title: Vascular pharmacotherapy and dementia.

abstract: The incidence of dementia is increasing dramatically with the ageing population. Increasing evidence indicates that vascular disease is associated with cognitive decline and with the most common form of dementia, Alzheimer's dis- ease (AD). Cardiovascular risk factors such as hyperlipidaemia, hypertension and type 2 diabetes have attracted attention as potential targets in the prevention of dementia. The present review aims to provide a concise overview of the recent ad- vances linking vascular disease with dementia (with a particular focus on AD) and to examine the evidence for efficacy, where possible, for utilising vascular pharmacotherapy as a treatment option for dementia.


title: Manifold Regularized Distribution Adaptation for Classification of Remote Sensing Images

abstract: We perform unsupervised domain adaptation for the classification of remote sensing data by learning a shared subspace in this paper. Maximum mean discrepancy (MMD) is applied to each class, making the approach able to minimize the domain shift on a per-class basis. Furthermore, manifold regularization is employed to constrain the data manifold of both the source and target data to be preserved in the subspace. The manifold regularization in conjunction with the per-class MMD strategy is called manifold regularized distribution adaptation (MRDA) algorithm. Since the class mean of target data should be estimated by the predicted labels, we integrate spatial information and overall mean coincidence (OMC) method to improve the prediction accuracy, resulting in Spa_OMC_MRDA approach. Experimental results on both multispectral and hyperspectral remote sensing data indicated the good performances of the proposed approach.


title: Pyramid-Based Multi-scale LBP Features for Face Recognition

abstract: To efficiently extract local and global features in face description and recognition, a pyramid-based multi-scale LBP approach is proposed. Firstly, the face image pyramid is constructed through multi-scale analysis. Then the LBP operator is applied to each level of the image pyramid to extract facial features under various scales. Finally, all the extracted features are concatenated into an enhanced feature vector which is used as the face descriptor. Experimental results on ORL and FERET face databases show that the proposed LBP representation is highly efficient with good performance in face recognition and is robust to illumination, facial expression and position variation.


Alignment for Advanced Machine Learning Systems


title: A video game description language for model-based or interactive learning

abstract: We propose a powerful new tool for conducting research on computational intelligence and games. `PyVGDL' is a simple, high-level description language for 2D video games, and the accompanying software library permits parsing and instantly playing those games. The streamlined design of the language is based on defining locations and dynamics for simple building blocks, and the interaction effects when such objects collide, all of which are provided in a rich ontology. It can be used to quickly design games, without needing to deal with control structures, and the concise language is also accessible to generative approaches. We show how the dynamics of many classical games can be generated from a few lines of PyVGDL. The main objective of these generated games is to serve as diverse benchmark problems for learning and planning algorithms; so we provide a collection of interfaces for different types of learning agents, with visual or abstract observations, from a global or first-person viewpoint. To demonstrate the library's usefulness in a broad range of learning scenarios, we show how to learn competent behaviors when a model of the game dynamics is available or when it is not, when full state information is given to the agent or just subjective observations, when learning is interactive or in batch-mode, and for a number of different learning algorithms, including reinforcement learning and evolutionary search.


title: A System for Denial-of-Service Attack Detection Based on Multivariate Correlation Analysis

abstract: Interconnected systems, such as Web servers, database servers, cloud computing servers and so on, are now under threads from network attackers. As one of most common and aggressive means, denial-of-service (DoS) attacks cause serious impact on these computing systems. In this paper, we present a DoS attack detection system that uses multivariate correlation analysis (MCA) for accurate network traffic characterization by extracting the geometrical correlations between network traffic features. Our MCA-based DoS attack detection system employs the principle of anomaly based detection in attack recognition. This makes our solution capable of detecting known and unknown DoS attacks effectively by learning the patterns of legitimate network traffic only. Furthermore, a triangle-area-based technique is proposed to enhance and to speed up the process of MCA. The effectiveness of our proposed detection system is evaluated using KDD Cup 99 data set, and the influences of both non-normalized data and normalized data on the performance of the proposed detection system are examined. The results show that our system outperforms two other previously developed state-of-the-art approaches in terms of detection accuracy.


title: A Helping Hand: Soft Orthosis with Integrated Optical Strain Sensors and EMG Control

abstract: Human fingers and hands are frequently injured because they are delicate, complex, and used constantly. More than 3 million people in the United States suffer from hand or forearm disabilities [1], and, worldwide, hand injuries account for one-third of all work injuries [2]. Due to the importance of hands and the prevalence of hand issues, there is an increasing effort toward developing hand orthotics. These efforts have resulted in active hand orthoses that have been used for rehabilitation training and restoring partial hand function [3]. To ensure safety and to reduce control complexity, some orthoses use mechanical compliances [4], [5] such as underactuated linkages [6] or low-stiffness materials and structures (e.g., rubbers and flexible wires) [7], [8]-[11]. The orthoses made of elastomeric materials tend to be more comfortable, perhaps because their low elastic modulus (10 kPa l G' l 1 MPa) [12] is similar to that of human skin (~100 kPa) [13].


title: Video-based emotion recognition using CNN-RNN and C3D hybrid networks

abstract: In this paper, we present a video-based emotion recognition system submitted to the EmotiW 2016 Challenge. The core module of this system is a hybrid network that combines recurrent neural network (RNN) and 3D convolutional networks (C3D) in a late-fusion fashion. RNN and C3D encode appearance and motion information in different ways. Specifically, RNN takes appearance features extracted by convolutional neural network (CNN) over individual video frames as input and encodes motion later, while C3D models appearance and motion of video simultaneously. Combined with an audio module, our system achieved a recognition accuracy of 59.02% without using any additional emotion-labeled video clips in training set, compared to 53.8% of the winner of EmotiW 2015. Extensive experiments show that combining RNN and C3D together can improve video-based emotion recognition noticeably.


title: Resilient Machines Through

abstract: Animals sustain the ability to operate after injury by creating qualitatively different compensatory behaviors. Although such robustness would be desirable in engineered systems, most machines fail in the face of unexpected damage. We describe a robot that can recover from such change autonomously, through continuous self-modeling. A four-legged machine uses actuation-sensation relationships to indirectly infer its own structure, and it then uses this self-model to generate forward locomotion. When a leg part is removed, it adapts the self-models, leading to the generation of alternative gaits. This concept may help develop more robust machines and shed light on self-modeling in animals.


title: Detecting Melodic Motifs from Audio for Hindustani Classical Music

abstract: Melodic motifs form essential building blocks in Indian Classical music. The motifs, or key phrases, provide strong cues to the identity of the underlying raga in both Hindustani and Carnatic styles of Indian music. Thus the automatic detection of such recurring basic melodic shapes from audio is of relevance in music information retrieval. The extraction of melodic attributes from polyphonic audio and the variability inherent in the performance, which does not follow a predefined score, make the task particularly challenging. In this work, we consider the segmentation of selected melodic motifs from audio signals by computing similarity measures on time series of automatically detected pitch values. The methods are investigated in the context of detecting the signature phrase of Hindustani vocal music compositions (bandish) within and across performances.


title: Moving Poselets: A Discriminative and Interpretable Skeletal Motion Representation for Action Recognition

abstract: Given a video or time series of skeleton data, action recognition systems perform classification using cues such as motion, appearance, and pose. For the past decade, actions have been modeled using low-level feature representations such as Bag of Features. More recent work has shown that mid-level representations that model body part movements (e.g., hand moving forward) can be very effective. However, these mid-level features are usually hand-crafted and the dictionary of representative features is learned using ad-hoc heuristics. While automatic feature learning methods such as supervised sparse dictionary learning or neural networks can be applied to learn feature representation and action classifiers jointly, the resulting features are usually uninterpretable. In contrast, our goal is to develop a principled feature learning framework to learn discriminative and interpretable skeletal motion patterns for action recognition. For this purpose, we propose a novel body-part motion based feature called Moving Poselet, which corresponds to a specific body part configuration undergoing a specific movement. We also propose a simple algorithm for jointly learning Moving Poselets and action classifiers. Experiments on MSR Action3D, MSR DailyActivity3D and Berkeley MHAD datasets show that our two-layer model outperforms other two-layer models using hand-crafted features, and achieves results comparable to those of recent multi-layer Hierarchical Recurrent Neural Network (HRNN) models, which use multiple layers of RNN to model the human body hierarchy.


title: A Polynomial QR Decomposition Based Turbo Equalization Technique for Frequency Selective MIMO Channels

abstract: In the case of a frequency flat multiple-input ::: multiple-output (MIMO) system, QR decomposition can be ::: applied to reduce the MIMO channel equalization problem to ::: a set of decision feedback based single channel equalization ::: problems. Using a novel technique for polynomial matrix QR ::: decomposition (PMQRD) based on Givens rotations, we extend ::: this work to frequency selective MIMO systems. A transmitter ::: design based on Diagonal Bell Laboratories Layered Space Time ::: (D-BLAST) encoding has been implemented. Turbo equalization ::: is utilized at the receiver to overcome the multipath delay spread ::: and to facilitate multi-stream data feedback. The effect of channel ::: estimation error on system performance has also been considered ::: to demonstrate the robustness of the proposed PMQRD scheme. ::: Average bit error rate simulations show a considerable improvement ::: over a benchmark orthogonal frequency division ::: multiplexing (OFDM) technique. The proposed scheme thereby ::: has potential applicability in MIMO communication applications, ::: particularly for TDMA systems with frequency selective channels.


title: Using MotSaRT to Support On-line Teachers in Student Motivation

abstract: In classrooms teachers know how to motivate their students and exploit this knowledge to adapt or optimize their instruction when a student shows signs of demotivation. In on-line learning environments it is much more difficult to assess the motivation of the student and to have adaptive intervention strategies and rules of application to help prevent attrition. We developed MotSaRT - a motivational strategies recommender tool - to support on-line teachers in motivating learners. The design is informed by Social Cognitive Theory and a survey on motivation intervention strategies carried out with sixty on-line teachers. The survey results were analysed using a data mining algorithm (J48 decision trees) which resulted in a set of decision rules for recommending motivational strategies. MotSaRT has been developed based on these decision rules. Its functionality enables the teacher to specify the learner's motivation profile. MotSaRT then recommends the most likely intervention strategies to increase motivation.


title: Multi-accent speech recognition with hierarchical grapheme based models

abstract: We train grapheme-based acoustic models for speech recognition using a hierarchical recurrent neural network architecture with connectionist temporal classification (CTC) loss. The models learn to align utterances with phonetic transcriptions in a lower layer and graphemic transcriptions in the final layer in a multi-task learning setting. Using the grapheme predictions from a hierarchical model trained on 3 million US English utterances results in 6.7% relative word error rate (WER) increase when compared to using the phoneme-based acoustic model trained on the same data. However, we show that hierarchical grapheme-based models trained on larger acoustic data (12 million utterances) jointly for grapheme and phoneme prediction task outperform phoneme only model by 6.9% relative WER. We train a single multi-dialect model using a combined US, British, Indian and Australian English data set and then adapt the model using US English data only. This adapted multi-accent model outperforms a model exclusively trained on US English. This process is repeated for phoneme-based and grapheme-based acoustic models for all four dialects and larger improvements are obtained with grapheme models. Additionally using a multi-accent grapheme model, we observe large recognition accuracy improvements for Indian-accented utterances in Google VoiceSearch US traffic with a 40% relative WER reduction.


title: Implementation of wireless body area networks for healthcare systems

abstract: This work describes the implementation of a complete wireless body-area network (WBAN) system to deploy in medical environments. Issues related to hardware implementations, software and wireless protocol designs are addressed. In addition to reviewing and discussing the current attempts in wireless body area network technology, a WBAN system that has been designed for healthcare applications will be presented in detail herein. The wireless system in the WBAN uses medical bands to obtain physiological data from sensor nodes. The medical bands are selected to reduce the interference and thus increase the coexistence of sensor node devices with other network devices available at medical centers. The collected data is transferred to remote stations with a multi-hopping technique using the medical gateway wireless boards. The gateway nodes connect the sensor nodes to the local area network or the Internet. As such facilities are already available in medical centers; medical professions can access patients’ physiological signals anywhere in the medical center. The data can also be accessed outside the medical center as they will be made available online.


title: On-chip boost DC-DC converter in color OLED driver & controller ICs for mobile application

abstract: On-chip boost DC-DC converter used in color OLED driver and controller ICs for mobile application is presented in this paper. For special systematic requirement, current-limited pulse-frequency modulation (PFM) control scheme is used. This converter is designed with 0.35mum 1P4M high-voltage CMOS process. Experimental results show that, the converter can operate with supply voltage from 2.5 to 4.2V, which is suitable for single-cell lithium-ion rechargeable battery supply application, and boost voltage up to 12~18V for illuminating OLED display pixels. The output ripple voltage is about 250mV with 6.8muF off-chip capacitor and 10muH off-chip inductor. The power efficiency is over 75% for 1~300mA load current, maximum to 87% at about 200mA load current


title: Psychology of human-computer interaction

abstract: Contents: Preface. An Applied Information-Processing Psychology. Part I: Science Base. The Human Information-Processor. Part II: Text-Editing. System and User Variability. An Exercise in Task Analysis. The GOMS Model of Manuscript Editing. Extensions of the GOMS Analysis. Models of Devices for Text Selection. Part III: Engineering Models. The Keystroke-Level Model. The Unit-Task Level of Analysis. Part IV: Extensions and Generalizations. An Exploration into Circuit Design. Cognitive Skill. Applying Psychology to Design Reprise.


title: On the effectiveness of linear models for one-class collaborative filtering

abstract: In many personalised recommendation problems, there are examples of items users prefer or like, but no examples of items they dislike. A state-of-the-art method for such implicit feedback, or one-class collaborative filtering (OC-CF), problems is SLIM, which makes recommendations based on a learned item-item similarity matrix. While SLIM has been shown to perform well on implicit feedback tasks, we argue that it is hindered by two limitations: first, it does not produce user-personalised predictions, which hampers recommendation performance; second, it involves solving a constrained optimisation problem, which impedes fast training. In this paper, we propose LRec, a variant of SLIM that overcomes these limitations without sacrificing any of SLIM's strengths. At its core, LRec employs linear logistic regression; despite this simplicity, LRec consistently and significantly outperforms all existing methods on a range of datasets. Our results thus illustrate that the OC-CF problem can be effectively tackled via linear classification models.


Leadership Style and Staff Retention in Organisations


title: A Non-Stochastic Learning Approach to Energy Efficient Mobility Management

abstract: Energy efficient mobility management is an important problem in modern wireless networks with heterogeneous cell sizes and increased nodes densities. We show that optimization-based mobility protocols cannot achieve long-term optimal energy consumption, particularly for ultra-dense networks (UDN). To address the complex dynamics of UDN, we propose a non-stochastic online-learning approach which does not make any assumption on the statistical behavior of the small base station (SBS) activities. In addition, we introduce handover cost to the overall energy consumption, which forces the resulting solution to explicitly minimize frequent handovers. The proposed Batched Randomization with Exponential Weighting (BREW) algorithm relies on batching to explore in bulk, and hence reduces unnecessary handovers. We prove that the regret of BREW is sublinear in time, thus guaranteeing its convergence to the optimal SBS selection. We further study the robustness of the BREW algorithm to delayed or missing feedback. Moreover, we study the setting where SBSs can be dynamically turned on and off. We prove that sublinear regret is impossible with respect to arbitrary SBS on/off, and then develop a novel learning strategy, called ranking expert (RE), that simultaneously takes into account the handover cost and the availability of SBS. To address the high complexity of RE, we propose a contextual ranking expert (CRE) algorithm that only assigns experts in a given context. Rigorous regret bounds are proved for both RE and CRE with respect to the best expert. Simulations show that not only do the proposed mobility algorithms greatly reduce the system energy consumption, but they are also robust to various dynamics which are common in practical ultra-dense wireless networks.


title: Efficient disparity computation without . . .

abstract: In order to improve the performance of correlation-based disparity computation of stereo vision algorithms, standard methods need to choose in advance the value of the maximum disparity (MD). This value corresponds to the maximum displacement of the projection of a physical point expected between the two images. It generally depends on the motion model, the camera intrinsic parameters and on the depths of the observed scene. In this paper, we show that there is no optimal MD value that minimizes the matching errors in all image regions simultaneously and we propose a novel approach of the disparity computation that does not rely on any a priori MD. Two variants of this approach will be presented. When compared to traditional correlation-based methods, we show that our approach improves not only the accuracy of the results but also the efficiency of the algorithm. A local energy minimization is also proposed for fast refinement of the results. An extensive comparative study with ground truth is carried out on classical stereo images and the results show that the proposed method clearly gives more accurate results and it is two times faster than the fastest possible implementation of traditional correlation-based methods.


Anonymous Identities for Permissioned Blockchains ( DRAFT v 05 – 1 / 20 / 2016 )


title: In-cylinder mixture excursions in a port-injected engine during fast throttle opening

abstract: Fast throttle opening in port-injected gasoline engines often results in a lean air-fuel ratio excursion lasting several engine cycles. Even when the engine is equipped with a three-way catalyst this lean excursion can lead to high tailpipe emissions. This paper will describe an in-cylinder method of measuring these air-fuel ratio excursions, using a fast flame ionisation detector. Examples will be given of air-fuel ratio excursions obtained on a four-valve-per-cylinder sequentially-injected gasoline engine equipped with a lambda sensor. The air-fuel ratio excursions together with measurements of the engine air flow are used to estimate me build up of the fuel film on the inlet manifold walls. Whilst air-fuel ratio excursions have been recorded previously by other investigators, their results were obtained from exhaust gas analysis using fast oxygen sensors. This paper will demonstrate that the duration of the excursions recorded from exhaust gas analysis is overestimated, probably due to mixing of the exhaust gases from successive engine cycles. © Copyright 1994 Society of Automotive Engineers, Inc.


Relevant Acoustic Group Features for Automatic Sleepiness Recognition


title: Multiband Antenna for WiFi and WiGig Communications

abstract: This letter presents a low-cost printed circuit board (PCB)-based dual-band antenna for future wireless local area network (WLAN) applications. The antenna is designed to fully cover both WiFi channels (2.4/5.2/5.8 GHz) and Wireless Gigabit Alliance (WiGig) channels (57-64 GHz). At the WiFi frequency bands, the antenna is based on a printed monopole, while at the WiGig frequency band, a wideband higher-order-mode patch antenna is adopted. A compact microstrip resonance cell (CMRC) low-pass filter is also designed to allow feeding the monopole antenna at WiFi frequency bands while isolating the monopole from the patch for WiGig operation. The design is fabricated by standard PCB and plated-through-hole technologies, and its performance is validated by measurement.


title: The FCS1: a language independent assessment of CS1 knowledge

abstract: A primary goal of many CS education projects is to determine the extent to which a given intervention has had an impact on student learning. However, computing lacks valid assessments for pedagogical or research purposes. Without such valid assessments, it is difficult to accurately measure student learning or establish a relationship between the instructional setting and learning outcomes. We developed the Foundational CS1 (FCS1) Assessment instrument, the first assessment instrument for introductory computer science concepts that is applicable across a variety of current pedagogies and programming languages. We applied methods from educational and psychological test development, adapting them as necessary to fit the disciplinary context. We conducted a large scale empirical study to demonstrate that pseudo-code was an appropriate mechanism for achieving programming language independence. Finally, we established the validity of the assessment using a multi-faceted argument, combining interview data, statistical analysis of results on the assessment, and CS1 exam scores.


title: Halftone Visual Cryptography Via Error Diffusion

abstract: Halftone visual cryptography (HVC) enlarges the area of visual cryptography by the addition of digital halftoning techniques. In particular, in visual secret sharing schemes, a secret image can be encoded into halftone shares taking meaningful visual information. In this paper, HVC construction methods based on error diffusion are proposed. The secret image is concurrently embedded into binary valued shares while these shares are halftoned by error diffusion-the workhorse standard of halftoning algorithms. Error diffusion has low complexity and provides halftone shares with good image quality. A reconstructed secret image, obtained by stacking qualified shares together, does not suffer from cross interference of share images. Factors affecting the share image quality and the contrast of the reconstructed image are discussed. Simulation results show several illustrative examples.


title: High Precision Load Current Sensing Using On-Line Calibration of Trace Resistance

abstract: A method for the on-line calibration of a circuit board trace resistance at the output of a buck converter is described. The input current is measured with a precision resistor and processed to obtain a DC reference for the output current. The voltage drop across a trace resistance at the output is amplified with a gain that is adaptively adjusted to match the DC reference. This method is applied to obtain an accurate and high-bandwidth measurement of the load current in the modern microprocessor voltage regulator application (VRM), thus enabling an accurate DC load-line regulation as well as a fast transient response. Experimental results show an accuracy well within the tolerance band of this application, and exceeding all other popular methods.


title: Google Android a mobile platform: A review

abstract: At present smart phone usage is increasing dramatically due to their extended functionality than cell phones. Smartphones are like small computer which accompany us everywhere and allow us to access various functionalities. Smart phone is a personnel device which provides entertainment, information, making call, writing SMS and accessing different applications like check the email, to browse the Internet or to play games with our friends. We have to install applications on our smartphone in order to take all the advantage that these devices offer. The increasing importance of smart phones has increased competition among technology giants to take over the bigger part of the market share for mobile platform. As a result, in 2005 Google introduced Android (developed by Andy Rubin the Director of Mobile Platforms for Google), an open source mobile platform for smart phone devices which is consisting of a Linux Kernel, runtime environment, development framework, libraries and key applications. This paper aims to deal with the comparison between different smartphones like Android OS (Google), iOS (Apple), Symbian (Nokia) & Blackberry OS (RIM).


title: Location-Aware Recommendation Systems: Where We Are and Where We Recommend to Go

abstract: Recommendation systems have been successfully used to provide items of interest to the users (e.g., movies, music, books, news, images). However, traditional recommendation systems do not take into account the location as a relevant factor when providing suggestions. On the other hand, nowadays, there exist an increasing amount of georeferenced data and users are usually interested only in nearby items (e.g., restaurants, museums, cinemas). Hence, the emergence of location-aware recommendation systems have acquired a great attention by the research community in the last decade. In this paper, we provide a survey of location-aware recommendation systems in mobile computing scenarios. Firstly, we describe briey the fundamentals of recommendation systems. Then, we introduce some of the most relevant existing approaches for location-aware recommendation. Moreover, we present the main applications of this type of systems in several recommendation scenarios, such as music, news, restaurants, etc. Finally, we discuss new avenues and open issues in the area.


title: Modeling developable folds on a strip

abstract: A common operation in clothing and shoe design is to design a folding pattern over a narrow strip and then superimpose it with a smooth surface; the shape of the folding pattern is controlled by the boundary curve of the strip. Previous research results studying folds focused mostly on cloth modeling or in animations, which are driven more by visual realism, but allow large elastic deformations and usually completely ignore or avoid the surface developability issue. In reality, most materials used in garment and shoe industry are inextensible and uncompressible and hence any feasible folded surface must be developable, since it eventually needs to be flattened to its 2D pattern for manufacturing. Borrowing the classical boundary triangulation concept from descriptive geometry, this paper describes a computer-based method that automatically generates a specialized boundary triangulation approximation of a developable surface that interpolates a given strip. The development is achieved by geometrically simulating the folding process of the sheet as it would occur when rolled from one end of the strip to the other. Ample test examples are presented to validate the feasibility of the proposed method.


A Review of Detection and Tracking of Object from Image and Video Sequences


title: Federated cloud resource management

abstract: Federation of clouds is the future of cloud computing, mobile cloud computing, Internet of things, and big data applications. The utilization of federated resources is envisioned to increase the quality of service, cost benefits, and reliability. Resource management in the federation of multiple clouds is a pressing issue owing to the lack of cross-domain knowledge, security, trust, and administrative policies. This study classifies these resource management functions in the federated cloud environment into resource pricing, resource discovery, resource selection, resource monitoring, resource allocation, and disaster management. Each federated resource management function is discussed, and insights into state-of-the-art research are then provided. These resource management functions are also compared based on the performance metrics that are suitable for an individual function. Finally, we indicate the open challenges for further research with regard to each classified resource management function.


title: Managing Dynamic Risks in Global IT Projects: Agile Risk-Management using the Principles of Service-Oriented Architecture

abstract: Risk management in global information technology (IT) projects is becoming a critical area of concern for practitioners. Global IT projects usually span multiple locations involving various culturally diverse groups that use multiple standards and technologies. These multiplicities cause dynamic risks through interactions among internal (i.e., people, process, and technology) and external elements (i.e., business and natural environments) of global IT projects. This study proposes an agile risk-management framework for global IT project settings. By analyzing the dynamic interactions among multiplicities (e.g., multi-locations, multi-cultures, multi-groups, and multi-interests) embedded in the project elements, we identify the dynamic risks threatening the success of a global IT project. Adopting the principles of service-oriented architecture (SOA), we further propose a set of agile management strategies for mitigating the dynamic risks. The mitigation strategies are conceptually validated. The proposed framework will help practitioners understand the potential risks in their global IT projects and resolve their complex situations when certain types of dynamic risks arise.


title: Slanted Stixels: Representing San Francisco's Steepest Streets

abstract: In this work we present a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced that uses an extremely efficient over-segmentation. In doing so, the computational complexity of the Stixel inference algorithm is reduced significantly, achieving real-time computation capabilities with only a slight drop in accuracy. We evaluate the proposed approach in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset.


title: Gigantomastia with bilateral axillary breasts: Acute onset in pregnancy

abstract: The case of a young pregnant woman who developed an acute gigantomastia during pregnancy is presented, in addition to the unusual finding of bilateral axillary breasts. The condition was treated by simple mastectomy and immediate reconstruction with a subpectoral silicone implant. The literature is reviewed, and this rare combination of pathology is discussed.


title: A fuzzy linear regression model with interval type-2 fuzzy coefficients

abstract: This paper presents a regression model for crisp data with interval type-2 fuzzy coefficients based on the least squares estimation technique. The basic idea is to determine aggregation intervals for type-1 fuzzy sets, membership functions of whose are low membership function and upper membership function of interval type-2 fuzzy set. These aggregation intervals were called weighted intervals. Low and upper membership functions of coefficients for developed regression models are considered as piecewise linear functions.


title: The Impact of Experimental Setup in Prepaid Churn Prediction for Mobile Telecommunications : What to Predict , for Whom and Does the Customer Experience Matter ?

abstract: Prepaid customers in mobile telecommunications are not bound by a contract and can therefore change operators (‘churn’) at their convenience and without notification. This makes the task of predicting prepaid churn both challenging and financially rewarding. This paper presents an explorative, real world study of prepaid churn modeling by varying the experimental setup on three dimensions: data, outcome definition and population sample. Firstly, we add Customer Experience Management (CEM) data to data traditionally used for prepaid churn prediction. Secondly, we vary the outcome definition of prepaid churn. Thirdly, we alter the sample of the customers included, based on their inactivity period at the time of recording. While adding CEM parameters did not influence the predictability of churn, one variation on the sample and especially a particular change in the outcome definition had a substantial


title: focus security Misuse Cases: Use Cases with Hostile Intent Misuse cases—

abstract: Humans have analyzed negative scenarios ever since they first sat around Ice Age campfires debating the dangers of catching a woolly rhinoceros: "What if it turns and charges us before it falls into the pit?" A more recent scenario is "What if the hackers launch a denial-of-service attack?" Modern systems engineers can employ a misuse case, the negative form of a use case, to document and analyze such scenarios. A misuse case is simply a use case from the point of view of an actor hostile to the system under design. Misuse cases have many possible applications and interact with use cases in interesting and helpful ways. The paper discusses the elicitation of safety requirements from failure cases and considers the interplay of design, functional, and nonfunctional requirements.


title: Inducing probabilistic relational rules from probabilistic examples

abstract: We study the problem of inducing logic programs in a probabilistic setting, in which both the example descriptions and their classification can be probabilistic. The setting is incorporated in the probabilistic rule learner ProbFOIL+, which combines principles of the rule learner FOIL with ProbLog, a probabilistic Prolog. We illustrate the approach by applying it to the knowledge base of NELL, the Never-Ending Language Learner.


title: Mathematical Formula Identification in PDF Documents

abstract: Recognizing mathematical expressions in PDF documents is a new and important field in document analysis. It is quite different from extracting mathematical expressions in image-based documents. In this paper, we propose a novel method by combining rule-based and learning-based methods to detect both isolated and embedded mathematical expressions in PDF documents. Moreover, various features of formulas, including geometric layout, character and context content, are used to adapt to a wide range of formula types. Experimental results show satisfactory performance of the proposed method. Furthermore, the method has been successfully incorporated into a commercial software package for large-scale Chinese e-Book production.


title: Modelling Interaction of Sentence Pair with coupled-LSTMs

abstract: Recently, there is rising interest in modelling the interactions of two sentences with deep neural networks. However, most of the existing methods encode two sequences with separate encoders, in which a sentence is encoded with little or no information from the other sentence. In this paper, we propose a deep architecture to model the strong interaction of sentence pair with two coupled-LSTMs. Specifically, we introduce two coupled ways to model the interdependences of two LSTMs, coupling the local contextualized interactions of two sentences. We then aggregate these interactions and use a dynamic pooling to select the most informative features. Experiments on two very large datasets demonstrate the efficacy of our proposed architecture and its superiority to state-of-the-art methods.


title: Allelopathic Potential of Invasive Plantago virginica on Four Lawn Species

abstract: Plantago virginica L. has invaded many lawn ecosystems in the Eastern part of China. The invasion has incurred an economic cost to remove them. In order to prevent the invasion, it is critical to understand the invasive mechanisms of this species. However, few studies have been conducted on the allelopathic mechanisms of its invasion. In this study, we examined allelopathic effects of P. virginica on germination of seeds and growth of seedlings of four widely used lawn species. We found extensive allelopathic potential of P. virginica on other lawn species, which varied with species and developmental stage. While most effects of the extracts of P. virginica were inhibitory, some variables in some species were promoted by the addition of the extracts. The extracts of P. virginica significantly inhibited seed germination of Agrostis matsumurae. While the overall differences in seed germination rate of Poa annua were significant among treatments, difference between control and any of the treatments was not significant. The height of seedlings of A. matsumurae and Cynodon dactylon was significantly lower under the treatments of adding extracts of P. virginica. In contrast, growth of seedlings of Festuca elata and P. annua did not show significant differences among treatments. The root length of A. matsumurae, C. dactylon and P. annua was suppressed by the extracts of P. virginica whereas root length of F. elata was not affected. Aboveground biomass of A. matsumurae and F. elata was significantly higher than control, except for F. elata at the concentration of 50mg/mL, whereas aboveground biomass of C. dactylon and P. annua was reduced at higher concentrations of the extracts. Except for A. matsumurae, root biomass of the other three lawn species declined under the treatments with the extracts of P. virginica. Our results revealed that P. virginica had allelopathic potential on four lawn species and supported the theory of “novel weapons hypothesis”. Invasion by P. virginica in lawn can be moderated by selecting those species that are not affected or promotionally affected by it.


title: A Compact 6.5-W PHEMT MMIC Power Amplifier for Ku-Band Applications

abstract: A compact 6.5-W AlGaAs/InGaAs/GaAs PHEMT monolithic microwave integrated circuit (MMIC) power amplifier (PA) for Ku-band applications is proposed. This two-stage amplifier with chip size of 8.554mm2 (3.64mmtimes2.35mm) is designed to fully match 50-Omega input and output impedance. Under 8V and 2000mA dc bias condition, the PA deliver 38.1dBm (6.5W) saturated output power, 10.5-dB small signal gain and peak power added efficiency of 24.6% from 13.6 to 14.2GHz. This MMIC also achieved the best power densities (760mW/mm2) at Ku band reported to date


title: Digitalism: The New Realism?

abstract: Today's society is increasingly digitalised, with mobile smartphones being routinely carried and used by a significant percentage of the population. This provides an augmented experience for the individual that does not depend on their geographical separation with respect to their community of friends and other contacts. This changes the nature of relationships between people. Individuals may live in a "digital bubble", close to others physically, but far away from them in their digital world. More specifically, digital images can be generated and shared with ever greater ease. Sometimes the digital image takes on an important part of the individual's experience of reality. This paper explores examples of the phenomenon, within the context of the arts in particular and culture in general. We also consider the assortment of terms used in a variety of ways by researchers in different fields with regard to our ever more digital society, such as digitalism, digitality, digitalisation, digital culture, digital philosophy, etc. We survey these terms, exploring them from alternative viewpoints, including sociological and philosophical aspects, and attempt to pinpoint some of these terms more precisely, especially in a cultural and artistic context.


title: Archiving scientific data

abstract: Archiving is important for scientific data, where it is necessary to record all past versions of a database in order to verify findings based upon a specific version. Much scientific data is held in a hierachical format and has a key structure that provides a canonical identification for each element of the hierarchy. In this article, we exploit these properties to develop an archiving technique that is both efficient in its use of space and preserves the continuity of elements through versions of the database, something that is not provided by traditional minimum-edit-distance diff approaches. The approach also uses timestamps. All versions of the data are merged into one hierarchy where an element appearing in multiple versions is stored only once along with a timestamp. By identifying the semantic continuity of elements and merging them into one data structure, our technique is capable of providing meaningful change descriptions, the archive allows us to easily answer certain temporal queries such as retrieval of any specific version from the archive and finding the history of an element. This is in contrast with approaches that store a sequence of deltas where such operations may require undoing a large number of changes or significant reasoning with the deltas. A suite of experiments also demonstrates that our archive does not incur any significant space overhead when contrasted with diff approaches. Another useful property of our approach is that we use XML format to represent hierarchical data and the resulting archive is also in XML. Hence, XML tools can be directly applied on our archive. In particular, we apply an XML compressor on our archive, and our experiments show that our compressed archive outperforms compressed diff-based repositories in space efficiency. We also show how we can extend our archiving tool to an external memory archiver for higher scalability and describe various index structures that can further improve the efficiency of some temporal queries on our archive.


title: The Media Frames Corpus: Annotations of Frames Across Issues

abstract: We describe the first version of the Media Frames Corpus: several thousand news articles on three policy issues, annotated in terms of media framing. We motivate framing as a phenomenon of study for computational linguistics and describe our annotation process.


title: PID control applied on a line-follower AGV using a RGB camera

abstract: This paper aims to develop a control algorithm for a unicycle type AGV (Automated Guided Vehicle) to follow a trajectory predefined by a line avoiding oscillation on its movements. At the first tests the PID controller will be applied. On some applications cameras are used to help a robot follow a track (instead of infra red sensors), determining if it should keep a forward movement or turn to any sides. On this application, the camera is used in the PID control loop as a sensor to give feedback information, thus evaluating the efficiency of the control system allied with computer vision. This type of application simplifies the sensing system of a line follower AGV and makes possible to use the camera to other means, such as token and color identification. Considering a simplified model and a classic control technique, the results presented have showed stability on lower speeds.


title: Learning Multi-Relational Semantics Using Neural-Embedding Models

abstract: In this paper we present a unified framework for modeling multi-relational representations, scoring, and learning, and conduct an empirical study of several recent multi-relational embedding models under the framework. We investigate the different choices of relation operators based on linear and bilinear transformations, and also the effects of entity representations by incorporating unsupervised vectors pre-trained on extra textual resources. Our results show several interesting findings, enabling the design of a simple embedding model that achieves the new state-of-the-art performance on a popular knowledge base completion task evaluated on Freebase.


title: Addiction and the Brain Antireward System

abstract: A neurobiological model of the brain emotional systems has been proposed to explain the persistent changes in motivation that are associated with vulnerability to relapse in addiction, and this model may generalize to other psychopathology associated with dysregulated motivational systems. In this framework, addiction is conceptualized as a cycle of decreased function of brain reward systems and recruitment of antireward systems that progressively worsen, resulting in the compulsive use of drugs. Counteradaptive processes, such as opponent process, that are part of the normal homeostatic limitation of reward function fail to return within the normal homeostatic range and are hypothesized to repeatedly drive the allostatic state. Excessive drug taking thus results in not only the short-term amelioration of the reward deficit but also suppression of the antireward system. However, in the long term, there is worsening of the underlying neurochemical dysregulations that ultimately form an allostatic state (de...


title: The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices

abstract: Abstract In this paper, we propose a robust methodology to assess the value of microblogging data to forecast stock market variables: returns, volatility and trading volume of diverse indices and portfolios. The methodology uses sentiment and attention indicators extracted from microblogs (a large Twitter dataset is adopted) and survey indices (AAII and II, USMC and Sentix), diverse forms to daily aggregate these indicators, usage of a Kalman Filter to merge microblog and survey sources, a realistic rolling windows evaluation, several Machine Learning methods and the Diebold-Mariano test to validate if the sentiment and attention based predictions are valuable when compared with an autoregressive baseline. We found that Twitter sentiment and posting volume were relevant for the forecasting of returns of S&P 500 index, portfolios of lower market capitalization and some industries. Additionally, KF sentiment was informative for the forecasting of returns. Moreover, Twitter and KF sentiment indicators were useful for the prediction of some survey sentiment indicators. These results confirm the usefulness of microblogging data for financial expert systems, allowing to predict stock market behavior and providing a valuable alternative for existing survey measures with advantages (e.g., fast and cheap creation, daily frequency).


title: NPE: Neural Personalized Embedding for Collaborative Filtering

abstract: Matrix factorization is one of the most efficient approaches in recommender systems. However, such algorithms, which rely on the interactions between users and items, perform poorly for "cold-users" (users with little history of such interactions) and at capturing the relationships between closely related items. To address these problems, we propose a neural personalized embedding (NPE) model, which improves the recommendation performance for cold-users and can learn effective representations of items. It models a user's click to an item in two terms: the personal preference of the user for the item, and the relationships between this item and other items clicked by the user. We show that NPE outperforms competing methods for top-N recommendations, specially for cold-user recommendations. We also performed a qualitative analysis that shows the effectiveness of the representations learned by the model.


Oracle Quantum Computing


title: Hand Vein Detection using Infrared Light for Web based Account

abstract: The most common account based password used for authentication is Textual passwords. But Textual passwords are in risk to phishing attack, burette force attack, social engineering and shoulder surfing. Biometrics is used for human recognition which consists of authentication, verification and recognition. Biometric passwords are introduced as alternative techniques to textual passwords. Many biometric systems exist today by using fingerprint, face, iris, etc but risk to duplicating a fake (For e.g.:“fingerprintgummy finger”). Palm vein authentication is one of the modern biometric techniques, which employs the vein pattern in the human palm to verify the person. The merits of palm vein on classical biometric (e.g. fingerprint, iris, face) are a low risk of falsification, difficulty of duplicated and stability. In this propose method, detecting a hand vein by using Near Infrared (NIR) Light method for web based account. A CCD camera will capture the image of person's palm vein region. The captured image will process through Mat lab software and emphasize to get vein structure. The detected palm vein structure which acts as a password for web based account for password security.


title: DANN: a deep learning approach for annotating the pathogenicity of genetic variants

abstract: Summary: Annotating genetic variants, especially non-coding variants, for the purpose of identifying pathogenic variants remains a challenge. Combined annotation-dependent depletion (CADD) is an algorithm designed to annotate both coding and non-coding variants, and has been shown to outperform other annotation algorithms. CADD trains a linear kernel support vector machine (SVM) to differentiate evolutionarily derived, likely benign, alleles from simulated, likely deleterious, variants. However, SVMs cannot capture non-linear relationships among the features, which can limit performance. To address this issue, we have developed DANN. DANN uses the same feature set and training data as CADD to train a deep neural network (DNN). DNNs can capture non-linear relationships among features and are better suited than SVMs for problems with a large number of samples and features. We exploit Compute Unified Device Architecture-compatible graphics processing units and deep learning techniques such as dropout and momentum training to accelerate the DNN training. DANN achieves about a 19% relative reduction in the error rate and about a 14% relative increase in the area under the curve (AUC) metric over CADD’s SVM methodology. ::: ::: Availability and implementation: All data and source code are available at https://cbcl.ics.uci.edu/public_data/DANN/. ::: ::: Contact: ude.icu.sci@xhx


title: First Insights on a Passive Major Depressive Disorder Prediction System with Incorporated Conversational Chatbot

abstract: This publication has emanated from research conducted ::: with the financial support of Science Foundation Ireland (SFI) under Grant ::: Number SFI/12/RC/2289 (Insight).


title: Algorithm 980: Sparse QR Factorization on the GPU

abstract: Sparse matrix factorization involves a mix of regular and irregular computation, which is a particular challenge when trying to obtain high-performance on the highly parallel general-purpose computing cores available on graphics processing units (GPUs). We present a sparse multifrontal QR factorization method that meets this challenge and is significantly faster than a highly optimized method on a multicore CPU. Our method factorizes many frontal matrices in parallel and keeps all the data transmitted between frontal matrices on the GPU. A novel bucket scheduler algorithm extends the communication-avoiding QR factorization for dense matrices by exploiting more parallelism and by exploiting the staircase form present in the frontal matrices of a sparse multifrontal method.


title: Embryonic Inner Ear Cells Reaggregate into Specific Patterns In Vitro

abstract: The sensory epithelia of the mammalian inner ear consist of a highly precise pattern of sensory hair cells and supporting cells. The mechanisms regulating this patterning are only beginning to be determined. The present study describes a method for culturing dissociated embryonic inner ear cells and the resulting patterning that occurs in these cultures. The results indicate that developing inner ear cells aggregate into precise patterns on a two-dimensional substrate, suggesting that intrinsic patterning mechanisms remain active in vitro. Using antibodies and scanning electron microscopy to detect hair cells and nonsensory cells, it was determined that only a subset of aggregates contained sensory hair cells. The hair cells were organized into specific patterns and surrounded by supporting cells, similar to the in vivo pattern. Additionally, hair cells increased their immunoreactivity and number of stereocilia over time, suggesting that hair cells continue to mature in vitro. Thus, the study reveals that the cells of the developing inner ear provide the necessary signals that direct sensory hair cells and supporting cells to reassociate into very precise patterns in vitro and that these patterns are reminiscent of the patterning that occurs in vivo.


title: Object detection using gabor filters

abstract: Abstract This paper pertains to the detection of objects located in complex backgrounds. A feature-based segmentation approach to the object detection problem is pursued, where the features are computed over multiple spatial orientations and frequencies. The method proceeds as follows: a given image is passed through a bank of even-symmetric Gabor filters. A selection of these filtered images is made and each (selected) filtered image is subjected to a nonlinear (sigmoidal like) transformation. Then, a measure of texture energy is computed in a window around each transformed image pixel. The texture energy (“Gabor features”) and their spatial locations are inputted to a squared-error clustering algorithm. This clustering algorithm yields a segmentation of the original image—it assigns to each pixel in the image a cluster label that identifies the amount of mean local energy the pixel possesses across different spatial orientations and frequencies. The method is applied to a number of visual and infrared images, each one of which contains one or more objects. The region corresponding to the object is usually segmented correctly, and a unique signature of “Gabor features” is typically associated with the segment containing the object(s) of interest. Experimental results are provided to illustrate the usefulness of this object detection method in a number of problem domains. These problems arise in IVHS, military reconnaissance, fingerprint analysis, and image database query.


title: Change point localization in dependent dynamic nonparametric random dot product graphs

abstract: In this paper, we study the change point localization problem in a sequence of dependent nonparametric random dot product graphs (e.g. Young and Scheinerman, 2007). To be specific, assume that at every time point, a network is generated from a nonparametric random dot product graph model, where the latent positions are generated from unknown underlying distributions. The underlying distributions are piecewise constant in time and change at unknown locations, called change points. Most importantly, we allow for dependence among networks generated between two consecutive change points. This setting incorporates the edge-dependence within networks and across-time dependence between networks, which is the most flexible setting in the published literature. ::: To fulfill the task of consistently localizing change points, we propose a novel change point detection algorithm, consisting of two steps. First, we estimate the latent positions of the random dot product model, the theoretical result thereof is a refined version of the state-of-the-art results, allowing the dimension of the latent positions to grow unbounded. Then, we construct a nonparametric version of CUSUM statistic (e.g. Page,1954; Padilla et al. 2019) that can handle across-time dependence. The consistency is proved theoretically and supported by extensive numerical experiments, which outperform existing methods.


title: Models for Hyperspectral Image Analysis: From Unmixing to Object-Based Classification

abstract: The recent advances in hyperspectral remote sensing technology allow the simultaneous acquisition of hundreds of spectral wavelengths for each image pixel. This rich spectral information of the hyperspectral data makes it possible to discriminate different physical substances, leading to a potentially more accurate classification and thus opening the door to numerous new applications. Throughout the history of remote sensing research, numerous methods for hyperspectral image analysis have been presented. Depending on the spatial resolution of the images, specific mathematical models must be designed to effectively analyze the imagery. Some of these models operate at a sub-pixel level, trying to decompose a mixed spectral signature into its pure constituents, while others operate at a pixel or even object level, seeking to assign unique labels to every pixel or object in the scene. The spectral mixing of the measurements and the high dimensionality of the data are some of the challenging features of hyperspectral imagery. This chapter presents an overview of unmixing and classification methods, intended to address these challenges for accurate hyperspectral data analysis.


title: A Content-Based Indexing Scheme for Large-Scale Unstructured Data

abstract: The sheer volume of multimedia contents generated by today's Internet services are stored in the cloud. The traditional indexing method associating the user-generated metadata with the content is vulnerable to the inaccuracy caused by the low quality of the metadata. While the content-based indexing does not depend on the error-prone metadata. However, the state-of-the-art research focuses on developing descriptive features and miss the system-oriented considerations when incorporating these features into the practical cloud computing systems. We propose an Update-Efficient and Parallel-Friendly content-based multimedia indexing system, called Partitioned Hash Forest (PHF). The PHF system incorporates the state-of-the-art content-based indexing models and multiple system-oriented optimizations. PHF contains an approximate content-based index and leverages the hierarchical memory system to support the high volume of updates. Additionally, the content-aware data partitioning and lock-free concurrency management module enable the parallel processing of the concurrent user requests. We evaluate PHF in terms of indexing accuracy and system efficiency by comparing it with the state-of-the-art content-based indexing algorithm and its variances. We achieve the significantly better accuracy with less resource consumption, around 37% faster in update processing and up to 2.5X throughput speedup in a multi-core platform comparing to other parallel-friendly designs.


title: Sequence-to-Sequence Learning as Beam-Search Optimization

abstract: Sequence-to-Sequence (seq2seq) modeling has rapidly become an important general-purpose NLP tool that has proven effective for many text-generation and sequence-labeling tasks. Seq2seq builds on deep neural language modeling and inherits its remarkable accuracy in estimating local, next-word distributions. In this work, we introduce a model and beam-search training scheme, based on the work of Daume III and Marcu (2005), that extends seq2seq to learn global sequence scores. This structured approach avoids classical biases associated with local training and unifies the training loss with the test-time usage, while preserving the proven model architecture of seq2seq and its efficient training approach. We show that our system outperforms a highly-optimized attention-based seq2seq system and other baselines on three different sequence to sequence tasks: word ordering, parsing, and machine translation.


title: A Novel Compact Balanced-to-Unbalanced Low-Temperature Co-Fired Ceramic Bandpass Filter With Three Coupled Lines Configuration

abstract: This paper proposes a novel compact balanced-to-unbalanced bandpass filter. Firstly, a pre-design circuit is presented, which is composed of an inductive coupled-line bandpass filter and an out-of-phase capacitive coupled-line bandpass filter. A novel compact circuit with three coupled lines configuration, derived from the pre-design circuit, is then proposed for miniaturizing the balanced-to-unbalanced bandpass filter. A 2.4-GHz multilayer ceramic chip type balanced-to-unbalanced bandpass filter with a size of 2.0 mm times 1.2 mm times 0.7 mm is developed to validate the feasibility of the proposed structure. The filter is designed by using circuit simulation, as well as full-wave electromagnetic simulation softwares, and fabricated by the use of low-temperature co-fired ceramic technology. The measured results agree quite well with the simulated. According to the measurement results, the maximum insertion loss is 1.65 dB, the maximum in-band phase imbalance is within 3deg, and the maximum in-band magnitude imbalance is less than 0.32 dB.


title: Colour and its changes in chillies.

abstract: In this review, attention is focussed on the complexity of chilli pigments and their storage stability. The following sections are included: pigments of chillies; isolation and purification of chilli pigments; colour measurement of chillies; colour degradation of chillies; and effect of various factors on the colour retention of chillies (moisture content, temp., light, fat content, antioxidants).


title: maskSLIC: Regional Superpixel Generation with Application to Local Pathology Characterisation in Medical Images

abstract: Supervoxel methods such as Simple Linear Iterative Clustering (SLIC) are an effective technique for partitioning an image or volume into locally similar regions, and are a common building block for the development of detection, segmentation and analysis methods. We introduce maskSLIC an extension of SLIC to create supervoxels within regions-of-interest, and demonstrate,on examples from 2-dimensions to 4-dimensions, that maskSLIC overcomes issues that affect SLIC within an irregular mask. We highlight the benefits of this method through examples, and show that it is able to better represent underlying tumour subregions and achieves significantly better results than SLIC on the BRATS 2013 brain tumour challenge data (p=0.001) - outperforming SLIC on 18/20 scans. Finally, we show an application of this method for the analysis of functional tumour subregions and demonstrate that it is more effective than voxel clustering.


title: Semidirect posterior composite restorations with a flexible die technique

abstract: BACKGROUND AND OVERVIEW ::: Besides indirect use in the laboratory and direct use for restorations, composites can be used in semidirect procedures. The authors describe the semidirect composite restoration technique by using a flexible die for large lesions in posterior teeth. ::: ::: ::: CASE DESCRIPTION ::: The authors present illustrations of the clinical steps and the outcomes immediately after the procedures. The authors placed chairside inlay, onlay, and overlay composite restorations. The final esthetic outcome, along with function and anatomic form recovery, demonstrated that this might be a viable cost-effective alternative technique to laboratory-fabricated indirect restorations. ::: ::: ::: CONCLUSIONS AND PRACTICAL IMPLICATIONS ::: Clinicians can restore large preparations in posterior teeth successfully with semidirect composite restorations in a single appointment by using the flexible die technique, resulting in satisfactory function and esthetic outcome.


Diagnosis of Takotsubo Cardiomyopathy – Mayo Clinic Criteria –


title: Octopus: A Framework for Cost-Quality-Time Optimization in Crowdsourcing

abstract: Managing micro-tasks on crowdsourcing marketplaces involves balancing conflicting objectives -- the quality of work, total cost incurred and time to completion. Previous agents have focused on cost-quality, or cost-time tradeoffs, limiting their real-world applicability. As a step towards this goal we present Octopus, the first AI agent that jointly manages all three objectives in tandem. Octopus is based on a computationally tractable, multi-agent formulation consisting of three components; one that sets the price per ballot to adjust the rate of completion of tasks, another that optimizes each task for quality and a third that performs task selection. We demonstrate that Octopus outperforms existing state-of-the-art approaches in simulation and experiments with real data, demonstrating its superior performance. We also deploy Octopus on Amazon Mechanical Turk to establish its ability to manage tasks in a real-world, dynamic setting.


title: A 5.2-GHz CMOS T/R Switch for Ultra-Low-Voltage Operations

abstract: A novel CMOS transmit/receive (T/R) switch suitable for ultra-low-voltage operations is presented in this paper. Due to the use of resonators in the receiving and transmitting paths, enhanced performance in terms of insertion losses and isolation can be achieved. In addition, the forward-body-bias and body-floating techniques are also introduced to minimize the on-resistance of the MOSFETs at a reduced bias voltage. Using a standard 0.18-mum CMOS process, a 5.2-GHz asymmetric T/R switch based on the proposed architecture is implemented. With a supply voltage of 0.6 V, the fabricated circuit exhibits 1.56-dB insertion loss, 17-dB isolation, and 11.2-dBm in the receiving mode while the measured results in the transmitting mode are 2.02 dB, 31 dB, and 29.6 dBm, respectively.


title: Smart Home System Based on IOT Technologies

abstract: The idea of applying IOT technologies to smart home system is introduced. An original architecture of the integrated system is analyzed with its detailed introduction. This architecture has great scalability. Based on this proposed architecture many applications can be integrated into the system through uniform interface. Agents are proposed to communicate with appliances through RFID tags. Key issues to be solved to promote the development of smart home system are also discussed.


title: Learning Entity Types from Query Logs via Graph-Based Modeling

abstract: Entities (e.g., person, movie or place) play an important role in real-world applications and learning entity types has attracted much attention in recent years. Most conventional automatic techniques use large corpora, such as news articles, to learn types of entities. However, such text corpora focus on general knowledge about entities in an objective way. Hence, it is difficult to satisfy those users with specific and personalized needs for an entity. Recent years have witnessed an explosive expansion in the mining of search query logs, which contain billions of entities. The word patterns and click-throughs in search logs are not found in text corpora, thus providing a complemental source for discovering entity types based on user behaviors. In this paper, we study the problem of learning entity types from search query logs and address the following challenges: (1) queries are short texts, and information related to entities is usually very sparse; (2) large amounts of irrelevant information exists in search logs, bringing noise in detecting entity types. In this paper, we first model query logs using a bipartite graph with entities and their auxiliary information, such as contextual words and clicked URLs. Then we propose a graph-based framework called ELP (Ensemble framework based on Lable Propagation) to simultaneously learn the types of both entities and auxiliary signals. In ELP, two separate strategies are designed to fix the problems of sparsity and noise in query logs. Extensive empirical studies are conducted on real search logs to evaluate the effectiveness of the proposed ELP framework.


title: Exact Discovery of Time Series Motifs

abstract: Time series motifs are pairs of individual time series, or subsequences of a longer time series, which are very similar to each other. As with their discrete analogues in computational biology, this similarity hints at structure which has been conserved for some reason and may therefore be of interest. Since the formalism of time series motifs in 2002, dozens of researchers have used them for diverse applications in many different domains. ::: ::: Because the obvious algorithm for computing motifs is quadratic in the number of items, more than a dozen approximate algorithms to discover motifs have been proposed in the literature. In this work, for the first time, we show a tractable exact algorithm to find time series motifs. As we shall show through extensive experiments, our algorithm is up to three orders of magnitude faster than brute-force search in large datasets. ::: ::: We further show that our algorithm is fast enough to be used as a subroutine in higher level data mining algorithms for anytime classification, near-duplicate detection and summarization, and we consider detailed case studies in domains as diverse as electroencephalograph interpretation and entomological telemetry data mining.


title: Is it beneficial to use Internet-communication for escaping from boredom? Boredom proneness interacts with cue-induced craving and avoidance expectancies in explaining symptoms of Internet-communication disorder

abstract: The use of online-communication applications including messengers (e.g. WhatsApp) or social networking services (e.g. Facebook) on the smartphone has turned into daily practice for billions of people, for example during waiting times. An increasing number of individuals show diminished control over their usage of these applications despite negative consequences in everyday life. This can be referred to as Internet-communication disorder (ICD). The current study investigated the effect of boredom proneness on symptoms of an ICD. It further examined the mediating role of cognitive and affective mechanisms, namely expectancies to avoid negative feelings online and cue-induced craving. The results of a structural equation model (N = 148) illustrate that boredom proneness is a risk factor for the development and maintenance of an ICD as it had a significant direct effect on ICD symptoms. Furthermore, boredom proneness predicted avoidance expectancies as well as cue-induced craving. Both in turn enhanced the risk of developing ICD tendencies. Moreover, both variables mediated the effect of boredom proneness on ICD and interacted among each other. In summary, the results demonstrate that people who have a higher susceptibility to experience boredom show higher expectancies to avoid negative emotions online, which promotes higher craving reactions when being confronted with specific cues (e.g. an incoming message), and could result in ICD tendencies.


title: Instrumented insole vs. force plate: A comparison of center of plantar pressure

abstract: Instrumented insoles allow analysis of gait outside of the confines of a motion analysis lab and capture motion data on every step. This study assesses the concurrent validity of center of plantar pressure (COPP) measurements during walking, and shows that our custom instrumented insoles compare favorably to an Advanced Mechanical Technology Inc. (AMTI) force plate in a clinical motion laboratory, particularly when the large difference in price is considered (an insole is nearly two orders of magnitude less expensive than a force plate). Deploying inexpensive insoles such as ours for ubiquitous health monitoring allows measurement of gait in more typical environments. This affords the opportunity to evaluate the gait of older adults in the home environment, and a future opportunity of providing real-time feedback corresponding to changes in gait.


title: Effect of a finite ground plane on microstrip-fed cavity-backed slot antennas

abstract: This paper examines the effect of a finite ground plane on microstrip-fed cavity-backed slot antennas. The uniform geometrical theory of diffraction is employed for calculating the diffracted fields from the edges of a slot antenna's finite ground plane. Measured and simulated results are presented to illustrate the edge effect of a finite ground plane on a slot antenna's return loss and radiation pattern. It is demonstrated that the finite ground plane has a small effect on a slot antenna's radiation performance, which suggests that slot antennas are a superior candidate for mobile terminals compared to microstrip patch antennas.


title: Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments

abstract: Real-time electricity pricing models can potentially lead to economic and environmental advantages compared to the current common flat rates. In particular, they can provide end users with the opportunity to reduce their electricity expenditures by responding to pricing that varies with different times of the day. However, recent studies have revealed that the lack of knowledge among users about how to respond to time-varying prices as well as the lack of effective building automation systems are two major barriers for fully utilizing the potential benefits of real-time pricing tariffs. We tackle these problems by proposing an optimal and automatic residential energy consumption scheduling framework which attempts to achieve a desired trade-off between minimizing the electricity payment and minimizing the waiting time for the operation of each appliance in household in presence of a real-time pricing tariff combined with inclining block rates. Our design requires minimum effort from the users and is based on simple linear programming computations. Moreover, we argue that any residential load control strategy in real-time electricity pricing environments requires price prediction capabilities. This is particularly true if the utility companies provide price information only one or two hours ahead of time. By applying a simple and efficient weighted average price prediction filter to the actual hourly-based price values used by the Illinois Power Company from January 2007 to December 2009, we obtain the optimal choices of the coefficients for each day of the week to be used by the price predictor filter. Simulation results show that the combination of the proposed energy consumption scheduling design and the price predictor filter leads to significant reduction not only in users' payments but also in the resulting peak-to-average ratio in load demand for various load scenarios. Therefore, the deployment of the proposed optimal energy consumption scheduling schemes is beneficial for both end users and utility companies.


title: Back-EMF Harmonic Analysis and Fault-Tolerant Control of Flux-Switching Permanent-Magnet Machine With Redundancy

abstract: A flux-switching permanent-magnet (PM) (FSPM) machine is a new class of stator-PM brushless machines, which offers the advantages of high reliability, high power density, and high efficiency. In this paper, by employing redundant winding in the existing FSPM machine topology, a new redundant FSPM (R-FSPM) machine is proposed for a fault-tolerant operation of critical applications. The redundant winding configuration is analyzed, and the corresponding suitable control strategy for the postfault operation is proposed. The electromagnetic performance of the R-FSPM motor drive under normal and fault conditions is analyzed by using a cosimulation method coupling magnetic and electrical circuit solvers. Finally, a 12-stator-tooth/10-rotor-pole R-FSPM machine is prototyped for verification. Both simulation and experimental results are presented, verifying the fault-tolerant characteristic of the proposed motor drive.


title: PeMo: Modifying application's permissions and preventing information stealing on smartphones

abstract: Android OS protects user data and system resources with the help of permission based model which requires the android application developers to declare the resources to be used by their application well in advance. The system resources and the user's data would not be accessible to the developer unless the user grants the acceptance to the permissions being asked by the developer. In order to install the application on the mobile, the user has to allow access to all the requested permissions. There is no method of granting some permission to the application and denying rest of the others. Such a case leads to accepting the permissions like reading messages or accessing contacts etc. and puts the user's data on stake. In this paper we present a permission enforcement framework for android called PeMo, which will allow the user to selectively grant permissions to application according to the need of the application, as there are many applications on the Play Store those are asking for some permissions which are not required for their working. We will also show how these constraints can be set by the user through an easy to use interface. Our permission enforcement framework is implemented and tested on Android OS and is compatible with the current security mechanism. The permission enforcement framework would help user to protect their private data from being misuse.


title: Media framing and public attitudes toward biofuels.

abstract: Media framing and public opinion on the issue of biofuels - transportation fuels made from plants, animal products, or organic waste - is investigated. Through a detailed content analysis of newspaper coverage, the paper documents an increase in negative frames between 1999 and 2008, especially frames focusing on the negative economic effects of biofuels on consumers. Next, using data from a 2010 Internet survey of a random sample of the U.S. public, the paper analyzes the relative influence of these new media frames on public attitudes toward biofuels compared with other common predictors of public opinion, such as party ID, regional economic interests, and personal identity as an environmentalist. In general, the results confirm that public attitudes toward biofuels appear to be shaped by these new media frames, especially among those who indicate a high degree of attention to the media. This suggests the relative importance of framing effects on policy attitudes for environmental and energy policies in general.


title: A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue

abstract: Objective: Recently, gene expression profiling using microarray techniques has been shown as a promising tool to improve the diagnosis and treatment of cancer. Gene expression data contain high level of noise and the overwhelming number of genes relative to the number of available samples. It brings out a great challenge for machine learning and statistic techniques. Support vector machine (SVM) has been successfully used to classify gene expression data of cancer tissue. In the medical field, it is crucial to deliver the user a transparent decision process. How to explain the computed solutions and present the extracted knowledge becomes a main obstacle for SVM. Material and methods: A multiple kernel support vector machine (MK-SVM) scheme, consisting of feature selection, rule extraction and prediction modeling is proposed to improve the explanation capacity of SVM. In this scheme, we show that the feature selection problem can be translated into an ordinary multiple parameters learning problem. And a shrinkage approach: 1-norm based linear programming is proposed to obtain the sparse parameters and the corresponding selected features. We propose a novel rule extraction approach using the information provided by the separating hyperplane and support vectors to improve the generalization capacity and comprehensibility of rules and reduce the computational complexity. Results and conclusion: Two public gene expression datasets: leukemia dataset and colon tumor dataset are used to demonstrate the performance of this approach. Using the small number of selected genes, MK-SVM achieves encouraging classification accuracy: more than 90% for both two datasets. Moreover, very simple rules with linguist labels are extracted. The rule sets have high diagnostic power because of their good classification performance.


title: SAR imaging via modern 2-D spectral estimation methods

abstract: Abstract : This report discusses the use of modern 2-D spectral estimation algorithms for SAR imaging, and makes two principal contributions to the field of adaptive SAR imaging. First, it is a comprehensive comparison of 2-D spectral estimation methods for SAR imaging. It provides a synopsis of the algorithms available, discusses their relative merits for SAR imaging, and illustrates their performance on simulated and collected SAR imagery. The discussion of autoregressive linear predictive techniques (ARLP), including the Tufts Kumaresan variant, is somewhat more general than appears in most of the literature, in that it allows the prediction element to be varied throughout the subaperture. This generality leads to a theoretical link between ARLP and one of Pisarenko's methods. The report also provides a theoretical analysis that predicts the impact of the adaptive sidelobe reduction (ASR) algorithm on target to clutter ratio and provides insight into order and constraint selection. Second, this work develops multi-channel variants of three related algorithms, minimum variance method (MVM), reduced rank MVM (RRMVM), and ASR to estimate both reflectivity intensity and interferometric height from polarimetric displaced-aperture interferometric data. Examples illustrate that MVM and ASR both offer significant advantages over Fourier methods for estimating both scattering intensity and interferometric height, and allow empirical comparison of the accuracies of Fourier, MVM, and- ometric height estimates.


title: PEIR, the personal environmental impact report, as a platform for participatory sensing systems research

abstract: PEIR, the Personal Environmental Impact Report, is a participatory sensing application that uses location data sampled from everyday mobile phones to calculate personalized estimates of environmental impact and exposure. It is an example of an important class of emerging mobile systems that combine the distributed processing capacity of the web with the personal reach of mobile technology. This paper documents and evaluates the running PEIR system, which includes mobile handset based GPS location data collection, and server-side processing stages such as HMM-based activity classification (to determine transportation mode); automatic location data segmentation into "trips''; lookup of traffic, weather, and other context data needed by the models; and environmental impact and exposure calculation using efficient implementations of established models. Additionally, we describe the user interface components of PEIR and present usage statistics from a two month snapshot of system use. The paper also outlines new algorithmic components developed based on experience with the system and undergoing testing for integration into PEIR, including: new map-matching and GSM-augmented activity classification techniques, and a selective hiding mechanism that generates believable proxy traces for times a user does not want their real location revealed.


title: Revisiting the Strange Stories: Revealing Mentalizing Impairments in Autism

abstract: A test of advanced theory of mind (ToM), first introduced by F. Happe (1994), was adapted for children (mental, human, animal, and nature stories plus unlinked sentences). These materials were closely matched for difficulty and were presented to forty-five 7- to 12-year-olds with autism and 27 control children. Children with autism who showed ToM impairment on independent tests performed significantly more poorly than controls solely on the mental, human, and animal stories with greatest impairment on the former and least on the latter. Thus, a mentalizing deficit may affect understanding of biologic agents even when this does not explicitly require understanding others' mental states.


title: Intestinal microbial dysbiosis aggravates the progression of Alzheimer’s disease in Drosophila

abstract: Neuroinflammation caused by local deposits of Aβ42 in the brain is key for the pathogenesis and progression of Alzheimer’s disease. However, inflammation in the brain is not always a response to local primary insults. Gut microbiota dysbiosis, which is recently emerging as a risk factor for psychiatric disorders, can also initiate a brain inflammatory response. It still remains unclear however, whether enteric dysbiosis also contributes to Alzheimer’s disease. Here we show that in a Drosophila Alzheimer’s disease model, enterobacteria infection exacerbated progression of Alzheimer’s disease by promoting immune hemocyte recruitment to the brain, thereby provoking TNF-JNK mediated neurodegeneration. Genetic depletion of hemocytes attenuates neuroinflammation and alleviated neurodegeneration. We further found that enteric infection increases the motility of the hemocytes, making them more readily attracted to the brain with an elevated oxidative stress status. This work highlights the importance of gut–brain crosstalk as a fundamental regulatory system in modulating Alzheimer’s disease neurodegeneration. Emerging evidence suggests that gut microbiota influences immune function in the brain and may play a role in neurological diseases. Here, the authors offer in vivo evidence from a Drosophila model that supports a role for gut microbiota in modulating the progression of Alzheimer’s disease.


title: Gradient Descent Provably Optimizes Over-parameterized Neural Networks

abstract: One of the mysteries in the success of neural networks is randomly initialized first order methods like gradient descent can achieve zero training loss even though the objective function is non-convex and non-smooth. This paper demystifies this surprising phenomenon for two-layer fully connected ReLU activated neural networks. For an $m$ hidden node shallow neural network with ReLU activation and $n$ training data, we show as long as $m$ is large enough and no two inputs are parallel, randomly initialized gradient descent converges to a globally optimal solution at a linear convergence rate for the quadratic loss function. ::: Our analysis relies on the following observation: over-parameterization and random initialization jointly restrict every weight vector to be close to its initialization for all iterations, which allows us to exploit a strong convexity-like property to show that gradient descent converges at a global linear rate to the global optimum. We believe these insights are also useful in analyzing deep models and other first order methods.


title: Brain2Object: Printing Your Mind from Brain Signals with Spatial Correlation Embedding

abstract: Electroencephalography (EEG) signals are known to manifest differential patterns when individuals visually concentrate on different objects (e.g., a car). In this work, we present an end-to-end digital fabrication system , Brain2Object, to print the 3D object that an individual is observing by solely decoding visually-evoked EEG brain signal streams. We propose a unified training framework which combines multi-class Common Spatial Pattern and deep Convolutional Neural Networks to support the backend computation. Specially, a Dynamical Graph Representation of EEG signals is learned for accurately capturing the structured spatial correlations of EEG channels in an adaptive manner. A user friendly interface is developed as the system front end. Brain2Object presents a streamlined end-to-end workflow which can serve as a template for deeper integration of BCI technologies to assist with our routine activities. The proposed system is evaluated extensively using offline experiments and through an online demonstrator. For the former, we use a rich widely used public dataset and a limited but locally collected dataset. The experiment results show that our approach consistently outperforms a wide range of baseline and state-of-the-art approaches. The proof-of-concept corroborates the practicality of our approach and illustrates the ease with which such a system could be deployed.


title: AutoLoss: Learning Discrete Schedules for Alternate Optimization

abstract: Many machine learning problems involve iteratively and alternately optimizing different task objectives with respect to different sets of parameters. Appropriately scheduling the optimization of a task objective or a set of parameters is usually crucial to the quality of convergence. In this paper, we present AutoLoss, a meta-learning framework that automatically learns and determines the optimization schedule. AutoLoss provides a generic way to represent and learn the discrete optimization schedule from metadata, allows for a dynamic and data-driven schedule in ML problems that involve alternating updates of different parameters or from different loss objectives. We apply AutoLoss on four ML tasks: d-ary quadratic regression, classification using a multi-layer perceptron (MLP), image generation using GANs, and multi-task neural machine translation (NMT). We show that the AutoLoss controller is able to capture the distribution of better optimization schedules that result in higher quality of convergence on all four tasks. The trained AutoLoss controller is generalizable -- it can guide and improve the learning of a new task model with different specifications, or on different datasets.


title: Can Simple GAs Solve Beehive Hidato Logic Puzzles? The Influence of Diversity Preservation and Genetic Operators

abstract: Beehive Hidato is a fill-in logic puzzle, similar to Sudoku, with hexagonal grid cells. Some hexagons are pre-filled with fixed numbers, while the remaining has to be filled by the player such that consecutive numbers stay connected to form a “path”, from 1 to n, the largest number in the grid. Each Hidato problem has only one correct answer and, despite its simple rules, finding the solution for these problems can be quite challenging. In this work, we analyzed the importance of diversity preservation, as well as, the influence of commonly used permutation genetic operators in a simple genetic algorithm (GA) for solving Beehive Hidato problems. The algorithm was evaluated on 21 instances of Beehive Hidato problems, with different complexity levels, divided into two classes according to its size. We found PMX crossover and swap mutation as the best operators among the ones tested. Apart from that, the results indicate that the use of a diversity preservation technique has a significant role in GA performance, mainly for solving larger problem instances.


title: A Virtual Feedback Assistance System for Remote Operation of a 3DOF Micromanipulator in Micro-Nanorobotic Manipulation

abstract: Manipulation in micro or nanoscale with robotic manipulators under observation of electron microscopes is a widely used strategy for fabrication of nanodevices and nanoscale material property characterization. These types of manipulation systems can handle the relatively larger scale of objects. However, the complexity of manipulation increases highly for 3D manipulation. Since the manipulation system consists of multiple components including manipulator, microscope, and also some end-effector tools, a proper offline visualization of the system is necessary for operation. Therefore, we propose a web-based virtual interface between the user and the actual manipulator operated under digital microscope initially. It gives the operator 3D positional feedback from the virtual model by mapping data read during remote operation. The same interface is used for remote operation of the manipulator within the SEM chamber and a manipulation task is performed.


title: Shared Subway Shuttle Bus Route Planning Based on Transport Data Analytics

abstract: The development requirements of shared buses are extremely urgent to alleviate urban traffic congestions by improving road resource utilization and to provide a neotype transportation mode with good user experiences. The key to shared bus implementation lies in accurately predicting travel requirements and planning dynamic routes. However, the sparseness and the high volatility of shared bus data bring a great resistance to accurate prediction of travel requirements. Based on the consideration of user experiences, optimization objectives of shared bus route planning are significantly different from traditional public transportation and shared bus route planning is far more challenging than online car-hailing services due to the relatively high number of passengers. In this paper, we put forward a two-stage approach (SubBus), which is composed of travel requirement prediction and dynamic routes planning, based on various crowdsourced shared bus data to generate dynamic routes for shared buses in the “last mile” scene. First, we analyze the resident travel behaviors to obtain five predictive features, such as flow, time, week, location, and bus, and utilize them to predict travel requirements accurately based on a machine learning model. Second, we design a dynamic programming algorithm to generate dynamic, optimal routes with fixed destinations for multiple operating buses utilizing prediction results based on operating characteristics of shared buses. Extensive experiments are performed on real crowdsourced shared subway shuttle bus data and demonstrate that SubBus outperforms other methods on dynamic route planning for the “last mile” scene. Note to Practitioners —This paper is inspired by the problem of shared subway shuttle bus dynamic route planning for the “last mile” scene, and it is also applicable to other scenes, including commuting scenes, urban transportation hub scenes, and destination scenes of the tourist market. Shared bus operation routes at such scenes are usually aimed at trips with fixed destinations. Existing approaches to planning routes are generally designed for traditional transportation, such as traditional buses and taxis. In this paper, we propose a novel two-stage dynamic route planning approach (SubBus) based on the operation characteristics of shared subway shuttle buses. We perform a resident travel behavior analysis to improve the accuracy of travel requirement prediction. After that, we combine the prediction results and station properties to gain shared bus optimal routes. We then display how to apply SubBus to optimize shared bus operation status based on crowdsourced shared subway shuttle bus data generated by Panda Bus Company. We keep a continuous collaboration with the company to optimize the approach details and experimental effects, which demonstrate that our approach can generate effective routes for shared subway shuttle buses to optimize operation status on the “last mile” issue.