title: A Coupled Resonator Decoupling Network for Two-Element Compact Antenna Arrays in Mobile Terminals

abstract: A new concept for decoupling two coupled antenna elements in a broad band using a coupled resonator decoupling network (CRDN) is proposed for the first time. A synthesis and design theory of a CRDN is presented. Based on the admittance parameters of a given antenna array, a set of required rational functions and, consequently, the coupling matrix for a second-order decoupling network is obtained analytically. To prove the concept, two prototypes using microstrip resonators are designed and experimentally studied. Measurement results have demonstrated that an isolation improvement of more than 10 dB can be achieved within more than 15% bandwidth in both examples. The benefits of using a CRDN for different levels of isolation in a MIMO terminal are investigated through experiments and simulations. The results have shown that, as compared to the existing decoupling scheme using a lumped element, the proposed CRDN scheme can significantly increase the radiation efficiency, reduce the correlation, improve the channel capacity, and above all enhance the throughput of a MIMO terminal. The technique is general and can be applied to both symmetric and asymmetric arrays.


title: Ventral phalloplasty.

abstract: PURPOSE ::: To present the surgical technique of ventral phalloplasty as an adjunct procedure to the classic prosthetic surgery. ::: ::: ::: MATERIALS AND METHODS ::: In this video we demonstrate how to perform a ventral phalloplasty in a patient that has undergone a penile prosthesis implantation. Our technique consists of: delineation of the penile scrotal web, excision of this redundant skin, and re-approximation of the wound to mimic the natural median raphe. ::: ::: ::: RESULTS ::: The ventral phalloplasty improves the perception of phallic length, as well as patients' satisfaction after prosthetic surgery. ::: ::: ::: CONCLUSION ::: Penile length perception is the main concern of patients that have undergone penile prosthesis implantation. In this video we demonstrate that the ventral phalloplasty can improve perception of phallic length, and can be an important adjunct to the classic prosthetic surgery.


title: Wafer-level MEMS packaging via thermally released metal-organic membranes

abstract: This paper reports on the design, implementation and characterization of wafer-level packaging technology for a wide range of microelectromechanical system (MEMS) devices. The encapsulation technique is based on thermal decomposition of a sacrificial polymer through a polymer overcoat to form a released thin-film organic membrane with scalable height on top of the active part of the MEMS. Hermiticity and vacuum operation are obtained by thin-film deposition of a metal such as chromium, aluminum or gold. The thickness of the overcoat can be optimized according to the size of the device and differential pressure to package a wide variety of MEMS such as resonators, accelerometers and gyroscopes. The key performance metrics of several batches of packaged devices do not degrade as a result of residues from the sacrificial polymer. A Q factor of 5000 at a resonant frequency of 2.5 MHz for the packaged resonator, and a static sensitivity of 2 pF g −1 for the packaged accelerometer were obtained. Cavities as small as 0.000 15 mm 3 for the resonator and as large as 1 mm 3 for the accelerometer have been made by this method. (Some figures in this article are in colour only in the electronic version)


title: Deviceless edge computing: extending serverless computing to the edge of the network

abstract: The serverless paradigm has been rapidly adopted by developers of cloud-native applications, mainly because it relieves them from the burden of provisioning, scaling and operating the underlying infrastructure. In this paper, we propose a novel computing paradigm - Deviceless Edge Computing that extends the serverless paradigm to the edge of the network, enabling IoT and Edge devices to be seamlessly integrated as application execution infrastructure. We also discuss open challenges to realize Deviceless Edge Computing, based on our experience in prototyping a deviceless platform.


title: A Simple yet Effective Model for Zero-Shot Learning

abstract: Zero-shot learning has tremendous application value in complex computer vision tasks, e.g. image classification, localization, image captioning, etc., for its capability of transferring knowledge from seen data to unseen data. Many recent proposed methods have shown that the formulation of a compatibility function and its generalization are crucial for the success of a zero-shot learning model. In this paper, we formulate a softmax-based compatibility function, and more importantly, propose a regularized empirical risk minimization objective to optimize the function parameter which leads to a better model generalization. In comparison to eight baseline models on four benchmark datasets, our model achieved the highest average ranking. Our model was effective even when the training set size was small and significantly outperforming an alternative state-of-the-art model in generalized zero-shot recognition tasks.


L OSS-AWARE W EIGHT Q UANTIZATION OF D EEP N ET-WORKS


title: NeuRec: On Nonlinear Transformation for Personalized Ranking

abstract: Modeling user-item interaction patterns is an important task for personalized recommendations. Many recommender systems are based on the assumption that there exists a linear relationship between users and items while neglecting the intricacy and non-linearity of real-life historical interactions. In this paper, we propose a neural network based recommendation model (NeuRec) that untangles the complexity of user-item interactions and establish an integrated network to combine non-linear transformation with latent factors. We further design two variants of NeuRec: userbased NeuRec and item-based NeuRec, by focusing on different aspects of the interaction matrix. Extensive experiments on four real-world datasets demonstrated their superior performances on personalized ranking task.


title: Weakly-supervised Contextualization of Knowledge Graph Facts

abstract: Knowledge graphs (KGs) model facts about the world, they consist of nodes (entities such as companies and people) that are connected by edges (relations such as founderOf). Facts encoded in KGs are frequently used by search applications to augment result pages. When presenting a KG fact to the user, providing other facts that are pertinent to that main fact can enrich the user experience and support exploratory information needs. KG fact contextualization is the task of augmenting a given KG fact with additional and useful KG facts. The task is challenging because of the large size of KGs, discovering other relevant facts even in a small neighborhood of the given fact results in an enormous amount of candidates. We introduce a neural fact contextualization method (NFCM) to address the KG fact contextualization task. NFCM first generates a set of candidate facts in the neighborhood of a given fact and then ranks the candidate facts using a supervised learning to rank model. The ranking model combines features that we automatically learn from data and that represent the query-candidate facts with a set of hand-crafted features we devised or adjusted for this task. In order to obtain the annotations required to train the learning to rank model at scale, we generate training data automatically using distant supervision on a large entity-tagged text corpus. We show that ranking functions learned on this data are effective at contextualizing KG facts. Evaluation using human assessors shows that it significantly outperforms several competitive baselines.


title: Information Discovery in E-commerce: Half-day SIGIR 2018 Tutorial

abstract: E-commerce (electronic commerce or EC) is the buying and selling of goods and services, or the transmitting of funds or data online. E-commerce platforms come in many kinds, with global players such as Amazon, Airbnb, Alibaba, eBay, JD.com and platforms targeting specific markets such as Bol.com and Booking.com. Information retrieval has a natural role to play in e-commerce, especially in connecting people to goods and services. Information discovery in e-commerce concerns different types of search (exploratory search vs. lookup tasks), recommender systems, and natural language processing in e-commerce portals. Recently, the explosive popularity of e-commerce sites has made research on information discovery in e-commerce more important and more popular. There is increased attention for e-commerce information discovery methods in the community as witnessed by an increase in publications and dedicated workshops in this space. Methods for information discovery in e-commerce largely focus on improving the performance of e-commerce search and recommender systems, on enriching and using knowledge graphs to support e-commerce, and on developing innovative question-answering and bot-based solutions that help to connect people to goods and services. Below we describe why we believe that the time is right for an introductory tutorial on information discovery in e-commerce, the objectives of the proposed tutorial, its relevance, as well as more practical details, such as the format, schedule and support materials.


A Degeneracy Framework for Graph Similarity


Modeling Time-Frequency Patterns with LSTM vs. Convolutional Architectures for LVCSR Tasks


title: Learning to detect unseen object classes by between-class attribute transfer

abstract: We study the problem of object classification when training and test classes are disjoint, i.e. no training examples of the target classes are available. This setup has hardly been studied in computer vision research, but it is the rule rather than the exception, because the world contains tens of thousands of different object classes and for only a very few of them image, collections have been formed and annotated with suitable class labels. In this paper, we tackle the problem by introducing attribute-based classification. It performs object detection based on a human-specified high-level description of the target objects instead of training images. The description consists of arbitrary semantic attributes, like shape, color or even geographic information. Because such properties transcend the specific learning task at hand, they can be pre-learned, e.g. from image datasets unrelated to the current task. Afterwards, new classes can be detected based on their attribute representation, without the need for a new training phase. In order to evaluate our method and to facilitate research in this area, we have assembled a new large-scale dataset, “Animals with Attributes”, of over 30,000 animal images that match the 50 classes in Osherson's classic table of how strongly humans associate 85 semantic attributes with animal classes. Our experiments show that by using an attribute layer it is indeed possible to build a learning object detection system that does not require any training images of the target classes.


title: Non-Local Auto-Encoder With Collaborative Stabilization for Image Restoration

abstract: Deep neural networks have been applied to image restoration to achieve the top-level performance. From a neuroscience perspective, the layerwise abstraction of knowledge in a deep neural network can, to some extent, reveal the mechanisms of how visual cues are processed in human brain. A pivotal property of human brain is that similar visual cues can stimulate the same neuron to induce similar neurological signals. However, conventional neural networks do not consider this property, and the resulting models are, as a result, unstable regarding their internal propagation. In this paper, we develop the (stacked) non-local auto-encoder, which exploits self-similar information in natural images for stability. We propose that similar inputs should induce similar network propagation. This is achieved by constraining the difference between the hidden representations of non-local similar image blocks during training. By applying the proposed model to image restoration, we then develop a collaborative stabilization step to further rectify forward propagation. To obtain a reliable deep model, we employ several strategies to simplify training and improve testing. Extensive image restoration experiments, including image denoising and super-resolution, demonstrate the effectiveness of the proposed method.


title: No-reference perceptual quality assessment of JPEG compressed images

abstract: Human observers can easily assess the quality of a distorted image without examining the original image as a reference. By contrast, designing objective No-Reference (NR) quality measurement algorithms is a very difficult task. Currently, NR quality assessment is feasible only when prior knowledge about the types of image distortion is available. This research aims to develop NR quality measurement algorithms for JPEG compressed images. First, we established a JPEG image database and subjective experiments were conducted on the database. We show that Peak Signal-to-Noise Ratio (PSNR), which requires the reference images, is a poor indicator of subjective quality. Therefore, tuning an NR measurement model towards PSNR is not an appropriate approach in designing NR quality metrics. Furthermore, we propose a computational and memory efficient NR quality assessment model for JPEG images. Subjective test results are used to train the model, which achieves good quality prediction performance.


title: Privacy for Smart Meters: Towards Undetectable Appliance Load Signatures

abstract: Smart grid privacy encompasses the privacy of information extracted by analysing smart metering data. In this paper, we suggest that home electrical power routing can be used to moderate the home's load signature in order to hide appliance usage information. In particular, 1) we introduce a power management model using a rechargeable battery, 2) we propose a power mixing algorithm, and 3) we evaluate its protection level by proposing three different privacy metrics: an information theoretic (relative entropy), a clustering classification, and a correlation/regression one; these are tested on different metering datasets. This paper sets the ground for further research on the subject of optimising home energy management with regards to hiding load signatures.


title: Multi-Task Rank Learning for Image Quality Assessment

abstract: In practice, images are distorted by more than one distortion. For image quality assessment (IQA), existing machine learning (ML)-based methods generally establish a unified model for all the distortion types, or each model is trained independently for each distortion type, which is therefore distortion aware. In distortion-aware methods, the common features among different distortions are not exploited. In addition, there are fewer training samples for each model training task, which may result in overfitting. To address these problems, we propose a multi-task learning framework to train multiple IQA models together, where each model is for each distortion type; however, all the training samples are associated with each model training task. Thus, the common features among different distortion types and the said underlying relatedness among all the learning tasks are exploited, which would benefit the generalization ability of trained models and prevent overfitting possibly. In addition, pairwise image quality ranking instead of image quality rating is optimized in our learning task, which is fundamentally departed from traditional ML-based IQA methods toward better performance. The experimental results confirm that the proposed multi-task rank-learning-based IQA metric is prominent against all state-of-the-art nonreference IQA approaches.


Recent Advances in Clustering: A Brief Survey


title: Mitigating DoS attacks against signature-based authentication in VANETs

abstract: Vehicular ad hoc networks (VANETs) are supposed to improve traffic safety and drivers' experiences. In a typical VANET, a vehicle broadcasts safety messages to its neighbors. Since behaviors based on safety messages could be life-critical, authentication of these messages must be guaranteed. Many signature-based schemes have been proposed for authentication in VANETs, but few of them have addressed the problem of denial of service (DoS) attacks against signature-based authentication. In such a DoS attack, attackers can broadcast forged messages with invalid signatures to force the receiving vehicles to perform lots of unnecessary signature verifications, and thus the benign vehicles cannot verify the messages from other legitimate vehicles. Our scheme features a pre-authentication process before signature verifying process to deal with this kind of DoS attack. The pre-authentication process takes advantage of the one-way hash chain and a group rekeying scheme. Evaluations show that the proposed scheme mitigates such DoS attacks effectively.


title: Nonlinear Co-simulation of Component and System Using a New Parameter for WiMAX

abstract: Many Low Noise Amplifier (LNA) are used in the transceiver systems of Worldwide Interoperability for Microwave Access (WiMAX). Error Vector Magnitude (EVM) that is caused by a nonlinear distortion of the LNA degrades the quality of WiMAX systems. In general, the LNA design using a linear model (S-parameter) has not been focusing on the EVM, although focusing on the gain and Noise Figure (NF).A co-simulation of the components and system was carried out to evaluate the EVM of the WiMAX system using the LNA. In the co-simulation of the linear model components and systems, no saturation was observed for the EVM. This meant the co-simulation using the linear model was not effective for the design of the EVM.


abstract: Comics on the Brain: Structure and Meaning in Sequential Image Comprehension Neil Cohn (neil.cohn@tufts.edu), Martin Paczynski, Phil Holcomb Department of Psychology, 490 Boston Ave Medford, MA 02155 USA Ray Jackendoff Center for Cognitive Studies, Miner Hall Medford, MA 02155 USA Gina Kuperberg Department of Psychiatry and Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital, Bldg 149, 13th Street Charlestown, MA 02129 USA Abstract Background Research on language comprehension has long drawn a distinction between meaning and structure. Most theoretical work pertaining to this separation has been at the level of the single sentence (Chomsky, 1965). However, this general divide is also relevant our understanding of how we make sense of text and discourse, beyond the single sentence. Much work on text and discourse has emphasized the local relationships which draw together related concepts into common semantic fields (Halliday & Hasan, 1985; van Dijk, 1977) or through the establishment of coherence relationships across individual sentences (Halliday & Hasan, 1976; Zwaan & Radvansky, 1998). Theories of narrative with a more global scope have also been articulated in “story grammars” (e.g. Mandler & Johnson, 1977; Rumelhart, 1975; Thorndyke, 1977), which base individual narrative categories around characters’ goals. These categories were organized within a formalistic generative grammar which imposed a global structure for understanding how characters navigated through events. Similar to verbal discourse, most theories describing sequential image structure have also focused on linear semantic relationships between individual images (McCloud, 1993). Recently though, Cohn (2003, In prep) has proposed a theoretical model for the structure of graphic narratives. Like story grammars’ treatment of Just as syntax differentiates coherent sentences from scrambled word strings, sequential images must also use a cognitive system to distinguish coherent narratives from random strings of images. We conducted experiments analogous to two classic psycholinguistic studies to examine structure and semantics in sequential images. We compared Normal comic strips with both structure and meaning to sequences with Semantics Only, Structure Only, or randomly Scrambled panels. Experiment 1 used a target-monitoring paradigm, and found that RTs were slowest to target panels in Scrambled sequences, intermediate in Structural Only and Semantic Only sequences, and fastest in Normal sequences. Experiment 2 measured ERPs to the same strips. The largest N400 appeared in Scrambled and Structural Only sequences, intermediate in Semantic Only sequences and smallest in Normal sequences. Together, these findings suggest that sequential image comprehension is guided by an interaction between a structure and meaning, broadly analogous to syntax and semantics in language. Keywords: Narrative; Comics; Visual Language; Discourse; ERPs; N400 Introduction Drawings have been conveying narratives through sequences of images for millennia, whether painted on cave walls, carved into reliefs, hung on medieval tapestries, or, in their modern context, appearing in comic books (McCloud, 1993). Compared to research on the structure and comprehension of verbal narrative, however, few studies have examined the driving forces behind understanding this visual language: what are the representations and mechanisms engaged during sequential image comprehension? And how is the meaning integrated with structure across a sequence of images?


title: Parallel and serial processes in visual search

abstract: How does the mind open up to acquire information? Arguably, this remains one of the central unresolved issues in modern psychology. With the rise of the information-processing paradigm in the social sciences, the issue of acquisition was reframed in terms of a one versus many distinction. Accordingly, the central problem of attention research became developing principled accounts of serial and parallel processing (Sternberg, 1966, 1975). For well over 2 decades, this dichotomy captivated much of the relevant psychophysical research and figured predominantly in numerous attempts to understand how attention operated during search (e.g., feature-integration theory: Treisman & Gelade, 1980; guided search: Wolfe, Cave, & Franzel, 1989). Although not always made explicit, the search problem was formulated in terms of single foveations and how attentional resources—and not the eyes per se—are directed. Unfortunately, it was soon realized (and slowly accepted) that the visual search method on which the early theories were built is inherently flawed—in short, the methods in use simply did not allow the serial–parallel distinction to be made (Townsend, 1972, 1974, 1990), provided that it existed in the


title: iWander: An Android application for dementia patients

abstract: Non-pharmacological management of dementia puts a burden on those who are taking care of a patient that suffer from this chronic condition. Caregivers frequently need to assist their patients with activities of daily living. However, they are also encouraged to promote functional independence. With the use of a discrete monitoring device, functional independence is increased among dementia patients while decreasing the stress put on caregivers. This paper describes a tool which improves the quality of treatment for dementia patients using mobile applications. Our application, iWander, runs on several Android based devices with GPS and communication capabilities. This allows for caregivers to cost effectively monitor their patients remotely. The data uncollected from the device is evaluated using Bayesian network techniques which estimate the probability of wandering behavior. Upon evaluation several courses of action can be taken based on the situation's severity, dynamic settings and probability. These actions include issuing audible prompts to the patient, offering directions to navigate them home, sending notifications to the caregiver containing the location of the patient, establishing a line of communication between the patient-caregiver and performing a party call between the caregiver-patient and patient's local 911. As patients use this monitoring system more, it will better learn and identify normal behavioral patterns which increases the accuracy of the Bayesian network for all patients. Normal behavior classifications are also used to alert the caregiver or help patients navigate home if they begin to wander while driving allowing for functional independence.


Ten Choices for Lexical Semantics


title: On Millimeter Wave and THz Mobile Radio Channel for Smart Rail Mobility

abstract: As a widely acknowledged efficient and green transportation model, rail traffic is expected to evolve into a new era of “smart rail mobility” whereby infrastructure, trains, and travelers will be interconnected to achieve optimized mobility, higher safety, and lower costs. Thus, a seamless high-data rate wireless connectivity with up to dozens of gigahertz bandwidth is required. Such a huge bandwidth requirement motivates the exploration of the underutilized millimeter (mm) wave and terahertz (THz) bands. In this paper, the motivations for developing millimeter wave and THz communications for railway are clarified by first defining the applications and scenarios required for smart rail mobility. Ray-tracing simulations at 100 GHz imply that to form high-gain directional antenna beams, dynamic beamforming strategies and advanced handover design are critical for the feasibility of THz communications to enable smart rail mobility.


title: The LV dataset: A realistic surveillance video dataset for abnormal event detection

abstract: In recent years, designing and testing video anomaly detection methods have focused on synthetic or unrealistic sequences. This has mainly four drawbacks: 1) events are controlled and predictable because they are usually performed by actors; 2) environmental conditions, e.g. camera motion and illumination, are usually ideal thus realistic conditions are not well reflected; 3) events are usually short and repetitive; and 4) the material is captured from scenarios that do not necessarily match the testing scenarios. This leads us to propose a new rich collection of realistic videos captured by surveillance cameras in challenging environmental conditions, the Live Videos (LV) dataset. We explore the performance of a number of state-of-the-art video anomaly detection methods on the LV dataset. Our results confirm the need to design methods that are capable of handling realistic videos captured by surveillance cameras with acceptable processing times. The proposed LV dataset, thus, will facilitate the design and testing of such new methods.


title: Weight, weight change, and coronary heart disease in women. Risk within the 'normal' weight range.

abstract: OBJECTIVE--To assess the validity of the 1990 US weight guidelines for women that support a substantial gain in weight at approximately 35 years of age and recommend a range of body mass index (BMI) (defined as weight in kilograms divided by the square of height in meters) from 21 to 27 kg/m2, in terms of coronary heart disease (CHD) risk in women. DESIGN--Prospective cohort study. SETTING--Female registered nurses in the United States. PARTICIPANTS--A total of 115,818 women aged 30 to 55 years in 1976 and without a history of previous CHD. MAIN OUTCOME MEASURE--Incidence of CHD defined as nonfatal myocardial infarction or fatal CHD. RESULTS--During 14 years of follow-up, 1292 cases of CHD were ascertained. After controlling for age, smoking, menopausal status, postmenopausal hormone use, and parental history of CHD and using as a reference women with a BMI of less than 21 kg/m2, relative risks (RRs) and 95% confidence intervals (CIs) for CHD were 1.19 (0.97 to 1.44) for a BMI of 21 to 22.9 kg/m2, 1.46 (1.20 to 1.77) for a BMI of 23 to 24.9 kg/m2, 2.06 (1.72 to 2.48) for a BMI of 25 to 28.9 kg/m2, and 3.56 (2.96 to 4.29) for a BMI of 29 kg/m2 or more. Women who gained weight from 18 years of age were compared with those with stable weight (+/- 5 kg) in analyses that controlled for the same variables as well as BMI at 18 years of age. The RRs and CIs were 1.25 (1.01 to 1.55) for a 5- to 7.9-kg gain, 1.64 (1.33 to 2.04) for an 8- to 10.9-kg gain, 1.92 (1.61 to 2.29) for an 11- to 19-kg gain, and 2.65 (2.17 to 3.22) for a gain of 20 kg or more. Among women with the BMI range of 18 to 25 kg/m2, weight gain after 18 years of age remained a strong predictor of CHD risk. CONCLUSIONS--Higher levels of body weight within the "normal" range, as well as modest weight gains after 18 years of age, appear to increase risks of CHD in middle-aged women. These data provide evidence that current US weight guidelines may be falsely reassuring to the large proportion of women older than 35 years who are within the current guidelines but have potentially avoidable risks of CHD.


title: Wireless node localization based on RSSI using a rotating antenna on a mobile robot

abstract: Determining the physical location of radio nodes is crucial in a wireless sensor network in order to geo-reference the sensor information. This paper presents a localization technique using a mobile radio node attached to a robot. Its position is given by a real time kinematic (RTK) GPS. We propose a new method to obtain the relative bearing between the mobile and the static node using commodity radio hardware. The method exploits the anisotropy of the antenna gain and is not only more precise than the mapping of the RSSI to distance only, it also allows to monitor the integrity of the measurement. Experimental results in an outdoor environment show a standard deviation of 5. for the angle of arrival. The applicability of the approach for the localization of static radio nodes is demonstated by means of a particle filter. The mean error of the position for experimental data is approximately 13 cm.


title: Contraception use and pregnancy among 15–24 year old South African women: a nationally representative cross-sectional survey

abstract: Background ::: Adolescent reproductive health has not continued to receive the attention it deserves since the start of the HIV epidemic. In South Africa, high numbers of adolescent women report pregnancies that are unwanted and yet few have accessed available termination of pregnancy services. Enabling contraception use is vital for meeting the goals of HIV prevention.


title: A backward pass through a CNN using a generative model of its activations

abstract: Neural networks have shown to be a practical way of building a very complex mapping between a pre-specified input space and output space. For example, a convolutional neural network (CNN) mapping an image into one of a thousand object labels is approaching human performance in this particular task. However the mapping (neural network) does not automatically lend itself to other forms of queries, for example, to detect/reconstruct object instances, to enforce top-down signal on ambiguous inputs, or to recover object instances from occlusion. One way to address these queries is a backward pass through the network that fuses top-down and bottom-up information. In this paper, we show a way of building such a backward pass by defining a generative model of the neural network's activations. Approximate inference of the model would naturally take the form of a backward pass through the CNN layers, and it addresses the aforementioned queries in a unified framework.


title: Automatic Three-Dimensional Cephalometric Annotation System Using Three-Dimensional Convolutional Neural Networks

abstract: ABSTRACTAutomatic annotation for three-dimensional (3D) cephalometric analysis has been limited by computational complexity and computing performance. The purpose of this study was to evaluate the ...


Do Motion Boundaries Improve Semantic Segmentation


title: Internet of Things application for implementation of smart agriculture system

abstract: Over the past few years, there has been significant interest in designing smart agricultural systems. The use of smart farming techniques can enhance the crop yield, while simultaneously generating more output from the same amount of input. But still, most of the farmers are unaware of the latest technologies and practices. In this paper a novel wireless mobile robot based on Internet of Things (IoT) is designed and implemented for performing various operations on the field. This proposed wireless robot is equipped with various sensors for measuring different environmental parameters. It also includes Raspberry Pi 2 model B hardware for executing the whole process. The main features of this novel intelligent wireless robot is that it can execute tasks such as moisture sensing, scaring birds and animals, spraying pesticides, moving forward or backward and switching ON/OFF electric motor. The robot is fitted with a wireless camera to monitor the activities in real time. The proposed wireless mobile robot has been tested in the fields, readings have been monitored and satisfactory results have been observed, which indicate that this system is very much useful for smart agricultural systems.


title: Linux kernel vulnerabilities: State-of-the-art defenses and open problems

abstract: Avoiding kernel vulnerabilities is critical to achieving security of many systems, because the kernel is often part of the trusted computing base. This paper evaluates the current state-of-the-art with respect to kernel protection techniques, by presenting two case studies of Linux kernel vulnerabilities. First, this paper presents data on 141 Linux kernel vulnerabilities discovered from January 2010 to March 2011, and second, this paper examines how well state-of-the-art techniques address these vulnerabilities. The main findings are that techniques often protect against certain exploits of a vulnerability but leave other exploits of the same vulnerability open, and that no effective techniques exist to handle semantic vulnerabilities---violations of high-level security invariants.


title: Management Rules to Detect Melanoma

abstract: Most melanomas are easy to be diagnosed clinically and dermoscopically. The question remains open concerning the correct strategies to detect those melanomas that look morphologically inconspicuous from a clinical and/or dermoscopic point of view. In our estimation, when morphology is not enough to recognize melanoma, one has to use specific management strategies. Herein we summarize the following 7 simple and practical rules that outline the need for a more general approach integrating clinical information with dermoscopic examination: (1) Look basically at all lesions. (2) Undress high-risk patients. (3) Use the 10 seconds rule in single lesions. (4) Compare and monitor multiple moles. (5) Excise doubtful nodular lesions. (6) Combine clinical and dermoscopic criteria. (7) Combine clinical and histopathologic criteria.


title: A model-driven framework for enterprise service management

abstract: With a steady pace of adoption of service-oriented architecture, enterprises have made significant progresses in implementing various kinds of Web services and converting existing applications to service-oriented architecture. As a significant number of services have been put into actual use, many service-oriented enterprises are faced with the problem of how to manage these services efficiently. In this paper, we propose a model-driven framework for a more efficient management of these services. In this framework, the creation and maintenance of enterprise integration solutions are modeled by flows, finite state machines (FSMs), role-based access control (RBAC) among other formal systems models as well as appropriate decision models. For instance, each enterprise integration solution would be modeled as composite services that can be described by respective flow, FSM and RBAC models. These solution models can then be stored, and later retrieved for the execution of these composite services. Furthermore, formal systems and decision models are also used to maintain and update these service-oriented solutions to improve the efficiency and quality of service management by taking advantage of the underlying service-oriented architecture.


title: Comprehensive Evaluation of Rectangular and Double-D Coil Geometry for 50 kW/85 kHz IPT System

abstract: In this paper, the influence of the inductive power transfer (IPT) coil geometry on the performance factors efficiency, power density, and stray field is studied for a public transport electric vehicle battery charging system. IPT coil geometries with rectangular winding and with double-D winding are compared based on the Pareto fronts obtained from a multi-objective optimization. In order to study the effect of the winding layout experimentally, two full-scale 50 kW/85 kHz hardware prototypes with the same outer coil dimensions ( $410\times 760\times 60$ mm3) and ferrite core structure are constructed. For both the prototypes, the measured dc–dc efficiency is approximately 95.5% at 50 kW with a 160 mm air gap and ideally positioned coils, which confirms the calculations. The positioning tolerance of the double-D prototype is inferior, because with coil misalignment the efficiency decays faster than for the rectangular winding prototype. Flux density measurements show that both the prototypes fulfill the ICNIRP 2010 standard at 800 mm lateral distance from the coil center. However, the measured magnetic stray field is a factor of two lower for the double-D prototype, which is a key advantage in high-power applications.


title: A low power, high accuracy amperometric potentiostat for NOx gas sensors

abstract: A low power, 1-V and an area efficient CMOS amperometric potentiostat is designed for gas sensors exploiting the benefits of room temperature ionic liquids. A regulated cascode current mirror is used as current replicating interface, which results in better performance in terms of gain, input/output resistance, bandwidth and linearity. The potentiostat is implemented in 65 nm CMOS standard process, resulting in detection of redox current of 0.2 nA–12.3 μA and having a power consumption of 11.5 μW, while having an area of 0.018 mm2.


title: Advanced approach for indoor identification and tracking using smart floor and pyroelectric infrared sensors

abstract: Research concern has been specified on automatic identification and tracking of people within the home environment to support smart home services. Although various approaches have been proposed to deal with this problem, solutions still remain untackled due to various reasons (e.g. user acceptance). The aim of this paper is to develop and implement an advanced approach that is able to accurately detect, identify, and track people within appropriate smart home environment. A novel approach for human identification and tracking within smart home environment is proposed. The proposed approach combined PIR sensors and floor pressure through unique designed integration strategy aiming to unite the advantages of the two sensor types and overcome their limitations.


title: The Common Traits of the ACC and PFC in Anxiety Disorders in the DSM-5: Meta-Analysis of Voxel-Based Morphometry Studies

abstract: Background ::: The core domains of social anxiety disorder (SAD), generalized anxiety disorder (GAD), panic disorder (PD) with and without agoraphobia (GA), and specific phobia (SP) are cognitive and physical symptoms that are related to the experience of fear and anxiety. It remains unclear whether these highly comorbid conditions that constitute the anxiety disorder subgroups of the Diagnostic and Statistical Manual for Mental Disorders – Fifth Edition (DSM-5) represent distinct disorders or alternative presentations of a single underlying pathology.


title: Big Data: The NoSQL and RDBMS review

abstract: The quantity of data transmitted in the network intensified rapidly with the increased dependency on social media applications, sensors for data acquisitions and smartphones utilizations. Typically, such data is unstructured and originates from multiple sources in different format. Consequently, the abstraction of data for rendering is difficult, that lead to the development of a computing system that is able to store data in unstructured format and support distributed parallel computing. To data, there exist approaches to handle big data using NoSQL. This paper provides a review and the comparison between NoSQL and Relational Database Management System (RDBMS). By reviewing each approach, the mechanics of NoSQL systems can be clearly distinguished from the RDBMS. Basically, such systems rely on multiple factors, that include the query language, architecture, data model and consumer API. This paper also defines the application that matches the system and subsequently able to accurately correlates to a specific NoSQL system.


title: Improving Neural Network Quantization without Retraining using Outlier Channel Splitting

abstract: Quantization can improve the execution latency and energy efficiency of neural networks on both commodity GPUs and specialized accelerators. The majority of existing literature focuses on training quantized DNNs, while this work examines the less-studied topic of quantizing a floating-point model without (re)training. DNN weights and activations follow a bell-shaped distribution post-training, while practical hardware uses a linear quantization grid. This leads to challenges in dealing with outliers in the distribution. Prior work has addressed this by clipping the outliers or using specialized hardware. In this work, we propose outlier channel splitting (OCS), which duplicates channels containing outliers, then halves the channel values. The network remains functionally identical, but affected outliers are moved toward the center of the distribution. OCS requires no additional training and works on commodity hardware. Experimental evaluation on ImageNet classification and language modeling shows that OCS can outperform state-of-the-art clipping techniques with only minor overhead.


title: Online Multi-Task Learning for Policy Gradient Methods

abstract: Policy gradient algorithms have shown considerable recent success in solving high-dimensional sequential decision making tasks, particularly in robotics. However, these methods often require extensive experience in a domain to achieve high performance. To make agents more sample-efficient, we developed a multi-task policy gradient method to learn decision making tasks consecutively, transferring knowledge between tasks to accelerate learning. Our approach provides robust theoretical guarantees, and we show empirically that it dramatically accelerates learning on a variety of dynamical systems, including an application to quadrotor control.


title: Evaluating distributed word representations for capturing semantics of biomedical concepts

abstract: Recently there is a surge in interest in learning vector representations of words using huge corpus in unsupervised manner. Such word vector representations, also known as word embedding, have been shown to improve the performance of machine learning models in several NLP tasks. However efficiency of such representation has not been systematically evaluated in biomedical domain. In this work our aim is to compare the performance of two state-of-the-art word embedding methods, namely word2vec and GloVe on a basic task of reflecting semantic similarity and relatedness of biomedical concepts. For this, vector representations of all unique words in the corpus of more than 1 million full-length research articles in biomedical domain are obtained from the two methods. These word vectors are evaluated for their ability to reflect semantic similarity and semantic relatedness of word-pairs in a benchmark data set of manually curated semantic similar and related words available at http:// rxinformatics.umn.edu. We observe that parameters of these models do affect their ability to capture lexicosemantic properties and word2vec with particular language modeling seems to perform better than others.


title: SEANO: Semi-supervised Embedding in Attributed Networks with Outliers

abstract: In this paper, we propose a novel framework, called Semi-supervised Embedding in Attributed Networks with Outliers (SEANO), to learn a low-dimensional vector representation that systematically captures the topological proximity, attribute affinity and label similarity of vertices in a partially labeled attributed network (PLAN). Our method is designed to work in both transductive and inductive settings while explicitly alleviating noise effects from outliers. Experimental results on various datasets drawn from the web, text and image domains demonstrate the advantages of SEANO over state-of-the-art methods in semi-supervised classification under transductive as well as inductive settings. We also show that a subset of parameters in SEANO is interpretable as outlier score and can significantly outperform baseline methods when applied for detecting network outliers. Finally, we present the use of SEANO in a challenging real-world setting -- flood mapping of satellite images and show that it is able to outperform modern remote sensing algorithms for this task.


title: An Empirical Study of Web Cookies

abstract: Web cookies are used widely by publishers and 3rd parties to track users and their behaviors. Despite the ubiquitous use of cookies, there is little prior work on their characteristics such as standard attributes, placement policies, and the knowledge that can be amassed via 3rd party cookies. In this paper, we present an empirical study of web cookie characteristics, placement practices and information transmission. To conduct this study, we implemented a lightweight web crawler that tracks and stores the cookies as it navigates to websites. We use this crawler to collect over 3.2M cookies from the two crawls, separated by 18 months, of the top 100K Alexa web sites. We report on the general cookie characteristics and add context via a cookie category index and website genre labels. We consider privacy implications by examining specific cookie attributes and placement behavior of 3rd party cookies. We find that 3rd party cookies outnumber 1st party cookies by a factor of two, and we illuminate the connection between domain genres and cookie attributes. We find that less than 1% of the entities that place cookies can aggregate information across 75% of web sites. Finally, we consider the issue of information transmission and aggregation by domains via 3rd party cookies. We develop a mathematical framework to quantify user information leakage for a broad class of users, and present findings using real world domains. In particular, we demonstrate the interplay between a domain's footprint across the Internet and the browsing behavior of users, which has significant impact on information transmission.


title: X-band T/R module in state-of-the-art GaN technology

abstract: In this paper a first iteration X-band T/R module based on a GaN-HEMT MMIC Front-End chip-set, comprising a power amplifier, robust low-noise amplifier and power switch will be presented. Even though ultimate T/R module performance cannot be achieved with current GaN-HEMT technological maturity the impact that this technology can have at systems level in terms of performance/cost trade-off will be illustrated by means of a preliminary innovative module architecture which foresees the elimination of more traditional T/R module components such as ferrite circulator and limiter for front-end signal routing and protection.


title: IMouse: Eyes Gesture Control System

abstract: A high number of people, affected with neuro-locomotor disabilities or those paralyzed by injury cannot use computers for basic tasks such as sending or receiving messages, browsing the internet, watch their favorite TV show or movies. Through a previous research study, it was concluded that eyes are an excellent candidate for ubiquitous computing since they move anyway during interaction with computing machinery. Using this underlying information from eye movements could allow bringing the use of computers back to such patients. For this purpose, we propose an imouse gesture control system which is completely operated by human eyes only. The purpose of this work is to design an open-source generic eye-gesture control system that can effectively track eye-movements and enable the user to perform actions mapped to specific eye movements/gestures by using computer webcam. It detects the pupil from the user’s face and then tracks its movements. It needs to be accurate in real-time so that the user is able to use it like other every-day devices with comfort.


title: SiMoNa: A Proof-of-concept Domain Specific Modeling Language for IoT Infographics

abstract: The Internet of Things (IoT) has emerged as one of the prominent concepts in academic discourse in recent times reflecting a wider trend by industry to connect physical objects to the Internet and to each other. The IoT is already generating an unprecedented volume of data in greater varieties and higher velocities. Making sense of such data is an emerging and significant challenge. Infographics are visual representations that provide a visual space for end users to compare and analyze data, information, and knowledge in a more efficient form than traditional forms. The nature of loT requires a continuum modification in how end users see information to achieve such efficiency gains. Conceptualizing and implementing Infographics in an loT system can thus require significant planning and development for both data scientists, graphic designers and developers resulting in both costs in terms of time and effort. To address this problem, this paper presents SiMoNa, a domain-specific modeling language (DSML) to create, connect, interact, and build interactive infographic presentations for loT systems efficiently based on the model-driven develonment (MDD) naradiam.


title: Design optimization of spoke-type PM motors for Formula E racing cars

abstract: This paper presents the performance trade-offs in the design optimization of spoke-type permanent magnet (PM) motors for high speed and very high torque density traction motors. An example 18-slot 16-pole machine for a direct drive Formula E race car over the Le Mans driving cycle is considered. Both low speed and extended speed/field-weakening operations are evaluated using high fidelity finite element (FE) simulations, to simultaneously increase the torque density and decrease the power losses over the high energy-throughput-zones of the machine torque-speed plane. The results of the design optimization process yielding 3,400 design candidates are utilized to quantify the performance trade-offs for increasing the power density in spoke-type PM motors. These trade-offs include the impacts on other performance metrics such as power losses, PM demagnetization, and torque ripple. The analysis is supplemented by multi-physics simulation of three counterpart optimized designs, and successful experimental verification of a prototype of one of those three designs which represents a record high power density motor in traction applications.


title: Characteristics of Mobile Web Content

abstract: The World Wide Web is no longer tethered to our desktops and laptops. The Web has gone mobile, providing instant access to information anywhere and anytime. The mobile Web can be considered a shadow of the World Wide Web, implemented using specialized markup languages and design techniques adapted for comparatively limited mobile phones and PDAs. Despite the growing importance and usage of the mobile Web, surprising little is known about it. This paper presents the results of a study of mobile Web content conducted in May and June of 2006. The study examines the content of over one-million mobile Web pages from around the world using a search-assisted crawling methodology to locate and study pages for three of the most popular mobile Web formats - WML 1.0, WML 2.0/XHTML mobile profile (XHTML-MP) and compact HTML (C-HTML). The objective is to study the relative characteristics of these mobile Web content formats, as well as compare them with a similar sampling of non-mobile (HTML) content. We found that WML is the dominant mobile Web content type, although regional differences do exist. We found that all three mobile content types studied were on the same order of magnitude for average page characteristics such as number of links (under 10) and number of images (around 1), but pages in the newest format, XHTML-MP, are 50% larger on average than those in WML. Not surprisingly, all of these characteristics are much smaller than for HTML content pages gathered with the same methodology. In terms of specific features, only 7 % of pages used WML cards, but 50% of XHTML-MP servers dynamically adapted the content served based on the user agent. Finally, we found less than 4% of mobile pages contained ad objects, which is much less than for HTML pages.


title: Predictability, Complexity, and Learning

abstract: We define predictive information Ipred(T) as the mutual information between the past and the future of a time series. Three qualitatively different behaviors are found in the limit of large observation times T:Ipred(T) can remain finite, grow logarithmically, or grow as a fractional power law. If the time series allows us to learn a model with a finite number of parameters, then Ipred(T) grows logarithmically with a coefficient that counts the dimensionality of the model space. In contrast, power-law growth is associated, for example, with the learning of infinite parameter (or nonparametric) models such as continuous functions with smoothness constraints. There are connections between the predictive information and measures of complexity that have been defined both in learning theory and the analysis of physical systems through statistical mechanics and dynamical systems theory. Furthermore, in the same way that entropy provides the unique measure of available information consistent with some simple and plausible conditions, we argue that the divergent part of Ipred(T) provides the unique measure for the complexity of dynamics underlying a time series. Finally, we discuss how these ideas may be useful in problems in physics, statistics, and biology.


title: Convolutional Neural Network Architectures for Matching Natural Language Sentences

abstract: Semantic matching is of central importance to many natural language tasks \cite{bordes2014semantic,RetrievalQA}. A successful matching algorithm needs to adequately model the internal structures of language objects and the interaction between them. As a step toward this goal, we propose convolutional neural network models for matching two sentences, by adapting the convolutional strategy in vision and speech. The proposed models not only nicely represent the hierarchical structures of sentences with their layer-by-layer composition and pooling, but also capture the rich matching patterns at different levels. Our models are rather generic, requiring no prior knowledge on language, and can hence be applied to matching tasks of different nature and in different languages. The empirical study on a variety of matching tasks demonstrates the efficacy of the proposed model on a variety of matching tasks and its superiority to competitor models.


title: The Toronto Paper Matching System: An automated paper-reviewer assignment system

abstract: One of the most important tasks of conference organizers is the assignment of papers to reviewers. Reviewers’ assessments of papers is a crucial step in determining the conference program, and in a certain sense to shape the direction of a field. However this is not a simple task: large conferences typically have to assign hundreds of papers to hundreds of reviewers, and time constraints make the task impossible for one person to accomplish. Furthermore other constraints, such as reviewer load have to be taken into account, preventing the process from being completely distributed. We built the first version of a system to suggest reviewer assignments for the NIPS 2010 conference, followed, in 2012, by a release that better integrated our system with Microsoft’s popular Conference Management Toolkit (CMT). Since then our system has been widely adopted by the leading conferences in both the machine learning and computer vision communities. This paper provides an overview of the system, a summary of learning models and methods of evaluation that we have been using, as well as some of the recent progress and open issues.


title: Contact-Free Support Structures for Part Overhangs in Powder-Bed Metal Additive Manufacturing

abstract: This study investigates the feasibility of a novel concept, contact-free support structures, for part overhangs in powder-bed metal additive manufacturing. The intent is to develop alternative support designs that require no or little post-processing, and yet, maintain effectiveness in minimizing overhang distortions. The idea is to build, simultaneously during part fabrications, a heat sink (called “heat support”), underneath an overhang to alter adverse thermal behaviors. Thermomechanical modeling and simulations using finite element analysis were applied to numerically research the heat support effect on overhang distortions. Experimentally, a powder-bed electron beam additive manufacturing system was utilized to fabricate heat support designs and examine their functions. The results prove the concept and demonstrate the effectiveness of contact-free heat supports. Moreover, the method was tested with different heat support parameters and applied to various overhang geometries. It is concluded that the heat support proposed has the potential to be implemented in industrial applications.


title: Insight and quality of life in long-term hospitalized Japanese patients with chronic schizophrenia

abstract: AIM ::: The aim of the study was to investigate the relationship between insight and quality of life (QOL) and the respective predictive factors in long-term hospitalized patients with chronic schizophrenia. ::: ::: ::: METHODS ::: The present subjects were 47 Japanese patients with chronic schizophrenia who were hospitalized for >1 year (mean hospitalization period, 9.8 years). Assessments were made using the Scale of Unawareness of Mental Disorder (SUMD) and the EuroQoL-5 Dimensions (EQ-5D) scale. Sociodemographic details and illness-related variables were also evaluated, including use of the Positive and Negative Syndrome Scale. ::: ::: ::: RESULTS ::: There was no association between SUMD and EQ-5D scores. Hallucinatory behavior was a predictor of good insight. Poor rapport was a predictor of bad insight. Poor attention was a predictor of bad QOL. ::: ::: ::: CONCLUSION ::: The relationship between insight and QOL and the respective predictive factors might be different between acute and chronic stages. Further studies are needed to investigate how these changes occur.


title: Social Internet Sites as a Source of Public Health Information

abstract: Social media websites, such as YouTube, Facebook, MySpace, Twitter, and Second Life® are rapidly emerging as popular sources of health information especially for teens and young adults. Social media marketing carries the advantages of low cost, rapid transmission through a wide community, and user interaction. Disadvantages include blind authorship, lack of source citation, and presentation of opinion as fact. Dermatologists and other health care providers should recognize the importance of social media websites and their potential usefulness for disseminating health information.


title: An extended SDN architecture for network function virtualization with a case study on intrusion prevention

abstract: In conventional software-defined networking (SDN), a controller classifies the traffic redirected from a switch to determine the path to network function virtualization (NFV) modules. The redirection generates a large volume of control-plane traffic. We propose an extended SDN architecture to reduce the traffic overhead to the controller for providing NFV. The extension includes two-layer traffic classification in the data plane, extended OpenFlow protocol messages and service chaining mechanisms. Network events are analyzed in the data plane instead of the control plane. The efficiency is evaluated with a case study of intrusion prevention. The evaluation shows that only 0.12 percent of the input traffic is handled by the controller, while 77.23 percent is handled on the controller in conventional SDN.


title: The ups and downs of memory.

abstract: Ever since the classic work of Ebbinghaus (1885/1964), the default view in scientific psychology has been that memory declines over time. Less well-known clinical and laboratory traditions suggest, however, that memory can also increase over time. Ballard (1913) demonstrated that, actually, memory simultaneously increases and decreases over time and thus has not 1 but 2 tendencies. When more than 1 recall test is administered, a later test invariably shows loss of some items remembered earlier (oblivescence), but later tests also invariably show that previously unremembered items are recovered in later tests (reminis- cence). Depending on a number of factors (e.g., the stimulus used), the overall balance between reminiscence and oblivescence may be positive (hypermnesia) or negative (amnesia). Modern multitrial recall studies have extensively documented hypermnesic memory in single laboratory sessions and, also, although less frequently, over pe- riods of days, weeks, and even months. With hypermnesic memory now established, hypnosis has been shown not to add anything to regular hypermnesia. This article presents an integration of the scattered literatures, which now, after a century of experimental and clinical effort, coheres into a solid empirical picture, with numerous implications (e.g., for the recovered memory controversy, eyewitness testimony, repression, and subliminal perception).


title: What Makes Popular Culture Popular? Product Features and Optimal Differentiation in Music:

abstract: In this article, we propose a new explanation for why certain cultural products outperform their peers to achieve widespread success. We argue that products’ position in feature space significantly predicts their popular success. Using tools from computer science, we construct a novel dataset allowing us to examine whether the musical features of nearly 27,000 songs from Billboard’s Hot 100 charts predict their levels of success in this cultural market. We find that, in addition to artist familiarity, genre affiliation, and institutional support, a song’s perceived proximity to its peers influences its position on the charts. Contrary to the claim that all popular music sounds the same, we find that songs sounding too much like previous and contemporaneous productions—those that are highly typical—are less likely to succeed. Songs exhibiting some degree of optimal differentiation are more likely to rise to the top of the charts. These findings offer a new perspective on success in cultural markets by spec...


THE DINU-MODEL – A PROCESS MODEL FOR THE DESIGN OF NUDGES


title: On-chip vs. off-chip memory: the data partitioning problem in embedded processor-based systems

abstract: Efficient utilization of on-chip memory space is extremely important in modern embedded system applications based on processor cores. In addition to a data cache that interfaces with slower off-chip memory, a fast on-chip SRAM, called Scratch-Pad memory, is often used in several applications, so that critical data can be stored there with a guaranteed fast access time. We present a technique for efficiently exploiting on-chip Scratch-Pad memory by partitioning the application's scalar and arrayed variables into off-chip DRAM and on-chip Scratch-Pad SRAM, with the goal of minimizing the total execution time of embedded applications. We also present extensions of our proposed memory assignment strategy to handle context switching between multiple programs, as well as a generalized memory hierarchy. Our experiments on code kernels from typical applications show that our technique results in significant performance improvements.


title: Noname Personnel Scheduling: Models and Complexity

abstract: Due to its complexity, its challenging features, and its practical relevance, personnel scheduling has been heavily investigated in the last few decades. However, there is a relatively low level of study on models and complexity in these important problems. In this paper, we present mathematical models which cover specific aspects in the personnel scheduling literature. Furthermore, we address complexity issues by identifying polynomial solvable and NP-hard special cases.


title: Future of Human-Building Interaction

abstract: In 2030, we will have a different interactive experience with our built environments, at home, at work, and even in public urban spaces. This is attributed to advancements in sensing and actuation systems that can integrate into the building infrastructures, in symbiosis with the new environmental concerns that call for new life, work, and mobility styles. This change, whether gradual or sudden, evident or seamless, can have a remarkable impact on our everyday experiences, and thus entails efforts to envision possible scenarios and plan for them. We believe that buildings, as they would embody our digital and physical interactive daily experiences, should be designed and nurtured in a dialogue with their users at the individual as well as social levels. This implies a responsibility of the HCI community to intervene and involve the user in the Human-Building Interaction (HBI) design practice. We propose bringing together expertise from the fields of human-computer interaction, building and urban architecture, and social sciences, and provide them with an occasion for collaboratively creating and sharing ``images' of HBI by 2030. The goal is to uncover research opportunities and challenges that will emerge through discussions and multi-faceted debates about the topics proposed.


title: Pure Pursuit Revisited: Field Testing of Autonomous Vehicles in Urban Areas

abstract: In this paper, we aim to explore path following. We implement a path following component by referring to the existing Pure Pursuit algorithm. Using the simulation and field operational test, we identified the problem in the path following component. The main problems identified were with respect to vehicles meandering off the path, turning a corner, and the instability of steering control. Therefore, we apply some modifications to the Pure Pursuit[1] algorithm. We have also conducted the simulation and field operational tests again to evaluate these modifications.


title: Monitoring of the daily living activities in smart home care

abstract: One of the key requirements for technological systems that are used to secure independent housing for seniors in their home environment is monitoring of daily living activities (ADL), their classification, and recognition of routine daily patterns and habits of seniors in Smart Home Care (SHC). To monitor daily living activities, the use of a temperature, CO2, humidity sensors, and microphones are described in experiments in this study. The first part of the paper describes the use of CO2 concentration measurement for detecting and monitoring room´s occupancy in SHC. In second part focuses this paper on the proposal of an implementation of Artificial Neural Network based on the Levenberg–Marquardt algorithm (LMA) for the detection of human presence in a room of SHC with the use of predictive calculation of CO2 concentrations from obtained measurements of temperature (indoor, outdoor) T ::: i, T ::: o and relative air humidity rH. Based on the long-term monitoring (1 month) of operational and technical functions (unregulated, uncontrolled) in an experimental Smart Home (SH), LMA was trained through the data picked up by the sensors of CO2, T and rH with the aim to indirectly predict CO2 leading to the elimination of CO2 sensor from the measurement process. Within the realized experiment, input parameters of the neuronal network and the number of neurons for LMA were optimized on the basis of calculated values of Root Mean Squared Error, the correlative coefficient (R) and the length of the measured training time ANN. With the use of the trained network ANN, we realized a strictly controlled short-term (11 h) experiment without the use of CO2 sensor. Experimental results verified high method accuracy (>95%) within the short-term and long-term experiments for learned ANN (1.6.2015–30.6.2015). For learned ANN (1.2.2014–27.2.2014) was verified worse method accuracy (>60%). The original contribution is a verification of a low-cost method for the detection of human presence in the real operating environment of SHC. In the third part of the paper is described the practical implementation of voice control of operating technical functions by the KNX technology in SHC by means of the in-house developed application HESTIA, intended for both the desktop system version and the mobile version of the Windows 10 operating system for mobile phones. The resultant application can be configured for any building equipped with the KNX bus system. Voice control implementation is an in-house solution, no third-party software is used here. Utilization of the voice communication application in SHC was proven on the experimental basis with the combination of measurement CO2 for ADL monitoring in SHC.


title: A survey of awareness, knowledge and perception of online spam

abstract: Online spam does not only create nuisance for the Internet users, they could also lead to further and bigger problems such as hacking, phishing, etc. It is well known that online spam are currently handled using detection and prevention methods. Despite the effectiveness of these methods in reducing the proliferation of online spam, it is also known that spammers will continue to find ways to promote spam. They deceive users who may lack awareness and knowledge about this crime. Therefore, it is important to investigate Internet users' awareness of online spam, how much knowledge they actually have about online spam, and their perception of online spam. This paper provides a descriptive analysis of public awareness, knowledge and perception drawn out from a web based survey involving 368 Internet users. To the best of our knowledge, this is the first such survey that has ever been conducted to highlight these issues.


title: Design and Control of a supercapacitor storage system for traction applications

abstract: The storage system in this paper is made of supercapacitors. The main goal is to ensure an efficient energy management in a series hybrid vehicle, even if braking resistors are still needed. Design considerations are discussed. In particular the influence of the inductor resistance on the system stability is described. A maximum control structure is then deduced from the energetic macroscopic representation of the storage system. Comparisons between experimentation and simulation are presented in order to highlight the influence of the inductor resistor. Experiments are then carried out on a normal operating cycle.


Direct Reduction and Magnetic Separating Experiment Study of Nickel Slag


title: Design of the pronunciation dictionary for an English CAPT system

abstract: Computer Assisted Pronunciation Training (CAPT) systems can judge the overall pronunciation quality and point out the pronunciation errors by recognition of the user's utterance to improve the users' oral ability. However, the performance of the current error detection systems can't satisfy the users' expectation. This paper carefully designs the pronunciation dictionary of the speech recognition engine of the CAPT system based on observation of the spectral properties of some phonemes. First, the phonetic symbol /R/ utilized in some modern English dictionaries is replaced by two symbols, /R/ (as in word result) and /ER/ (as in word bear), due to the large spectral deviation in these two cases. Moreover, rather than using a consonant sequence to represent a consonant cluster, we use one symbol to represent the whole for some consonant clusters, according to the phonetic properties of the consonants. Finally, we divide the phoneme /l/ into clear /l/ and dark /l/ according to the distinction in the manner of articulation and the place of articulation. So is /n/. As a result, we get a new pronunciation dictionary which is more suitable for the speech recognition engine of the CAPT system. The HMMs are trained by using the TIMIT database, and evaluated on a database involving 40 Chinese undergraduates. Experimental results illustrate the effectiveness of the new pronunciation dictionary.


title: Hypothesis Only Baselines in Natural Language Inference

abstract: We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI). Especially when an NLI dataset assumes inference is occurring based purely on the relationship between a context and a hypothesis, it follows that assessing entailment relations while ignoring the provided context is a degenerate solution. Yet, through experiments on ten distinct NLI datasets, we find that this approach, which we refer to as a hypothesis-only model, is able to significantly outperform a majority class baseline across a number of NLI datasets. Our analysis suggests that statistical irregularities may allow a model to perform NLI in some datasets beyond what should be achievable without access to the context.


title: The EM Side-Channel(s)

abstract: We present results of a systematic investigation of leakage of compromising information via electromagnetic (EM) emanations from CMOS devices. These emanations are shown to consist of a multiplicity of signals, each leaking somewhat different information about the underlying computation. We show that not only can EM emanations be used to attack cryptographic devices where the power side-channel is unavailable, they can even be used to break power analysis countermeasures.


title: Unsupervised Meta-Learning for Reinforcement Learning

abstract: Meta-learning algorithms use past experience to learn to quickly solve new tasks. In the context of reinforcement learning, meta-learning algorithms acquire reinforcement learning procedures to solve new problems more efficiently by utilizing experience from prior tasks. The performance of meta-learning algorithms depends on the tasks available for meta-training: in the same way that supervised learning generalizes best to test points drawn from the same distribution as the training points, meta-learning methods generalize best to tasks from the same distribution as the meta-training tasks. In effect, meta-reinforcement learning offloads the design burden from algorithm design to task design. If we can automate the process of task design as well, we can devise a meta-learning algorithm that is truly automated. In this work, we take a step in this direction, proposing a family of unsupervised meta-learning algorithms for reinforcement learning. We motivate and describe a general recipe for unsupervised meta-reinforcement learning, and present an instantiation of this approach. Our conceptual and theoretical contributions consist of formulating the unsupervised meta-reinforcement learning problem and describing how task proposals based on mutual information can be used to train optimal meta-learners. Our experimental results indicate that unsupervised meta-reinforcement learning effectively acquires accelerated reinforcement learning procedures without the need for manual task design and these procedures exceed the performance of learning from scratch.


title: Quaternion Recurrent Neural Networks

abstract: Recurrent neural networks (RNNs) are powerful architectures to model sequential data, due to their capability to learn short and long-term dependencies between the basic elements of a sequence. Nonetheless, popular tasks such as speech or images recognition, involve multi-dimensional input features that are characterized by strong internal dependencies between the dimensions of the input vector. We propose a novel quaternion recurrent neural network (QRNN), alongside with a quaternion long-short term memory neural network (QLSTM), that take into account both the external relations and these internal structural dependencies with the quaternion algebra. Similarly to capsules, quaternions allow the QRNN to code internal dependencies by composing and processing multidimensional features as single entities, while the recurrent operation reveals correlations between the elements composing the sequence. We show that both QRNN and QLSTM achieve better performances than RNN and LSTM in a realistic application of automatic speech recognition. Finally, we show that QRNN and QLSTM reduce by a maximum factor of 3.3x the number of free parameters needed, compared to real-valued RNNs and LSTMs to reach better results, leading to a more compact representation of the relevant information.


The Thermal and Mechanical Properties of High Performance Polyimide Fibers


title: Assessing Indirect Environmental Effects of Information and Communication Technology (ICT): A Systematic Literature Review

abstract: Indirect environmental effects of information and communication technology (ICT) are those effects of ICT that change patterns of production or consumption in domains other than ICT, or more precisely, the environmental consequences of these changes. Digitalization as the societal process of ICT-driven change has created increasing interest in the indirect environmental effects of this technology. Assessments of indirect effects face various methodological challenges, such as the definition of the system boundary, the definition of a baseline as a reference or the occurrence of rebound effects. Existing studies use various approaches or methods to assess a spectrum of ICT use cases in several application domains. In view of the large number of assessments that have been conducted, the choices made when applying assessment methods, and the variety of ICT use cases in different application domains investigated, we present a systematic literature review of existing assessments of indirect environmental effects of ICT. The review provides a state-of-the-art overview of the methods used in the research field and is intended to support researchers in designing sound assessments which yield significant results. We identified 54 studies in seven main application domains using 15 different assessment approaches. The most common application domains are virtual mobility (e.g., telecommuting), virtual goods (e.g., digital media), and smart transport (e.g., route optimization). Life-cycle assessment, partial footprint, and the “ICT enablement method” are the most common approaches. The major part of the assessments focuses on patterns of production (e.g., production of paper-based books vs. e-books), a smaller part on patterns of consumption (e.g., changes in media consumption). Based on these results, we identify as a research gap the investigation of ICT impacts on consumer behavior, which could, for example, focus on social practices, and account for the dynamic implications of change. Elaborating such an approach could provide valuable insights into ICT’s impact on society and the resulting environmental consequences.


title: Early flood alerts using Short Message Service (SMS)

abstract: Early flood alerts system is use to create a tool to measure water level, which powered by solar cell panel. The system is supported by microcontroller and GSM modem to send the measured data via SMS (Short Message Service) into two specific clients' phone numbers that have previously been inputted into the system. Besides the capability to predict the remaining time before the flood, this system can also detect three types of water level: early flood level, flood level and low tide level after the flood with a maximum water level detection of four meters. This system focused on an accurate measurement of the height of the water level and sending a warning message of the threshold height set before to the clients. For each of water level, the system will send three messages for each client number. The setting for client phone numbers and the water level can be programmed by sending the SMS that has been formatted in the system. The early flood alerts system is based on ATMEL AT89S51 microcontroller and the measurement of the water level is processed by ultrasonic HC-SR04.


title: Deep multimodal hashing with orthogonal regularization

abstract: Hashing is an important method for performing efficient similarity search. With the explosive growth of multimodal data, how to learn hashing-based compact representations for multimodal data becomes highly non-trivial. Compared with shallow-structured models, deep models present superiority in capturing multimodal correlations due to their high nonlinearity. However, in order to make the learned representation more accurate and compact, how to reduce the redundant information lying in the multimodal representations and incorporate different complexities of different modalities in the deep models is still an open problem. In this paper, we propose a novel deep multimodal hashing method, namely Deep Multimodal Hashing with Orthogonal Regularization (DMHOR), which fully exploits intra-modality and inter-modality correlations. In particular, to reduce redundant information, we impose orthogonal regularizer on the weighting matrices of the model, and theoretically prove that the learned representation is guaranteed to be approximately orthogonal. Moreover, we find that a better representation can be attained with different numbers of layers for different modalities, due to their different complexities. Comprehensive experiments on WIKI and NUS-WIDE, demonstrate a substantial gain of DMHOR compared with state-of-the-art methods.


title: Effective LSTMs for Target-Dependent Sentiment Classification

abstract: Target-dependent sentiment classification remains a challenge: modeling the semantic relatedness of a target with its context words in a sentence. Different context words have different influences on determining the sentiment polarity of a sentence towards the target. Therefore, it is desirable to integrate the connections between target word and context words when building a learning system. In this paper, we develop two target dependent long short-term memory (LSTM) models, where target information is automatically taken into account. We evaluate our methods on a benchmark dataset from Twitter. Empirical results show that modeling sentence representation with standard LSTM does not perform well. Incorporating target information into LSTM can significantly boost the classification accuracy. The target-dependent LSTM models achieve state-of-the-art performances without using syntactic parser or external sentiment lexicons.


title: Triboelectrostatic separation for granular plastic waste recycling: a review.

abstract: The world's plastic consumption has increased incredibly in recent decades, generating more and more plastic waste, which makes it a great public concern. Recycling is the best treatment for plastic waste since it cannot only reduce the waste but also reduce the consumption of oil for producing new virgin plastic. Mechanical recycling is recommended for plastic waste to avoid the loss of its virgin value. As a mechanical separation technology, triboelectrostatic separation utilizes the difference between surface properties of different materials to get them oppositely charged, deflected in the electric field and separately collected. It has advantages such as high efficiency, low cost, no concern of water disposal or secondary pollution and a relatively wide processing range of particle size especially suitable for the granular plastic waste. The process of triboelectrostatic separation for plastic waste is reviewed in this paper. Different devices have been developed and proven to be effective for separation of plastic waste. The influence factors are also discussed. It can be concluded that the triboelectrostatic separation of plastic waste is a promising technology. However, more research is required before it can be widely applied in industry.


title: Variational message passing

abstract: Bayesian inference is now widely established as one of the principal foundations for machine learning. In practice, exact inference is rarely possible, and so a variety of approximation techniques have been developed, one of the most widely used being a deterministic framework called variational inference. In this paper we introduce Variational Message Passing (VMP), a general purpose algorithm for applying variational inference to Bayesian Networks. Like belief propagation, VMP proceeds by sending messages between nodes in the network and updating posterior beliefs using local operations at each node. Each such update increases a lower bound on the log evidence (unless already at a local maximum). In contrast to belief propagation, VMP can be applied to a very general class of conjugate-exponential models because it uses a factorised variational approximation. Furthermore, by introducing additional variational parameters, VMP can be applied to models containing non-conjugate distributions. The VMP framework also allows the lower bound to be evaluated, and this can be used both for model comparison and for detection of convergence. Variational message passing has been implemented in the form of a general purpose inference engine called VIBES ('Variational Inference for BayEsian networkS') which allows models to be specified graphically and then solved variationally without recourse to coding.


title: Design and Strategy for the Deployment of Energy Storage Systems in a Distribution Feeder With Penetration of Renewable Resources

abstract: Integration of distributed energy resources has the potential to impact the operation of the distribution systems by affecting the equipment reliability and customer power quality. The stochastic nature of the distributed energy resources have been accommodated using the energy storage systems along with providing economical benefits to the distribution systems. This paper provides a generalized framework for strategic deployment of a lithium-ion-based energy storage system to increase the benefits in a distribution feeder. Convex optimization is specifically developed to exploit the energy price arbitrage along the optimization time horizon. A significant amount of work has been performed in this paper for detailed characterization of the life cycle costs of the battery energy storage system (BESS). A novel approach for effective utilization of the energy storage system to mitigate the intermittent nature of the photovoltaic (PV) generation has been presented in this paper. The objectives include reduction of the substation transformer losses, reduction of the life cycle cost of the battery storage system, and accommodate PV variability. A convex optimization with a quadratic objective has been formulated to calculate the charge/discharge schedule of the energy storage system. A field validated model of the distribution feeder in OpenDSS has been used to determine the impact of optimal scheduling of the BESS on the substation transformer losses and the feeder head voltage.


title: Minimum intervention dentistry principles and objectives

abstract: Minimum intervention dentistry (MID) is the modern medical approach to the management of caries, utilizing caries risk assessment, and focusing on the early prevention and interception of disease. Moving the focus away from the restoration of teeth allows the dentist to achieve maximum intervention, with minimal invasive treatments. The four core principles of MID can be considered to be: (1) Recognition - early identification and assessment of potential caries risk factors through lifestyle analysis, saliva testing and using plaque diagnostic tests; (2) Reduction - to eliminate or minimize caries risk factors by altering diet and lifestyle habits and increasing the pH of the oral environment; (3) Regeneration - to arrest and reverse incipient lesions, using appropriate topical agents including fluorides and casein phosphopeptides-amorphous calcium phosphates (CPP-ACP); (4) Repair - when cavitation is present and surgical intervention is required, conservative caries removal is carried out to maximize the repair potential of the tooth and retain tooth structure. Bioactive materials are used to restore the tooth and promote internal healing of the dentine. Effective implementation of MID involves integrating each of these four elements into patient assessment and treatment planning. This review paper discusses the key principles of MID as a philosophy of patient care, and the practical objectives which flow into individual patient care.


title: Usability of mobile applications: literature review and rationale for a new usability model

abstract: The usefulness of mobile devices has increased greatly in recent years allowing users to perform more tasks in a mobile context. This increase in usefulness has come at the expense of the usability of these devices in some contexts. We conducted a small review of mobile usability models and found that usability is usually measured in terms of three attributes; effectiveness, efficiency and satisfaction. Other attributes, such as cognitive load, tend to be overlooked in the usability models that are most prominent despite their likely impact on the success or failure of an application. To remedy this we introduces the PACMAD (People At the Centre of Mobile Application Development) usability model which was designed to address the limitations of existing usability models when applied to mobile devices. PACMAD brings together significant attributes from different usability models in order to create a more comprehensive model. None of the attributes that it includes are new, but the existing prominent usability models ignore one or more of them. This could lead to an incomplete usability evaluation. We performed a literature search to compile a collection of studies that evaluate mobile applications and then evaluated the studies using our model.


title: Logical Physical Clocks and Consistent Snapshots in Globally Distributed Databases

abstract: There is a gap between the theory and practice of distributed systems in terms of the use of time. The theory of distributed systems shunned the notion of time, and introduced “causality tracking” as a clean abstraction to reason about concurrency. The practical systems employed physical time (NTP) information but in a best effort manner due to the difficulty of achieving tight clock synchronization. In an effort to bridge this gap and reconcile the theory and practice of distributed systems on the topic of time, we propose a hybrid logical clock, HLC, that combines the best of logical clocks and physical clocks. HLC captures the causality relationship like logical clocks, and enables easy identification of consistent snapshots in distributed systems. Dually, HLC can be used in lieu of physical/NTP clocks since it maintains its logical clock to be always close to the NTP clock. Moreover HLC fits in to 64 bits NTP timestamp format, and is masking tolerant to NTP kinks and uncertainties. We show that HLC has many benefits for wait-free transaction ordering and performing snapshot reads in multiversion globally distributed databases.


title: Active Learning for Large-Scale Entity Resolution

abstract: Entity resolution (ER) is the task of identifying different representations of the same real-world object across datasets. Designing and tuning ER algorithms is an error-prone, labor-intensive process, which can significantly benefit from data-driven, automated learning methods. Our focus is on "big data'' scenarios where the primary challenges include 1) identifying, out of a potentially massive set, a small subset of informative examples to be labeled by the user, 2) using the labeled examples to efficiently learn ER algorithms that achieve both high precision and high recall, and 3) executing the learned algorithm to determine duplicates at scale. Recent work on learning ER algorithms has employed active learning to partially address the above challenges by aiming to learn ER rules in the form of conjunctions of matching predicates, under precision guarantees. While successful in learning a single rule, prior work has been less successful in learning multiple rules that are sufficiently different from each other, thus missing opportunities for improving recall. In this paper, we introduce an active learning system that learns, at scale, multiple rules each having significant coverage of the space of duplicates, thus leading to high recall, in addition to high-precision. We show the superiority of our system on real-world ER scenarios of sizes up to tens of millions of records, over state-of-the-art active learning methods that learn either rules or committees of statistical classifiers for ER, and even over sophisticated methods based on first-order probabilistic models.


title: Deep Convolutional Networks are Hierarchical Kernel Machines

abstract: In i-theory a typical layer of a hierarchical architecture consists of HW modules pooling the dot products of the inputs to the layer with the transformations of a few templates under a group. Such layers include as special cases the convolutional layers of Deep Convolutional Networks (DCNs) as well as the non-convolutional layers (when the group contains only the identity). Rectifying nonlinearities -- which are used by present-day DCNs -- are one of the several nonlinearities admitted by i-theory for the HW module. We discuss here the equivalence between group averages of linear combinations of rectifying nonlinearities and an associated kernel. This property implies that present-day DCNs can be exactly equivalent to a hierarchy of kernel machines with pooling and non-pooling layers. Finally, we describe a conjecture for theoretically understanding hierarchies of such modules. A main consequence of the conjecture is that hierarchies of trained HW modules minimize memory requirements while computing a selective and invariant representation.


title: Survey on Histogram Equalization method based Image Enhancement techniques

abstract: Image Enhancement is the process of improving the image quality for better visibility of images. The visibility and look of image will be decided by human eyes, which varies from person to person. The images resulted by quality measures made by hardware and software may not be always good and are not having natural look. Many methods are introduced to enhance images. Histogram Equalization is the method in which, the histograms of the input images are altered to obtain the enhanced images. In this paper, we studied various methods of Histogram Equalization, the different types are briefly explained and placed in chronological order in table.


title: Bayesian Affect Control Theory

abstract: Affect Control Theory is a mathematical representation of the interactions between two persons, in which it is posited that people behave in a way so as to minimize the amount of deflection between their cultural emotional sentiments and the transient emotional sentiments that are created by each situation. Affect Control Theory presents a maximum likelihood solution in which optimal behaviours or identities can be predicted based on past interactions. Here, we formulate a probabilistic and decision theoretic model of the same underlying principles, and show this to be a generalisation of the basic theory. The model is more expressive than the original theory, as it can maintain multiple hypotheses about behaviours and identities simultaneously as a probability distribution. This allows the model to generate affectively believable interactions with people by learning about their identity and predicting their behaviours. We demonstrate this generalisation with a set of simulations. We then show how our model can be used as an emotional "plug-in" for systems that interact with humans. We demonstrate human-interactive capability by building a simple intelligent tutoring application and pilot-testing it in an experiment with 20 participants.


title: Controlling User Perceptions of Linguistic Style: Trainable Generation of Personality Traits

abstract: Recent work in natural language generation has begun to take linguistic variation into account, developing algorithms that are capable of modifying the system's linguistic style based either on the user's linguistic style or other factors, such as personality or politeness. While stylistic control has traditionally relied on handcrafted rules, statistical methods are likely to be needed for generation systems to scale to the production of the large range of variation observed in human dialogues. Previous work on statistical natural language generation (SNLG) has shown that the grammaticality and naturalness of generated utterances can be optimized from data; however these data-driven methods have not been shown to produce stylistic variation that is perceived by humans in the way that the system intended. This paper describes Personage, a highly parameterizable language generator whose parameters are based on psychological findings about the linguistic reflexes of personality. We present a novel SNLG method which uses parameter estimation models trained on personality-annotated data to predict the generation decisions required to convey any combination of scalar values along the five main dimensions of personality. A human evaluation shows that parameter estimation models produce recognizable stylistic variation along multiple dimensions, on a continuous scale, and without the computational cost incurred by overgeneration techniques.