title: Dynamic Graph Modules for Modeling Higher-Order Interactions in Activity Recognition

abstract: Video action recognition, as a critical problem towards video understanding, has attracted increasing attention recently. To identify an action involving higher-order object interactions, we need to consider: 1) spatial relations among objects in a single frame; 2) temporal relations between different/same objects across multiple frames. However, previous approaches, e.g., 2D ConvNet + LSTM or 3D ConvNet, are either incapable of capturing relations between objects, or unable to handle streaming videos. In this paper, we propose a novel dynamic graph module to model object interactions in videos. We also devise two instantiations of our graph module: (i) visual graph, to capture visual similarity changes between objects; (ii) location graph, to capture relative location changes between objects. Distinct from previous models, the proposed graph module has the ability to process streaming videos in an aggressive manner. Combined with existing 3D action recognition ConvNets, our graph module can also boost ConvNets' performance, which demonstrates the flexibility of the module. We test our graph module on Something-Something dataset and achieve the state-of-the-art performance.


title: Hashing fingerprints for identity de-duplication

abstract: Fraudulent identities of multiple enrollments usually link to fraud and serious breaches of law. With the vast biometric data collection, identity de-duplication has become the processing bottleneck of biometric enrollments. Recently, locality sensitive hashing (LSH) based methods have been introduced for fast retrieval of biometric identities. Most of them are working in the binary space. This paper proposes a new fingerprint indexing method based on a variant of LSH called spherical LSH (S-LSH). The proposed S-LSH based algorithm is able to hash fingerprint templates directly in the original feature space and thus avoid the intermediate step of binary transformation. In this way, S-LSH can better preserve the interpoint similarity of minutiae points. We demonstrate the effectiveness and efficiency of the new S-LSH based approach by performing fingerprint indexing experiments on the FVC2002 DB1 database and comparing it with a state-of-the-art hashing based fingerprint indexing method.


title: Developing Competencies by Playing Digital sports-games

abstract: The idea of digital game-based learning (DGBL) is that students (or players) learn something by playing a computer or video game and that an educator can employ digital games to assist and boost both formal and informal learning. There is game software that is not specifically produced for educational use but which is nonetheless regularly implemented in educational settings by educators. These so-called COTS (commercial off-the-shelf) games are particularly effective in socialization processes. COTS sports computer and video games can be divided into three main categories: sports simulation games, sports arcade games and sports management games. After taking a closer look at these sports computer and video games, specifically sports simulation games, it is possible to posit dimensions of competencies that are developed by playing those games. Various examples for each dimension of competencies can be generated: motor competence, cognitive competence, meta-cognitive competence, social competence, emotional competence, personal competence and media competence. Furthermore, examples of implementing digital sports-games in physical education can easily be generated. After comparing the postulated dimensions of competencies of COTS digital sports-simulations with those of ”real” sports, the following question arises: Are their respective educational results comparable?


title: Classification of thermophilic streptomycetes, including the description of Streptomyces thermoalcalitolerans sp. nov.

abstract: A polyphasic taxonomic study was undertaken to clarify relationships within and between representative thermophilic alkalitolerant streptomycetes isolated from soil and appropriate marker strains. The resultant data, notably those from DNA-DNA relatedness studies, support the taxonomic integrity of the validly described species Streptomyces thermodiastaticus, Streptomyces thermoviolaceus and Streptomyces thermovulgaris. However, the genotypic and phenotypic data clearly show that Streptomyces thermonitrificans Desai and Dhala 1967 and S. thermovulgaris (Henssen 1957) Goodfellow et al. 1987 represent a single species. On the basis of priority, S. thermonitrificans is a later subjective synonym of S. thermovulgaris. Similarly, 10 out of the 11 representative thermophilic alkalitolerant isolates had a combination of properties consistent with their classification as S. thermovulgaris. The remaining thermophilic alkalitolerant isolate, Streptomyces strain TA56, merited species status. The name Streptomyces thermoalcalitolerans sp. nov. is proposed for this strain. A neutrophilic thermophilic isolate, Streptomyces strain NAR85, was identified as S. thermodiastaticus.


title: Laterality Influences Agility Performance in Elite Soccer Players

abstract: Introduction: Laterality (i.e., handedness, footedness, and eyedness) could have an impact on highly repeated soccer movements and thus, could influence performance. The purpose of this study was to examine the laterality of high-level football players and its effects on 180° left and right U-turn movements. Materials and Methods: Handedness, footedness, and eyedness were determined in 72 elite football players (EFP, 18.2 ± 2.2 years) from the Stade Rennais Football Club (French League 1) and 9 amateur football players (AFP, 19.6 ± 2.1 years). Players performed a visual-motor task on a synthetic pitch consisting of 180° left and right rotations as fast as possible in response to a visual light on a computer screen. Movement times and reactive times for each left and right rotation were recorded with an accelerometer and video display. Results: Laterality profiles showed a majority (χ2 = 9.42, df = 2, p = 0.031) of crossed formulas (i.e., dominant leg or hand is controlateral to the dominant eye) for EFP (53 ± 7%) and a majority of non-crossed formulas for AFP (63 ± 9%). Reaction times were significantly faster (p = 0.028, effect size = 0.148, trivial) in EFP right-eyed (568.2 ± 55.5 ms) than in AFP (610.0 ± 43.9 ms). For the left rotation and for right-footed players, movement times were significantly different (p = 0.043, effect size = 0.413, small) between EFP (1.15 ± 0.07 s) and AFP (1.17 ± 0.07 s). A significant difference (p < 0.033) was observed between footedness and rotation movement times in the EFP. Conclusion: Our results showed that laterality profiles differed between EFP and AFP. Hence, in EFP, reaction times depended on the side of the visual stimulus. Moreover, leg laterality of EFP influenced 180° left or right rotation speed. Our results indicate the importance of determining laterality in soccer players and identifying deficits in performance when turning.


title: Performance modeling from software components

abstract: When software products are assembled from pre-defined components, performance prediction should be based on the components also. This supports rapid model-building, using previously calibrated sub-models or "performance components", in sync with the construction of the product. The specification of a performance component must be tied closely to the software component specification, but it also includes performance related parameters (describing workload characteristics and demands), and it abstracts the behaviour of the component in various ways (for reasons related to practical factors in performance analysis). A useful set of abstractions and parameters are already defined for layered performance modeling. This work extends them to accommodate software components, using a new XML-based language called Component-Based Modeling Language (CBML). With CBML, compatible components can be inserted into slots provided in a hierarchical component specification based on the UML component model.


title: Challenges for MapReduce in Big Data

abstract: In the Big Data community, MapReduce has been seen as one of the key enabling approaches for meeting continuously increasing demands on computing resources imposed by massive data sets. The reason for this is the high scalability of the MapReduce paradigm which allows for massively parallel and distributed execution over a large number of computing nodes. This paper identifies MapReduce issues and challenges in handling Big Data with the objective of providing an overview of the field, facilitating better planning and management of Big Data projects, and identifying opportunities for future research in this field. The identified challenges are grouped into four main categories corresponding to Big Data tasks types: data storage (relational databases and NoSQL stores), Big Data analytics (machine learning and interactive analytics), online processing, and security and privacy. Moreover, current efforts aimed at improving and extending MapReduce to address identified challenges are presented. Consequently, by identifying issues and challenges MapReduce faces when handling Big Data, this study encourages future Big Data research.


title: Be Prepared: The EMV Preplay Attack

abstract: The leading system for smart card-based payments worldwide, EMV (which stands for Europay, MasterCard, and Visa), is widely deployed in Europe and is starting to be introduced in the US as well. Despite this wide deployment, a series of significant vulnerabilities make EMV vulnerable to the preplay attack. Specifically, weak and defective random number generators and various protocol failures leave the system open to fraud at scale.


title: Overview of the improvement of the ring-stage survival assay-a novel phenotypic assay for the detection of artemisinin-resistant Plasmodium falciparum

abstract: Artemisinin resistance in Plasmodium falciparum threatens the remarkable efficacy of artemisinin-based combination therapies worldwide. Thus, greater insight into the resistance mechanism using monitoring tools is essential. The ring-stage survival assay is used for phenotyping artemisinin-resistance or decreased artemisinin sensitivity. Here, we review the progress of this measurement assay and explore its limitations and potential applications.


title: On the Preliminary Investigation of Selfish Mining Strategy with Multiple Selfish Miners

abstract: Eyal and Sirer's selfish mining strategy has demonstrated that Bitcoin system is not secure even if 50% of total mining power is held by altruistic miners. Since then, researchers have been investigating either to improve the efficiency of selfish mining, or how to defend against it, typically in a single selfish miner setting. Yet there is no research on a selfish mining strategies concurrently used by multiple miners in the system. The effectiveness of such selfish mining strategies and their required mining power under such multiple selfish miners setting remains unknown. ::: In this paper, a preliminary investigation and our findings of selfish mining strategy used by multiple miners are reported. In addition, the conventional model of Bitcoin system is slightly redesigned to tackle its shortcoming: namely, a concurrency of individual mining processes. Although a theoretical analysis of selfish mining strategy under this setting is yet to be established, the current findings based on simulations is promising and of great interest. In particular, our work shows that a lower bound of power threshold required for selfish mining strategy decreases in proportion to a number of selfish miners. Moreover, there exist Nash equilibria where all selfish miners in the system do not change to an honest mining strategy and simultaneously earn their unfair amount of mining reward given that they equally possess sufficiently large mining power. Lastly, our new model yields a power threshold for mounting selfish mining strategy slightly greater than one from the conventional model.


title: Block4Forensic: An Integrated Lightweight Blockchain Framework for Forensics Applications of Connected Vehicles

abstract: Today's vehicles are becoming cyber-physical systems that do not only communicate with other vehicles but also gather various information from hundreds of sensors within them. These developments help create smart and connected (e.g., self-driving) vehicles that will introduce significant information to drivers, manufacturers, insurance companies and maintenance service providers for various applications. One such application that is becoming crucial with the introduction of self-driving cars is the forensic analysis for traffic accidents. The utilization of vehicle-related data can be instrumental in post-accident scenarios to find out the faulty party, particularly for self-driving vehicles. With the opportunity of being able to access various information on the cars, we propose a permissioned blockchain framework among the various elements involved to manage the collected vehicle-related data. Specifically, we first integrate Vehicular Public Key Management (VPKI) to the proposed blockchain to provide membership establishment and privacy. Next, we design a fragmented ledger that will store detailed data related to vehicle such as maintenance information/history, car diagnosis reports, etc. The proposed forensic framework enables trustless, traceable and privacy-aware post-accident analysis with minimal storage and processing overhead.


title: IoT security: Review, blockchain solutions, and open challenges

abstract: Abstract With the advent of smart homes, smart cities, and smart everything, the Internet of Things (IoT) has emerged as an area of incredible impact, potential, and growth, with Cisco Inc. predicting to have 50 billion connected devices by 2020. However, most of these IoT devices are easy to hack and compromise. Typically, these IoT devices are limited in compute, storage, and network capacity, and therefore they are more vulnerable to attacks than other endpoint devices such as smartphones, tablets, or computers. In this paper, we present and survey major security issues for IoT. We review and categorize popular security issues with regard to the IoT layered architecture, in addition to protocols used for networking, communication, and management. We outline security requirements for IoT along with the existing attacks, threats, and state-of-the-art solutions. Furthermore, we tabulate and map IoT security problems against existing solutions found in the literature. More importantly, we discuss, how blockchain, which is the underlying technology for bitcoin, can be a key enabler to solve many IoT security problems. The paper also identifies open research problems and challenges for IoT security.


title: Growth of Lactococcus lactis strains at low water activity: correlation with the ability to accumulate glycine betaine.

abstract: Abstract Lactococcus lactis strains were divided into two groups based on their ability to grow in the presence of an upper limit of either 2% w/v NaCl (sensitive) or 4% w/v NaCl (tolerant). Growth inhibition of NaCl tolerant strains was substantially relieved by glycine betaine which was accumulated in significant amounts when growing at low water activities ( a w ). Very little accumulation of glycine betaine occurred during growth of the NaCl sensitive strains. The NaCl tolerant strains had substantial levels of glycine betaine transport activity in vitro, whereas the NaCl sensitive strains had little or no such activity. A low a w sensitive mutant of L. lactis subsp. cremoris MG1363 (NaCl tolerant) was isolated following IS S1 insertional mutagenesis. This mutant was inhibited at an a w of 0.988 produced by addition of 2% w/v NaCl or the equivalent glucose concentration (0.58 M). The mutant did not accumulate glycine betaine when growing at low a w , and did not transport glycine betaine when assayed in vitro.


title: GroudSim: an event-based simulation framework for computational grids and clouds

abstract: We present GroudSim, a Grid and Cloud simulation toolkit for scientific applications based on a scalable simulation-independent discrete-event core. GroudSim provides a comprehensive set of features for complex simulation scenarios from simple job executions on leased computing resources to calculation of costs, and background load on resources. Simulations can be parameterised and are easily extendable by probability distribution packages for failures which normally occur in complex environments. Experimental results demonstrate the improved scalability of GroudSim compared to a related process-based approach.


USING NEURAL NETWORKS FOR CREDIT SCORING Prof


Keyword Extraction using Semantic Analysis


title: Safety and Effectiveness of Insulin Detemir in Different Age-Groups in the A1chieve Study

abstract: Diabetes therapy should balance glycemic control with risk of adverse events. This sub-analysis of the A1chieve study evaluated clinical safety and effectiveness of insulin detemir in different age-groups (≤40 years, >40–65 years, and >65 years) of insulin-experienced and insulin-naïve people with type 2 diabetes. A1chieve was an international, open-label, non-interventional, 24-week study in 66,726 people with type 2 diabetes starting/switching to therapy with biphasic insulin aspart 30, insulin detemir or insulin aspart (alone/in combination) in routine clinical practice. This sub-analysis evaluated clinical safety and effectiveness in patients starting/switching to insulin detemir (±oral glucose-lowering drugs). In total, 15,241 patients were included in the sub-analysis. In all age-groups, the proportion of participants experiencing any, major or nocturnal hypoglycemia was significantly (all p < 0.05) reduced relative to baseline, except in insulin-naïve patients for any and nocturnal hypoglycemia, where there was a significant increase or no significant change in patients aged >65 years and >40–65 years, respectively, and no significant change in major hypoglycemia in insulin-naïve patients aged ≤40 years. Seven serious adverse drug reactions were reported. Body weight was significantly reduced in patients aged ≤40 years and >40–65 years and significantly increased in insulin-naïve patients aged >65 years at 24 weeks. At 24 weeks, glycated hemoglobin was reduced by 2.3%, 2.0%, and 1.8%, in the ≤40 years, >40–65 years, and >65 years age-groups, respectively (all p < 0.001). Fasting and post-prandial plasma glucose were significantly reduced and health-related quality of life (HRQoL) significantly improved across all patient cohorts (all p < 0.001). After 24-week treatment with insulin detemir, all age-groups of insulin-experienced and insulin-naïve patients had significantly improved glycemic control and HRQoL. The proportion of patients experiencing hypoglycemia was reduced in all age-groups but unchanged in insulin-naïve patients aged >40–65 years and increased in insulin-naïve patients aged >65 years. The safety and effectiveness of insulin detemir may benefit all age-groups.


title: Entropie causality anc greedy minimum entropy coupling

abstract: We study the problem of identifying the causal relationship between two discrete random variables from observational data. We recently proposed a novel framework called entropic causality that works in a very general functional model but makes the assumption that the unobserved exogenous variable has small entropy in the true causal direction. ::: This framework requires the solution of a minimum entropy coupling problem: Given marginal distributions of m discrete random variables, each on n states, find the joint distribution with minimum entropy, that respects the given marginals. This corresponds to minimizing a concave function of nm variables over a convex polytope defined by nm linear constraints, called a transportation polytope. Unfortunately, it was recently shown that this minimum entropy coupling problem is NP-hard, even for 2 variables with n states. Even representing points (joint distributions) over this space can require exponential complexity (in n, m) if done naively. ::: In our recent work we introduced an efficient greedy algorithm to find an approximate solution for this problem. In this paper we analyze this algorithm and establish two results: that our algorithm always finds a local minimum and also is within an additive approximation error from the unknown global optimum.


title: Determinant factors of information communication technology (ICT) adoption by government-owned universities in Nigeria: A qualitative approach

abstract: Purpose – The purpose of this paper is to specifically investigate and prioritize the effects of 13 factors in determining ICT adoption in Nigerian universities.Design/methodology/approach – The constructs of theoretical framework of technology‐organization‐environment (T‐O‐E) underpin the survey. The survey adopted in‐depth unstructured and semi‐structured interviews with 30 senior executives drawn purposefully from at least one university in each of the five state capitals in south‐eastern Nigeria.Findings – Evidence from the study shows that, irrespective of the perceived competitive pressures and perceived benefits of ICT solutions, government‐owned universities are yet to exploit its full potentials in their operations. This behaviour is informed by incessant corrupt practices; irregular energy supply and internet connectivity/accessibility; lack of financial capacity, expert skills, managerial and technical flexibility/support; and poor regulatory policies and government supports.Originality/value –...


title: Low costs and high efficiency asynchronous machine with stator cage winding

abstract: This paper presents a new multi-phase stator cage winding suitable for different synchronous and asynchronous machine types. The new winding construction is characterized with high quality MMF distribution, simple construction, and extremely short end winding length. The stator consists of a stack of iron lamination, but with massive conductors in each slot, being short-circuited at one axial end, and with each slot conductor supplied separately at the second end of the machine. To show the capability and the efficiency of the new machine concept, an asynchronous motor (ASM) for traction application in electric vehicles (EV) is designed and investigated. The simulation results show high power density, high efficiency, and thermally robust for the new ASM design.


title: Introduction to wide-area control of power systems

abstract: A key element in the development of smart power transmission systems over the past decade is the tremendous advancement of the Wide-Area Measurement System (WAMS) technology, also commonly referred to as the Synchrophasor technology. Sophisticated digital recording devices called Phasor Measurement Units or PMUs are currently being installed at different points in the North American grid, especially under the smart grid initiatives of the US Department of Energy, to record and communicate GPS-synchronized, high sampling rate (6-60 samples/sec), dynamic power system data. Significant research efforts have been made on techniques to useWAMS for monitoring and situational awareness of large power networks dispersed across wide geographical areas. In contrast, use of WAMS for automatic feedback control has received less attention from the research community. The objective of this paper is to bridge this gap by formulating wide-area control problems for oscillation damping, voltage control, wide-area protection, and disturbance localization. We present the main research challenges that need to be overcome to realize the benefits of wide area control in power systems. Our discussion begins with a review of the fundamental physical models of different characteristic components of a large transmission-level power grid such as synchronous generators, transmission lines, and loads, followed by a description of how these subsystem-level models can be integrated to form the overall system model. We pose ten distinct control-theoretic problems. The first two problems are on using PMU measurements from selected nodes in the system to identify such system models in different resolutions in real-time, and the remaining on how the identified models can be used for designing output-feedback based damping controllers, for understanding voltage fluctuations at different nodes of the network graph, and for detecting malicious inputs entering the system dynamics via faults or extraneous attacks. We also propose two new control paradigms, namely a scheduling approach for appropriate controller selection based on online estimation of oscillation modes, and distributed phasor-based control using model estimation. We illustrate our ideas via representative examples, many of which are inspired by well-known power transfer paths in the US west coast grid, also referred to as the Western Electricity Coordinating Council (WECC).


title: Exploring part-aware segmentation for fine-grained visual categorization

abstract: It is challenge to segment fine-grained objects due to appearance variations and clutter of backgrounds. Most of existing segmentation methods hardly separate small parts of the instance from its background with sufficient accuracy. However, such small parts usually contain important semantic information, which is crucial in fine-grained categorization. Observing that fine-grained objects almost share the same configuration of parts, we present a novel part-aware segmentation method, which explicitly detects semantic parts and preserve these parts during segmentation. We firstly design a hybrid part localization method, which generates accurate part proposals with moderate computation. Then we iteratively update the segmentation outputs and the part proposals, which obtains better foreground segmentation results. Experiments demonstrate the superiority of the proposed method, as compared to state-of-the-art segmentation approaches for fine-grained categorization.


title: Knowledge management: a review of theoretical frameworks and industrial cases

abstract: It has been observed that the major competition advantage for a corporation lies in the corporation's knowledge and therefore "knowledge management" has become a critical issue. In order to accelerate the research progress in knowledge management, this paper has integrated previous knowledge management frameworks into a comprehensive theoretical framework. Based on this framework, eight cases involving knowledge management were collected and analyzed. The analysis results gave us a clear picture about why, what and how knowledge management was performed in these industries. Hopefully, the integrated theoretical framework and a whole picture of knowledge management in practice will serve as foundations of further research and development.


title: Bridging epistemologies: The generative dance between organizational knowledge and organizational knowing

abstract: Bridgin Epistemologies: The Generative Dance between Organizational Knowledge and Organizational Knowing - Much current work on organizational knowledge, intellectual capital, knowledge-creating organizations, knowledge work, and the like rests on a single, traditional understanding of the nature of knowledge. We call this understanding the epistemology of possession. since it treats knowledge as something people possess. Yet, this epistemology cannot account for the knowing found in individual and group practice. Knowing as action calls for an epistemology of practice, Moreover, the epistemology of possession tends to privilege explicit over tacit knowledge, and knowledge possessed by individuals over that possessed by groups. Current work on organizations is limited by this privileging and by the scant attention given to knowing in its own right. Organizations are better understood if explicit, tacit, individual and group knowledge are treated as four distinct and coequal forms of knowledge (each doing work the others cannot), and if knowledge and knowing are seen as mutually enabling (not competing). We hold that knowledge Is a tool of knowing, that knowing is an aspect of our interaction with the social and physical world, and that the interplay of knowledge and knowing can generate new knowledge and new ways of knowing. We believe this generative dance between knowledge and knowing is a powerful source of organizational innovation. Harnessing this innovation calls for organizational and technological infrastructures that support the interplay of knowledge and knowing. Ultimately, these concepts make possible a more robust framing of such epistemologically-centered concerns as core competencies, the management of intellectual capital, etc. We explore these views through three brief case studies drawn from recent research.


title: Colt: Concept Lineage Tool for Data Flow Metadata Capture and Analysis

abstract: Most organizations are becoming increasingly data-driven, often processing data from many different sources to enable critical business operations. Beyond the well-addressed challenge of storing and processing large volumes of data, financial institutions in particular are increasingly subject to federal regulations requiring high levels of accountability for the accuracy and lineage of this data. For companies like GE Capital, which maintain data across a globally interconnected network of thousands of systems, it is becoming increasingly challenging to capture an accurate understanding of the data flowing between those systems. To address this problem, we designed and developed a concept lineage tool allowing organizational data flows to be modeled, visualized and interactively explored. This tool has novel features that allow a data flow network to be contextualized in terms of business-specific metadata such as the concept, business, and product for which it applies. Key analysis features have been implemented, including the ability to trace the origination of particular datasets, and to discover all systems where data is found that meets some user-defined criteria. This tool has been readily adopted by users at GE Capital and in a short time has already become a business-critical application, with over 2,200 data systems and over 1,000 data flows captured.


title: Redefine statistical significance

abstract: We propose to change the default P-value threshold for statistical significance from 0.05 to 0.005 for claims of new discoveries.


title: Convolutional Sequence Modeling Revisited

abstract: This paper revisits the problem of sequence modeling using convolutional architectures. Although both convolutional and recurrent architectures have a long history in sequence prediction, the current "default" mindset in much of the deep learning community is that generic sequence modeling is best handled using recurrent networks. The goal of this paper is to question this assumption. Specifically, we consider a simple generic temporal convolution network (TCN), which adopts features from modern ConvNet architectures such as a dilations and residual connections. We show that on a variety of sequence modeling tasks, including many frequently used as benchmarks for evaluating recurrent networks, the TCN outperforms baseline RNN methods (LSTMs, GRUs, and vanilla RNNs) and sometimes even highly specialized approaches. We further show that the potential "infinite memory" advantage that RNNs have over TCNs is largely absent in practice: TCNs indeed exhibit longer effective history sizes than their recurrent counterparts. As a whole, we argue that it may be time to (re)consider ConvNets as the default "go to" architecture for sequence modeling.


title: Digital Control Techniques Based on Voltage Source Inverters in Renewable Energy Applications: A Review

abstract: In the modern era, distributed generation is considered as an alternative source for power generation. Especially, need of the time is to provide the three-phase loads with smooth sinusoidal voltages having fixed frequency and amplitude. A common solution is the integration of power electronics converters in the systems for connecting distributed generation systems to the stand-alone loads. Thus, the presence of suitable control techniques, in the power electronic converters, for robust stability, abrupt response, optimal tracking ability and error eradication are inevitable. A comprehensive review based on design, analysis, validation of the most suitable digital control techniques and the options available for the researchers for improving the power quality is presented in this paper with their pros and cons. Comparisons based on the cost, schemes, performance, modulation techniques and coordinates system are also presented. Finally, the paper describes the performance evaluation of the control schemes on a voltage source inverter (VSI) and proposes the different aspects to be considered for selecting a power electronics inverter topology, reference frames, filters, as well as control strategy.


title: The ground truth about metadata and community detection in networks

abstract: Across many scientific domains, there is a common need to automatically extract a simplified view or coarse-graining of how a complex system’s components interact. This general task is called community detection in networks and is analogous to searching for clusters in independent vector data. It is common to evaluate the performance of community detection algorithms by their ability to find so-called ground truth communities. This works well in synthetic networks with planted communities because these networks’ links are formed explicitly based on those known communities. However, there are no planted communities in real-world networks. Instead, it is standard practice to treat some observed discrete-valued node attributes, or metadata, as ground truth. We show that metadata are not the same as ground truth and that treating them as such induces severe theoretical and practical problems. We prove that no algorithm can uniquely solve community detection, and we prove a general No Free Lunch theorem for community detection, which implies that there can be no algorithm that is optimal for all possible community detection tasks. However, community detection remains a powerful tool and node metadata still have value, so a careful exploration of their relationship with network structure can yield insights of genuine worth. We illustrate this point by introducing two statistical techniques that can quantify the relationship between metadata and community structure for a broad class of models. We demonstrate these techniques using both synthetic and real-world networks, and for multiple types of metadata and community structures.


title: Reduced resting-state functional connectivity between amygdala and orbitofrontal cortex in social anxiety disorder

abstract: Abstract Social anxiety disorder patients suffer from excessive anxious responses in social interaction leading to avoidance behavior and social impairment. Although the amygdala has a central role in perception and processing of threatening cues, little is known about the involved networks and corresponding dysfunctions in social anxiety. Therefore, this study aims to investigate the functional connectivity network of the amygdala in patients with social anxiety disorder and to identify regions that might influence amygdalar reactivity via modulatory pathways. Ten patients with anxiety disorders (social and/or panic) and 27 healthy controls underwent a facial emotion processing task as well as 6-min functional MRI at resting state. Individual voxel-wise functional connectivity maps were calculated using the amygdala as seed region. Group comparisons were done by random-effects analysis in SPM. Patients exhibited an amygdala hyperactivation during the emotional task and decreased functional coupling of the left amygdala with the medial orbitofrontal cortex and the posterior cingulate cortex/precuneus. The strength of this functional connectivity showed a negative association with the severity of state anxiety. In addition, an exploratory analysis revealed further reduced functional connectivity and a marked functional separation between the medial orbitofrontal and anterior cingulate cortices in the patient group. Our results suggest alterations within the amygdalar functional connectivity network in social anxiety disorder. Combined with the amygdalar hyperactivation our findings corroborate the proposed dysfunction of the fronto-amygdalar inhibition in anxiety disorders and indicate a modulatory influence of the anterior and posterior cingulate cortices on threat perception and processing.


title: Compact Omnidirectional Circularly Polarized Antenna

abstract: In certain radar and wireless communication applications, omnidirectional circularly polarized (CP) antennas are desired but existing solutions have complex 3-D structures and are large in size. In this paper, a compact and low profile CP antenna having omnidirectional pattern on azimuthal plane is demonstrated. The CP waveform is synthesized by combining two linearly polarized and omnidirectional antennas with 90° phase difference on their terminals. The most challenging part of the design is the realization of the horizontally polarized antenna with omnidirectional radiation pattern. This antenna is designed using a novel two-faced slot antenna structure capable of emitting in-phase radiation of equal magnitude on both sides of the antenna. The two faces of the low-profile slot antenna are connected by a thin meander parallel plate waveguide to provide antiparallel field distributions over the two faces of the slot radiators. This special design removes radiation nulls on E-plane that exists on conventional slot antennas, the mechanism of which has been carefully discussed in this paper. The other antenna is a compatible pair of planar inverted-F antennas constructed on the sides of the finite-ground plane two-faced slot antenna for the provision of vertically polarized omnidirectional radiation. A feed network with power splitter and phase shifter is also accommodated on the compact CP antenna. The antenna is designed and fabricated, and its performance is measured against numerical simulations. It is shown that the fabricated antenna at 2.4 GHz can provide measured axial ratio of lower than 1.8 and gain variations of less than ±1.5 dB over all azimuth angles.


title: Improved Bat Algorithm Applied to Multilevel Image Thresholding

abstract: Multilevel image thresholding is a very important image processing technique that is used as a basis for image segmentation and further higher level processing. However, the required computational time for exhaustive search grows exponentially with the number of desired thresholds. Swarm intelligence metaheuristics are well known as successful and efficient optimization methods for intractable problems. In this paper, we adjusted one of the latest swarm intelligence algorithms, the bat algorithm, for the multilevel image thresholding problem. The results of testing on standard benchmark images show that the bat algorithm is comparable with other state-of-the-art algorithms. We improved standard bat algorithm, where our modifications add some elements from the differential evolution and from the artificial bee colony algorithm. Our new proposed improved bat algorithm proved to be better than five other state-of-the-art algorithms, improving quality of results in all cases and significantly improving convergence speed.


title: Combination of audio and lyrics features for genre classification in digital audio collections

abstract: In many areas multimedia technology has made its way into mainstream. In the case of digital audio this is manifested in numerous online music stores having turned into profitable businesses. The widespread user adaption of digital audio both on home computers and mobile players show the size of this market. Thus, ways to automatically process and handle the growing size of private and commercial collections become increasingly important; along goes a need to make music interpretable by computers. The most obvious representation of audio files is their sound - there are, however, more ways of describing a song, for instance its lyrics, which describe songs in terms of content words. Lyrics of music may be orthogonal to its sound, and differ greatly from other texts regarding their (rhyme) structure. Consequently, the exploitation of these properties has potential for typical music information retrieval tasks such as musical genre classification; so far, there is a lack of means to efficiently combine these modalities. In this paper, we present findings from investigating advanced lyrics features such as the frequency of certain rhyme patterns, several parts-of-speech features, and statistic features such as words per minute (WPM). We further analyse in how far a combination of these features with existing acoustic feature sets can be exploited for genre classification and provide experiments on two test collections.


title: Ball and plate control system based on sliding mode control with uncertain items observe compensation

abstract: Aiming at Ball and plate system is a nonlinear, and under-actuated system system. the sliding mode control with uncertain items observe compensation is proposed, the nonlinear and uncertain items can be estimated in the control method, from the compensation uncertain items perspective, the decoupling and Linearization can be realized. The result of simulation and experiments indicate the proposed scheme can effectively solve the problems of trajectory tracking in the system has nonlinear and under-actuated.


title: Illusory conjunctions in the perception of objects

abstract: In perceiving objects we may synthesize conjunctions of separable features by directing attention serially to each item in turn (A. Treisman and G. Gelade, Cognitive Psychology, 1980, 12, 97–136). This feature-integration theory predicts that when attention is diverted or overloaded, features may be wrongly recombined, giving rise to “illusory conjunctions.” The present paper confirms that illusory conjunctions are frequently experienced among unattended stimuli varying in color and shape, and that they occur also with size and solidity (outlined versus filled-in shapes). They are shown both in verbal recall and in simultaneous and successive matching tasks, making it unlikely that they depend on verbal labeling or on memory failure. They occur as often between stimuli differing on many features as between more similar stimuli, and spatial separation has little effect on their frequency. Each feature seems to be coded as an independent entity and to migrate, when attention is diverted, with few constraints from the other features of its source or destination.


title: Body sensor networks for baseball swing training: Coordination analysis of human movements using motion transcripts

abstract: Becoming proficient in a sport requires significant investment in training. Wearable sensor devices can improve training due to the high level of mobility, ubiquity and intelligent feedback offered. In this paper, we present a wearable platform that provides baseball players with corrective feedback based on multidimensional physiological data collected from a body sensor network. We attempt to generate motion transcripts, which simplify interpretation of complex movements. We then use transcripts to measure coordination among limb segments and joints of the body. The starting times of key events are found in the transcripts, and the coordination between these times is analyzed.


title: Real-time classification and sensor fusion with a spiking deep belief network

abstract: Deep Belief Networks (DBNs) have recently shown impressive performance on a broad range of classification problems. Their generative properties allow better understanding of the performance, and provide a simpler solution for sensor fusion tasks. However, because of their inherent need for feedback and parallel update of large numbers of units, DBNs are expensive to implement on serial computers. This paper proposes a method based on the Siegert approximation for Integrate-and-Fire neurons to map an offline-trained DBN onto an efficient event-driven spiking neural network suitable for hardware implementation. The method is demonstrated in simulation and by a real-time implementation of a 3-layer network with 2694 neurons used for visual classification of MNIST handwritten digits with input from a 128 × 128 Dynamic Vision Sensor (DVS) silicon retina, and sensory-fusion using additional input from a 64-channel AER-EAR silicon cochlea. The system is implemented through the open-source software in the jAER project and runs in real-time on a laptop computer. It is demonstrated that the system can recognize digits in the presence of distractions, noise, scaling, translation and rotation, and that the degradation of recognition performance by using an event-based approach is less than 1%. Recognition is achieved in an average of 5.8 ms after the onset of the presentation of a digit. By cue integration from both silicon retina and cochlea outputs we show that the system can be biased to select the correct digit from otherwise ambiguous input.


title: Family Violence and Football: The Effect of Unexpected Emotional Cues on Violent Behavior

abstract: Family violence is a pervasive and costly problem, yet there is no consensus on how to interpret the phenomenon of violence by one family member against another. Some analysts assume that violence has an instrumental role in intra-family incentives. Others argue that violent episodes represent a loss of control that the offender immediately regrets. In this paper we specify and test a behavioral model of the latter form. Our key hypothesis is that negative emotional cues - benchmarked relative to a rationally expected reference point - make a breakdown of control more likely. We test this hypothesis using data on police reports of family violence on Sundays during the professional football season. Controlling for location and time fixed effects, weather factors, the pre-game point spread, and the size of the local viewing audience, we find that upset losses by the home team (losses in games that the home team was predicted to win by more than 3 points) lead to an 8 percent increase in police reports of at-home male-on-female intimate partner violence. There is no corresponding effect on female-on-male violence. Consistent with the behavioral prediction that losses matter more than gains, upset victories by the home team have (at most) a small dampening effect on family violence. We also find that unexpected losses in highly salient or frustrating games have a 50% to 100% larger impact on rates of family violence. The evidence that payoff-irrelevant events affect the rate of family violence leads us to conclude that at least some fraction of family violence is better characterized as a breakdown of control than as rationally directed instrumental violence.


title: Deep Specialized Network for Illuminant Estimation

abstract: Illuminant estimation to achieve color constancy is an ill-posed problem. Searching the large hypothesis space for an accurate illuminant estimation is hard due to the ambiguities of unknown reflections and local patch appearances. In this work, we propose a novel Deep Specialized Network (DS-Net) that is adaptive to diverse local regions for estimating robust local illuminants. This is achieved through a new convolutional network architecture with two interacting sub-networks, i.e. an hypotheses network (HypNet) and a selection network (SelNet). In particular, HypNet generates multiple illuminant hypotheses that inherently capture different modes of illuminants with its unique two-branch structure. SelNet then adaptively picks for confident estimations from these plausible hypotheses. Extensive experiments on the two largest color constancy benchmark datasets show that the proposed ‘hypothesis selection’ approach is effective to overcome erroneous estimation. Through the synergy of HypNet and SelNet, our approach outperforms state-of-the-art methods such as [1, 2, 3].


title: Early detection of lung cancer using SVM classifier in biomedical image processing

abstract: Image processing techniques are now commonly used in the medical field for early detection of diseases. This research aims to improve accuracy, sensitivity and specificity of early detection of lung cancer through a combination of image processing techniques and data mining. The Computed Tomography (CT) scan image of the lungs is pre-processed and the Region of Interest (ROI) segmented, retained and compressed using a DWT (Discrete Waveform Transform) technique. The resulting ROI image is decomposed into four sub frequencies, bands LL, HL, LH, and HH. Again, the LL sub frequency is decomposed into four sub-bands, applying a 2-level DWT to the ROI based image. Further, features such as entropy, co-relation, energy, variance and homogeneity are extracted from the 2-level DWT images using a GLCM (Gray level Co-occurrence Matrix) with classification effected by means of an SVM (Support Vector Machine). Classification identifies whether the CT image is normal or cancerous. The Lung Image Database Consortium dataset (LIDC) has been used for training and testing purpose for this study. A Receiver Operating Characteristics (ROC) curve is used to analyze the performance of the system. Overall the system has accuracy of 95.16%, sensitivity of 98.21% and specificity of 78.69%.


Character Identification on Multiparty Conversation: Identifying Mentions of Characters in TV Shows


title: Do static type systems improve the maintainability of software systems? An empirical study

abstract: Static type systems play an essential role in contemporary programming languages. Despite their importance, whether static type systems influence human software development capabilities remains an open question. One frequently mentioned argument for static type systems is that they improve the maintainability of software systems — an often used claim for which there is no empirical evidence. This paper describes an experiment which tests whether static type systems improve the maintainability of software systems. The results show rigorous empirical evidence that static type are indeed beneficial to these activities, except for fixing semantic errors.


title: Human Gender Classification: A Review

abstract: Gender contains a wide range of information regarding to the characteristics difference between male and female. Successful gender recognition is essential and critical for many applications in the commercial domains such as applications of human-computer interaction and computer-aided physiological or psychological analysis. Some have proposed various approaches for automatic gender classification using the features derived from human bodies and/or behaviors. First, this paper introduces the challenge and application for gender classification research. Then, the development and framework of gender classification are described. Besides, we compare these state-of-the-art approaches, including vision-based methods, biological information-based method, and social network information-based method, to provide a comprehensive review in the area of gender classification. In mean time, we highlight the strength and discuss the limitation of each method. Finally, this review also discusses several promising applications for the future work.


title: Agent-based demand forecast in multi-echelon supply chain

abstract: Supply chain management (SCM) is an emerging field that has commanded attention and support from the industrial community. Demand forecast taking inventory into consideration is an important issue in SCM. There are many diverse inventory systems, in theory or practice, which are operated by entities (companies) in a supply chain. In order to increase supply chain effectiveness, minimize total cost, and reduce the bullwhip effect, integration and coordination of these different systems in the supply chain (SC) are required using information technology and effective communication. The paper develops a multi-agent system to simulate a supply chain, where agents operate these entities with different inventory systems. Agents are coordinated to control inventory and minimize the total cost of a SC by sharing information and forecasting knowledge. The demand is forecasted with a genetic algorithm (GA) and the ordering quantity is offered at each echelon incorporating the perspective of "systems thinking". By using this agent-based system, the results show that total cost decreases and the ordering variation curve becomes smooth.


Extortion on the Internet: the Rise of Crypto-Ransomware


title: MATLAB model for spiking neural networks

abstract: Spiking Neural Networks are the most realistic model compared to its biological counterpart. This paper introduces a MATLAB toolbox that is specifically designed for simulating spiking neural networks. The toolbox includes a set of functions that are useful for: creating and organizing the desired architecture; updating stimuli signals, adapting synapses and simulating the network; extracting and visualizing the simulation results.


title: A 1-V MOSFET-only fully-differential dynamic comparator for use in low-voltage pipelined A/D converters

abstract: Low-voltage low-power comparators implemented in standard digital processes are of increasing importance in different integrated applications. In this paper a 1-V fully-differential low-power MOSFET-only comparator with rail-to-rail input swing is presented which can be suitably used in very low-voltage low-power pipelined A/D converters. This comparator utilizes a resistive divider configuration with a MOSFET-only clock booster to supply a higher voltage for the dynamic latch in the intervals that a comparison is to be made. HSPICE simulations of the proposed comparator in a 0.25-mm CMOS process, confirm the rail-to-rail input range of the fully-differential comparator with a supply voltage of 1 V with a decision time of less than 1 ns while driving a capacitive load of 0.2pF. The worst-case dynamic power consumption of the proposed comparator is less than 100mW with a clock frequency of 50MHz.


title: From handcrafted to deep local invariant features

abstract: This paper presents an overview of the evolution of local features from handcrafted to deep-learning-based methods, followed by a discussion of several benchmarks and papers evaluating such local features. Our investigations are motivated by 3D reconstruction problems, where the precise location of the features is important. As we describe these methods, we highlight and explain the challenges of feature extraction and potential ways to overcome them. We first present handcrafted methods, followed by methods based on classical machine learning and finally we discuss methods based on deep-learning. This largely chronologically-ordered presentation will help the reader to fully understand the topic of image and region description in order to make best use of it in modern computer vision applications. In particular, understanding handcrafted methods and their motivation can help to understand modern approaches and how machine learning is used to improve the results. We also provide references to most of the relevant literature and code.


title: Scheduling Computation Graphs of Deep Learning Models on Manycore CPUs

abstract: For a deep learning model, efficient execution of its computation graph is key to achieving high performance. Previous work has focused on improving the performance for individual nodes of the computation graph, while ignoring the parallelization of the graph as a whole. However, we observe that running multiple operations simultaneously without interference is critical to efficiently perform parallelizable small operations. The attempt of executing the computation graph in parallel in deep learning frameworks usually involves much resource contention among concurrent operations, leading to inferior performance on manycore CPUs. To address these issues, in this paper, we propose Graphi, a generic and high-performance execution engine to efficiently execute a computation graph in parallel on manycore CPUs. Specifically, Graphi minimizes the interference on both software/hardware resources, discovers the best parallel setting with a profiler, and further optimizes graph execution with the critical-path first scheduling. Our experiments show that the parallel execution consistently outperforms the sequential one. The training times on four different neural networks with Graphi are 2.1x to 9.5x faster than those with TensorFlow on a 68-core Intel Xeon Phi processor.


title: Understanding Consumer Decision Making for Complex Choices: The Effects of Individual and Contextual Factors

abstract: E-commerce consumers are facing increasingly complex purchasing decisions. Due to their cognitive capacity, however, consumers may not always achieve their goal of optimal product choice. Existing research has focused on providing aids to consumers to help them make rational and conscious information choices for complex purchasing decisions. Recently, a dual-process Unconscious Thought Theory (UTT) has suggested otherwise. It shows that due to the limited cognitive resources, unconscious information processing may outperform conscious information processing for complex decisions. Drawing on the UTT, the study proposes that strategically designed interventions would interact with other contextual and individual factors in consumer information processing, and ultimately lead to superior consumer choice under certain choice environment. An experiment was conducted to test the research model. Focusing on the unconscious information processing in online shopping, this study has important implications for Web-specific human-computer interaction research and e-commerce practitioners.


Routerless Networks-on-Chip


title: Robotic grasping and contact: a review

abstract: In this paper, we survey the field of robotic grasping and the work that has been done in this area over the last two decades, with a slight bias toward the development of the theoretical framework and analytical results in this area.


title: GRAPE: A Graph-Based Framework for Disambiguating People Appearances in Web Search

abstract: Finding information about people using search engines is one of the most common activities on the Web. However, search engines usually return a long list of Web pages, which may be relevant to many namesakes, especially given the explosive growth of Web data. To address the challenge caused by name ambiguity in Web people search, this paper proposes a novel graph-based framework, GRAPE (abbr. a Graph-based fRamework for disAmbiguating People appEarances in Web search). In GRAPE, people tag information (e.g., people name, organization, and email address) surrounding the queried people name is extracted from the search results, a graph-based unsupervised algorithm is then developed to cluster the extracted tags, where a new method, Cohesion, is introduced to measure the importance of a tag for clustering, and each final cluster of tags represents a unique people entity. Experimental results show that our proposed framework outperforms the state-of-the-art Web people name disambiguation approaches.


title: 13C NMR chemical shift and electronic structure of an infinite polymer chain as studied by tight‐binding theory within the CNDO/2 framework: Polyethylene and cis and trans polyacetylenes

abstract: Formulas for the calculation of the 13C NMR chemical shift of infinite polymer chains were derived by the tight‐binding theory within the CNDO/2 framework incorporated with the sum‐over‐state method. This formalism was applied to the calculation of the 13C NMR chemical shift tensor of polyethylene and cis and trans polyacetylenes. The calculated results were found to agree with the experimental data.


title: Nimble cybersecurity incident management through visualization and defensible recommendations

abstract: Analysts engaged in real-time monitoring of cybersecurity incidents must quickly and accurately respond to alerts generated by intrusion detection systems. We investigated two complementary approaches to improving analyst performance on this vigilance task: a graph-based visualization of correlated IDS output and defensible recommendations based on machine learning from historical analyst behavior. We tested our approach with 18 professional cybersecurity analysts using a prototype environment in which we compared the visualization with a conventional tabular display, and the defensible recommendations with limited or no recommendations. Quantitative results showed improved analyst accuracy with the visual display and the defensible recommendations. Additional qualitative data from a "talk aloud" protocol illustrated the role of displays and recommendations in analysts' decision-making process. Implications for the design of future online analysis environments are discussed.


title: Learning to Update for Object Tracking.

abstract: Model update lies at the heart of object tracking.Generally, model update is formulated as an online learning problem where a target model is learned over the online training dataset. Our key innovation is to \emph{learn the online learning algorithm itself using large number of offline videos}, i.e., \emph{learning to update}. The learned updater takes as input the online training dataset and outputs an updated target model. As a first attempt, we design the learned updater based on recurrent neural networks (RNNs) and demonstrate its application in a template-based tracker and a correlation filter-based tracker. Our learned updater consistently improves the base trackers and runs faster than realtime on GPU while requiring small memory footprint during testing. Experiments on standard benchmarks demonstrate that our learned updater outperforms commonly used update baselines including the efficient exponential moving average (EMA)-based update and the well-designed stochastic gradient descent (SGD)-based update. Equipped with our learned updater, the template-based tracker achieves state-of-the-art performance among realtime trackers on GPU.


A genetic programming approach to telecommunications fraud detection and classification


title: Executives’ perceptions of the business value of information technology: A process-oriented approach

abstract: Despite significant progress in evaluating the productivity payoffs from information technology (IT), the inability of traditional firm-level economic analy- sis to account fully for the intangible impacts of IT has led to calls for a more inclu- sive and comprehensive approach to measuring IT business value. In response to this call, we develop a process-oriented model to assess the impacts of IT on critical business activities~within the value chaiiirOur model incorporates corporate goals for IT and management practices as key^erminants of realized IT payoffs. Using survey data from 304 business executives worldwide, we foimd that corporate goals for IT can be classified into one of four types: unfocused, operations focus, market focus, and dual focus. Our analysis confirms that these goals are useful indicators of payoffs from IT in that executives in fums with more focused goals for IT perceive greater payoffs from IT across the value chain. In addition, we found that manage- ment practices such as strategic alignment and IT investment evaluation contribute to higher perceived levels of IT business value.


title: An Improved Algorithm for Incremental Induction of Decision Trees

abstract: This paper presents an algorithm for incremental induction of decision trees that is able to handle both numeric and symbolic variables. In order to handle numeric variables, a new tree revision operator called 'slewing' is introduced. Finally, a non-incremental method is given for finding a decision tree based on a direct metric of a candidate tree.


title: A 10-GS/s 4-Bit Single-Core Digital-to-Analog Converter for Cognitive Ultrawidebands

abstract: This brief delineates the design and realization of a 10-GS/s 4-bit digital-to-analog converter (DAC) for the cognitive ultrawideband (CUWB), an emerging solution for low interference and efficient spectrum utilization in communication networks. The DAC serves as the data converter for the adaptive waveform transmitter therein, largely to reduce its power dissipation and hardware complexity. For reasons of low power dissipation and low-cost CUWB application, the resolution of the DAC is 4 bits, its realization is in standard 65-nm CMOS, and the architecture is a single core. The binary current-steering DAC includes critical building blocks such as current sources and a novel deglitcher circuit. The current sources are designed for small area with high linearity based on our derived relationship between current-source output resistance and linearity parameters [integral nonlinearity (INL) and spurious-free dynamic range (SFDR)]. The deglitcher design includes high-speed source followers as high-speed low voltage swing buffers to improve the linearity by decreasing the output glitch energy. The DAC embodies an in situ hardware efficient (small integrated-circuit area and reduced input/output pinout) tester that generates 4 $\times$ 10-Gb/s test-data pattern to facilitate functional verification. The designed DAC achieves ≤ 0.16-least significant bit INL/differential nonlinearity and > 23-dBc SFDR over the Nyquist bandwidth up to 4.53 GHz, and features the most competitive figures-of-merit of all similar DACs reported to date.


title: Learning to group discrete graphical patterns

abstract: We introduce a deep learning approach for grouping discrete patterns common in graphical designs. Our approach is based on a convolutional neural network architecture that learns a grouping measure defined over a pair of pattern elements. Motivated by perceptual grouping principles, the key feature of our network is the encoding of element shape, context, symmetries, and structural arrangements. These element properties are all jointly considered and appropriately weighted in our grouping measure. To better align our measure with human perceptions for grouping, we train our network on a large, human-annotated dataset of pattern groupings consisting of patterns at varying granularity levels, with rich element relations and varieties, and tempered with noise and other data imperfections. Experimental results demonstrate that our deep-learned measure leads to robust grouping results.


title: Organ support therapy in the intensive care unit and return to work: a nationwide, register-based cohort study

abstract: The association between severity of illness and ability to return to work is unclear. Therefore, we investigated return to work and associations with measures of illness severity in ICU survivors. We conducted this cohort study using Danish registry data for the period 2005–2014 on ICU patients who were discharged alive from hospital, had an ICU length of stay (LOS) of at least 72 h, were not treated with dialysis before hospital admission and were working prior to admission. We assessed (1) the cumulative incidence (chance) of return to work (2005–2017) and receipt of social benefits after discharge from a hospital stay with ICU admission and (2) the association between organ support therapies (renal replacement therapy, cardiovascular support and mechanical ventilation), and during 2011–2014 SAPS II and ICU LOS, and return to work, using multivariable Cox regression. Among 5762 ICU survivors, 68% returned to work within 2 years after hospital discharge. Disability and sickness benefits constituted 89% of social benefits among patients not returning to work and 59% among patients withdrawing from work following an initial return to work. Mechanical ventilation (HR 0.70, 95% CI [0.65–0.77]), but not RRT (HR 0.85, 95% CI [0.71–1.02]), cardiovascular support (HR 0.93, 95% CI [0.82–1.07]) and increasing SAPS II, was associated with decreased chance of return to work. Increasing ICU LOS was also associated with a decreased chance of return to work. The majority of a nationwide cohort of ICU survivors returned to work. Sick leave and receipt of disability pension were common following ICU admission. Mechanical ventilation and longer ICU LOS were associated with reduced chances of return to work.


title: A 63W 14MHz Class-E amplifier for wireless power transmission

abstract: This paper presents a 14MHz Class-E power amplifier to be used for wireless power transmission. The Class-E power amplifier was built to consider the VSWR and the frequency bandwidth. Tw o kinds of circuits were designed: the high and low quality factor amplifiers. The low quality factor amplifier is confirmed to have larger bandwidth than the high quality factor amplifier. It has also possessed less sensitive characteristics. Therefore, the low quality factor amplifier circuit was adopted and tested. The effect of gate driving input source is studied. The efficiency of the Class-E amplifier reaches 85.5% at 63W.


title: Corrosion Study and Intermetallics Formation in Gold and Copper Wire Bonding in Microelectronics Packaging

abstract: A comparison study on the reliability of gold (Au) and copper (Cu) wire bonding is conducted to determine their corrosion and oxidation behavior in different environmental conditions. The corrosion and oxidation behaviors of Au and Cu wire bonding are determined through soaking in sodium chloride (NaCl) solution and high temperature storage (HTS) at 175 °C, 200 °C and 225 °C. Galvanic corrosion is more intense in Cu wire bonding as compared to Au wire bonding in NaCl solution due to the minimal formation of intermetallics in the former. At all three HTS annealing temperatures, the rate of Cu-Al intermetallic formation is found to be three to five times slower than Au-Al intermetallics. The faster intermetallic growth rate and lower activation energy found in this work for both Au/Al and Cu/Al as compared to literature could be due to the thicker Al pad metallization which removed the rate-determining step in previous studies due to deficit in Al material.


title: Factors of Transferability for a Generic ConvNet Representation

abstract: Evidence is mounting that Convolutional Networks (ConvNets) are the most effective representation learning method for visual recognition tasks. In the common scenario, a ConvNet is trained on a large labeled dataset (source) and the feed-forward units activation of the trained network, at a certain layer of the network, is used as a generic representation of an input image for a task with relatively smaller training set (target). Recent studies have shown this form of representation transfer to be suitable for a wide range of target visual recognition tasks. This paper introduces and investigates several factors affecting the transferability of such representations. It includes parameters for training of the source ConvNet such as its architecture, distribution of the training data, etc. and also the parameters of feature extraction such as layer of the trained ConvNet, dimensionality reduction, etc. Then, by optimizing these factors, we show that significant improvements can be achieved on various (17) visual recognition tasks. We further show that these visual recognition tasks can be categorically ordered based on their similarity to the source task such that a correlation between the performance of tasks and their similarity to the source task w.r.t. the proposed factors is observed.


title: Missile trajectory shaping using sampling-based path planning

abstract: This paper presents missile guidance as a complex robotic problem: a hybrid non-linear system moving in a heterogeneous environment. The proposed solution to this problem combines a sampling-based path planner, Dubins' curves and a locally-optimal guidance law. This algorithm aims to find feasible trajectories that anticipate future flight conditions, especially the loss of manoeuverability at high altitude. Simulated results demonstrate the substantial performance improvements over classical midcourse guidance laws and the benefits of using such methods, well-known in robotics, in the missile guidance field of research.


title: Unmanned aerial surveillance system for hazard collision avoidance in autonomous shipping

abstract: Autonomous ships require a sense-and-collision-avoidance functionality based on surveillance of the ocean surface in order to detect unmapped and potentially non-cooperative obstacles and hazards, and to engage into evasive manoeuvres to avoid impending collisions. In this paper, we study the concept of using an autonomous ship being assisted by an unmanned aerial surveillance system (UASS) that provides information to the ship in order to implement collision avoidance in compliance with the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS). The motivation is that a UASS provides complementary sensing capabilities that can be combined with a conventional maritime radar and Automatic Identification System (AIS) to detect smaller obstacles that may be hidden in clutter, behind other objects, or submerged close to the surface. We propose a system architecture and implement a simulation environment to illustrate the concept and study the feasibility of the key control algorithms based on receding-horizon optimization.


title: High-Frequency Trading Strategy Based on Deep Neural Networks

abstract: This paper presents a high-frequency strategy based on Deep Neural Networks (DNNs). The DNN was trained on current time (hour and minute), and \( n \)-lagged one-minute pseudo-returns, price standard deviations and trend indicators in order to forecast the next one-minute average price. The DNN predictions are used to build a high-frequency trading strategy that buys (sells) when the next predicted average price is above (below) the last closing price. The data used for training and testing are the AAPL tick-by-tick transactions from September to November of 2008. The best-found DNN has a 66 % of directional accuracy. This strategy yields an 81 % successful trades during testing period.


title: Achieving eye contact in a one-to-many 3D video teleconferencing system

abstract: We present a set of algorithms and an associated display system capable of producing correctly rendered eye contact between a three-dimensionally transmitted remote participant and a group of observers in a 3D teleconferencing system. The participant's face is scanned in 3D at 30Hz and transmitted in real time to an autostereoscopic horizontal-parallax 3D display, displaying him or her over more than a 180° field of view observable to multiple observers. To render the geometry with correct perspective, we create a fast vertex shader based on a 6D lookup table for projecting 3D scene vertices to a range of subject angles, heights, and distances. We generalize the projection mathematics to arbitrarily shaped display surfaces, which allows us to employ a curved concave display surface to focus the high speed imagery to individual observers. To achieve two-way eye contact, we capture 2D video from a cross-polarized camera reflected to the position of the virtual participant's eyes, and display this 2D video feed on a large screen in front of the real participant, replicating the viewpoint of their virtual self. To achieve correct vertical perspective, we further leverage this image to track the position of each audience member's eyes, allowing the 3D display to render correct vertical perspective for each of the viewers around the device. The result is a one-to-many 3D teleconferencing system able to reproduce the effects of gaze, attention, and eye contact generally missing in traditional teleconferencing systems.


title: Computational implications of biophysical diversity and multiple timescales in neurons and synapses for circuit performance

abstract: Despite advances in experimental and theoretical neuroscience, we are still trying to identify key biophysical details that are important for characterizing the operation of brain circuits. Biological mechanisms at the level of single neurons and synapses can be combined as ‘building blocks’ to generate circuit function. We focus on the importance of capturing multiple timescales when describing these intrinsic and synaptic components. Whether inherent in the ionic currents, the neuron's complex morphology, or the neurotransmitter composition of synapses, these multiple timescales prove crucial for capturing the variability and richness of circuit output and enhancing the information-carrying capacity observed across nervous systems.


title: A fraud management system architecture for next-generation networks

abstract: This paper proposes an original architecture for a fraud management system (FMS) for convergent. Nextgeneration networks (NGNs), which are based on the Internet protocol (IP). The architecture has the potential to satisfy the requirements of flexibility and application-independency for effective fraud detection in NGNs that cannot be met by traditional FMSs. The proposed architecture has a thorough four-stage detection process that analyses billing records in IP detail record (IPDR) format – an emerging IP-based billing standard – for signs of fraud. Its key feature is its usage of neural networks in the form of self-organising maps (SOMs) to help uncover unknown NGN fraud scenarios. A prototype was implemented to test the effectiveness of using a SOM for fraud detection and is also described in the paper.


title: For Most Large Underdetermined Systems of Linear Equations the Minimal ℓ1-norm Solution is also the Sparsest Solution

abstract: We consider linear equations y = Φx where y is a given vector in ℝn and Φ is a given n × m matrix with n 0 so that for large n and for all Φ's except a negligible fraction, the following property holds: For every y having a representation y = Φx0by a coefficient vector x0 ∈ ℝmwith fewer than ρ · n nonzeros, the solution x1of the 1-minimization problem ::: ::: ::: ::: is unique and equal to x0. In contrast, heuristic attempts to sparsely solve such systems—greedy algorithms and thresholding—perform poorly in this challenging setting. The techniques include the use of random proportional embeddings and almost-spherical sections in Banach space theory, and deviation bounds for the eigenvalues of random Wishart matrices. © 2006 Wiley Periodicals, Inc.


title: Effect of Fructose on Glycemic Control in Diabetes A systematic review and meta-analysis of controlled feeding trials

abstract: OBJECTIVE The effect of fructose on cardiometabolic risk in humans is controversial. We conducted a systematic review and meta-analysis of controlled feeding trials to clarify the effect of fructose on glycemic control in individuals with diabetes. ::: ::: RESEARCH DESIGN AND METHODS We searched MEDLINE, EMBASE, and the Cochrane Library (through 22 March 2012) for relevant trials lasting ≥7 days. Data were aggregated by the generic inverse variance method (random-effects models) and expressed as mean difference (MD) for fasting glucose and insulin and standardized MD (SMD) with 95% CI for glycated hemoglobin (HbA1c) and glycated albumin. Heterogeneity was assessed by the Cochran Q statistic and quantified by the I 2 statistic. Trial quality was assessed by the Heyland methodological quality score (MQS). ::: ::: RESULTS Eighteen trials ( n = 209) met the eligibility criteria. Isocaloric exchange of fructose for carbohydrate reduced glycated blood proteins (SMD −0.25 [95% CI −0.46 to −0.04]; P = 0.02) with significant intertrial heterogeneity ( I 2 = 63%; P = 0.001). This reduction is equivalent to a ∼0.53% reduction in HbA1c. Fructose consumption did not significantly affect fasting glucose or insulin. A priori subgroup analyses showed no evidence of effect modification on any end point. ::: ::: CONCLUSIONS Isocaloric exchange of fructose for other carbohydrate improves long-term glycemic control, as assessed by glycated blood proteins, without affecting insulin in people with diabetes. Generalizability may be limited because most of the trials were <12 weeks and had relatively low MQS (<8). To confirm these findings, larger and longer fructose feeding trials assessing both possible glycemic benefit and adverse metabolic effects are required.


title: Anubis - Speeding Up Computer-Aided Translation

abstract: In this paper, the idea of Computer-Aided Translation is first introduced and a modern approach to CAT is then presented. Next, we provide a more detailed description of one of the state-of-art CAT systems - memoQ. Then, the author’s approach to the idea - the Anubis system - is described and evaluated. While Anubis is comparable to memoQ in terms of the precision of provided translation memory matches, it outperforms memoQ when it comes to the speed of searching the translation memory. In the experiments carried out, Anubis turned out to be over 430% faster than memoQ. The result was achieved thanks to the author’s algorithm of minimizing the size of translation memory index, so it can be stored in computer’s RAM memory.


title: Preliminary evaluation of a controlled-brake orthosis for FES-aided gait

abstract: A hybrid functional-electrical stimulation (FES) gait system that incorporates a computer-controlled orthosis system has been developed to address the problems of rapid muscle fatigue and poor movement control that are characteristic of FES-aided gait. The orthosis is a long-leg brace that contains controllable friction brakes at both hip and knee joints. The system achieves desirable limb trajectories by utilizing the stimulated muscles as a source of unregulated power and regulating the power at each joint by computer control of the friction brakes. Muscle fatigue is reduced by locking the controllable brakes to provide the isometric joint torques necessary during stance. The hybrid gait system was evaluated and compared to conventional four channel FES-aided gait using four subjects with paraplegia. The results demonstrated significant reduction in muscle fatigue and improvement in trajectory control when using the orthosis combined with FES compared to using FES alone. Results for distance and speed improvements varied across subjects. Considerable work remains in the design of the hardware before the system is feasible for use outside the laboratory.


title: Boltzmann Chains and Hidden Markov Models

abstract: We propose a statistical mechanical framework for the modeling of discrete time series. Maximum likelihood estimation is done via Boltzmann learning in one-dimensional networks with tied weights. We call these networks Boltzmann chains and show that they contain hidden Markov models (HMMs) as a special case. Our framework also motivates new architectures that address particular shortcomings of HMMs. We look at two such architectures: parallel chains that model feature sets with disparate time scales, and looped networks that model long-term dependencies between hidden states. For these networks, we show how to implement the Boltzmann learning rule exactly, in polynomial time, without resort to simulated or mean-field annealing. The necessary computations are done by exact decimation procedures from statistical mechanics.


title: Supervised and Unsupervised Aspect Category Detection for Sentiment Analysis with Co-occurrence Data

abstract: Using online consumer reviews as electronic word of mouth to assist purchase-decision making has become increasingly popular. The Web provides an extensive source of consumer reviews, but one can hardly read all reviews to obtain a fair evaluation of a product or service. A text processing framework that can summarize reviews, would therefore be desirable. A subtask to be performed by such a framework would be to find the general aspect categories addressed in review sentences, for which this paper presents two methods. In contrast to most existing approaches, the first method presented is an unsupervised method that applies association rule mining on co-occurrence frequency data obtained from a corpus to find these aspect categories. While not on par with state-of-the-art supervised methods, the proposed unsupervised method performs better than several simple baselines, a similar but supervised method, and a supervised baseline, with an $F_{1}$ -score of 67%. The second method is a supervised variant that outperforms existing methods with an $F_{1}$ -score of 84%.


title: Cementum formation around a titanium implant: a case report.

abstract: Animal studies have shown that a periodontal ligament may be produced around a titanium implant when it is in contact with fractured and retained roots. Formation of cementum and attachment connective tissue around titanium implants confirms that cementum progenitor cells are located in the periodontal ligament, since cementum and periodontal ligament are present at the implant-root interface, whereas the remainder of the implant, which is not in contact with the root, shows osseointegration. The aim was to evaluate histologically the characteristics of the tissue present between a titanium implant and a retained root, which were subsequently extracted as a result of peri-implantitis. The histologic examination revealed a continuous layer of cementum and numerous cementocytes on the implant surface. No blood vessel or collagen fibers were detected in the periodontal space. In contrast to experimental studies carried out on animals, the lack of connective tissue fibers and the presence of hypercementosis in this specimen could have been caused by the inflammatory process. Furthermore, the extrusive movement of the root might explain the presence of cementum hypertrophy. Further studies are required to establish whether the neoformation of cementum and collagen fibers on an implant in the presence of root residues occurs only in animal models or whether it may also occur in humans.


title: Microscopic Blood Smear Segmentation and Classification Using Deep Contour Aware CNN and Extreme Machine Learning

abstract: Recent advancement in genomics technologies has opened a new realm for early detection of diseases that shows potential to overcome the drawbacks of manual detection technologies. In this work, we have presented efficient contour aware segmentation approach based based on fully conventional network whereas for classification we have used extreme machine learning based on CNN features extracted from each segmented cell. We have evaluated system performance based on segmentation and classification on publicly available dataset. Experiment was conducted on 64000 blood cells and dataset is divided into 80% for training and 20% for testing. Segmentation results are compared with the manual segmentation and found that proposed approach provided with 98.12% and 98.16% for RBC and WBC respectively whereas classification accuracy is shown on publicly available dataset 94.71% and 98.68% for RBC & its abnormalities detection and WBC respectively.


title: Utilizing correlated node mobility for efficient DTN routing

abstract: In a delay tolerant network (DTN), nodes are connected intermittently and the future node connections are mostly not known. Therefore, effective forwarding based on limited knowledge of contact behavior of nodes is challenging. Most of the previous studies assumed that mobility of a node is independent from mobility of other nodes and looked at only the pairwise node relations to decide routing. In contrast, in this paper, we analyze the temporal correlation between the meetings of each node with other nodes and utilize this correlation for efficient routing. We introduce a new metric called conditional intermeeting time (CIT), which computes the average intermeeting time between two nodes relative to a meeting with a third node. Then, we modify existing DTN routing protocols using the proposed metric to improve their performance. Extensive simulations based on real and synthetic DTN traces show that the modified algorithms perform better than the original ones.


title: Ridge Regression, Hubness, and Zero-Shot Learning

abstract: This paper discusses the effect of hubness in zero-shot learning, when ridge regression is used to find a mapping between the example space to the label space. Contrary to the existing approach, which attempts to find a mapping from the example space to the label space, we show that mapping labels into the example space is desirable to suppress the emergence of hubs in the subsequent nearest neighbor search step. Assuming a simple data model, we prove that the proposed approach indeed reduces hubness. This was verified empirically on the tasks of bilingual lexicon extraction and image labeling: hubness was reduced with both of these tasks and the accuracy was improved accordingly.


title: Unsupervised Entity Linking with Abstract Meaning Representation

abstract: Most successful Entity Linking (EL) methods aim to link mentions to their referent entities in a structured Knowledge Base (KB) by comparing their respective contexts, often using similarity measures. While the KB structure is given, current methods have suffered from impoverished information representations on the mention side. In this paper, we demonstrate the effectiveness of Abstract Meaning Representation (AMR) (Banarescu et al., 2013) to select high quality sets of entity “collaborators” to feed a simple similarity measure (Jaccard) to link entity mentions. Experimental results show that AMR captures contextual properties discriminative enough to make linking decisions, without the need for EL training data, and that system with AMR parsing output outperforms hand labeled traditional semantic roles as context representation for EL. Finally, we show promising preliminary results for using AMR to select sets of “coherent” entity mentions for collective entity linking 1 .


title: An Inductive and Capacitive Combined Wireless Power Transfer System With LC-Compensated Topology

abstract: This paper proposes a combined inductive and capacitive wireless power transfer (WPT) system with LC -compensated topology for electric vehicle charging application. The circuit topology is a combination of the LCC -compensated inductive power transfer (IPT) system and the LCLC -compensated capacitive power transfer (CPT) system. The working principle of the combined circuit topology is analyzed in detail, providing the relationship between the circuit parameters and the system power. The design of the inductive and capacitive coupling is implemented by the finite-element analysis. The equivalent circuit model of the coupling plates is derived. A 3.0-kW WPT system is designed and implemented as an example of combined inductive and capacitive coupling. The inductive coupler size is 300 mm × 300 mm and the capacitive coupler is 610 mm × 610 mm. The air-gap distance is 150 mm for both couplers. The output power of the combined system is the sum of the IPT and CPT system. The prototype has achieved 2.84-kW output power with 94.5% efficiency at 1-MHz switching frequency, and performs better under misalignment than the IPT System. This demonstrates that the inductive–capacitive combined WPT system is a potential solution to the electric vehicle charging application.


title: Construction and animation of anatomically based human hand models

abstract: The human hand is a masterpiece of mechanical complexity, able to perform fine motor manipulations and powerful work alike. Designing an animatable human hand model that features the abilities of the archetype created by Nature requires a great deal of anatomical detail to be modeled. In this paper, we present a human hand model with underlying anatomical structure. Animation of the hand model is controlled by muscle contraction values. We employ a physically based hybrid muscle model to convert these contraction values into movement of skin and bones. Pseudo muscles directly control the rotation of bones based on anatomical data and mechanical laws, while geometric muscles deform the skin tissue using a mass-spring system. Thus, resulting animations automatically exhibit anatomically and physically correct finger movements and skin deformations. In addition, we present a deformation technique to create individual hand models from photographs. A radial basis warping function is set up from the correspondence of feature points and applied to the complete structure of the reference hand model, making the deformed hand model instantly animatable.


title: DispSegNet: Leveraging Semantics for End-to-End Learning of Disparity Estimation from Stereo Imagery

abstract: Recent work has shown that convolutional neural networks (CNNs) can be applied successfully in disparity estimation, but these methods still suffer from errors in regions of low texture, occlusions, and reflections. Concurrently, deep learning for semantic segmentation has shown great progress in recent years. In this letter, we design a CNN architecture that combines these two tasks to improve the quality and accuracy of disparity estimation with the help of semantic segmentation. Specifically, we propose a network structure in which these two tasks are highly coupled. One key novelty of this approach is the two-stage refinement process. Initial disparity estimates are refined with an embedding learned from the semantic segmentation branch of the network. The proposed model is trained using an unsupervised approach, in which images from one half of the stereo pair are warped and compared against images from the other camera. Another key advantage of the proposed approach is that a single network is capable of outputting disparity estimates and semantic labels. These outputs are of great use in autonomous vehicle operation; with real-time constraints being key, such performance improvements increase the viability of driving applications. Experiments on KITTI and Cityscapes datasets show that our model can achieve state-of-the-art results and that leveraging embedding learned from semantic segmentation improves the performance of disparity estimation.


title: Scaling Binarized Neural Networks on Reconfigurable Logic

abstract: Binarized neural networks (BNNs) are gaining interest in the deep learning community due to their significantly lower computational and memory cost. They are particularly well suited to reconfigurable logic devices, which contain an abundance of fine-grained compute resources and can result in smaller, lower power implementations, or conversely in higher classification rates. Towards this end, the FINN framework was recently proposed for building fast and flexible field programmable gate array (FPGA) accelerators for BNNs. FINN utilized a novel set of optimizations that enable efficient mapping of BNNs to hardware and implemented fully connected, non-padded convolutional and pooling layers, with per-layer compute resources being tailored to user-provided throughput requirements. However, FINN was not evaluated on larger topologies due to the size of the chosen FPGA, and exhibited decreased accuracy due to lack of padding. In this paper, we improve upon FINN to show how padding can be employed on BNNs while still maintaining a 1-bit datapath and high accuracy. Based on this technique, we demonstrate numerous experiments to illustrate flexibility and scalability of the approach. In particular, we show that a large BNN requiring 1.2 billion operations per frame running on an ADM-PCIE-8K5 platform can classify images at 12 kFPS with 671 μs latency while drawing less than 41 W board power and classifying CIFAR-10 images at 88.7% accuracy. Our implementation of this network achieves 14.8 trillion operations per second. We believe this is the fastest classification rate reported to date on this benchmark at this level of accuracy.


title: Integration of DNN based speech enhancement and ASR.

abstract: Speech enhancement employing Deep Neural Networks (DNNs) is gaining strength as a data-driven alternative to classical Minimum Mean Square Error (MMSE) enhancement approaches. In the past, Observation Uncertainty approaches to integrate MMSE speech enhancement with Automatic Speech Recognition (ASR) have yielded good results as a lightweight alternative for robust ASR. In this paper we thus explore the integration of DNN-based speech enhancement with ASR by employing Observation Uncertainty techniques. For this purpose, we explore various techniques and approximations that allow propagating the uncertainty of inference of the DNN into feature domain. This uncertainty can then be used to dynamically compensate the ASR model utilizing techniques like uncertainty decoding. We test the proposed techniques on the AURORA4 corpus and show that notable improvements can be attained over the already effective DNN enhancement.


title: Adversarial Deep Learning Against Intrusion Detection Classifiers

abstract: Traditional approaches in network intrusion detection follow a signature-based ap- proach, however the use of anomaly detection approaches based on machine learning techniques have been studied hea ...


title: Politics and Deficit Finance

abstract: The government faces both economic and political constraints on its ability to raise revenue. Political factors can limit the government's revenue-generating ability to levels below those dictated by economic constraints. Taxation is more directly constrained by the political process than deficit finance because taxes are voted on directly by the legislature. Deficits are a residual that results from the aggregation of all tax and expenditure bills. Increased electoral safety weakens the political constraint against deficit finance because the only electoral check on deficit finance occurs when representatives run for reelection. Empirical estimates show that the increased security of incumbents has contributed to increases in deficit finance and has increased the overall level of public expenditures.