title: Data Augmentation for Support Vector Machines

abstract: Summary This paper presents a latent variable representation of regularized support vector machines (SVM’s) that enables EM, ECME or MCMC algorithms to provide parameter estimates. We verify our representation by demonstrating that minimizing the SVM optimality criterion together with the parameter regularization penalty is equivalent to finding the mode of a mean-variance mixture of normals pseudo-posterior distribution. The latent variables in the mixture representation lead to EM and ECME point estimates of SVM parameters, as well as MCMC algorithms based on Gibbs sampling that can bring Bayesian tools for Gaussian linear models to bear on SVM’s. We show how to implementing SVM’s with spike-and-slab priors and running them against data from a standard spam filtering data set.


title: Medical Smart Textiles Based on Fiber Optic Technology: An Overview

abstract: The growing interest in the development of smart textiles for medical applications is driven by the aim to increase the mobility of patients who need a continuous monitoring of such physiological parameters. At the same time, the use of fiber optic sensors (FOSs) is gaining large acceptance as an alternative to traditional electrical and mechanical sensors for the monitoring of thermal and mechanical parameters. The potential impact of FOSs is related to their good metrological properties, their small size and their flexibility, as well as to their immunity from electromagnetic field. Their main advantage is the possibility to use textile based on fiber optic in a magnetic resonance imaging environment, where standard electronic sensors cannot be employed. This last feature makes FOSs suitable for monitoring biological parameters (e.g., respiratory and heartbeat monitoring) during magnetic resonance procedures. Research interest in combining FOSs and textiles into a single structure to develop wearable sensors is rapidly growing. In this review we provide an overview of the state-of-the-art of textiles, which use FOSs for monitoring of mechanical parameters of physiological interest. In particular we briefly describe the working principle of FOSs employed in this field and their relevant advantages and disadvantages. Also reviewed are their applications for the monitoring of mechanical parameters of physiological interest.


title: Microstrip Yagi-Uda Antenna for 2.45 GHz RFID Handheld Reader

abstract: This paper proposes a compact microstrip Yagi-Uda antenna for 2.45 GHz radio frequency identification (RFID) handheld reader applications. The proposed antenna is etched on a piece of FR4 substrate with an overall size of 65 mm × 55 mm ×1.6 mm and consists of a microstrip balun, a dipole, and a director. The ground plane is designed to act as a reflector that contributes to enhancing the antenna gain. The measured 10-dB return loss bandwidth and peak gain achieved by the proposed antenna are 380 MHz and 7.5 dBi, respectively. In addition, a parametric study is conducted to facilitate the design and optimization processes for engineers.


title: Sorted random projections for robust rotation-invariant texture classification

abstract: This paper presents a simple, novel, yet very powerful approach for robust rotation-invariant texture classification based on random projection. The proposed sorted random projection maintains the strengths of random projection, in being computationally efficient and low-dimensional, with the addition of a straightforward sorting step to introduce rotation invariance. At the feature extraction stage, a small set of random measurements is extracted from sorted pixels or sorted pixel differences in local image patches. The rotation invariant random features are embedded into a bag-of-words model to perform texture classification, allowing us to achieve global rotation invariance. The proposed unconventional and novel random features are very robust, yet by leveraging the sparse nature of texture images, our approach outperforms traditional feature extraction methods which involve careful design and complex steps. We report extensive experiments comparing the proposed method to six state-of-the-art methods, RP, Patch, LBP, WMFS and the methods of Lazebnik et al. and Zhang et al., in texture classification on five databases: CUReT, Brodatz, UIUC, UMD and KTH-TIPS. Our approach leads to significant improvements in classification accuracy, producing consistently good results on each database, including what we believe to be the best reported results for Brodatz, UMD and KTH-TIPS.


title: AUTISM: A Window Onto the Development of the Social and the Analytic Brain

abstract: Although the neurobiological understanding of autism has been increasing exponentially, the diagnosis of autism spectrum conditions still rests entirely on behavioral criteria. Autism is therefore most productively approached using a combination of biological and psychological theory. The triad of behavioral abnormalities in social function, communication, and restricted and repetitive behaviors and interests can be explained psychologically by an impaired capacity for empathizing, or modeling the mental states governing the behavior of people, along with a superior capacity for systemizing, or inferring the rules governing the behavior of objects. This empathizing-systemizing theory explains other psychological models such as impairments of executive function or central coherence, and may have a neurobiological basis in abnormally low activity of brain regions subserving social cognition, along with abnormally high activity of regions subserving lower-level, perceptual processing—a pattern that may resul...


title: Semantic Role Features for Machine Translation

abstract: We propose semantic role features for a Tree-to-String transducer to model the reordering/deletion of source-side semantic roles. These semantic features, as well as the Tree-to-String templates, are trained based on a conditional log-linear model and are shown to significantly outperform systems trained based on Max-Likelihood and EM. We also show significant improvement in sentence fluency by using the semantic role features in the log-linear model, based on manual evaluation.


title: Intrusion Prevention/Intrusion Detection System (IPS/IDS) for Wifi Networks

abstract: The nature of wireless networks itself created new vulnerabilities that in the classical wired networks do not exist. This results in an evolutional requirement to implement new sophisticated security mechanism in form of Intrusion Detection and Prevention Systems. This paper deals with security issues of small office and home office wireless networks. The goal of our work is to design and evaluate wireless IDPS with use of packet injection method. Decrease of attacker’s traffic by 95% was observed when compared to attacker’s traffic without deployment of proposed IDPS system.


Fiduciary Access to Digital Assets: A Review of the Uniform Law Conference of Canada’s Proposed Uniform Act and Comparable American Model Legislation


title: Epidemiology of paediatric presentations with musculoskeletal problems in primary care

abstract: Musculoskeletal disease is a common cause of morbidity, but there is a paucity of musculoskeletal research focusing on paediatric populations, particularly in primary care settings. In particular, there is limited information on population consultation frequency in paediatric populations, and frequency varies by age and sex. Few studies have examined paediatric musculoskeletal consultation frequency for different body regions. The objective was to determine the annual consultation prevalence of regional musculoskeletal problems in children in primary care. Musculoskeletal codes within the Read morbidity Code system were identified and grouped into body regions. Consultations for children aged three to seventeen in 2006 containing these codes were extracted from recorded consultations at twelve general practices contributing to a general practice consultation database (CiPCA). Annual consultation prevalence per 10,000 registered persons for the year 2006 was determined, stratified by age and sex, for problems in individual body regions. Over 8 % (8.27%, 95% CI 7.86 to 8.68%) of the 16,862 children consulted with a musculoskeletal problem during 2006. Annual consultation prevalence for any musculoskeletal problem was significantly higher in males than females (male: female prevalence ratio 1.18, 95% CI 1.06 to 1.31). Annual consultation prevalence increased with age and the most common body regions consulted for were the foot, knee and back all of which had over 100 consultations (109, 104 and 101 respectively) per 10,000 persons per year. This study provides new and detailed information on patterns of paediatric musculoskeletal consultations in primary care. Musculoskeletal problems in children are varied and form a significant part of the paediatric primary care workload. The findings of this study may be used as a resource for planning future studies.


title: DexHunter: Toward Extracting Hidden Code from Packed Android Applications

abstract: The rapid growth of mobile application (or simply app) economy provides lucrative and profitable targets for hackers. Among OWASP’s top ten mobile risks for 2014, the lack of binary protections makes it easy to reverse, modify, and repackage Android apps. Recently, a number of packing services have been proposed to protect Android apps by hiding the original executable file (i.e., dex file). However, little is known about their effectiveness and efficiency. In this paper, we perform the first systematic investigation on such services by answering two questions: (1) what are the major techniques used by these services and their effects on apps? (2) can the original dex file in a packed app be recovered? If yes, how? We not only reveal their techniques and evaluate their effects, but also propose and develop a novel system, named DexHunter, to extract dex files protected by these services. It is worth noting that DexHunter supports both the Dalvik virtual machine (DVM) and the new Android Runtime (ART). The experimental results show that DexHunter can extract dex files from packed apps effectively and efficiently.


title: A fast triangle to triangle intersection test for collision detection

abstract: The triangle-to-triangle intersection test is a basic component of all collision detection data structures and algorithms. This paper presents a fast method for testing whether two triangles embedded in three dimensions intersect. Our technique solves the basic sets of linear equations associated with the problem and exploits the strong relations between these sets to speed up their solution. Moreover, unlike previous techniques, with very little additional cost, the exact intersection coordinates can be determined. Finally, our technique uses general principles that can be applied to similar problems such as rectangle-to-rectangle intersection tests, and generally to problems where several equation sets are strongly related. We show that our algorithm saves about 20% of the mathematical operations used by the best previous triangle-to-triangle intersection algorithm. Our experiments also show that it runs 18.9% faster than the fastest previous algorithm on average for typical scenarios of collision detection (on Pentium 4). Copyright © 2006 John Wiley & Sons, Ltd.


title: Implementing a Distribution-Network Decision-Support System at Pfizer/Warner-Lambert

abstract: We constructed a decision-support system (DSS) to help the distribution network of Pfizer/Warner Lambert to plan its operations. Using OR tools, databases, programming languages, and spreadsheets, this system supports decisions concerning daily operations, annual planning, and long-run strategic planning. This DSS improved the firm's ability to make rapid, better-informed decisions in many different areas of distribution and supply chain ranging from individual customer deliveries to long-term manufacturing location and technology.


title: A Survey on Malware Detection Using Data Mining Techniques

abstract: In the Internet age, malware (such as viruses, trojans, ransomware, and bots) has posed serious and evolving security threats to Internet users. To protect legitimate users from these threats, anti-malware software products from different companies, including Comodo, Kaspersky, Kingsoft, and Symantec, provide the major defense against malware. Unfortunately, driven by the economic benefits, the number of new malware samples has explosively increased: anti-malware vendors are now confronted with millions of potential malware samples per year. In order to keep on combating the increase in malware samples, there is an urgent need to develop intelligent methods for effective and efficient malware detection from the real and large daily sample collection. In this article, we first provide a brief overview on malware as well as the anti-malware industry, and present the industrial needs on malware detection. We then survey intelligent malware detection methods. In these methods, the process of detection is usually divided into two stages: feature extraction and classification/clustering. The performance of such intelligent malware detection approaches critically depend on the extracted features and the methods for classification/clustering. We provide a comprehensive investigation on both the feature extraction and the classification/clustering techniques. We also discuss the additional issues and the challenges of malware detection using data mining techniques and finally forecast the trends of malware development.


title: Computational analysis of memory capacity in echo state networks

abstract: Reservoir computing became very popular due to its potential for efficient design of recurrent neural networks, exploiting the computational properties of the reservoir structure. Various approaches, ranging from appropriate reservoir initialization to its optimization by training have been proposed. In this paper, we extend our previous work and focus on short-term memory capacity, introduced by Jaeger in case of echo state networks. Memory capacity has been previously shown to peak at criticality, when the network switches from a stable regime to an unstable dynamic regime. Using computational experiments with nonlinear ESNs, we systematically analyze the memory capacity from the perspective of several parameters and their relationship, namely the input and reservoir weights scaling, reservoir size and its sparsity. We also derive and test two gradient descent based orthogonalization procedures for recurrent weights matrix, which considerably increase the memory capacity, approaching the upper bound, which is equal to the reservoir size, as proved for linear reservoirs. Orthogonalization procedures are discussed in the context of existing methods and their benefit is assessed.


title: Lipreading: A Graph Embedding Approach

abstract: In this paper, we propose a novel graph embedding method for the problem of lipreading. To characterize the temporal connections among video frames of the same utterance, a new distance metric is defined on a pair of frames and graphs are constructed to represent the video dynamics based on the distances between frames. Audio information is used to assist in calculating such distances. For each utterance, a subspace of the visual feature space is learned from a well-defined intrinsic and penalty graph within a graph-embedding framework. Video dynamics are found to be well preserved along some dimensions of the subspace. Discriminatory cues are then decoded from curves of the projected visual features to classify different utterances.


title: Breakdown Voltage for Superjunction Power Devices With Charge Imbalance: An Analytical Model Valid for Both Punch Through and Non Punch Through Devices

abstract: An analytical model for the electric field and the breakdown voltage (BV) of an unbalanced superjunction (SJ) device is presented in this paper. The analytical technique uses a superposition approach treating the asymmetric charge in the pillars as an excess charge component superimposed on a balanced charge component. The proposed double-exponential model is able to accurately predict the electric field and the BV for unbalanced SJ devices in both punch through and non punch through conditions. The model is also reasonably accurate at extremely high levels of charge imbalance when the devices behave similarly to a PiN diode or to a high-conductance layer. The analytical model is compared against numerical simulations of charge unbalanced SJ devices and against experimental results.


title: Drones for smart cities: Issues in cybersecurity, privacy, and public safety

abstract: It is expected that drones will take a major role in the connected smart cities of the future. They will be delivering goods and merchandise, serving as mobile hot spots for broadband wireless access, and maintaining surveillance and security of smart cities. However, pervasive use of drones for future smart cities also brings together several technical and societal concerns and challenges that needs to be addressed, including in the areas of cybersecurity, privacy, and public safety. Drones, while can be used for the betterment of the society, can also be used by malicious entities to conduct physical and cyber attacks, and threaten the society. The goal of this survey paper is to review various aspects of drones in future smart cities, relating to cybersecurity, privacy, and public safety. We will also provide representative results on cyber attacks using drones.


Leibniz: What Kind of Legal Rationalism?


Perceptions of Presence: Discursive and Embodied Experiences in Global Collaboration


title: Human-Inspired Control of Bipedal Walking Robots

abstract: This paper presents a human-inspired control approach to bipedal robotic walking: utilizing human data and output functions that appear to be intrinsic to human walking in order to formally design controllers that provably result in stable robotic walking. Beginning with human walking data, outputs-or functions of the kinematics-are determined that result in a low-dimensional representation of human locomotion. These same outputs can be considered on a robot, and human-inspired control is used to drive the outputs of the robot to the outputs of the human. The main results of this paper are that, in the case of both under and full actuation, the parameters of this controller can be determined through a human-inspired optimization problem that provides the best fit of the human data while simultaneously provably guaranteeing stable robotic walking for which the initial condition can be computed in closed form. These formal results are demonstrated in simulation by considering two bipedal robots-an underactuated 2-D bipedal robot, AMBER, and fully actuated 3-D bipedal robot, NAO-for which stable robotic walking is automatically obtained using only human data. Moreover, in both cases, these simulated walking gaits are realized experimentally to obtain human-inspired bipedal walking on the actual robots.


title: A deep learning SAR ATR system using regularization and prioritized classes

abstract: Recently an increased interest in deep learning for radar and particularly SAR ATR systems has been observed. Many authors proposed systems that outperform established classification systems on benchmark datasets like MSTAR. In this paper we present a new implementation of our recently proposed convolutional neural network classifier, which has a more flexible structure and at the same time less free parameters and thus a reduced training time. Furthermore, the training itself is improved through regularization techniques that improve the convergence properties of the network. Another feature of this new implementation is the dependency of the learning rate on the target class. With this feature the network can focus on classes that cause higher costs of misclassification.


title: A Vector Space for Distributional Semantics for Entailment

abstract: Distributional semantics creates vector-space representations that capture many forms of semantic similarity, but their relation to semantic entailment has been less clear. We propose a vector-space model which provides a formal foundation for a distributional semantics of entailment. Using a mean-field approximation, we develop approximate inference procedures and entailment operators over vectors of probabilities of features being known (versus unknown). We use this framework to reinterpret an existing distributional-semantic model (Word2Vec) as approximating an entailment-based model of the distributions of words in contexts, thereby predicting lexical entailment relations. In both unsupervised and semi-supervised experiments on hyponymy detection, we get substantial improvements over previous results.


title: Identifying Computer-Supported Collaborative Learning (CSCL) Research in Selected Journals Published from 2003 to 2012: A Content Analysis of Research Topics and Issues.

abstract: This study aims to identity the emerging research trends in the field of computed-supported collaborative learning (CSCL) so as to provide insights for researchers and educators into research topics and issues for further exploration. This paper analyzed the research topics, methods and technology adoptionof CSCLfrom 2003 to 2012. A total of 706 articles from 9 leading SSCI journals were selected and analyzed through the lens of research topics, research design, research methods, data sources, data analysis methods, research settings, research sample groups, research learning domain, types of collaborative learning, and technology for supporting collaborative learning. The results indicated that technologyin support of collaborative learning, interaction pattern and analysis, CSCL evaluation, and CSCL practice or applications in naturalistic teaching settings and community environment were the four major research topics. Qualitative research approach and descriptive research method were found in most of CSCL publications. Furthermore, it was found that the research topics significantly correlated with the research design and research methods. In the future, researchers and educators should pay more attention to technical affordance and facilitation in collaborative learning.


title: Posterior Circumflex Humeral Artery Aneurysm: Case Report and Systematic Literature Review

abstract: Posterior circumflex humeral artery (PCHA) aneurysm is a rare condition. It mostly affects young professional sports player and might be career threatening. This is the report of the case of a 28 year old volleyball player presenting with symptoms of right digital ischaemia caused by a PCHA aneurysm. Surgical treatment by resection of the aneurysm without revascularisation was performed. The post-operative course was uneventful and the patient returned to professional competition one month after surgery. A literature review is also presented in an attempt to provide recommendations for PCHA aneurysm diagnosis and management.


title: Development of whole-body emotion expression humanoid robot

abstract: Personal robots and robot technology (RT)-based assistive devices are expected to play a major role in our elderly-dominated society, with an active participation to joint works and community life with humans, as partner and as friends for us. The authors think that the emotion expression of a robot is effective in joint activities of human and robot. In addition, we also think that bipedal walking is necessary to robots which are active in human living environment. But, there was no robot which has those functions. And, it is not clear what kinds of functions are effective actually. Therefore we developed a new bipedal walking robot which is capable to express emotions. In this paper, we present the design and the preliminary evaluation of the new head of the robot with only a small number of degrees of freedom for facial expression.


title: Causality in distributed systems

abstract: Understanding distributed computations and mastering their complexity strongly depends on our ability to observe and to analyze their causal structure. In this paper, we briefly introduce the notion of causality in distributed, asynchronous systems, and demonstrate the importance of this concept by analyzing its impact on the problem of global predicate detection.


title: Clothing genre classification by exploiting the style elements

abstract: This paper presents a novel approach to automatically classify the upperwear genre from a full-body input image with no restrictions of model poses, image backgrounds, and image resolutions. Five style elements, that are crucial for clothing recognition, are identified based on the clothing design theory. The corresponding features of each of these style elements are also designed. We illustrate the effectiveness of our approach by showing that the proposed algorithm achieved overall precision of 92.04%, recall of 92.45%, and F score of 92.25% with 1,077 clothing images crawled from popular online stores.


title: AGGRESSION AND VIOLENCE IN THE INNER CITY Effects of Environment via Mental Fatigue

abstract: S. Kaplan suggested that one outcome of mental fatigue may be an increased propensity for outbursts of anger and even violence. If so, contact with nature, which appears to mitigate mental fatigue,...


title: Boosting complete-code tool for partial program

abstract: To improve software quality, researchers and practitioners have proposed static analysis tools for various purposes (e.g., detecting bugs, anomalies, and vulnerabilities). Although many such tools are powerful, they typically need complete programs where all the code names (e.g., class names, method names) are resolved. In many scenarios, researchers have to analyze partial programs in bug fixes (the revised source files can be viewed as a partial program), tutorials, and code search results. As a partial program is a subset of a complete program, many code names in partial programs are unknown. As a result, despite their syntactical correctness, existing complete-code tools cannot analyze partial programs, and existing partial-code tools are limited in both their number and analysis capability. Instead of proposing another tool for analyzing partial programs, we propose a general approach, called GRAPA, that boosts existing tools for complete programs to analyze partial programs. Our major insight is that after unknown code names are resolved, tools for complete programs can analyze partial programs with minor modifications. In particular, GRAPA locates Java archive files to resolve unknown code names, and resolves the remaining unknown code names from resolved code names. To illustrate GRAPA, we implement a tool that leverages the state-of-the-art tool, WALA, to analyze Java partial programs. We thus implemented the first tool that is able to build system dependency graphs for partial programs, complementing existing tools. We conduct an evaluation on 8,198 partial-code commits from four popular open source projects. Our results show that GRAPA fully resolved unknown code names for 98.5% bug fixes, with an accuracy of 96.1% in total. Furthermore, our results show the significance of GRAPA's internal techniques, which provides insights on how to integrate with more complete-code tools to analyze partial programs.


title: An 8.5-Gb/s Fully Integrated CMOS Optoelectronic Receiver Using Slope-Detection Adaptive Equalizer

abstract: An 8.5-Gb/s single-chip optoelectronic integrated circuit (OEIC) for short-distance optical communications is realized in a 0.13-μm CMOS process. The OEIC consists of an on-chip silicon photodiode, a transimpedance amplifier with modified regulated cascode input configuration, an adaptive equalizer based upon slope-detection algorithm, and a limiting amplifier with merged negative impedance circuits. The proposed slope-detection adaptive equalizer compensates the limited bandwidth and the temperature variation of the integrated silicon photodiode. Measured results demonstrate 120-dB Ω transimpedance gain, 5.9-GHz bandwidth, -3.2-dBm optical sensitivity for 10-12 BER, and 47-mW power dissipation from a single 1.5-V supply. The OEIC chip core occupies the area of 0.1 mm2.


title: Auditing Search Engines for Differential Satisfaction Across Demographics

abstract: Many online services, such as search engines, social media platforms, and digital marketplaces, are advertised as being available to any user, regardless of their age, gender, or other demographic factors. However, there are growing concerns that these services may systematically underserve some groups of users. In this paper, we present a framework for internally auditing such services for differences in user satisfaction across demographic groups, using search engines as a case study. We first explain the pitfalls of naively comparing the behavioral metrics that are commonly used to evaluate search engines. We then propose three methods for measuring latent differences in user satisfaction from observed differences in evaluation metrics. To develop these methods, we drew on ideas from the causal inference literature and the multilevel modeling literature. Our framework is broadly applicable to other online services, and provides general insight into interpreting their evaluation metrics.


title: Why Business Modeling is Crucial in the Development of eHealth Technologies

abstract: The impact and uptake of information and communication technologies that support health care are rather low. Current frameworks for eHealth development suffer from a lack of fitting infrastructures, inability to find funding, complications with scalability, and uncertainties regarding effectiveness and sustainability. These issues can be addressed by defining a better implementation strategy early in the development of eHealth technologies. A business model, and thus business modeling, help to determine such an implementation strategy by involving all important stakeholders in a value-driven dialogue on what the technology should accomplish. This idea also seems promising to eHealth, as it can contribute to the whole development of eHealth technology. We therefore suggest that business modeling can be used as an effective approach to supporting holistic development of eHealth technologies. The contribution of business modeling is elaborated in this paper through a literature review that covers the latest business model research, concepts from the latest eHealth and persuasive technology research, evaluation and insights from our prior eHealth research, as well as the review conducted in the first paper of this series. Business modeling focuses on generating a collaborative effort of value cocreation in which all stakeholders reflect on the value needs of the others. The resulting business model acts as the basis for implementation. The development of eHealth technology should focus more on the context by emphasizing what this technology should contribute in practice to the needs of all involved stakeholders. Incorporating the idea of business modeling helps to cocreate and formulate a set of critical success factors that will influence the sustainability and effectiveness of eHealth technology.


title: Abstract and Concrete Sentences, Embodiment, and Languages

abstract: One of the main challenges of embodied theories is accounting for meanings of abstract words. The most common explanation is that abstract words, like concrete ones, are grounded in perception and action systems. According to other explanations, abstract words, differently from concrete ones, would activate situations and introspection; alternatively, they would be represented through metaphoric mapping. However, evidence provided so far pertains to specific domains. To be able to account for abstract words in their variety we argue it is necessary to take into account not only the fact that language is grounded in the sensorimotor system, but also that language represents a linguistic-social experience. To study abstractness as a continuum we combined a concrete (C) verb with both a concrete and an abstract (A) noun; and an abstract verb with the same nouns previously used (grasp vs. describe a flower vs. a concept). To disambiguate between the semantic meaning and the grammatical class of the words, we focused on two syntactically different languages: German and Italian. Compatible combinations (CC, AA) were processed faster than mixed ones (CA, AC). This is in line with the idea that abstract and concrete words are processed preferentially in parallel systems - abstract in the language system and concrete more in the motor system, thus costs of processing within one system are the lowest. This parallel processing takes place most probably within different anatomically predefined routes. With mixed combinations, when the concrete word preceded the abstract one (CA), participants were faster, regardless of the grammatical class and the spoken language. This is probably due to the peculiar mode of acquisition of abstract words, as they are acquired more linguistically than perceptually. Results confirm embodied theories which assign a crucial role to both perception-action and linguistic experience for abstract words.


title: shapeShift: A Mobile Tabletop Shape Display for Tangible and Haptic Interaction

abstract: shapeShift is a compact, high-resolution (7 mm pitch), mobile tabletop shape display. We explore potential interaction techniques in both passive and active mobile scenarios. In the passive case, the user is able to freely move and spin the display as it renders elements. We introduce use cases for rendering lateral I/O elements, exploring volumetric datasets, and grasping and manipulating objects. On an active omnidirectional-robot platform, shapeShift can display moving objects and provide both vertical and lateral kinesthetic feedback. We use the active platform as an encounter-type haptic device combined with a head-mounted display to dynamically simulate the presence of virtual content.


title: Emotion recognition in the wild challenge 2016

abstract: The fourth Emotion Recognition in the Wild (EmotiW) challenge is a grand challenge in the ACM International Conference on Multimodal Interaction 2016, Tokyo. EmotiW is a series of benchmarking and competition effort for researchers working in the area of automatic emotion recognition in the wild. The fourth EmotiW has two sub-challenges: Video based emotion recognition (VReco) and Group-level emotion recognition (GReco). The VReco sub-challenge is being run for the fourth time and GReco is a new sub-challenge this year.


title: Performance analysis of MIMO systems with arbitrary number transmit antenna selection and OSTBC in the presence of imperfect CSI

abstract: In this paper, the performance analysis of MIMO systems with arbitrary number transmit antenna selection (TAS) and orthogonal space-time block coding (STBC) in Rayleigh fading channels for imperfect channel state information (CSI) is presented. For the performance analysis, the moment generating function of the system effective SNR as well as its upper and lower bounds are derived. Then, accurate and approximate expressions of bit error rate (BER) of MIMO-TAS-STBC with MPSK and MQAM are further derived. Using the approximate BER formula and imperfect CSI, an adaptive antenna selection scheme is developed for minimizing the BER. The diversity gain and coding gain are analyzed at high SNR. The results indicate that the MIMO-TAS-STBC for imperfect CSI can only achieve partial diversity order KN and the coding gain is affected by K, N, code rate, modulation pattern, and channel correlation coefficients, when K transmit antennas are selected and N receive antennas are used. Simulation results show that the theoretical analysis matches the simulation result well, and the approximate expressions are close to the accurate ones but have a lower complexity.


title: Simultaneous Localization and Appearance Estimation with a Consumer RGB-D Camera

abstract: Acquiring general material appearance with hand-held consumer RGB-D cameras is difficult for casual users, due to the inaccuracy in reconstructed camera poses and geometry, as well as the unknown lighting that is coupled with materials in measured color images. To tackle these challenges, we present a novel technique for estimating the spatially varying isotropic surface reflectance, solely from color and depth images captured with an RGB-D camera under unknown environment illumination. The core of our approach is a joint optimization, which alternates among solving for plausible camera poses, materials, the environment lighting and normals. To refine camera poses, we exploit the rich spatial and view-dependent variations of materials, treating the object as a localization-self-calibrating model. To recover the unknown lighting, measured color images along with the current estimate of materials are used in a global optimization, efficiently solved by exploiting the sparsity in the wavelet domain. We demonstrate the substantially improved quality of estimated appearance on a variety of daily objects.


title: A Scalable Hybrid MAC Protocol for Massive M2M Networks

abstract: In Machine to Machine (M2M) networks, a robust Medium Access Control (MAC) protocol is crucial to enable numerous machine-type devices to concurrently access the channel. Most literatures focus on developing simplex (reservation or contention based) MAC protocols which cannot provide a scalable solution for M2M networks with large number of devices. In this paper, a frame-based Hybrid MAC scheme, which consists of a contention period and a transmission period, is proposed for M2M networks. In the proposed scheme, the devices firstly contend the transmission opportunities during the contention period, only the successful devices will be assigned a time slot for transmission during the transmission period. To balance the tradeoff between the contention and transmission period in each frame, an optimization problem is formulated to maximize the system throughput by finding the optimal contending probability during contention period and optimal number of devices that can transmit during transmission period. A practical hybrid MAC protocol is designed to implement the proposed scheme. The analytical and simulation results demonstrate the effectiveness of the proposed Hybrid MAC protocol.


title: Optimal Control of a Dual Three-Level Inverter System for Medium-Voltage Drives

abstract: Actual developments in medium-voltage drives aim at increasing the power capability of the PWM inverter. Parallel connection of power semiconductor devices permits increasing the output current, while series connected devices increase the output voltage. In either case are additional means required for balancing the current or voltage stress of the devices. The three-level neutral-point clamped (NPC) inverter topology offers an inherent means to ensure dynamic voltage balancing of a maximum of two series connected devices. Higher operating voltages can be achieved by series connection of entire inverter topologies. Two circuit topologies are considered that behave as five-level units. Synchronous optimal pulsewidth modulation is applied for their control. This permits reducing the switching frequency to the 100-Hz region without sacrificing on harmonic distortion.


title: Discovering Conditional Matching Rules

abstract: Matching dependencies (MDs) have recently been proposed to make data dependencies tolerant to various information representations, and found useful in data quality applications such as record matching. Instead of the strict equality function used in traditional dependency syntax (e.g., functional dependencies), MDs specify constraints based on similarity and identification. However, in practice, MDs may still be too strict and applicable only in a subset of tuples in a relation. Thereby, we study the conditional matching dependencies (CMDs), which bind matching dependencies only in a certain part of a table, i.e., MDs conditionally applicable in a subset of tuples. Compared to MDs, CMDs have more expressive power that enables them to satisfy wider application needs. In this article, we study several important theoretical and practical issues of CMDs, including irreducible CMDs with respect to the implication, discovery of CMDs from data, reliable CMDs agreed most by a relation, approximate CMDs almost satisfied in a relation, and finally applications of CMDs in record matching and missing value repairing. Through an extensive experimental evaluation in real data sets, we demonstrate the efficiency of proposed CMDs discovery algorithms and effectiveness of CMDs in real applications.


title: Abnormally Long Penis in Children with Vesical Calculus

abstract: Dear Editor, ::: ::: A 31-month-old boy was investigated for increased frequency of urination for the past 4 months. He voided a small quantity of urine every 20 to 30 min. However, other urinary symptoms such as pain, hematuria, and fever were conspicuously absent. His physical examination was unremarkable except for an abnormally long penis (Fig. 1). The penis in flaccid state measured 7 cm, and its stretched length was 9 cm (Fig. 2). This is approximately equal to the average penile length of Indian adults [1]. The stretched penis length expected for his age group, according to European standards, is 5.15 ± 0.46 cm (range, 4.2 to 6 cm) [2]. A 4 × 3 × 3-cm calcium oxalate stone in the urinary bladder was diagnosed by plain radiograph, and the stone was surgically removed. Sensitized by this experience, I carefully observed the length of penis in two subsequent boys with vesical calculus. In both of them, penile length was slightly more than two standard deviations of the expected mean of age-matched penile length. ::: ::: ::: ::: Fig. 1 ::: ::: Clinical photograph of genitalia showing an abnormally long penis in a child with vesical calculus ::: ::: ::: ::: ::: ::: Fig. 2 ::: ::: Measurement of stretched penis length of the boy shown in Fig. 1 (The asterisk marks the pubic symphysis) ::: ::: ::: ::: It is possible that penile length is influenced by the presence of a bladder stone. In vesical calculus, pain is often referred to the tip of the penis. Therefore, young boys with a bladder stone are known to repeatedly pull and squeeze the penis in a futile attempt of alleviating pain [3]. Physical stretching of the penis or tumescence caused by repeated massage could be responsible for lengthening of the penis. This hypothesis is supported by a recent publication [4] wherein prolonged semi-erection of high-flow priapism in a prepubertal boy was shown to increase penile size. This interesting clinical observation definitely warrants further scientific studies. Confirmation of any correlation between penile length and vesical calculus will be truly fascinating as it will not only aid clinical diagnosis but also will widen our understanding of penis-lengthening procedures.


title: Heterogeneous Face Recognition Using Kernel Prototype Similarities

abstract: Heterogeneous face recognition (HFR) involves matching two face images from alternate imaging modalities, such as an infrared image to a photograph or a sketch to a photograph. Accurate HFR systems are of great value in various applications (e.g., forensics and surveillance), where the gallery databases are populated with photographs (e.g., mug shot or passport photographs) but the probe images are often limited to some alternate modality. A generic HFR framework is proposed in which both probe and gallery images are represented in terms of nonlinear similarities to a collection of prototype face images. The prototype subjects (i.e., the training set) have an image in each modality (probe and gallery), and the similarity of an image is measured against the prototype images from the corresponding modality. The accuracy of this nonlinear prototype representation is improved by projecting the features into a linear discriminant subspace. Random sampling is introduced into the HFR framework to better handle challenges arising from the small sample size problem. The merits of the proposed approach, called prototype random subspace (P-RS), are demonstrated on four different heterogeneous scenarios: 1) near infrared (NIR) to photograph, 2) thermal to photograph, 3) viewed sketch to photograph, and 4) forensic sketch to photograph.


title: Measurements using orthodontic analysis software on digital models obtained by 3D scans of plaster casts

abstract: Objective ::: The purpose of this work was to determine the reliability and validity of measurements performed on digital models with a desktop scanner and analysis software in comparison with measurements performed manually on conventional plaster casts.


title: Adaptive neural FOPID controller applied for missile guidance system

abstract: Adaptive Neural Fractional Order Proportional Integral Derivative (FOPID) controller is designed to control a missile using Proportional Navigation Guidance PNG system. The proposed FOPID controller is intended to improve the performance of PNG system in terms of miss distance accuracy and stability. A new tuning procedure has been proposed by applying hybrid neural genetic algorithm in order to align the missile attitude with the Line of Sight (LOS) angle. Genetic algorithm is used first in which it has fast convergence speed at the initial tuning stages, but near the global optimum values the tuning process becomes very slow, therefore neural technique is used to train a neural network which has faster tuning speed near the optimal values, and the tuning accuracy becomes much better. The tuning method has been compared with Ziegler-Nichols which is applied on PID controller and the results showed the superiority of the proposed tuning method. The need for fractional order type of feedback control system is justified by the nonlinear nature of the proportional navigation system and the dynamics of the missiles, to which many alternatives were applied in the literature using less accurate controllers while the proposed control system proved to have more accuracy hitting the target with less value of miss distance and more stability in the angle of attack and the motion during flight as well as the performance of the controller in terms of second and infinity norms.


title: Chinese Song Iambics Generation with Neural Attention-based Model

abstract: Learning and generating Chinese poems is a charming yet challenging task. Traditional approaches involve various language modeling and machine translation techniques, however, they perform not as well when generating poems with complex pattern constraints, for example Song iambics, a famous type of poems that involve variable-length sentences and strict rhythmic patterns. This paper applies the attention-based sequence-to-sequence model to generate Chinese Song iambics. Specifically, we encode the cue sentences by a bi-directional Long-Short Term Memory (LSTM) model and then predict the entire iambic with the information provided by the encoder, in the form of an attention-based LSTM that can regularize the generation process by the fine structure of the input cues. Several techniques are investigated to improve the model, including global context integration, hybrid style training, character vector initialization and adaptation. Both the automatic and subjective evaluation results show that our model indeed can learn the complex structural and rhythmic patterns of Song iambics, and the generation is rather successful.


title: Scenic Spots: Chinese Tourism, the State, and Cultural Authority

abstract: Preface1. What's in a Site?: The Making of "Scenic Spots"2. Two Sites and a Non-Site: Mounts Emei, Jiuzhaigou, and Songpan3. Making Sense of Scenic Spots4. Scenic Spots Beyond the Border: Migration, Tourism, and Cultural AuthorityNotesBibliographyIndex


title: Market Making via Reinforcement Learning

abstract: Market making is a fundamental trading problem in which an agent provides liquidity by continually offering to buy and sell a security. The problem is challenging due to inventory risk, the risk of accumulating an unfavourable position and ultimately losing money. In this paper, we develop a high-fidelity simulation of limit order book markets, and use it to design a market making agent using temporal-difference reinforcement learning. We use a linear combination of tile codings as a value function approximator, and design a custom reward function that controls inventory risk. We demonstrate the effectiveness of our approach by showing that our agent outperforms both simple benchmark strategies and a recent online learning approach from the literature.


title: Heavy Metals in Water and Sediment: A Case Study of Tembi River

abstract: This study was carried out to examine heavy metals concentration in water and sediment of upstream and downstream of the entry of the sewage to the Tembi River, Iran. Samples were collected from upstream and downstream and were analyzed for Cd, Cr, Cu, Fe, Pb, Ni, and Zn by atomic absorption spectrophotometer. The results indicated that the average concentration of the metals in water and sediment on downstream was more than that of upstream. The comparison of the mean concentrations of heavy metals in water of the Tembi River with drinking water standards and those in the water used for agriculture suggests that the mean concentration of Cu and Zn lies within the standard range for drinking water and the mean concentration of Mn, Zn, and Pb lies within the standard range of agricultural water. The highest average concentration on downstream for Pb in water and for Mn in sediment was 1.95 and 820.5 ppm, respectively. Also, the lowest average concentration on upstream was identified for Cd in water and sediment 0.07 and 10 ppm, respectively. With regard to the results, it gets clear that using the water for recreational purposes, washing, and fishing is detrimental to human health and the environment.


title: QTL Mapping and Epistasis Analysis of Rice Root Growth Ability at Seedling Stage

abstract: A doubled haploid (DH) population derived from anther culture of Zhaiyeqing 8 / Jingxi 17 F 1, a typical indica and japonica hybrid, was used to analyze root growth ability of DH population at seedling stage by using floating cultural method. QTLs and epistasis for this trait were identified by using the constructed molecular linkage map and QTLMapperV1 0 software . A QTL controlling root growth ability was detected between C63 and CT125 on chromosome 3. Five pairs of epistatic loci associated with root growth ability were located on chromosome 2, 3, 5, 6, 7 and 12, respectively. Two pairs of epistatic loci for root length and root number were detected, respectively. One of them was simultaneously identified both in root length and root number.


title: Isometric projection

abstract: Recently the problem of dimensionality reduction has received a lot of interests in many fields of information processing. We consider the case where data is sampled from a low dimensional manifold which is embedded in high dimensional Euclidean space. The most popular manifold learning algorithms include Locally Linear Embedding, ISOMAP, and Laplacian Eigenmap. However, these algorithms are nonlinear and only provide the embedding results of training samples. In this paper, we propose a novel linear dimensionality reduction algorithm, called Isometric Projection. Isometric Projection constructs a weighted data graph where the weights are discrete approximations of the geodesic distances on the data manifold. A linear subspace is then obtained by preserving the pairwise distances. In this way, Isometric Projection can be defined everywhere. Comparing to Principal Component Analysis (PCA) which is widely used in data processing, our algorithm is more capable of discovering the intrinsic geometrical structure. Specially, PCA is optimal only when the data space is linear, while our algorithm has no such assumption and therefore can handle more complex data space. Experimental results on two real life data sets illustrate the effectiveness of the proposed method.


title: Mitigation of warpage for large 2.5D Through Silicon Interposer (TSI) package assembly

abstract: In recent years, tremendous research and attention have been focused on 2.5D/3D IC (integrated circuit) integration within a TSI (Through Silicon Interposer) package. Integration of multiple ICs with the use of TSI technology could bring about higher integration density, shorter interconnection path and smaller device structure for the next-generation semiconductor devices. In this paper, we investigated the warpage behavior of our Chip-on-Chip-on-Substrate (CoC)-oS TSI package using the Chip-on-Chip (CoC) first assembly approach. The C4 (controlled collapse chip connection) bumps on the CoC TSI package was attached using both flux-based ball drop process and laser solder ball jetting process. Warpage analysis and profile for each assembly part/package in the CoC-first assembly was measured using the shadow moire method. Two guest dies were assembled onto a large (38.70mm × 26.70mm) and thin (0.10mm) TSI package using flip-chip thermo-compression bonding. The assembled CoC TSI package was then mounted onto an organic substrate. Warpage measurement was conducted from a temperature range of 25°C to 250°C. From the shadow moire measurement results, the warpage difference between the organic substrate and the assembled CoC TSI package with C4 bumps via laser solder ball jetting process is much smaller than using the ball drop process, at room temperature during the initial package placement prior to the reflow process. High warpage difference between the organic substrate and the assembled CoC TSI package with C4 bumps would result in opened and stretched solder joint interconnections issues, especially around the edges and peripheral of the package. Opened solder joint would result in no electrical connection and stretched solder joint would increase the risk of solder joint breakage and affect the long-term reliability of the package. Hence, the initial warpage difference between the assembled package and organic substrate during the assembly process can induce significant effects. In summary, we have demonstrated that the assembly process with the use of laser solder ball jetting process would reduce the warpage of CoC TSI package with C4 bumps, thus reduce the warpage difference with the organic substrate. Hence, the reduction in warpage of the assembled CoC TSI package can improve the process control of the CoC-first assembly flow.


title: Brave new (interactive) worlds: A review of the design affordances and constraints of two 3D virtual worlds as interactive learning environments

abstract: Three-dimensional virtual worlds are an emerging medium currently being used in both traditional classrooms and for distance education. Three-dimensional (3D) virtual worlds are a combination of desk-top interactive Virtual Reality within a chat environment. This analysis provides an overview of Active Worlds Educational Universe and Adobe Atmosphere and the pedagogical affordances and constraints of the inscription tools, discourse tools, experiential tools, and resource tools of each application. The purpose of this review is to discuss the implications of using each application for educational initiatives by exploring how the various design features of each may support and enhance the design of interactive learning environments.


title: Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks

abstract: A detailed environment perception is a crucial component of automated vehicles. However, to deal with the amount of perceived information, we also require segmentation strategies. Based on a grid map environment representation, well-suited for sensor fusion, free-space estimation and machine learning, we detect and classify objects using deep convolutional neural networks. As input for our networks we use a multi-layer grid map efficiently encoding 3D range sensor information. The inference output consists of a list of rotated bounding boxes with associated semantic classes. We conduct extensive ablation studies, highlight important design considerations when using grid maps and evaluate our models on the KITTI Bird's Eye View benchmark. Qualitative and quantitative benchmark results show that we achieve robust detection and state of the art accuracy solely using top-view grid maps from range sensor data.


title: CRISPR/Cas9-mediated targeted mutagenesis in upland cotton (Gossypium hirsutum L.)

abstract: The clustered, regularly interspaced, short palindromic repeats (CRISPR)/CRISPR associated (Cas)9 protein system has emerged as a simple and efficient tool for genome editing in eukaryotic cells. It has been shown to be functional in several crop species, yet there are no reports on the application of this or any other genome editing technologies in the cotton plant. Cotton is an important crop that is grown mainly for its fiber, but its seed also serves as a useful source of edible oil and feed protein. Most of the commercially-grown cotton is tetraploid, thus making it much more difficult to target both sets of homeologous alleles. Therefore, in order to understand the efficacy of the CRISPR/Cas9 system to target a gene within the genome of cotton, we made use of a transgenic cotton line previously generated in our laboratory that had a single copy of the green fluorescent protein (GFP) gene integrated into its genome. We demonstrate, for the first time, the use of this powerful new tool in targeted knockout of a gene residing in the cotton genome. By following the loss of GFP fluorescence, we were able to observe the cells that had undergone targeted mutations as a result of CRISPR/Cas9 activity. In addition, we provide examples of the different types of indels obtained by Cas9-mediated cleavage of the GFP gene, guided by three independent sgRNAs. The results provide useful information that will help us target important native genes in the cotton plant in future.


title: Object oriented extensions of IEC 61131–3 as an enabling technology of software product lines

abstract: Software product lines (SPL) relying on UML technology have been a breakthrough in software reuse in the IT domain. In the industrial automation domain, SPL are not yet established in industrial practice. One reason for this is that conventional function block programming techniques do not adequately support SPL architecture definition and product configuration, while UML tools are not industrially accepted for control software development. In this paper, the use of object oriented (OO) extensions of IEC 61131–3 are used to bridge this gap. The SPL architecture and product specifications are expressed as UML class diagrams, which serve as straightforward specifications for configuring the IEC 61131–3 control application with OO extensions. A product configurator tool has been developed using PLCopen XML technology to support the generation of an executable IEC 61131–3 application according to chosen product options. The approach is demonstrated using a mobile elevating working platform as a case study.


title: Muscle Spindle Distribution, Morphology, and Density in Longus Colli and Multifidus Muscles of the Cervical Spine

abstract: Study Design.Tissue blocks comprising muscle and bone from C5 to C7 segments were harvested at autopsy from 16 individuals ranging in age from 4 to 77 years. The prevertebral longus colli and postvertebral multifidus muscle pairs from one side in each individual were randomly selected for this study


title: Syllabification algorithm based on syllable rules matching for Malay language

abstract: In this paper, we present a new syllabification algorithm for Malay language. Syllabification is the process to extract or divide syllable from words. Syllabification process is language dependent where each language can have its own set of syllable structure. Syllabication is an important component in speech synthesizer, speech recognition and transliteration system. Syllabification algorithms have been proposed in many languages including English, Spanish, Myanmar, Singhala, Chinese and ect. Unfortunately, there are not many information regarding evaluation of syllabification scheme for Malay. In this paper, we propose an efficient algorithm based on syllable rules matching. In order to evaluate the algorithm, a prototype has been developed to measure the accuracy of syllabification. We evaluate our method using Bernama, Kamus Dewan and Overlap data collection. The syllable rules matching achieved 60.7% accuracy on BERNAMA collection, 77.4% on Kamus Dewan Collection and 71.6% on Overlap collection.


title: On Multi-Column Foreign Key Discovery

abstract: A foreign/primary key relationship between relational tables is one of the most important constraints in a database. From a data analysis perspective, discovering foreign keys is a crucial step in understanding and working with the data. Nevertheless, more often than not, foreign key constraints are not specified in the data, for various reasons; e.g., some associations are not known to designers but are inherent in the data, while others become invalid due to data inconsistencies. This work proposes a robust algorithm for discovering single-column and multi-column foreign keys. Previous work concentrated mostly on discovering single-column foreign keys using a variety of rules, like inclusion dependencies, column names, and minimum/maximum values. We first propose a general rule, termed Randomness, that subsumes a variety of other rules. We then develop efficient approximation algorithms for evaluating randomness, using only two passes over the data. Finally, we validate our approach via extensive experiments using real and synthetic datasets.


title: MAC: Magnetostatic Active Contour Model

abstract: We propose an active contour model using an external force field that is based on magnetostatics and hypothesized magnetic interactions between the active contour and object boundaries. The major contribution of the method is that the interaction of its forces can greatly improve the active contour in capturing complex geometries and dealing with difficult initializations, weak edges, and broken boundaries. The proposed method is shown to achieve significant improvements when compared against six well-known and state-of-the-art shape recovery methods, including the geodesic snake, the generalized version of gradient vector flow (GVF) snake, the combined geodesic and GVF snake, and the charged particle model.


title: Practical verification of WPA-TKIP vulnerabilities

abstract: We describe three attacks on the Wi-Fi Protected Access Temporal Key Integrity Protocol (WPA-TKIP). The first attack is a Denial of Service attack that can be executed by injecting only two frames every minute. The second attack demonstrates how fragmentation of 802.11 frames can be used to inject an arbitrary amount of packets, and we show that this can be used to perform a portscan on any client. The third attack enables an attacker to reset the internal state of the Michael algorithm. We show that this can be used to efficiently decrypt arbitrary packets sent towards a client. We also report on implementation vulnerabilities discovered in some wireless devices. Finally we demonstrate that our attacks can be executed in realistic environments.


title: End-to-end Active Object Tracking via Reinforcement Learning

abstract: We study active object tracking, where a tracker takes as input the visual observation (i.e., frame sequence) and produces the camera control signal (e.g., move forward, turn left, etc.). Conventional methods tackle the tracking and the camera control separately, which is challenging to tune jointly. It also incurs many human efforts for labeling and many expensive trial-and-errors in realworld. To address these issues, we propose, in this paper, an end-to-end solution via deep reinforcement learning, where a ConvNet-LSTM function approximator is adopted for the direct frame-toaction prediction. We further propose an environment augmentation technique and a customized reward function, which are crucial for a successful training. The tracker trained in simulators (ViZDoom, Unreal Engine) shows good generalization in the case of unseen object moving path, unseen object appearance, unseen background, and distracting object. It can restore tracking when occasionally losing the target. With the experiments over the VOT dataset, we also find that the tracking ability, obtained solely from simulators, can potentially transfer to real-world scenarios.


title: Optimal Decisions from Probabilistic Models: The Intersection-over-Union Case

abstract: A probabilistic model allows us to reason about the world and make statistically optimal decisions using Bayesian decision theory. However, in practice the intractability of the decision problem forces us to adopt simplistic loss functions such as the 0/1 loss or Hamming loss and as result we make poor decisions through MAP estimates or through low-order marginal statistics. In this work we investigate optimal decision making for more realistic loss functions. Specifically we consider the popular intersection-over-union (IoU) score used in image segmentation benchmarks and show that it results in a hard combinatorial decision problem. To make this problem tractable we propose a statistical approximation to the objective function, as well as an approximate algorithm based on parametric linear programming. We apply the algorithm on three benchmark datasets and obtain improved intersection-over-union scores compared to maximum-posterior-marginal decisions. Our work points out the difficulties of using realistic loss functions with probabilistic computer vision models.


title: A longitudinal study of the social and emotional predictors and consequences of cyber and traditional bullying victimisation

abstract: Objectives ::: Few longitudinal studies have investigated how cyberbullying interacts with traditional bullying among young people, who are increasingly using online environments to seek information, entertainment and to socialise. This study aimed to identify the associations between the relative contribution of cyberbullying victimisation and traditional bullying victimisation on social and emotional antecedents and outcomes among adolescents.


title: Enhanced High Dimensional Data Visualization through Dimension Reduction and Attribute Arrangement

abstract: Researchers and users are well aware of the difficulties related to finding an appropriate configuration of the axes mapping attributes in multidimensional visualization techniques, particularly in visualizations that show a large number of attributes simultaneously. We address this problem with a simple strategy that offers both dimension ordering and dimension reduction. Dimension ordering is based on attribute similarity heuristics, and the basic rationale is extended to support dimension reduction. We discuss the performance of our algorithms and present some results of their application to several data sets. The algorithms improve the capability of visualization techniques to segregate clusters present in the data and reduce the visual clutter aggravated by arbitrary distributions of the axes.


title: Effects of Position Bias on Click-Based Recommender Evaluation

abstract: Measuring the quality of recommendations produced by a recommender system RS is challenging. Labels used for evaluation are typically obtained from users of a RS, by asking for explicit feedback, or inferring labels from implicit feedback. Both approaches can introduce significant biases in the evaluation process. We investigate biases that may affect labels inferred from implicit feedback. Implicit feedback is easy to collect but can be prone to biases, such as position bias. We examine this bias using click models, and show how bias following these models would affect the outcomes of RS evaluation. We find that evaluation based on implicit and explicit feedback can agree well, but only when the evaluation metrics are designed to take user behavior and preferences into account, stressing the importance of understanding user behavior in deployed RSs.


title: Design of parameters self-tuning fuzzy PID control for DC motor

abstract: A DC motor model is built, and a controller is designed in this paper. The controller is simulated on Matlab Simulink. The simulation result shows the difference of conventional PID controller and the parameters self-tuning fuzzy PID. From these simulation results, all performance indexes of the fuzzy PID controller with parameters self-tuning are vastly superior to those of the conventional PID controller.


title: Multi-sensor payload detection and acquisition for truck-trailer AGVs

abstract: A fundamental task of automated guided vehicles is transporting heavy payloads. These payloads are often given in the form of large containers that are mounted onto a cart with four caster wheels. In this paper we investigate a combined detection and control architecture that allows an AGV to reliably detect and acquire such containers at any location, independently of a global localization system. We propose a solution using a horizontally mounted 2D laser scanner and a forward facing 3D time-of-flight camera to detect the payload. Furthermore we demonstrate the algorithm on an asymmetric AGV that consists of a differentially driven base and a trailer with a lifting unit.


title: Extended development of LinuxCNC for control of a Delta robot

abstract: In recent years, open architecture motion controllers, including those for CNC machines and robots, have received much interest and support among the global control and automation community. This paper presents work done in extending a well-known and supported open-source control software called LinuxCNC for the control of a Delta robot, a translational parallel mechanism. Key features in the development process are covered and discussed and the final customized system based on LinuxCNC described.


title: Understanding the Linux Kernel

abstract: In order to thoroughly understand what makes Linux tick and why it works so well on a wide variety of systems, you need to delve deep into the heart of the kernel. The kernel handles all interactions between the CPU and the external world, and determines which programs will share processor time, in what order. It manages limited memory so well that hundreds of processes can share the system efficiently, and expertly organizes data transfers so that the CPU isn't kept waiting any longer than necessary for the relatively slow disks. The third edition of Understanding the Linux Kernel takes you on a guided tour of the most significant data structures, algorithms, and programming tricks used in the kernel. Probing beyond superficial features, the authors offer valuable insights to people who want to know how things really work inside their machine. Important Intel-specific features are discussed. Relevant segments of code are dissected line by line. But the book covers more than just the functioning of the code; it explains the theoretical underpinnings of why Linux does things the way it does.This edition of the book covers Version 2.6, which has seen significant changes to nearly every kernel subsystem, particularly in the areas of memory management and block devices. The book focuses on the following topics:Memory management, including file buffering, process swapping, and Direct memory Access (DMA)The Virtual Filesystem layer and the Second and Third Extended FilesystemsProcess creation and schedulingSignals, interrupts, and the essential interfaces to device driversTimingSynchronization within the kernelInterprocess Communication (IPC)Program executionUnderstanding the Linux Kernel will acquaint you with all the inner workings of Linux, but it's more than just an academic exercise. You'll learn what conditions bring out Linux's best performance, and you'll see how it meets the challenge of providing good system response during process scheduling, file access, and memory management in a wide variety of environments. This book will help you make the most of your Linux system.


title: On the exploitation of process mining for security audits: the process discovery case

abstract: This paper reports on the potential of process mining as a basis for security audits of business process and corresponding business process management systems. In particular, it focuses on process discovery as a means to reconstruct process-related structures from event logs, such as the process' control flow, social network and data flows. Based on this information, security analysis to determine the compliance with security and privacy requirements can be automated.


title: A Non-Parametric Topic Model for Short Texts Incorporating Word Coherence Knowledge

abstract: Mining topics in short texts (e.g. tweets, instant messages) can help people grasp essential information and understand key contents, and is widely used in many applications related to social media and text analysis. The sparsity and noise of short texts often restrict the performance of traditional topic models like LDA. Recently proposed Biterm Topic Model (BTM) which models word co-occurrence patterns directly, is revealed effective for topic detection in short texts. However, BTM has two main drawbacks. It needs to manually specify topic number, which is difficult to accurately determine when facing new corpora. Besides, BTM assumes that two words in same term should belong to the same topic, which is often too strong as it does not differentiate two types of words (i.e. general words and topical words). To tackle these problems, in this paper, we propose a non-parametric topic model npCTM with the above distinction. Our model incorporates the Chinese restaurant process (CRP) into the BTM model to determine topic number automatically. Our model also distinguishes general words from topical words by jointly considering the distribution of these two word types for each word as well as word coherence information as prior knowledge. We carry out experimental studies on real-world twitter dataset. The results demonstrate the effectiveness of our method to discover coherent topics compared with the baseline methods.


title: SVM based forest fire detection using static and dynamic features

abstract: A novel approach is proposed in this paper for automatic forest fire detection from video. Based on 3D point cloud of the collected sample fire pixels, Gaussian mixture model is built and helps segment some possible flame regions in single image. Then the new specific flame pattern is defined for forest, and three types of fire colors are labeled accordingly. With 11 static features including color distributions, texture parameters and shape roundness, the static SVM classifier is trained and filters the segmented results. Using defined overlapping degree and varying degree, the remained candidate regions are matched among consecutive frames. Subsequently the variations of color, texture, roundness, area, contour are computed, then the average and the mean square deviation of them are obtained. Together with the flickering frequency from temporal wavelet based Fourier descriptors analysis of flame contour, 27 dynamic features are used to train the dynamic SVM classifier, which is applied for final decision. Our approach has been tested with dozens of video clips, and it can detect forest fire while recognize the fire like objects, such as red house, bright light and flying flag. Except for the acceptable accuracy, our detection algorithm performs in real time, which proves its value for computer vision based forest fire surveillance. KEYWORDS: Forest flame, Color segmentation, Static feature, Shapematching, Dynamic feature, SVM Language: en


title: Post-quantum Key Exchange for the Internet and the Open Quantum Safe Project

abstract: Designing public key cryptosystems that resist attacks by quantum computers is an important area of current cryptographic research and standardization. To retain confidentiality of today’s communications against future quantum computers, applications and protocols must begin exploring the use of quantum-resistant key exchange and encryption. In this paper, we explore post-quantum cryptography in general and key exchange specifically. We review two protocols for quantum-resistant key exchange based on lattice problems: BCNS15, based on the ring learning with errors problem, and Frodo, based on the learning with errors problem. We discuss their security and performance characteristics, both on their own and in the context of the Transport Layer Security (TLS) protocol. We introduce the Open Quantum Safe project, an open-source software project for prototyping quantum-resistant cryptography, which includes liboqs, a C library of quantum-resistant algorithms, and our integrations of liboqs into popular open-source applications and protocols, including the widely used OpenSSL library.


Automatic Ontology-based Knowledge Extraction and Tailored Biography Generation from the Web


title: Blocks That Shout: Distinctive Parts for Scene Classification

abstract: The automatic discovery of distinctive parts for an object or scene class is challenging since it requires simultaneously to learn the part appearance and also to identify the part occurrences in images. In this paper, we propose a simple, efficient, and effective method to do so. We address this problem by learning parts incrementally, starting from a single part occurrence with an Exemplar SVM. In this manner, additional part instances are discovered and aligned reliably before being considered as training examples. We also propose entropy-rank curves as a means of evaluating the distinctiveness of parts shareable between categories and use them to select useful parts out of a set of candidates. We apply the new representation to the task of scene categorisation on the MIT Scene 67 benchmark. We show that our method can learn parts which are significantly more informative and for a fraction of the cost, compared to previous part-learning methods such as Singh et al. [28]. We also show that a well constructed bag of words or Fisher vector model can substantially outperform the previous state-of-the-art classification performance on this data.


title: Density estimation using Real NVP

abstract: Unsupervised learning of probabilistic models is a central yet challenging problem in machine learning. Specifically, designing models with tractable learning, sampling, inference and evaluation is crucial in solving this task. We extend the space of such models using real-valued non-volume preserving (real NVP) transformations, a set of powerful invertible and learnable transformations, resulting in an unsupervised learning algorithm with exact log-likelihood computation, exact sampling, exact inference of latent variables, and an interpretable latent space. We demonstrate its ability to model natural images on four datasets through sampling, log-likelihood evaluation and latent variable manipulations.


title: A simple unpredictable pseudo random number generator

abstract: Two closely-related pseudo-random sequence generators are presented: The ${1 / P}$generator, with input P a prime, outputs the quotient digits obtained on dividing 1 by P. The $x^2 \bmod N$generator with inputs N, $x_0 $ (where $N = P \cdot Q$ is a product of distinct primes, each congruent to 3 mod 4, and $x_0 $ is a quadratic residue $\bmod N$), outputs $b_0 b_1 b_2 \cdots $ where $b_i = {\operatorname{parity}}(x_i )$ and $x_{i + 1} = x_i^2 \bmod N$.From short seeds each generator efficiently produces long well-distributed sequences. Moreover, both generators have computationally hard problems at their core. The first generator’s sequences, however, are completely predictable (from any small segment of $2|P| + 1$ consecutive digits one can infer the “seed,” P, and continue the sequence backwards and forwards), whereas the second, under a certain intractability assumption, is unpredictable in a precise sense. The second generator has additional interesting properties: from knowledge of $x_0 $ and N but n...


Corn Productivity: The Role of Management and Biotechnology


title: Item-based top-N recommendation algorithms

abstract: The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems---a personalized information filtering technology used to identify a set of items that will be of interest to a certain user. User-based collaborative filtering is the most successful technology for building recommender systems to date and is extensively used in many commercial recommender systems. Unfortunately, the computational complexity of these methods grows linearly with the number of customers, which in typical commercial applications can be several millions. To address these scalability concerns model-based recommendation techniques have been developed. These techniques analyze the user--item matrix to discover relations between the different items and use these relations to compute the list of recommendations.In this article, we present one such class of model-based recommendation algorithms that first determines the similarities between the various items and then uses them to identify the set of items to be recommended. The key steps in this class of algorithms are (i) the method used to compute the similarity between the items, and (ii) the method used to combine these similarities in order to compute the similarity between a basket of items and a candidate recommender item. Our experimental evaluation on eight real datasets shows that these item-based algorithms are up to two orders of magnitude faster than the traditional user-neighborhood based recommender systems and provide recommendations with comparable or better quality.


title: The smart car seat: personalized monitoring of vital signs in automotive applications

abstract: Embedded wireless sensors are important components of mobile distributed computing networks, and one of the target applications areas is health care. The preservation of mobility for senior citizens is one of the key issues in maintaining an independent lifestyle. Thus health technologies inside a car can contribute both to safety issues (supervision of driver fitness) as well as healthcare issues by monitoring vitals signs imperceptibly. In this paper, three embedded measurement techniques for non-contact monitoring of vital signals have been investigated. Specifically, capacitive electrocardiogram (cECG) monitoring, mechanical movement analysis (ballistocardiogram, BCG) using piezo-foils and inductive impedance monitoring were examined regarding their potential for integration into car seats. All three sensing techniques omit the need for electroconductive contact to the human body, but require defined mechanical boundary conditions (stable distances or, in the case of BCG, frictional connection). The physical principles of operation, the specific boundary conditions regarding automotive integration and the results during wireless operation in a running car are presented. All three sensors were equipped with local intelligence by incorporating a microcontroller. To eliminate the need for additional cabling, a wireless Bluetooth communication module was added and used to transmit data to a measurement PC. Finally, preliminary results obtained during test drives on German city roads and highways are discussed.


title: Sphere Detection in Kinect Point Clouds via the 3D Hough Transform

abstract: We introduce a fast, robust and accurate Hough Transform (HT) based algorithm for detecting spherical structures in 3D point clouds. To our knowledge, our algorithm is the first HT based implementation that detects spherical structures in typical in 3D point clouds generated by consumer depth sensors such as the Microsoft Kinect. Our approach has been designed to be computationally efficient; reducing an established limitation of HT based approaches. We provide experimental analysis of the achieved results, showing a robust performance against occlusion, and we show superior performance to the only other HT based algorithm for detecting spheres in point clouds available in literature.


title: Deep Predictive Models in Interactive Music

abstract: Automatic music generation is a compelling task where much recent progress has been made with deep learning models. In this paper, we ask how these models can be integrated into interactive music systems; how can they encourage or enhance the music making of human users? Musical performance requires prediction to operate instruments, and perform in groups. We argue that predictive models could help interactive systems to understand their temporal context, and ensemble behaviour. Deep learning can allow data-driven models with a long memory of past states. ::: We advocate for predictive musical interaction, where a predictive model is embedded in a musical interface, assisting users by predicting unknown states of musical processes. We propose a framework for incorporating such predictive models into the sensing, processing, and result architecture that is often used in musical interface design. We show that our framework accommodates deep generative models, as well as models for predicting gestural states, or other high-level musical information. We motivate the framework with two examples from our recent work, as well as systems from the literature, and suggest musical use-cases where prediction is a necessary component.


title: PubMed Labs: An experimental platform for improving biomedical literature search

abstract: PubMed is a freely accessible system for searching the biomedical literature, with approximately 2.5 million users worldwide on an average workday. We have recently developed PubMed Labs (www.pubmed.gov/labs), an experimental platform for users to test new features/tools and provide feedback, which enables us to make more informed decisions about potential changes to improve the search quality and overall usability of PubMed. In doing so, we hope to better meet our user needs in an era of information overload. Another novel aspect of PubMed Labs lies in its mobile-first and responsive layout, which offers better support for accessing PubMed on the increasingly popular use of mobile and small-screen devices. Currently, PubMed Labs only includes a core subset of PubMed functionalities, e.g. search, facets. We encourage users to test PubMed Labs and share their experience with us, based on which we expect to continuously improve PubMed Labs with more advanced features and better user experience.


title: A FAST AND SIMPLE METHOD OF BUILDING DETECTION FROM LIDAR DATA BASED ON SCAN LINE ANALYSIS

abstract: One of the major problems in processing LiDAR (Light Detection And Ranging) data is its huge data volume which causes very high computational load when dealing with large areas with high point density. A fast and simple algorithm based on scan line analysis is proposed for automatic detection of building points from LiDAR data. At first, ground/non-ground classification is performed to filter out the ground points. Douglas–Peucker algorithm is then used to segment the scan line into segment objects based on height variation. These objects are preliminarily classified into buildings and vegetation based on local analysis using simple rules. At last, the region growing method is used to improve the quality of the extraction. The test data provided by the ISPRS test project on urban object extraction, containing a lot of buildings with complex roof structures, various sizes, and different heights, is used to test the algorithm. The experimental results show that the proposed algorithm can extract building regions effectively.


title: Invited Review Article Wild emmer wheat, Triticum dicoccoides, occupies a pivotal position in wheat domestication process

abstract: Domestication of plants and animals is the major factor underlying human civilization. Cultivated wheats refer mainly to two types: the hexaploid bread wheat (Triticum aestivum ) accounting for about 95% of world wheat production, and the tetraploid durum wheat (T. durum ) accounting for the other 5%. T. aestivum derived from a cross between domesticated emmer T. dicoccum and the goat grass Aegilops tauschii, which most probably originated in the south or west of the Caspian Sea about 9,000 years ago. T. dicoccoides , the wild emmer wheat, is the progenitor of cultivated wheats, has the same genome formula as durum wheat and has contributed two genomes to bread wheat, and has played a core role to wheat domestication. This process of wheat domestication fits the gradual and multi-site model rather than the fast and single-site model. Domestication has genetically not only transformed the brittle rachis, tenacious glume and non-free threshability, but also modified yield and yield components in wheat. Wheat domestication is only involved in a limited number of chromosome regions, or domestication syndrome factors, though many relevant quantitative trait loci were detected. The available crop genome sequences and genome sequencing of wheat can transform today’s biology, dramatically advancing both theory and application of wheat domestication study. The nonrandom adaptive processes and complexes in T. dicoccoides and other wheat relatives could provide the basis for wheat improvement as single genes, QTLs, and interacting biochemical networks. Genome sequencing of diploid wild wheat, either T. urartu or Ae. tauschii , could be helpful for isolation of domestication syndrome factors and other relevant genes. The distinct adaptive complexes of T. dicoccoides to environmental stresses is of great importance for improvement of bread wheat.


title: PULSAR: Stateful Black-Box Fuzzing of Proprietary Network Protocols

abstract: The security of network services and their protocols critically depends on minimizing their attack surface. A single flaw in an implementation can suffice to compromise a service and expose sensitive data to an attacker. The discovery of vulnerabilities in protocol implementations, however, is a challenging task: While for standard protocols this process can be conducted with regular techniques for auditing, the situation becomes difficult for proprietary protocols if neither the program code nor the specification of the protocol are easily accessible. As a result, vulnerabilities in closed-source implementations can often remain undiscovered for a longer period of time. In this paper, we present Pulsar, a method for stateful black-box fuzzing of proprietary network protocols. Our method combines concepts from fuzz testing with techniques for automatic protocol reverse engineering and simulation. It proceeds by observing the traffic of a proprietary protocol and inferring a generative model for message formats and protocol states that can not only analyze but also simulate communication. During fuzzing this simulation can effectively explore the protocol state space and thereby enables uncovering vulnerabilities deep inside the protocol implementation. We demonstrate the efficacy of Pulsar in two case studies, where it identifies known as well as unknown vulnerabilities.


title: Midfield Wireless Power Transfer for Bioelectronics

abstract: Midfield wireless powering enables electronics to be designed at the millimeter-scale and operated at nearly any location in the body. Theoretical studies show a wireless power transfer paradigm with a maximum bound orders of magnitude higher than conventional near-field inductive coupling. Furthermore, higher frequency of operation for midfield coupling lends itself to miniaturization of the implant. Midfield powering experiments using an external patterned metal structure and a millimeter sized coil show significant power delivery within safe levels of tissue heating in the chest and the head of a porcine model, and will enable implantable bioelectronics to reach new levels of functionality and one day to become as ubiquitous as over-the-counter pharmaceuticals.


title: Risk assessment of hand washing efficacy using literature and experimental data.

abstract: This study simulated factors that influence the levels of bacteria on foodservice workers' hands. Relevant data were collected from the scientific literature and from laboratory experiments. Literature information collected included: initial bacterial counts on hands and water faucet spigots, bacterial population changes during hand washing as effected by soap type, sanitizing agent, drying method, and the presence of rings. Experimental data were also collected using Enterobacter aerogenes as a surrogate for transient bacteria. Both literature and experimental data were translated into appropriate discrete or probability distribution functions. The appropriate statistical distribution for each phase of the hand washing process was determined. These distributions were: initial count on hands, beta (2.82, 2.32, 7.5); washing reduction using regular soap, beta (3.01, 1.91, -3.00, 0.60); washing reduction using antimicrobial soap, beta (4.19, 2.99, -4.50, 1.50); washing reduction using chlorhexidine gluconate (CHG), triangular (-4.75, -1.00, 0); reductions from hot air drying, beta (3.52, 1.92, -0.20, 1.00); reduction from paper towel drying, triangular (-2.25, -0.75, 0); reduction due to alcohol sanitizer, gamma (-1.23, 4.42) -5.8; reduction due to alcohol-free sanitizer, gamma (2.22, 5.38) -5.00; and the effect of rings, beta (8.55, 23.35, 0.10, 0.45). Experimental data were fit to normal distributions (expressed as log percentage transfer rate): hand-to-spigot transfer, normal (-0.80, 1.09); spigot to hand, normal (0.36, 0.90). Soap with an antimicrobial agent (in particular, CHG) was observed to be more effective than regular soap. Hot air drying had the capacity to increase the amount of bacterial contamination on hands, while paper towel drying caused a slight decrease in contamination. There was little difference in the efficacy of alcohol and alcohol-free sanitizers. Ring wearing caused a slight decrease in the efficacy of hand washing. The experimental data validated the simulated combined effect of certain hand washing procedures based on distributions derived from reported studies. The conventional hand washing system caused a small increase in contamination on hands vs. the touch-free system. Sensitivity analysis revealed that the primary factors influencing final bacteria counts on the hand were sanitizer, soap, and drying method. This research represents an initial framework from which sound policy can be promulgated to control bacterial transmission via hand contacts.


title: A case of monocephalus rachipagus tribrachius tetrapus in a puppy

abstract: Caudal duplication (dipygus) is an uncommon pathologic of conjoined twinning. The conjoined malformation is classified according to the nature and site of the union. We report the presence of this malformation in a female crossbreed puppy. The puppy was delivered by caesarean section following a prolonged period of dystocia. External findings showed a single head (monocephalus) and a normal cranium with no fissure in the medial line detected. The thorax displayed a caudal duplication arising from the lumbosacral region (rachipagus). The puppy had three upper limbs, a right and left, and a third limb in the dorsal region where the bifurcation began. The subsequent caudal duplication appeared symmetrical. Necropsy revealed internal abnormalities consisting of a complete duplication of the urogenital system and a duplication of the large intestines arising from a bifurcation of the caudal ileum. Considering the morphophysiological description the malformation described would be classified as the first case in the dog of a monocephalus rachipagus tribrachius tetrapus.


Architecture and Simulation of Timing Synchronization Circuits for the FPGA Implementation of Narrowband Waveforms


title: Guidelines for human electromyographic research

abstract: Guidelines are proposed for the collection, analysis, and description of electromyographic (EMG) data. The guidelines cover technological issues in EMG recording, social aspects of EMG experimentation, and limits to inferences that can be drawn in EMG research. An atlas is proposed for facial EMG electrode placements, and standard EMG terminology is suggested.


title: ICCDetector: ICC-Based Malware Detection on Android

abstract: Most existing mobile malware detection methods (e.g., Kirin and DroidMat) are designed based on the resources required by malwares (e.g., permissions, application programming interface (API) calls, and system calls). These methods capture the interactions between mobile apps and Android system, but ignore the communications among components within or cross application boundaries. As a consequence, the majority of the existing methods are less effective in identifying many typical malwares, which require a few or no suspicious resources, but leverage on inter-component communication (ICC) mechanism when launching stealthy attacks. To address this challenge, we propose a new malware detection method, named ICCDetector. ICCDetector outputs a detection model after training with a set of benign apps and a set of malwares, and employs the trained model for malware detection. The performance of ICCDetector is evaluated with 5264 malwares, and 12026 benign apps. Compared with our benchmark, which is a permission-based method proposed by Peng et al. in 2012 with an accuracy up to 88.2%, ICCDetector achieves an accuracy of 97.4%, roughly 10% higher than the benchmark, with a lower false positive rate of 0.67%, which is only about a half of the benchmark. After manually analyzing false positives, we discover 43 new malwares from the benign data set, and reduce the number of false positives to seven. More importantly, ICCDetector discovers 1708 more advanced malwares than the benchmark, while it misses 220 obvious malwares, which can be easily detected by the benchmark. For the detected malwares, ICCDetector further classifies them into five newly defined malware categories, which help understand the relationship between malicious behaviors and ICC characteristics. We also provide a systemic analysis of ICC patterns of benign apps and malwares.