title: Cocaine as a risk factor for neuroleptic-induced acute dystonia.

abstract: Background: A prospective study was conducted to test the hypothesis that cocaine use is a risk factor for neuroleptic-induced acute dystonia (NIAD). Method: The study sample consisted of a high-risk group for NIAD, males aged 17-45 years who had received high-potency neuroleptics within 24 hours of admission and had not used neuroleptics in the month prior to admission. Patients were excluded if they suffered from a neurodegenerative disorder or were exposed to anticholinergics, benzodiazepines, promethazine, carbamazepine, phenytoin, or levodopa during the study. Twenty-nine patients-9 cocaine users and 20 nonusers-entered the study, which lasted 2 years. Patients were followed for 7 days. Results: Cocaine-using psychiatric patients developed significantly more NIAD than did nonusers (relative risk = 4.4, 95% Cl = 1.4 to 13.9). Conclusion: Cocaine use is a major risk factor for NIAD and should be added to the list of well-known risk factors. The authors strongly suggest that cocaine-using psychiatric patients who are started on a regimen of neuroleptics should also be administered an anticholinergic for at least 7 days to prevent NIAD.


title: 5G: The Convergence of Wireless Communications

abstract: As the rollout of 4G mobile communication networks takes place, representatives of industry and academia have started to look into the technological developments toward the next generation (5G). Several research projects involving key international mobile network operators, infrastructure manufacturers, and academic institutions, have been launched recently to set the technological foundations of 5G. However, the architecture of future 5G systems, their performance, and mobile services to be provided have not been clearly defined. In this paper, we put forth the vision for 5G as the convergence of evolved versions of current cellular networks with other complementary radio access technologies. Therefore, 5G may not be a single radio access interface but rather a “network of networks”. Evidently, the seamless integration of a variety of air interfaces, protocols, and frequency bands, requires paradigm shifts in the way networks cooperate and complement each other to deliver data rates of several Gigabits per second with end-to-end latency of a few milliseconds. We provide an overview of the key radio technologies that will play a key role in the realization of this vision for the next generation of mobile communication networks. We also introduce some of the research challenges that need to be addressed.


title: On building an accurate stereo matching system on graphics hardware

abstract: This paper presents a GPU-based stereo matching system with good performance in both accuracy and speed. The matching cost volume is initialized with an AD-Census measure, aggregated in dynamic cross-based regions, and updated in a scanline optimization framework to produce the disparity results. Various errors in the disparity results are effectively handled in a multi-step refinement process. Each stage of the system is designed with parallelism considerations such that the computations can be accelerated with CUDA implementations. Experimental results demonstrate the accuracy and the efficiency of the system: currently it is the top performer in the Middlebury benchmark, and the results are achieved on GPU within 0.1 seconds. We also provide extra examples on stereo video sequences and discuss the limitations of the system.


title: Learning Blind Motion Deblurring

abstract: As handheld video cameras are now commonplace and available in every smartphone, images and videos can be recorded almost everywhere at anytime. However, taking a quick shot frequently yields a blurry result due to unwanted camera shake during recording or moving objects in the scene. Removing these artifacts from the blurry recordings is a highly ill-posed problem as neither the sharp image nor the motion blur kernel is known. Propagating information between multiple consecutive blurry observations can help restore the desired sharp image or video. Solutions for blind deconvolution based on neural networks rely on a massive amount of ground-truth data which is hard to acquire. In this work, we propose an efficient approach to produce a significant amount of realistic training data and introduce a novel recurrent network architecture to deblur frames taking temporal information into account, which can efficiently handle arbitrary spatial and temporal input sizes. We demonstrate the versatility of our approach in a comprehensive comparison on a number of challening real-world examples.


title: Tracking Noisy Targets: A Review of Recent Object Tracking Approaches

abstract: Visual object tracking is an important computer vision problem with numerous real-world applications including human-computer interaction, autonomous vehicles, robotics, motion-based recognition, video indexing, surveillance and security. In this paper, we aim to extensively review the latest trends and advances in the tracking algorithms and evaluate the robustness of trackers in the presence of noise. The first part of this work comprises a comprehensive survey of recently proposed tracking algorithms. We broadly categorize trackers into correlation filter based trackers and the others as non-correlation filter trackers. Each category is further classified into various types of trackers based on the architecture of the tracking mechanism. In the second part of this work, we experimentally evaluate tracking algorithms for robustness in the presence of additive white Gaussian noise. Multiple levels of additive noise are added to the Object Tracking Benchmark (OTB) 2015, and the precision and success rates of the tracking algorithms are evaluated. Some algorithms suffered more performance degradation than others, which brings to light a previously unexplored aspect of the tracking algorithms. The relative rank of the algorithms based on their performance on benchmark datasets may change in the presence of noise. Our study concludes that no single tracker is able to achieve the same efficiency in the presence of noise as under noise-free conditions; thus, there is a need to include a parameter for robustness to noise when evaluating newly proposed tracking algorithms.


Game Theory as an Instrument for the Analysis of International Relations


title: Customizable 3D Printed Tactile Maps as Interactive Overlays

abstract: Though tactile maps have been shown to be useful tools for visually impaired individuals, their availability has been limited by manufacturing and design costs. In this paper, we present a system that uses 3D printing to (1) make tactile maps more affordable to produce, (2) allow visually impaired individuals to independently design and customize maps, and (3) provide interactivity using widely available mobile devices. Our system consists of three parts: a web interface, a modeling algorithm, and an interactive touchscreen application. Our web interface, hosted at www.tactilemaps.net, allows visually impaired individuals to create maps of any location on the globe while specifying (1) what features to map, (2) how the features should be represented by textures, and (3) where to place markers and labels. Our modeling algorithm accommodates user specifications to create map models with (1) multiple layers of continuously varying textures and (2) markers of various geometric shapes or braille characters. Our interactive application uses a novel approach to 3D printing tactile maps using conductive filament to provide touchscreen overlays that allow users to dynamically interact with the maps on a wide range of mobile devices. This paper details the implementation of our system. We also present findings from a user study validating the usability of our mapping interface and the utility of the maps produced. Finally, we discuss the limitations of our current implementation and the plans we have to improve our system based on feedback from our user study and additional interviews.


title: Neuroimaging acupuncture effects in the human brain.

abstract: Acupuncture is an ancient East Asian healing modality that has been in use for more than 2000 years. Unfortunately, its mechanisms of action are not well understood, and controversy regarding its clinical efficacy remains. Importantly, acupuncture needling often evokes complex somatosensory sensations and may modulate the cognitive/affective perception of pain, suggesting that many effects are supported by the brain and extending central nervous system (CNS) networks. Modern neuroimaging techniques such as functional magnetic resonance imaging, positron emission tomography, electroencephalography, and magnetoencephalography provide a means to safely monitor brain activity in humans and may be used to help map the neurophysiological correlates of acupuncture. In this review, we will summarize data from acupuncture neuroimaging research and discuss how these findings contribute to current hypotheses of acupuncture action.


title: Identification of SLR tool needs - results of a community workshop

abstract: Context: With the increasing popularity of the Systematic Literature Review (SLR) process, there is also an increasing need for tool support. Objective:The goal of this work was to consult the software engineering researchers who conduct SLRs to identify and prioritize the necessary SLR tool features. Method: To gather information required to address this goal, we invited SLR authors to participate in an interactive 2?h workshop structured around the Nominal Group Technique. Results: The workshop outcomes indicated that Search & Selection and Collaboration are the two highest priority tool features. The results also showed that most of the high-priority features are not well-supported in current tools. Conclusion: These results support and extend the results of prior work. SLR tool authors can use these findings to guide future development efforts.


title: "It won't happen to me!": self-disclosure in online social networks

abstract: Despite the considerable amount of self-disclosure in Online Social Networks (OSN), the motivation behind this phenomenon is still little understood. Building on the Privacy Calculus theory, this study fills this gap by taking a closer look at the factors behind individual self-disclosure decisions. In a Structural Equation Model with 237 subjects we find Perceived Enjoyment and Privacy Concerns to be significant determinants of information revelation. We confirm that the privacy concerns of OSN users are primarily determined by the perceived likelihood of a privacy violation and much less by the expected damage. These insights provide a solid basis for OSN providers and policy-makers in their effort to ensure healthy disclosure levels that are based on objective rationale rather than subjective misconceptions.


title: Design of millimeter-wave wideband gap waveguide transitions considering integration into the antenna system

abstract: Two wideband vertical transitions from inverted microstrip gap waveguide to rectangular waveguide (WR-15) for 60 GHz antenna array applications are proposed. These transitions are aimed to interconnect the WR-15 with the gap waveguide feed-network employed to provide feeding to the radiating elements of slot/horn antenna arrays. The wideband field transformation is achieved without the need of adding a quarter-wavelength cavity backshort on a metal block placed over the transition. First, we replace this backshort by creating a cavity in the radiating layer when this is made in Substrate Integrated Waveguide (SIW) technology, and thereafter we use an air-filled pin cavity in the radiating layer. The simulated Sparameters show that the presented transitions cover the unlicensed 60 GHz band (57–64 GHz) with good margins.


title: On the security issues of NFC enabled mobile phones

abstract: In this paper, we investigate the possibility that a Near Field Communication (NFC) enabled mobile phone, with an embedded secure element (SE), could be used as a mobile token cloning and skimming platform. We show how an attacker could use an NFC mobile phone as such an attack platform by exploiting the existing security controls of the embedded SE and the available contactless APIs. To illustrate the feasibility of these actions, we also show how to practically skim and emulate certain tokens typically used in payment and access control applications with a NFC mobile phone. We also discuss how to capture and analyse legitimate transaction information from contactless systems. Although such attacks can also be implemented on other contactless platforms, such as custom-built card emulators and modified readers, the NFC enabled mobile phone has a legitimate form factor, which would be accepted by merchants and arouse less suspicion in public. Finally, we propose several security countermeasures for NFC phones that could prevent such misuse.


title: Development of drug-in-adhesive patch formulations for transdermal delivery of fluoxetine: In vitro and in vivo evaluations

abstract: Abstract The aim of this study was to develop drug-in adhesive (DIA) patch formulations for the transdermal delivery of fluoxetine (FX). The DIA patch formulations containing FX were prepared and optimized with various pressure sensitive adhesives, drug contents and enhancers. Among the various formulations, DuroTak 87-502B-based patch formulation containing 20% (w/w) FX with no enhancer was selected for in vivo pharmacokinetic study based on in vitro permeation studies using hairless mouse, rat and human cadaver skins. The C max of FX after the transdermal administration of the optimized patch formulation in rats was 52.38 ng/ml, and plasma concentration of FX was maintained for 36 h. Moreover, the predicted human C ss (55.79 ng/ml) and C max (27.35 ng/ml) were in good agreement with the reported plasma levels (15–55 ng/ml) of oral FX in clinical applications. These results suggest that the optimized patch formulation could be further developed for clinical applications, providing the therapeutic plasma level of FX over an extended period. To the best of our knowledge, our results are the first reported in vitro and in vivo data on the preparation and optimization of FX-loaded DIA patch, showing its feasibility as an effective transdermal delivery system for FX.


title: EStore: An effective optimized data placement structure for Hive

abstract: The data warehouse system Hive has emerged as an important facility for supporting data computing and storage. In particular, RCFile is a tailor-made data placement structure implemented in Hive, which is designed for the data processing efficiency. In this paper, we propose several optimized schemes based on RCFile and introduce EStore, which is an optimized data placement structure that is able to improve the query rate and reduce storage space for Hive. Specifically, it adopts both row-store and column-store in blocks, and further classifies the columns by the frequency of each table-column. Moreover, we also employ the classic RDP code to store files of the data table. We conduct experiments on a real cluster, and the results show that EStore has better features in terms of data query rate and storage space compared with RCFile.


title: You are who you know: inferring user profiles in online social networks

abstract: Online social networks are now a popular way for users to connect, express themselves, and share content. Users in today's online social networks often post a profile, consisting of attributes like geographic location, interests, and schools attended. Such profile information is used on the sites as a basis for grouping users, for sharing content, and for suggesting users who may benefit from interaction. However, in practice, not all users provide these attributes. In this paper, we ask the question: given attributes for some fraction of the users in an online social network, can we infer the attributes of the remaining users? In other words, can the attributes of users, in combination with the social network graph, be used to predict the attributes of another user in the network? To answer this question, we gather fine-grained data from two social networks and try to infer user profile attributes. We find that users with common attributes are more likely to be friends and often form dense communities, and we propose a method of inferring user attributes that is inspired by previous approaches to detecting communities in social networks. Our results show that certain user attributes can be inferred with high accuracy when given information on as little as 20% of the users.


title: A Double-Tail Latch-Type Voltage Sense Amplifier with 18ps Setup+Hold Time

abstract: A latch-type voltage sense amplifier in 90nm CMOS is designed with a separated input and cross-coupled stage. This separation enables fast operation over a wide common-mode and supply voltage range. With a 1-sigma offset of 8mV, the circuit consumes 92fJ/decision with a 1.2V supply. It has an input equivalent noise of 1.5mV and requires 18ps setup-plus-hold time


title: Dynamic Configuration of IPv4 Link-Local Addresses

abstract: To participate in wide-area IP networking, a host needs to be ::: configured with IP addresses for its interfaces, either manually by ::: the user or automatically from a source on the network such as a ::: Dynamic Host Configuration Protocol (DHCP) server. Unfortunately, such ::: address configuration information may not always be available. It is ::: therefore beneficial for a host to be able to depend on a useful ::: subset of IP networking functions even when no address configuration ::: is available. This document describes how a host may automatically ::: configure an interface with an IPv4 address within the 169.254/16 ::: prefix that is valid for communication with other devices connected to ::: the same physical (or logical) link. IPv4 Link-Local addresses are ::: not suitable for communication with devices not directly connected to ::: the same physical (or logical) link, and are only used where stable, ::: routable addresses are not available (such as on ad hoc or isolated ::: networks). This document does not recommend that IPv4 Link-Local ::: addresses and routable addresses be configured simultaneously on the ::: same interface. [STANDARDS-TRACK]


title: Cerebro-cerebellar circuits in autism spectrum disorder

abstract: The cerebellum is one of the most consistent sites of abnormality in autism spectrum disorder (ASD) and cerebellar damage is associated with an increased risk of ASD symptoms, suggesting that cerebellar dysfunction may play a crucial role in the etiology of ASD. The cerebellum forms multiple closed-loop circuits with cerebral cortical regions that underpin movement, language, and social processing. Through these circuits, cerebellar dysfunction could impact the core ASD symptoms of social and communication deficits and repetitive and stereotyped behaviors. The emerging topography of sensorimotor, cognitive, and affective subregions in the cerebellum provides a new framework for interpreting the significance of regional cerebellar findings in ASD and their relationship to broader cerebro-cerebellar circuits. Further, recent research supports the idea that the integrity of cerebro-cerebellar loops might be important for early cortical development; disruptions in specific cerebro-cerebellar loops in ASD might impede the specialization of cortical regions involved in motor control, language, and social interaction, leading to impairments in these domains. Consistent with this concept, structural and functional differences in sensorimotor regions of the cerebellum and sensorimotor cerebro-cerebellar circuits are associated with deficits in motor control and increased repetitive and stereotyped behaviors in ASD. In contrast, communication and social impairments are associated with atypical activation and structure in cerebro-cerebellar loops underpinning language and social cognition. Finally, there is converging evidence from structural, functional, and connectivity neuroimaging studies that cerebellar right Crus I/II abnormalities are related to more severe ASD impairments in all domains. We propose that cerebellar abnormalities may disrupt optimization of both structure and function in specific cerebro-cerebellar circuits in ASD.


Learning good policies from suboptimal demonstrations


Digital Phenotyping: Technology for a New Science of Behavior.


title: Detecting authorship deception: a supervised machine learning approach using . . .

abstract: We describe a new supervised machine learning approach for detecting author- ship deception, a specific type of authorship attribution task particularly relevant for cybercrime forensic investigations, and demonstrate its validity on two case studies drawn from realistic online data sets. The core of our approach involves identifying uncharacteristic behavior for an author, based on a writeprint ex- tracted from unstructured text samples of the author's writing. The writeprints used here involve stylometric features and content features derived from topic models, an unsupervised approach for identifying relevant keywords that relate to the content areas of a document. One innovation of our approach is to trans- form the writeprint feature values into a representation that individually balances characteristic and uncharacteristic traits of an author, and we subsequently apply a Sparse Multinomial Logistic Regression classifier to this novel representation. Our method yields high accuracy for authorship deception detection on the two case studies, confirming its utility.


title: Link analysis ranking: algorithms, theory, and experiments

abstract: The explosive growth and the widespread accessibility of the Web has led to a surge of research activity in the area of information retrieval on the World Wide Web. The seminal papers of Kleinberg [1998, 1999] and Brin and Page [1998] introduced Link Analysis Ranking, where hyperlink structures are used to determine the relative authority of a Web page and produce improved algorithms for the ranking of Web search results. In this article we work within the hubs and authorities framework defined by Kleinberg and we propose new families of algorithms. Two of the algorithms we propose use a Bayesian approach, as opposed to the usual algebraic and graph theoretic approaches. We also introduce a theoretical framework for the study of Link Analysis Ranking algorithms. The framework allows for the definition of specific properties of Link Analysis Ranking algorithms, as well as for comparing different algorithms. We study the properties of the algorithms that we define, and we provide an axiomatic characterization of the INDEGREE heuristic which ranks each node according to the number of incoming links. We conclude the article with an extensive experimental evaluation. We study the quality of the algorithms, and we examine how different structures in the graphs affect their performance.


title: MODELING AND SIMULATION OF CHOPPERS SWITCHING VIA MATLAB/SIMULINK

abstract: Modeling and simulating the converter using the modelling equations is necessary to design of power electronic converter circuit with the use of closed loop scheme. This can easily be done with the help of state equations and MATLAB/SIMULINK as a tool for simulation of those state equations. An attempt has been made in this paper to simulate all basic non-isolated power converters. So that these models can be readily used for any close loop design (say using PI, fuzzy, or sliding mode control etc.).


A Compact, Modular Series Elastic Actuator


title: Design of a Concurrent Dual-Band 1.8–2.4-GHz GaN-HEMT Doherty Power Amplifier

abstract: In this paper, the design, implementation, and experimental results of a high-efficiency dual-band GaN-HEMT Doherty power amplifier (DPA) are presented. An extensive discussion about the design of the passive structures is presented showing different possible topologies of the dual-band DPA. One of the proposed topologies is used to design a dual-band DPA in hybrid technology for the frequency bands 1.8 and 2.4 GHz with the second efficiency peak at 6-dB output power back-off (OBO). For a continuous-wave output power of 20 W, the measured power-added efficiency (PAE) is 64% and 54% at 1.8 and 2.4 GHz, respectively. At -dB OBO, the resulting measured PAEs were 60% and 44% in the two frequency bands. Linearized concurrent modulated measurement using 10-MHz LTE signal with 7-dB peak-to-average-ratio (PAR) at 1.8 GHz and 10-MHz WiMAX signal with 8.5-dB PAR at 2.4 GHz shows an average PAE of 34%, at an adjacent channel leakage ratio of -48 dBc and -46 dBc at 1.8 and 2.4 GHz, respectively.


title: Requirements Engineering in Agile Software Development

abstract: Finding out, analyzing, documenting, and checking requirements are important activities in all development approaches, including agile development. This paper discusses problems concerned with the conduction of requirements engineering activities in agile software development processes and suggests some improvements to solve some challenges caused by agile requirements engineering practices in large projects, like properly handling and identifying sensitive (including non- functional) requirements, documenting and managing requirements documentation, keeping agile teams in contact with outside customers. The paper also discusses the requirements traceability problem in agile software development and the relationships between the traceability and refactoring processes and their impact on each other.


title: Omnichannel Retail Operations with Buy-Online-and-Pick-up-in-Store

abstract: Many retailers have recently started to offer customers the option to buy online and pick up in store (BOPS). We study the impact of the BOPS initiative on store operations. We build a stylized model where a retailer operates both online and offline channels. Consumers strategically make channel choices. The BOPS option affects consumer choice in two ways: by providing real-time information about inventory availability and by reducing the hassle cost of shopping. We obtain three findings. First, not all products are well-suited for in-store pickup; specifically, it may not be profitable to implement BOPS on products that sell well in stores. Second, BOPS enables retailers to reach new customers, but for existing customers, the shift from online fulfillment to store fulfillment may decrease profit margins when the latter is less cost effective. Finally, in a decentralized retail system where store and online channels are managed separately, BOPS revenue can be shared across channels to alleviate incentive conflicts; it is rarely efficient to allocate all the revenue to a single channel.


abstract: Over the past decade, analysts have proposed several frameworks to explain the process of radicalization into violent extremism (RVE). These frameworks are based primarily on rational, conceptual models which are neither guided by theory nor derived from systematic research. This article reviews recent (post-9/11) conceptual models of the radicalization process and recent (post-9/11) empirical studies of RVE. It emphasizes the importance of distinguishing between ideological radicalization and terrorism involvement, though both issues deserve further empirical inquiry.Finally, it summarizes some recent RVE-related research efforts, identifies seven things that social science researchers and operational personnel still need to know about violent radicalization, and offers a set of starting assumptions to move forward with a research agenda that might help to thwart tomorrow's terrorists. This article is available in Journal of Strategic Security: http://scholarcommons.usf.edu/jss/vol4/iss4/3 Journal of Strategic Security Volume 4 Issue 4 2011, pp. 37-62 DOI: 10.5038/1944-0472.4.4.2 Journal of Strategic Security (c) 2011 ISSN: 1944-0464 eISSN: 1944-0472 37 Radicalization into Violent Extremism II: A Review of Conceptual Models and Empirical Research Randy Borum University of South Florida wborum@usf.edu


title: Hybrid Cryptographic Access Control for Cloud-Based EHR Systems

abstract: Cloud-based electronic health record (EHR) systems are next-generation big data systems for facilitating efficient and scalable storage and fostering collaborative care, clinical research, and development. Mobility and the use of multiple mobile devices in collaborative healthcare increases the need for robust privacy preservation. Thus, large-scale EHR systems require secure access to privacy-sensitive data, data storage, and management. The authors provide a comprehensive solution with a cryptographic role-based technique to distribute session keys to establish communications and information retrieval using the Kerberos protocol; location- and biometrics-based authentication to authorize users; and a wavelet-based steganographic technique to embed EHR data securely using electrocardiography (ECG) signals as the host in a trusted cloud storage. A comprehensive security analysis demonstrates that the model is scalable, secure, and reliable for accessing and managing EHR data.


title: Evaluating diversified search results using per-intent graded relevance

abstract: Search queries are often ambiguous and/or underspecified. To accomodate different user needs, search result diversification has received attention in the past few years. Accordingly, several new metrics for evaluating diversification have been proposed, but their properties are little understood. We compare the properties of existing metrics given the premises that (1) queries may have multiple intents; (2) the likelihood of each intent given a query is available; and (3) graded relevance assessments are available for each intent. We compare a wide range of traditional and diversified IR metrics after adding graded relevance assessments to the TREC 2009 Web track diversity task test collection which originally had binary relevance assessments. Our primary criterion is discriminative power, which represents the reliability of a metric in an experiment. Our results show that diversified IR experiments with a given number of topics can be as reliable as traditional IR experiments with the same number of topics, provided that the right metrics are used. Moreover, we compare the intuitiveness of diversified IR metrics by closely examining the actual ranked lists from TREC. We show that a family of metrics called D#-measures have several advantages over other metrics such as α-nDCG and Intent-Aware metrics.


title: Casting doubts on the viability of WiFi offloading

abstract: With the advent of the smartphone, mobile data usage has exploded which in turn has created tremendous pressure on cellular data networks. A promising candidate to reduce the impact of cellular data growth is WiFi offloading. However, recent data from our study of two hundred student smartphone users casts doubts on the reductions that can be gained from WiFi offloading. Despite the users operating in a dense university WiFi environment, cellular consumption still dominated overall data usage. We believe the root cause of lesser WiFi utilization can be traced to the WiFi being optimized for laptop WiFi reception rather than the more constrained smartphone WiFi reception. Our work examines the relationship of WiFi versus 3G usage through a variety of aspects including active phone usage, application types, and traffic volume over an eight week period from the Spring of 2012.


title: POSTER: Semi-supervised Classification for Dynamic Android Malware Detection

abstract: Manually labeling the large number of samples of Android APKs into benign or different malicious families requires tremendous human effort, while it is comparably easy and cheap to obtain a large amount of unlabeled APKs from various sources. Moreover, the fast-paced evolution of Android malware continuously generates derivative and new malware families. These families often contain new signatures, which can escape detection that uses static analysis. These practical challenges can also cause classical supervised machine learning algorithms to degrade in performance. We propose a framework that uses model-based semi-supervised (MBSS) classification scheme built using dynamic Android API call logs. The semi-supervised approach efficiently uses the labeled and unlabeled APKs to estimate a finite mixture model of Gaussian distributions via conditional expectation-maximization and efficiently detects malware during out-of-sample testing. We compare MBSS with the popular malware detection classifiers such as support vector machine (SVM), k-nearest neighbor (kNN) and linear discriminant analysis (LDA). Under the ideal classification setting, MBSS has competitive performance with 98% accuracy and very low false positive rate for in-sample classification. For out-of-sample testing, the out-of-sample test data exhibit similar behavior of retrieving phone information and sending to the network, compared with in-sample training set. When this similarity is strong, MBSS and SVM with linear kernel maintain 90% detection rate while kNN and LDA suffer great performance degradation. When this similarity is slightly weaker, all classifiers degrade in performance, but MBSS still performs significantly better than other classifiers.


title: Warrior Model for Human Performance and Injury Prevention: Eagle Tactical Athlete Program (ETAP) Part I.

abstract: INTRODUCTION ::: Physical training for United States military personnel requires a combination of injury prevention and performance optimization to counter unintentional musculoskeletal injuries and maximize warrior capabilities. Determining the most effective activities and tasks to meet these goals requires a systematic, research-based approach that is population specific based on the tasks and demands of the warrior. ::: ::: ::: OBJECTIVE ::: We have modified the traditional approach to injury prevention to implement a comprehensive injury prevention and performance optimization research program with the 101st Airborne Division (Air Assault) at Ft. Campbell, KY. This is Part I of two papers that presents the research conducted during the first three steps of the program and includes Injury Surveillance, Task and Demand Analysis, and Predictors of Injury and Optimal Performance. ::: ::: ::: METHODS ::: Injury surveillance based on a self-report of injuries was collected on all Soldiers participating in the study. Field-based analyses of the tasks and demands of Soldiers performing typical tasks of 101st Soldiers were performed to develop 101st-specific laboratory testing and to assist with the design of the intervention (Eagle Tactical Athlete Program (ETAP)). Laboratory testing of musculoskeletal, biomechanical, physiological, and nutritional characteristics was performed on Soldiers and benchmarked to triathletes to determine predictors of injury and optimal performance and to assist with the design of ETAP. ::: ::: ::: RESULTS ::: Injury surveillance demonstrated that Soldiers of the 101st are at risk for a wide range of preventable unintentional musculoskeletal injuries during physical training, tactical training, and recreational/sports activities. The field-based analyses provided quantitative data and qualitative information essential to guiding 101st specific laboratory testing and intervention design. Overall the laboratory testing revealed that Soldiers of the 101st would benefit from targeted physical training to meet the specific demands of their job and that sub-groups of Soldiers would benefit from targeted injury prevention activities. ::: ::: ::: CONCLUSIONS ::: The first three steps of the injury prevention and performance research program revealed that Soldiers of the 101st suffer preventable musculoskeletal injuries, have unique physical demands, and would benefit from targeted training to improve performance and prevent injury.


title: Hybrid Optimization Algorithm of Improved Binary Particle Swarm Optimization (iBPSO) And Cuckoo Search for Review Spam Detection

abstract: With the development of the Internet, people are interested to share their views and opinions about the product on the web, forums, blogs etc. These online reviews are important for individual users and organization. Recently, it is a common tendency to the user to read the reviews or comments before purchasing some products or services. The online reviews are helpful for the business organizations in order to promote their product. However, in practice, these online reviews may be fake in order to promote or devalue the product. These fake reviews are called as opinion spam. Objective of the research paper is that, to select the best feature subset for detecting the fake review. To select a small subset of features out of the thousands of feature is important for accurate classification of review spam detection. Therefore, a good feature selection method is needed in order to speed up the processing rate, predictive accuracy. In this paper hybrid improved Binary Particle Swarm optimization (iBPSO) and cuckoo search (CS) is used for feature selection and Naive Bayes and k Nearest Neighbor classifier is used for classifying the review as spam and ham. Experimental results have shown that the proposed algorithm has yielded the best performance compared with the swarm intelligence techniques Binary Particle Swarm Optimization (BPSO) and Shuffled Frog Leaping (SFL).


title: The Cognitive Ecology of the Internet

abstract: In this chapter, we analyze the relationships between the Internet and its users in terms of situated cognition theory. We first argue that the Internet is a new kind of cognitive ecology , providing almost constant access to a vast amount of digital information that is increasingly more integrated into our cognitive routines. We then briefly introduce situated cognition theory and its species of embedded, embodied, extended, distributed and collective cognition. Having thus set the stage, we begin by taking an embedded cognition view and analyze how the Internet aids certain cognitive tasks. After that, we conceptualize how the Internet enables new kinds of embodied interaction , extends certain aspects of our embodiment, and examine how wearable technologies that monitor physiological, behavioral and contextual states transform the embodied self. On the basis of the degree of cognitive integration between a user and Internet resource, we then look at how and when the Internet extends our cognitive processes. We end this chapter with a discussion of distributed and collective cognition as facilitated by the Internet.


title: Scaling Agile Development in Mechatronic Organizations - A Comparative Case Study

abstract: Agile software development principles enable companies to successfully and quickly deliver software by meeting their customers' expectations while focusing on high quality. Many companies working with pure software systems have adopted these principles, but implementing them in companies dealing with non-pure software products is challenging. We identified a set of goals and practices to support large-scale agile development in companies that develop software-intense mechatronic systems. We used an inductive approach based on empirical data collected during a longitudinal study with six companies in the Nordic region. The data collection took place over two years through focus group workshops, individual on-site interviews, and complementary surveys. The primary benefit of large-scale agile development is improved quality, enabled by practices that support regular or continuous integration between teams delivering software, hardware, and mechanics. In this regard, the most beneficial integration cycle for deliveries is every four weeks; while continuous integration on a daily basis would favor software teams, other disciplines does not seem to benefit from faster integration cycles. We identified 108 goals and development practices supporting agile principles among the companies, most of them concerned with integration; therefrom, 26 agile practices are unique to the mechatronics domain to support adopting agile beyond pure software development teams. 16 of these practices are considered as key enablers, confirmed by our control cases.


title: Subject access in online catalogs: A design model

abstract: A model based on strikingly different philosophical as. sumptions from those currently popular is proposed for the design of online subject catalog access. Three design principles are presented and discussed: uncertainty (subject indexing is indeterminate and probabilistic beyond a certain point), variety (by Ashby’s law of requisite variety, variety of searcher query must equal variety of document indexing), and complexity (the search process, particularly during the entry and orientation phases, is subtler and more complex, on several grounds, than current models assume). Design features presented are an access phase, including entry and orientation, a hunting phase, and a selection phase. An end-user thesaurus and a front-end system mind are presented as examples of online catalog system components to improve searcher success during entry and orientation. The proposed model is “wrapped around” existing Library of Congress subject-heading indexing in such a way as to enhance access greatly without requiring reindexing. It is argued that both for cost reasons and in principle this is a superior approach to other design philosophies.


title: Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model

abstract: We present a novel training framework for neural sequence models, particularly for grounded dialog generation. The standard training paradigm for these models is maximum likelihood estimation (MLE), or minimizing the cross-entropy of the human responses. Across a variety of domains, a recurring problem with MLE trained generative neural dialog models (G) is that they tend to produce 'safe' and generic responses ("I don't know", "I can't tell"). In contrast, discriminative dialog models (D) that are trained to rank a list of candidate human responses outperform their generative counterparts; in terms of automatic metrics, diversity, and informativeness of the responses. However, D is not useful in practice since it cannot be deployed to have real conversations with users. ::: Our work aims to achieve the best of both worlds -- the practical usefulness of G and the strong performance of D -- via knowledge transfer from D to G. Our primary contribution is an end-to-end trainable generative visual dialog model, where G receives gradients from D as a perceptual (not adversarial) loss of the sequence sampled from G. We leverage the recently proposed Gumbel-Softmax (GS) approximation to the discrete distribution -- specifically, an RNN augmented with a sequence of GS samplers, coupled with the straight-through gradient estimator to enable end-to-end differentiability. We also introduce a stronger encoder for visual dialog, and employ a self-attention mechanism for answer encoding along with a metric learning loss to aid D in better capturing semantic similarities in answer responses. Overall, our proposed model outperforms state-of-the-art on the VisDial dataset by a significant margin (2.67% on recall@10). The source code can be downloaded from this https URL


title: On the role of positive and negative affectivity in job performance: a meta-analytic investigation.

abstract: Although interest regarding the role of dispositional affect in job behaviors has surged in recent years, the true magnitude of affectivity's influence remains unknown. To address this issue, the authors conducted a qualitative and quantitative review of the relationships between positive and negative affectivity (PA and NA, respectively) and various performance dimensions. A series of meta-analyses based on 57 primary studies indicated that PA and NA predicted task performance in the hypothesized directions and that the relationships were strongest for subjectively rated versus objectively rated performance. In addition, PA was related to organizational citizenship behaviors but not withdrawal behaviors, and NA was related to organizational citizenship behaviors, withdrawal behaviors, counterproductive work behaviors, and occupational injury. Mediational analyses revealed that affect operated through different mechanisms in influencing the various performance dimensions. Regression analyses documented that PA and NA uniquely predicted task performance but that extraversion and neuroticism did not, when the four were considered simultaneously. Discussion focuses on the theoretical and practical implications of these findings.


title: Scalable performance of the panasas parallel file system

abstract: The Panasas file system uses parallel and redundant access to object storage devices (OSDs), per-file RAID, distributed metadata management, consistent client caching, file locking services, and internal cluster management to provide a scalable, fault tolerant, high performance distributed file system. The clustered design of the storage system and the use of client-driven RAID provide scalable performance to many concurrent file system clients through parallel access to file data that is striped across OSD storage nodes. RAID recovery is performed in parallel by the cluster of metadata managers, and declustered data placement yields scalable RAID rebuild rates as the storage system grows larger. This paper presents performance measures of I/O, metadata, and recovery operations for storage clusters that range in size from 10 to 120 storage nodes, 1 to 12 metadata nodes, and with file system client counts ranging from 1 to 100 compute nodes. Production installations are as large as 500 storage nodes, 50 metadata managers, and 5000 clients.


title: Efficient Reformulation of Solid Phase Diffusion in Electrochemical-Mechanical Coupled Models for Lithium-Ion Batteries: Effect of Intercalation Induced Stresses

abstract: Lithium-ion batteries are typically modeled using porous electrode theory coupled with various transport and reaction mechanisms with an appropriate discretization or approximation for the solid phase diffusion within the electrode particle. One of the major difficulties insimulating Li-ionbatterymodels is theneedforsimulating solid-phase diffusioninthe secondradial dimensionrwithin the particle. It increases the complexity of the model as well as the computation time/cost to a great extent. This is particularly true for the inclusion of pressure induced diffusion inside particles experiencing volume change. A computationally efficient representation for solid-phase diffusion is discussed in this paper. The operating condition has a significant effect on the validity, accuracy, and efficiency of various approximations for the solid-phase transport governed by pressure induced diffusion. This paper introduces efficient methods for solid phase reformulation ‐ (1) parabolic profile approach and (2) a mixed order finite difference method for approximating/representing solid-phase concentration variations within the active materials of porous electrodes for macroscopic models for lithium-ion batteries.


title: The Goldilocks zone: Towards better understanding of neural network loss landscapes

abstract: We explore the loss landscape of fully-connected and convolutional neural networks using random, low-dimensional hyperplanes and hyperspheres. Evaluating the Hessian, $H$, of the loss function on these hypersurfaces, we observe 1) an unusual excess of the number of positive eigenvalues of $H$, and 2) a large value of $\mathrm{Tr}(H) / ||H||$ at a well defined range of configuration space radii, corresponding to a thick, hollow, spherical shell we refer to as the \textit{Goldilocks zone}. We observe this effect for fully-connected neural networks over a range of network widths and depths on MNIST and CIFAR-10 datasets with the $\mathrm{ReLU}$ and $\tanh$ non-linearities, and a similar effect for convolutional networks. Using our observations, we demonstrate a close connection between the Goldilocks zone, measures of local convexity/prevalence of positive curvature, and the suitability of a network initialization. We show that the high and stable accuracy reached when optimizing on random, low-dimensional hypersurfaces is directly related to the overlap between the hypersurface and the Goldilocks zone, and as a corollary demonstrate that the notion of intrinsic dimension is initialization-dependent. We note that common initialization techniques initialize neural networks in this particular region of unusually high convexity/prevalence of positive curvature, and offer a geometric intuition for their success. Furthermore, we demonstrate that initializing a neural network at a number of points and selecting for high measures of local convexity such as $\mathrm{Tr}(H) / ||H||$, number of positive eigenvalues of $H$, or low initial loss, leads to statistically significantly faster training on MNIST. Based on our observations, we hypothesize that the Goldilocks zone contains an unusually high density of suitable initialization configurations.


title: Retinal blood vessels semantic segmentation method based on modified U-Net

abstract: Automatic segmentation of retinal blood vessels from fundus images plays a key role in the computer aided diagnostic system, which is helpful for the early treatment of many fundus diseases including diabetic retinopathy, glaucoma and hypertension. In this paper, a modified U-Net is proposed to train semantic segmentation models for retinal blood vessels. In addition, we use Condition Random Field to integrate the global information. The comparison between our method and other typical methods is given to evaluate the proposed method. Our experiments shows that our scheme leads to improved accuracy on DRIVE datasets and having obtained an average accuracy of 86.5% for retinal blood vessels segmentation task.


title: Joint 3D Object and Layout Inference from a Single RGB-D Image

abstract: Inferring 3D objects and the layout of indoor scenes from a single RGB-D image captured with a Kinect camera is a challenging task. Towards this goal, we propose a high-order graphical model and jointly reason about the layout, objects and superpixels in the image. In contrast to existing holistic approaches, our model leverages detailed 3D geometry using inverse graphics and explicitly enforces occlusion and visibility constraints for respecting scene properties and projective geometry. We cast the task as MAP inference in a factor graph and solve it efficiently using message passing. We evaluate our method with respect to several baselines on the challenging NYUv2 indoor dataset using 21 object categories. Our experiments demonstrate that the proposed method is able to infer scenes with a large degree of clutter and occlusions.


title: On compressive sensing applied to radar

abstract: Compressive sensing (CS) techniques offer a framework for the detection and allocation of sparse signals with a reduced number of samples. Today, modern radar systems operate with high bandwidths-demanding high sample rates according to the Shannon-Nyquist theorem-and a huge number of single elements for phased array antennas. Often only a small amount of target parameters is the final output, arising the question, if CS could not be a good mean to reduce data size, complexity, weight, power consumption and costs of radar systems. There is only a small number of publications addressing the application of CS to radar, leaving several open questions. This paper addresses some aspects as a further step to CS-radar by presenting generic system architectures and implementation considerations. It is not the aim of this paper to investigate numerically efficient algorithms but to point to promising applications as well as arising problems. Three possible applications are considered: pulse compression, radar imaging, and air space surveillance with array antennas. Some simulation results are presented and enriched by the evaluation of real data acquired by an experimental radar system of Fraunhofer FHR.


title: Affective Benchmarking of Movies Based on the Physiological Responses of a Real Audience

abstract: We propose here an objective study of the emotional impact of a movie on an audience. An affective benchmarking solution is introduced making use of a low-intrusive measurement of the well-known Electro Dermal Answer. A dedicated processing of this biosignal produces a time-variant and normalized affective signal related to the significant excitation variations of the audience. Besides the new methodology, the originality of this paper stems from the fact that this framework has been tested on a real audience during regular cinema shows and a film festival, for five different movies and a total of 128 audience members.


title: Trends in big data analytics

abstract: Abstract One of the major applications of future generation parallel and distributed systems is in big-data analytics. Data repositories for such applications currently exceed exabytes and are rapidly increasing in size. Beyond their sheer magnitude, these datasets and associated applications’ considerations pose significant challenges for method and software development. Datasets are often distributed and their size and privacy considerations warrant distributed techniques. Data often resides on platforms with widely varying computational and network capabilities. Considerations of fault-tolerance, security, and access control are critical in many applications (Dean and Ghemawat, 2004; Apache hadoop). Analysis tasks often have hard deadlines, and data quality is a major concern in yet other applications. For most emerging applications, data-driven models and methods, capable of operating at scale, are as-yet unknown. Even when known methods can be scaled, validation of results is a major issue. Characteristics of hardware platforms and the software stack fundamentally impact data analytics. In this article, we provide an overview of the state-of-the-art and focus on emerging trends to highlight the hardware, software, and application landscape of big-data analytics.


title: Improving biocuration of microRNAs in diseases: a case study in idiopathic pulmonary fibrosis

abstract: MicroRNAs (miRNAs) are small and non-coding RNA molecules that inhibit gene expression posttranscriptionally. They play important roles in several biological processes, and in recent years there has been an interest in studying how they are related to the pathogenesis of diseases. Although there are already some databases that contain information for miRNAs and their relation with illnesses, their curation represents a significant challenge due to the amount of information that is being generated every day. In particular, respiratory diseases are poorly documented in databases, despite the fact that they are of increasing concern regarding morbidity, mortality and economic impacts. In this work, we present the results that we obtained in the BioCreative Interactive Track (IAT), using a semiautomatic approach for improving biocuration of miRNAs related to diseases. Our procedures will be useful to complement databases that contain this type of information. We adapted the OntoGene text mining pipeline and the ODIN curation system in a full-text corpus of scientific publications concerning one specific respiratory disease: idiopathic pulmonary fibrosis, the most common and aggressive of the idiopathic interstitial cases of pneumonia. We curated 823 miRNA text snippets and found a total of 246 miRNAs related to this disease based on our semiautomatic approach with the system OntoGene/ODIN. The biocuration throughput improved by a factor of 12 compared with traditional manual biocuration. A significant advantage of our semiautomatic pipeline is that it can be applied to obtain the miRNAs of all the respiratory diseases and offers the possibility to be used for other illnesses. ::: ::: ::: Database URL ::: http://odin.ccg.unam.mx/ODIN/bc2015-miRNA/.


Job Recommendation Systems for Enhancing E-recruitment Process


abstract: A recent proof of NP-hardness of Euclidean sum-of-squares clustering, due to Drineas et al. (Mach. Learn. 56:9---33, 2004), is not valid. An alternate short proof is provided.


title: Training data pseudo-shuffling and direct decoding framework for recurrent neural network based acoustic modeling

abstract: We propose two techniques to enhance the performance of recurrent neural network (RNN)-based acoustic models. The first technique addresses training efficiency. Because RNNs require sequential input, it is difficult to randomly shuffle training samples to accelerate stochastic gradient descent based training. We propose a "pseudo-shuffling" procedure that instead augments training sample unexpectedness by skipping successive samples. The second proposed technique is a novel "direct decoding" framework in which the posterior probability of the RNN is inputted into a decoder without conversion into a hidden Markov model emission probability. In our large vocabulary speech recognition experiments with English lecture recordings, the first technique significantly improved RNN training efficiency, showing a 14.3% relative word error rate (WER) improvement. The second technique further achieved an additional 3.1% relative WER improvement. Our sigmoid-type RNN achieved a 10.7% better WER than same-sized deep neural networks without using long short-term memory cells.


Melody Extraction on Vocal Segments Using Multi-Column Deep Neural Networks


title: Towards smart traffic management systems: Vacant on-street parking spot detection based on video analytics

abstract: Smart Cities rely on the use of ICTs for a more efficient and intelligent use of resources, whilst improving citizens' quality of life and reducing the environmental footprint. As far as the livability of cities is concerned, traffic is one of the most frequent and complex factors directly affecting citizens. Particularly, drivers in search of a vacant parking spot are a non-negligible source of atmospheric and acoustic pollution. Although some cities have installed sensor-based vacant parking spot detectors in some neighbourhoods, the cost of this approach makes it unfeasible at large scale. As an approach to implement a sustainable solution to the vacant parking spot detection problem in urban environments, this work advocates fusing the information from small-scale sensor-based detectors with that obtained from exploiting the widely-deployed video surveillance camera networks. In particular, this paper focuses on how video analytics can be exploited as a prior step towards Smart City solutions based on data fusion. Through a set of experiments carefully planned to replicate a real-world scenario, the vacant parking spot detection success rate of the proposed system is evaluated through a critical comparison of local and global visual features (either alone or fused at feature level) and different classifier systems applied to the task. Furthermore, the system is tested under setup scenarios of different complexities, and experimental results show that while local features are best when training with small amounts of highly accurate on-site data, they are outperformed by their global counterparts when training with more samples from an external vehicle database.


title: Cascaded H-bridge multilevel converter for grid connected photovoltaic generators with independent maximum power point tracking of each solar array

abstract: This paper introduces a new control method and proportional PWM modulation of the cascaded H-bridge multilevel converter for grid-connected photovoltaic systems. This control makes each H-bridge module supply different power levels, allowing therefore for each module an independent maximum power point tracking of the corresponding photovoltaic array.


title: Robotic Single-Site Surgery for Female-to-Male Transsexuals: Preliminary Experience

abstract: Hysterectomy with bilateral salpingo-oophorectomy is a part of gender reassignment surgery for the treatment of female-to-male transsexualism. Over the last years many efforts were made in order to reduce invasiveness of laparoscopic and robotic surgery such as the introduction of single-site approach. We report our preliminary experience on single-site robotic hysterectomy for cross-sex reassignment surgery. Our data suggest that single-site robotic hysterectomy is feasible and safe in female-to-male transsexualism with some benefits in terms of postoperative pain and aesthetic results.


title: Service-oriented Communities: Models and Concepts Towards Fractal Social Organizations

abstract: It is described an abstract model for the definition and the dynamic evolution of "communities of actants" originating from a given reference society of roles. Multiple representations are provided, showing how communities evolve with respect to their reference societies. In particular we show how such representations are self-similar and factorisable into "prime" constituents. An operating model is then introduced that describes the life-cycle of the communities of actants. After this a software component is presented -- the service-oriented community -- and its features are described in terms of the above mentioned models. Finally it is shown how such component can constitute the building block of a novel architecture for the design of fractal social organizations.


title: BIM-Integrated Construction Operation Simulation for Just-In-Time Production Management

abstract: Traditional construction planning, which depends on historical data and heuristic modification, prevents the integration of managerial details such as productivity dynamics. Specifically, the distance between planning and execution brings cost overruns and duration extensions. To minimize variations, this research presents a Building Information Modeling (BIM)-integrated simulation framework for predicting productivity dynamics at the construction planning phase. To develop this framework, we examined critical factors affecting productivity at the operational level, and then forecast the productivity dynamics. The resulting plan includes specific commands for retrieving the required information from BIM and executing operation simulations. It consists of the following steps: (1) preparing a BIM model to produce input data; (2) composing a construction simulation at the operational level; and (3) obtaining productivity dynamics from the BIM-integrated simulation. To validate our framework, we applied it to a structural steel model; this was due to the significance of steel erections. By integrating BIM with construction operation simulations, we were able to create reliable construction plans that adapted to project changes. Our results show that the developed framework facilitates the reliable prediction of productivity dynamics, and can contribute to improved schedule reliability, optimized resource allocation, cost savings associated with buffers, and reduced material waste.


An LSTM Attention-based Network for Reading Comprehension


title: An efficient MFCC extraction method in speech recognition

abstract: This paper introduces a new algorithm of extracting MFCC for speech recognition. The new algorithm reduces the computation power by 53% compared to the conventional algorithm. Simulation results indicate the new algorithm has a recognition accuracy of 92.93%. There is only a 1.5% reduction in recognition accuracy compared to the conventional MFCC extraction algorithm, which has an accuracy of 94.43%. However, the number of logic gates required to implement the new algorithm is about half of the MFCC algorithm, which makes the new algorithm very efficient for hardware implementation.


title: Gambling in a rigged casino: The adversarial multi-armed bandit problem

abstract: In the multi-armed bandit problem, a gambler must decide which arm of K non-identical slot machines to play in a sequence of trials so as to maximize his reward. This classical problem has received much attention because of the simple model it provides of the trade-off between exploration (trying out each arm to find the best one) and exploitation (playing the arm believed to give the best payoff). Past solutions for the bandit problem have almost always relied on assumptions about the statistics of the slot machines. In this work, we make no statistical assumptions whatsoever about the nature of the process generating the payoffs of the slot machines. We give a solution to the bandit problem in which an adversary, rather than a well-behaved stochastic process, has complete control over the payoffs. In a sequence of T plays, we prove that the expected per-round payoff of our algorithm approaches that of the best arm at the rate O(T/sup -1/3/), and we give an improved rate of convergence when the best arm has fairly low payoff. We also consider a setting in which the player has a team of "experts" advising him on which arm to play; here, we give a strategy that will guarantee expected payoff close to that of the best expert. Finally, we apply our result to the problem of learning to play an unknown repeated matrix game against an all-powerful adversary.


title: Marchand Balun Embedded Probe

abstract: Planar-type Marchand baluns are embedded into on-wafer probes in order to provide accurate differential measurement capabilities for conventional single-ended measurement equipments such as the two-port vector network analyzer. In designing the planar-type Marchand balun, the values for differential- and common-mode characteristic impedances of the coupled transmission lines are analytically derived by using general transmission line theories. The derived characteristic impedances are realized using a double-sided single-layer printed circuit board surrounded by conductive fixtures. The proposed coupled transmission line structure provides convenient port interfaces to the coaxial cables and it is free from bond wires and vias, enabling one to achieve high cost efficiency and reproducibility.


title: Return-Oriented Flush-Reload Side Channels on ARM and Their Implications for Android Devices

abstract: Cache side-channel attacks have been extensively studied on x86 architectures, but much less so on ARM processors. The technical challenges to conduct side-channel attacks on ARM, presumably, stem from the poorly documented ARM cache implementations, such as cache coherence protocols and cache flush operations, and also the lack of understanding of how different cache implementations will affect side-channel attacks. This paper presents a systematic exploration of vectors for flush-reload attacks on ARM processors. flush-reload attacks are among the most well-known cache side-channel attacks on x86. It has been shown in previous work that they are capable of exfiltrating sensitive information with high fidelity. We demonstrate in this work a novel construction of flush-reload side channels on last-level caches of ARM processors, which, particularly, exploits return-oriented programming techniques to reload instructions. We also demonstrate several attacks on Android OS (e.g., detecting hardware events and tracing software execution paths) to highlight the implications of such attacks for Android devices.


title: Robust 3D Patch-Based Face Hallucination

abstract: Incorporating 3D information has proven to be effective in many computer vision tasks and it is no exception in the context of facial analysis. However, limited application has been witnessed in face hallucination (FH), probably due to the difficulty of fitting 3D models onto low-resolution (LR) images. This paper presents a pure 3D approach to address this problem. By extending the LR image formation process to the 3D domain, the classic Lucas–Kanade algorithm is exploited to improve the precision of the error-prone 3D model fitting on LR images. The established correspondence between the input image and 3D training textures then facilitates reconstruction of high-resolution (HR) patches directly on the mesh, which can be employed to render realistic frontal faces for recognition. Extensive evaluation on several publicly available datasets reveals superior qualitative and quantitative results over state-of-the-art methods in fitting, FH and recognition, which shows the advantage of the proposed 3D framework over its 2D rivals, especially for non-frontal head poses and low image quality.


title: SentiCap: Generating Image Descriptions with Sentiments

abstract: The recent progress on image recognition and language modeling is making automatic description of image content a reality. However, stylized, non-factual aspects of the written description are missing from the current systems. One such style is descriptions with emotions, which is commonplace in everyday communication, and influences decision-making and interpersonal relationships. We design a system to describe an image with emotions, and present a model that automatically generates captions with positive or negative sentiments. We propose a novel switching recurrent neural network with word-level regularization, which is able to produce emotional image captions using only 2000+ training sentences containing sentiments. We evaluate the captions with different automatic and crowd-sourcing metrics. Our model compares favourably in common quality metrics for image captioning. In 84.6% of cases the generated positive captions were judged as being at least as descriptive as the factual captions. Of these positive captions 88% were confirmed by the crowd-sourced workers as having the appropriate sentiment.


title: The future of urban biodiversity research: Moving beyond the ‘low-hanging fruit’

abstract: In this era of rapidly urbanising human populations, urban practitioners are under increasing pressure to create resilient and sustainable cities and towns. Urban ecologists currently have a unique opportunity to apply solid, evidence-based research to help create biodiversity-rich and sustainable cities and towns for the future. Unfortunately, there is currently a mismatch between the questions planners, designers and decision-makers are asking urban ecologists that would allow them to improve the biodiversity outcomes in urban areas, and the questions urban ecologists must ask to contribute to the development and application of the science of urban ecology. For a number of reasons, urban ecologists over the past 25 years have primarily focused on describing the patterns of biodiversity in cities and towns using broad, aggregate predictor variables (e.g., distance to city center, land-use, percent cover of impermeable surfaces and vegetation, etc.). We refer to these results as ‘low-hanging fruit’. If the discipline of urban ecology is going to provide the necessary information to inform actions to preserve and enhance urban biodiversity, we need to move beyond place-based research, and work towards the development of confirmed generalizations regarding the relationship between the structure and function of urban ecosystems and biodiversity. We propose three essential strategies for achieving this refined understanding: 1) defining the study window to place the study into a broader global context, 2) collecting and using more explicit question-driven measures of the urban condition in order to improve our understanding of urban ecological drivers, as well as recording more detailed ecological responses to provide insights into the ecological mechanisms underlying an observed response, and 3) expanding studies to include multiple cities, regions and countries. These strategies will help to expedite the ability of urban ecology to contribute to the creation of biodiversity-rich, healthy, resilient cities and towns.


Learning to Probabilistically Identify Authoritative Documents


title: Investigating effects of magnetizing inductance on coupled-inductor voltage regulators

abstract: Coupled inductors are an emerging topology for computing power supplies as next generation CPU requirements for high efficiency and fast transient response pose great challenge on voltage regulator design. This paper addresses the effects of magnetizing inductance on current-balancing, dynamic response, and current-sensing of multiphase voltage regulator with coupled inductors. State Space Averaging and other mathematical analysis methods are employed. Experimental results are provided to verify the analysis.


title: The Seduction Script: Psychological and Cultural Norms of Interpersonal Approaches As Markers for Sexual Aggression and Abuse

abstract: Hardly any subjects enjoy greater – public or private – interest than the art of flirtation and seduction. However, interpersonal approach behavior not only paves the way for sexual interaction and reproduction, but it simultaneously integrates non-sexual psychobiological and cultural standards regarding consensus and social norms. In the present paper, we use script theory as a concept that extends across psychological and cultural science to assess behavioral options during interpersonal approaches. Specifically, we argue that approaches follow scripted event sequences that entail ambivalence as an essential communicative element. On the one hand, ambivalence may facilitate interpersonal approaches by maintaining and provoking situational uncertainty, so that the outcome of an action – even after several approaches and dates – remains ambiguous. On the other hand, ambivalence may increase the risk for sexual aggression or abuse, depending on the individual’s abilities, the circumstances, and the intentions of the interacting partners. Recognizing latent sequences of sexually aggressive behavior, in terms of their rigid structure and behavioral options, may thus enable individuals to use resources efficiently, avoid danger, and extricate themselves from assault situations. We conclude that interdisciplinary script knowledge about ambivalence as a core component of the seduction script may be helpful for counteracting subtly aggressive intentions and preventing sexual abuse. We discuss this with regard to the nature-nurture debate as well as phylogenetic and ontogenetic aspects of interpersonal approach behavior and its medial implementation.


title: The Current State of Business Intelligence

abstract: Business intelligence (BI) is now widely used, especially in the world of practice, to describe analytic applications. BI is currently the top-most priority of many chief information officers. BI has become a strategic initiative and is now recognized by CIOs and business leaders as instrumental in driving business effectiveness and innovation. BI is a process that includes two primary activities: getting data in and getting data out. Getting data in, traditionally referred to as data warehousing, involves moving data from a set of source systems into an integrated data warehouse. Getting data in delivers limited value to an enterprise; only when users and applications access the data and use it to make decisions does the organization realize the full value from its data warehouse. Thus, getting data out receives most attention from organizations. This second activity, which is commonly referred to as BI, consists of business users and applications accessing data from the data warehouse to perform enterprise reporting, OLAP, querying, and predictive analytics.


title: A content analysis of depression-related tweets

abstract: This study examines depression-related chatter on Twitter to glean insight into social networking about mental health. We assessed themes of a random sample (n?=?2000) of depression-related tweets (sent 4-11 to 5-4-14). Tweets were coded for expression of DSM-5 symptoms for Major Depressive Disorder (MDD). Supportive or helpful tweets about depression was the most common theme (n?=?787, 40%), closely followed by disclosing feelings of depression (n?=?625; 32%). Two-thirds of tweets revealed one or more symptoms for the diagnosis of MDD and/or communicated thoughts or ideas that were consistent with struggles with depression after accounting for tweets that mentioned depression trivially. Health professionals can use our findings to tailor and target prevention and awareness messages to those Twitter users in need. Many posts about depression on Twitter are supportive/helpful tweets.Twitter users also commonly disclose feelings of depression in their tweets.Tweets reveal symptoms consistent with the diagnosis of Major Depressive Disorder.Research is needed on how tweets relate to self-reported depression.


title: Mechanisms of Fluid Transport Across Renal Tubules

abstract: The sections in this article are: ::: ::: ::: 1 ::: Theoretical Foundations of Water Transport in Epithelia ::: ::: 2 ::: Volume Absorption in the Proximal Tubule ::: ::: 3 ::: Water Permeability of the Proximal Tubule ::: 3.1 ::: Measurement of Water Permeability ::: ::: 3.2 ::: Importance of the Osmotic Water Permeability ::: ::: ::: ::: ::: 4 ::: Location of the Osmotic Gradient ::: 4.1 ::: Luminal Hypotonicity Produced by Solute Absorption ::: ::: 4.2 ::: Absorbate Hypertonicity ::: ::: ::: ::: ::: 5 ::: “Passive” Driving Forces for Volume Absorption ::: 5.1 ::: NaCl Diffusion in Volume Absorption ::: ::: 5.2 ::: Reflection Coefficient Differences ::: ::: ::: ::: ::: 6 ::: Models of Solute-Solvent Coupling in Proximal Volume Absorption ::: ::: 7 ::: Routes of Volume Movement in the Proximal Nephron ::: 7.1 ::: Measurement of Cell Membrane Osmotic Water Permeabilities ::: ::: ::: ::: ::: 8 ::: Consequences of Transcellular Volume Flow ::: ::: 9 ::: The ADH-Sensitive Distal Nephron ::: 9.1 ::: Measurement of Permeability Changes Produced by ADH ::: ::: 9.2 ::: Wafer Permeability of ADH-Sensitive Nephron Segments in Vivo and in Vitro ::: ::: ::: ::: ::: 10 ::: Site of the Change in Water Permeability with ADH ::: ::: 11 ::: Evaluation of the Pf/PDw Ratio ::: 11.1 ::: Large Pore Hypothesis ::: ::: 11.2 ::: Unstirred Layer Effects ::: ::: ::: ::: ::: 12 ::: The Narrow Channel Hypothesis: Single-File Diffusion Through Small Aqueous Channels ::: ::: 13 ::: ADH Increases the Number of Narrow Aqueous Channels in Apical Plasma Membranes ::: ::: 14 ::: The Apparent EA for Water Transport in Cortical Collecting Tubules ::: 14.1 ::: The Raw Data ::: ::: 14.2 ::: Correction for Diffusion Constraints in Series with Apical Membranes ::: ::: 14.3 ::: The “True” EA for Water Transport ::: ::: ::: ::: ::: 15 ::: Pseudo-“Breaks” in EA Measurements ::: ::: 16 ::: Comparison of ADH-Dependent Apical Membrane Water Channels with Gramicidin A Channels ::: ::: 17 ::: Parallel Paths for Water and Solute Permeation ::: ::: 18 ::: Morphologic Studies ::: ::: 19 ::: Intracellular Mediators of ADH Action ::: 19.1 ::: Modulation of ADH Action—α-Adrenergic Agents ::: ::: 19.2 ::: Atrial Natriuretic Peptide ::: ::: ::: ::: ::: 20 ::: Prostaglandins ::: 20.1 ::: Calcium ::: ::: 20.2 ::: Protein Kinase C ::: ::: ::: ::: ::: 21 ::: Summary


title: Design of a Novel Consequent-Pole Transverse-Flux Machine With Improved Permanent Magnet Utilization

abstract: Severe leakage flux is a long-existed drawback in the transverse-flux permanent magnet machines (TFPMs), which results in a low permanent magnet (PM) utilization rate and prominent cogging torque in TFPMs. Aiming to solve this problem, this paper presents a novel consequent-pole transverse-flux machine (CP-TFM). The key is to artificially shift the upper and lower salient-pole rotors with a half-pole pitch and connect them with a ring-shaped core. Therefore, a complementary magnetic circuit is constructed to reduce the flux leakage between the adjacent poles as well as improve the PM utilization rate. To verify the feasibility of design, a 3-D finite-element model is established and its electromagnetic performance is evaluated. With the same peripheral dimension and copper loss, a quantitative comparison is carried out between the proposed CP-TFM and conventional TFPM. Finite-element simulation results reveal that compared to its contrast, the proposed solution can almost double the PM utilization ratio and effectively reduce the cogging torque.


title: Embedded Wafer Level Ball Grid Array (eWLB)

abstract: The main challenges of today's device packaging are miniaturization, continuously increasing operating frequencies/high data rates, high number of I/Os, reliability, and thermal requirements. One of the major package trends driven by mobile-phone applications is the Wafer Level Ball Grid Array (WLB). Drivers for the implementation of WLB technology are cost reduction, smaller form factor and better electrical performance with respect to high frequency applications. Thin-film WLB technology consists in realizing additional redistribution layers above the passivation of a semiconductor chip using standard thin-film techniques to rearrange peripheral pads on the wafer in an array pattern. A hard limit will be reached with this technology, when the number of I/Os reaches a larger number dian can be fitted on the silicon chip at a given pitch. We introduce Infineon's embedded Wafer Level Ball Grid Array technology, which allows fitting interconnects onto a so-called fan-out area extending the chip area. The core process of this emerging technology is the encapsulation of silicon dice by compression molding. The eWLB technology is a forward-looking development of the WLB technology, upholding the known benefits such as small package dimensions, excellent electrical and thermal performance, and maximum connection density. However, this technology significantly increases the functionality and application spread. Due to eWLB. complex semiconductor chips such as modem and processor chips for applications in mobile communications require a high number of solder connections with standardized contact spacing to be produced with a minimal footprint. At the same time, the packages can be provided with as many solder contacts as needed. The possibility of additional wiring area around the chip proper means that the wafer-level packaging technology also lends itself to new. space-sensitive applications. We demonstrate the capabilities of Infineon's molded embedded Wafer Level Package Technology and show how we extended it towards a Platform Technology. The qualified Platform we introduce here covers currently a range of package sizes up to 8×8mm2 at a ball pitch of 0.5mm. The Qualification Criteria we have applied follow the tests described in JEDEC Standard Number 26-A.


title: Comment Volume Prediction using Regression

abstract: The latest decade lead to a unconstrained advancement of the importance of online networking. Due to the gigantic measures of records appearing in web organizing, there is a colossal necessity for the programmed examination of such records. Online networking customer's comments expect a basic part in building or changing the one's acknowledgments concerning some specific indicate or making it standard. This paper demonstrates a preliminary work to exhibit the sufficiency of machine learning prescient calculations on the remarks of most well known long range informal communication site, Facebook. We showed the customer remark patters, over the posts on Facebook Pages and expected that what number of remarks a post is depended upon to get in next H hrs. To automate the technique, we developed an item display containing the crawler, information processor and data disclosure module. For prediction, we used the Linear Regression model (Simple Linear model, Linear relapse model and Pace relapse model) and Non-Linear Regression model(Decision tree, MLP) on different data set varieties and evaluated them under the appraisal estimations Hits@10, AUC@10, Processing Time and Mean Absolute Error.


title: Logic-based approach to semantic query optimization

abstract: The purpose of semantic query optimization is to use semantic knowledge (e.g., integrity constraints) for transforming a query into a form that may be answered more efficiently than the original version. In several previous papers we described and proved the correctness of a method for semantic query optimization in deductive databases couched in first-order logic. This paper consolidates the major results of these papers emphasizing the techniques and their applicability for optimizing relational queries. Additionally, we show how this method subsumes and generalizes earlier work on semantic query optimization. We also indicate how semantic query optimization techniques can be extended to databases that support recursion and integrity constraints that contain disjunction, negation, and recursion.


title: Modeling Distributive and Integrative Negotiations. Review and Revised Characterization

abstract: The development of user-friendly negotiation support systems enabled negotiators to obtain advice directly from the system rather than via an intermediary. The emergence of e-commerce and the development of negotiating software agents further contributed to the automation of negotiation activities. These developments exposed inconsistencies in the descriptions of integrative and distributed negotiations. They also showed limitations of the existing modeling methods. These methods were designed to support negotiation experts who themselves had to make distinctions between distributive and integrative processes. Inconsistent descriptions and the lack of formal models that could be embedded in systems often contribute to a mechanical approach to negotiations compounding the difficulty in the design and development of software that can be used in real-life situations. The contradictions between the characteristics of integrative and distributive negotiations are discussed and assumptions for these two types as well as qualitative differences between them are proposed. Negotiation literature suggests that it is the negotiators' perception of the problem that leads to the their focus on either distributive or integrative conflict resolution. This may be the case for negotiations that are not supported with software. In case of the latter it is the design principles and information processing that that differentiates these two types of negotiations. Negotiation representation based on the information requirements for different types of conflict is proposed.


title: A Systematic Literature Review of Adaptive Parameter Control Methods for Evolutionary Algorithms

abstract: Evolutionary algorithms (EAs) are robust stochastic optimisers that perform well over a wide range of problems. Their robustness, however, may be affected by several adjustable parameters, such as mutation rate, crossover rate, and population size. Algorithm parameters are usually problem-specific, and often have to be tuned not only to the problem but even the problem instance at hand to achieve ideal performance. In addition, research has shown that different parameter values may be optimal at different stages of the optimisation process. To address these issues, researchers have shifted their focus to adaptive parameter control, in which parameter values are adjusted during the optimisation process based on the performance of the algorithm. These methods redefine parameter values repeatedly based on implicit or explicit rules that decide how to make the best use of feedback from the optimisation algorithm. In this survey, we systematically investigate the state of the art in adaptive parameter control. The approaches are classified using a new conceptual model that subdivides the process of adapting parameter values into four steps that are present explicitly or implicitly in all existing approaches that tune parameters dynamically during the optimisation process. The analysis reveals the major focus areas of adaptive parameter control research as well as gaps and potential directions for further development in this area.


title: Robust nonlinear predictive control using feedback linearization and linear matrix inequalities

abstract: This paper combines two features that has proven to be valuable in robust and nonlinear predictive control, namely feedback linearization and LMI-based predictive controller design. The algorithm consists of three steps, first a feedback linearization, secondly the derivation of an uncertainty description and finally the LMI-based optimization. A simulation example shows the effect of the feedback linearization step.


title: Real-Time Grading Evaluation of Quality of Strokes in Chinese Characters Handwriting

abstract: To realize exercises of Chinese character writing under unattended operation, a real-time evaluation technology for quality of strokes of handwritten Chinese characters is proposed. Its advantages lie in the fact that it decomposes the features of handwritten strokes into macrocosmic and microcosmic views. Macrocosmically, it can effectively identify various open clerical errors by inspecting topological constraints of each key point on path of strokes, microcosmically, it trains the relevant parameters by using decision tree and gets different evaluation rules at two grades, "loose" and "strict", so as to provide appropriate guidance for people with different backgrounds who learn Chinese character writing. Based on this technology, we have realized a real-time evaluation engine for Chinese character handwriting, and the experiments show it has played a good helping role in learning writing strokes of Chinese character.


title: Modular Cascaded H-Bridge Multilevel PV Inverter With Distributed MPPT for Grid-Connected Applications

abstract: This paper presents a modular cascaded H-bridge multilevel photovoltaic (PV) inverter for single- or three-phase grid-connected applications. The modular cascaded multilevel topology helps to improve the efficiency and flexibility of PV systems. To realize better utilization of PV modules and maximize the solar energy extraction, a distributed maximum power point tracking control scheme is applied to both single- and three-phase multilevel inverters, which allows independent control of each dc-link voltage. For three-phase grid-connected applications, PV mismatches may introduce unbalanced supplied power, leading to unbalanced grid current. To solve this issue, a control scheme with modulation compensation is also proposed. An experimental three-phase seven-level cascaded H-bridge inverter has been built utilizing nine H-bridge modules (three modules per phase). Each H-bridge module is connected to a 185-W solar panel. Simulation and experimental results are presented to verify the feasibility of the proposed approach.


title: Semi-supervised Multi-Label Topic Models for Document Classification and Sentence Labeling

abstract: Extracting parts of a text document relevant to a class label is a critical information retrieval task. We propose a semi-supervised multi-label topic model for jointly achieving document and sentence-level class inferences. Under our model, each sentence is associated with only a subset of the document's labels (including possibly none of them), with the label set of the document the union of the labels of all of its sentences. For training, we use both labeled documents, and, typically, a larger set of unlabeled documents. Our model, in a semisupervised fashion, discovers the topics present, learns associations between topics and class labels, predicts labels for new (or unlabeled) documents, and determines label associations for each sentence in every document. For learning, our model does not require any ground-truth labels on sentences. We develop a Hamiltonian Monte Carlo based algorithm for efficiently sampling from the joint label distribution over all sentences, a very high-dimensional discrete space. Our experiments show that our approach outperforms several benchmark methods with respect to both document and sentence-level classification, as well as test set log-likelihood. All code for replicating our experiments is available from https://github.com/hsoleimani/MLTM.


title: Classification and adulteration detection of vegetable oils based on fatty acid profiles.

abstract: The detection of adulteration of high priced oils is a particular concern in food quality and safety. Therefore, it is necessary to develop authenticity detection method for protecting the health of customers. In this study, fatty acid profiles of five edible oils were established by gas chromatography coupled with mass spectrometry (GC/MS) in selected ion monitoring mode. Using mass spectral characteristics of selected ions and equivalent chain length (ECL), 28 fatty acids were identified and employed to classify five kinds of edible oils by using unsupervised (principal component analysis and hierarchical clustering analysis), supervised (random forests) multivariate statistical methods. The results indicated that fatty acid profiles of these edible oils could classify five kinds of edible vegetable oils into five groups and are therefore employed to authenticity assessment. Moreover, adulterated oils were simulated by Monte Carlo method to establish simultaneous adulteration detection model for five kinds of edible oils by random forests. As a result, this model could identify five kinds of edible oils and sensitively detect adulteration of edible oil with other vegetable oils about the level of 10%.


title: The relation between forest structure and soil burn severity

abstract: A study funded through National Fire Plan evaluates the relation between pre-wildfire forest structure and post-wildfire soil burn severity across three forest types: dry, moist, and cold forests. Over 73 wildfires were sampled in Idaho, Oregon, Montana, Colorado, and Utah, which burned between 2000 and 2003. Because of the study’s breadth, the results are applicable for understanding how forest structure relates to post-wildfire soil burn severity within Rocky Mountains forests. This paper discusses a burn severity classification that integrates fire intensity, fire severity, and post wildfire response; and discusses the relations wildfire setting (fire group), tree crown ratio, tree canopy cover, surface fuel condition, and tree size have with different soil burn severity outcomes.


title: Designing and Evaluating Explanations for Recommender Systems

abstract: This chapter gives an overview of the area of explanations in recommender systems. We approach the literature from the angle of evaluation: that is, we are interested in what makes an explanation “good”, and suggest guidelines as how to best evaluate this. We identify seven benefits that explanations may contribute to a recommender system, and relate them to criteria used in evaluations of explanations in existing systems, and how these relate to evaluations with live recommender systems. We also discuss how explanations can be affected by how recommendations are presented, and the role the interaction with the recommender system plays w.r.t. explanations. Finally, we describe a number of explanation styles, and how they may be related to the underlying algorithms. Examples of explanations in existing systems are mentioned throughout.


title: Identifying features for detecting fraudulent loan requests on P2P platforms

abstract: This exploratory study is intended to address the problem of fraudulent loan requests on peer-to-peer (P2P) platforms. We propose a set of features that capture the behavioral characteristics (e.g., learning, past performance, social networking, and herding manipulation) of malevolent borrowers, who intentionally create loan requests to acquire funds from lenders but default later on. We found that using the widely adopted classification methods such as Random Forest and Support Vector Machines, the proposed feature set outperform the baseline feature set in helping detect fraudulent loan requests. Although the performance (e.g., Recall or Sensitivity) is still not up to its optimum, this study demonstrates that by analyzing the transaction records of confirmed malevolent borrowers, it is possible to capture some useful behavioral patterns for fraud detection. Such features and methods would possibly help lenders identify loan request frauds and avoid financial losses.


title: Channel Coding Rate in the Finite Blocklength Regime

abstract: This paper investigates the maximal channel coding rate achievable at a given blocklength and error probability. For general classes of channels new achievability and converse bounds are given, which are tighter than existing bounds for wide ranges of parameters of interest, and lead to tight approximations of the maximal achievable rate for blocklengths n as short as 100. It is also shown analytically that the maximal rate achievable with error probability ? isclosely approximated by C - ?(V/n) Q-1(?) where C is the capacity, V is a characteristic of the channel referred to as channel dispersion , and Q is the complementary Gaussian cumulative distribution function.


title: Device-to-Device Communication Underlaying Cellular Communications Systems

abstract: In this article we propose to facilitate local peer-to-peer communication by a Device-to-Device (D2D) radio that operates as an underlay network to an IMT-Advanced cellular network. It is expected that local services may utilize mobile peer-to-peer communication instead of central server based communication for rich multimedia services. The main challenge of the underlay radio in a multi-cell environment is to limit the interference to the cellular network while achieving a reasonable link budget for the D2D radio. We propose a novel power control mechanism for D2D connections that share cellular uplink resources. The mechanism limits the maximum D2D transmit power utilizing cellular power control information of the devices in D2D communication. Thereby it enables underlaying D2D communication even in interference-limited networks with full load and without degrading the performance of the cellular network. Secondly, we study a single cell scenario consisting of a device communicating with the base station and two devices that communicate with each other. The results demonstrate that the D2D radio, sharing the same resources as the cellular network, can provide higher capacity (sum rate) compared to pure cellular communication where all the data is transmitted through the base station.