title: FA*IR: A Fair Top-k Ranking Algorithm

abstract: In this work, we define and solve the Fair Top-k Ranking problem, in which we want to determine a subset of k candidates from a large pool of n » k candidates, maximizing utility (i.e., select the "best" candidates) subject to group fairness criteria. Our ranked group fairness definition extends group fairness using the standard notion of protected groups and is based on ensuring that the proportion of protected candidates in every prefix of the top-k ranking remains statistically above or indistinguishable from a given minimum. Utility is operationalized in two ways: (i) every candidate included in the top-k should be more qualified than every candidate not included; and (ii) for every pair of candidates in the top-k, the more qualified candidate should be ranked above. An efficient algorithm is presented for producing the Fair Top-k Ranking, and tested experimentally on existing datasets as well as new datasets released with this paper, showing that our approach yields small distortions with respect to rankings that maximize utility without considering fairness criteria. To the best of our knowledge, this is the first algorithm grounded in statistical tests that can mitigate biases in the representation of an under-represented group along a ranked list.


Windows Registry Forensics: An Imperative Step in Tracking Data Theft via USB Devices


title: Complex systems analysis of series of blackouts: cascading failure, critical points, and self-organization

abstract: We give an overview of a complex systems approach to large blackouts of electric power transmission systems caused by cascading failure. Instead of looking at the details of particular blackouts, we study the statistics and dynamics of series of blackouts with approximate global models. Blackout data from several countries suggest that the frequency of large blackouts is governed by a power law. The power law makes the risk of large blackouts consequential and is consistent with the power system being a complex system designed and operated near a critical point. Power system overall loading or stress relative to operating limits is a key factor affecting the risk of cascading failure. Power system blackout models and abstract models of cascading failure show critical points with power law behavior as load is increased. To explain why the power system is operated near these critical points and inspired by concepts from self-organized criticality, we suggest that power system operating margins evolve slowly to near a critical point and confirm this idea using a power system model. The slow evolution of the power system is driven by a steady increase in electric loading, economic pressures to maximize the use of the grid, and the engineering responses to blackouts that upgrade the system. Mitigation of blackout risk should account for dynamical effects in complex self-organized critical systems. For example, some methods of suppressing small blackouts could ultimately increase the risk of large blackouts.


title: Practical attacks against WEP and WPA

abstract: In this paper, we describe two attacks on IEEE 802.11 based wireless LANs. The first attack is an improved key recovery attack on WEP, which reduces the average number of packets an attacker has to intercept to recover the secret key. The second attack is (according to our knowledge) the first practical attack on WPA secured wireless networks, besides launching a dictionary attack when a weak pre-shared key (PSK) is used. The attack works if the network is using TKIP to encrypt the traffic. An attacker, who has about 12-15 minutes access to the network is then able to decrypt an ARP request or response and send 7 packets with custom content to network.


title: Comparison of edge computing implementations: Fog computing, cloudlet and mobile edge computing

abstract: When it comes to storage and computation of large scales of data, Cloud Computing has acted as the de-facto solution over the past decade. However, with the massive growth in intelligent and mobile devices coupled with technologies like Internet of Things (IoT), V2X Communications, Augmented Reality (AR), the focus has shifted towards gaining real-time responses along with support for context-awareness and mobility. Due to the delays induced on the Wide Area Network (WAN) and location agnostic provisioning of resources on the cloud, there is a need to bring the features of the cloud closer to the consumer devices. This led to the birth of the Edge Computing paradigm which aims to provide context aware storage and distributed Computing at the edge of the networks. In this paper, we discuss the three different implementations of Edge Computing namely Fog Computing, Cloudlet and Mobile Edge Computing in detail and compare their features. We define a set of parameters based on which one of these implementations can be chosen optimally given a particular use-case or application and present a decision tree for the selection of the optimal implementation.


title: On-Road Multivehicle Tracking Using Deformable Object Model and Particle Filter With Improved Likelihood Estimation

abstract: This paper proposes a novel method for multivehicle detection and tracking using a vehicle-mounted monocular camera. In the proposed method, the features of vehicles are learned as a deformable object model through the combination of a latent support vector machine (LSVM) and histograms of oriented gradients (HOGs). The detection algorithm combines both global and local features of the vehicle as a deformable object model. Detected vehicles are tracked through a particle filter, which estimates the particles' likelihood by using a detection scores map and template compatibility for both root and parts of the vehicle while considering the deformation cost caused by the movement of vehicle parts. Tracking likelihoods are iteratively used as a priori probability to generate vehicle hypothesis regions and update the detection threshold to reduce false negatives of the algorithm presented before. Extensive experiments in urban scenarios showed that the proposed method can achieve an average vehicle detection rate of 97% and an average vehicle-tracking rate of 86% with a false positive rate of less than 0.26%.


Scienstein: A Research Paper Recommender System


title: Active Frame, Location, and Detector Selection for Automated and Manual Video Annotation

abstract: We describe an information-driven active selection approach to determine which detectors to deploy at which location in which frame of a video to minimize semantic class label uncertainty at every pixel, with the smallest computational cost that ensures a given uncertainty bound. We show minimal performance reduction compared to a "paragon" algorithm running all detectors at all locations in all frames, at a small fraction of the computational cost. Our method can handle uncertainty in the labeling mechanism, so it can handle both "oracles" (manual annotation) or noisy detectors (automated annotation).


title: Fast Measurement Technique for Phased Array Calibration

abstract: A novel measurement method is proposed in order to measure the active electric fields of the individual antenna elements in a phased array antenna. Fast and accurate measurements can be realized by the proposed method because the electric fields of multiple elements can be obtained simultaneously and no phase measurements are required. Hence, it can be easily applied to the on-board diagnostics and re-calibration in the operating phased array antenna systems. In the first step with this method, the phases of multiple antenna elements are successively shifted with the specified phase intervals while the array power variations are measured. Next, the measured power variation is expanded into a Fourier series and the terms are rearranged to put them into the form of the rotating element electric field vector (REV) method. Finally, the REV solution is used to identify the electric fields of the individual elements. Additionally, a theoretical study is carried out on the accuracy of the proposed measurement method. Simple, closed-form equations have been successfully derived for the measurement errors and the calibration accuracy is theoretically estimated. The proposed measurement method is validated with experimental results and the measurement accuracy is compared with the theoretical prediction.


title: A study of transfer function generation for time-varying volume data

abstract: The proper usage and creation of transfer functions for time-varying data sets is an often ignored problem in volume visualization. Although methods and guidelines exist for time-invariant data, little formal study for the timevarying case has been performed. This paper examines this problem, and reports the study that we have conducted to determine how the dynamic behavior of time-varying data may be captured by a single or small set of transfer functions. The criteria which dictate when more than one transfer function is needed were also investigated. Four data sets with different temporal characteristics were used for our study. Results obtained using two different classes of methods are discussed, along with lessons learned. These methods, including a new multiresolution opacity map approach, can be used for semi-automatic generation of transfer functions to explore large-scale time-varying data sets.


Gondwanan glaciation, cyclothems, continental positioning, and climate change


title: Force transmission in joint-less tendon driven wearable robotic hand

abstract: This paper presents the design analysis and the fabrication process of Tendon Anchoring Support(TA Support). TA Support is developed to deliver the wire tension effectively to target actuation point, fingertip, while developing SNU Exo-Glove, which is joint-less tendon driven robot. Based on the force analysis of SNU Exo-Glove, TA Support was developed with several design considerations, especially customization to maximize force transmission. For that, fabrication process for customization to patient's hand has been established and introduced in this paper. To check the viability of customization, pressure measurement experiment has been performed. Engineers developing the soft wearable robot with tendon driven mechanism will always encounter force transmission problem, delivering force from actuator to target actuation point, as stated in this paper and this paper can suggest one possible effective solution.


title: A methodology for time-frequency image processing applied to the classification of non-stationary multichannel signals using instantaneous frequency descriptors with application to newborn EEG signals

abstract: This article presents a general methodology for processing non-stationary signals for the purpose of classification and localization. The methodology combines methods adapted from three complementary areas: time-frequency signal analysis, multichannel signal analysis and image processing. The latter three combine in a new methodology referred to as multichannel time-frequency image processing which is applied to the problem of classifying electroencephalogram (EEG) abnormalities in both adults and newborns. A combination of signal related features and image related features are used by merging key instantaneous frequency descriptors which characterize the signal non-stationarities. The results obtained show that, firstly, the features based on time-frequency image processing techniques such as image segmentation, improve the performance of EEG abnormalities detection in the classification systems based on multi-SVM and neural network classifiers. Secondly, these discriminating features are able to better detect the correlation between newborn EEG signals in a multichannel-based newborn EEG seizure detection for the purpose of localizing EEG abnormalities on the scalp.


PornographyandtheMaleSexualScript : AnAnalysisofConsumption and Sexual Relations


title: BlenSor: blender sensor simulation toolbox

abstract: This paper introduces a novel software package for the simulation of various types of range scanners. The goal is to provide researchers in the fields of obstacle detection, range data segmentation, obstacle tracking or surface reconstruction with a versatile and powerful software package that is easy to use and allows to focus on algorithmic improvements rather than on building the software framework around it. The simulation environment and the actual simulations can be efficiently distributed with a single compact file. Our proposed approach facilitates easy regeneration of published results, hereby highlighting the value of reproducible research.


title: Effects of an Intervention to Increase Bed Alarm Use to Prevent Falls in Hospitalized Patients

abstract: BACKGROUND ::: Bed alarm systems intended to prevent hospital falls have not been formally evaluated. ::: ::: ::: OBJECTIVE ::: To investigate whether an intervention aimed at increasing bed alarm use decreases hospital falls and related events. ::: ::: ::: DESIGN ::: Pair-matched, cluster randomized trial over 18 months. Nursing units were allocated by computer-generated randomization on the basis of baseline fall rates. Patients and outcome assessors were blinded to unit assignment; outcome assessors may have become unblinded. (ClinicalTrials.gov registration number: NCT00183053) ::: ::: ::: SETTING ::: 16 nursing units in an urban community hospital. ::: ::: ::: PATIENTS ::: 27 672 inpatients in general medical, surgical, and specialty units. ::: ::: ::: INTERVENTION ::: Education, training, and technical support to promote use of a standard bed alarm system (intervention units); bed alarms available but not formally promoted or supported (control units). ::: ::: ::: MEASUREMENTS ::: Pre-post difference in change in falls per 1000 patient-days (primary end point); number of patients who fell, fall-related injuries, and number of patients restrained (secondary end points). ::: ::: ::: RESULTS ::: Prevalence of alarm use was 64.41 days per 1000 patient-days on intervention units and 1.79 days per 1000 patient-days on control units (P = 0.004). There was no difference in change in fall rates per 1000 patient-days (risk ratio, 1.09 [95% CI, 0.85 to 1.53]; difference, 0.41 [CI, -1.05 to 2.47], which corresponds to a greater difference in falls in control vs. intervention units) or in the number of patients who fell, injurious fall rates, or the number of patients physically restrained on intervention units compared with control units. ::: ::: ::: LIMITATION ::: The study was conducted at a single site and was slightly underpowered compared with the initial design. ::: ::: ::: CONCLUSION ::: An intervention designed to increase bed alarm use in an urban hospital increased alarm use but had no statistically or clinically significant effect on fall-related events or physical restraint use. ::: ::: ::: PRIMARY FUNDING SOURCE ::: National Institute on Aging.


title: A Unified Multilingual Semantic Representation of Concepts

abstract: Semantic representation lies at the core of several applications in Natural Language Processing. However, most existing semantic representation techniques cannot ::: be used effectively for the representation of individual word senses. We put forward a novel multilingual concept representation, called MUFFIN, which not only enables accurate representation of word senses in different languages, but also provides multiple advantages over existing approaches. MUFFIN represents a given concept in a unified semantic space irrespective of the language of interest, enabling cross-lingual comparison of different ::: concepts. We evaluate our approach in two different evaluation benchmarks, semantic similarity and Word Sense Disambiguation, reporting state-of-the-art performance ::: on several standard datasets.


title: Dopamine selectively remediates ‘model-based’ reward learning: a computational approach

abstract: Patients with loss of dopamine due to Parkinson’s disease are impaired at learning from reward. However, it remains unknown precisely which aspect of learning is impaired. In particular, learning from reward, or reinforcement learning, can be driven by two distinct computational processes. One involves habitual stamping-in of stimulus-response associations, hypothesized to arise computationally from ‘model-free’ learning. The other, ‘model-based’ learning, involves learning a model of the world that is believed to support goal-directed behaviour. Much work has pointed to a role for dopamine in model-free learning. But recent work suggests model-based learning may also involve dopamine modulation, raising the possibility that model-based learning may contribute to the learning impairment in Parkinson’s disease. To directly test this, we used a two-step reward-learning task which dissociates model-free versus model-based learning. We evaluated learning in patients with Parkinson’s disease tested ON versus OFF their dopamine replacement medication and in healthy controls. Surprisingly, we found no effect of disease or medication on model-free learning. Instead, we found that patients tested OFF medication showed a marked impairment in model-based learning, and that this impairment was remediated by dopaminergic medication. Moreover, model-based learning was positively correlated with a separate measure of working memory performance, raising the possibility of common neural substrates. Our results suggest that some learning deficits in Parkinson’s disease may be related to an inability to pursue reward based on complete representations of the environment. ::: ::: * Abbreviation ::: : UPDRS ::: : Unified Parkinson’s Disease Rating Scale


title: Disability in court: intersectionality and rule of law

abstract: Intersectionality is a commonly used perspective in issues regarding social inequality and injustice in several fields, and has also been introduced in disability studies. In legal systems, social inequality and rule of law are closely connected. The court is an arena that is known both to produce and reproduce social inequality. The purpose of this article is to raise and discuss the question of how intersectionality may serve as a productive theoretical approach in research on disability and rule of law. We discuss in what ways intersectionality as a perspective may contribute to rule of law for disabled people in Western legal systems. Our intention is to investigate the potential of intersectionality as a sensitizing perspective within a process-oriented model for analytical sensibility. Intersectionality as a perspective in relation to disability and rule of law is explored with categorization and gender in focus.


title: Wearable Sensors for Human Activity Monitoring: A Review

abstract: An increase in world population along with a significant aging portion is forcing rapid rises in healthcare costs. The healthcare system is going through a transformation in which continuous monitoring of inhabitants is possible even without hospitalization. The advancement of sensing technologies, embedded systems, wireless communication technologies, nano technologies, and miniaturization makes it possible to develop smart systems to monitor activities of human beings continuously. Wearable sensors detect abnormal and/or unforeseen situations by monitoring physiological parameters along with other symptoms. Therefore, necessary help can be provided in times of dire need. This paper reviews the latest reported systems on activity monitoring of humans based on wearable sensors and issues to be addressed to tackle the challenges.


title: From Fall Detection to Fall Prevention: A Generic Classification of Fall-Related Systems

abstract: Falls are a major health problem for the frail community dwelling old people. For more than two decades, falls have been extensively investigated by medical institutions to mitigate their impact (e.g., lack of independence and fear of falling) and minimize their consequences (e.g., cost of hospitalization and so on). However, the problem of elderly falling does not only concern health-professionals but has also drawn the interest of the scientific community. In fact, falls have been the object of many research studies and the purpose of many commercial products from academia and industry. These studies have tackled the problem using fall detection approaches exhausting a variety of sensing methods. Lately, researcher has shifted their efforts to fall prevention where falls might be spotted before they even happen. Despite their restriction to clinical studies, early fall prediction systems have started to emerge. At the same time, current reviews in this field lack a common ground classification. In this context, the main contribution of this paper is to give a comprehensive overview on elderly falls and to propose a generic classification of fall-related systems based on their sensor deployment. An extensive research scheme from fall detection to fall prevention systems have also been conducted based on this common ground classification. Data processing techniques in both fall detection and fall prevention tracks are also highlighted. The objective of this paper is to deliver medical technologists in the field of public health a good position regarding fall-related systems.


title: Ground estimation and point cloud segmentation using SpatioTemporal Conditional Random Field

abstract: Whether it be to feed data for an object detection-and-tracking system or to generate proper occupancy grids, 3D point cloud extraction of the ground and data classification are critical processing tasks, on their efficiency can drastically depend the whole perception chain. Flat-ground assumption or form recognition in point clouds can either lead to systematic error, or massive calculations. This paper describes an adaptive method for ground labeling in 3D Point clouds, based on a local ground elevation estimation. The system proposes to model the ground as a Spatio-Temporal Conditional Random Field (STCRF). Spatial and temporal dependencies within the segmentation process are unified by a dynamic probabilistic framework based on the conditional random field (CRF). Ground elevation parameters are estimated in parallel in each node, using an interconnected Expectation Maximization (EM) algorithm variant. The approach, designed to target high-speed vehicle constraints and performs efficiently with highly-dense (Velodyne-64) and sparser (Ibeo-Lux) 3D point clouds, has been implemented and deployed on experimental vehicle and platforms, and are currently tested on embedded systems (Nvidia Jetson TX1, TK1). The experiments on real road data, in various situations (city, countryside, mountain roads,…), show promising results.


title: UROBOROS: Instrumenting Stripped Binaries with Static Reassembling

abstract: Software instrumentation techniques are widely used in program analysis tasks such as program profiling, vulnerability discovering, and security-oriented transforming. In this paper, we present an instrumentation tool called UROBOROS, which supports static instrumentation on stripped binaries. Due to the lack of relocation and debug information, reverse engineering of stripped binaries is challenging. Compared with the previous work, UROBOROS can provide complete, easy-to-use, transparent, and efficient static instrumentation on stripped binaries. UROBOROS supports complete instrumentation by statically recovering the relocatable program (including both code and data sections) and the control flow structures from binary code. UROBOROS provides a rich API to access and manipulate different levels of the program tructure. The instrumentation facilities of UROBOROS are easy-to-use, users with no binary rewriting and patching skills can directly manipulate stripped binaries to perform smooth program transformations. Distinguished from most instrumentation tools that need to patch the instrumentation code as new sections, UROBOROS can directly inline the instrumentation code into the disassembled program, which provides transparent instrumentation on stripped binaries. For efficiency, in the rewritten output of existing tools, frequent control transfers between the attached and original sections can incur a considerable performance penalty. However, the output from UROBOROS incurs no extra cost because the original and instrumentation code are connected by "fall-through" transfers. We perform comparative evaluations between UROBOROS and the state-of-the-art binary instrumentation tools, including DynInst and Pin. To demonstrate the versatility of UROBOROS, we also implement two real-world reengineering tasks which could be challenging for other instrumentation tools to accomplish. Our experimental results show that UROBOROS outperforms theexisting binary instrumentation tools with better performance, lower labor cost, and a broader scope of applications.


title: A Solar Power Generation System With a Seven-Level Inverter

abstract: This paper proposes a new solar power generation system, which is composed of a dc/dc power converter and a new seven-level inverter. The dc/dc power converter integrates a dc-dc boost converter and a transformer to convert the output voltage of the solar cell array into two independent voltage sources with multiple relationships. This new seven-level inverter is configured using a capacitor selection circuit and a full-bridge power converter, connected in cascade. The capacitor selection circuit converts the two output voltage sources of dc-dc power converter into a three-level dc voltage, and the full-bridge power converter further converts this three-level dc voltage into a seven-level ac voltage. In this way, the proposed solar power generation system generates a sinusoidal output current that is in phase with the utility voltage and is fed into the utility. The salient features of the proposed seven-level inverter are that only six power electronic switches are used, and only one power electronic switch is switched at high frequency at any time. A prototype is developed and tested to verify the performance of this proposed solar power generation system.


title: The PROMPT Suite: Interactive Tools for Ontology Merging and Mapping

abstract: Researchers in the ontology-design field have developed the content for ontologies in many domain areas. This distributed nature of ontology development has led to a large number of ontologies covering overlapping domains. In order for these ontologies to be reused, they first need to be merged or aligned to one another. We developed a suite of tools for managing multiple ontologies. These suite provides users with a uniform framework for comparing, aligning, and merging ontologies, maintaining versions, translating between different formalisms. Two of the tools in the suite support semi-automatic ontology merging: IPROMPT is an interactive ontology-merging tool that guides the user through the merging process, presenting him with suggestions for next steps and identifying inconsistencies and potential problems. ANCHORPROMPT uses a graph structure of ontologies to find correlation between concepts and to provide additional information for IPROMPT.


title: Dissemination in opportunistic social networks: the role of temporal communities

abstract: Epidemic content dissemination in opportunistic social networks (OSN) has been analyzed in depth, theoretically and empirically. Most related works have studied the pairwise contact history among nodes in conference or campus environments. We claim that given the nature of these networks, this approach leads to a biased understanding of the content dissemination process. We design a methodology to break OSN traces down into 'temporal communities', i.e., groups of people who meet periodically during an experiment. We show that these communities correlate with people's social communities. As in previous works, we observe that efficient content dissemination is mostly due to high contact rate nodes. However, we show that high contact rate nodes that are more frequently involved in temporal communities contribute less to the dissemination process, leading us to conjecture that social communities tend to limit dissemination in OSNs.


title: Domo: a force sensing humanoid robot for manipulation research

abstract: Humanoid robots found in research and commercial use today typically lack the ability to operate in unstructured and unknown environments. Force sensing and compliance at each robot joint can allow the robot to safely act in these environments. However, these features can be difficult to incorporate into robot designs. We present a new force sensing and compliant humanoid under development in the humanoid robotics group at MIT CSAIL. The robot, named Domo, is to be a research platform for exploring issues in general dexterous manipulation, visual perception, and learning. In this paper we describe aspects of the design, detail proposed research directions for the robot, and illustrate how the design of humanoid robots can be informed by the desired research goals.


Learning Long-Term Dependencies with Gradient Descent is Difficult


title: Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks

abstract: Existing question answering methods infer answers either from a knowledge base or from raw text. While knowledge base (KB) methods are good at answering compositional questions, their performance is often affected by the incompleteness of the KB. Au contraire, web text contains millions of facts that are absent in the KB, however in an unstructured form. {\it Universal schema} can support reasoning on the union of both structured KBs and unstructured text by aligning them in a common embedded space. In this paper we extend universal schema to natural language question answering, employing \emph{memory networks} to attend to the large body of facts in the combination of text and KB. Our models can be trained in an end-to-end fashion on question-answer pairs. Evaluation results on \spades fill-in-the-blank question answering dataset show that exploiting universal schema for question answering is better than using either a KB or text alone. This model also outperforms the current state-of-the-art by 8.5 $F_1$ points.\footnote{Code and data available in \url{https://rajarshd.github.io/TextKBQA}}


title: Analyzing Emergency Evacuation Strategies for Mass Gatherings using Crowd Simulation And Analysis framework: Hajj Scenario

abstract: Hajj is one of the largest mass gatherings where Muslims from all over the world gather in Makah each year for pilgrimage. A mass assembly of such scale bears a huge risk of disaster either natural or man-made. In the past few years, thousands of casualties have occurred while performing different Hajj rituals, especially during the Circumambulation of Kaba (Tawaf) due to stampede or chaos. During such calamitous situations, an appropriate evacuation strategy can help resolve the problem and mitigate further risk of causalities. It is however a daunting research problem to identify an optimal course of action based on several constraints. Modeling and analyzing such a problem of real-time and spatially explicit complexity requires a microscale crowd simulation and analysis framework. Which not only allows the modeler to express the spatial dimensions and features of the environment in real scale, but also provides modalities to capture complex crowd behaviors. In this paper, we propose an Agent-based Crowd Simulation & Analysis framework that incorporates the use of Anylogic Pedestrian library and integrates/interoperate Anylogic Simulation environment with the external modules for optimization and analysis. Hence provides a runtime environment for analyzing complex situations, e.g., emergency evacuation strategies. The key features of the proposed framework include: (i) Ability to model large crowd in a spatially explicit environment at real-scale; (ii) Simulation of complex crowd behavior such as emergency evacuation; (iii) Interoperability of optimization and analysis modules with simulation runtime for evaluating evacuation strategies. We present a case study of Hajj scenario as a proof of concept and a test bed for identifying and evaluating optimal strategies for crowd evacuation


title: Compound Identification Using Penalized Linear Regression on Metabolomics

abstract: Compound identification is often achieved by matching the experimental mass spectra to the mass spectra stored in a reference library based on mass spectral similarity. Because the number of compounds in the reference library is much larger than the range of mass-to-charge ratio (m/z) values so that the data become high dimensional data suffering from singularity. For this reason, penalized linear regressions such as ridge regression and the lasso are used instead of the ordinary least squares regression. Furthermore, two-step approaches using the dot product and Pearson’s correlation along with the penalized linear regression are proposed in this study.


title: Curcumin attenuates bevacizumab-induced toxicity via suppressing oxidative stress and preventing mitochondrial dysfunction in heart mitochondria

abstract: Heart failure was subsequently noted in 2-4% of patients on bevacizumab (BEV). Whereas mitochondria play an important role in myocardial tissue homeostasis, deterioration in mitochondrial function will eventually lead to cardiomyocyte cell death and consequently cardiovascular dysfunction. Therefore, the aim of our study is to search the effects of BEV on isolated rat heart mitochondria and cardiomyocytes, and survey the effect of curcumin as a mitochondrial protective and cardioprotective agent. Rat heart mitochondria and cardiomyocytes were isolated from adult rat heart ventricular. By using biochemical and flow cytometry evaluations, the parameters of mitochondrial toxicity including succinate dehydrogenase (SDH) activity, mitochondrial swelling, mitochondrial membrane potential (MMP) collapse, reactive oxygen species (ROS) formation and lipid peroxidation (LP), and cellular assays such as cytotoxicity and MMP collapse were evaluated. Results revealed that BEV (up to 50 μg/ml) induced a concentration- and time-dependent rise in mitochondrial ROS formation, MMP collapse, mitochondrial swelling, LP, and inhibition of SDH in rat heart mitochondria. Our results showed that curcumin (10-100 μM) significantly ameliorated BEV-induced mitochondrial toxicities. Also, our results in cellular assays confirmed amelioration effect of curcumin against BEV toxicity. These results indicate that the cardiotoxic effects of BEV are associated with mitochondrial dysfunction and ROS formation, which finally ends in MMP collapse and mitochondrial swelling as the "point of no return" in the cascade of events leading to apoptosis. Also, results of this study suggest that probably the combination therapy of BEV and curcumin could decrease mitochondrial effects of this drug.


title: Unsupervised Domain Adaptation with Residual Transfer Networks

abstract: The recent success of deep neural networks relies on massive amounts of labeled data. For a target task where labeled data is unavailable, domain adaptation can transfer a learner from a different source domain. In this paper, we propose a new approach to domain adaptation in deep networks that can jointly learn adaptive classifiers and transferable features from labeled data in the source domain and unlabeled data in the target domain. We relax a shared-classifier assumption made by previous methods and assume that the source classifier and target classifier differ by a residual function. We enable classifier adaptation by plugging several layers into deep network to explicitly learn the residual function with reference to the target classifier. We fuse features of multiple layers with tensor product and embed them into reproducing kernel Hilbert spaces to match distributions for feature adaptation. The adaptation can be achieved in most feed-forward models by extending them with new residual layers and loss functions, which can be trained efficiently via back-propagation. Empirical evidence shows that the new approach outperforms state of the art methods on standard domain adaptation benchmarks.


title: Security Challenges of Small Cell as a Service in Virtualized Mobile Edge Computing Environments

abstract: Research on next-generation 5G wireless networks is currently attracting a lot of attention in both academia and industry. While 5G development and standardization activities are still at their early stage, it is widely acknowledged that 5G systems are going to extensively rely on dense small cell deployments, which would exploit infrastructure and network functions virtualization (NFV), and push the network intelligence towards network edges by embracing the concept of mobile edge computing (MEC). As security will be a fundamental enabling factor of small cell as a service (SCaaS) in 5G networks, we present the most prominent threats and vulnerabilities against a broad range of targets. As far as the related work is concerned, to the best of our knowledge, this paper is the first to investigate security challenges at the intersection of SCaaS, NFV, and MEC. It is also the first paper that proposes a set of criteria to facilitate a clear and effective taxonomy of security challenges of main elements of 5G networks. Our analysis can serve as a staring point towards the development of appropriate 5G security solutions. These will have crucial effect on legal and regulatory frameworks as well as on decisions of businesses, governments, and end-users.


title: Wheelchair motion control guide using eye gaze and blinks based on bug 2 algorithm

abstract: In the present study, we put forward a new approach together with the classic method, to direct the powered wheelchair by means of eye movements. At the same time as the wheelchair automatically travels towards the location of the desired destination, the user is granted to look around without restraints. Only Electro-Oclography (EOG) signals are used to guide the wheelchair; eye gazes and blinks. Switching between guiding methods; typical manual method and developed automatic method, can be done any time by the user. In the proposed guiding approach; by scheming the gaze angle of the wheelchair user, the control unit is able to obtain the desired point location; distance and direction. Bug algorithms are employed to guide the wheelchair in the automatic guiding approach. Two different Bug algorithms are utilized to navigate the wheelchair in the auto controlling method. A comparison is discussed between Bug2 and Tangent-Bug, algorithms. Experimental tests show slightly different results than theory, because the bug algorithms cannot continuously update the robot's position data in experiments.


title: Multidimensional materials and device architectures for future hybrid energy storage

abstract: With the continued miniaturization of electronics, there are increasing efforts to engineer small, powerful energy storage devices. Here the authors review the cutting edge of this rapidly developing field, highlighting the most promising materials and architectures for our future energy storage requirements.


title: Scattering Parameters and Surface Normals from Homogeneous Translucent Materials Using Photometric Stereo

abstract: This paper proposes a novel photometric stereo solution to jointly estimate surface normals and scattering parameters from a globally planar, homogeneous, translucent object. Similar to classic photometric stereo, our method only requires as few as three observations of the translucent object under directional lighting. Naively applying classic photometric stereo results in blurred photometric normals. We develop a novel blind deconvolution algorithm based on inverse rendering for recovering the sharp surface normals and the material properties. We demonstrate our method on a variety of translucent objects.


title: On-Chip Microwave Quantum Hall Circulator

abstract: Circulators are non-reciprocal circuit elements integral to technologies including radar systems, microwave communication transceivers, and the readout of quantum information devices. Their non-reciprocity arises from the interference of microwaves over the centimetre-scale of the signal wavelength in the presence of bulky magnetic media that break time-reversal symmetry. Here we realize a completely passive on-chip microwave circulator with size one-thousandth the wavelength by exploiting the chiral, slow-light response of a 2-dimensional electron gas (2DEG) in the quantum Hall regime. For an integrated GaAs device with 330 um diameter and 1 GHz centre frequency, a non-reciprocity of 25 dB is observed over a 50 MHz bandwidth. Furthermore, the direction of circulation can be selected dynamically by varying the magnetic field, an aspect that may enable reconfigurable passive routing of microwave signals on-chip.


title: Few-Shot Object Recognition from Machine-Labeled Web Images

abstract: With the tremendous advances made by Convolutional Neural Networks (ConvNets) on object recognition, we can now easily obtain adequately reliable machine-labeled annotations easily from predictions by off-the-shelf ConvNets. In this work, we present an abstraction memory based framework for few-shot learning, building upon machine-labeled image annotations. Our method takes large-scale machine-annotated dataset (e.g., OpenImages) as an external memory bank. In the external memory bank, the information is stored in the memory slots in the form of key-value, in which image feature is regarded as the key and the label embedding serves as the value. When queried by the few-shot examples, our model selects visually similar data from the external memory bank and writes the useful information obtained from related external data into another memory bank, i.e., abstraction memory. Long Short-Term Memory (LSTM) controllers and attention mechanisms are utilized to guarantee the data written to the abstraction memory correlates with the query example. The abstraction memory concentrates information from the external memory bank to make the few-shot recognition effective. In the experiments, we first confirm that our model can learn to conduct few-shot object recognition on clean human-labeled data from the ImageNet dataset. Then, we demonstrate that with our model, machine-labeled image annotations are very effective and abundant resources for performing object recognition on novel categories. Experimental results show that our proposed model with machine-labeled annotations achieves great results, with only a 1% difference in accuracy between the machine-labeled annotations and the human-labeled annotations.


title: Robotic validation of visual odometry for wireless capsule endoscopy

abstract: Wireless capsule endoscopy (WCE) is the prime diagnostic modality for the small-bowel. It consists in a swallowable color camera that enables the visual detection and assessment of abnormalities, without patient discomfort. The localization of the capsule is currently performed in the 3D abdominal space using radiofrequency (RF) triangulation. However, this approach does not provide sufficient information for the localization of the capsule, and therefore for the localization of the detected abnormalities, within the gastrointestinal (GI) lumen. To cope with this problem, we have recently proposed a method for visual tracking of the capsule endoscope (CE). It is based solely on visual features extracted from the captured images during the journey of the CE in the GI tract, enabling therefore visual odometry. Due to lack of ex-vivo or in-vivo ground truth data, the feasibility of that method was assessed using relative measurements in an image-based simulation experiment. In this paper, we make one step forward towards the assessment of the absolute localization capabilities of visual odometry using a calibrated in-vitro experimental setup. The obtained results validate the feasibility of the proposed approach, highlight the difficulty of this complex problem, and reveal the challenges ahead.


Automatic Learning Content Sequence via Linked Open Data


title: EDoS-Shield - A Two-Steps Mitigation Technique against EDoS Attacks in Cloud Computing

abstract: Cloud computing is currently one of the most hyped information technology fields and it has become one of the fastest growing segments of IT. Cloud computing allows us to scale our servers in magnitude and availability in order to provide services to a greater number of end users. Moreover, adopters of the cloud service model are charged based on a pay-per-use basis of the cloud's server and network resources, aka utility computing. With this model, a conventional DDoS attack on server and network resources is transformed in a cloud environment to a new breed of attack that targets the cloud adopter's economic resource, namely Economic Denial of Sustainability attack (EDoS). In this paper, we advocate a novel solution, named EDoS-Shield, to mitigate the Economic Denial of Sustainability (EDoS) attack in the cloud computing systems. We design a discrete simulation experiment to evaluate its performance and the results show that it is a promising solution to mitigate the EDoS.


Catalytic Stability Study of a Pd-Ni2 P/C Catalyst for Formic Acid Electrooxidation


title: Measuring performance in a changing business environment

abstract: The design and use of performance measurement systems has received considerable attention in recent years. Many organisations have redesigned their measurement systems to ensure that they reflect their current environment and strategies. However, increasingly the environment in which organisations compete is dynamic and rapidly changing, requiring constant modification of strategies and operations to reflect these changing circumstances. Despite this, few organisations appear to have systematic processes in place to ensure that their performance measurement systems continue to reflect their environment and strategies. This paper presents case study research that investigates what actions organisations can take to ensure that their measurement systems evolve over time.


title: ICFHR2014 Competition on Handwritten Document Image Binarization (H-DIBCO 2014)

abstract: Document image binarization is an important step in the document image analysis and recognition pipeline. H-DIBCO 2014 is the International Document Image Binarization Competition which is dedicated to handwritten document images organized in conjunction with ICFHR 2014 conference. The objective of the contest is to identify current advances in handwritten document image binarization using meaningful evaluation performance measures. This paper reports on the contest details including the evaluation measures used as well as the performance of the 7 submitted methods along with a short description of each method.


title: TextProposals: a Text-specific Selective Search Algorithm for Word Spotting in the Wild

abstract: Motivated by the success of powerful while expensive techniques to recognize words in a holistic way, object proposals techniques emerge as an alternative to the traditional text detectors. In this paper we introduce a novel object proposals method that is specifically designed for text. We rely on a similarity based region grouping algorithm that generates a hierarchy of word hypotheses. Over the nodes of this hierarchy it is possible to apply a holistic word recognition method in an efficient way. ::: Our experiments demonstrate that the presented method is superior in its ability of producing good quality word proposals when compared with class-independent algorithms. We show impressive recall rates with a few thousand proposals in different standard benchmarks, including focused or incidental text datasets, and multi-language scenarios. Moreover, the combination of our object proposals with existing whole-word recognizers shows competitive performance in end-to-end word spotting, and, in some benchmarks, outperforms previously published results. Concretely, in the challenging ICDAR2015 Incidental Text dataset, we overcome in more than 10 percent f-score the best-performing method in the last ICDAR Robust Reading Competition. Source code of the complete end-to-end system is available at this https URL


title: Hooked on Facebook: The Role of Social Anxiety and Need for Social Assurance in Problematic Use of Facebook

abstract: Abstract There is a growing concern that excessive and uncontrolled use of Facebook not only interferes with performance at school or work but also poses threats to physical and psychological well-being. The present research investigated how two individual difference variables—social anxiety and need for social assurance—affect problematic use of Facebook. Drawing on the basic premises of the social skill model of problematic Internet use, we hypothesized that social anxiety and need for social assurance would be positively correlated with problematic use of Facebook. Furthermore, it was predicted that need for social assurance would moderate the relationship between social anxiety and problematic use. A cross-sectional online survey was conducted with a college student sample in the United States (N = 243) to test the proposed hypotheses. Results showed that both social anxiety and need for social assurance had a significant positive association with problematic use of Facebook. More importantly, the dat...


title: Occluded object reconstruction for first responders with augmented reality glasses using conditional generative adversarial networks

abstract: Firefighters suffer a variety of life-threatening risks, including line-of-duty deaths, injuries, and exposures to hazardous substances. Support for reducing these risks is important. We built a partially occluded object reconstruction method on augmented reality glasses for first responders. We used a deep learning based on conditional generative adversarial networks to train associations between the various images of flammable and hazardous objects and their partially occluded counterparts. Our system then reconstructed an image of a new flammable object. Finally, the reconstructed image was superimposed on the input image to provide"transparency". The system imitates human learning about the laws of physics through experience by learning the shape of flammable objects and the flame characteristics.


title: Joint inference of entities, relations, and coreference

abstract: Although joint inference is an effective approach to avoid cascading of errors when inferring multiple natural language tasks, its application to information extraction has been limited to modeling only two tasks at a time, leading to modest improvements. In this paper, we focus on the three crucial tasks of automated extraction pipelines: entity tagging, relation extraction, and coreference. We propose a single, joint graphical model that represents the various dependencies between the tasks, allowing flow of uncertainty across task boundaries. Since the resulting model has a high tree-width and contains a large number of variables, we present a novel extension to belief propagation that sparsifies the domains of variables during inference. Experimental results show that our joint model consistently improves results on all three tasks as we represent more dependencies. In particular, our joint model obtains 12% error reduction on tagging over the isolated models.


title: Atraumatic tears of the ligamentum teres are more frequent in professional ballet dancers than a sporting population

abstract: OBJECTIVES ::: To compare the frequency of atraumatic ligamentum teres (LT) tear in professional ballet dancers with that of athletes, and to determine the relationship with clinical and imaging findings. ::: ::: ::: METHODS ::: Forty-nine male and female professional ballet dancers (98 hips) and 49 age and sex-matched non-dancing athletes (98 hips) completed questionnaires on hip symptoms and physical activity levels, underwent hip rotation range of movement (ROM) and hypermobility testing, and 3.0-Tesla magnetic resonance imaging (3 T MRI) on both hips to detect LT tears, acetabular labral tears, and articular cartilage defects, and to measure the lateral centre edge angles (LCE). ::: ::: ::: RESULTS ::: A higher frequency of LT tear was found in dancers (55 %) compared with athletes (22 %, P = 0.001). The frequency and severity of LT tears in dancers increased with older age (P = 0.004, P = 0.006, respectively). The Hip and Groin Outcome Score (HAGOS) pain scores or hip rotation ROM did not differ significantly among participants with normal, partial, or complete tears of LT (P > 0.01 for all). Neither the frequency of generalised joint hypermobility (P = 0.09) nor the LCE angles (P = 0.32, P = 0.16, left and right hips respectively) differed between those with and those without LT tear. In most hips, LT tear co-existed with either a labral tear or a cartilage defect, or both. ::: ::: ::: CONCLUSION ::: The higher frequency of atraumatic LT tears in professional ballet dancers suggests that the LT might be abnormally loaded in ballet, and caution is required when evaluating MRI, as LT tears may be asymptomatic. A longitudinal study of this cohort is required to determine if LT tear predisposes the hip joint to osteoarthritis.


title: A software-based dynamic-warp scheduling approach for load-balancing the Viola-Jones face detection algorithm on GPUs

abstract: Face detection is a key component in applications such as security surveillance and human-computer interaction systems, and real-time recognition is essential in many scenarios. The Viola-Jones algorithm is an attractive means of meeting the real time requirement, and has been widely implemented on custom hardware, FPGAs and GPUs. We demonstrate a GPU implementation that achieves competitive performance, but with low development costs. Our solution treats the irregularity inherent to the algorithm using a novel dynamic warp scheduling approach that eliminates thread divergence. This new scheme also employs a thread pool mechanism, which significantly alleviates the cost of creating, switching, and terminating threads. Compared to static thread scheduling, our dynamic warp scheduling approach reduces the execution time by a factor of 3. To maximize detection throughput, we also run on multiple GPUs, realizing 95.6 FPS on 5 Fermi GPUs.


title: Ultra High Speed operation of BLDC motor with enhanced motor dynamics for space limited applications

abstract: This paper proposes new method of operating BLDC motor which extends operating speed beyond the limit set by conventional 120° conduction mode of operation, without field weakening and having same inverter dc bus voltage. Concepts of Phase Extension mode and Ultra High Speed mode are introduced. Also in the paper it is shown that the proposed ‘phase extension’ mode of operation can produce additional average torque, above the conventionally available torque. Thus increasing torque-to-weight ratio of BLDC motor drive system makes it desirable for space constrained applications like Aerospace. Proposed modes of operation will enhance the motor dynamics in the wide speed range. Analysis, simulation results and pictorial descriptions are presented.


title: Learning to match and cluster large high-dimensional data sets for data integration

abstract: Part of the process of data integration is determining which sets of identifiers refer to the same real-world entities. In integrating databases found on the Web or obtained by using information extraction methods, it is often possible to solve this problem by exploiting similarities in the textual names used for objects in different databases. In this paper we describe techniques for clustering and matching identifier names that are both scalable and adaptive, in the sense that they can be trained to obtain better performance in a particular domain. An experimental evaluation on a number of sample datasets shows that the adaptive method sometimes performs much better than either of two non-adaptive baseline systems, and is nearly always competitive with the best baseline system.


title: Artificial Redirection of Sensation From Prosthetic Fingers to the Phantom Hand Map on Transradial Amputees: Vibrotactile Versus Mechanotactile Sensory Feedback

abstract: This work assesses the ability of transradial amputees to discriminate multi-site tactile stimuli in sensory discrimination tasks. It compares different sensory feedback modalities using an artificial hand prosthesis in: 1) a modality matched paradigm where pressure recorded on the five fingertips of the hand was fed back as pressure stimulation on five target points on the residual limb; and 2) a modality mismatched paradigm where the pressures were transformed into mechanical vibrations and fed back. Eight transradial amputees took part in the study and were divided in two groups based on the integrity of their phantom map; group A had a complete phantom map on the residual limb whereas group B had an incomplete or nonexisting map. The ability in localizing stimuli was compared with that of 10 healthy subjects using the vibration feedback and 11 healthy subjects using the pressure feedback (in a previous study), on their forearms, in similar experiments. Results demonstrate that pressure stimulation surpassed vibrotactile stimulation in multi-site sensory feedback discrimination. Furthermore, we demonstrate that subjects with a detailed phantom map had the best discrimination performance and even surpassed healthy participants for both feedback paradigms whereas group B had the worst performance overall. Finally, we show that placement of feedback devices on a complete phantom map improves multi-site sensory feedback discrimination, independently of the feedback modality.


title: Anti theft control system design using embedded system

abstract: In this paper we present a novel anti theft control system for automobiles that tries to prevent the theft of a vehicle. This system makes use of an embedded chip that has an inductive proximity sensor, which senses the key during insertion and sends a text message to the owner's mobile stating that the car is being accessed. This is followed by the system present in the car asking the user to enter a unique password. The password consists of few characters and the car key number. If the user fails to enter the correct password in three trials, a text message is sent to the police with the vehicle number and the location tracked using a GPS module. The message is also sent to the owner about the unauthorized usage. Further the fuel injector of the car is deactivated so that the user cannot start the car by any means. At the same time a secret lock system gets activated and the unauthorized user gets trapped inside the car and only the owner who is equipped with the key to the secret lock system can deactivate the mechanism. This technique helps in taking fast steps towards an attempt to steal the. The design is robust and simple.


title: Do burnout and work engagement predict depressive symptoms and life satisfaction? A three-wave seven-year prospective study

abstract: Abstract Background Burnout and work engagement have been viewed as opposite, yet distinct states of employee well-being. We investigated whether work-related indicators of well-being (i.e. burnout and work engagement) spill-over and generalize to context-free well-being (i.e. depressive symptoms and life satisfaction). More specifically, we examined the causal direction: does burnout/work engagement lead to depressive symptoms/life satisfaction, or the other way around? Methods Three surveys were conducted. In 2003, 71% of all Finnish dentists were surveyed ( n = 3255), and the response rate of the 3-year follow-up was 84% ( n = 2555). The second follow-up was conducted four years later with a response rate of 86% ( n = 1964). Structural equation modeling was used to investigate the cross-lagged associations between the study variables across time. Results Burnout predicted depressive symptoms and life dissatisfaction from T1 to T2 and from T2 to T3. Conversely, work engagement had a negative effect on depressive symptoms and a positive effect on life satisfaction, both from T1 to T2 and from T2 to T3, even after adjusting for the impact of burnout at every occasion. Limitations The study was conducted among one occupational group, which limits its generalizability. Conclusions Work-related well-being predicts general wellbeing in the long-term. For example, burnout predicts depressive symptoms and not vice versa. In addition, burnout and work engagement are not direct opposites. Instead, both have unique, incremental impacts on life satisfaction and depressive symptoms.


title: Multisite prediction of 4-week and 52-week treatment outcomes in patients with first-episode psychosis: a machine learning approach

abstract: Summary Background At present, no tools exist to estimate objectively the risk of poor treatment outcomes in patients with first-episode psychosis. Such tools could improve treatment by informing clinical decision-making before the commencement of treatment. We tested whether such a tool could be successfully built and validated using routinely available, patient-reportable information. Methods By applying machine learning to data from 334 patients in the European First Episode Schizophrenia Trial (EUFEST; International Clinical Trials Registry Platform number, ISRCTN68736636), we developed a tool to predict poor versus good treatment outcome (Global Assessment of Functioning [GAF] score ≥65 vs GAF Findings The generalisability of our outcome predictions were estimated with cross-validation (test-fold balanced accuracy [BAC] of 75·0% for 4-week outcomes and 73·8% for and 52-week outcomes), and leave-site-out validation across 44 European sites (BAC of 72·1% for 4-week outcomes and 71·1% for 52-week outcomes). We identified a smaller group of ten predictors still providing a BAC of 71·7% in 108 patients never used for model discovery. Unemployment, poor education, functional deficits, and unmet psychosocial needs predicted both endpoints, whereas previous depressive episodes, male sex, and suicidality additionally predicted poor 1-year outcomes. 52-week predictions identified patients at risk for symptom persistence, non-adherence to treatment, readmission to hospital and poor quality of life. Specifically among these patients, amisulpride and olanzapine showed superior efficacy versus haloperidol, quetiapine, and ziprasidone. Interpretation Our results suggest that prognostic models operating on brief, patient-reportable pre-treatment data might provide useful insight into individualised outcome trajectories, optimising treatment selection, and targeted clinical trial designs. To embed these tools into real-world care, replication is needed in external first-episode samples with overlapping variables, which are not available in the field at present. Funding The European Group for Research in Schizophrenia.


title: Discrete-Continuous Depth Estimation from a Single Image

abstract: In this paper, we tackle the problem of estimating the depth of a scene from a single image. This is a challenging task, since a single image on its own does not provide any depth cue. To address this, we exploit the availability of a pool of images for which the depth is known. More specifically, we formulate monocular depth estimation as a discrete-continuous optimization problem, where the continuous variables encode the depth of the superpixels in the input image, and the discrete ones represent relationships between neighboring superpixels. The solution to this discrete-continuous optimization problem is then obtained by performing inference in a graphical model using particle belief propagation. The unary potentials in this graphical model are computed by making use of the images with known depth. We demonstrate the effectiveness of our model in both the indoor and outdoor scenarios. Our experimental evaluation shows that our depth estimates are more accurate than existing methods on standard datasets.


title: Procedural Generation of Rock Piles using Aperiodic Tiling

abstract: In this paper, we present a tiling method for generating piles of rocks without any computationally demanding physically-based simulation. Previous techniques rely on a periodic tiling of rocks and generate unrealistic repetitive patterns. In contrast, our approach relies on a modified corner cube algorithm to generate a set of aperiodic tiles. We generalize the construction method so that the geometry of rocks should straddle corner cubes with a view to avoiding unrealistic gaps in the arrangement of rocks. Moreover, we propose an original technique to control the shape of rocks into contact by computing the Voronoi cells using a parameterized anisotropic distance. Our method has been successfully used to generate landscapes and stone huts and walls with thousands of rocks piled together.


title: Hopsworks: Improving User Experience and Development on Hadoop with Scalable, Strongly Consistent Metadata

abstract: Hadoop is a popular system for storing, managing,and processing large volumes of data, but it has bare-bonesinternal support for metadata, as metadata is a bottleneck andless means more scalability. The result is a scalable platform withrudimentary access control that is neither user- nor developer-friendly. Also, metadata services that are built on Hadoop, suchas SQL-on-Hadoop, access control, data provenance, and datagovernance are necessarily implemented as eventually consistentservices, resulting in increased development effort and morebrittle software. In this paper, we present a new project-based multi-tenancymodel for Hadoop, built on a new distribution of Hadoopthat provides a distributed database backend for the HadoopDistributed Filesystem's (HDFS) metadata layer. We extendHadoop's metadata model to introduce projects, datasets, andproject-users as new core concepts that enable a user-friendly, UI-driven Hadoop experience. As our metadata service is backed bya transactional database, developers can easily extend metadataby adding new tables and ensure the strong consistency ofextended metadata using both transactions and foreign keys.


title: Novel multilevel inverter carrier-based PWM methods

abstract: The advent of the transformerless multilevel inverter topology has brought forth various pulsewidth modulation (PWM) schemes as a means to control the switching of the active devices in each of the multiple voltage levels in the inverter. An analysis of how existing multilevel carrier-based PWM affects switch utilization for the different levels of a diode-clamped inverter is conducted. Two novel carrier-based multilevel PWM schemes are presented which help to optimize or balance the switch utilization in multilevel inverters. A 10 kW prototype six-level diode-clamped inverter has been built and controlled with the novel PWM strategies proposed in this paper to act as a voltage-source inverter for a motor drive.


title: A 40 GHz/9 ps Sampling Head for Wideband Applications

abstract: The purpose of this research is the evaluation and the design of a 40 GHz/9 ps wideband sampling head with commercially available microwave components and without the use of digital compensation techniques or cryogenic cooling techniques. This paper discusses the considerable increase in bandwidth and the novel compensation techniques of the AC-blow-by and initial-charge blow-by of the traveling-wave trapped-charge sampling gate [1] since these are the current limitations of this most promising approach for wideband sampling. The actual circuit is realized on alumina substrate (?r = 9.9, h = 10 mil). The same method was employed on RT/duroid (?r = 10.8, h = 10 mil) which yields an impressive bandwidth of 21 GHz.


title: Optimal exact least squares rank minimization

abstract: In multivariate analysis, rank minimization emerges when a low-rank structure of matrices is desired as well as a small estimation error. Rank minimization is nonconvex and generally NP-hard, imposing one major challenge. In this paper, we consider a nonconvex least squares formulation, which seeks to minimize the least squares loss function with the rank constraint. Computationally, we develop efficient algorithms to compute a global solution as well as an entire regularization solution path. Theoretically, we show that our method reconstructs the oracle estimator exactly from noisy data. As a result, it recovers the true rank optimally against any method and leads to sharper parameter estimation over its counterpart. Finally, the utility of the proposed method is demonstrated by simulations and image reconstruction from noisy background.


title: Determinants of a sustainable competitive advantage due to an IT-enabled strategy

abstract: Information technology (IT) has been asserted to be a source of sustainable competitive advantage. Empirical evidence has shown that IT can improve a company's performance and competitive position. We examine the factors that are believed to lead to a sustainable competitive advantage due to an IT-enabled strategy, and test these factors empirically. Our findings show that managerial IT skills are positively related to sustainability, and competitor's knowledge of competitive advantage is negatively related to sustainability. There was no support for technical IT skills or IT infrastructure as a source of sustainable competitive advantage.


title: Large Eddy Simulation of Vertical Axis Wind Turbine Wakes

abstract: In this study, large-eddy simulation (LES) is combined with a turbine model to investigate the wake behind a vertical-axis wind turbine (VAWT) in a three dimensional turbulent flow. Two methods are used to model the subgrid-scale (SGS) stresses: (a) the Smagorinsky model, and (b) the modulated gradient model. To parameterize the effects of the VAWT on the flow, two VAWT models are developed: (a) the actuator surface model (ASM), in which the time-averaged turbine-induced forces are distributed on a surface swept by the turbine blades, i.e. the actuator surface, and (b) the actuator line model (ALM), in which the instantaneous blade forces are only spatially distributed on lines representing the blades, i.e. the actuator lines. This is the first time that LES is applied and validated for simulation of VAWT wakes by using either the ASM or the ALM techniques. In both models, blade-element theory is used to calculate the lift and drag forces on the blades. The results are compared with flow measurements in the wake of a model straight-bladed VAWT, carried out in the Institute de Mechanique et Statistique de la Turbulence (IMST) water channel. Different combinations of SGS models with VAWT models are studied and a fairly good overall agreement between simulation results and measurement data is observed. In general, the ALM is found to better capture the unsteady-periodic nature of the wake and shows a better agreement with the experimental data compared with the ASM. The modulated gradient model is also found to be a more reliable SGS stress modeling technique, compared with the Smagorinsky model, and it yields reasonable predictions of the mean flow and turbulence characteristics of a VAWT wake using its theoretically-determined model coefficient.


title: Simple Open Stance Classification for Rumour Analysis

abstract: Stance classification determines the attitude, or stance, in a (typically short) text. The task has powerful applications, such as the detection of fake news or the automatic extraction of attitudes toward entities or events in the media. This paper describes a surprisingly simple and efficient classification approach to open stance classification in Twitter, for rumour and veracity classification. The approach profits from a novel set of automatically identifiable problem-specific features, which significantly boost classifier accuracy and achieve above state-of-the-art results on recent benchmark datasets. This calls into question the value of using complex sophisticated models for stance classification without first doing informed feature extraction.


title: Bigtable: A Distributed Storage System for Structured Data

abstract: Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers. Many projects at Google store data in Bigtable, including web indexing, Google Earth, and Google Finance. These applications place very different demands on Bigtable, both in terms of data size (from URLs to web pages to satellite imagery) and latency requirements (from backend bulk processing to real-time data serving). Despite these varied demands, Bigtable has successfully provided a flexible, high-performance solution for all of these Google products. In this paper we describe the simple data model provided by Bigtable, which gives clients dynamic control over data layout and format, and we describe the design and implementation of Bigtable.


title: Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces

abstract: Motivated by the success of reinforcement learning (RL) for discrete-time tasks such as AlphaGo and Atari games, there has been a recent surge of interest in using RL for continuous-time control of physical systems (cf. many challenging tasks in OpenAI Gym and DeepMind Control Suite). Since discretization of time is susceptible to error, it is methodologically more desirable to handle the system dynamics directly in continuous time. However, very few techniques exist for continuous-time RL and they lack flexibility in value function approximation. In this paper, we propose a novel framework for model-based continuous-time value function approximation in reproducing kernel Hilbert spaces. The resulting framework is so flexible that it can accommodate any kind of kernel-based approach, such as Gaussian processes and kernel adaptive filters, and it allows us to handle uncertainties and nonstationarity without prior knowledge about the environment or what basis functions to employ. We demonstrate the validity of the presented framework through experiments.


title: Building Bayesian Neural Networks with Blocks: On Structure, Interpretability and Uncertainty

abstract: We provide simple schemes to build Bayesian Neural Networks (BNNs), block by block, inspired by a recent idea of computation skeletons. We show how by adjusting the types of blocks that are used within the computation skeleton, we can identify interesting relationships with Deep Gaussian Processes (DGPs), deep kernel learning (DKL), random features type approximation and other topics. We give strategies to approximate the posterior via doubly stochastic variational inference for such models which yield uncertainty estimates. We give a detailed theoretical analysis and point out extensions that may be of independent interest. As a special case, we instantiate our procedure to define a Bayesian {\em additive} Neural network -- a promising strategy to identify statistical interactions and has direct benefits for obtaining interpretable models.


title: Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms

abstract: An important application of reinforcement learning (RL) is to finite-state control problems and one of the most difficult problems in learning for control is balancing the exploration/exploitation tradeoff. Existing theoretical results for RL give very little guidance on reasonable ways to perform exploration. In this paper, we examine the convergence of single-step on-policy RL algorithms for control. On-policy algorithms cannot separate exploration from learning and therefore must confront the exploration problem directly. We prove convergence results for several related on-policy algorithms with both decaying exploration and persistent exploration. We also provide examples of exploration strategies that can be followed during learning that result in convergence to both optimal values and optimal policies.


title: Understanding the Information Security Awareness Process in Real Estate Organizations Using the Seci Model

abstract: Information security awareness plays a key role in organizations using information systems for their business operations. Recent trends have indicated that not only critical infrastructure sectors but also organizations such as retail services are targeted by criminals as long as there is data of interest and the level of information security (and awareness) is relatively low. One such example concerns real estate organizations where they process their customers’ personal and sensitive information. In this study, we applied Nonaka’s Socialization, Externalization, Internalization, and Combination (SECI) model as the theoretical lens to understand how real estate business employees gain awareness and understanding of information security. Data collected from 105 real estate organizations is tested using the structural equation modelling approach. The results indicate Socialization, Internalization and Combination were found to support the development of information security awareness among employees while data analysis does not support Externalization.


title: Ultracompact optrode with integrated laser diode chips and SU-8 waveguides for optogenetic applications

abstract: This paper reports on the design, fabrication and characterization of an innovative silicon-based neural probe with optical functionality. This so-called optrode is intended as an ultracompact tool for optogenetic applications in neuroscientific research. Beside platinum microe-lectrodes for electrical recording applications, bare laser diode (LD) chips combined with waveguides (WGs) implemented in the negative photoresist SU-8 are integrated on the probe. The assembly of the bare LD chips applies flip-chip and wire bonding and benefits of a lateral alignment accuracy better ±5 μm required for the efficient coupling of light into the 15-μm-wide and 13-μm-high WGs. Undesired tissue illumination due to stray light is effectively blocked using a micromachined cover chip adhesively bonded to the probe base. Probe shafts with a length of up to 8 mm and a thickness of 50 μm carrying four electrodes and two WGs each have been realized. The maximum optical output power per WG was measured to be 29.7 mW/mm2 for LDs with a center wavelength of 650 nm.


title: Detecting algorithmically generated malicious domain names

abstract: Recent Botnets such as Conficker, Kraken and Torpig have used DNS based "domain fluxing" for command-and-control, where each Bot queries for existence of a series of domain names and the owner has to register only one such domain name. In this paper, we develop a methodology to detect such "domain fluxes" in DNS traffic by looking for patterns inherent to domain names that are generated algorithmically, in contrast to those generated by humans. In particular, we look at distribution of alphanumeric characters as well as bigrams in all domains that are mapped to the same set of IP-addresses. We present and compare the performance of several distance metrics, including KL-distance, Edit distance and Jaccard measure. We train by using a good data set of domains obtained via a crawl of domains mapped to all IPv4 address space and modeling bad data sets based on behaviors seen so far and expected. We also apply our methodology to packet traces collected at a Tier-1 ISP and show we can automatically detect domain fluxing as used by Conficker botnet with minimal false positives.


title: End-to-End Communication Architecture for Smart Grids

abstract: Smart grids heavily depend on communication in order to coordinate the generation, distribution, and consumption of energy-even more so if distributed power plants based on renewable energies are taken into account. Given the variety of communication partners, a heterogeneous network infrastructure consisting of IP-based and suitable field-level networks is the most appropriate solution. This paper investigates such a two-tier infrastructure and possible field-level networks with particular attention to metering and supervisory control and data acquisition applications. For the problem of network integration, a combination of gateway and tunneling solutions is proposed which allows a semitransparent end-to-end connection between application servers and field nodes. The feasibility of the approach and implementation details are discussed at the example of powerline communication and IP-based networks investigated in the European research project on real-time energy management via powerlines and internet. Nevertheless, it is shown that the communication architecture is versatile enough to serve as a generic solution for smart grids.


title: New directions on agile methods: a comparative analysis

abstract: Agile software development methods have caught the attention of software engineers and researchers worldwide. Scientific research is yet scarce. This paper reports results from a study, which aims to organize, analyze and make sense out of the dispersed field of agile software development methods. The comparative analysis is performed using the method's life-cycle coverage, project management support, type of practical guidance, fitness-for-use and empirical evidence as the analytical lenses. The results show that agile software development methods, without rationalization, cover certain/different phases of the software development life-cycle and most of the them do not offer adequate support for project management. Yet, many methods still attempt to strive for universal solutions (as opposed to situation appropriate) and the empirical evidence is still very limited Based on the results, new directions are suggested In principal it is suggested to place emphasis on methodological quality -- not method quantity.


title: Symmetrizing the Kullback-Leibler Distance

abstract: We define a new distance measure the resistor-average distance between two probability distributions that is closely related to the Kullback-Leibler distance. While the KullbackLeibler distance is asymmetric in the two distributions, the resistor-average distance is not. It arises from geometric considerations similar to those used to derive the Chernoff distance. Determining its relation to well-known distance measures reveals a new way to depict how commonly used distance measures relate to each other.


title: Multiple UAV cooperative search under collision avoidance and limited range communication constraints

abstract: This paper uses a team of unmanned air vehicles (UAVs) to cooperatively search, an area of interest that contains regions of opportunity and regions of potential hazard. The objective of the UAV team is to visit as many opportunities as possible, while avoiding as many hazards as possible. To enable cooperation, the UAVs are constrained to stay within communication range of one another. Collision avoidance is also required. Algorithms for team-optimal and individually-optimal/team-suboptimal solutions are developed and their computational complexity compared. Simulation results demonstrating the feasibility of the cooperative search algorithms are presented.


A Broad View of the Ecosystem of Socially Engineered Exploit Documents


title: 18 kW three phase inverter system using hermetically sealed SiC phase-leg power modules

abstract: Power electronics play an important role in electricity utilization from generation to end customers. Thus, high-efficiency power electronics help to save energy and conserve energy resources. Research on silicon carbide (SiC) power electronics has shown their better efficiency compared to Si power electronics due to the significant reduction in both conduction and switching losses. Combined with their high-temperature capability, SiC power electronics are more reliable and compact. This paper focuses on the development of such a high efficiency, high temperature inverter based on SiC JFET and diode modules. It involves the work on high temperature packaging (>200 °C), inverter design and prototype development, device characterization, and inverter testing. A SiC inverter prototype with a power rating of 18 kW is developed and demonstrated. When tested at moderate load levels compared to the inverter rating, an efficiency of 98.2% is achieved by the initial prototype without optimization, which is higher than most Si inverters.


title: Systematic review of dermoscopy and digital dermoscopy/ artificial intelligence for the diagnosis of melanoma

abstract: Summary ::: Background Dermoscopy improves diagnostic accuracy of the unaided eye for melanoma, and digital dermoscopy with artificial intelligence or computer diagnosis has also been shown useful for the diagnosis of melanoma. At present there is no clear evidence regarding the diagnostic accuracy of dermoscopy compared with artificial intelligence. ::: ::: Objectives To evaluate the diagnostic accuracy of dermoscopy and digital dermoscopy/artificial intelligence for melanoma diagnosis and to compare the diagnostic accuracy of the different dermoscopic algorithms with each other and with digital dermoscopy/artificial intelligence for the detection of melanoma. ::: ::: Methods A literature search on dermoscopy and digital dermoscopy/artificial intelligence for melanoma diagnosis was performed using several databases. Titles and abstracts of the retrieved articles were screened using a literature evaluation form. A quality assessment form was developed to assess the quality of the included studies. Heterogeneity among the studies was assessed. Pooled data were analysed using meta-analytical methods and comparisons between different algorithms were performed. ::: ::: Results Of 765 articles retrieved, 30 studies were eligible for meta-analysis. Pooled sensitivity for artificial intelligence was slightly higher than for dermoscopy (91% vs. 88%; P = 0·076). Pooled specificity for dermoscopy was significantly better than artificial intelligence (86% vs. 79%; P < 0·001). Pooled diagnostic odds ratio was 51·5 for dermoscopy and 57·8 for artificial intelligence, which were not significantly different (P = 0·783). There were no significance differences in diagnostic odds ratio among the different dermoscopic diagnostic algorithms. ::: ::: Conclusions Dermoscopy and artificial intelligence performed equally well for diagnosis of melanocytic skin lesions. There was no significant difference in the diagnostic performance of various dermoscopy algorithms. The three-point checklist, the seven-point checklist and Menzies score had better diagnostic odds ratios than the others; however, these results need to be confirmed by a large-scale high-quality population-based study.


title: An Empirical Evaluation of Deep Learning for ICD-9 Code Assignment using MIMIC-III Clinical Notes

abstract: Abstract Background and Objective Code assignment is of paramount importance in many levels in modern hospitals, from ensuring accurate billing process to creating a valid record of patient care history. However, the coding process is tedious and subjective, and it requires medical coders with extensive training. This study aims to evaluate the performance of deep-learning-based systems to automatically map clinical notes to ICD-9 medical codes. Methods The evaluations of this research are focused on end-to-end learning methods without manually defined rules. Traditional machine learning algorithms, as well as state-of-the-art deep learning methods such as Recurrent Neural Networks and Convolution Neural Networks, were applied to the Medical Information Mart for Intensive Care (MIMIC-III) dataset. An extensive number of experiments was applied to different settings of the tested algorithm. Results Findings showed that the deep learning-based methods outperformed other conventional machine learning methods. From our assessment, the best models could predict the top 10 ICD-9 codes with 0.6957 F1 and 0.8967 accuracy and could estimate the top 10 ICD-9 categories with 0.7233 F1 and 0.8588 accuracy. Our implementation also outperformed existing work under certain evaluation metrics. Conclusion A set of standard metrics was utilized in assessing the performance of ICD-9 code assignment on MIMIC-III dataset. All the developed evaluation tools and resources are available online, which can be used as a baseline for further research.


title: SOPJ: A Scalable Online Provenance Join for Data Integration

abstract: Data integration is a technique used to combine different sources of data together to provide an unified view among them. MOMIS[1] is an open-source data integration framework developed by the DBGroup1. The goal of our work is to make MOMIS be able to scale-out as the input data sources increase without introducing noticeable performance penalty. In particular, we present a full outer join method capable to efficiently integrate multiple sources at the same time by using data streams and provenance information. To evaluate the scalability of this innovative approach, we developed a join engine employing a distributed data processing framework. Our solution is able to process input data sources in the form of continuous stream, execute the join operation on-the-fly and produce outputs as soon as they are generated. In this way, the join can return partial results before the input streams have been completely received or processed optimizing the entire execution. Encouraging results of adopting the proposed approach on real datasets closes the paper.


title: Convexity of stiffness matrix eigenvalues for a position controlled limb of mobile climbing robots

abstract: This paper presents a simple model of the structure stiffness of a position controlled limb designed for mobile climbing robot. The stiffness matrices of the robot limb at different configurations are investigated. Experiments are conducted to demonstrate that this model is able to predict the general trend of change of stiffness matrix as a function of end effector position. Experiments also show that the maximum eigenvalue of stiffness matrix is a convex function with respect to the effector position, which matches the prediction of theory. Other testing includes measuring the effect of motor backlash on stiffness coefficients, and gauge the amount of contribution to total compliance from joint compliance and structure compliance.


title: DIYABC v2.0: a software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data

abstract: DIYABC is a software package for a comprehensive analysis of population history using approximate Bayesian computation on DNA polymorphism data. Version 2.0 implements a number of new features and analytical methods. It allows (i) the analysis of single nucleotide polymorphism data at large number of loci, apart from microsatellite and DNA sequence data, (ii) efficient Bayesian model choice using linear discriminant analysis on summary statistics and (iii) the serial launching of multiple post-processing analyses. DIYABC v2.0 also includes a user-friendly graphical interface with various new options. It can be run on three operating systems: GNU/Linux, Microsoft Windows and Apple Os X. Freely available with a detailed notice document and example projects to academic users at http://www1.montpellier.inra.fr/CBGP/diyabc CONTACT: estoup@supagro.inra.fr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


title: Turning a Mobile Device into a Mouse in the Air

abstract: A mouse has been one of the most successful user interfaces due to its intuitive use. As more devices are equipped with displays and offer rich options for users to choose from, a traditional mouse that requires a surface to operate is no longer sufficient. While different types of air mice are available in the market, they rely on accelerometers and gyroscopes, which significantly limit the accuracy and ease of use. In this paper, we develop a system that can accurately track hand movement to realize a mouse. A unique advantage of our scheme is that it achieves high tracking accuracy using the existing hardware already available in the mobile devices (e.g., smart phones and smart watches) and equipment to be controlled (e.g., smart TVs). More specifically, our approach sends inaudible sound pulses at a few selected frequencies, and uses the frequency shifts to estimate the speed and distance traveled. We then develop techniques to quickly calibrate the distance between speakers and narrow down the device's initial position using its movement trajectory. Based on the information, we continuously track the device's new position in real time. This is feasible because many devices, such as smart TVs, PCs, and laptops, already have multiple speakers. When only one speaker is available, we can leverage the frequency shift of sound along with the phase of received WiFi signal to enable tracking. Our evaluation and user study demonstrate that our system achieves high tracking accuracy (e.g., median error of around 1.4 cm) and ease of use.


title: SDN-based security framework for the IoT in distributed grid

abstract: Software-defined networking (SDN), is evolving as a new paradigm for the next generation of network architecture. The separation of control plane and data plane within SDN, brings the flexibility to manage, configure, secure, and optimize network resources using dynamic software programs. From a security point of view SDN has the ability to collect information from the network devices and allow applications to program the forwarding devices, which unleashes a powerful technology for proactive and smart security policy. These functions enable the integration of security tools that can be used in distributed scenarios, unlike the traditional security solutions based on a static firewall programmed by an administrator such as Intrusion Detection and Prevention System (IDS/IPS). This network programmability may be integrated to create a new communication platform for the Internet of Things (IoT). In this paper, we present our preliminary study that is focused on the understanding of an effective approach to build a cluster network using SDN. By using network virtualization and OpenFlow technologies to generate virtual nodes, we simulate a prototype system of over 500 devices controlled by SDN, and it represents a cluster. The results show that the network devices are only able to forward the packets by predefined rules on the controller. For this reason, we propose a method to control the IP header at the application-level to overcome this problem using Opflex within SDN architecture.


title: A new look at psychological climate and its relationship to job involvement, effort, and performance.

abstract: This study investigated the process by which employee perceptions of the organizational environment are related to job involvement, effort, and performance. The researchers developed an operational definition of psychological climate that was based on how employees perceive aspects of the organizational environment and interpret them in relation to their own well-being. Perceived psychological climate was then related to job involvement, effort, and performance in a path-analytic framework. Results showed that perceptions of a motivating and involving psychological climate were related to job involvement, which in turn was related to effort. Effort was also related to work performance. Results revealed that a modest but statistically significant effect of job involvement on performance became nonsignificant when effort was inserted into the model, indicating the mediating effect of effort on the relationship. The results cross-validated well across 2 samples of outside salespeople, indicating that relationships are generalizable across these different sales contexts. Unleashing the power of human potential in the workplace through creation of an involving and motivating organizational environment has been acclaimed as a key source of competitive advantage for business organizations (e.g., Lawler, 1992; Pfeffer, 1994). A prominent stream of literature has argued that when employees perceive the potential for satisfying their psychological needs in the workplace, they engage themselves more completely and invest greater time and effort in the organization's work (Kahn, 1990; Pfeffer, 1994). It has been argued that such processes lead to greater organizational productivity and competitiveness. Although anecdotal evidence suggests that favorable employee perceptions of organizational environments lead to superior performance, empirical research has not shown how (or whether) psychological climate and job involvement translate into higher performance. We investigated whether employee effort constitutes the mediating link relating psychological climate and job involvement to work performance. Our purpose was to investigate the process by which


title: The Balance Attack Against Proof-Of-Work Blockchains: The R3 Testbed as an Example

abstract: In this paper, we identify a new form of attack, called the Balance attack, against proof-of-work blockchain systems. The novelty of this attack consists of delaying network communications between multiple subgroups of nodes with balanced mining power. Our theoretical analysis captures the precise tradeoff between the network delay and the mining power of the attacker needed to double spend in Ethereum with high probability. ::: We quantify our probabilistic analysis with statistics taken from the R3 consortium, and show that a single machine needs 20 minutes to attack the consortium. Finally, we run an Ethereum private chain in a distributed system with similar settings as R3 to demonstrate the feasibility of the approach, and discuss the application of the Balance attack to Bitcoin. Our results clearly confirm that main proof-of-work blockchain protocols can be badly suited for consortium blockchains.