonomous vehicles serve this very same purpose to foster research and development in a particular direction. The 2007 DARPA Urban Challenge 16


title: Self-determination theory

abstract: This chapter provides an introduction to Self-Determination Theory, a macro-theory that details the origins and outcomes of human agentic action. Basic psychological needs for autonomy, competence, and relatedness in Self-Determination Theory are introduced as is the relationship of need satisfaction to intrinsic motivation and the regulation of extrinsic motivation. Mini-theories associated with self-determination are introduced, and research on self-determination summarized.


Tales of Damage and (Retro-)Causation


title: Frame Interpolation with Multi-Scale Deep Loss Functions and Generative Adversarial Networks

abstract: Frame interpolation attempts to synthesise intermediate frames given one or more consecutive video frames. In recent years, deep learning approaches, and in particular convolutional neural networks, have succeeded at tackling low- and high-level computer vision problems including frame interpolation. There are two main pursuits in this line of research, namely algorithm efficiency and reconstruction quality. In this paper, we present a multi-scale generative adversarial network for frame interpolation (FIGAN). To maximise the efficiency of our network, we propose a novel multi-scale residual estimation module where the predicted flow and synthesised frame are constructed in a coarse-to-fine fashion. To improve the quality of synthesised intermediate video frames, our network is jointly supervised at different levels with a perceptual loss function that consists of an adversarial and two content losses. We evaluate the proposed approach using a collection of 60fps videos from YouTube-8m. Our results improve the state-of-the-art accuracy and efficiency, and a subjective visual quality comparable to the best performing interpolation method.


title: The Agile Teaching/Learning Methodology and Its e-Learning Platform

abstract: The Agile Teaching/Learning Methodology (ATLM) is a teaching/learning methodology designed for higher-education based on the best practices and ideas from the field of software engineering and leveraging upon concepts from agile software methodologies. Although ATLM was designed using concepts borrowed from software engineering, the methodology itself can easily be applied to a wide variety of courses that might require agility in teaching and learning. This paper explains the objectives behind ATLM and the process architecture of the methodology. ATLM emphasizes agility, communication and the learning process. The paper also presents the e-learning platform we have developed to support this ATLM approach to teaching/learning and the technologies behind this platform. The ATLM e-learning platform makes use of a number of modern collaboration and knowledge sharing technologies such as blogging, commenting, instant messaging, wiki and XML RSS.


title: Towards Instrument Segmentation for Music Content Description: a Critical Review of Instrument Classification Techniques

abstract: A system capable of describing the musical content of any kind of sound file or sound stream, as it is supposed to be done in MPEG7-compliant applications, should provide an account of the different moments where a certain instrument can be listened to. In this paper we concentrate on reviewing the different techniques that have been so far proposed for automatic classification of musical instruments. As most of the techniques to be discussed are usable only in "solo" performances we will evaluate their applicability to the more complex case of describing sound mixes. We conclude this survey discussing the necessity of developing new strategies for classifying sound mixes without a priori separation of sound sources.


title: Recognition without Correspondence using Multidimensional Receptive Field Histograms

abstract: The appearance of an object is composed of local structure. This local structure can be described and characterized by a vector of local features measured by local operators such as Gaussian derivatives or Gabor filters. This article presents a technique where appearances of objects are represented by the joint statistics of such local neighborhood operators. As such, this represents a new class of appearance based techniques for computer vision. Based on joint statistics, the paper develops techniques for the identification of multiple objects at arbitrary positions and orientations in a cluttered scene. Experiments show that these techniques can identify over 100 objects in the presence of major occlusions. Most remarkably, the techniques have low complexity and therefore run in real-time.


title: Convergent priorities and tensions: a qualitative study of the integration of complementary and alternative therapies with conventional cancer treatment

abstract: Demand for complementary and alternative medicine (CAM) is high among cancer patients. This, alongside growing evidence for the efficacy of some CAM therapies, is driving change within cancer centres, where evidence-based CAM therapies are increasingly provided alongside standard cancer treatments. In Australia, commitment to equitable access to healthcare is strong, and some cancer centres are now providing integrative services at no cost to the patient. This represents a significant shift in healthcare provision. This study aimed to examine health professional and patient dynamics in an integrated cancer service where CAM is provided at no cost to patients alongside standard cancer treatments. It specifically sought to understand what might drive or hinder further integration of CAM with standard treatment in the cancer context. Qualitative interviews were undertaken with twenty key stakeholders—cancer patients, cancer nurses, and oncologists—who were delivering or receiving care in an Australian public hospital where acupuncture services are provided at no cost to patients alongside standard chemotherapy and radiation treatments. Findings point to key areas where the concerns and priorities of cancer patients, cancer nurses, and oncologists converge and diverge in ways that reflect core personal and professional interests regarding patient care needs, the evidence base for CAM efficacy and safety, and rising healthcare costs. Understanding points of convergence and divergence could assist clinicians and service providers in negotiating ways forward for integrative cancer services.


title: Text Classification by Combining Grouping, LSA and kNN

abstract: A grouping method of the similar words, is proposed for the classification of documents, which is applied to Reuters international news and it is shown that the grouping of words has equivalent ability to the Latent Semantic Analysis(LSA) in the classification accuracy. Further, a new combining method is proposed for the documents classification, which consists of Grouping, Latent Semantic Analysis followed by the k-Nearest Neighbor classification (k- NN). The combining method proposed here, shows the higher accuracy in the classification than the conventional methods of the kNN, and the LSA followed by the kNN.


title: Automatic Generation of Constructable Brick Sculptures

abstract: Fabrication of LEGO models in large scale requires careful pre-planning to produce constructable and stable models. We propose a system that, starting with a voxelization of a 3D mesh, merges voxels to form larger bricks, and then analyzes and repairs structural problems, finally outputting a set of building instructions. We also present extensions such as producing hollow models, fulfilling limits on the number of bricks of each size, and including colors. Results (both real and virtual) and timings show significant improvements over previous work.


title: Privacy, security, risk, and trust concerns in e-commerce

abstract: Electronic commerce has increased dramatically in recent years, because of the revolution in information technology. The services provided by e-commerce companies could be affected by several factors such as privacy, security, trusts and perceived risks. In this paper, we propose a preliminary theoretical model that investigates the relationship between privacy, security concerns, and perceived risks, and how it would relate to customers' level of trust in e-commerce. A survey that involves 100 participants has been conducted and analyzed testing a number of hypotheses focused on studying the attributes of each factor, and its effect on the perceived risks associated with unexpected situations. The results of this research study show that a positive relationship exists between the perceived risks and some of the privacy attributes such as control and notice. A positive relationship also exists between the perceived risks and some of the security attributes such as confidentially and integrity. Further, the study demonstrates a positive relationship between perceived risks and customers' level of trust.


title: Real-Time Object Tracking on a Drone With Multi-Inertial Sensing Data

abstract: Real-time object tracking on a drone under a dynamic environment has been a challenging issue for many years, with existing approaches using off-line calculation or powerful computation units on board. This paper presents a new lightweight real-time onboard object tracking approach with multi-inertial sensing data, wherein a highly energy-efficient drone is built based on the Snapdragon flight board of Qualcomm. The flight board uses a digital signal processor core of the Snapdragon 801 processor to realize PX4 autopilot, an open-source autopilot system oriented toward inexpensive autonomous aircraft. It also uses an ARM core to realize Linux, robot operating systems, open-source computer vision library, and related algorithms. A lightweight moving object detection algorithm is proposed that extracts feature points in the video frame using the oriented FAST and rotated binary robust independent elementary features algorithm and adapts a local difference binary algorithm to construct the image binary descriptors. The K-nearest neighbor method is then used to match the image descriptors. Finally, an object tracking method is proposed that fuses inertial measurement unit data, global positioning system data, and the moving object detection results to calculate the relative position between coordinate systems of the object and the drone. All the algorithms are run on the Qualcomm platform in real time. Experimental results demonstrate the superior performance of our method over the state-of-the-art visual tracking method.


title: Shredded banknotes reconstruction using AKAZE points.

abstract: Shredded banknote reconstruction is a recent topic and can be viewed as solving large-scale jigsaw puzzles. Also, problems such as reconstruction of fragmented documents, photographs and historical artefacts are closely related with this topic. The high computational complexity of these problems increases the need for the development of new methods Reconstruction of shredded banknotes consists of three main stages. (1) Matching fragments with a reference banknote. (2) Aligning the fragments by rotating at certain angles. (3) Assembling the fragments. The existing methods can successfully applied to synthetic banknote fragments which are created in computer environment. But when real banknote reconstruction problem is considered, different sub problems arise and make the existing methods inadequate. In this study, a keypoint based method, named AKAZE, was used to make the matching process effective. This is the first study that uses the AKAZE method in the reconstruction of shredded banknotes. A new method for fragment alignment has also been proposed. In this method, the convex hulls that contain all true matched AKAZE keypoints were found on reference banknote and fragments. The orientations of fragments were estimated accurately by comparing these convex polygons. Also, a new criterion was developed to reveal the success rates of reconstructed banknotes. In addition, two different data sets including real and synthetic banknote fragments of different countries were created to test the success of proposed method.


title: Who plays, how much, and why? Debunking the stereotypical gamer profile

abstract: Online games have exploded in popularity, but for many researchers access to players has been difficult. The study reported here is the first to collect a combination of survey and behavioral data with the cooperation of a major virtual world operator. In the current study, 7,000 players of the massively multiplayer online game (MMO) EverQuest 2 were surveyed about their offline characteristics, their motivations and their physical and mental health. These self-report data were then combined with data on participants’ actual in-game play behaviors, as collected by the game operator. Most of the results defy common stereotypes in surprising and interesting ways and have implications for communication theory and for future investigations of games.


title: Distractor-aware Siamese Networks for Visual Object Tracking

abstract: Recently, Siamese networks have drawn great attention in visual tracking community because of their balanced accuracy and speed. However, features used in most Siamese tracking approaches can only discriminate foreground from the non-semantic backgrounds. The semantic backgrounds are always considered as distractors, which hinders the robustness of Siamese trackers. In this paper, we focus on learning distractor-aware Siamese networks for accurate and long-term tracking. To this end, features used in traditional Siamese trackers are analyzed at first. We observe that the imbalanced distribution of training data makes the learned features less discriminative. During the off-line training phase, an effective sampling strategy is introduced to control this distribution and make the model focus on the semantic distractors. During inference, a novel distractor-aware module is designed to perform incremental learning, which can effectively transfer the general embedding to the current video domain. In addition, we extend the proposed approach for long-term tracking by introducing a simple yet effective local-to-global search region strategy. Extensive experiments on benchmarks show that our approach significantly outperforms the state-of-the-arts, yielding 9.6% relative gain in VOT2016 dataset and 35.9% relative gain in UAV20L dataset. The proposed tracker can perform at 160 FPS on short-term benchmarks and 110 FPS on long-term benchmarks.


title: Vector Embedding of Wikipedia Concepts and Entities

abstract: Using deep learning for different machine learning tasks such as image classification and word embedding has recently gained many attentions. Its appealing performance reported across specific Natural Language Processing (NLP) tasks in comparison with other approaches is the reason for its popularity. Word embedding is the task of mapping words or phrases to a low dimensional numerical vector. In this paper, we use deep learning to embed Wikipedia Concepts and Entities. The English version of Wikipedia contains more than five million pages, which suggest its capability to cover many English Entities, Phrases, and Concepts. Each Wikipedia page is considered as a concept. Some concepts correspond to entities, such as a person's name, an organization or a place. Contrary to word embedding, Wikipedia Concepts Embedding is not ambiguous, so there are different vectors for concepts with similar surface form but different mentions. We proposed several approaches and evaluated their performance based on Concept Analogy and Concept Similarity tasks. The results show that proposed approaches have the performance comparable and in some cases even higher than the state-of-the-art methods.


title: DigiTaps: eyes-free number entry on touchscreens with minimal audio feedback

abstract: Eyes-free input usually relies on audio feedback that can be difficult to hear in noisy environments. We present DigiTaps, an eyes-free number entry method for touchscreen devices that requires little auditory attention. To enter a digit, users tap or swipe anywhere on the screen with one, two, or three fingers. The 10 digits are encoded by combinations of these gestures that relate to the digits' semantics. For example, the digit 2 is input with a 2-finger tap. We conducted a longitudinal evaluation with 16 people and found that DigiTaps with no audio feedback was faster but less accurate than with audio feedback after every input. Throughout the study, participants entered numbers with no audio feedback at an average rate of 0.87 characters per second, with an uncorrected error rate of 5.63%.


title: Enterprise Data Analysis and Visualization: An Interview Study

abstract: Organizations rely on data analysts to model customer engagement, streamline operations, improve production, inform business decisions, and combat fraud. Though numerous analysis and visualization tools have been built to improve the scale and efficiency at which analysts can work, there has been little research on how analysis takes place within the social and organizational context of companies. To better understand the enterprise analysts' ecosystem, we conducted semi-structured interviews with 35 data analysts from 25 organizations across a variety of sectors, including healthcare, retail, marketing and finance. Based on our interview data, we characterize the process of industrial data analysis and document how organizational features of an enterprise impact it. We describe recurring pain points, outstanding challenges, and barriers to adoption for visual analytic tools. Finally, we discuss design implications and opportunities for visual analysis research.


T.: Crowdsourcing linked open data for disaster management


title: Detecting Nitrous Oxide Reductase (nosZ) Genes in Soil Metagenomes: Method Development and Implications for the Nitrogen Cycle

abstract: ABSTRACT Microbial activities in soils, such as (incomplete) denitrification, represent major sources of nitrous oxide (N 2 O), a potent greenhouse gas. The key enzyme for mitigating N 2 O emissions is NosZ, which catalyzes N 2 O reduction to N 2 . We recently described “atypical” functional NosZ proteins encoded by both denitrifiers and nondenitrifiers, which were missed in previous environmental surveys (R. A. Sanford et al., Proc. Natl. Acad. Sci. U. S. A. 109:19709–19714, 2012, doi:10.1073/pnas.1211238109). Here, we analyzed the abundance and diversity of both nosZ types in whole-genome shotgun metagenomes from sandy and silty loam agricultural soils that typify the U.S. Midwest corn belt. First, different search algorithms and parameters for detecting nosZ metagenomic reads were evaluated based on in silico -generated (mock) metagenomes. Using the derived cutoffs, 71 distinct alleles (95% amino acid identity level) encoding typical or atypical NosZ proteins were detected in both soil types. Remarkably, more than 70% of the total nosZ reads in both soils were classified as atypical, emphasizing that prior surveys underestimated nosZ abundance. Approximately 15% of the total nosZ reads were taxonomically related to Anaeromyxobacter , which was the most abundant genus encoding atypical NosZ-type proteins in both soil types. Further analyses revealed that atypical nosZ genes outnumbered typical nosZ genes in most publicly available soil metagenomes, underscoring their potential role in mediating N 2 O consumption in soils. Therefore, this study provides a bioinformatics strategy to reliably detect target genes in complex short-read metagenomes and suggests that the analysis of both typical and atypical nosZ sequences is required to understand and predict N 2 O flux in soils. IMPORTANCE Nitrous oxide (N 2 O) is a potent greenhouse gas with ozone layer destruction potential. Microbial activities control both the production and the consumption of N 2 O, i.e., its conversion to innocuous dinitrogen gas (N 2 ). Until recently, consumption of N 2 O was attributed to bacteria encoding “typical” nitrous oxide reductase (NosZ). However, recent phylogenetic and physiological studies have shown that previously uncharacterized, functional, “atypical” NosZ proteins are encoded in genomes of diverse bacterial groups. The present study revealed that atypical nosZ genes outnumbered their typical counterparts, highlighting their potential role in N 2 O consumption in soils and possibly other environments. These findings advance our understanding of the diversity of microbes and functional genes involved in the nitrogen cycle and provide the means (e.g., gene sequences) to study N 2 O fluxes to the atmosphere and associated climate change.


title: Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control

abstract: Deep reinforcement learning (RL) algorithms can learn complex robotic skills from raw sensory inputs, but have yet to achieve the kind of broad generalization and applicability demonstrated by deep learning methods in supervised domains. We present a deep RL method that is practical for real-world robotics tasks, such as robotic manipulation, and generalizes effectively to never-before-seen tasks and objects. In these settings, ground truth reward signals are typically unavailable, and we therefore propose a self-supervised model-based approach, where a predictive model learns to directly predict the future from raw sensory readings, such as camera images. At test time, we explore three distinct goal specification methods: designated pixels, where a user specifies desired object manipulation tasks by selecting particular pixels in an image and corresponding goal positions, goal images, where the desired goal state is specified with an image, and image classifiers, which define spaces of goal states. Our deep predictive models are trained using data collected autonomously and continuously by a robot interacting with hundreds of objects, without human supervision. We demonstrate that visual MPC can generalize to never-before-seen objects---both rigid and deformable---and solve a range of user-defined object manipulation tasks using the same model.


title: Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs

abstract: In this work, we provide a new formulation for Graph Convolutional Neural Networks (GCNNs) for link prediction on graph data that addresses common challenges for biomedical knowledge graphs (KGs). We introduce a regularized attention mechanism to GCNNs that not only improves performance on clean datasets, but also favorably accommodates noise in KGs, a pervasive issue in real-world applications. Further, we explore new visualization methods for interpretable modelling and to illustrate how the learned representation can be exploited to automate dataset denoising. The results are demonstrated on a synthetic dataset, the common benchmark dataset FB15k-237, and a large biomedical knowledge graph derived from a combination of noisy and clean data sources. Using these improvements, we visualize a learned model's representation of the disease cystic fibrosis and demonstrate how to interrogate a neural network to show the potential of PPARG as a candidate therapeutic target for rheumatoid arthritis.


title: A new look at survey propagation and its generalizations

abstract: This paper provides a new conceptual perspective on survey propagation, which is an iterative algorithm recently introduced by the statistical physics community that is very effective in solving random k-SAT problems even with densities close to the satisfiability threshold. We first describe how any SAT formula can be associated with a novel family of Markov random fields (MRFs), parameterized by a real number \rho \in [0,1]. We then show that applying belief propagation--a well-known ``message-passing'' technique for estimating marginal probabilities--to this family of MRFs recovers a known family of algorithms, ranging from pure survey propagation at one extreme (\rho = 1) to standard belief propagation on the uniform distribution over SAT assignments at the other extreme (\rho = 0). Configurations in these MRFs have a natural interpretation as partial satisfiability assignments, on which a partial order can be defined. We isolate cores as minimal elements in this partial ordering, which are also fixed points of survey propagation and the only assignments with positive probability in the MRF for \rho=1. Our experimental results for k=3 suggest that solutions of random formulas typically do not possess non-trivial cores. This makes it necessary to study the structure of the space of partial assignments for \rho 0 can be viewed as a ``smoothed'' version of the uniform distribution over satisfying assignments (\rho=0). Finally, we isolate properties of Gibbs sampling and message-passing algorithms that are typical for an ensemble of k-SAT problems.


title: CMiner: Opinion Extraction and Summarization for Chinese Microblogs

abstract: Sentiment analysis of microblog texts has drawn lots of attention in both the academic and industrial fields. However, most of the current work only focuses on polarity classification. In this paper, we present an opinion mining system for Chinese microblogs called CMiner. Instead of polarity classification, CMiner focuses on more complicated opinion mining tasks - opinion target extraction and opinion summarization. Novel algorithms are developed for the two tasks and integrated into the end-to-end system. CMiner can help to effectively understand the users’ opinion towards different opinion targets in a microblog topic. Specially, we develop an unsupervised label propagation algorithm for opinion target extraction. The opinion targets of all messages in a topic are collectively extracted based on the assumption that similar messages may focus on similar opinion targets. In addition, we build an aspect-based opinion summarization framework for microblog topics. After getting the opinion targets of all the microblog messages in a topic, we cluster the opinion targets into several groups and extract representative targets and summaries for each group. A co-ranking algorithm is proposed to rank both the opinion targets and microblog sentences simultaneously. Experimental results on a benchmark dataset show the effectiveness of our system and the algorithms.


title: Dual voltage regulations of single switch flyback converter using variable switching frequency

abstract: This paper proposes a dual voltage regulations of single switch flyback converter using variable frequency control. By adding an adjusted filter to secondary windings, each output shows different tendency according to the switching frequency. This means that even if the switching duty is same, the output voltage might be not equal at the different switching frequency. So the output voltage can be regulated by not only the switching duty control but also the switching frequency control. Though the adjusted filter is added to each secondary windings, the simplicity of circuit and control algorithm which are main features of the flyback converter is not disturbed. The dual voltage regulations of the proposed method is compared with that of the conventional method and the feasibility and performance of the proposed method is verified through experiment using a proto-type converter.


title: A Genetic Algorithm Solving a Weekly Course-Timetabling Problem

abstract: In this paper we describe a heavily constrained university timetabling problem, and our genetic algorithm based approach to solve it. A problem-specific chromosome representation and knowledge-augmented genetic operators have been developed; these operators ‘intelligently’ avoid building illegal timetables. The prototype timetabling system which is presented has been implemented in C and PROLOG, and includes an interactive graphical user interface. Tests with real data from our university were performed and yield promising results.


title: An Information Theoretic Feature Selection Framework for Big Data under Apache Spark

abstract: With the advent of extremely high dimensional datasets, dimensionality reduction techniques are becoming mandatory. Among many techniques, feature selection has been growing in interest as an important tool to identify relevant features on huge datasets --both in number of instances and features--. The purpose of this work is to demonstrate that standard feature selection methods can be parallelized in Big Data platforms like Apache Spark, boosting both performance and accuracy. We thus propose a distributed implementation of a generic feature selection framework which includes a wide group of well-known Information Theoretic methods. Experimental results on a wide set of real-world datasets show that our distributed framework is capable of dealing with ultra-high dimensional datasets as well as those with a huge number of samples in a short period of time, outperforming the sequential version in all the cases studied.


title: Reinforcement Learning-Based Plug-in Electric Vehicle Charging With Forecasted Price

abstract: This paper proposes a novel demand response method that aims at reducing the long-term cost of charging the battery of an individual plug-in electric vehicle (PEV). The problem is cast as a daily decision-making problem for choosing the amount of energy to be charged in the PEV battery within a day. We model the problem as a Markov decision process (MDP) with unknown transition probabilities. A batch reinforcement-learning (RL) algorithm is proposed for learning an optimum cost-reducing charging policy from a batch of transition samples and making cost-reducing charging decisions in new situations. In order to capture the day-to-day differences of electricity charging costs, the method makes use of actual electricity prices for the current day and predicted electricity prices for the following day. A Bayesian neural network is employed for predicting the electricity prices. For constructing the RL training dataset, we use historical prices. A linear-programming-based method is developed for creating a dataset of optimal charging decisions. Different charging scenarios are simulated for each day of the historical time frame using the set of past electricity prices. Simulation results using real-world pricing data demonstrate cost savings of 10%–50% for the PEV owner when using the proposed charging method.


title: A Wavelet-Based Data Compression Technique for Smart Grid

abstract: This paper proposes a wavelet-based data compression approach for the smart grid (SG). In particular, wavelet transform (WT)-based multiresolution analysis (MRA), as well as its properties, are studied for its data compression and denoising capabilities for power system signals in SG. Selection of the Order 2 Daubechies wavelet and scale 5 as the best wavelet function and the optimal decomposition scale, respectively, for disturbance signals is demonstrated according to the criterion of the maximum wavelet energy of wavelet coefficients (WCs). To justify the proposed method, phasor data are simulated under disturbance circumstances in the IEEE New England 39-bus system. The results indicate that WT-based MRA can not only compress disturbance signals but also depress the sinusoidal and white noise contained in the signals.


title: How to draw illustrative figures?

abstract: Simplified illustrations are effective for medical purposes in ways: they can quickly convey stories to patients, and avoid unnecessary graphic depiction. Despite their simplicity, such illustrations are not trivial to improvise. In this talk, we propose a new system that automatically transforms medical images into friendly 2D illustrations via an optimization problem.


title: Ultrasonographic evaluation of long-term results of nasal tip defatting in rhinoplasty cases

abstract: OBJECTIVES/HYPOTHESIS ::: Nasal skin thickness has an important role in aesthetic results of rhinoplasty. The aim of this study was to evaluate the long-term results of tip and supratip skin defatting technique in rhinoplasty subjects using ultrasonography. ::: ::: ::: STUDY DESIGN ::: Prospective, randomized, case-control study. ::: ::: ::: METHODS ::: Among 111 rhinoplasty cases referred to a university hospital between February 2010 and September 2011, after physical examination and measuring the nasal tip and supratip skin thickness by ultrasonography, a total of 55 patients with thick and moderate skin were randomly allocated for rhinoplasty using one of the following methods: rhinoplasty with (case group) and without (control group) defatting tip and supratip skin. Ultrasonographic evaluation of the skins was repeated 1 and 12 months after surgery, and the data were analyzed by Wilcoxon and repeated measure tests using SPSS 17 software. ::: ::: ::: RESULTS ::: Twenty-eight of 55 candidates (10 men, 45 women; mean age, 25.1 ± 7.6 years) underwent skin defatting during rhinoplasty; the other 27 patients did not undergo this procedure. Forty-four patients completed the study. Thickness of tip and supratip skin was not statistically different before surgery and during follow-up evaluations in defatting and nondefatting technique groups (P = .7). ::: ::: ::: CONCLUSIONS ::: Defatting techniques have no effect on reducing tip and supratip skin thickness after rhinoplasty in moderate to thick skins.


title: Anthropomorphism influences perception of computer-animated characters ’ actions

abstract: Computer-animated characters are common in popular culture and have begun to be used as experimental tools in social cognitive neurosciences. Here we investigated how appearance of these characters’ influences perception of their actions. Subjects were presented with different characters animated either with motion data captured from human actors or by interpolating between poses (keyframes) designed by an animator, and were asked to categorize the motion as biological or artificial. The response bias towards ‘biological’, derived from the Signal Detection Theory, decreases with characters’ anthropomorphism, while sensitivity is only affected by the simplest rendering style, point-light displays. fMRI showed that the response bias correlates positively with activity in the mentalizing network including left temporoparietal junction and anterior cingulate cortex, and negatively with regions sustaining motor resonance. The absence of significant effect of the characters on the brain activity suggests individual differences in the neural responses to unfamiliar artificial agents. While computer-animated characters are invaluable tools to investigate the neural bases of social cognition, further research is required to better understand how factors such as anthropomorphism affect their perception, in order to optimize their appearance for entertainment, research or therapeutic purposes.


title: Lead-me interface for a pulling sensation from hand-held devices

abstract: When a small mass in a hand-held device oscillates along a single axis with asymmetric acceleration (strongly peaked in one direction and diffuse in the other), the holder typically experiences a kinesthetic illusion characterized by the sensation of being continuously pushed or pulled by the device. This effect was investigated because of its potential application to a hand-held, nongrounded, haptic device that can convey a sense of a continuous translational force in one direction, which is a key missing piece in haptic research. A 1 degree-of-freedom (DOF) haptic device based on a crank-slider mechanism was constructed. The device converts the constant rotation of an electric motor into the constrained movement of a small mass with asymmetric acceleration. The frequency that maximizes the perceived movement offered by the haptic device was investigated. Tests using three subjects showed that for the prototype, the best frequencies were 5 and 10 cycles per second.


title: Hybrid-priority real-time scheduling

abstract: A hybrid scheduling algorithm is proposed, which integrates features of the fixed priority (FP) and earliest deadline first (EDF) scheduling policies. It is shown that this hybrid scheduling algorithm is a generalization of both FP and EDF, and tends to retain most of the desirable properties and features of both individual policies. Two exact (i.e., necessary and sufficient) tests are derived for sporadic task systems scheduled by the hybrid scheduling algorithm.


title: Thickness Reduction and Performance Enhancement of Steerable Square Loop Antenna Using Hybrid High Impedance Surface

abstract: In order to reduce the thickness of a steerable beam square loop antenna, the effects of combining it with various periodic high impedance surfaces (HISs) are investigated. When a via-less HIS is used, the radiation pattern has high side lobes, which are shown to be due to surface waves propagating in the HIS lattice. Using a HIS with vias between the plates and ground removes the surface waves, but the beam is distorted due to strong coupling between the HIS's vias and the antenna element. Consequently a hybrid HIS is designed. This uses a via-less lattice beneath the loop, with vias at the edge of the HIS to suppress surface wave propagation. Consequently, a square loop antenna with four feeds on a hybrid HIS substrate is proposed for beam steering applications. This antenna has a low-profile with a total thickness of 4.69 mm for a test frequency band of 4.3 GHz to 5.0 GHz. It exhibits a gain of 8.65 dB at the test frequency (4.7 GHz). Compared to the earlier reported steerable square loop antenna, the new antenna achieves a 61% reduction in substrate thickness, a bandwidth enhancement by 150 MHz and an increase in gain by 1.95 dB.


title: Improvement of speech emotion recognition with neural network classifier by using speech spectrogram

abstract: This research presents a novel algorithm for detecting human emotion via speech recognition by using speech spectrogram. The proposed algorithm aims to detect the emotional by using information inside the spectrogram. Neural network was used for being the classifier. A new approach to feature extraction based on analysis of two dimensions time-frequency representation of a speech signal have been presented. The algorithm was tested with EMO-Database. The experimental results show that the proposed framework can efficiently find the correct speech emotion compared to using the comparing method.


Complementary Metal-Oxide Semiconductor Sensors


title: The Relative Entropy Rate For Two Hidden Markov Processes

abstract: The relative entropy rate is a natural and usefull measure of distance between two stochastic processes. In this paper we study the relative entropy rate between two Hidden Markov Processes (HMPs), which is of both theoretical and practical importance. We give new results showing analyticity, representation using Lyapunov exponents, and Taylor expansion for the relative entropy rate of two discrete-time finite-alphabet HMPs.


title: Curriculum Learning and Minibatch Bucketing in Neural Machine Translation

abstract: We examine the effects of particular orderings of sentence pairs on the on-line training of neural machine translation (NMT). We focus on two types of such orderings: (1) ensuring that each minibatch contains sentences similar in some aspect and (2) gradual inclusion of some sentence types as the training progresses (so called "curriculum learning"). In our English-to-Czech experiments, the internal homogeneity of minibatches has no effect on the training but some of our "curricula" achieve a small improvement over the baseline.


title: Diffusion of deception in social media: Social contagion effects and its antecedents

abstract: What makes deceptive attacks on social media particularly virulent is the likelihood of a contagion effect, where a perpetrator takes advantage of the connections among people to deceive them. To examine this, the current study experimentally stimulates a phishing type attack, termed as farcing, on Facebook users. Farcing attacks occur in two stages: a first stage where phishers use a phony profile to friend victims, and a second stage, where phishers solicit personal information directly from victims. In the present study, close to one in five respondents fell victim to the first stage attack and one in ten fell victim to the second stage attack. Individuals fell victim to a level 1 attack because they relied primarily on the number of friends or the picture of the requester as a heuristic cue and made snap judgments. Victims also demonstrated a herd mentality, gravitating to a phisher whose page showed more connections. Such profiles caused an upward information cascade, where each victim attracted many more victims through a social contagion effect. Individuals receiving a level 2 information request on Facebook peripherally focused on the source of the request by using the sender's picture in the message as a credibility cue.


title: Merging What's Cracked, Cracking What's Merged: Adaptive Indexing in Main-Memory Column-Stores

abstract: Adaptive indexing is characterized by the partial creation and refinement of the index as side effects of query execution. Dynamic or shifting workloads may benefit from preliminary index structures focused on the columns and specific key ranges actually queried --- without incurring the cost of full index construction. The costs and benefits of adaptive indexing techniques should therefore be compared in terms of initialization costs, the overhead imposed upon queries, and the rate at which the index converges to a state that is fully-refined for a particular workload component. ::: ::: Based on an examination of database cracking and adaptive merging, which are two techniques for adaptive indexing, we seek a hybrid technique that has a low initialization cost and also converges rapidly. We find the strengths and weaknesses of database cracking and adaptive merging complementary. One has a relatively high initialization cost but converges rapidly. The other has a low initialization cost but converges relatively slowly. We analyze the sources of their respective strengths and explore the space of hybrid techniques. We have designed and implemented a family of hybrid algorithms in the context of a column-store database system. Our experiments compare their behavior against database cracking and adaptive merging, as well as against both traditional full index lookup and scan of unordered data. We show that the new hybrids significantly improve over past methods while at least two of the hybrids come very close to the "ideal performance" in terms of both overhead per query and convergence to a final state.


title: Experimental protein malnutrition in primates—cytochemistry of the nervous system

abstract: Experimental protein malnutrition was induced in groups of young juvenile squirrel monkeys by feeding them ad libitum diets very low in protein content, whereas a diet containing 25% protein content was fed to the control animals. Detailed cytochemical studies have clearly shown the sensitivity of the nervous system to dietary abuse. The motor neurons of the spinal cord and the Purkinje cells of the cerebellum are very sensitive to protein deficiency, and the number of oligodendroglia cells increases sharply. Gallocyanin stained preparations from the malnourished animals show significant decrease in the amount of RNA in the Purkinje cells of cerebellum and the anterior horn cells of the spinal cord. The larger neurons show prominent chromatolysis with concomitant increase in the number of oligodendrocytes surrounding such nerve cells. The different layers of the cerebellar cortex of the malnourished animals, as well as some of the neurons of the spinal cord, showed decreased activity of succinate dehydrogenase and increased levels of thiamine pyrophosphatase and lactate dehydrogenase. Although ATPase activity appeared to remain unchanged quantitatively, it showed profound disturbance in its in situ localization, especially in the mitochondrial ATPase located in the cytoplasm. The enzyme activity in the nuclear membrane appeared unchanged. These studies emphasize the importance of studying in situ changes in the anatomically heterogeneous nervous tissue, rather than of working only with biochemical methods using homogenized material.


title: A Magnetic Resonance Imaging Study of the Cerebellar Vermis in Chronically Treated and Treatment-Naïve Children with Attention-Deficit/Hyperactivity Disorder Combined Type

abstract: Background Because of its dense connections to the prefrontal cortex and basal ganglia, the cerebellum is thought to play an important role in cognition. Numerous magnetic resonance imaging (MRI) studies have found abnormalities in the cerebellum in children with attention-deficit/hyperactivity disorder (ADHD). While some studies in animal and human models suggest that certain brain structures are affected by chronic stimulant medication, it is unclear whether the cerebellum is also affected. The purpose of the current study was to determine if cerebellar morphology was different in treatment-naive versus chronically treated children with ADHD. Methods There were 32 boys and 15 girls total ( N = 47) that comprised three groups: ADHD-combined type (ADHD-C) children with no history of stimulant medication treatment ( n = 14), ADHD-C children chronically treated with stimulant medication ( n = 18), and typically developing control children ( n = 15). Results Treatment-naive children with ADHD had significantly smaller area in the posterior inferior vermis (lobules VIII–X) than both chronically treated children with ADHD ( p = .004) and typically developing control children ( p = .001). No differences were observed between chronically treated children with ADHD and control children. Conclusions The results from this study suggest that chronic stimulant treatment may normalize the development of important areas of the cerebellar vermis in children with ADHD.


title: Individual Differences in Personality Masculinity-Femininity: Examining the Effects of Genes, Environment, and Prenatal Hormone Transfer

abstract: Males and females score differently on some personality traits, but the underlying etiology of these differences is not well understood. This study examined genetic, environmental, and prenatal hor ...


title: Information Evolution in Social Networks

abstract: Social networks readily transmit information, albeit with less than perfect fidelity. We present a large-scale measurement of this imperfect information copying mechanism by examining the dissemination and evolution of thousands of memes, collectively replicated hundreds of millions of times in the online social network Facebook. The information undergoes an evolutionary process that exhibits several regularities. A meme's mutation rate characterizes the population distribution of its variants, in accordance with the Yule process. Variants further apart in the diffusion cascade have greater edit distance, as would be expected in an iterative, imperfect replication process. Some text sequences can confer a replicative advantage; these sequences are abundant and transfer "laterally" between different memes. Subpopulations of the social network can preferentially transmit a specific variant of a meme if the variant matches their beliefs or culture. Understanding the mechanism driving change in diffusing information has important implications for how we interpret and harness the information that reaches us through our social networks.


title: Learning To Use Formulas To Solve Simple Arithmetic Problems

abstract: Solving simple arithmetic word problems is one of the challenges in Natural Language Understanding. This paper presents a novel method to learn to use formulas to solve simple arithmetic word problems. Our system, analyzes each of the sentences to identify the variables and their attributes; and automatically maps this information into a higher level representation. It then uses that representation to recognize the presence of a formula along with its associated variables. An equation is then generated from the formal description of the formula. In the training phase, it learns to score the pair from the systematically generated higher level representation. It is able to solve 86.07% of the problems in a corpus of standard primary school test questions and beats the state-of-the-art by


title: The EDA Challenges in the Dark Silicon Era ( Temperature , Reliability , and Variability Perspectives )

abstract: Technology scaling has resulted in smaller and faster transistors in successive technology generations. However, transistor power consumption no longer scales commensurately with integration density and, consequently, it is projected that in future technology nodes it will only be possible to simultaneously power on a fraction of cores on a multi-core chip in order to stay within the power budget. The part of the chip that is powered off is referred to as dark silicon and brings new challenges as well as opportunities for the design community, particularly in the context of the interaction of dark silicon with thermal, reliability and variability concerns. In this perspectives paper we describe these new challenges and opportunities, and provide preliminary experimental evidence in their support.


title: WBCD breast cancer database classification applying artificial metaplasticity neural network

abstract: The correct diagnosis of breast cancer is one of the major problems in the medical field. From the literature it has been found that different pattern recognition techniques can help them to improve in this domain. These techniques can help doctors form a second opinion and make a better diagnosis. In this paper we present a novel improvement in neural network training for pattern classification. The proposed training algorithm is inspired by the biological metaplasticity property of neurons and Shannon's information theory. During the training phase the Artificial metaplasticity Multilayer Perceptron (AMMLP) algorithm gives priority to updating the weights for the less frequent activations over the more frequent ones. In this way metaplasticity is modeled artificially. AMMLP achieves a more effcient training, while maintaining MLP performance. To test the proposed algorithm we used the Wisconsin Breast Cancer Database (WBCD). AMMLP performance is tested using classification accuracy, sensitivity and specificity analysis, and confusion matrix. The obtained AMMLP classification accuracy of 99.26%, a very promising result compared to the Backpropagation Algorithm (BPA) and recent classification techniques applied to the same database.


title: An evaluation of the RGB-D SLAM system

abstract: We present an approach to simultaneous localization and mapping (SLAM) for RGB-D cameras like the Microsoft Kinect. Our system concurrently estimates the trajectory of a hand-held Kinect and generates a dense 3D model of the environment. We present the key features of our approach and evaluate its performance thoroughly on a recently published dataset, including a large set of sequences of different scenes with varying camera speeds and illumination conditions. In particular, we evaluate the accuracy, robustness, and processing time for three different feature descriptors (SIFT, SURF, and ORB). The experiments demonstrate that our system can robustly deal with difficult data in common indoor scenarios while being fast enough for online operation. Our system is fully available as open-source.


title: Self-Alignment Mechanisms for Assistive Wearable Robots: A Kinetostatic Compatibility Method

abstract: The field of wearable robotics is gaining momentum thanks to its potential application in rehabilitation engineering, assistive robotics, and power augmentation. These devices are designed to be used in direct contact with the user to aid with movement or increase the power of specific skeletal joints. The design of the so-called physical human-robot interface is critical, since it determines not only the efficacy of the robot but the kinematic compatibility of the device with the human skeleton and the degree of adaptation to different anthropometries as well. Failing to deal with these problems causes misalignments between the robot and the user joint. Axes misalignment leads to the impossibility of controlling the torque effectively transmitted to the user joint and causes undesired loading forces on articulations and soft tissues. In this paper, we propose a general analytical method for the design of exoskeletons able to assist human joints without being subjected to misalignment effects. This method is based on a kinetostatic analysis of a coupled mechanism (robot-human skeleton) and can be applied in the design of self-aligning mechanisms. The method is exemplified in the design of an assistive robotic chain for a two-degree-of-freedom (DOF) human articulation.


title: The Menpo Benchmark for Multi-pose 2D and 3D Facial Landmark Localisation and Tracking

abstract: In this article, we present the Menpo 2D and Menpo 3D benchmarks, two new datasets for multi-pose 2D and 3D facial landmark localisation and tracking. In contrast to the previous benchmarks such as 300W and 300VW, the proposed benchmarks contain facial images in both semi-frontal and profile pose. We introduce an elaborate semi-automatic methodology for providing high-quality annotations for both the Menpo 2D and Menpo 3D benchmarks. In Menpo 2D benchmark, different visible landmark configurations are designed for semi-frontal and profile faces, thus making the 2D face alignment full-pose. In Menpo 3D benchmark, a united landmark configuration is designed for both semi-frontal and profile faces based on the correspondence with a 3D face model, thus making face alignment not only full-pose but also corresponding to the real-world 3D space. Based on the considerable number of annotated images, we organised Menpo 2D Challenge and Menpo 3D Challenge for face alignment under large pose variations in conjunction with CVPR 2017 and ICCV 2017, respectively. The results of these challenges demonstrate that recent deep learning architectures, when trained with the abundant data, lead to excellent results. We also provide a very simple, yet effective solution, named Cascade Multi-view Hourglass Model, to 2D and 3D face alignment. In our method, we take advantage of all 2D and 3D facial landmark annotations in a joint way. We not only capitalise on the correspondences between the semi-frontal and profile 2D facial landmarks but also employ joint supervision from both 2D and 3D facial landmarks. Finally, we discuss future directions on the topic of face alignment.


title: A Communications-Oriented Perspective on Traffic Management Systems for Smart Cities: Challenges and Innovative Approaches

abstract: The growing size of cities and increasing population mobility have determined a rapid increase in the number of vehicles on the roads, which has resulted in many challenges for road traffic management authorities in relation to traffic congestion, accidents, and air pollution. Over the recent years, researchers from both industry and academia have been focusing their efforts on exploiting the advances in sensing, communication, and dynamic adaptive technologies to make the existing road traffic management systems (TMSs) more efficient to cope with the aforementioned issues in future smart cities. However, these efforts are still insufficient to build a reliable and secure TMS that can handle the foreseeable rise of population and vehicles in smart cities. In this survey, we present an up-to-date review of the different technologies used in the different phases involved in a TMS and discuss the potential use of smart cars and social media to enable fast and more accurate traffic congestion detection and mitigation. We also provide a thorough study of the security threats that may jeopardize the efficiency of the TMS and endanger drivers' lives. Furthermore, the most significant and recent European and worldwide projects dealing with traffic congestion issues are briefly discussed to highlight their contribution to the advancement of smart transportation. Finally, we discuss some open challenges and present our own vision to develop robust TMSs for future smart cities.


title: The theoretical bases of indirect calorimetry: A review

abstract: Abstract Indirect calorimetry is the method by which the type and rate of substrate utilization, and energy metabolism are estimated in vivo starting from gas exchange measurements. This technique provides unique information, is noninvasive, and can be advantageously combined with other experimental methods to investigate numerous aspects of nutrient assimilation, thermogenesis, the energetics of physical exercise, and the pathogenesis of metabolic diseases. Since its use as a research tool in metabolism is growing, the theoretical bases of indirect calorimetry are here reviewed in a detailed and orderly fashion. Special cases, such as the occurrence of net lipid synthesis or gluconeogenesis, are formally considered with derivation of explicit stoichiometric equations. The limitations of indirect calorimetry, both theoretical and technical, are discussed in the context of circumstances of clinical interest in metabolism.


title: iSensestress: Assessing stress through human-smartphone interaction analysis

abstract: Stress condition, if experienced for an extended amount of time, can negatively affect individual's health. Several external sensors monitoring different physiological states correlated with stress, or smartphone apps that monitor individuals context, have been leveraged to assess stress state in everyday life. The less intrusive "human-smartphone interaction" have been under-investigated so far. In our research we leverage 'swipe', 'scroll' and 'text input' interactions to assess the stress state of smartphone users. Based on data collected from 13 participants, we leverage 'swipe' and 'scroll' data to assess stress with an average F-measure of 79-85% for a within-subject model, and of 70-80% when building a global model. Moreover, 'text input' via a virtual keyboard has been analyzed, showing how several easy to calculate features enable to differentiate between stress and nostress state. To the best of our knowledge, this is the first attempt to leverage human-smartphone interaction, and in particular 'swipe', 'scroll' and 'text input' interactions, to accurately assess stress state in individuals without using any external sensor or leveraging privacy-sensitive context information.


title: Causally Driven Incremental Multi Touch Attribution Using a Recurrent Neural Network

abstract: This paper describes a practical system for Multi Touch Attribution (MTA) for use by a publisher of digital ads. We developed this system for JD.com, an eCommerce company, which is also a publisher of digital ads in China. The approach has two steps. The first step ('response modeling') fits a user-level model for purchase of a product as a function of the user's exposure to ads. The second ('credit allocation') uses the fitted model to allocate the incremental part of the observed purchase due to advertising, to the ads the user is exposed to over the previous T days. To implement step one, we train a Recurrent Neural Network (RNN) on user-level conversion and exposure data. The RNN has the advantage of flexibly handling the sequential dependence in the data in a semi-parametric way. The specific RNN formulation we implement captures the impact of advertising intensity, timing, competition, and user-heterogeneity, which are known to be relevant to ad-response. To implement step two, we compute Shapley Values, which have the advantage of having axiomatic foundations and satisfying fairness considerations. The specific formulation of the Shapley Value we implement respects incrementality by allocating the overall incremental improvement in conversion to the exposed ads, while handling the sequence-dependence of exposures on the observed outcomes. The system is under production at JD.com, and scales to handle the high dimensionality of the problem on the platform (attribution of the orders of about 300M users, for roughly 160K brands, across 200+ ad-types, served about 80B ad-impressions over a typical 15-day period).


title: Modeling and Analysis of Network Dynamics in Complex Communication Networks Using Social Network Methods

abstract: Modern communication networks are inherently complex in nature. First of all, they have a large number of heterogeneous components. Secondly, their connectivity is extremely dynamic. Nodes can come and go, links can be removed and added over time. Traditional modeling and simulation techniques amalgamate or ignore such dynamics and therefore, are unable to represent them. Complex communication networks are therefore better modeled as mathematical structures called graphs. Modeling as graphs allows for the application of complex techniques such as various network based analysis techniques. While this is a very important and much needed skill for communication networks researchers and engineers, to the best of our knowledge, currently there is no resource describing these details. In this paper, we give a concise but comprehensive review of modeling complex communication networks as graphs. We also show how to apply complex social network analysis on these models besides a demonstration of formal modeling network dynamics.


title: Solving the team orienteering problem using effective multi-start simulated annealing

abstract: The team orienteering problem (TOP) is known as an NP-complete problem. A set of locations is provided and a score is collected from the visit to each location. The objective is to maximize the total score given a fixed time limit for each available tour. Given the computational complexity of this problem, a multi-start simulated annealing (MSA) algorithm which combines a simulated annealing (SA) based meta-heuristic with a multi-start hill climbing strategy is proposed to solve TOP. To verify the developed MSA algorithm, computational experiments are performed on well-known benchmark problems involving numbers of locations ranging between 42 and 102. The experimental results demonstrate that the multi-start hill climbing strategy can significantly improve the performance of the traditional single-start SA. Meanwhile, the proposed MSA algorithm is highly effective compared to the state-of-the-art meta-heuristics on the same benchmark instances. The proposed MSA algorithm obtained 135 best solutions to the 157 benchmark problems, including five new best solutions. In terms of both solution quality and computational expense, this study successfully constructs a high-performance method for solving this challenging problem.


title: Single chip enabled high frequency link based isolated bias supply for silicon carbide MOSFET six-pack power module gate drives

abstract: Regarded as one of the most successful wide bandgap (WBG) devices, Silicon Carbide (SiC) metal-oxide-semiconductor field-transistors (MOSFETs) are being considered in an increasing number of power electronics applications. One of those applications is the hybrid and electric vehicle (HEV/EV) traction inverters where high-efficiency and high-power density is essential. From the system-level perspective, the gate driver circuit design for such device is challenging considering the device's fast switching speed and compact system structure. This paper presents a low profile (6 mm) isolated bias supply design using commercially available components for the SiC MOSFET modules targeting an HEV/EV traction inverter application. A single chip MAX 13256 (3 mm∗3 mm) is adopted to form the high-frequency link for entire power module gate drive supply. Distributed transformer strategy is highlighted to provide multiple isolated output and compact structure with minimized parasitic capacitance between all the isolation barriers. The featured low profile optimization reduces the parasitic parameters that might deteriorate the system performance for the fast switching WBG devices. Moreover, the open-loop high-frequency link architecture allows easy configuration for customized output voltage level, polarity and higher reliability. A prototype gate driver has been built for 1.2 kV, 50 A SiC six-pack MOSFET power module, and experimental results are presented.


title: Rhipicephalus sanguineus (ACARI: IXODIDAE) BITING A HUMAN BEING IN PORTO ALEGRE CITY, RIO GRANDE DO SUL, BRAZIL

abstract: We report the finding of a female brown dog tick, Rhipicephalus sanguineus (Acari: Ixodidae) on the scalp of a male patient inPorto Alegre, Rio Grande do Sul, Brazil. Human parasitism by this tick is rare and has seldomly been reported in the literature, despite its recognized importance since it can act as a vector of Rickettsia rickettsii, the agent of spotted fever.


title: Get To The Point: Summarization with Pointer-Generator Networks

abstract: Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.


title: A CMOS Class-D Line Driver Employing a Phase-Locked Loop Based PWM Generator

abstract: A Class-D line driver that utilizes a phase-locked loop (PLL) for PWM generation is presented. The principle of operation and implementation details relating to loop stability, linearity and noise performance are analyzed. An implementation is presented in a 130 nm CMOS process. The amplifier can deliver 1.2 W into a 6.8 Ω load with a 4.8 V power supply. The architecture eliminates the requirements for a high-quality carrier generator and a high-speed voltage comparator that are often required in PWM implementations. It can achieve a THD of -65 dB, for a sinusoidal input with a frequency of 60 kHz, while employing a switching frequency that can be as high as 20 MHz. The peak efficiency is 83% for output power larger than 1 W for a switching frequency of 10 MHz. The die area is 2.25 mm2.


title: Influence Analysis of Geometrical Parameters of Detonation Initiation with Hot Jet by Adaptive Mesh Refinement Method

abstract: The open-source program AMROC, implementing a block-structured adaptive mesh refinement method, was adopted for the fine structure numerical simulation of detonation initiation in quiescent and supersonic combustible mixtures. The effects of various geometry parameters, including the diameter of hot jet, the height of the channel and the location of the hot jet, were considered and analyzed in these simulations. Results showed that for certain kind of hot jet, the energy strength was determined by the diameter of hot jet in quiescent combustible mixtures. There existed a critical diameter of the hot jet. Whether the two-time wall reflection was realized effectively or not was depend on the location of hot jet. The height of the detonation channel was a key factor for the strength of the reflected shock wave and played a important role on the realization of the valid two-time wall reflection. In supersonic combustible mixtures, there existed a critical diameter of hot jet too. The height of detonation channel made important impacts on the shock reflection on the upper wall, and whether the local detonation combustion would be realized directly or not on the upper wall was depend on the strength of reflection shock.


Real Time Text Mining on Twitter Data


title: Mind the denominator: towards a more effective measurement system for cybersecurity

abstract: ABSTRACTCrime statistics in the physical world are routinely normalised around the population of a city or country. Such normalisations are essential, as they provide both a propensity-based perspective on crime (e.g. the odds of being murdered are 1 in 100,000) and correct for the simple fact that a larger population should have more crimes. Unfortunately, many cybersecurity metrics tend to be uncorrected counts of malicious phenomena such as the number of phishing websites. While normalisation cannot make bad measures good, a failure to normalise even the best cybersecurity metrics can lead to bias. A failure to normalise count statistics around the size of the ecosystem, sensor density or risk mitigation personnel has implications for the observed trends, often making the state of cybersecurity seem worse than it actually is. In short, normalisation of malicious count data is a crucial measurement step and has significant impacts for both firm- and economy-wide risk management strategies and policy ass...


title: Robust Principal Component Analysis with Non-Greedy l1-Norm Maximization

abstract: Principal Component Analysis (PCA) is one of the most important methods to handle high-dimensional data. However, the high computational complexity makes it hard to apply to the large scale data with high dimensionality, and the used l2-norm makes it sensitive to outliers. A recent work proposed principal component analysis based on l1-normmaximization, which is efficient and robust to outliers. In that work, a greedy strategy was applied due to the difficulty of directly solving the l1-norm maximization problem, which is easy to get stuck in local solution. In this paper, we first propose an efficient optimization algorithmto solve a general l1-norm maximization problem, and then propose a robust principal component analysis with non-greedy l1-norm maximization. Experimental results on real world datasets show that the nongreedy method always obtains much better solution than that of the greedy method.


title: Telerobotic system for cell manipulation

abstract: In this paper, telerobotic system for cell injection applications is proposed. Fidelity and stability are contradicting factors in teleoperation. Most of cells manipulations nowadays are performed manually by the human operators. These highly precise operations require high-skilled professional operators. However, the success and survival rate of the cells is very low due to the great sensitivity of cells. Moreover, while manipulating, operator cannot feel any interaction with the cells because of their negligible mass, therefore all the operations are based on the visual information provided by the high-precision microscope. In addition, during every operation the humanpsilas hand has a certain vibration that can affect the quality of teleoperation, especially in telesurgery, nanomanipuation and other precise tasks. All of the reasons listed above show that the cells manipulation is an exclusively complex task for a human operator to perform. Therefore, applying telerobotic system for the cells manipulation may provide us with many advantages and could help us to overcome the problems listed above.


title: LC Passive Wireless Sensors Toward a Wireless Sensing Platform: Status, Prospects, and Challenges

abstract: Inductor–capacitor ( $LC$ ) passive wireless sensors use a transformer with loose coupling between an external readout coil and an inductor that receives power through this inductive coupling. Changes in the sensor are wirelessly and remotely detected by the readout coil, which makes them highly useful in applications that require the sensor to be powered remotely and to occupy a small volume, such as harsh and sealed environments, where physical access to the sensor is difficult. Although the sensor to accomplish this function dates from the 1960’s, its rapid extension over the past decades has benefited from microelectromechanical systems. This paper provides an overview of the status and challenges in the $LC$ passive wireless sensor toward a wireless sensing platform. The basic sensing principles are first categorized into detecting changes of the sensor in response to the capacitance, resistance, inductance, or coupling distance due to the parameter of interest through monitoring the impedance magnitude and phase spectrum. The present state of the art in sensor applications for pressure, strain, temperature, humidity, biochemical, gas, and so on is then reviewed and compared. For emerging applications from many Internet of Things scenarios, geometrical constraints, such as small and non-invasive coils, reduce the magnetic coupling between the sensor and the readout coil, resulting in a limited interrogation distance. Furthermore, an increasing number of applications also require the simultaneous measurement of multiple parameters. Recent efforts to increase the interrogation distance and to extend the measurement of single parameter to multiple parameters are finally outlined. [2016-0093]


title: Aggregate Reinforcement Learning for multi-agent territory division

abstract: In many applications in Robotics such as disaster rescuing, mine detection, robotic surveillance and warehouse systems, it is crucial to build multi-agent systems (MAS) in which agents cooperate to complete a sequence of tasks. For better performance in such systems, e.g. minimizing duplicate work, agents need to agree on how to divide and plan that sequence of tasks among themselves. This paper targets the problem of territory division in the children's game of Hide-and-Seek as a test-bed for our proposed approach. The problem is solved in a hierarchical learning scheme using Reinforcement Learning (RL). Based on Q-learning, our learning model is presented in detail; definition of composite states, actions, and reward function to deal with multiple agent learning. In addition, a revised version of the standard updating rule of the Q-learning is proposed to cope with multiple seekers. The model is examined on a set of different maps, on which it converges to the optimal solutions. After the complexity analysis of the algorithm, we enhanced it by using state aggregation (SA) to alleviate the state space explosion. Two levels of aggregation are devised: topological and hiding aggregation. After elaborating how the learning model is modified to handle the aggregation technique, the enhanced model is examined by some experiments. Results indicate promising performance with higher convergence rate and up to 10i? space reduction.


title: Women Administrators Negotiate Work-Family Conflicts in Changing Times: An Intergenerational Perspective

abstract: Background:Concerns about work-family conflicts are becoming an increasing problem for women administrators. Yet these concerns have been overshadowed in the educational leadership scholarship, which has focused on barriers related to discrimination in hiring and promotion and lack of sponsoring and mentoring.Purpose:To illuminate differences and commonalities in how women administrators from different generations and racial/ethnic identities negotiate work-family conflicts.Research Methodology:A qualitative life course design was employed in this intergenerational study of 31 Black and White women administrators.Main Findings:The participants’ efforts to enact the multiple and competing roles of administrator, wife, mother, and caretaker proved to be conflicting across generation and race/ethnicity, especially for women born after the civil rights and women’s movements, who confronted a strikingly different gender context than did their older counterparts. To reconcile these conflicts, the participants e...


title: Plant disease detection using CNNs and GANs as an augmentative approach

abstract: Almost 40% of the world's crop yield is lost to diseases and pest infestations. According to a 2012 survey, Maharashtra has the highest rate of farmer suicides and one of the major reasons for this is the failure of crops. This paper presents an image-based classification system for identification of plant diseases. Since existing datasets have diluted focus across several countries and there are none that pertain to India specifically, there is a need for establishing a local dataset to be of use to Indian farmers. It uses Generative Adversarial Networks (GANs) to augment the limited number of local images available. The classification is done by a Convolutional Neural Network (CNN) model deployed in a smart phone app.


title: Deep reinforcement learning based neuro-control for a two-dimensional magnetic positioning system

abstract: This paper presents a control scheme based on deep reinforcement learning for a two-dimensional positioning system with electromagnetic actuators. Two neuro-controllers are trained and used for controlling the X-Y position of an object. The neuro-controllers learning approach is based on the actor-critic architecture and the deep deterministic policy gradient (DDPG) algorithm using the Q-learning method. The performance of the control system is verified for different setpoints and working conditions.


title: Set-based Similarity Search for Time Series

abstract: A fundamental problem of time series is k nearest neighbor (k-NN) query processing. However, existing methods are not fast enough for large dataset. In this paper, we propose a novel approach, STS3, to process k-NN queries by transforming time series to sets and measure the similarity under Jaccard metric. Our approach is more accurate than Dynamic Time Warping(DTW) in our suitable scenarios and it is faster than most of the existing methods, due to the efficient similarity search for sets. Besides, we also developed an index, a pruning and an approximation technique to improve the k-NN query procedure. As shown in the experimental results, all of them could accelerate the query processing effectively.