title: Automatic Generation of Social Event Storyboard From Image Click-Through Data

abstract: Recent studies have shown that a noticeable percentage of Web search traffic is about social events. While traditional Web sites can only show human-edited events, in this paper, we present a novel system to automatically detect events from search log data and generate storyboards where the events are arranged chronologically. We chose image search log as the resource for event mining, as search logs can directly reflect people’s interests. To discover events from log data, we present a smooth nonnegative matrix factorization framework, which combines the information of query semantics, temporal correlations, search logs, and time continuity. Moreover, we consider the time factor to be an important element, since different events will develop in different time tendencies. In addition, to provide a media-rich and visually appealing storyboard, each event is associated with a set of representative photos arranged along a timeline. These relevant photos are automatically selected from image search results by analyzing image content features. We use celebrities as our test domain, which takes a large percentage of image search traffic. Experiments consisting of Web search traffic on 200 celebrities, for a period of six months, show very encouraging results compared with handcrafted editorial storyboards.


title: Web Tracking: Mechanisms, Implications, and Defenses

abstract: This articles surveys the existing literature on the methods currently used by web services to track the user online as well as their purposes, implications, and possible user’s defenses. ::: A significant majority of reviewed articles and web resources are from years 2012–2014. Privacy seems to be the Achilles’ heel of today’s web. Web services make continuous efforts to obtain as much information as they can about the things we search, the sites we visit, the people with who we contact, and the products we buy. Tracking is usually performed for commercial purposes. We present 5 main groups of methods used for user tracking, which are based on sessions, client ::: storage, client cache, fingerprinting, or yet other approaches. ::: A special focus is placed on mechanisms that use web caches, operational caches, and fingerprinting, as they are usually very rich in terms of using various creative methodologies. We also show how the users can be identified on the web and associated with their real names, e-mail addresses, phone numbers, or even street addresses. We show why tracking is being used and its possible implications for the users. For example, we describe recent cases of price discrimination, assessing financial credibility, determining insurance coverage, government surveillance, and identity theft. For each of the tracking methods, we present possible defenses. Some of them are specific to a particular tracking approach, while others are more universal (block more than one threat) and they are discussed separately. Apart from describing the methods and tools used for keeping the personal data away from being tracked, we also present several tools that were used for research purposes – their main goal is to discover how and by which entity the users are being tracked on their desktop computers or smartphones, provide this information to the users, and visualize it in an accessible and easy to follow way. ::: Finally, we present the currently proposed future approaches to ::: track the user and show that they can potentially pose significant ::: threats to the users’ privacy.


Forecasting Emerging Trends from Scientific Literature.


title: WiFall: Device-free fall detection by wireless networks

abstract: Injuries that are caused by falls have been regarded as one of the major health threats to the independent living for the elderly. Conventional fall detection systems have various limitations. In this work, we first look for the correlations between different radio signal variations and activities by analyzing radio propagation model. Based on our observation, we propose WiFall, a truly unobtrusive fall detection system. WiFall employs physical layer Channel State Information (CSI) as the indicator of activities. It can detect fall of the human without hardware modification, extra environmental setup, or any wearable device. We implement WiFall on desktops equipped with commodity 802.11n NIC, and evaluate the performance in three typical indoor scenarios with several layouts of transmitter-receiver (Tx-Rx) links. In our area of interest, WiFall can achieve fall detection for a single person with high accuracy. As demonstrated by the experimental results, WiFall yields 90 percent detection precision with a false alarm rate of 15 percent on average using a one-class SVM classifier in all testing scenarios. It can also achieve average 94 percent fall detection precisions with 13 percent false alarm using Random Forest algorithm.


title: TAMU at KBP 2017: Event Nugget Detection and Coreference Resolution

abstract: In this paper, we describe TAMU's system submitted to the TAC KBP 2017 event nugget detection and coreference resolution task. Our system builds on the statistical and empirical observations made on training and development data. We found that modifiers of event nuggets tend to have unique syntactic distribution. Their parts-of-speech tags and dependency relations provides them essential characteristics that are useful in identifying their span and also defining their types and realis status. We further found that the joint modeling of event span detection and realis status identification performs better than the individual models for both tasks. Our simple system designed using minimal features achieved the micro-average F1 scores of 57.72, 44.27 and 42.47 for event span detection, type identification and realis status classification tasks respectively. Also, our system achieved the CoNLL F1 score of 27.20 in event coreference resolution task.


title: Defending false data injection attack on smart grid network using adaptive CUSUM test

abstract: In modern smart grid networks, the traditional power grid is enabled by the technological advances in sensing, measurement, and control devices with two-way communications between the suppliers and customers. The smart grid integration helps the power grid networks to be smarter, but it also increases the risk of adversaries because of the currently obsoleted cyber-infrastructure. Adversaries can easily paralyzes the power facility by misleading the energy management system with injecting false data. In this paper, we proposes a defense strategy to the malicious data injection attack for smart grid state estimation at the control center. The proposed “adaptive CUSUM algorithm”, is recursive in nature, and each recursion comprises two inter-leaved stages: Stage 1 introduces the linear unknown parameter solver technique, and Stage 2 applies the multi-thread CUSUM algorithm for quickest change detection. The proposed scheme is able to determine the possible existence of adversary at the control center as quickly as possible without violating the given constraints such as a certain level of detection accuracy and false alarm. The performance of the proposed algorithm is evaluated by both mathematic analysis and numerical simulation.


title: 1.5-GHz CMOS voltage-controlled oscillator based on thickness-field-excited piezoelectric AlN contour-mode MEMS resonators

abstract: This paper reports on the first demonstration of a 1.5 GHz CMOS oscillator based on thickness-field-excited (TFE) piezoelectric AlN MEMS contour-mode resonators (CMRs). The measured phase noise is −85 dBc/Hz at 10 kHz offset frequency and −151 dBc/Hz at 1 MHz. This is the highest frequency MEMS oscillator ever reported using a laterally vibrating mechanical resonator. The high frequency operation has been enabled by optimizing the geometrical design and micro-fabrication process of TFE AlN CMRs, so that a low effective motional resistance around 50 Ω is achieved together with a high unloaded quality factor (Q u ) approaching 2500 and simultaneously high k2 t up to 1.96%. A tunable-supply oscillator design is proposed for fine frequency tuning (or trimming) over a narrow bandwidth. The circuit design enables a novel GHz voltage-controlled oscillator (VCO) without the use of any low-Q tunable component. The 1.5 GHz VCO exhibits a 1500 ppm tuning range by a DC voltage change of 2.5 V. This technique can be utilized for fine frequency trimming and temperature compensation applications.


title: Physical-Layer Security and Reliability Challenges for Industrial Wireless Sensor Networks

abstract: The open nature of radio propagation makes wireless transmissions exposed to unauthorized users and become vulnerable to both the jamming and eavesdropping attacks. In industrial environments, wireless transmissions are also adversely affected by the presence of large-bodied obstructing machinery, metallic friction-induced impairments, and equipment noise. This may result in the failure of wireless transmissions and thus wastes the precious energy resources for industrial wireless sensor networks (IWSNs). This paper is motivated to present a review on the challenges and solutions of improving the physical-layer security and reliability for IWSNs. We first discuss some wireless reliability enhancement techniques for mitigating the background interference, path loss, multipath fading, and link failure. Then, we provide an overview of wireless jamming and eavesdropping attacks along with their countermeasures, where a jammer attempts to emit an interfering radio signal for disrupting the desired communications between a wireless sensor and its sink, while an eavesdropper intends to tap the confidential sensor-sink transmissions. Additionally, we evaluate the tradeoff between the security and reliability, called security-reliability tradeoff , in the context of wireless sensor-sink transmissions. Finally, we discuss a range of open challenges and future trends for IWSNs, including the energy-efficient security and reliability designs, joint jamming-and-eavesdropping defense mechanisms, as well as the energy harvesting for IWSNs.


title: Bottlenecks and their performance implications in e-commerce systems

abstract: We present a detailed workload characterization of a multi-tiered system that hosts an e-commerce site. Using the TPC-W workload and via experimental measurements, we illustrate how workload characteristics affect system behavior and operation, focusing on the statistical properties of dynamic page generation. This analysis allows to identify bottlenecks and the system conditions under which there is degradation in performance. Consistent with the literature, we find that the distribution of the dynamic page generation is heavy-tailed, which is caused by the interaction of the database server with the storage system. Furthermore, by examining the queuing behavior at the database server, we present experimental evidence of the existence of statistical correlation in the distribution of dynamic page generation times, especially under high load conditions. We couple this observation with the existence (and switching) of bottlenecks in the system.


title: Asking for Help Using Inverse Semantics

abstract: Robots inevitably fail, often without the ability to recover autonomously. We demonstrate an approach for enabling a robot to recover from failures by communicating its need for specific help to a human partner using natural language. Our approach automatically detects failures, then generates targeted spoken-language requests for help such as “Please give me the white table leg that is on the black table.” Once the human partner has repaired the failure condition, the system resumes full autonomy. We present a novel inverse semantics algorithm for generating effective help requests. In contrast to forward semantic models that interpret natural language in terms of robot actions and perception, our inverse semantics algorithm generates requests by emulating the human’s ability to interpret a request using the Generalized Grounding Graph (G) framework. To assess the effectiveness of our approach, we present a corpusbased online evaluation, as well as an end-to-end user study, demonstrating that our approach increases the effectiveness of human interventions compared to static requests for help.


title: The concept of perceived value : a systematic review of the research

abstract: The purpose of this article is to present a systematic review of the extensive research that has been conducted on the conceptualization of perceived value. The major conclusions of the present study are: (i) that both uni-dimensional and multi-dimensional models of value have their roles to play in providing simplified (uni-dimensional) and complex (multi-dimensional) understandings of the concept; (ii) that the nature of perceived value is complex and multi-dimensional; (iii) that the concept of perceived value implies an interaction between a consumer and a product; (iv) that value is relative by virtue of its comparative, personal, and situational nature; and (v) that value is preferential, perceptual, and cognitive-affective in nature. By organizing and synthesizing the major research streams and the individual studies within them, the present study thus provides a comprehensive framework for future studies of the dimensionality of perceived value.


Deep Learning for Depression Detection of Twitter Users


Isolation of Streptococcus gallolyticus with very high degradability of condensed tannins from feces of the wild Japanese rock ptarmigans on Mt. Tateyama


title: Noise characterization in millimeter sized micromanipulation systems

abstract: Abstract Efficient and dexterous manipulation of very small (micrometer and millimeter sized) objects require the use of high precision micromanipulation systems. The accuracy of the positioning is nevertheless limited by the noise due to vibrations of the end effectors making it difficult to achieve precise micrometer and nanometer displacements to grip small objects. The purpose of this paper is to analyze the sources of noise and to take it into account in dynamic models of micromanipulation systems. Environmental noise is studied considering the following sources of noise: ground motion and acoustic noises. Each source of noise is characterized in different environmental conditions and a separate description of their effects is investigated on micromanipulation systems using millimeter sized cantilevers as end effectors. Then, using the finite difference method (FDM), a dynamic model taking into account studied noises is used. Ground motion is described as a disturbance transmitted by the clamping to the tip of the cantilever and acoustic noises as external uniform and orthogonal waves. For model validation, an experimental setup including cantilevers of different lengths is designed and a high resolution laser interferometer is used for vibration measurements. Results show that the model allows a physical interpretation about the sources of noise and vibrations in millimeter sized micromanipulation systems leading to new perspectives for high positioning accuracy in robotics micromanipulation through active noise control.


title: Implicit self-esteem compensation: Automatic threat defense.

abstract: Four experiments demonstrated implicit self-esteem compensation (ISEC) in response to threats involving gender identity (Experiment 1), implicit racism (Experiment 2), and social rejection (Experiments 3–4). Under conditions in which people might be expected to suffer a blow to self-worth, they instead showed high scores on 2 implicit self-esteem measures. There was no comparable effect on explicit self-esteem. However, ISEC was eliminated following self-affirmation (Experiment 3). Furthermore, threat manipulations increased automatic intergroup bias, but ISEC mediated these relationships (Experiments 2–3). Thus, a process that serves as damage control for the self may have negative social consequences. Finally, pretest anxiety mediated the relationship between threat and ISEC (Experiment 3), whereas ISEC negatively predicted anxiety among high-threat participants (Experiment 4), suggesting that ISEC may function to regulate anxiety. The implications of these findings for automatic emotion regulation, intergroup bias, and implicit self-esteem measures are discussed.


title: A survey on vision-based human action recognition

abstract: Vision-based human action recognition is the process of labeling image sequences with action labels. Robust solutions to this problem have applications in domains such as visual surveillance, video retrieval and human-computer interaction. The task is challenging due to variations in motion performance, recording settings and inter-personal differences. In this survey, we explicitly address these challenges. We provide a detailed overview of current advances in the field. Image representations and the subsequent classification process are discussed separately to focus on the novelties of recent research. Moreover, we discuss limitations of the state of the art and outline promising directions of research.


The Labeled Segmentation of Printed Books


title: Quality driven web services composition

abstract: The process-driven composition of Web services is emerging as a promising approach to integrate business applications within and across organizational boundaries. In this approach, individual Web services are federated into composite Web services whose business logic is expressed as a process model. The tasks of this process model are essentially invocations to functionalities offered by the underlying component services. Usually, several component services are able to execute a given task, although with different levels of pricing and quality. In this paper, we advocate that the selection of component services should be carried out during the execution of a composite service, rather than at design-time. In addition, this selection should consider multiple criteria (e.g., price, duration, reliability), and it should take into account global constraints and preferences set by the user (e.g., budget constraints). Accordingly, the paper proposes a global planning approach to optimally select component services during the execution of a composite service. Service selection is formulated as an optimization problem which can be solved using efficient linear programming methods. Experimental results show that this global planning approach outperforms approaches in which the component services are selected individually for each task in a composite service.


title: A 2.1-Channel Class-D Amplifier Exploited Coupling Virtual-Audio-Image to Enhance Stereo

abstract: This brief presents a novel 2.1-channel stereo-enhanced class-D audio amplifier to improve the quality of stereophonic sound in portable devices. By a way of cross coupling, the input audio signal from one channel can be amplified at the same time, with different gains in the two channels. The ratio of gains in the two channels can be adjusted by changing the resistance and then making the position of the virtual-audio-image change, which improves the stereo effect. The bass signals from different channels, which frequency range between 30 and 300 Hz, are superimposed and then amplified in the independent bass channel by using the Sallen–Key bandpass filter. A practical implementation in the SMIC 0.18- $\mu\hbox{m}$ CMOS process has been done to validate the theoretical results. When a 200- $\hbox{mV}_{\rm PP}$ sinusoidal signal at 1 kHz is applied in one channel and a dc signal applied in the other channel, the amplifier demonstrates a gain difference of $-$ 9.4 dB. The simulation and experiment results show that the amplifier can deliver 2.5 W $\times$ 2 into 4- $\Omega$ loads at 3.6-V supply in the left or right channel, with the total harmonic distortion plus noise (THD $+$ N) of less than 0.1%. The gain in the bass channel is up to 30 dB at 3.6-V power supply, with the THD $+$ N of about 0.05%.


title: Zero-Shot Relation Extraction via Reading Comprehension

abstract: We show that relation extraction can be reduced to answering simple reading comprehension questions, by associating one or more natural-language questions with each relation slot. This reduction has several advantages: we can (1) learn relation-extraction models by extending recent neural reading-comprehension techniques, (2) build very large training sets for those models by combining relation-specific crowd-sourced questions with distant supervision, and even (3) do zero-shot learning by extracting new relation types that are only specified at test-time, for which we have no labeled training examples. Experiments on a Wikipedia slot-filling task demonstrate that the approach can generalize to new questions for known relation types with high accuracy, and that zero-shot generalization to unseen relation types is possible, at lower accuracy levels, setting the bar for future work on this task.


title: The emergence of social order in a robotic society

abstract: The article presents a general model of the emergence of social order in multi-agent-systems (MAS). The agents consist of two types of neural networks that have the task to generate social actions as their output and to adjust these actions to the actions of other agents. The result is a form of social order, i.e., a set of rules of interaction. The agents can generalize these rules by applying them on new but similar agents. An example is given how this model could be applied to the interaction of humans and roboters for some tasks of NASA.


Bundle adjustment rules


title: The Cambridge Face Memory Test: Results for neurologically intact individuals and an investigation of its validity using inverted face stimuli and prosopagnosic participants

abstract: The two standardized tests of face recognition that are widely used suffer from serious shortcomings [Duchaine, B. & Weidenfeld, A. (2003). An evaluation of two commonly used tests of unfamiliar face recognition. Neuropsychologia, 41, 713–720; Duchaine, B. & Nakayama, K. (2004). Developmental prosopagnosia and the Benton Facial Recognition Test. Neurology, 62, 1219–1220]. Images in the Warrington Recognition Memory for Faces test include substantial non-facial information, and the simultaneous presentation of faces in the Benton Facial Recognition Test allows feature matching. Here, we present results from a new test, the Cambridge Face Memory Test, which builds on the strengths of the previous tests. In the test, participants are introduced to six target faces, and then they are tested with forced choice items consisting of three faces, one of which is a target. For each target face, three test items contain views identical to those studied in the introduction, five present novel views, and four present novel views with noise. There are a total of 72 items, and 50 controls averaged 58. To determine whether the test requires the special mechanisms used to recognize upright faces, we conducted two experiments. We predicted that controls would perform much more poorly when the face images are inverted, and as predicted, inverted performance was much worse with a mean of 42. Next we assessed whether eight prosopagnosics would perform poorly on the upright version. The prosopagnosic mean was 37, and six prosopagnosics scored outside the normal range. In contrast, the Warrington test and the Benton test failed to classify a majority of the prosopagnosics as impaired. These results indicate that the new test effectively assesses face recognition across a wide range of abilities.


title: Modeling, Control, and Seamless Transition of the Bidirectional Battery-Driven Switched Reluctance Motor/Generator Drive Based on Integrated Multiport Power Converter for Electric Vehicle Applications

abstract: This paper proposes a generalized control method for battery-driven switched reluctance motor (SRM) drives based on integrated multiport power converter (IMPC) with small-ripple bidirectional power flow ability in electric vehicles. Large power ripple caused by the current commutation is a challenge issue for SRM drives, which would lower the system efficiency, requires extra heat dissipation in the battery and shortens its lifetime. The detailed model of the SRM drive and IMPC is developed which covers both motoring and generating modes of operation. Based on this model, an advanced multiobjective power flow control method with repetitive controller is proposed to restrain battery current ripple. Because of the unified model, a seamless transition between various modes can also be achieved. Finally, simulation and experimental results demonstrate the feasibility and superior performance of the proposed control method.


title: Constant frequency aircraft electric power systems with harmonic reduction

abstract: The ever-increasing number of power electronic converters connected to the aircraft electric power system significantly increases harmonic levels and voltage transients in that system. Stringent limits on harmonic current distortion and perturbation of the aircraft electric power supply demands accurate simulation and development of high performance filters for the mitigation of harmonics and minimization of system transients. This paper presents the simulation, modeling, and transient analysis of conventional and advanced aircraft electric power systems with harmonic mitigation using passive harmonic filters. The aircraft electric power system is analyzed considering equivalent passive AC and DC loads under transient and steady state operating conditions. The electric power source is simulated by a controlled motor-generator set to provide a constant frequency and a constant output voltage source. The DC power is generated using 12-pulse converters. Moreover, to mitigate the harmonics generated by the converters a passive input filter is designed to meet the harmonic standards for the AC side of the aircraft electric power system.


title: Thread Priority Sensitive Simultaneous Multi-Threading Fair Scheduling Strategy

abstract: Simultaneous multi-threading processors can improve performance through permitting multiple independent threads to issue multiple instructions each cycle. However, all the existing simultaneous multi-threading resource allocation strategies allocate the resources according to the status of instructions from the threads and without considering the thread priorities that operating system endows. In this paper, a thread-priority sensitive simultaneous multi-threading fair scheduling strategy, which combines with the instructions executed status and thread priority and takes advantage of ICOUNT and improved DWarn strategy, runs the threads of simultaneous multi-threading fairly from the perspective of the operating system under the conditions of no loss of processor performance.


title: Inverse Combinatorial Optimization: A Survey on Problems, Methods, and Results

abstract: Given a (combinatorial) optimization problem and a feasible solution to it, the corresponding inverse optimization problem is to find a minimal adjustment of the cost function such that the given solution becomes optimum.


DESIGN AND PERFORMANCE ANALYSIS OF MILLIMETRE-WAVE ROTMAN LENS-BASED ARRAY BEAMFORMING NETWORKS FOR LARGE-SCALE ANTENNA SUBSYSTEMS


title: Leaf classification using marginalized shape context and shape+texture dual-path deep convolutional neural network

abstract: Identifying plant species based on photographs of their leaves is an important problem in computer vision and biology. Previous approaches for leaf image classification typically rely on hand-crafted shape features or texture features. In contrast, we propose a dual-path deep convolutional neural network (CNN) to (i) learn joint feature representations for leaf images, exploiting their shape and texture characteristics, and (ii) optimize these features for the classification task. We compare our CNN approach against (i) vanilla CNN classifiers and (ii) popular hand-crafted shape features, including a novel shape-context based feature that is extremely computationally efficient, which we call the marginalized shape context. Our results on three large public datasets demonstrate that our dual-path CNN leads to higher accuracy and consistency than the state of the art.


title: Sponsor-event congruence effects: The moderating role of sport involvement and mediating role of sponsor attitudes

abstract: Abstract The authors examine the moderating effect of sport involvement in the association between sponsor-event congruence and consumer responses. University students (N = 118) took part in the experiment. The partial least squares structural equation modeling (PLS-SEM) results show that sport involvement moderates the effectiveness of sponsor-event congruence on sponsor credibility, influencing attitude toward the sponsor and intention to purchase the sponsor’s product. Research findings imply that a sponsorship campaign, in which sponsor-event congruence occurs, may have the power to deliver a product relevant message to consumers who are involved in sports via a central route.


title: Developing Monolingual English Corpus for Plagiarism Detection using Human Annotated Paraphrase Corpus

abstract: In this paper, we describe an approach to create monolingual English plagiarism detection corpus for the task of text alignment corpus construction in PAN 2015 competition. We propose two different obfuscation methods to fragment obfuscation for creating the cases of plagiarism. The first method is an artificial obfuscation which consists of variety of obfuscation strategies such as synonym substitution, random change of order, POS preserving change of order and addition/deletion. The second obfuscation method is a simulated obfusca- tion, in which the SemEval dataset is used for creating the cases of plagiarism by using pairs of sentences with their similarity scores.


title: The Selfish Side of Self‐Control

abstract: Self-control is a powerful tool that promotes goal pursuit by helping individuals curb personal desires, follow norms, and adopt rational thinking. In interdependent social contexts, the socially acceptable (i.e. normative) and rational approach to secure long-term goals is prosocial behaviour. Consistent with that, much research associates self-control with prosociality. The present research demonstrates that when norm salience is reduced (i.e. social relations are no longer interdependent), high self-control leads to more selfish behaviour when it is economically rational. In three studies, participants were asked to allocate an endowment between themselves and another person (one-round, zero-sum version of the dictator game), facing a conflict between a socially normative and an economically rational approach. Across the studies, norm salience was manipulated [through manipulation of social context (private/public; Studies 1 and 2), measurement of social desirability (Studies 1 and 3), and measurement (Study 2) and manipulation (Study 3) of social power] such that some participants experienced low normative pressure. Findings showed that among individuals in a low normative pressure context, self-control led to economically rational, yet selfish, behaviour. The findings highlight the role of self-control in regulating behaviour so as to maximize situational adaptation. Copyright © 2014 European Association of Personality Psychology


title: Autonomous driving simulator for educational purposes

abstract: Interest in robotics field as a teaching tool to promote the STEM areas — Science, Technology, Engineering and Mathematics has grown in the past years. The search for costless solutions to promote robotics is a major challenge and the use of real robots always increases associated costs. An alternative to this is the use of a simulator. The construction of a simulator related with the Portuguese Autonomous Driving Competition using Gazebo as 3D simulator and Robotics Operating System (ROS) as a middleware connection to promote, attract, and enthusiasm university students to the mobile robotics challenges is presented. The proposed simulator focus on the autonomous driving competition task, such as semaphore recognition, localization, and motion control. An evaluation of the simulator is also performed, leading to an absolute error of 5.11% and a relative error of 2.76% on best case scenarios relating to the odometry tests and an accuracy of 99.37% regarding to the semaphore recognition tests performed.


title: Image super-resolution using gradient profile prior

abstract: In this paper, we propose an image super-resolution approach using a novel generic image prior - gradient profile prior, which is a parametric prior describing the shape and the sharpness of the image gradients. Using the gradient profile prior learned from a large number of natural images, we can provide a constraint on image gradients when we estimate a hi-resolution image from a low-resolution image. With this simple but very effective prior, we are able to produce state-of-the-art results. The reconstructed hi-resolution image is sharp while has rare ringing or jaggy artifacts.


Haptic Feedback for Virtual Reality


title: Spatial Augmented Reality: Merging Real and Virtual Worlds

abstract: Like virtual reality, augmented reality is becoming an emerging platform in new application areas for museums, edutainment, home entertainment, research, industry, and the art communities using novel approaches which have taken augmented reality beyond traditional eye-worn or hand-held displays. In this book, the authors discuss spatial augmented reality approaches that exploit optical elements, video projectors, holograms, radio frequency tags, and tracking technology, as well as interactive rendering algorithms and calibration techniques in order to embed synthetic supplements into the real environment or into a live video of the real environment. Special Features: - Comprehensive overview - Detailed mathematical equations - Code fragments - Implementation instructions - Examples of Spatial AR displays The authors have put together a preliminary collection of Errata. Updates will be posted to this site as necessary.


title: A kernel approach to comparing distributions

abstract: We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a Reproducing Kernel Hilbert Space. We apply this technique to construct a two-sample test, which is used for determining whether two sets of observations arise from the same distribution. We use this test in attribute matching for databases using the Hungarian marriage method, where it performs strongly. We also demonstrate excellent performance when comparing distributions over graphs, for which no alternative tests currently exist.


title: Spatiotemporal QRST cancellation method using separate QRS and T-waves templates

abstract: The standard ECG remains the most common non-invasive tool for diagnosing and studying atrial fibrillation (AF). Due to the much higher amplitude of the electrical ventricular activity in the surface ECG, isolation of the atrial activity component is crucial to the analysis and characterization of AF. An average beat subtraction (ABS) based method is developed to perform this QRST cancellation. In contrast with standard methods, two sets of templates are created instead of one: one set for the QRS complexes and one for the T-waves. The QRS complexes are clustered according to their morphology; the T-waves, using both their preceding RR interval and their morphology. Next, spatial optimization (rotation and scaling) is applied to the QRS templates. ECG signals generated by a biophysical model are used to evaluate the performance of the proposed method in comparison with two other QRST cancellation methods. The proposed method decreases the averaged relative error by 19.7% and 29.0% in comparison with the standard ABS and the standard spatiotemporal method, respectively


title: A machine program for theorem-proving

abstract: The programming of a proof procedure is discussed in connection with trial runs and possible improvements.


title: Program-Adaptive Mutational Fuzzing

abstract: We present the design of an algorithm to maximize the number of bugs found for black-box mutational fuzzing given a program and a seed input. The major intuition is to leverage white-box symbolic analysis on an execution trace for a given program-seed pair to detect dependencies among the bit positions of an input, and then use this dependency relation to compute a probabilistically optimal mutation ratio for this program-seed pair. Our result is promising: we found an average of 38.6% more bugs than three previous fuzzers over 8 applications using the same amount of fuzzing time.


title: Software-based resolver-to-digital converter using the PLL tracking algorithm

abstract: In this paper, an all-digital resolver-to-digital conversion (RDC) system using phase-locked loop (PLL) tracking algorithm is proposed. Excitation signal is obtained from PWM module of a TMS320F2812 digital signal processor (DSP). According to the excitation signal, the resolver outputs differential sine and cosine signals, which are sampled by a 16-bit peripheral AD7606 module. The rotor angle position is estimated with the PLL tracking algorithm based on DSP. The proposed RDC system is applied to a servo synchronous motor with a resolver coupled to the shaft, and the electrical error of the resolver is ±10′. Finally, responding simulations and experiments are carried out. The results demonstrate that the RDC system has the features with simple hardware structure and low cost, and high-precision resolver-to-digital conversion is achieved.


title: Conservative surgical management of subungual (matrix derived) melanoma: report of seven cases and literature review

abstract: BACKGROUND ::: Subungual melanoma (SUM) is a rare entity, comprising approximately 0·7-3·5% of all melanoma subtypes. SUM histopathologically belongs to the acral lentiginous pathological subtype of malignant melanoma. Its diagnosis is helped by dermoscopy but pathological examination of doubtful cases is required. Classical management of SUM is based on radical surgery, namely distal phalanx amputation. Conservative treatment with nonamputative wide excision of the nail unit followed by a skin graft has been insufficiently reported in the medical literature even though it is performed in many centres. ::: ::: ::: OBJECTIVES ::: To report a series of patients with in situ or minimally invasive SUM treated by conservative surgery, to investigate the postoperative evolution and to evaluate the outcome with a review of the literature. ::: ::: ::: METHODS ::: We performed a retrospective extraction study from our melanoma register of all patients with in situ and minimally invasive SUM treated with conservative surgery in the University Hospital Department of Dermatology, Lyon, France from 2004 to 2009. The patient demographics, disease presentation, delay to diagnosis, histopathology and postoperative evolution were reviewed. ::: ::: ::: RESULTS ::: Seven cases of SUM treated as such were identified in our melanoma database. All cases had a clinical presentation of melanonychia striata. The mean delay to diagnosis was 2years. Surgical excision of the entire nail unit with a 5-10mm safety margin without bone resection followed by full-thickness skin graft taken from the arm was performed in all cases. No recurrence was observed with a mean follow-up of 45months. Functional results were found satisfactory by all patients and their referring physicians. Sixty-two other cases have been found in the literature and are also discussed. ::: ::: ::: CONCLUSIONS ::: Conservative surgical management in patients with in situ or minimally invasive SUM is a procedure with good cosmetic and functional outcome and, in our cases as well as in the literature, the prognosis is not changed.


Hereditary type IIb protein S deficiency in a patient with recurrent venous ulcers.


title: Incoherence of a concept image and erroneous conclusions in the case of differentiability

abstract: The level of the coherence of a concept image conveys how well the cognitive structure concerning the concept is organized. This study considers the relationship between deficiencies in the coherence of the concept image and erroneous conclusions in the case of differentiability. The study is based on an interview where the student made conclusions contradictory to the formal theory of mathematics. He used an erroneous method to study the differentiability of piecewise defined functions. This method became the key factor which maintained the internal coherence of the concept image. It made it possible to build a cognitive structure whose basis was erroneous.


Multi-Transfer: Transfer Learning with Multiple Views and Multiple Sources


title: Knowledge for the good of the individual and society: linking philosophy, disciplinary goals, theory, and practice.

abstract: Nursing as a profession has a social mandate to contribute to the good of society through knowledge-based practice. Knowledge is built upon theories, and theories, together with their philosophical bases and disciplinary goals, are the guiding frameworks for practice. This article explores a philosophical perspective of nursing's social mandate, the disciplinary goals for the good of the individual and society, and one approach for translating knowledge into practice through the use of a middle-range theory. It is anticipated that the integration of the philosophical perspective and model into nursing practice will strengthen the philosophy, disciplinary goal, theory, and practice links and expand knowledge within the discipline. With the focus on humanization, we propose that nursing knowledge for social good will embrace a synthesis of the individual and the common good. This approach converges vital and agency needs described by Hamilton and the primacy of maintaining the heritage of the good within the human species as outlined by Maritain. Further, by embedding knowledge development in a changing social and health care context, nursing focuses on the goals of clinical reasoning and action. McCubbin and Patterson's Double ABCX Model of Family Adaptation was used as an example of a theory that can guide practice at the community and global level. Using the theory-practice link as a foundation, the Double ABCX model provides practising nurses with one approach to meet the needs of individuals and society. The integration of theory into nursing practice provides a guide to achieve nursing's disciplinary goals of promoting health and preventing illness across the globe. When nursing goals are directed at the synthesis of the good of the individual and society, nursing's social and moral mandate may be achieved.


title: Multimodal two-photon imaging using a second harmonic generation-specific dye

abstract: Second harmonic generation (SHG) imaging can be used to visualize unique biological phenomena, but currently available dyes limit its application owing to the strong fluorescent signals that they generate together with SHG. Here we report the first non-fluorescent and membrane potential-sensitive SHG-active organic dye Ap3. Ap3 is photostable and generates SH signals at the plasma membrane with virtually no fluorescent signals, in sharp contrast to the previously used fluorescent dye FM4-64. When tested in neurons, Ap3-SHG shows linear membrane potential sensitivity and fast responses to action potentials, and also shows significantly reduced photodamage compared with FM4-64. The SHG-specific nature of Ap3 allows simultaneous and completely independent imaging of SHG signals and fluorescent signals from various reporter molecules, including markers of cellular organelles and intracellular calcium. Therefore, this SHG-specific dye enables true multimodal two-photon imaging in biological samples.


title: Political campaigning 2.0: The influence of online news and social networking sites on attitudes and behavior

abstract: This study aimed to examine differences in influence between online news (e.g., New York Times) and social networking sites (e.g., Facebook and Twitter) on attitudes in political campaigns. In a we ...


title: Georgia tech information security center hands-on network security laboratory

abstract: An undergraduate internetwork, security-teaching laboratory, which includes both defensive and offensive security laboratory experimentation, is described. This laboratory is oriented toward an introductory internetworking security class and is intended to complement more theoretical network security classes while sparking student interest. The laboratory is unique in that it uses an isolated laboratory network that provides a simple model of the Internet, including an enterprise network component, a university component, a "good" Internet service provider, and a "bad" Internet service provider. This setup is in contrast to typical educational laboratories, which use only a few physical computers with virtual machines. All of the laboratory assignments are available on the Internet for general community use and modification (the Internetwork Security Class Home Page is available at http://users.ece.gatech.edu//spl ap/owen/).


title: Forward Thinking: Building and Training Neural Networks One Layer at a Time

abstract: We present a general framework for training deep neural networks without backpropagation. This substantially decreases training time and also allows for construction of deep networks with many sorts of learners, including networks whose layers are defined by functions that are not easily differentiated, like decision trees. The main idea is that layers can be trained one at a time, and once they are trained, the input data are mapped forward through the layer to create a new learning problem. The process is repeated, transforming the data through multiple layers, one at a time, rendering a new data set, which is expected to be better behaved, and on which a final output layer can achieve good performance. We call this forward thinking and demonstrate a proof of concept by achieving state-of-the-art accuracy on the MNIST dataset for convolutional neural networks. We also provide a general mathematical formulation of forward thinking that allows for other types of deep learning problems to be considered.


The cmu motion of body (mobo) database


title: A Dual-Polarized Two-Dimensional Beam-Steering Fabry–Pérot Cavity Antenna With a Reconfigurable Partially Reflecting Surface

abstract: A dual-polarized two-dimensional beam steering Fabry-Perot cavity antenna with a reconfigurable partially reflecting surface (PRS) is proposed. A polarization reconfigurable aperture coupled patch antenna, which can work in two different linear polarizations, is adopted as the exciter. By controlling the PIN diodes, the PRS can have different reflection phase distributions, giving the antenna different beam tilted states in eight azimuthal directions. The PRS has the same function on different linear polarization waves, so the antenna can realize polarization reconfiguration and beam steering simultaneously. A prototype antenna is fabricated and measured. The measured results show that, beam angles greater than 8° were obtained for each azimuthal direction at 5.5 GHz. Good agreement between the simulated and measured results was achieved, which confirms the correctness of the design principle.


title: A Survey on Unit Testing Practices and Problems

abstract: Unit testing is a common practice where developers write test cases together with regular code. Automation frameworks such as JUnit for Java have popularised this approach, allowing frequent and automatic execution of unit test suites. Despite the appraisals of unit testing in practice, software engineering researchers see potential for improvement and investigate advanced techniques such as automated unit test generation. To align such research with the needs of practitioners, we conducted a survey amongst 225 software developers, covering different programming languages and 29 countries, using a global online marketing research platform. The survey responses confirm that unit testing is an important factor in software development, and suggest that there is indeed potential and need for research on automation of unit testing. The results help us to identify areas of importance on which further research will be necessary (e.g., Maintenance of unit tests), and also provide insights into the suitability of online marketing research platforms for software engineering surveys.


title: Deep Neural Networks with Relativity Learning for facial expression recognition

abstract: Facial expression recognition aims to classify facial expression as one of seven basic emotions including “neutral”. This is a difficult problem due to the complexity and subtlety of human facial expressions, but the technique is needed in important applications such as social interaction research. Deep learning methods have achieved state-of-the-art performance in many tasks including face recognition and person re-identification. Here we present a deep learning method termed Deep Neural Networks with Relativity Learning (DNNRL), which directly learns a mapping from original images to a Euclidean space, where relative distances correspond to a measure of facial expression similarity. DNNRL updates the model parameters according to sample importance. This strategy results in an adjustable and robust model. Experiments on two representative facial expression datasets (FER-2013 and SFEW 2.0) are performed to demonstrate the robustness and effectiveness of DNNRL.


title: Big data in Parkinson’s disease: using smartphones to remotely detect longitudinal disease phenotypes

abstract: OBJECTIVE ::: To better understand the longitudinal characteristics of Parkinson's disease (PD) through the analysis of finger tapping and memory tests collected remotely using smartphones. ::: ::: ::: APPROACH ::: Using a large cohort (312 PD subjects and 236 controls) of participants in the mPower study, we extract clinically validated features from a finger tapping and memory test to monitor the longitudinal behaviour of study participants. We investigate any discrepancy in learning rates associated with motor and non-motor tasks between PD subjects and healthy controls. The ability of these features to predict self-assigned severity measures is assessed whilst simultaneously inspecting the severity scoring system for floor-ceiling effects. Finally, we study the relationship between motor and non-motor longitudinal behaviour to determine if separate aspects of the disease are dependent on one another. ::: ::: ::: MAIN RESULTS ::: We find that the test performances of the most severe subjects show significant correlations with self-assigned severity measures. Interestingly, less severe subjects do not show significant correlations, which is shown to be a consequence of floor-ceiling effects within the mPower self-reporting severity system. We find that motor performance after practise is a better predictor of severity than baseline performance suggesting that starting performance at a new motor task is less representative of disease severity than the performance after the test has been learnt. We find PD subjects show significant impairments in motor ability as assessed through the alternating finger tapping (AFT) test in both the short- and long-term analyses. In the AFT and memory tests we demonstrate that PD subjects show a larger degree of longitudinal performance variability in addition to requiring more instances of a test to reach a steady state performance than healthy subjects. ::: ::: ::: SIGNIFICANCE ::: Our findings pave the way forward for objective assessment and quantification of longitudinal learning rates in PD. This can be particularly useful for symptom monitoring and assessing medication response. This study tries to tackle some of the major challenges associated with self-assessed severity labels by designing and validating features extracted from big datasets in PD, which could help identify digital biomarkers capable of providing measures of disease severity outside of a clinical environment.


title: Litz wire design for wireless power transfer in electric vehicles

abstract: Eddy current losses in wireless charging systems for electric vehicles must be minimized in consideration of constraints like the available space, the strand diameter in litz wire and the cost. A mathematical model for eddy current losses is needed to find the optimum geometrical design for a given application. This paper gives the fundamental equations to calculate skin effect, proximity effect and dc losses in litz wire. Litz wire consists of strands, which are twisted to form multiple bundles. Therefore eddy current losses occur not only on the strand level but the bundle level as well. The losses caused by different effects and bundle levels are compared, and the influence of strand diameter and strand number on the total loss is examined. Finally, aluminum litz wire is analyzed. It becomes apparent that the overall loss can be reduced with the aid of replacing copper litz wire by aluminum in some areas of application. Material choice and geometrical design require detailed knowledge of the systems nominal operation point.


title: Dual-task interference in simple tasks: Data and theory

abstract: People often have trouble performing 2 relatively simple tasks concurrently. The causes of this interference and its implications for the nature of attentional limitations have been controversial for 40 years, but recent experimental findings are beginning to provide some answers. Studies of the psychological refractory period effect indicate a stubborn bottleneck encompassing the process of choosing actions and probably memory retrieval generally, together with certain other cognitive operations. Other limitations associated with task preparation, sensory-perceptual processes, and timing can generate additional and distinct forms of interference. These conclusions challenge widely accepted ideas about attentional resources and probe reaction time methodologies. They also suggest new ways of thinking about continuous dual-task performance, effects of extraneous stimulation (e.g.. stop signals), and automaticity. Implications for higher mental processes are discussed. For more than 100 years, psychologists have been interested in people's ability (or inability) to perform two or more activities concurrently. One reason these limitations provoke curiosity is simply that people wonder what is humanly possible. This question has obvious significance for practical problems such as designing interfaces to prevent operators from becoming overloaded or predicting what a pilot can do in an emergency. There is also an important scientific reason to try to understand dual-task performance limitations: Overloading a system is often one of the best ways to figure out what the parts of the system are and how these parts function together. For this reason, studying dual-task interference provides an important window on basic questions about the functional architecture of the brain. For certain of these questions—such as whether human cognitive architecture includes a central processor—dual-task studies may provide the only avenue of study. Ordinarily, people are not aware of having much difficulty performing different activities at the same time unless the tasks are either physically incompatible (e.g., typing and drinking coffee) or intellectually demanding (e.g.. conversing and adding up the check in a restaurant). Casual observation of people's behavior outside the laboratory seems to support this impression: People apparently have conversations at the same time they are driving, read magazines while they run exercise bicycles, chew gum while they walk, and so forth. It might seem, therefore, that one would have to look at rather exceptional activities to find much dual-task interference. Laboratory studies show just the opposite, however: Many pairs of tasks interfere with each other quite drastically, even though they are neither intellectually challenging nor physically incompatible.


title: Reliability and validity of the Japanese version of the child abuse potential inventory abuse scale

abstract: Introduction: The present study examined the reliability, validity, and cutoff scores of the Japanese version of the Child Abuse Potential Inventory (CAPI) abuse scale, which screens for parents at risk of child abuse. ::: ::: Methods: Samples consisted of 1,809 parents, 109 students, and 33 child abusers in Japan. The CAPI was administered to all participants, and twice to the student sample at a 2-week interval. Internal consistency was evaluated by Cronbach's α, and construct validity by principal factor analysis with the parent sample. Test-retest reliability was assessed with Pearson's r with the student sample. With regard to predictive validity, the cutoff scores and classification rates were calculated through discriminant analysis between abusers and matched non-abusers. ::: ::: Results: Internal consistency (Cronbach's α=0.88), test-retest reliability (Pearson's r=0.93; two-tailed P<0.001), and predictive validity (overall diagnostic power=90%) were all highly satisfactory. Regarding construct validity, the six-factor structure of the original version was not replicated; only three factors were obtained. The discriminant analysis showed the basic cutoff score as 159. The conservative cutoff score for the upper 5% of the parent sample was 218. ::: ::: Discussion: The Japanese version of the CAPI abuse scale showed highly satisfactory internal consistency, test-retest reliability, and predictive validity. The construct correspondence with the original version was more compromised. This version of the CAPI identified parents with scores of 159 or greater as being at risk of abusing a child.


title: Arch Sex Behav (2010) 39:880–897 DOI 10.1007/s10508-009-9546-y ORIGINAL PAPER Women’s Sexual Responses to Heterosexual and Lesbian Erotica: The Role of Stimulus Intensity, Affective Reaction, and Sexual History

abstract: Past research has demonstrated that women do not show a “category-specific” genital response to erotic stimuli. That is, on average, heterosexual and lesbian women are indistinguishable in terms of their physiological genital responses to heterosexual versus lesbian erotica. In two studies with heterosexual women (n = 28 for Study 1; n = 30 for Study 2) and lesbians (n = 24 for Study 1; n = 25 for Study 2), results confirmed that, on average, women did not show category-specific genital responses or category-specific subjective sexual arousal. However, there was evidence of notable within-group variability; many women did respond to the stimuli in a category-specific manner. Heterosexual women were more likely than lesbian women to demonstrate category-specificity. Findings also revealed that category-specificity was associated with multiple factors, including affective responses to the erotic stimuli and sexual history. Results of this study highlight the complexity of women’s sexual identities and sexual responses.


title: Distributed analogical idea generation: inventing with crowds

abstract: Harnessing crowds can be a powerful mechanism for increasing innovation. However, current approaches to crowd innovation rely on large numbers of contributors generating ideas independently in an unstructured way. We introduce a new approach called distributed analogical idea generation, which aims to make idea generation more effective and less reliant on chance. Drawing from the literature in cognitive science on analogy and schema induction, our approach decomposes the creative process in a structured way amenable to using crowds. In three experiments we show that distributed analogical idea generation leads to better ideas than example-based approaches, and investigate the conditions under which crowds generate good schemas and ideas. Our results have implications for improving creativity and building systems for distributed crowd innovation.


title: Optimization of electrochemical hydride generation coupled to microwave-induced plasma atomic emission spectrometry for the determination of arsenic and its use for the analysis of biological tissue samples

abstract: Argon and helium MIPs operated in a surfatron and in a TE101 resonator were coupled to an electrochemical HG cell for the determination of As by AES. The optimization of this new type of HG used for the direct introduction of AsH3 into the plasma, included variations of the microwave power, sample flow rate, acid flow rate, acid concentration, gas flow rate and current in the electrochemical flow cell. Compared with chemical HG using NaBH4, the detection limits for As could be improved by a factor of two. The best detection limit (3σ) for As was achieved with the Ar MIP operated in a surfatron (1 ng ml–1). It was also shown that interferences of transition metals and of other hydride forming elements are lower than with classical HG. The TE101 resonator, which shows better stability and less interferences than the surfatron, was connected to a Pd-coated graphite furnace for preconcentration of the AsH3 by hot trapping at temperatures of between 50 and 200 °C. The transient As signal has good reproducibility (6%) and with preconcentration times of 1 min the detection limit for As is 25 pg ml–1. With a set-up combining electrochemical flow cell HG, preconcentration in a Pd-coated graphite furnace and MIP-AES using a TE101 resonator, reliable determinations of As in various digested biological samples at the 0.05–10µg g–1 level could be performed.


title: Design of Half Mode Substrate Integrated Waveguide (HMSIW) filter with series Defective Microstrip Structure (DMS) for Ku band operation

abstract: Design of a Half Mode SIW Filter (HMSIW) for Ku band operation has been presented in this paper. Defective Microstrip Structure (DMS) consisting of a single and multiple slots has been introduced and the effect has been studied. The structure is smaller in size and also cost effective due to the half TE10 mode. The effect of introducing single and multiple resonant slots to create stopband within the passband of the filter has been studied validated with simulation results. The proposed technique has been found quite useful in designing HMSIW bandpass filters.


title: Combining the Best of Graphical Models and ConvNets for Semantic Segmentation

abstract: We present a two-module approach to semantic segmentation that incorporates Convolutional Networks (CNNs) and Graphical Models. Graphical models are used to generate a small (5-30) set of diverse segmentations proposals, such that this set has high recall. Since the number of required proposals is so low, we can extract fairly complex features to rank them. Our complex feature of choice is a novel CNN called SegNet, which directly outputs a (coarse) semantic segmentation. Importantly, SegNet is specifically trained to optimize the corpus-level PASCAL IOU loss function. To the best of our knowledge, this is the first CNN specifically designed for semantic segmentation. This two-module approach achieves $52.5\%$ on the PASCAL 2012 segmentation challenge.


title: A Visual Category Filter for Google Images

abstract: We extend the constellation model to include heterogeneous parts which may represent either the appearance or the geometry of a region of the object. The parts and their spatial configuration are learnt simultaneously and automatically, without supervision, from cluttered images.


title: A Bayesian Foundation for Individual Learning Under Uncertainty

abstract: Computational learning models are critical for understanding mechanisms of adaptive behavior. However, the two major current frameworks, reinforcement learning (RL) and Bayesian learning, both have certain limitations. For example, many Bayesian models are agnostic of inter-individual variability and involve complicated integrals, making online learning difficult. Here, we introduce a generic hierarchical Bayesian framework for individual learning under multiple forms of uncertainty (e.g., environmental volatility and perceptual uncertainty). The model assumes Gaussian random walks of states at all but the first level, with the step size determined by the next higher level. The coupling between levels is controlled by parameters that shape the influence of uncertainty on learning in a subject-specific fashion. Using variational Bayes under a mean field approximation and a novel approximation to the posterior energy function, we derive trial-by-trial update equations which (i) are analytical and extremely efficient, enabling real-time learning, (ii) have a natural interpretation in terms of RL, and (iii) contain parameters representing processes which play a key role in current theories of learning, e.g., precision-weighting of prediction error. These parameters allow for the expression of individual differences in learning and may relate to specific neuromodulatory mechanisms in the brain. Our model is very general: it can deal with both discrete and continuous states and equally accounts for deterministic and probabilistic relations between environmental events and perceptual states (i.e., situations with and without perceptual uncertainty). These properties are illustrated by simulations and analyses of empirical time series. Overall, our framework provides a novel foundation for understanding normal and pathological learning that contextualizes RL within a generic Bayesian scheme and thus connects it to principles of optimality from probability theory.


title: A Survey of the Challenges, Opportunities and Use of Multiple Antennas in Current and Future 5G Small Cell Base Stations

abstract: Small cell base stations (SBSs) and multiple antennas are seen as fundamental technologies in the emergence of the next generation [i.e., 5th generation (5G)] of cellular wireless technology. This paper provides a comprehensive survey of literature relating to the applications and challenges associated with using multiple antennas in SBSs. The use of multiple antenna techniques in conventional wireless base stations has undergone much study and is widespread. With heterogeneity in current networks and a furthering of this theme together with greater densification expected in 5G systems, their use in SBSs is at an evolutionary stage. In this paper, unique design challenges associated with size, cost, and performance in SBSs are presented. We present a clear understanding of this increasingly important research area, identifying a clear classification of use and design guidelines. We present a state-of-the-art review of the literature to show how researchers are using and considering the use of multiple antennas in small cells. Attention is given to current generation networks, and with SBSs being a dominant technology necessary for 5G, we also provide insights into the design challenges in such possible future networks.


title: Modeling and Analysis of Attacks and Counter Defense Mechanisms for Cyber Physical Systems

abstract: In this paper, we develop an analytical model based on stochastic Petri nets to capture the dynamics between adversary behavior and defense for cyber physical systems. We consider several types of failures including attrition failure, pervasion failure, and exfiltration failure which can happen to a cyber physical system. Using a modernized electrical grid as an example, we illustrate the parameterization process. Our results reveal optimal design conditions, including the intrusion detection interval, and the redundancy level, under which the modernized electrical grid's mean time to failure is maximized. Further, there exists a design tradeoff between exfiltration failure, attrition failure, and pervasion failure when using redundancy to improve the overall system reliability.


Mental health awareness: The Indian scenario


title: Generating digital signatures on mobile devices

abstract: With the explosion of the mobile communication market, more and more handheld devices act as clients in the Internet. People use these devices to purchase books, play games, receive emails, etc. For protecting privacies, such applications should integrate digital signature schemes. Since handheld devices have poor computational capabilities and limited battery life, traditional computation intensive digital signature protocols that are based on asymmetric cryptographic algorithms are not suitable for mobile devices. We propose a server based signature (SBS) scheme for mobile devices. Besides achieving the same security level of the traditional digital signature protocols, the SBS scheme also: 1) reduces the computation complexity on the mobile devices; 2) reduces the communication consumption between signer and verifier. Application results show that our scheme is very useful for mobile communication systems.


title: Semi-Amortized Variational Autoencoders

abstract: Amortized variational inference (AVI) replaces instance-specific local inference with a global inference network. While AVI has enabled efficient training of deep generative models such as variational autoencoders (VAE), recent empirical work suggests that inference networks can produce suboptimal variational parameters. We propose a hybrid approach, to use AVI to initialize the variational parameters and run stochastic variational inference (SVI) to refine them. Crucially, the local SVI procedure is itself differentiable, so the inference network and generative model can be trained end-to-end with gradient-based optimization. This semi-amortized approach enables the use of rich generative models without experiencing the posterior-collapse phenomenon common in training VAEs for problems like text generation. Experiments show this approach outperforms strong autoregressive and variational baselines on standard text and image datasets.


title: Physically based shading in theory and practice

abstract: Real-Time Line and Disk Light Shading ::: Eric Heitz and Stephen Hill ::: At SIGGRAPH 2016, we presented a new real-time area lighting technique for polygonal sources. In this talk, we will show how the underlying framework, based on Linearly Transformed Cosines (LTCs), can be extended to support line and disk lights. We will discuss the theory behind these approaches as well as practical implementation tips and tricks concerning numerical precision and performance. ::: Physically Based Shading at DreamWorks Animation ::: Feng Xie and Jon Lanz ::: PDI/DreamWorks was one of the first animation studios to adopt global illumination in production rendering. Concurrently, we have also been developing and applying physically based shading principles to improve the consistency and realism of our material models, while balancing the need for intuitive artistic control required for feature animations. ::: In this talk, we will start by presenting the evolution of physically based shading in our films. Then we will present some fundamental principles with respect to importance sampling and energy conservation in our BSDF framework with a pragmatic and efficient approach to transimssion fresnel modeling. Finally, we will present our new set of physically plausible production shaders for our new path tracer, which includes our new hard surface shader, our approach to material layering and some new developments in fabric and glitter shading. ::: Volumetric Skin and Fabric Shading at Framestore ::: Nathan Walster ::: Recent advances in shading have led to the use of free-path sampling to better solve complex light transport within volumetric materials. In this talk, we describe how we have implemented these ideas and techniques within a production environment, their application on recent shows---such as Guardians of the Galaxy Vol. 2 and Alien: Covenant---and the effect this has had on artists' workflow within our studio. ::: Practical Multilayered Materials in Call of Duty: Infinite Warfare ::: Michał Drobot ::: This talk presents a practical approach to multilayer, physically based surface rendering, specifically optimized for Forward+ rendering pipelines. The presented pipeline allows for the creation of complex surface by decomposing them into different mediums, each represented by a simple BRDF/BSSRDF and set of simple, physical macro properties, such as thickness, scattering and absorption. The described model is explained via practical examples of common multilayer materials such as car paint, lacquered wood, ice, and semi-translucent plastics. Finally, the talk describes intrinsic implementation details for achieving a low performance budget for 60 Hz titles as well as supporting multiple rendering modes: opaque, alpha blend, and refractive blend. ::: Pixar's Foundation for Materials: PxrSurface and PxrMarschnerHair ::: Christophe Hery and Junyi Ling ::: Pixar's Foundation Materials, PxrSurface and PxrMarschnerHair, began shipping with RenderMan 21. ::: PxrSurface is the standard surface shader developed in the studio for Finding Dory and used more recently for Cars 3 and Coco. This shader contains nine lobes that cover the entire gamut of surface materials for these two films: diffuse, three specular, iridescence, fuzz, subsurface, single scatter and a glass lobe. Each of these BxDF lobes is energy conserving, but conservation is not enforced between lobes on the surface level. We use parameter layering methods to feed a PxrSurface with pre-layered material descriptions. This simultaneously allows us the flexibility of a multilayered shading pipeline together with efficient and consistent rendering behavior. ::: We also implemented our individual BxDFs with the latest state-of-the-art techniques. For example, our three specular lobes can be switched between Beckmann and GGX modes. Many compound materials have multiple layers of specular; these lobes interact with each other modulated by the Fresnel effect of the clearcoat layer. We also leverage LEADR mapping to recreate sub-displacement micro features such as metal flakes and clearcoat scratches. ::: Another example is that PxrSurface ships with Jensen, d'Eon and Burley diffusion profiles. Additionally, we implemented a novel subsurface model using path-traced volumetric scattering, which represents a significant advancement. It captures zero and single scattering events of subsurface scattering implicit to the path-tracing algorithm. The user can adjust the phase-function of the scattering events and change the extinction profiles, and it also comes with standardized color inversion features for intuitive albedo input. To the best of our knowledge, this is the first commercially available rendering system to model these features and the rendering cost is comparable to classic diffusion subsurface scattering models. ::: PxrMarschnerHair implements Marschner's seminal hair illumination model with importance sampling. We also account for the residual energy left after the R, TT, TRT and glint lobes, through a fifth diffuse lobe. We show that this hair surface shader can reproduce dark and blonde hair effectively in a path-traced production context. Volumetric scattering from fiber to fiber changes the perceived hue and saturation of a groom, so we also provide a color inversion scheme to invert input albedos, such that the artistic inputs are straightforward and intuitive. ::: Revisiting Physically Based Shading at Imageworks ::: Christopher Kulla and Alejandro Conty ::: Two years ago, the rendering and shading groups at Sony Imageworks embarked on a project to review the structure of our physically based shaders in an effort to simplify their implementation, improve quality and pave the way to take advantage of future improvements in light transport algorithms. ::: We started from classic microfacet BRDF building blocks and investigated energy conservation and artist friendly parametrizations. We continued by unifying volume rendering and subsurface scattering algorithms and put in place a system for medium tracking to improve the setup of nested media. Finally, from all these building blocks, we rebuilt our artist-facing shaders with a simplified interface and a more flexible layering approach through parameter blending. ::: Our talk will discuss the details of our various building blocks, what worked and what didn't, as well as some future research directions we are still interested in exploring.


title: Media multitasking and well-being of university students

abstract: This study examines the impact of media multitasking behaviors on university students' social and psychological well-being (indicated by social success, normalcy, and self-control measures). To address inconsistent findings in recent literature, we characterized media multitasking behaviors by motivations, characteristics, and contexts. In particular, we examined the motivation of the primary task and the synchronicity of the task when social interactions were involved. Synchronous social interactions were found to be significantly and positively associated with social success, normalcy, and self-control. However, as predicted, media multitasking during synchronous social interactions was associated with lower social success. Further, although increased media multitasking during cognitive activities was linked with decreased self-control, media multitasking during entertainment activities was correlated with increased social success, normalcy, and self-control. Media multitasking in different contexts influenced well-being variables differently.Media multitasking during synchronous social interactions was associated with lower social success.Increased media multitasking during cognitive activities was linked with decreased level of self-control.Media multitasking during entertainment activities was correlated to increased social success, normalcy, and self-control.


title: Cross-Lingual Sentiment Analysis Without (Good) Translation

abstract: Current approaches to cross-lingual sentiment analysis try to leverage the wealth of labeled English data using bilingual lexicons, bilingual vector space embeddings, or machine translation systems. Here we show that it is possible to use a single linear transformation, with as few as 2000 word pairs, to capture fine-grained sentiment relationships between words in a cross-lingual setting. We apply these cross-lingual sentiment models to a diverse set of tasks to demonstrate their functionality in a non-English context. By effectively leveraging English sentiment knowledge without the need for accurate translation, we can analyze and extract features from other languages with scarce data at a very low cost, thus making sentiment and related analyses for many languages inexpensive.


title: Wheelchair for physically disabled people with voice, ultrasonic and infrared sensor control

abstract: This paper describes a wheelchair for physically disabled people developed within the UMIDAM Project. A dependent-user recognition voice system and ultrasonic and infrared sensor systems has been integrated in this wheelchair. In this way we have obtained a wheelchair which can be driven with using voice commands and with the possibility of avoiding obstacles and downstairs or hole detection. The wheelchair has also been developed to allow autonomous driving (for example, following walls). The project, in which two prototypes have been produced, has been carried out totally in the Electronics Department of the University of Alcala (Spain). It has been financed by the ONCE. Electronic system configuration, a sensor system, a mechanical model, control (low level control, control by voice commands), voice recognition and autonomous control are considered. The results of the experiments carried out on the two prototypes are also given.


title: Brain-Inspired Deep Networks for Image Aesthetics Assessment

abstract: Image aesthetics assessment has been challenging due to its subjective nature. Inspired by the scientific advances in the human visual perception and neuroaesthetics, we design Brain-Inspired Deep Networks (BDN) for this task. BDN first learns attributes through the parallel supervised pathways, on a variety of selected feature dimensions. A high-level synthesis network is trained to associate and transform those attributes into the overall aesthetics rating. We then extend BDN to predicting the distribution of human ratings, since aesthetics ratings are often subjective. Another highlight is our first-of-its-kind study of label-preserving transformations in the context of aesthetics assessment, which leads to an effective data augmentation approach. Experimental results on the AVA dataset show that our biological inspired and task-specific BDN model gains significantly performance improvement, compared to other state-of-the-art models with the same or higher parameter capacity.


title: How Generative Adversarial Nets and its variants Work: An Overview of GAN.

abstract: Generative Adversarial Networks gets wide attention in machine learning field because of its massive potential to learn high dimensional, complex real data. Specifically, it does not need to do further distribution assumption and can simply infer real-like samples from latent space. This powerful property leads GAN to be applied various applications such as image synthesis, image attribute editing and semantically decomposing of image. In this review paper, we look into details of GAN that firstly show how it operates and fundamental meaning of objective functions and point to GAN variants applied to vast amount of tasks.


DESIGN SCIENCE IN INFORMATION SYSTEMS RESEARCH 1


The BellKor solution to the Netflix Prize


title: Super Resolution of the Partial Pixelated Images With Deep Convolutional Neural Network

abstract: The problem of super resolution of partial pixelated images is considered in this paper. Partial pixelated images are more and more common in nowadays due to public safety etc. However, in some special cases, for instance criminal investigation, some images are pixelated intentionally by criminals and partial pixelate make it hard to reconstruct images even a higher resolution images. Hence, a method is proposed to handle this problem based on the deep convolutional neural network, termed depixelate super resolution CNN(DSRCNN). Given the mathematical expression pixelates, we propose a model to reconstruct the image from the pixelation and map to a higher resolution by combining the adversarial autoencoder with two depixelate layers. This model is evaluated on standard public datasets in which images are pixelated randomly and compared to the state of arts methods, shows very exciting performance.


title: Trading rules on stock markets using genetic network programming with sarsa learning

abstract: In this paper, the Genetic Network Programming (GNP) for creating trading rules on stocks is described. GNP is an evolutionary computation, which represents its solutions using graph structures and has some useful features inherently. It has been clarified that GNP works well especially in dynamic environments since GNP can create quite compact programs and has an implicit memory function. In this paper, GNP is applied to creating a stock trading model. There are three important points: The first important point is to combine GNP with Sarsa Learning which is one of the reinforcement learning algorithms. Evolution-based methods evolve their programs after task execution because they must calculate fitness values, while reinforcement learning can change programs during task execution, therefore the programs can be created efficiently. The second important point is that GNP uses candlestick chart and selects appropriate technical indices to judge the buying and selling timing of stocks. The third important point is that sub-nodes are used in each node to determine appropriate actions (buying/selling) and to select appropriate stock price information depending on the situation. In the simulations, the trading model is trained using the stock prices of 16 brands in 2001, 2002 and 2003. Then the generalization ability is tested using the stock prices in 2004. From the simulation results, it is clarified that the trading rules of the proposed method obtain much higher profits than Buy&Hold method and its effectiveness has been confirmed.


title: Approximating Attack Surfaces with Stack Traces

abstract: Security testing and reviewing efforts are a necessity for software projects, but are time-consuming and expensive to apply. Identifying vulnerable code supports decision-making during all phases of software development. An approach for identifying vulnerable code is to identify its attack surface, the sum of all paths for untrusted data into and out of a system. Identifying the code that lies on the attack surface requires expertise and significant manual effort. This paper proposes an automated technique to empirically approximate attack surfaces through the analysis of stack traces. We hypothesize that stack traces from user-initiated crashes have several desirable attributes for measuring attack surfaces. The goal of this research is to aid software engineers in prioritizing security efforts by approximating the attack surface of a system via stack trace analysis. In a trial on Windows 8, the attack surface approximation selected 48.4% of the binaries and contained 94.6% of known vulnerabilities. Compared with vulnerability prediction models (VPMs) run on the entire codebase, VPMs run on the attack surface approximation improved recall from .07 to .1 for binaries and from .02 to .05 for source files. Precision remained at .5 for binaries, while improving from .5 to .69 for source files.


title: Cognitive function and walking velocity in people with dementia; a comparison of backward and forward walking

abstract: How forward and backward walking, both central to everyday life, relate to cognition are relatively unexplored in people with dementia. This study aimed to investigate if forward and backward walki ...


title: Can Twitter replace Newswire for breaking news?

abstract: Twitter is often considered to be a useful source of real-time news, potentially replacing newswire for this purpose. But is this true? In this paper, we examine the extent to which news reporting in newswire and Twitter overlap and whether Twitter often reports news faster than traditional newswire providers. In particular, we analyse 77 days worth of tweet and newswire articles with respect to both manually identified major news events and larger volumes of automatically identified news events. Our results indicate that Twitter reports the same events as newswire providers, in addition to a long tail of minor events ignored by mainstream media. However, contrary to popular belief, neither stream leads the other when dealing with major news events, indicating that the value that Twitter can bring in a news setting comes predominantly from increased event coverage, not timeliness of reporting.


title: Towards active detection of identity clone attacks on online social networks

abstract: Online social networks (OSNs) are becoming increasingly popular and Identity Clone Attacks (ICAs) that aim at creating fake identities for malicious purposes on OSNs are becoming a significantly growing concern. Such attacks severely affect the trust relationships a victim has built with other users if no active protection is applied. In this paper, we first analyze and characterize the behaviors of ICAs. Then we propose a detection framework that is focused on discovering suspicious identities and then validating them. Towards detecting suspicious identities, we propose two approaches based on attribute similarity and similarity of friend networks. The first approach addresses a simpler scenario where mutual friends in friend networks are considered; and the second one captures the scenario where similar friend identities are involved. We also present experimental results to demonstrate flexibility and effectiveness of the proposed approaches. Finally, we discuss some feasible solutions to validate suspicious identities.


Evaporative Cooling of Actuators for Humanoid Robots


title: Is ontology-based activity recognition really effective?

abstract: While most activity recognition systems rely on data-driven approaches, the use of knowledge-driven techniques is gaining increasing interest. Research in this field has mainly concentrated on the use of ontologies to specify the semantics of activities, and ontological reasoning to recognize them based on context information. However, at the time of writing, the experimental evaluation of these techniques is limited to computational aspects; their actual effectiveness is still unknown. As a first step to fill this gap, in this paper, we experimentally evaluate the effectiveness of the ontological approach, using an activity dataset collected in a smart-home setting. Preliminary results suggest that existing ontological techniques underperform data-driven ones, mainly because they lack support for reasoning with temporal information. Indeed, we show that, when ontological techniques are extended with even simple forms of temporal reasoning, their effectiveness is comparable to the one of a state-of-the-art technique based on Hidden Markov Models. Then, we indicate possible research directions to further improve the effectiveness of ontology-based activity recognition through temporal reasoning.


title: Gender role attitudes in Italy: 1988–2008. A path-dependency story of traditionalism

abstract: ABSTRACTConsidering gender role attitudes as part of a broader cultural change related to the modernization process, this study adopts a path-dependency approach to analyze the support for the role of women in the public sphere in Italy since 1988. Modernization processes varied across Italian regions and the paper explores how different gender patterns developed accordingly. Using pooled data from European Values Survey, World Values Survey, and International Social Survey Program, the author assesses if this specific change is part of the postmaterialist shift and investigates the mechanisms of change carrying out cohort decomposition methods. The results address a reinforcement of traditionalism mainly due to the period effect that shows regional differences given by history.


title: NOMA-Based Spatial Modulation

abstract: Spatial modulation (SM) has emerged as a low-complexity and energy-efficient multiple-input multiple-output transmission technique, where the information bits are not only transmitted by amplitude phase modulation but also conveyed by the index of activated transmit antenna (TA). By deploying SM in downlink multi-user (DL-MU) scenarios, conventional orthogonal multiple access-based SM (OMA-SM) allocates exclusive time-frequency resources to users, but suffers from low spectral efficiency. TA grouping-based SM (TAG-SM) divides TAs into sub-groups to serve different users independently, but suffers from severe inter-user interference. By introducing non-OMA (NOMA) into SM for DL-MU transmission, NOMA-based SM (NOMA-SM) is proposed to mitigate inter-user interference, while maintaining high spectral efficiency. Specifically, by applying successive interference cancellation at user side, the inter-user interference could be effectively eliminated with the sacrifice of increased computational complexity. Afterward, based on a symbol error rate analysis, a low-complexity power allocation scheme is provided to achieve high spectral efficiency through power domain multiplexing. When considering near-far effect from user distribution, user pairing issue is also discussed. Numerical simulation compares NOMA-SM with OMA-SM and TAG-SM, and verifies the effectiveness of the proposed low-complexity power allocation and user pairing methodologies.


title: Integrating Extractive and Abstractive Models for Long Text Summarization

abstract: With the explosive growth of information on the Internet, it becomes more and more important to improve the efficiency of information acquisition. Automatic text summarization provides a good means for quick acquisition of information through compression and refinement. While existing methods for automatic text summarization achieve elegant performance on short sequences, however, they are facing the challenges of low efficiency and accuracy when dealing with long text. In this paper, we present a twophase approach towards long text summarization, namely, EA-LTS. In the extraction phase, it conceives a hybrid sentence similarity measure by combining sentence vector and Levenshtein distance, and integrates it into graph model to extract key sentences. In the abstraction phase, it constructs a recurrent neural network based encoder-decoder, and devises pointer and attention mechanisms to generate summaries. We test our model on a real-life long text corpora, collected from sina.com, experimental results verify the accuracy and validity of the proposed method, which is demonstrated to be superior to state-of-the-art methods.


title: Cyberspace Security Using Adversarial Learning and Conformal Prediction

abstract: This paper advances new directions for ::: cyber security using adversarial learning and conformal prediction in order to ::: enhance network and computing services defenses against adaptive, malicious, ::: persistent, and tactical offensive threats. Conformal prediction is the ::: principled and unified adaptive and learning framework used to design, develop, ::: and deploy a multi-faceted self-managing defensive shield to detect, ::: disrupt, and deny intrusive attacks, hostile and malicious behavior, and ::: subterfuge. Conformal prediction leverages apparent relationships between immunity ::: and intrusion detection using non-conformity measures characteristic of ::: affinity, a typicality, and surprise, to recognize patterns and messages as ::: friend or foe and to respond to them accordingly. The solutions proffered ::: throughout are built around active learning, meta-reasoning, randomness, ::: distributed semantics and stratification, and most important and above all ::: around adaptive Oracles. The motivation for using conformal prediction and its ::: immediate off-spring, those of semi-supervised learning and transduction, comes ::: from them first and foremost supporting discriminative and non-parametric ::: methods characteristic of principled demarcation using cohorts and sensitivity ::: analysis to hedge on the prediction outcomes including negative selection, on one ::: side, and providing credibility and confidence indices that assist ::: meta-reasoning and information fusion.


title: A Fast KNN Algorithm for Text Categorization

abstract: The KNN algorithm applied to text categorization is a simple, valid and non-parameter method. The traditional KNN has a fatal defect that the time of similarity computing is huge. The practicality will be lost when the KNN algorithm is applied to text categorization with the high dimension and huge samples. In this paper, a method called TFKNN(Tree-Fast-K-Nearest-Neighbor) is presented, which can search the exact k nearest neighbors quickly. In the method, a SSR tree for searching K nearest neighbors is created, in which all child nodes of each non-leaf node are ranked according to the distances between their central points and the central point of their parent. Then the searching scope is reduced based on the tree. Subsequently , the time of similarity computing is decreased largely.


title: Data Sets: Word Embeddings Learned from Tweets and General Data

abstract: A word embedding is a low-dimensional, dense and real- valued vector representation of a word. Word embeddings have been used in many NLP tasks. They are usually gener- ated from a large text corpus. The embedding of a word cap- tures both its syntactic and semantic aspects. Tweets are short, noisy and have unique lexical and semantic features that are different from other types of text. Therefore, it is necessary to have word embeddings learned specifically from tweets. In this paper, we present ten word embedding data sets. In addition to the data sets learned from just tweet data, we also built embedding sets from the general data and the combination of tweets with the general data. The general data consist of news articles, Wikipedia data and other web data. These ten embedding models were learned from about 400 million tweets and 7 billion words from the general text. In this paper, we also present two experiments demonstrating how to use the data sets in some NLP tasks, such as tweet sentiment analysis and tweet topic classification tasks.


title: Airborne laser scanning — an introduction and overview

abstract: Abstract This tutorial paper gives an introduction and overview of various topics related to airborne laser scanning (ALS) as used to measure range to and reflectance of objects on the earth surface. After a short introduction, the basic principles of laser, the two main classes, i.e., pulse and continuous-wave lasers, and relations with respect to time-of-flight, range, resolution, and precision are presented. The main laser components and the role of the laser wavelength, including eye safety considerations, are explained. Different scanning mechanisms and the integration of laser with GPS and INS for position and orientation determination are presented. The data processing chain for producing digital terrain and surface models is outlined. Finally, a short overview of applications is given.


title: Describing the orthology signal in a PPI network at a functional, complex level

abstract: In recent work, stable evolutionary signal induced by orthologous proteins has been observed in a Yeast protein-protein interaction (PPI) network. This finding suggests more connected subgraphs of a PPI network to be potential mediators of evolutionary information. Because protein complexes are also likely to be present in such subgraphs, it is interesting to characterize the bias of the orthology signal on the detection of putative protein complexes. To this aim, we propose a novel methodology for quantifying the functionality of the orthology signal in a PPI network at a protein complex level. The methodology performs a differential analysis between the functions of those complexes detected by clustering a PPI network using only proteins with orthologs in another given species, and the functions of complexes detected using the entire network or sub-networks generated by random sampling of proteins. We applied the proposed methodology to a Yeast PPI network using orthology information from a number of different organisms. The results indicated that the proposed method is capable to isolate functional categories that can be clearly attributed to the presence of an evolutionary (orthology) signal and quantify their distribution at a finegrained protein level.