title: MEASURING THE PERFORMANCE OF ORDINAL CLASSIFICATION

abstract: Ordinal classification is a form of multiclass classification for which there is an inherent order between the classes, but not a meaningful numeric difference between them. The performance of such classifiers is usually assessed by measures appropriate for nominal classes or for regression. Unfortunately, these do not account for the true dimension of the error. The goal of this work is to show that existing measures for evaluating ordinal classification models suffer from a number of important shortcomings. For this reason, we propose an alternative measure defined directly in the confusion matrix. An error coefficient appropriate for ordinal data should capture how much the result diverges from the ideal prediction and how "inconsistent" the classifier is in regard to the relative order of the classes. The proposed coefficient results from the observation that the performance yielded by the Misclassification Error Rate coefficient is the benefit of the path along the diagonal of the confusion matrix. We carry out an experimental study which confirms the usefulness of the novel metric.


title: Network-Coding-Assisted Data Dissemination via Cooperative Vehicle-to-Vehicle/-Infrastructure Communications

abstract: Vehicle-to-vehicle/vehicle-to-infrastructure (referred to as V2X) communications have potential to revolutionize current road transportation systems with respect to vehicle safety, transportation efficiency, and travel experience. This paper puts the first effort on applying network coding in cooperative V2X communication environments to improve bandwidth efficiency and enhance data service performance. Specifically, we investigate new arising challenges on network-coding-assisted data dissemination by considering both communication constraints and application requirements in vehicular networks. We present the system model and give an insight into the characteristics of cooperative data dissemination with network coding. On this basis, we formulate the problem and propose a network-coding-assisted scheduling algorithm to enable the hybrid of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications and exploit their joint effects on providing efficient data services. We design a cache strategy that allows vehicles to retrieve their unrequested data items. This strategy not only increases the opportunity of data sharing among vehicles but also gives higher probability of packet decoding, which in turn enhances the data service performance. We give an intensive analysis on the scheduling overhead, which shows the scalability of the algorithm. Finally, we build the simulation model and conduct a comprehensive performance evaluation to demonstrate the superiority of the proposed solution.


title: Query-Efficient GAN Based Black-Box Attack Against Sequence Based Machine and Deep Learning Classifiers

abstract: In this paper we present an efficient and generic black-box attack demonstrated against API call based machine learning malware classifiers. We generate adversarial examples combining sequences (API call sequences) and other features (e.g., printable strings) that will be misclassified by the classifier without affecting the malware functionality. Opposed to previous studies, our attack minimizes the number of target classifier queries and only requires access to the predicted label of the attacked model (without the confidence level). We evaluate the attack's effectiveness against a variety of classifiers, including recurrent neural network variants, deep neural networks, support vector machines, and gradient-boosted decision trees. We show that the attack requires fewer queries and less knowledge about the attacked model's architecture than other existing black-box attacks, making it practical for attacking cloud based models at a minimal cost. We also implement a software framework that can be used to recraft any malware binary so it will not be detected by classifiers, without access to the malware source code. Finally, we discuss the robustness of this attack to existing defense mechanisms.


title: A Single-Stage Three-Phase Grid-Connected Photovoltaic System With Modified MPPT Method and Reactive Power Compensation

abstract: Single-stage grid-connected photovoltaic (PV) systems have advantages such as simple topology, high efficiency, etc. However, since all the control objectives such as the maximum power point tracking (MPPT), synchronization with the utility voltage, and harmonics reduction for output current need to be considered simultaneously, the complexity of the control scheme is much increased. This paper presents the implementation of a single-stage three-phase grid-connected PV system. In addition to realize the aforementioned control objectives, the proposed control can also remarkably improve the stability of the MPPT method with a modified incremental conductance MPPT method. The reactive power compensation for local load is also realized, so as to alleviate grid burden. A DSP is employed to implement the proposed MPPT controller and reactive power compensation unit. Simulation and experimental results show the high stability and high efficiency of this single-stage three-phase grid-connected PV system.


title: FUNDAMENTAL ECONOMIC STRUCTURE AND STRUCTURAL CHANGE IN REGIONAL ECONOMIES: A METHODOLOGICAL APPROACH

abstract: Regional economic structure is defined as the composition and patterns of various components of the regional economy such as: produc-tion, employment, consumption, trade, and gross regional product. Structur-al change is conceptualized as the change in relative importance of the aggregate indicators of the economy. The process of regional development and structural change are intertwined, implying as economic development takes place the strength and direction of intersectoral relationships change leading to shifts in the importance, direction and interaction of economic sectors such as: primary, secondary, tertiary, quaternary and quinary sec-tors. The fundamental economic structure (FES) concept implies that selected characteristics of an economy will vary predictably with region size. The identification of FES leads to an improved understanding of the space-time evolution of regional economic activities at different geograph-ical scales. The FES based economic activities are predictable, stable and important. This paper reviews selected themes in manifesting an improved understanding of the relationship among intersectoral transactions and economic size leading to the identification of FES. The following four ques-tions are addressed in this paper: (1) What are the relationships among sector composition and structural change in the process of economic devel-opment? (2) What are the approaches utilized to study structural change analysis? (3) Can a methodology be developed to identify FES for regional economies? (4) Would the identification of FES manifest an improved con-ception of the taxonomy of economies?


Social Media Usage and Cultural Dimensions: an Empirical Investigation


title: Patrol Team Language Identification System for DARPA RATS P1 Evaluation

abstract: This paper describes the language identification (LID) system developed by the Patrol team for the first phase of the DARPA RATS (Robust Automatic Transcription of Speech) program, which seeks to advance state of the art detection capabilities on audio from highly degraded communication channels. We show that techniques originally developed for LID on telephone speech (e.g., for the NIST language recognition evaluations) remain effective on the noisy RATS data, provided that careful consideration is applied when designing the training and development sets. In addition, we show significant improvements from the use of Wiener filtering, neural network based and language dependent i-vector modeling, and fusion.


title: Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension

abstract: We propose a neural machine-reading model that constructs dynamic knowledge graphs from procedural text. It builds these graphs recurrently for each step of the described procedure, and uses them to track the evolving states of participant entities. We harness and extend a recently proposed machine reading comprehension (MRC) model to query for entity states, since these states are generally communicated in spans of text and MRC models perform well in extracting entity-centric spans. The explicit, structured, and evolving knowledge graph representations that our model constructs can be used in downstream question answering tasks to improve machine comprehension of text, as we demonstrate empirically. On two comprehension tasks from the recently proposed PROPARA dataset (Dalvi et al., 2018), our model achieves state-of-the-art results. We further show that our model is competitive on the RECIPES dataset (Kiddon et al., 2015), suggesting it may be generally applicable. We present some evidence that the model's knowledge graphs help it to impose commonsense constraints on its predictions.


title: A Basis for Analyzing Software Architecture Analysis Methods

abstract: A software architecture is a key asset for any organization that builds complex software-intensive systems. Because of an architecture's central role as a project blueprint, organizations should analyze the architecture before committing resources to it. An analysis helps to ensure that sound architectural decisions are made. Over the past decade a large number of architecture analysis methods have been created, and at least two surveys of these methods have been published. This paper examines the criteria for analyzing architecture analysis methods, and suggests a new set of criteria that focus on the essence of what it means to be an architecture analysis method. These criteria could be used to compare methods, to help understand the suitability of a method, or to improve a method. We then examine two methods--the Architecture Tradeoff Analysis Method and Architecture-level Modifiability Analysis--in light of these criteria, and provide some insight into how these methods can be improved.


title: Multiscale ultrawide foveated video extrapolation

abstract: Video extrapolation is the task of extending a video beyond its original field of view. Extrapolating video in a manner that is consistent with the original video and visually pleasing is difficult. In this work we aim at very wide video extrapolation which increases the complexity of the task. Some video extrapolation methods simplify the task by using a rough color extrapolation. A recent approach focuses on artifact avoidance and run time reduction using foveated video extrapolation, but fails to preserve the structure of the scene. This paper introduces a multi-scale method which combines a coarse to fine approach with foveated video extrapolation. Foveated video extrapolation reduces the effective number of pixels that need to be extrapolated, making the extrapolation less time consuming and less prone to artifacts. The coarse to fine approach better preserves the structure of the scene while preserving finer details near the domain of the input video. The combined method gains improvement both visually and in processing time.


title: Novel in-wheel axial-flux segmented switched reluctance motor

abstract: In this paper a novel axial-flux segmented rotor switched reluctance motor (AFSSRM) with a stator sandwiched by two rotors with a particular form of the segments is presented. The machine has magnetic short paths with no flux reversal and the active magnetic parts are made of soft magnetic composites. An in-wheel AFSSRM, specially intended for direct drive of light motorcycles, is designed. Electromagnetic analysis of the AFSSRM is carried out using 3D finite element. Matlab-Simulink simulations of the whole in-wheel AFSSRM drive are used to verify its behavior.


title: Model compression

abstract: Often the best performing supervised learning models are ensembles of hundreds or thousands of base-level classifiers. Unfortunately, the space required to store this many classifiers, and the time required to execute them at run-time, prohibits their use in applications where test sets are large (e.g. Google), where storage space is at a premium (e.g. PDAs), and where computational power is limited (e.g. hea-ring aids). We present a method for "compressing" large, complex ensembles into smaller, faster models, usually without significant loss in performance.


Tagging and Chunking with Subword 2 Word models


title: Interactive Indirect Illumination Using Voxel Cone Tracing

abstract: Indirect illumination is an important element for realistic image synthesis, but its computation is expensive and highly dependent on the complexity of the scene and of the BRDF of the involved surfaces. While off-line computation and pre-baking can be acceptable for some cases, many applications (games, simulators, etc.) require real-time or interactive approaches to evaluate indirect illumination. We present a novel algorithm to compute indirect lighting in real-time that avoids costly precomputation steps and is not restricted to low-frequency illumination. It is based on a hierarchical voxel octree representation generated and updated on the fly from a regular scene mesh coupled with an approximate voxel cone tracing that allows for a fast estimation of the visibility and incoming energy. Our approach can manage two light bounces for both Lambertian and glossy materials at interactive framerates (25-70FPS). It exhibits an almost scene-independent performance and can handle complex scenes with dynamic content thanks to an interactive octree-voxelization scheme. In addition, we demonstrate that our voxel cone tracing can be used to efficiently estimate Ambient Occlusion.


Nested Named Entity Recognition Revisited


title: Privacy preserving mining of association rules

abstract: We present a framework for mining association rules from transactions consisting of categorical items where the data has been randomized to preserve privacy of individual transactions. While it is feasible to recover association rules and preserve privacy using a straightforward "uniform" randomization, the discovered rules can unfortunately be exploited to find privacy breaches. We analyze the nature of privacy breaches and propose a class of randomization operators that are much more effective than uniform randomization in limiting the breaches. We derive formulae for an unbiased support estimator and its variance, which allow us to recover itemset supports from randomized datasets, and show how to incorporate these formulae into mining algorithms. Finally, we present experimental results that validate the algorithm by applying it on real datasets.


title: Inter-AS traffic engineering with SDN

abstract: Egress selection in an Internet Service Provider (ISP) is the process of selecting an egress router to route interdomain traffic across the ISP such that a traffic engineering objective is achieved. In traditional ISP networks, traffic through the ISP is carried through the network and exits via an egress that is closest to the source in an attempt to minimize network resources for transit traffic. This exit strategy is known as Hot Potato Routing (HPR). The emerging field of Software-Defined Networking (SDN) has opened up many possibilities and promised to bring new flexibility to the rigid traditional paradigm of networking. In an ISP network, however, completely replacing legacy network devices with SDN nodes is neither simple nor straightforward. This has led to the idea of incremental and selective deployment of SDN nodes in an ISP network. Such a hybrid network gives us control over traffic flows that pass through the SDN nodes without requiring extensive changes to an existing ISP network. In this paper, we look at the problem of choosing an optimal set of egress routers to route inter-domain transit traffic in a hybrid SDN network such that the maximum link utilization of the egress links is minimized. We formulate the optimization problem, show that it is related to the makespan scheduling problem of unrelated parallel machines, and propose heuristics to solve it. We perform simulations to evaluate our heuristic on a real ISP topology and show that even with a small number of SDN nodes in the network, the maximum link utilization on the egress links can be lower than that of traditional HPR.


title: Autonomous Vehicle Security: A Taxonomy of Attacks and Defences

abstract: In recent years, we have seen significant advancement in technologies to bring about smarter cities worldwide. The interconnectivity of things is the key enabler in these initiatives. An important building block is smart mobility, and it revolves around resolving land transport challenges in cities with dense populations. A transformative direction that global stakeholders are looking into is autonomous vehicles and the transport infrastructure to interconnect them to the traffic management system (that is, vehicle to infrastructure connectivity), as well as to communicate with one another (that is, vehicle to vehicle connectivity) to facilitate better awareness of road conditions. A number of countries had also started to take autonomous vehicles to the roads to conduct trials and are moving towards the plan for larger scale deployment. However, an important consideration in this space is the security of the autonomous vehicles. There has been an increasing interest in the attacks and defences of autonomous vehicles as these vehicles are getting ready to go onto the roads. In this paper, we aim to organize and discuss the various methods of attacking and defending autonomous vehicles, and propose a comprehensive attack and defence taxonomy to better categorize each of them. Through this work, we hope that it provides a better understanding of how targeted defences should be put in place for targeted attacks, and for technologists to be more mindful of the pitfalls when developing architectures, algorithms and protocols, so as to realise a more secure infrastructure composed of dependable autonomous vehicles.


title: Corporate Social Responsibility Theories: Mapping the Territory

abstract: The Corporate Social Responsibility (CSR) field presents not only a landscape of theories but also a proliferation of approaches, which are controversial, complex and unclear. This article tries to clarify the sit- uation, ''mapping the territory'' by classifying the main CSR theories and related approaches in four groups: (1) instrumental theories, in which the corporation is seen as only an instrument for wealth creation, and its social activities are only a means to achieve economic results; (2) political theories, which concern themselves with the power of corporations in society and a responsible use of this power in the political arena; (3) integrative theories, in which the corporation is focused on the satisfaction of social demands; and (4) ethical theories, based on ethical responsibilities of corporations to society. In practice, each CSR theory presents four dimensions related to profits, political performance, social demands and ethical values. The findings suggest the necessity to develop a new theory on the business and society relationship, which should integrate these four dimensions.


title: Application of Machine Learning To Epileptic Seizure Detection

abstract: We present and evaluate a machine learning approach to constructing patient-specific classifiers that detect the onset of an epileptic seizure through analysis of the scalp EEG, a non-invasive measure of the brain's electrical activity. This problem is challenging because the brain's electrical activity is composed of numerous classes with overlapping characteristics. The key steps involved in realizing a high performance algorithm included shaping the problem into an appropriate machine learning framework, and identifying the features critical to separating seizure from other types of brain activity. When trained on 2 or more seizures per patient and tested on 916 hours of continuous EEG from 24 patients, our algorithm detected 96% of 173 test seizures with a median detection delay of 3 seconds and a median false detection rate of 2 false detections per 24 hour period. We also provide information about how to download the CHB-MIT database, which contains the data used in this study.


title: The relation between perception and behavior or how to win a game of Trivial Pursuit

abstract: The authors tested and confirmed the hypothesis that priming a stereotype or trait leads to complex overt behavior in line with this activated stereotype or trait. Specifically, 4 experiments established that priming the stereotype of professors or the trait intelligent enhanced participants' performance on a scale measuring general knowledge. Also, priming the stereotype of soccer hooligans or the trait stupid reduced participants' performance on a general knowledge scale. Results of the experiments revealed (a) that prolonged priming leads to more pronounced behavioral effects and (b) that there is no sign of decay of the effects for at least 15 min. The authors explain their results by claiming that perception had a direct and pervasive impact on overt behavior (cf. J.A. Bargh, M. Chen, & L. Burrows, 1996). Implications for human social behavior are discussed. Language: en


title: Dual-Band Rectangular Patch Hybrid Coupler

abstract: This paper presents, for the first time, the design of a rectangular patch hybrid coupler that can operate at two arbitrary frequency bands with the same quadrature phase relation between the two outputs. The design procedure of the hybrid coupler is presented. For demonstration, a dual-band rectangular patch hybrid coupler is designed to operate in the 2.4- and 5-GHz bands corresponding to the IEEE 802.11 a/b/g bands. The measured result shows 9.3% and 18.2% bandwidth for the lower and upper frequency bands.


title: c-Maf Is Required for the Development of Dorsal Horn Laminae III/IV Neurons and Mechanoreceptive DRG Axon Projections

abstract: Establishment of proper connectivity between peripheral sensory neurons and their central targets is required for an animal to sense and respond to various external stimuli. Dorsal root ganglion(DRG) neurons convey sensory signals of different modalities via their axon projections to distinct laminae in the dorsal horn of the spinal cord. In this study, we found that c-Maf was expressed predominantly in the interneurons of laminae III/IV, which primarily receive inputs from mechanoreceptive DRG neurons. In the DRG, c-Maf(+) neurons also coexpressed neurofilament-200, a marker for the medium-and large-diameter myelinated afferents that transmit non-noxious information. Furthermore, mouse embryos deficient in c-Maf displayed abnormal development of dorsal horn laminae III/IV neurons, as revealed by the marked reduction in the expression of several marker genes for these neurons, including those for transcription factors MafA and Rora, GABA(A) receptor subunit alpha 5, and neuropeptide cholecystokinin. In addition, among the four major subpopulations of DRG neurons marked by expression of TrkA, TrkB, TrkC, and MafA/GFR alpha 2/Ret, c-Maf was required selectively for the proper differentiation of MafA (+)/Ret (+)/GFR alpha 2 (+) low-threshold mechano-receptors (LTMs). Last, we found that the central and peripheral projections of mechanoreceptive DRG neurons were compromised in c-Maf deletion mice. Together, our results indicate that c-Maf is required for the proper development of MafA (+)/Ret (+)/GFR alpha 2 (+) LTMs in the DRG, their afferent projections in the dorsal horn and Pacinian corpuscles, as well as neurons in laminae III/IV of the spinal cord.


title: Contents and time sensitive document ranking of scientific literature

abstract: A new link-based document ranking framework is devised with at its heart, a contents and time sensitive random literature explorer designed to more accurately model the behaviour of readers of scientific documents. In particular, our ranking framework dynamically adjusts its random walk parameters according to both contents and age of encountered documents, thus incorporating the diversity of topics and how they evolve over time into the score of a scientific publication. Our random walk framework results in a ranking of scientific documents which is shown to be more effective in facilitating literature exploration than PageRank measured against a proxy gold standard based on papers’ potential usefulness in facilitating later research. One of its many strengths lies in its practical value in reliably retrieving and placing promisingly useful papers at the top of its ranking.


title: A three-phase ZVS PWM DC/DC converter with asymmetrical duty cycle for high power applications

abstract: This paper proposes the application of the asymmetrical duty cycle to the three-phase DC/DC PWM isolated converter. Thus soft commutation is achieved for a wide load range using the leakage inductance of the transformer and the intrinsic capacitance of the switches, as no additional semiconductor devices are needed. The resulting topology is characterized by an increase in the input current and output current frequency, by a factor of three compared to the full bridge converter, which reduces the filters size. In addition, the RMS current through the power components is lower, implying an improved thermal distribution of the losses. Besides, the three-phase transformer allows the reduction of the core size. In this paper, a mathematical analysis, the main waveforms, a design procedure as well as simulation and experimental results obtained in a prototype of 6 kW are presented.


title: Projector calibration by inverse camera calibration

abstract: The accuracy of 3-D reconstructions depends substantially on the accuracy of active vision system calibration. In this work, the problem of video projector calibration is solved by inverting the standard camera calibration work flow. The calibration procedure requires a single camera, which does not need to be calibrated and which is used as the sensor whether projected dots and calibration pattern landmarks, such as the checkerboard corners, coincide. The method iteratively adjusts the projected dots to coincide with the landmarks and the final coordinates are used as inputs to a camera calibration method. The otherwise slow iterative adjustment is accelerated by estimating a plane homography between the detected landmarks and the projected dots, which makes the calibration method fast.


title: Distributed heterogeneous event processing: enhancing scalability and interoperability of CEP in an industrial context

abstract: Although a significant amount of research has investigated the benefits of distributed CEP in terms of scalability and extensibility, there is an ongoing reluctance in deploying distributed CEP in an industrial context. In this paper we present the DHEP system developed together with the IBM® laboratory in Boblingen. It addresses some of the key problems in increasing the acceptance of distributed CEP, for example supporting interoperability between heterogeneous event processing systems. We present the concepts behind the DHEP system and show how those concepts help to achieve scalable and extensible event processing in an industrial context. Moreover, we verify in an evaluation study that the additional cost imposed by the DHEP system is moderate and 'affordable' for the benefits provided.


title: The business case for corporate social responsibility: A company-level measurement approach for CSR

abstract: Summary Although theoretical and empirical research often points to a positive relation between CSR and company competitiveness, approaches to measure the company-specific business impacts of CSR are missing in the current literature. However, such an approach could strengthen the overall CSR involvement and support rational decision-making in this area. This paper thus focuses on the question how to measure the business impact of CSR activities from a company perspective. Using a theoretical approach a multi-step measurement model is developed that allows managers to evaluate their company-specific business case for CSR. A case example illustrates the use of the model in practice.


Investigating the influence of perceived organizational support, psychological empowerment and organizational learning on job performance: an empirical investigation


WikiTalk: A Spoken Wikipedia-based Open-Domain Knowledge Access System


title: An Acute Bout of Self-Myofascial Release Increases Range of Motion Without a Subsequent Decrease in Muscle Activation or Force

abstract: AbstractMacDonald, GZ, Penney, MDH, Mullaley, ME, Cuconato, AL, Drake, CDJ, Behm, DG, and Button, DC. An acute bout of self-myofascial release increases range of motion without a subsequent decrease in muscle activation or force. J Strength Cond Res 27(3): 812–821, 2013. Foam rolling is thought to i


title: Cutaneous leishmaniasis: new dermoscopic findings.

abstract: BACKGROUND ::: Cutaneous leishmaniasis (CL) has a worldwide distribution. Delayed and incorrect diagnosis may cause the significant spread of this disease and consequent increases in morbidity and drug resistance. ::: ::: ::: OBJECTIVES ::: We aimed to determine dermoscopic features of CL that may help to improve the accuracy of clinical diagnosis of the disease. ::: ::: ::: METHODS ::: A total of 145 lesions in 102 patients were evaluated dermoscopically. Following the diagnosis of CL, all lesions were evaluated by experienced dermoscopists. ::: ::: ::: RESULTS ::: A total of 51 papules, 40 nodulo-ulcerative lesions, 31 plaques, and 23 nodules were evaluated by dermoscopy. Generalized erythema appeared in all lesions (100%), yellow tears in 58 lesions, both crust and ulcer in 51 lesions, white starburst-like patterns in 27 lesions, ovoid salmon-colored structures in 19 lesions, and a perilesional hypopigmented halo pattern in four lesions. Various vascular structures were present in 126 lesions. The most common vascular structure observed was an irregular linear pattern in 78 lesions, followed by a tree-like pattern in 53 lesions. The rest of the vascular structures included hairpin vessels in 25 lesions, glomerulus-like vessels in 24 lesions, dotted vessels in 23 lesions, comma-shaped vessels in six lesions, and polymorphous/atypical vessels in four lesions. We did not identify the types of parasite involved. ::: ::: ::: CONCLUSIONS ::: Yellow tears, white starburst-like patterns and salmon-colored ovoid structures seem to appear specifically in CL lesions. In geographical areas in which CL is common, dermoscopy may be utilized as a useful diagnostic tool that is practical and non-invasive.


title: COMPACT WIDE-SLOT TRI-BAND ANTENNA FOR WLAN/WIMAX APPLICATIONS

abstract: In this paper, a wide-slot triple band antenna fed by a coplanar waveguide (CPW) for WLAN/WiMAX applications is proposed. The antenna mainly comprises a ground with a wide square slot in the center, a rectangular feeding strip and two pairs of planar inverted L strips (PIL) connecting with the slotted ground. By introducing the two pairs of PIL's, three resonant frequencies, 2.4/5.5GHz for WLAN, and 3.5GHz for WiMAX, are excited. Prototypes of the antenna are fabricated and tested. The simulated and measured results show that the proposed antenna has three good impedance bandwidths (S11 better than i10dB) of 300MHz (about 12.6% centered at 2.39GHz), 280MHz (about 8% centered at 3.49GHz) and 790MHz (about 14.5% centered at 5.43GHz), which make it easily cover the required bandwidths for WLAN band (2.4{ 2.48GHz, 5.15{5.35GHz, and 5.725{5.825GHz) and WiMAX (3.4- 3.6GHz) applications. Moreover, the obtained radiation patterns demonstrate that the proposed antenna has flgure-eight patterns in E-plane, and is omni-directional in H-plane. The gains of the antenna at operation bands are stable.


title: Going beyond the surface: studying multi-layer interaction above the tabletop

abstract: Lightweight spatially aware displays (Tangible Magic Lenses) are an effective approach for exploring complex information spaces within a tabletop environment. One way of using the 3D space above a horizontal surface is to divide it into discrete parallel layers stacked upon each other. Horizontal and vertical lens movements are essential tasks for the style of multi-layer interaction associated with it. We conducted a comprehensive user study with 18 participants investigating fundamental issues such as optimal number of layers and their thickness, movement and holding accuracies, and physical boundaries of the interaction volume. Findings include a rather limited overall interaction height (44 cm), a different minimal layer thickness for vertical and horizontal search tasks (1 cm/4 cm), a reasonable maximum number of layers depending on the primary task, and a convenience zone in the middle for horizontal search. Derived from that, design guidelines are also presented.


title: Observations on the Urine Metabolic Ratio of Oxymorphone to Oxycodone in Pain Patients

abstract: David A. Yee1, Brookie M. Best1,3, Rabia S. Atayee1,2 and Amadeo J. Pesce4,5* University of California, San Diego (UCSD), Skaggs School of Pharmacy & Pharmaceutical Sciences, San Diego, CA, UCSD Department of Internal Medicine, UCSD Medical Center, San Diego, CA, UCSD Department of Pediatrics, Rady Children’s Hospital, San Diego, CA, Millennium Laboratories, San Diego, CA, and Department of Pathology and Laboratory Medicine, UCSD School of Medicine


title: User Friendly Implementation for Efficiently Conducting Hammersmith Infant Neurological Examination

abstract: The aim of this work is to design a semi-automatic application that can be used as an aid by the doctors for smoothly conducting Hammersmith Infant Neurological Examination (HINE). A simplified version of the examination which provides a quantitative neurological assessment is used to design the application. The application includes a methodology of conducting HINE examination suited to inexperienced staff, applicable to both neonatal and post-neonatal infants. It also provides a facility to go through the previous records of a patient that can help in diagnosing patients with high risk of neurological disorder. A semi-automatic approach is proposed for skeleton generation. The application has been installed in hospitals and currently in operation. It is expected to increase the efficiency of conducting HINE using the proposed application.


title: Integrated cut and place module for high productive manufacturing of lithium-ion cells

abstract: Abstract A main reason for the high costs of lithium-ion cells is the complexity of the assembly process. Especially, creating the cell stack is an inefficient process due to an immature handling and aligning of the limp electrodes and separators. This paper focuses therefore on the methodical development of a functionally integrated assembly module that combines cutting and handling of electrode sheets to increase stacking accuracy, process reliability and productivity. Based on an analysis of requirements and functions, a technical solution has been identified and will be illustrated. At the end, the realized module is described and its performance is proven by experimental validation.


title: An IoT-based appliance control system for smart homes

abstract: With the development of the social economy, more and more appliances have been presented in a house. It comes out a problem that how to manage and control these increasing various appliances efficiently and conveniently so as to achieve more comfortable, security and healthy space at home. In this paper, a smart control system base on the technologies of internet of things has been proposed to solve the above problem. The smart home control system uses a smart central controller to set up a radio frequency 433 MHz wireless sensor and actuator network (WSAN). A series of control modules, such as switch modules, radio frequency control modules, have been developed in the WSAN to control directly all kinds of home appliances. Application servers, client computers, tablets or smart phones can communicate with the smart central controller through a wireless router via a Wi-Fi interface. Since it has WSAN as the lower control layer, a appliance can be added into or withdrawn from the control system very easily. The smart control system embraces the functions of appliance monitor, control and management, home security, energy statistics and analysis.


title: Implementation of recurrent neural network algorithm for shortest path calculation in network routing

abstract: This paper describes the architecture and implementation of a shortest-path processor, both in reconfigurable hardware and VLSI. This processor is based on the principles of a recurrent spatiotemporal neural network. The processor's operation is similar to E.W. Dijkstra's (1959) algorithm and it can be used for network routing calculations. The objective of the processor is to find the least-cost path in a weighted graph between a given node and one or more destinations. The digital implementation, which exhibits a regular interconnect structure and uses simple processing elements, is well-suited for VLSI implementation and reconfigurable hardware.


title: Why model-based test automation is different and what you should know to get started

abstract: Test engineers, their managers, as well as the project developers often have many different views and misconceptions about tools and methods that provide test automation support. This paper compares model-based testing with three other generations of test automation. Model-based test automation can be considered a fourth generation test automation. It supports defect prevention, early requirement defect identification, and automatic generation of tests from models, which eliminates manual test design and reduces cost. However, to be effective at adopting model-based test automation, there are specific skills that will be required to incorporate this type of test automation into an organization. The paper discusses the organizational, personnel, and development lessons learned from working with numerous companies and projects over the past several years. It recommends how to get started, how to select a project, and how to organize a project. Finally, the paper describes how to measure, track and estimate the project completion date during the first non-pilot project.


title: Rehabilitative Outcomes after Hip Fracture in a Special Care Unit for Persons with Dementia and Behavioral and Psychotic Symptoms

abstract: Aim: Hip fracture implies severe problems to older people; special concerns regard persons with dementia, due either to cognitive impairment, or to behavioral and psychic symptoms. This study illustrates rehabilitative outcomes of these patients discharged by a special care unit ruled by “GentleCare” principles. Method: 54 patients [89% females, aged 82.3 years (range 66 - 94)] followed a post-surgery rehabilitative program carried out by a physiotherapist and an occupational therapist, supported by a psychologist. The multidimensional assessment consisted of cognition evaluation (Mini Mental State Examination, Clinical Dementia Rating, Global Deterioration Scale), functional evaluation (Barthel Index, Tinetti Gait and Balance, Bedford Alzheimer Nursing Severity scale), behavioral evaluation (UCLA Neuropsychiatric Inventory) and comorbidity evaluation (Cumulative Illness Rating Scale). Results: All parameters improved, including the 5 most frequent behavioral and psychic symptoms that usually preclude admission in ordinary rehabilitation units. 24% of improvement in Barthel Index total score was explained by agitation and apathy at discharge, in a multiple linear regression model: better functional levels corresponded to smoother behavioral problems. Most patients improved; 70.5% of them were discharged to home. Conclusion: A prosthetic approach enables valuable results in the rehabilitation of severely demented patients with hip fracture also in presence of behavioral symptoms.


title: Open3D: A Modern Library for 3D Data Processing

abstract: Open3D is an open-source library that supports rapid development of software that deals with 3D data. The Open3D frontend exposes a set of carefully selected data structures and algorithms in both C++ and Python. The backend is highly optimized and is set up for parallelization. Open3D was developed from a clean slate with a small and carefully considered set of dependencies. It can be set up on different platforms and compiled from source with minimal effort. The code is clean, consistently styled, and maintained via a clear code review mechanism. Open3D has been used in a number of published research projects and is actively deployed in the cloud. We welcome contributions from the open-source community.


title: Social psychological aspects of computer-mediated communication.

abstract: As more and more people use computers for communicating, the behavioral and societal effects of computer-mediated communication are becoming critical research topics. This article describes some of the issues raised by electronic communication, illustrates one empirical approach for investigating its social psychological effects, and discusses why social psychological research might contribute to a deeper understanding of computer-mediated communication specifically and of computers and technological change in society more generally. One objective of our research is to explore how people participate in computer-mediated communication and how computerization affects group efforts to reach consensus. In experiments, we have shown differences in participation, decisions, and interaction among groups meeting face to face and in simultaneous computer-linked discourse and communication by electronic mail. We discuss these results and the design of subsequent research to highlight the many researchable social psychological issues raised by computing and technological change. Computer technologies are improving so swiftly these days that few of us comprehend even a small part of the change. Computers are transforming work and, in some cases, lives. Whether eager for this or resistant, many people believe the organizational, social, and personal effects of computers will be deeply felt (De Sola Poole, 1977; Hiltz & Turoff, 1978; Kling, 1980). Today, no one can predict in any detail the nature of the transformations that computers will bring, but one aspect of life that will certainly be affected is communication. The use of electronic mail and messages, long-distance blackboards, computer bulletin boards, instantaneously transferable data banks, and simultaneous computer conferences is reportedly advancing "like an avalanche" (Stockton, 1981; also see Kraemer, 1981). The U.S. federal judiciary, for example, is using electronic mail to speed the circulation of appellate opinion drafts among panels of judges (Weis, 1983). Computer conferences are being used for such legal proceedings as admission of evidence, trial scheduling, giving parties access to documents, and expert interrogation (Bentz & Potrykus, 1976; "Party-Line Plea," 1981). Other government agencies, such as the Department of Defense, as well as private firms, such as Westinghouse Corporation and Xerox Corporation, and some universities, use computer-mediated communication extensively for both routine transfer of data and nonroutine interpersonal communication and project work (e.g., Licklider & Vezza, 1978; U.S. Department of Commerce, 1977; Wang Corporation, 1982). Computer-mediated communication was once confined to technical users and was considered somewhat arcane. This no longer holds true. Computer-mediated communication is a key component of the emerging technology of computer networks. In networks, people can exchange, store, edit, broadcast, and copy any written document. They can send data and messages instantaneously, easily, at low cost, and over long distances. Two or more people can look at a document and revise it together, consult with each other on critical matters without meeting together or setting up a telephone conference, or ask for and give assistance interactively (Hiltz & Turoff, 1978; Williams, 1977). Networks, and hence computer-mediated communications, are proliferating at a tremendous rate. In addition to the older long-distance networks that connect thousands of scientists, professionals, and managers (e.g., the Department of Defense's ARPANET, GTE's TELENET), there are more and more local-area networks that link up computers within a region, city, or organization (e.g., Nestar System's CLUSTERBUS, Xerox's ETHERNET, Ford Aerospace's FLASHNET, and Wang Laboratories' WANGNET). Stimulating this growth are the decreasing costs and the advantages of networks over stand-alone systems, such as sharing high-speed printers and access to a common interface for otherwise incompatible equipment. The future of this technology cannot be foretold, but it is far from arcane. The functions and impact of computer-mediated communication are still poorly understood. Critical information (such as who uses it for what purposes) October 1984 • American Psychologist Copyright 1984 by the American Psychological Aisociation, Inc. Vol. 39, No. 10, 1123-1134 1123 is lacking, and the social psychological significance is controversial (see, e.g., Turoff, 1982). Computers could make communication easier, just as the canning of perishables and the development of can openers made food preparation easier, or they could have much more complex implications. For instance, access to electronic communication may change the flow of information within organizations, altering status relations and organizational hierarchy. When a manager can receive electronic mail from 10,000 employees, what happens to existing controls over participation and information? When people can publish and distribute their own electronic newspaper at no cost, does the distribution of power change too? When communication is rapid and purely textual, do working groups find it easier or harder to resolve conflict? These unanswered questions illustrate that, although the technology may be impressive, little systematic research exists on its psychological, social, and cultural significance. Given such conditions it seems sensible to try to understand the fundamental behavioral, social, and organizational processes that surround computer-mediated communication. We believe that ideas and approaches from social psychology and other areas of behavioral science can be applied to these questions. This article is meant to describe some of the issues raised by electronic communication; to illustrate, from our own work, one empirical approach for investigating them; and to show why social psychological research might contribute to a deeper understanding of electronic communication specifically and of computers and technological change in society more generally. We begin by citing some existing research on computer-mediated communication. Most of this research addresses the technical capabilities of the electronic technologies. Next, we consider the possible social psychological impact, and we discuss some hypotheses and some possible implications for the outcomes of communication. Finally, we describe some of our own experiments on social psychological aspects of computer-mediated communication, using these to indicate potential lines of future research.


title: Social support and protection from depression: systematic review of current findings in Western countries

abstract: Background ::: ::: Numerous studies report an association between social support and protection from depression, but no systematic review or meta-analysis exists on this topic. ::: ::: Aims ::: ::: To review systematically the characteristics of social support (types and source) associated with protection from depression across life periods (childhood and adolescence; adulthood; older age) and by study design (cross-sectional v. cohort studies). ::: ::: Method ::: ::: A systematic literature search conducted in February 2015 yielded 100 eligible studies. Study quality was assessed using a critical appraisal checklist, followed by meta-analyses. ::: ::: Results ::: ::: Sources of support varied across life periods, with parental support being most important among children and adolescents, whereas adults and older adults relied more on spouses, followed by family and then friends. Significant heterogeneity in social support measurement was noted. Effects were weaker in both magnitude and significance in cohort studies. ::: ::: Conclusions ::: ::: Knowledge gaps remain due to social support measurement heterogeneity and to evidence of reverse causality bias.


title: Netflix recommendations for groups

abstract: In this era of overabundant information and content, people increasingly rely on recommender systems to identify those information items that best meet their needs and interests. Movie recommender systems, like the one used by Netflix, attempt to predict which films a given person will enjoy watching. While these systems help single individuals making decisions, they provide limited support for groups of people. This work explores how to create recommender systems for groups that can combine multiple user profiles and predict which movies a group of users will collectively enjoy the most. We built a prototype using Netflix REST API based on the results of a formative study of the watching habits of 60 actual Netflix users and examined their views of how a group recommendation system would fit in with their current habits. We conducted a preliminary evaluation with a focus group which validated our approach to group recommendations, revealing that this type of system could facilitate social interactions by sparking discussion about movies, directors, and actors among viewers. This prototype provides a valuable platform for further exploring group decision making in this context.


title: A comparative study of classification methods for automatic multimodal brain tumor segmentation

abstract: This paper presents a comparative study of various classification methods in the application of automatic brain tumor segmentation. The data used in the study are 3D MRI volumes from MICCAI2016 brain tumor segmentation (BRATS) benchmark. 30 volumes are chosen randomly as a training set and 57 volumes are randomly chosen as a test set. The volumes are preprocessed and a feature vector is retrieved from each volume's four modalities (T1, T1 contrast-enhanced, T2 and Fluid-attenuated inversion recovery). The popular Dice score is used as an accuracy measure to record each classifier recognition results. All classifiers are implemented in the popular machine learning suit of algorithms, WEKA.


title: Spatially-Adaptive Filter Units for Deep Neural Networks

abstract: Classical deep convolutional networks increase receptive field size by either gradual resolution reduction or application of hand-crafted dilated convolutions to prevent increase in the number of parameters. In this paper we propose a novel displaced aggregation unit (DAU) that does not require hand-crafting. In contrast to classical filters with units (pixels) placed on a fixed regular grid, the displacement of the DAUs are learned, which enables filters to spatially-adapt their receptive field to a given problem. We extensively demonstrate the strength of DAUs on a classification and semantic segmentation tasks. Compared to ConvNets with regular filter, ConvNets with DAUs achieve comparable performance at faster convergence and up to 3-times reduction in parameters. Furthermore, DAUs allow us to study deep networks from novel perspectives. We study spatial distributions of DAU filters and analyze the number of parameters allocated for spatial coverage in a filter.


title: Characterisation and Estimation of the Key Rank Distribution in the Context of Side Channel Evaluations

abstract: Quantifying the side channel security of implementations has been a significant research question for several years in academia but also among real world side channel practitioners. As part of security evaluations, efficient key rank estimation algorithms were devised, which in contrast to analyses based on subkey recovery, give a holistic picture of the security level after a side channel attack. However, it has been observed that outcomes of rank estimations show a huge spread in precisely the range of key ranks where enumeration could lead to key recovery. These observations raise the question whether this is because of insufficient rank estimation procedures, or, if this is an inherent property of the key rank. Furthermore, if this was inherent, how could key rank outcomes be translated into practically meaningful figures, suitable to analysing the risk that real world side channel attacks pose? This paper is a direct response to these questions. We experimentally identify the key rank distribution and show that it is independent of different distinguishers and signal-to-noise ratios. Then we offer a theoretical explanation for the observed key rank distribution and determine how many samples thereof are required for a robust estimation of some key parameters. We discuss how this can be naturally integrated into real world side channel evaluation practices. We conclude our research by connecting non-parametric order statistics, in particular percentiles, in a practically meaningful way with business goals.


title: Low cost high performance uncertainty quantification

abstract: Uncertainty quantification in risk analysis has become a key application. In this context, computing the diagonal of inverse covariance matrices is of paramount importance. Standard techniques, that employ matrix factorizations, incur a cubic cost which quickly becomes intractable with the current explosion of data sizes. In this work we reduce this complexity to quadratic with the synergy of two algorithms that gracefully complement each other and lead to a radically different approach. First, we turned to stochastic estimation of the diagonal. This allowed us to cast the problem as a linear system with a relatively small number of multiple right hand sides. Second, for this linear system we developed a novel, mixed precision, iterative refinement scheme, which uses iterative solvers instead of matrix factorizations. We demonstrate that the new framework not only achieves the much needed quadratic cost but in addition offers excellent opportunities for scaling at massively parallel environments. We based our implementation on BLAS 3 kernels that ensure very high processor performance. We achieved a peak performance of 730 TFlops on 72 BG/P racks, with a sustained performance 73% of theoretical peak. We stress that the techniques presented in this work are quite general and applicable to several other important applications.


title: Towards a Multi-cluster Analytical Engine for Transportation Data

abstract: In the new digital age, the pace and volume of growing transportation related data is exceeding our ability to manage and analyze it. In this position paper, we present a data engine, Godzilla, to ingest real-time traffic data and support analytic and data mining over traffic data. Godzilla is a multi-cluster approach to handle large volumes of growing data, changing workloads and varying number of users. The data originates at multiple sources, and consists of multiple types. Meanwhile, the workloads belong to three camps, namely batch processing, interactive queries and graph analysis. Godzilla support multiple language abstractions from scripting to SQL-like language.


title: Large-scale malware indexing using function-call graphs

abstract: A major challenge of the anti-virus (AV) industry is how to effectively process the huge influx of malware samples they receive every day. One possible solution to this problem is to quickly determine if a new malware sample is similar to any previously-seen malware program. In this paper, we design, implement and evaluate a malware database management system called SMIT (Symantec Malware Indexing Tree) that can efficiently make such determination based on malware's function-call graphs, which is a structural representation known to be less susceptible to instruction-level obfuscations commonly employed by malware writers to evade detection of AV software. Because each malware program is represented as a graph, the problem of searching for the most similar malware program in a database to a given malware sample is cast into a nearest-neighbor search problem in a graph database. To speed up this search, we have developed an efficient method to compute graph similarity that exploits structural and instruction-level information in the underlying malware programs, and a multi-resolution indexing scheme that uses a computationally economical feature vector for early pruning and resorts to a more accurate but computationally more expensive graph similarity function only when it needs to pinpoint the most similar neighbors. Results of a comprehensive performance study of the SMIT prototype using a database of more than 100,000 malware demonstrate the effective pruning power and scalability of its nearest neighbor search mechanisms.


title: Being with virtual others: Neural correlates of social interaction

abstract: Examined the cortical activity associated with personal involvement in social interaction. While fMRI data were recorded, 18 male subjects (mean age 26 years) watched a virtual scene of social interaction. The subjects were either looked at by one of the characters or observed him looking at someone else. Moreover, the virtual character showed either arbitrary facial movements or socially relevant facial expressions such as used in greetings. Cortical activity was stronger in the anterior medial prefrontal cortex when participants were gazed at, whereas there was more activity in the precuneus when they merely observed an interaction. Socially relevant facial expressions increased activity in the ventral medial prefrontal cortex, and arbitrary facial movements led to more activation of the middle temporal gyrus. It is concluded that different areas of the medial prefrontal cortex play differential roles in social cognition, with ventral areas supporting the analysis of interaction-relevant facial expressions and dorsal areas responsible for the detection of self-relevance in social interaction.


title: A 63 dB 16 mW 20 MHz BW double-sampled ΔΣs analog-to-digital converter with an embedded-adder quantizer

abstract: A wideband ΔΣ ADC using a novel double-sampling scheme with a single set of capacitors and a dynamic embedded-adder quantizer is presented. The proposed quantizer eliminates static currents in the adder of a low-distortion architecture. Fabricated in 0.18 µm CMOS process, the prototype chip operates with a 320 MHz sampling frequency and achieves 63 dB SNDR in a 20 MHz signal band while consuming 16 mW power.


title: Documenting software architectures: views and beyond

abstract: This lecture maps the concepts and templates explored in this tutorial with well-known architectural prescriptions, including the 4+1 approach of the Rational Unified Process, the Siemens Four Views approach, and the ANSI/IEEE-1471-2000 recommended best practice for documenting architectures for software-intensive systems. The lecture concludes by re-capping the highlights of the tutorial, and asking for feedback.


title: Applications of fuzzy rough set theory in machine learning: a survey

abstract: Data used in machine learning applications is prone to contain both vague and incom- plete information. Many authors have proposed to use fuzzy rough set theory in the development of new techniques tackling these characteristics. Fuzzy sets deal with vague data, while rough sets allow to model incomplete information. As such, the hybrid setting of the two paradigms is an ideal candidate tool to confront the separate challenges. In this paper, we present a thorough review on the use of fuzzy rough sets in machine learning applications. We recall their integration in preprocess- ing methods and consider learning algorithms in the supervised, unsupervised and semi-supervised domains and outline future challenges. Throughout the paper, we highlight the interaction between theoretical advances on fuzzy rough sets and practical machine learning tools that take advantage of them.


title: Attacking the baseband modem of mobile phones to breach the users' privacy and network security

abstract: As people are using their smartphones more frequently, cyber criminals are focusing their efforts on infecting smartphones rather than computers. This paper presents the design and implementation of a new type of mobile malware, named (U)SimMonitor for Android and iPhone devices, which attacks the baseband modem of mobile phones. In particular, the mobile malware is capable of stealing security credentials and sensitive information of the cellular technology including permanent and temporary identities, encryption keys and location of users. The developed malware operates in the background in a stealthy manner without disrupting the normal operation of the phone. We elaborate on the software architecture of (U)SimMonitor and provide implementation details for the specific AT commands used by the malware. We analyse the security impacts of (U)SimMonitor malware and we show that it can entirely breach the privacy of mobile users and the security of cellular networks. In particular, a mobile user with an infected phone can be identified and all his/her movements can be tracked. Moreover, all his/her encrypted phone calls and data sessions can be disclosed.


title: Automation of Automation — Definition, components and challenges

abstract: The increasing number and range of automation functions along with the decrease of qualified personal makes an upgraded engineering process necessary. This article gives a general overview of one approach, called the Automation of Automation, i.e. the automated execution of human tasks related to the engineering process of automation systems. Starting with a definition and a model describing the typical engineering process, some solutions for the needed framework are presented. Occurring problems within parts of this process model are discussed and possible solutions are presented.


title: Armadillo-inspired wheel-leg retractable module

abstract: In this paper, a new category of wheel-leg hybrid robot inspired by retracting configuration of the armadillo is presented. The proposed mechanism can compose large wheel diameter compared with the previous hybrid robot to realize high ability to climb obstacles. A prototype model of one Wheel-Leg module of the proposed robot mechanism has been developed to illustrate the concept. Actual design and mode changing experiment with a test mechanical module is also presented. Basic movement tests and the test to see the basic property of the rotational fingertip are also shown.


title: Graphcut textures: image and video synthesis using graph cuts

abstract: In this paper we introduce a new algorithm for image and video texture synthesis. In our approach, patch regions from a sample image or video are transformed and copied to the output and then stitched together along optimal seams to generate a new (and typically larger) output. In contrast to other techniques, the size of the patch is not chosen a-priori, but instead a graph cut technique is used to determine the optimal patch region for any given offset between the input and output texture. Unlike dynamic programming, our graph cut technique for seam optimization is applicable in any dimension. We specifically explore it in 2D and 3D to perform video texture synthesis in addition to regular image synthesis. We present approximative offset search techniques that work well in conjunction with the presented patch size optimization. We show results for synthesizing regular, random, and natural images and videos. We also demonstrate how this method can be used to interactively merge different images to generate new scenes.


title: LASeR: Lightweight Authentication and Secured Routing for NDN IoT in Smart Cities

abstract: Recent literature suggests that the Internet of Things (IoT) scales much better in an Information-Centric Networking (ICN) model instead of the current host-centric Internet Protocol (IP) model. In particular, the Named Data Networking (NDN) project (one of the ICN architecture flavors) offers features exploitable by IoT applications, such as stateful forwarding, in- network caching, and built-in assurance of data provenance. Though NDN-based IoT frameworks have been proposed, none have adequately and holistically addressed concerns related to secure onboarding and routing. Additionally, emerging IoT applications such as smart cities require high scalability and thus pose new challenges to NDN routing. Therefore, in this work, we propose and evaluate a novel, scalable framework for lightweight authentication and hierarchical routing in the NDN IoT (ND- NoT). Our ns-3 based simulation analyses demonstrate that our framework is scalable and efficient. It supports deployment densities as high as 40,000 nodes/km2 with an average onboarding convergence time of around 250 seconds and overhead of less than 20 KiB per node. This demonstrates its efficacy for emerging large-scale IoT applications such as smart cities.


title: Creating Capsule Wardrobes from Fashion Images

abstract: We propose to automatically create capsule wardrobes. Given an inventory of candidate garments and accessories, the algorithm must assemble a minimal set of items that provides maximal mix-and-match outfits. We pose the task as a subset selection problem. To permit efficient subset selection over the space of all outfit combinations, we develop submodular objective functions capturing the key ingredients of visual compatibility, versatility, and user-specific preference. Since adding garments to a capsule only expands its possible outfits, we devise an iterative approach to allow near-optimal submodular function maximization. Finally, we present an unsupervised approach to learn visual compatibility from "in the wild" full body outfit photos; the compatibility metric translates well to cleaner catalog photos and improves over existing methods. Our results on thousands of pieces from popular fashion websites show that automatic capsule creation has potential to mimic skilled fashionistas in assembling flexible wardrobes, while being significantly more scalable.


title: Actor-Critic Fictitious Play in Simultaneous Move Multistage Games

abstract: Fictitious play is a game theoretic iterative procedure meant to learn an equilibrium in normal form games. However, this algorithm requires that each player has full knowledge of other players' strategies. Using an architecture inspired by actor-critic algorithms, we build a stochastic approximation of the fictitious play process. This procedure is on-line, decentralized (an agent has no information of others' strategies and rewards) and applies to multistage games (a generalization of normal form games). In addition, we prove convergence of our method towards a Nash equilibrium in both the cases of zero-sum two-player multistage games and cooperative multistage games. We also provide empirical evidence of the soundness of our approach on the game of Alesia with and without function approximation.


Context Driven Technique for Document Classification


title: Image Interpolation and Resampling

abstract: This chapter presents a survey of interpolation and resampling techniques in the context of exact, separable interpolation of regularly sampled data. In this context, the traditional view of interpolation is to represent an arbitrary continuous function as a discrete sum of weighted and shifted synthesis functions—in other words, a mixed convolution equation. An important issue is the choice of adequate synthesis functions that satisfy interpolation properties. Examples of finite-support ones are the square pulse (nearest-neighbor interpolation), the hat function (linear interpolation), the cubic Keys' function, and various truncated or windowed versions of the sinc function. On the other hand, splines provide examples of infinite-support interpolation functions that can be realized exactly at a finite, surprisingly small computational cost. We discuss implementation issues and illustrate the performance of each synthesis function. We also highlight several artifacts that may arise when performing interpolation, such as ringing, aliasing, blocking and blurring. We explain why the approximation order inherent in the synthesis function is important to limit these interpolation artifacts, which motivates the use of splines as a tunable way to keep them in check without any significant cost penalty.


title: Machine Learning of Jazz Grammars

abstract: In the context of an educational software tool that can generate novel jazz solos using a probabilistic grammar (Keller 2007), this article describes the automated learning of such grammars. Learning takes place from a corpus of transcriptions, typically from a single performer, and our methods attempt to improvise solos representative of such a style. In order to capture idiomatic gestures of a specific soloist, we extend an earlier grammar representation (Keller and Morrison 2007) with a technique for representing melodic contour. Representative contours are extracted from a corpus using clustering, and sequencing among contours is done using Markov chains that are encoded into the grammar. This article first defines the basic building blocks for contours of typical jazz solos, which we call slopes, then shows how these slopes may be incorporated into a grammar wherein the notes are chosen according to tonal categories relevant to jazz playing. We show that melodic contours can be accurately portrayed using slopes learned from a corpus. Experimental results, including blind comparisons of solos generated from grammars based on several corpora, are reported.


title: Elimination of transformer inrush currents by controlled switching. I. Theoretical considerations

abstract: Transformer inrush currents are high-magnitude, harmonic-rich currents generated when transformer cores are driven into saturation during energization. These currents have undesirable effects, including potential damage or loss-of-life to the transformer, protective relay misoperation, and reduced power quality on the system. Controlled transformer switching can potentially eliminate these transients if residual core and core flux transients are taken into account in the closing algorithm. This paper explores the theoretical considerations of core flux transients, Based on these studies algorithms were developed which allow controlled energization of most transformers without inrush current.


title: Database Design for NoSQL Systems

abstract: We propose a database design methodology for NoSQL systems. The approach is based on NoAM (NoSQL Abstract Model), a novel abstract data model for NoSQL databases, which exploits the commonalities of various NoSQL systems and is used to specify a system-independent representation of the application data. This intermediate representation can be then implemented in target NoSQL databases, taking into account their specific features. Overall, the methodology aims at supporting scalability, performance, and consistency, as needed by next-generation web applications.


title: Business Analytics for Supply Chain: a Dynamic-Capabilities Framework

abstract: Supply chain management has become more important as an academic topic due to trends in globalization leading to massive reallocation of production related advantages. Because of the massive amount of data that is generated in the global economy, new tools need to be developed in order to manage and analyze the data, as well as to monitor organizational performance worldwide. This paper proposes a framework of business analytics for supply chain analytics (SCA) as IT-enabled, analytical dynamic capabilities composed of data management capability, analytical supply chain process capability, and supply chain performance management capability. This paper also presents a dynamic-capabilities view of SCA and extensively describes a set of its three capabilities: data management capability, analytical supply chain process capability, and supply chain performance management capability. Next, using the SCM best practice, sales & operations planning (S&OP), the paper demonstrates opportunities to apply SCA in an integrated way. In discussing the implications of the proposed framework, finally, the paper examines several propositions predicting the positive impact of SCA and its individual capability on SCM performance.


title: Finite Element Modeling of Tall Buildings: The Importance of Considering Foundation Systems for Lateral Stiffness

abstract: Structural analysis and design is often conducted under the assumption of rigid base boundary conditions, particularly if the foundation system extends to bedrock, though the extent to which the actual flexibility of the soil-foundation system affects the predicted periods of vibration depends on the application. While soil-structure interaction has mostly received attention in seismic applications, lateral flexibility below the ground surface may in some cases influence the dynamic properties of tall, flexible structures, generally greater than 50 stories and dominated by wind loads. This study will explore this issue and develop a hybrid framework within which these effects can be captured and eventually be applied to existing finite element models of two tall buildings in the Chicago Full-Scale Monitoring Program. It is hypothesized that the extent to which the rigid base condition assumption applies in these buildings depends on the relative role of cantilever and frame actions in their structural systems. In this hybrid approach, the lateral and axial flexibility of the foundation systems are first determined in isolation and then introduced to the existing finite element models of the buildings as springs, replacing the rigid boundary conditions assumed by designers in the original finite element model development. The evaluation of the periods predicted by this hybrid framework, validated against companion studies and full-scale data, are used to quantify the sensitivity of foundation modeling to the super-structural system primary deformation mechanisms and soil type. Not only will this study demonstrate the viability of this hybrid approach, but also illustrate situations under which foundation flexibility in various degrees of freedom should be considered in the modeling process.


title: A lazy evaluator

abstract: A different way to execute pure LISP programs is presented. It delays the evaluation of parameters and list structures without ever having to perform more evaluation steps than the usual method. Although the central idea can be found in earlier work this paper is of interest since it treats a rather well-known language and works out an algorithm which avoids full substitution. A partial correctness proof using Scott-Strachey semantics is sketched in a later section.


title: An exploration of relations between visual appeal, trustworthiness and perceived usability of homepages

abstract: Extremely high correlations between repeated judgments of visual appeal of homepages shown for 50 milliseconds have been interpreted as evidence for a mere exposure effect [Lindgaard et al. 2006]. Continuing that work, the present research had two objectives. First, it investigated the relationship between judgments differing in cognitive demands. Second, it began to identify specific visual attributes that appear to contribute to different judgments. Three experiments are reported. All used the stimuli and viewing time as before. Using a paradigm known to disrupt processing beyond the stimulus offset, Experiment 1 was designed to ensure that the previous findings could not be attributed to such continued processing. Adopting a within-subject design, Experiment 2 investigated the extent to which judgments differing in cognitive demands (visual appeal, perceived usability, trustworthiness) may be driven by the visual characteristics of a Web page. It also enabled analyses of visual attributes that contributed most to the different judgments. Experiment 3 replicated Experiment 2 but using a between-subject design to ensure that no practice effect could occur. The results suggest that all three types of judgments are largely driven by visual appeal, but that cognitively demanding judgments are processed in a qualitatively different manner than visual appeal, and that they rely on somewhat different visual attributes. A model accounting for the results is provided.


title: BCI meeting 2005-workshop on BCI signal processing: feature extraction and translation

abstract: This paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop charged with reviewing and evaluating the current state of and issues relevant to brain-computer interface (BCI) feature extraction and translation. The issues discussed include a taxonomy of methods and applications, time-frequency spatial analysis, optimization schemes, the role of insight in analysis, adaptation, and methods for quantifying BCI feedback.


title: Sequence classification using the high-level features extracted from deep neural networks

abstract: The recent success of deep neural networks (DNNs) in speech recognition can be attributed largely to their ability to extract a specific form of high-level features from raw acoustic data for subsequent sequence classification or recognition tasks. Among the many possible forms of DNN features, what forms are more useful than others and how effective these DNN features are in connection with the different types of downstream sequence recognizers remained unexplored and are the focus of this paper. We report our recent work on the construction of a diverse set of DNN features, including the vectors extracted from the output layer and from various hidden layers in the DNN. We then apply these features as the inputs to four types of classifiers to carry out the identical sequence classification task of phone recognition. The experimental results show that the features derived from the top hidden layer of the DNN perform the best for all four classifiers, especially for the autoregressive-moving-average (ARMA) version of a recurrent neural network. The feature vector derived from the DNN’s output layer performs slightly worse but better than any of the hidden layers in the DNN except the top one.


title: Self-heating in advanced CMOS technologies

abstract: On advanced technology nodes, increases in power density, non-planar architectures and different material systems can exacerbate local self-heating due to active power dissipation, which can affect device performance and reliability in various ways. This paper presents an overview of the research on self-heating in transistors and discusses modulators, measurement schemes, spatio-temporal sensitivities, and impacts on performance and reliability. As the industry continues to scale dimensions and power densities, the significance of self-heating effects will continue to grow, and a robust frame-work to fully assess it, and deal with its impacts to circuits and IP blocks are essential.


title: Design & analysis of K-means algorithm for cognitive fatigue detection in vehicular driver using Respiration signal

abstract: Vehicular drivers and shift workers in industry are at most risk of handling life critical tasks. The drivers traveling long distances or when they are tired, are at risk of a meeting an accident. The early hours of the morning and the middle of the afternoon are the peak times for fatigue driven accidents. The difficulty in determining the incidence of fatigue-related accidents is due, at least in part, to the difficulty in identifying fatigue as a causal or causative factor in accidents. In this paper we propose an alternative approach for fatigue detection in vehicular drivers using Respiration (RSP) signal to reduce the losses of the lives and vehicular accidents those occur due to cognitive fatigue of the driver. We are using basic K-means algorithm with proposed two modifications as classifier for detection of Respiration signal two state fatigue data recorded from the driver. The K-means classifiers [11] were trained and tested for wavelet feature of Respiration signal. The extracted features were treated as individual decision making parameters. From test results it could be found that some of the wavelet features could fetch 100 % classification accuracy.


title: Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore

abstract: In this study, with Singapore as an example, we demonstrate how we can use mobile phone call detail record (CDR) data, which contains millions of anonymous users, to extract individual mobility networks comparable to the activity-based approach. Such an approach is widely used in the transportation planning practice to develop urban micro simulations of individual daily activities and travel; yet it depends highly on detailed travel survey data to capture individual activity-based behavior. We provide an innovative data mining framework that synthesizes the state-of-the-art techniques in extracting mobility patterns from raw mobile phone CDR data, and design a pipeline that can translate the massive and passive mobile phone records to meaningful spatial human mobility patterns readily interpretable for urban and transportation planning purposes. With growing ubiquitous mobile sensing, and shrinking labor and fiscal resources in the public sector globally, the method presented in this research can be used as a low-cost alternative for transportation and planning agencies to understand the human activity patterns in cities, and provide targeted plans for future sustainable development.


title: Face Recognition Despite Wearing Glasses

abstract: In this paper we address the challenge of performing face recognition on human faces that are wearing glasses. This is a common problem for face recognition and automatic identity checking at airports, as passengers frequently forget to remove their glasses when passing through customs. In order to solve this problem, we first propose an automatic glasses presence detection model based on the tree-pictorial-structured face detection model and such model can detect the presence of glasses and further assign landmarks on the rim, hinge, and bridge of the glasses on frontal faces. Experimental results show that the glasses detection rate is highly satisfactory for various face databases. Secondly, based on the landmarks on glasses, we apply the non-local colour total variation (CTV) inpainting approach in an attempt to remove the glasses; also, we apply the deep learning technique to further remove the traces of glasses and light reflection on lenses by regarding them as noises. Finally, experiments for face recognition after glasses removal are conducted by using some typical approaches and the results show that our glasses removal framework can improve face recognition accuracy significantly.


title: Adaptive Scheme for Caching YouTube Content in a Cellular Network: Machine Learning Approach

abstract: Content caching at base stations is a promising solution to address the large demands for mobile data services over cellular networks. Content caching is a challenging problem as it requires predicting the future popularity of the content and the operating characteristics of the cellular networks. In this paper, we focus on constructing an algorithm that improves the users’ quality of experience (QoE) and reduces network traffic. The algorithm accounts for users’ behavior and properties of the cellular network (e.g. cache size, bandwidth, and load). The constructed content and network aware adaptive caching scheme uses an extreme-learning machine neural network to estimate the popularity of content, and mixed-integer linear programming to compute where to place the content and select the physical cache sizes in the network. The proposed caching scheme simultaneously performs efficient cache deployment and content caching. Additionally, a simultaneous perturbation stochastic approximation method is developed to reduce the number of neurons in the extreme-learning machine method while ensuring a sufficient predictive performance is maintained. Using real-world data from YouTube and a NS-3 simulator, we demonstrate how the caching scheme improves the QoE of users and network performance compared with industry standard caching schemes.


title: Adaptive application scheduling under interference in Kubernetes

abstract: Containers are rapidly replacing Virtual Machines (VMs) as the compute instance in cloud-based deployments. The significantly lower overhead of deploying containers (compared to VMs) has often been cited as one reason for this. However, interference caused by the limited isolation in shared resources can impact into the performance of hosted applications. We develop a Reference Net-based model of resource management within Kubernetes, primarily to better characterise such performance issues. Our model makes use of data obtained from a Kubernetes deployment, and can be used as a basis to design scalable (and potentially interference tolerant) applications that make use of Kubernetes.


Twitter Use by the U.S. Congress


title: Differentiation therapy revisited

abstract: The concept of differentiation therapy emerged from the fact that hormones or cytokines may promote differentiation ex vivo, thereby irreversibly changing the phenotype of cancer cells. Its hallmark success has been the treatment of acute promyelocytic leukaemia (APL), a condition that is now highly curable by the combination of retinoic acid (RA) and arsenic. Recently, drugs that trigger differentiation in a variety of primary tumour cells have been identified, suggesting that they are clinically useful. This Opinion article analyses the basis for the clinical successes of RA or arsenic in APL by assessing the respective roles of terminal maturation and loss of self-renewal. By reviewing other successful examples of drug-induced tumour cell differentiation, novel approaches to transform differentiating drugs into more efficient therapies are proposed.


title: Local Optima Smoothing for Global Optimization

abstract: It is widely believed that in order to solve large-scale global optimization problems, an appropriate mixture of local approximation and global exploration is necessary. Local approximation, if first-order information on the objective function is available, is efficiently performed by means of local optimization methods. Unfortunately, global exploration, in absence of some kind of global information on the problem, is a ‘blind’ procedure, aimed at placing observations as evenly as possible in the search domain. Often, this procedure reduces to uniform random sampling (like in Multistart algorithms or in techniques based on clustering). In this paper, we propose a new framework for global exploration which tries to guide random exploration towards the region of attraction of low-level local optima. The main idea originated by the use of smoothing techniques (based on Gaussian convolutions): the possibility of applying a smoothing transformation not to the objective function but to the result of local sear...


title: Narrative Generation through Characters ’ Point of View

abstract: Virtual Actors are at the heart of Interactive Storytelling systems and in recent years multiple approaches have been described to specify their autonomous behaviour. One well known problem is how to achieve a balance between the characters' autonomy, defined in terms of their individual roles and motivations, and the global structure of the plot, which tends to emphasise narrative phenomena and the coordination of multiple characters. In this paper we report a new approach to the definition of virtual characters aimed at achieving a balance between character autonomy and global plot structure. Where previous approaches have tended to focus on individual actions our objective is to reincorporate higher-level narrative elements in the behaviour of individual actors and address the relation between character and plot at the level of behaviour representation. To this end we introduce the notion of a characters' Point of View and show how it enables a story to be described from the perspective of a number of different characters: it is not merely a presentation effect it is also a different way to tell a story. ::: ::: As an illustration, we have developed an Interactive Narrative based on Shakespeare's Merchant of Venice. The system, which features a novel planning approach to story generation, can generate very different stories depending on the Point of View adopted and support dynamic modification of the story world which results in different story consequences. In the paper, we illustrate this approach using example narratives generated using our fully implemented prototype.


title: Link prediction by de-anonymization: How We Won the Kaggle Social Network Challenge

abstract: This paper describes the winning entry to the IJCNN 2011 Social Network Challenge run by Kaggle.com. The goal of the contest was to promote research on real-world link prediction, and the dataset was a graph obtained by crawling the popular Flickr social photo sharing website, with user identities scrubbed. By de-anonymizing much of the competition test set using our own Flickr crawl, we were able to effectively game the competition. Our attack represents a new application of de-anonymization to gaming machine learning contests, suggesting changes in how future competitions should be run. We introduce a new simulated annealing-based weighted graph matching algorithm for the seeding step of de-anonymization. We also show how to combine de-anonymization with link prediction—the latter is required to achieve good performance on the portion of the test set not de-anonymized—for example by training the predictor on the de-anonymized portion of the test set, and combining probabilistic predictions from de-anonymization and link prediction.


title: RACE: Remote Analysis Computation for gene Expression data

abstract: The Remote Analysis Computation for gene Expression data (RACE) suite is a collection of bioinformatics web tools designed for the analysis of DNA microarray data. RACE performs probe-level data preprocessing, extensive quality checks, data visualization and data normalization for Affymetrix GeneChips. In addition, it offers differential expression analysis on normalized expression levels from any array platform. RACE estimates the false discovery rates of lists of potentially regulated genes and provides a Gene Ontology-term analysis tool for GeneChip data to support the biological interpretation and annotation of results. The analysis is fully automated but can be customized by flexible parameter settings. To offer a convenient starting point for subsequent analyses, and to provide maximum transparency, the R scripts used to generate the results can be downloaded along with the output files. RACE is freely available for use at http://race.unil.ch.


title: Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research

abstract: Molecular modelers and cheminformaticians typically analyze experimental data generated by other scientists. Consequently, when it comes to data accuracy, cheminformaticians are always at the mercy of data providers who may inadvertently publish (partially) erroneous data. Thus, dataset curation is crucial for any cheminformatics analysis such as similarity searching, clustering, QSAR modeling, virtual screening, etc., especially nowadays when the availability of chemical datasets in public domain has skyrocketed in recent years. Despite the obvious importance of this preliminary step in the computational analysis of any dataset, there appears to be no commonly accepted guidance or set of procedures for chemical data curation. The main objective of this paper is to emphasize the need for a standardized chemical data curation strategy that should be followed at the onset of any molecular modeling investigation. Herein, we discuss several simple but important steps for cleaning chemical records in a database including the removal of a fraction of the data that cannot be appropriately handled by conventional cheminformatics techniques. Such steps include the removal of inorganic and organometallic compounds, counterions, salts and mixtures; structure validation; ring aromatization; normalization of specific chemotypes; curation of tautomeric forms; and the deletion of duplicates. To emphasize the importance of data curation as a mandatory step in data analysis, we discuss several case studies where chemical curation of the original “raw” database enabled the successful modeling study (specifically, QSAR analysis) or resulted in a significant improvement of model's prediction accuracy. We also demonstrate that in some cases rigorously developed QSAR models could be even used to correct erroneous biological data associated with chemical compounds. We believe that good practices for curation of chemical records outlined in this paper will be of value to all scientists working in the fields of molecular modeling, cheminformatics, and QSAR studies.


title: An ISU Dialogue System Exhibiting Reinforcement Learning Of Dialogue Policies: Generic Slot-Filling In The Talk In-Car System

abstract: We demonstrate a multimodal dialogue system using reinforcement learning for in-car scenarios, developed at Edinburgh University and Cambridge University for the TALK project. This prototype is the first "Information State Update" (ISU) dialogue system to exhibit reinforcement learning of dialogue strategies, and also has a fragmentary clarification feature. This paper describes the main components and functionality of the system, as well as the purposes and future use of the system, and surveys the research issues involved in its construction. Evaluation of this system (i.e. comparing the baseline system with handcoded vs. learnt dialogue policies) is ongoing, and the demonstration will show both.


title: A (Very) Brief History of Artificial Intelligence

abstract: In this brief history, the beginnings of artificial intelligence are traced to philosophy, fiction, and imagination. Early inventions in electronics, engineering, and many other disciplines have influenced AI. Some early milestones include work in problems solving which included basic work in learning, knowledge representation, and inference as well as demonstration programs in language understanding, translation, theorem proving, associative memory, and knowledge-based systems. The article ends with a brief examination of influential organizations and current issues facing the field.


Ethics, Performance Measure Choice, and Accounting Manipulation


Adaptive resonance theory


title: A formal analysis of IEEE 802.11w deadlock vulnerabilities

abstract: Formal methods can be used to discover obscure denial of service (DoS) vulnerabilities in wireless network protocols. The application of formal methods to the analysis of DoS vulnerabilities in communication protocols is not a mature research area. Although several formal models have been proposed, they lack a clear and convincing demonstration of their usefulness and practicality. This paper bridges the gap between theory and practice, and shows how a simple protocol model can be used to discover protocol deadlock vulnerabilities. A deadlock vulnerability is the most severe form of DoS vulnerabilities, thus checking for deadlock vulnerabilities is an essential part of robust protocol design. We demonstrate the usefulness of the proposed method through the discovery and experimental validation of deadlock vulnerabilities in the published IEEE 802.11w amendment to the 802.11 standard. We present the complete procedure of our approach, from model construction to verification and validation. An Appendix includes the complete model source code, which facilitates the replication and extension of our results. The source code can also be used as a template for modeling other protocols.


title: SHAPED: Shared-Private Encoder-Decoder for Text Style Adaptation

abstract: Supervised training of abstractive language generation models results in learning conditional probabilities over language sequences based on the supervised training signal. When the training signal contains a variety of writing styles, such models may end up learning an 'average' style that is directly influenced by the training data make-up and cannot be controlled by the needs of an application. We describe a family of model architectures capable of capturing both generic language characteristics via shared model parameters, as well as particular style characteristics via private model parameters. Such models are able to generate language according to a specific learned style, while still taking advantage of their power to model generic language phenomena. Furthermore, we describe an extension that uses a mixture of output distributions from all learned styles to perform on-the fly style adaptation based on the textual input alone. Experimentally, we find that the proposed models consistently outperform models that encapsulate single-style or average-style language generation capabilities.


title: Can they hear me now?: a security analysis of law enforcement wiretaps

abstract: Although modern communications services are susceptible to third-party eavesdropping via a wide range of possible techniques, law enforcement agencies in the US and other countries generally use one of two technologies when they conduct legally-authorized interception of telephones and other communications traffic. The most common of these, designed to comply with the 1994 Communications Assistance for Law Enforcement Act(CALEA), use a standard interface provided in network switches. This paper analyzes the security properties of these interfaces. We demonstrate that the standard CALEA interfaces are vulnerable to a range of unilateral attacks by the intercept target. In particular, because of poor design choices in the interception architecture and protocols, our experiments show it is practical for a CALEA-tapped target to overwhelm the link to law enforcement with spurious signaling messages without degrading her own traffic, effectively preventing call records as well as content from being monitored or recorded. We also identify stop-gap mitigation strategies that partially mitigate some of our identified attacks.