title: Cumulative citation recommendation: classification vs. ranking

abstract: Cumulative citation recommendation refers to the task of filtering a time-ordered corpus for documents that are highly relevant to a predefined set of entities. This task has been introduced at the TREC Knowledge Base Acceleration track in 2012, where two main families of approaches emerged: classification and ranking. In this paper we perform an experimental comparison of these two strategies using supervised learning with a rich feature set. Our main finding is that ranking outperforms classification on all evaluation settings and metrics. Our analysis also reveals that a ranking-based approach has more potential for future improvements.


title: Fatal mycotic dermatitis in captive brown tree snakes (Boiga irregularis).

abstract: Cutaneous fungal infections occurred in four captive brown tree snakes (Boiga irregularis). The ventral scales were most commonly affected, and lesions began as areas of erythema and edema with vesicle formation, followed by development of caseous brown plaques. Lesions usually started where ventral scales overlapped and spread rapidly. All snakes died within 14 days after clinical signs were first noted. The deaths of three of the snakes were directly attributable to the cutaneous disease; the other snake died from renal failure and visceral gout, most likely induced by gentamicin therapy. Histologically, lesions consisted of epidermal hyperplasia and hyperkeratosis, with foci of epidermal necrosis, intraepidermal vesicle formation, and subacute inflammation of the underlying dermis. These lesions were associated with bacteria and numerous septate, branched fungal hyphae within the epidermis and overlying serocelluar crusts. Hyphae that penetrated through the superficial surface of the epidermis often formed terminal arthroconidia. The same species of fungus was isolated in pure culture from the skin of three snakes, but fungal cultures were not performed on samples from the fourth snake. The fungus has been identified as the Chrysosporium anamorph of Nannizziopsis vriesii based on its formation of solitary dermatophytelike aleurioconidia and alternate and fission arthroconidia. The source of the fungus in this outbreak was not determined; however, the warm, moist conditions under which the snakes were housed likely predisposed them to opportunistic cutaneous fungal infections.


title: Unilateral amnesic stroke. Six new cases and a review of the literature

abstract: Although persistent amnesia due to bilateral limbic system infarction is well described, reports of amnesic syndromes due to unilateral stroke have appeared infrequently and unsystematically. We report six new cases and review previously published reports to expand and consolidate knowledge regarding amnesic stroke. ::: ::: Six patients developed acute amnesia associated with unilateral stroke. Brain computed tomography or magnetic resonance imaging revealed lesions in limbic structures, which accounted for the memory deficit. Based on these as well as similar cases in the literature, we delineate three distinct syndromes of unilateral amnesic stroke involving the territories of the posterior cerebral, anterior choroidal, and thalamic penetrating arteries. Eighty-five percent of reported cases have involved the left hemisphere. ::: ::: Persistent as well as transient amnesia may be the sole or primary manifestation of unilateral hemispheric stroke. The vascular mechanisms of amnesic stroke are diverse. Patients presenting with acute amnesia possibly related to cerebral ischemia should be classified by documented or presumed lesion site as well as the involved vascular territory. Left amygdalohippocampal or diencephalic dysfunction may produce a particular vulnerability to global amnesia.


title: Interactive music visualization for music player using processing

abstract: Most of the music players are often featured with music visualization that includes patterns such as splashes of colours and animated shapes which move to the rhythm of a song. This paper is presented an interactive theme-based design of music player using Processing. It is featured with interaction of music visualization that based on the music' s low-level features to correlate with the shapes colours, and player control functions, and results emotional-based of the visualization. Visual stimulus is able to enhance the connection between the music and user through visuals, especially when the auditory and visual stimuli are coordinated in a meaningful way. Therefore, we extracted the low-level features like size of sound, beat and frequency spectrum to form a specific visual pattern using shapes like freeform, cylinder and triangles to represent a defined content-the rainy day. Then, colours and player control functions are used to interact with the contents by changing the brightness of the colours to distinguish the different mood of the music. The resulting of music player is an interactive music visualization that integrate the low-level features through the rainy day contents via shapes and colors with real-time animation and player control interactions.


title: Flash Organizations: Crowdsourcing Complex Work by Structuring Crowds As Organizations

abstract: This paper introduces flash organizations: crowds structured like organizations to achieve complex and open-ended goals. Microtask workflows, the dominant crowdsourcing structures today, only enable goals that are so simple and modular that their path can be entirely pre-defined. We present a system that organizes crowd workers into computationally-represented structures inspired by those used in organizations - roles, teams, and hierarchies - which support emergent and adaptive coordination toward open-ended goals. Our system introduces two technical contributions: 1) encoding the crowd's division of labor into de-individualized roles, much as movie crews or disaster response teams use roles to support coordination between on-demand workers who have not worked together before; and 2) reconfiguring these structures through a model inspired by version control, enabling continuous adaptation of the work and the division of labor. We report a deployment in which flash organizations successfully carried out open-ended and complex goals previously out of reach for crowdsourcing, including product design, software development, and game production. This research demonstrates digitally networked organizations that flexibly assemble and reassemble themselves from a globally distributed online workforce to accomplish complex work.


title: A preliminary study on multi-wavelength PPG based pulse transit time detection for cuffless blood pressure measurement

abstract: Pulse transit time (PTT) has been widely studied as an index of blood pressure (BP) changes. In recent years, some prototypes of PTT-based wearable BP measurement devices have been developed, which can relieve users from the discomfort caused by the inflating cuff used in auscultatory and oscillometric BP measurement techniques. However, in the common practice for PTT detection, multi-site sensor implementation on human body is required, making it difficult for the integration of wearable devices. Since multi-wavelength (MW) photoplethysmogram (PPG) signals carry blood pulsation information of different blood vessels embedded in different skin depths, the time difference between different wavelength PPG signals collected at the same body site can be treated as a special PTT on a short length of blood vessels beneath the skin. In this work, the time difference between MW PPG, denoted as PTT_MW, was explored to track BP changes as a substitute of infrared (IR) PTT_EP. (PTT_EP is the time interval between electrocardiogram (ECG) and IR PPG.) Ten healthy adult subjects participated in the experiment, and their continuous BP, ECG and fingertip MW PPG signals generated from blue, green, yellow and IR light were recorded after 2-minute static handgrip exercise at 33% maximal voluntary contraction. The results showed that the correlation between Systolic BP (SBP) and IR-Blue PTT_MW (|r|= 0.52) was comparable to the correlation between SBP and IR PTT_EP (|r|= 0.59). Moreover, we optimized the wavelength combination of PTT_MWs for each subject and found the average value of optimal correlation between SBP and PTT_MW reached 0.76, which was significantly (p<0.01) higher than the correlation between IR PTT_EP and SBP. This study reveals that the time difference between MW PPG can be potentially used as PTT for cuffless BP measurement with its unique advantage in simple sensor implementation at only one body site.


Data Encryption Scheme Based on Rules of Cellular Automata and Chaotic Map Function for Information Security


title: Feature selection with linked data in social media

abstract: Feature selection is widely used in preparing highdimensional data for eective data mining. Increasingly popular social media data presents new challenges to feature selection. Social media data consists of (1) traditional high-dimensional, attribute-value data such as posts, tweets, comments, and images, and (2) linked data that describes the relationships between social media users as well as who post the posts, etc. The nature of social media also determines that its data is massive, noisy, and incomplete, which exacerbates the already challenging problem of feature selection. In this paper, we illustrate the dierences between attributevalue data and social media data, investigate if linked data can be exploited in a new feature selection framework by taking advantage of social science theories, extensively evaluate the eects of user-user and user-post relationships manifested in linked data on feature selection, and discuss some research issues for future work.


title: A Novel Circularly Polarized Antenna With Coin-Shaped Patches and a Ring-Shaped Strip for Worldwide UHF RFID Applications

abstract: A circularly polarized radio frequency identification (RFID) reader antenna is presented for worldwide ultra- high frequency (UHF) applications. The antenna is composed of two coin-shaped patches, a ring-shaped strip, and a suspended feeding strip with open-circuited termination. The measurements show that the antenna has an impedance bandwidth ( ${ S}_{11} ) of 22.4% (768–962 MHz) and a stable gain with a peak value of 9.8 dBic. Moreover, it is immune to polarization mismatch with a 3-dB axial-ratio (AR) bandwidth of 15.9% (816–957 MHz) and a 3-dB AR beamwidth of more than 66 $^\circ $ . The proposed antenna can be a candidate for universal RFID readers at the UHF band of 840–955 MHz. Due to the fact that the square holes in the center of the coin-shaped patches can embed one additional small antenna, the proposed antenna has the potential to be a dual- or tri-band combined RFID reader antenna, spanning both the conventional UHF and the ISM band of 2.45 or 5.8 GHz.


title: A Scalable Geospatial Web Service for Near Real-Time, High-Resolution Land Cover Mapping

abstract: A land cover classification service is introduced toward addressing current challenges on the handling and online processing of big remote sensing data. The geospatial web service has been designed, developed, and evaluated toward the efficient and automated classification of satellite imagery and the production of high-resolution land cover maps. The core of our platform consists of the Rasdaman array database management system for raster data storage and the open geospatial consortium web coverage processing service for data querying. Currently, the system is fully covering Greece with Landsat 8 multispectral imagery, from the beginning of its operational orbit. Datasets are stored and preprocessed automatically. A two-stage automated classification procedure was developed which is based on a statistical learning model and a multiclass support vector machine classifier, integrating advanced remote sensing and computer vision tools like Orfeo Toolbox and OpenCV. The framework has been trained to classify pansharpened images at 15-m ground resolution toward the initial detection of 31 spectral classes. The final product of our system is delivering, after a postclassification and merging procedure, multitemporal land cover maps with 10 land cover classes. The performed intensive quantitative evaluation has indicated an overall classification accuracy above 80%. The system in its current alpha release, once receiving a request from the client, can process and deliver land cover maps, for a 500- $\text{km}^2$ region, in about 20 s, allowing near real-time applications.


title: Artificial Intelligence Techniques for Cancer Detection and Classification: Review Study

abstract: Cancer is the general name for a group of more than 100 diseases. Although cancer includes different types of diseases, they all start because abnormal cells grow out of control. Without treatment, cancer can cause serious health problems and even loss of life. Early detection of cancer may reduce mortality and morbidity. This paper presents a review of the detection methods for lung, breast, and brain cancers. These methods used for diagnosis include artificial intelligence techniques, such as support vector machine neural network, artificial neural network, fuzzy logic, and adaptive neuro-fuzzy inference system, with medical imaging like X-ray, ultrasound, magnetic resonance imaging, and computed tomography scan images. Imaging techniques are the most important approach for precise diagnosis of human cancer. We investigated all these techniques to identify a method that can provide superior accuracy and determine the best medical images for use in each type of cancer.


title: Efficient and Scalable Runtime Monitoring for Cyber–Physical System

abstract: Our reliance on cyber–physical systems (CPSs) is increasingly widespread, but scalable methods for the analysis of such systems remain a significant challenge. Runtime verification of CPSs provides a reasonable middle ground between formal verification and simulation approaches, but it comes with its own challenges. A runtime verification system must run directly on the deployed application. In the CPS domain, it is therefore critical that a runtime verification system exhibits low overhead and good scalability so that the verification does not interfere with the analyzed CPS application. In this paper, we introduce Brace , a runtime verification system whose focus is on ensuring these performance qualities for applications in the CPS domain. Brace strives to bound the computation overhead for CPS runtime verification while preserving a high level of monitoring accuracy in terms of the number of false positive and false negative reports. Brace is particularly suitable to systems in which scheduling is distributed across networked CPS components. We evaluate Brace to determine how effectively and efficiently it can detect injected errors in two existing real-life CPS applications with distributed scheduling. Our results demonstrate that Brace efficiently detects those errors and a few true bugs and is able to bound both the memory and computation overhead even in systems with large numbers of observed events.


title: Understanding Second-Order Theory of Mind

abstract: Theory of mind is a key factor in the effectiveness of robots and humans working together as a team. Here, we further our understanding of theory of mind by extending a theory of mind model to account for a more complicated, second-order theory of mind task. Ultimately, this will provide robots with a deeper understanding of their human teammates, enabling them to better perform in human-robot teams.


A Novel 8 T SRAM Cell using 16 nm FinFET Technology


title: Intelligent legged climbing service robot for remote maintenance applications in hazardous environments

abstract: Abstract Wheeled-tracked vehicles are mainly suitable for relatively flat terrain. Legged-vehicles, on the other hand, have the potential to handle wide variety of terrain. Robug IIs is a legged climbing robot designed to work in relatively unstructured and rough terrain. It has the capability of walking, climbing vertical surfaces and performing autonomous floor to wall transfer. The sensing technique used in Robug IIs is mainly tactile and ultrasonic sensing. A set of reflexive rules has been developed for the robot to react to the uncertainty of the working environment. The robot also has the intelligence to seek and verify its own foot-holds.


Customers Churn Prediction and Attribute Selection in Telecom Industry Using Kernelized Extreme Learning Machine and Bat Algorithms


Learning How to Simplify From Explicit Labeling of Complex-Simplified Text Pairs


title: Maximum Boost Control for 7-level Z-source Cascaded H-Bridge Inverter

abstract: This paper proposes maximum boost control for 7-level z-source cascaded h-bridge inverter and their affiliation between voltage boost gain and modulation index. Z-source network avoids the usage of external dc-dc boost converter and improves output voltage with minimised harmonic content. Z-source network utilises distinctive LC impedance combination with 7-level cascaded inverter and it conquers the conventional voltage source inverter. The maximum boost controller furnishes voltage boost and maintain constant voltage stress across power switches, which provides better output voltage with variation of duty cycles. Single phase 7-level z-source cascaded inverter simulated using matlab/simulink.


title: Model Learning for Look-ahead Exploration in Continuous Control

abstract: We propose an exploration method that incorporates lookahead search over basic learnt skills and their dynamics, and use it for reinforcement learning (RL) of manipulation policies. Our skills are multi-goal policies learned in isolation in simpler environments using existing multigoal RL formulations, analogous to options or macroactions. Coarse skill dynamics, i.e., the state transition caused by a (complete) skill execution, are learnt and are unrolled forward during lookahead search. Policy search benefits from temporal abstraction during exploration, though itself operates over low-level primitive actions, and thus the resulting policies does not suffer from suboptimality and inflexibility caused by coarse skill chaining. We show that the proposed exploration strategy results in effective learning of complex manipulation policies faster than current state-of-the-art RL methods, and converges to better policies than methods that use options or parametrized skills as building blocks of the policy itself, as opposed to guiding exploration. We show that the proposed exploration strategy results in effective learning of complex manipulation policies faster than current state-of-the-art RL methods, and converges to better policies than methods that use options or parameterized skills as building blocks of the policy itself, as opposed to guiding exploration.


title: Seeing in the dark with recurrent convolutional neural networks

abstract: Classical convolutional neural networks (cCNNs) are very good at categorizing objects in images. But, unlike human vision which is relatively robust to noise in images, the performance of cCNNs declines quickly as image quality worsens. Here we propose to use recurrent connections within the convolutional layers to make networks robust against pixel noise such as could arise from imaging at low light levels, and thereby significantly increase their performance when tested with simulated noisy video sequences. We show that cCNNs classify images with high signal to noise ratios (SNRs) well, but are easily outperformed when tested with low SNR images (high noise levels) by convolutional neural networks that have recurrency added to convolutional layers, henceforth referred to as gruCNNs. Addition of Bayes-optimal temporal integration to allow the cCNN to integrate multiple image frames still does not match gruCNN performance. Additionally, we show that at low SNRs, the probabilities predicted by the gruCNN (after calibration) have higher confidence than those predicted by the cCNN. We propose to consider recurrent connections in the early stages of neural networks as a solution to computer vision under imperfect lighting conditions and noisy environments; challenges faced during real-time video streams of autonomous driving at night, during rain or snow, and other non-ideal situations.


title: On-The-Fly Academic Linked Data Integration

abstract: The web of Linked Open Data (LOD) has a prominent and rapid evolution recently. Over the last few years, LOD had developed to involve a wide range of various domains. Due to these facts, and the great interconnections among linked open datasets, linked data integration task had gained a huge attention and became a focal point of research. LOD applications aim to incorporate data from different LOD sources. Unfortunately, these sources of data are heterogeneous in schema and/or in vocabularies. Due to this heterogeneity, numerous challenges are emerging that have to be overcome. In this paper, a LOD integration framework is proposed, which aims to tackle these challenges. It works on integrating academic LOD datasets that reside in different LOD repositories with intrinsic schema and vocabularies heterogeneity. An automatic mapping technique in the integration processes is proposed in this paper. Consequently, an obvious decrease in execution time for the entire integration process, as well as, a great progress in the integrated data quality assessment metrics has been achieved.


title: Protocol Design Beyond Graph-Based Models

abstract: In this paper we shed new light on the fundamental gap between graph-based models used by protocol designers and fading channel models used by communication theorists in wireless networks. We experimentally demonstrate that graph-based models capture real-world phenomena inadequately. Consequentially, we advocate studying models beyond graphs even for protocol-design. In the main part of the paper we present an archetypal multi-hop situation. We show that the theoretical limits of any protocol which obeys the laws of graph-based models can be broken by a protocol explicitly defined for the physical model. Finally, we discuss possible applications, from data gathering to media access control.


title: Diffusion Adaptation Strategies for Distributed Optimization and Learning over Networks

abstract: We propose an adaptive diffusion mechanism to optimize global cost functions in a distributed manner over a network of nodes. The cost function is assumed to consist of a collection of individual components. Diffusion adaptation allows the nodes to cooperate and diffuse information in real-time; it also helps alleviate the effects of stochastic gradient noise and measurement noise through a continuous learning process. We analyze the mean-square-error performance of the algorithm in some detail, including its transient and steady-state behavior. We also apply the diffusion algorithm to two problems: distributed estimation with sparse parameters and distributed localization. Compared to well-studied incremental methods, diffusion methods do not require the use of a cyclic path over the nodes and are robust to node and link failure. Diffusion methods also endow networks with adaptation abilities that enable the individual nodes to continue learning even when the cost function changes with time. Examples involving such dynamic cost functions with moving targets are common in the context of biological networks.


title: Exploiting Multi-modal Curriculum in Noisy Web Data for Large-scale Concept Learning

abstract: Learning video concept detectors automatically from the big but noisy web data with no additional manual annotations is a novel but challenging area in the multimedia and the machine learning community. A considerable amount of videos on the web are associated with rich but noisy contextual information, such as the title, which provides weak annotations or labels about the video content. To leverage the big noisy web labels, this paper proposes a novel method called WEbly-Labeled Learning (WELL), which is established on the state-of-the-art machine learning algorithm inspired by the learning process of human. WELL introduces a number of novel multi-modal approaches to incorporate meaningful prior knowledge called curriculum from the noisy web videos. To investigate this problem, we empirically study the curriculum constructed from the multi-modal features of the videos collected from YouTube and Flickr. The efficacy and the scalability of WELL have been extensively demonstrated on two public benchmarks, including the largest multimedia dataset and the largest manually-labeled video set. The comprehensive experimental results demonstrate that WELL outperforms state-of-the-art studies by a statically significant margin on learning concepts from noisy web video data. In addition, the results also verify that WELL is robust to the level of noisiness in the video data. Notably, WELL trained on sufficient noisy web labels is able to achieve a comparable accuracy to supervised learning methods trained on the clean manually-labeled data.


title: Semantic Role Labeling of Implicit Arguments for Nominal Predicates

abstract: Nominal predicates often carry implicit arguments. Recent work on semantic role labeling has focused on identifying arguments within the local context of a predicate; implicit arguments, however, have not been systematically examined. To address this limitation, we have manually annotated a corpus of implicit arguments for ten predicates from NomBank. Through analysis of this corpus, we find that implicit arguments add 71% to the argument structures that are present in NomBank. Using the corpus, we train a discriminative model that is able to identify implicit arguments with an F1 score of 50%, significantly outperforming an informed baseline model. This article describes our investigation, explores a wide variety of features important for the task, and discusses future directions for work on implicit argument identification.


title: A vision-based system for automatic hand washing quality assessment

abstract: Hand washing is a critical activity in preventing the spread of infection in health-care environments and food preparation areas. Several guidelines recommended a hand washing protocol consisting of six steps that ensure that all areas of the hands are thoroughly cleaned. In this paper, we describe a novel approach that uses a computer vision system to measure the user’s hands motions to ensure that the hand washing guidelines are followed. A hand washing quality assessment system needs to know if the hands are joined or separated and it has to be robust to different lighting conditions, occlusions, reflections and changes in the color of the sink surface. This work presents three main contributions: a description of a system which delivers robust hands segmentation using a combination of color and motion analysis, a single multi-modal particle filter (PF) in combination with a k-means-based clustering technique to track both hands/arms, and the implementation of a multi-class classification of hand gestures using a support vector machine ensemble. PF performance is discussed and compared with a standard Kalman filter estimator. Finally, the global performance of the system is analyzed and compared with human performance, showing an accuracy close to that of human experts.


title: Automatically Generating Extraction Patterns from Untagged Text

abstract: Many corpus-based natural language processing systems rely on text corpora that have been manually annotated with syntactic or semantic tags. In particular, all previous dictionary construction systems for information extraction have used an annotated training corpus or some form of annotated input. We have developed a system called AutoSlog-TS that creates dictionaries of extraction patterns using only untagged text. AutoSlog-TS is based on the AutoSlog system, which generated extraction patterns using annotated text and a set of heuristic rules. By adapting AutoSlog and combining it with statistical techniques, we eliminated its dependency on tagged text. In experiments with the MUG-4 terrorism domain, AutoSlog-TS created a dictionary of extraction patterns that performed comparably to a dictionary created by AutoSlog, using only preclassified texts as input.


title: Low-rank tensor decomposition based anomaly detection for hyperspectral imagery

abstract: Anomaly detection becomes increasingly important in hyper-spectral image analysis, since it can now uncover many material substances which were previously unresolved by multi-spectral sensors. In this paper, we propose a Low-rank Tensor Decomposition based anomaly Detection (LTDD) algorithm for Hyperspectral Imagery. The HSI data cube is first modeled as a dense low-rank tensor plus a sparse tensor. Based on the obtained low-rank tensor, LTDD further decomposes the low-rank tensor using Tucker decomposition to extract the core tensor which is treated as the “support” of the anomaly spectral signatures. LTDD then adopts an unmixing approach to the reconstructed core tensor for anomaly detection. The experiments based on both simulated and real hyperspectral data sets verify the effectiveness of our algorithm.


title: A Survey on the Cryptographic Encryption Algorithms

abstract: Security is the major concern when the sensitive information is stored and transferred across the internet where the information is no longer protected by physical boundaries. Cryptography is an essential, effective and efficient component to ensure the secure communication between the different entities by transferring unintelligible information and only the authorized recipient can be able to access the information. The right selection of cryptographic algorithm is important for secure communication that provides more security, accuracy and efficiency. In this paper, we examine the security aspects and processes involved in the design and implementation of most widely used symmetric encryption algorithms such as Data Encryption Standard (DES), Triple Data Encryption Standard (3DES), Blowfish, Advanced Encryption Standard (AES) and Hybrid Cubes Encryption Algorithm (HiSea). Furthermore, this paper evaluated and compared the performance of these encryption algorithms based on encryption and decryption time, throughput, key size, avalanche effect, memory, correlation assessment and entropy. Thus, amongst the existing cryptographic algorithm, we choose a suitable encryption algorithm based on different parameters that are best fit to the user requirements.


title: Challenges and Opportunities in Edge Computing

abstract: Many cloud-based applications employ a data centers as a central server to process data that is generated by edge devices, such as smartphones, tablets and wearables. This model places ever increasing demands on communication and computational infrastructure with inevitable adverse effect on Quality-of-Service and Experience. The concept of Edge Computing is predicated on moving some of this computational load towards the edge of the network to harness computational capabilities that are currently untapped in edge nodes, such as base stations, routers and switches. This position paper considers the challenges and opportunities that arise out of this new direction in the computing landscape.


title: Network hubs in the human brain

abstract: Virtually all domains of cognitive function require the integration of distributed neural activity. Network analysis of human brain connectivity has consistently identified sets of regions that are critically important for enabling efficient neuronal signaling and communication. The central embedding of these candidate ‘brain hubs' in anatomical networks supports their diverse functional roles across a broad range of cognitive tasks and widespread dynamic coupling within and across functional networks. The high level of centrality of brain hubs also renders them points of vulnerability that are susceptible to disconnection and dysfunction in brain disorders. Combining data from numerous empirical and computational studies, network approaches strongly suggest that brain hubs play important roles in information integration underpinning numerous aspects of complex cognitive function.


title: Predicting Mortgage Default using Convolutional Neural Networks

abstract: Abstract We predict mortgage default by applying convolutional neural networks to consumer transaction data. For each consumer we have the balances of the checking account, savings account, and the credit card, in addition to the daily number of transactions on the checking account, and amount transferred into the checking account. With no other information about each consumer we are able to achieve a ROC AUC of 0.918 for the networks, and 0.926 for the networks in combination with a random forests classifier.


title: Malware Classification with Deep Convolutional Neural Networks

abstract: In this paper, we propose a deep learning framework for malware classification. There has been a huge increase in the volume of malware in recent years which poses a serious security threat to financial institutions, businesses and individuals. In order to combat the proliferation of malware, new strategies are essential to quickly identify and classify malware samples so that their behavior can be analyzed. Machine learning approaches are becoming popular for classifying malware, however, most of the existing machine learning methods for malware classification use shallow learning algorithms (e.g. SVM). Recently, Convolutional Neural Networks (CNN), a deep learning approach, have shown superior performance compared to traditional learning algorithms, especially in tasks such as image classification. Motivated by this success, we propose a CNN-based architecture to classify malware samples. We convert malware binaries to grayscale images and subsequently train a CNN for classification. Experiments on two challenging malware classification datasets, Malimg and Microsoft malware, demonstrate that our method achieves better than the state-of-the-art performance. The proposed method achieves 98.52% and 99.97% accuracy on the Malimg and Microsoft datasets respectively.


title: Identifying the most Informative Variables for Decision-Making Problems - a Survey of Recent Approaches and Accompanying Problems

abstract: We provide an overview of problems related to variable selection (also known as feature selection) techniques in decision-making problems based on machine learning with a particular emphasis on recent knowledge. Several popular methods are reviewed and assigned to a taxonomical context. Issues related to the generalization-versus-performance trade-off, inherent in currently used variable selection approaches, are addressed and illustrated on real-world examples.


title: SQLrand: Preventing SQL Injection Attacks

abstract: We present a practical protection mechanism against SQL injection attacks. Such attacks target databases that are accessible through a web front-end, and take advantage of flaws in the input validation logic of Web components such as CGI scripts. We apply the concept of instruction-set randomization to SQL, creating instances of the language that are unpredictable to the attacker. Queries injected by the attacker will be caught and terminated by the database parser. We show how to use this technique with the MySQL database using an intermediary proxy that translates the random SQL to its standard language. Our mechanism imposes negligible performance overhead to query processing and can be easily retrofitted to existing systems.


title: Flipper: A Systematic Approach to Debugging Training Sets

abstract: As machine learning methods gain popularity across different fields, acquiring labeled training datasets has become the primary bottleneck in the machine learning pipeline. Recently generative models have been used to create and label large amounts of training data, albeit noisily. The output of these generative models is then used to train a discriminative model of choice, such as logistic regression or a complex neural network. However, any errors in the generative model can propagate to the subsequent model being trained. Unfortunately, these generative models are not easily interpretable and are therefore difficult to debug for users. To address this, we present our vision for Flipper, a framework that presents users with high-level information about why their training set is inaccurate and informs their decisions as they improve their generative model manually. We present potential tools within the Flipper framework, inspired by observing biomedical experts working with generative models, which allow users to analyze the errors in their training data in a systematic fashion. Finally, we discuss a prototype of Flipper and report results of a user study where users create a training set for a classification task and improve the discriminative model's accuracy by 2.4 points in less than an hour with feedback from Flipper.


title: Chisio: A Compound Graph Editing and Layout Framework

abstract: We introduce a new free, open-source compound graph editing and layout framework named Chisio, based on the Eclipse Graph Editing Framework (GEF) and written in Java. Chisio can be used as a finished graph editor with its easy-to-use graphical interface. The framework has an architecture suitable for easy customization of the tool for end-user's specific needs as well. Chisio comes with a variety of graph layout algorithms, most supporting compound structures and non-uniform node dimensions. Furthermore, new algorithms are straightforward to add, making Chisio an ideal test environment for layout algorithm developers.


title: Deep Knowledge Tracing

abstract: Knowledge tracing—where a machine models the knowledge of a student as they interact with coursework—is a well established problem in computer supported education. Though effectively modeling student knowledge would have high educational impact, the task has many inherent challenges. In this paper we explore the utility of using Recurrent Neural Networks (RNNs) to model student learning. The RNN family of models have important advantages over previous methods in that they do not require the explicit encoding of human domain knowledge, and can capture more complex representations of student knowledge. Using neural networks results in substantial improvements in prediction performance on a range of knowledge tracing datasets. Moreover the learned model can be used for intelligent curriculum design and allows straightforward interpretation and discovery of structure in student tasks. These results suggest a promising new line of research for knowledge tracing and an exemplary application task for RNNs.


title: Multi-view Supervision for Single-View Reconstruction via Differentiable Ray Consistency

abstract: We study the notion of consistency between a 3D shape and a 2D observation and propose a differentiable formulation which allows computing gradients of the 3D shape given an observation from an arbitrary view. We do so by reformulating view consistency using a differentiable ray consistency (DRC) term. We show that this formulation can be incorporated in a learning framework to leverage different types of multi-view observations e.g. foreground masks, depth, color images, semantics etc. as supervision for learning single-view 3D prediction. We present empirical analysis of our technique in a controlled setting. We also show that this approach allows us to improve over existing techniques for single-view reconstruction of objects from the PASCAL VOC dataset.


title: Discrete Event, Continuous Time RNNs

abstract: We investigate recurrent neural network architectures for event-sequence processing. Event sequences, characterized by discrete observations stamped with continuous-valued times of occurrence, are challenging due to the potentially wide dynamic range of relevant time scales as well as interactions between time scales. We describe four forms of inductive bias that should benefit architectures for event sequences: temporal locality, position and scale homogeneity, and scale interdependence. We extend the popular gated recurrent unit (GRU) architecture to incorporate these biases via intrinsic temporal dynamics, obtaining a continuous-time GRU. The CT-GRU arises by interpreting the gates of a GRU as selecting a time scale of memory, and the CT-GRU generalizes the GRU by incorporating multiple time scales of memory and performing context-dependent selection of time scales for information storage and retrieval. Event time-stamps drive decay dynamics of the CT-GRU, whereas they serve as generic additional inputs to the GRU. Despite the very different manner in which the two models consider time, their performance on eleven data sets we examined is essentially identical. Our surprising results point both to the robustness of GRU and LSTM architectures for handling continuous time, and to the potency of incorporating continuous dynamics into neural architectures.


Representation Learning of Users and Items for Review Rating Prediction Using Attention-based Convolutional Neural Network


title: Multimodal recognition of personality traits in social interactions

abstract: This paper targets the automatic detection of personality traits in a meeting environment by means of audio and visual features; information about the relational context is captured by means of acoustic features designed to that purpose. Two personality traits are considered: Extraversion (from the Big Five) and the Locus of Control. The classification task is applied to thin slices of behaviour, in the form of 1-minute sequences. SVM were used to test the performances of several training and testing instance setups, including a restricted set of audio features obtained through feature selection. The outcomes improve considerably over existing results, provide evidence about the feasibility of the multimodal analysis of personality, the role of social context, and pave the way to further studies addressing different features setups and/or targeting different personality traits.


title: A scalable accuracy fuzzy logic controller on FPGA

abstract: A FLC has the characteristics of the distributed, scalable and flexible structure.The FLC is scaled by fuzzy accuracy factor in terms of fuzzy system elements.Scalable FLC reduces the complexity of design, speeds up fuzzy logic operation. A distributed, scalable and flexible fuzzy logic controller (FLC) without increasing additional hardware cost by fuzzy accuracy factor is proposed. In order to improve fuzzy logic operation speed, multi-input/multi-output (MIMO) fuzzy system is decomposed into several independent two-input/single-output (TISO) subsystems in parallel. The decomposed TISO FLC can deal with scalable requirements in terms of number of input language variables, output language variables, data accuracy types and fuzzy rules. In this paper, the systematic design methods are presented in detail, and the scalable TISO FLC architectures are addressed with fuzzification of fuzzy accuracy factor and analog-to-digital conversion (ADC) quantizer, fuzzy rules and fuzzy inference engine, and the defuzzification by a scalable divider. At last, A JTAG-TCL tool is developed to compare the scalable TISO FLC with other FLCs in resource, accuracy, and speed. The experiment results of photovoltaic (PV) system indicate that the proposed FLC has flexible control accuracy, fast response and better tracking performance.


title: Deceiving Google’s Cloud Video Intelligence API Built for Summarizing Videos

abstract: Despite the rapid progress of the techniques for image classification, video annotation has remained a challenging task. Automated video annotation would be a breakthrough technology, enabling users to search within the videos. Recently, Google introduced the Cloud Video Intelligence API for video analysis. As per the website, the system can be used to"separate signal from noise, by retrieving relevant information at the video, shot or per frame"level. A demonstration website has been also launched, which allows anyone to select a video for annotation. The API then detects the video labels (objects within the video) as well as shot labels (description of the video events over time). In this paper, we examine the usability of the Google's Cloud Video Intelligence API in adversarial environments. In particular, we investigate whether an adversary can subtly manipulate a video in such a way that the API will return only the adversary-desired labels. For this, we select an image, which is different from the video content, and insert it, periodically and at a very low rate, into the video. We found that if we insert one image every two seconds, the API is deceived into annotating the video as if it only contained the inserted image. Note that the modification to the video is hardly noticeable as, for instance, for a typical frame rate of 25, we insert only one image per 50 video frames. We also found that, by inserting one image per second, all the shot labels returned by the API are related to the inserted image. We perform the experiments on the sample videos provided by the API demonstration website and show that our attack is successful with different videos and images.


title: A mobile application of American sign language translation via image processing algorithms

abstract: Due to the relative lack of pervasive sign language usage within our society, deaf and other verbally-challenged people tend to face difficulty in communicating on a daily basis. Our study thus aims to provide research into a sign language translator applied on the smartphone platform, due to its portability and ease of use. In this paper, a novel framework comprising established image processing techniques is proposed to recognise images of several sign language gestures. More specifically, we initially implement Canny edge detection and seeded region growing to segment the hand gesture from its background. Feature points are then extracted with Speeded Up Robust Features (SURF) algorithm, whose features are derived through Bag of Features (BoF). Support Vector Machine (SVM) is subsequently applied to classify our gesture image dataset; where the trained dataset is used to recognize future sign language gesture inputs. The proposed framework has been successfully implemented on smartphone platforms, and experimental results show that it is able to recognize and translate 16 different American Sign Language gestures with an overall accuracy of 97.13%.


title: Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition

abstract: Recurrent Neural Networks (RNNs) and their variants, such as Long-Short Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, have achieved promising performance in sequential data modeling. The hidden layers in RNNs can be regarded as the memory units, which are helpful in storing information in sequential contexts. However, when dealing with high dimensional input data, such as video and text, the input-to-hidden linear transformation in RNNs brings high memory usage and huge computational cost. This makes the training of RNNs very difficult. To address this challenge, we propose a novel compact LSTM model, named as TR-LSTM, by utilizing the low-rank tensor ring decomposition (TRD) to reformulate the input-to-hidden transformation. Compared with other tensor decomposition methods, TR-LSTM is more stable. In addition, TR-LSTM can complete an end-to-end training and also provide a fundamental building block for RNNs in handling large input data. Experiments on real-world action recognition datasets have demonstrated the promising performance of the proposed TR-LSTM compared with the tensor-train LSTM and other state-of-the-art competitors.


title: A Scalable Implementation of Fully Homomorphic Encryption Built on NTRU

abstract: In this paper we report on our work to design, implement and evaluate a Fully Homomorphic Encryption (FHE) scheme. Our FHE scheme is an NTRU-like cryptosystem, with additional support for efficient key switching and modulus reduction operations to reduce the frequency of bootstrapping operations. Ciphertexts in our scheme are represented as matrices of 64-bit integers. The basis of our design is a layered software services stack to provide high-level FHE operations supported by lower-level lattice-based primitive implementations running on a computing substrate. We implement and evaluate our FHE scheme to run on a commodity CPU-based computing environment. We implemented our FHE scheme to run in a compiled C environment and use parallelism to take advantage of multi-core processors. We provide experimental results which show that our FHE implementation provides at least an order of magnitude improvement in runtime as compared to recent publicly known evaluation results of other FHE software implementations.


title: Gender inequalities, intimate partner violence and HIV preventive practices: findings of a South African cross-sectional study.

abstract: The aim of the paper is to investigate associations between a range of markers of gender inequity, including financial, psychological and physical violence, and two proximal practices in HIV prevention, namely discussion of HIV between partners and the woman suggesting condom use. The paper presents an analysis of data from a cross-sectional study of a representative sample of women from three South African Provinces which was primarily undertaken as an epidemiological study of gender-based violence. A multi-stage sampling design was used with clusters sampled with probability proportional to number of households. Households were randomly selected from within clusters. One randomly selected woman aged 18-49 years was interviewed in each selected home. One thousand three hundred and six women were interviewed (90.3% of eligible women). One thousand one hundred sixty four women had a partner in the previous year and were asked questions related to HIV prevention and gender inequalities in the relationship. The results indicate that discussion of HIV was significantly positively associated with education, living in Mpumalanga Province, the man being a migrant, the woman having multiple partners in the past year and having no confidante. It was significantly negatively associated with living in the Northern Province, the relationship being poor and there being a substantial age difference between partners. The woman suggesting condom use was significantly positively associated with her education, her having multiple partners, domestic violence prior to the past year and financial abuse. It was negatively associated with the relationship being poor. We conclude that this suggests that some indicators of gender inequalities are significantly associated with discussion of HIV and condom use but the direction of association found was both positive and negative. This highlights the need for a more nuanced understanding of gender inequalities and their relationship to HIV risk. Suggestions for key research questions are made.


title: Improving the scaffolds of a mobile-assisted Chinese character forming game via a design-based research cycle

abstract: This paper reports on one cycle of a design-based research (DBR) study in which mCSCL was explored through an iterative process of (re)designing and testing the collaboration and learning approach with students. A unique characteristic of our mCSCL approach is the student-led emergent formation of groups. The mCSCL application assigns each student a component of a Chinese character and requires them to form groups that can assemble a Chinese character using the components held by the group members. The enactment of the learning design in two modes (with and without the digital technology) was observed, and the actual process of students being scaffolded technologically or socially to accomplish their task was analyzed. Students were found to favor the card mode over the phone mode due to the emergent game strategy (social scaffold) of ''trial and error'' that they found it comfortable in applying. That triggered us to examine the scaffolding strategies by conducting another round of literature review. We explored domain-oriented theories (i.e. in Chinese character learning) to inform and guide them in deciding how they should further accommodate or rectify the students' use of the strategy. This cycle of DBR in Chinese-PP project has effectively reshaped the overall learning model design. This paper brings to the fore the value of the interplay and iterations of theories, implementations and reflections, in no fixed order, as advocated by DBR.


title: A Behavior-Based Approach to Securing Email Systems

abstract: The Malicious Email Tracking (MET) system, reported in a prior publication, is a behavior-based security system for email services. The Email Mining Toolkit (EMT) presented in this paper is an offline email archive data mining analysis system that is designed to assist computing models of malicious email behavior for deployment in an online MET system. EMT includes a variety of behavior models for email attachments, user accounts and groups of accounts. Each model computed is used to detect anomalous and errant email behaviors. We report on the set of features implemented in the current version of EMT, and describe tests of the system and our plans for extensions to the set of models.


title: A distributed in-situ analysis method for large-scale scientific data

abstract: Recently, a massive amount of data is generated in a wide range of scientific applications such as NASA's satellite, the large hadron collider, and large synoptic survey telescope. Most of scientific data follows the array model, and there are various kinds of standard array formats such as HDF, NetCDF, MDSplus, and ROOT. SciDB is the most well-known DBMS that stores the array-based scientific data and processes queries on it. SciDB is a distributed DBMS, and so, is scalable in terms of query performance. However, it has a severe drawback that takes a huge amount of time for loading a massive amount of scientific data into DBMS. That is, it is not scalable in terms of data loading. To overcome that problem, we propose a distributed in-situ analysis method that allows processing queries on raw scientific data in a distributed manner without explicit data loading. In detail, we propose the in-situ scan operator that scans necessary data of the array format and passes it to upper operators of the pipeline of a query plan. It also performs repartitioning during in-situ scanning, which is required for correct query results. Through experiments using real datasets, we have shown that the SciDB system using our method significantly outperforms the original SciDB system by orders of magnitude in terms of the performance of the first query.


title: Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity

abstract: Spike timing dependent plasticity (STDP) is a learning rule that modifies synaptic strength as a function of the relative timing of pre- and postsynaptic spikes. When a neuron is repeatedly presented with similar inputs, STDP is known to have the effect of concentrating high synaptic weights on afferents that systematically fire early, while postsynaptic spike latencies decrease. Here we use this learning rule in an asynchronous feedforward spiking neural network that mimics the ventral visual pathway and shows that when the network is presented with natural images, selectivity to intermediate-complexity visual features emerges. Those features, which correspond to prototypical patterns that are both salient and consistently present in the images, are highly informative and enable robust object recognition, as demonstrated on various classification tasks. Taken together, these results show that temporal codes may be a key to understanding the phenomenal processing speed achieved by the visual system and that STDP can lead to fast and selective responses.


title: Embryonic development of the grass pufferfish ( Takifugu niphobles ): From egg to larvae

abstract: Tetraodontidae (pufferfish) family members carry the smallest genomes among vertebrates, and these pocket-sized genomes have directly contributed to our understanding of the structure and evolution of higher animals. The grass pufferfish (Takifugu niphobles) could be considered a potential new model organism for comparative genomics and development due to the potential access to embryos, and availability of sequence data for two similar genomes: that of spotted green pufferfish (Tetraodon nigroviridis) and Fugu (Takifugu rubripes). In this study, we provide the first description of the normal embryonic development of T. niphobles, by drawing comparisons with the closely related species cited above. Embryos were obtained by in vitro fertilization of eggs, and subsequent development was monitored at a constant temperature consistent with natural conditions. T. niphobles development was divided into seven periods of embryogenesis: the zygote, cleavage, blastula, gastrula, segmentation, pharyngula, and hatching periods; and stages subdividing these periods are defined based on morphological characteristics. The developmental stage series described in this study aims to provide the utilization of T. niphobles as an experimental model organism for comparative developmental studies.


title: EMI Filter Design for a 1 MHz, 10 kW Three-Phase/Level PWM Rectifier

abstract: The attenuation characteristics of electromagnetic interference (EMI) filters in practice often differ from theoretical predictions and minor changes can result in a significant improvement in performance. The performance of the differential-mode (DM) filter stage can usually be well predicted, but the common mode (CM) behavior is more difficult to handle. This is especially true for three-phase pulsewidth modulation (PWM) rectifier systems, which inherently show a large high-frequency CM voltage at the rectifier output. Possible CM noise current paths of a three-phase/level PWM rectifier are analyzed in this paper where parasitic capacitances to the heat sink and to earth are considered. In addition, a concept to significantly reduce CM emissions is discussed in detail. Based on the proposed models, an EMI filter design for a system with 1 MHz switching frequency is shown. Experimental verification of the designed EMI filter is presented by impedance and conducted emission (CE) measurements taken from a 10 kW prototype. Several practical aspects of filter implementation such as component arrangement, shielding layers, magnetic coupling, etc., are discussed and verified by measurements.


title: Quantitative effects of Nf1 inactivation on in vivo hematopoiesis

abstract: The NF1 tumor-suppressor gene is frequently inactivated in juvenile myelomonocytic leukemia, and Nf1 mutant mice model this myeloproliferative disorder (MPD). Competitive repopulation assays were performed to quantify the proliferative advantage of Nf1–/– hematopoietic cells in vivo. Nf1 mutant stem cells demonstrated a growth advantage that was greatest in myeloid lineage cells and least pronounced in T lymphocytes. Surprisingly, although low numbers of Nf1-deficient cells consistently outcompeted wild-type cells, levels of chimerism were stable over months of observation, and MPD was not observed unless threshold numbers of mutant cells were injected. These data showing that normal competitor cells can strongly modulate the growth of mutant populations in vivo have general implications for modeling cancer in the mouse. In particular, strains in which cancer-associated mutations are expressed in fields of target cells may not accurately model early events in tumorigenesis because they eliminate the requirement for a mutant clone to outcompete resident normal cells.


title: ProvChain: A Blockchain-Based Data Provenance Architecture in Cloud Environment with Enhanced Privacy and Availability

abstract: Cloud data provenance is metadata that records the history of the creation and operations performed on a cloud data object. Secure data provenance is crucial for data accountability, forensics and privacy. In this paper, we propose a decentralized and trusted cloud data provenance architecture using blockchain technology. Blockchain-based data provenance can provide tamper-proof records, enable the transparency of data accountability in the cloud, and help to enhance the privacy and availability of the provenance data. We make use of the cloud storage scenario and choose the cloud file as a data unit to detect user operations for collecting provenance data. We design and implement ProvChain, an architecture to collect and verify cloud data provenance, by embedding the provenance data into blockchain transactions. ProvChain operates mainly in three phases: (1) provenance data collection, (2) provenance data storage, and (3) provenance data validation. Results from performance evaluation demonstrate that ProvChain provides security features including tamper-proof provenance, user privacy and reliability with low overhead for the cloud storage applications.


title: Improved gait recognition by gait dynamics normalization

abstract: Potential sources for gait biometrics can be seen to derive from two aspects: gait shape and gait dynamics. We show that improved gait recognition can be achieved after normalization of dynamics and focusing on the shape information. We normalize for gait dynamics using a generic walking model, as captured by a population hidden Markov model (pHMM) defined for a set of individuals. The states of this pHMM represent gait stances over one gait cycle and the observations are the silhouettes of the corresponding gait stances. For each sequence, we first use Viterbi decoding of the gait dynamics to arrive at one dynamics-normalized, averaged, gait cycle of fixed length. The distance between two sequences is the distance between the two corresponding dynamics-normalized gait cycles, which we quantify by the sum of the distances between the corresponding gait stances. Distances between two silhouettes from the same generic gait stance are computed in the linear discriminant analysis space so as to maximize the discrimination between persons, while minimizing the variations of the same subject under different conditions. The distance computation is constructed so that it is invariant to dilations and erosions of the silhouettes. This helps us handle variations in silhouette shape that can occur with changing imaging conditions. We present results on three different, publicly available, data sets. First, we consider the HumanID gait challenge data set, which is the largest gait benchmarking data set that is available (122 subjects), exercising five different factors, i.e., viewpoint, shoe, surface, carrying condition, and time. We significantly improve the performance across the hard experiments involving surface change and briefcase carrying conditions. Second, we also show improved performance on the UMD gait data set that exercises time variations for 55 subjects. Third, on the CMU Mobo data set, we show results for matching across different walking speeds. It is worth noting that there was no separate training for the UMD and CMU data sets.


title: Disease Mention Recognition with Specific Features

abstract: Despite an increasing amount of research on biomedical named entity recognition, there has been not enough work done on disease mention recognition. Difficulty of obtaining adequate corpora is one of the key reasons which hindered this particular research. Previous studies argue that correct identification of disease mentions is the key issue for further improvement of the disease-centric knowledge extraction tasks. In this paper, we present a machine learning based approach that uses a feature set tailored for disease mention recognition and outperforms the state-of-the-art results. The paper also discusses why a feature set for the well studied gene/protein mention recognition task is not necessarily equally effective for other biomedical semantic types such as diseases.


title: A noise-immune stylus analog front-end using adjustable frequency modulation and linear-interpolating data reconstruction for both electrically coupled resonance and active styluses

abstract: As the demand for intuitive and interactive displays is increasing in mobile devices such as smartphones and tablets, a pressure-sensitive stylus pen solution is needed for advanced user experiences [1-5]. Figure 10.4.1 compares electromagnetic resonance (EMR), active, and electrically coupled resonance (ECR) stylus systems, and shows cross-sectional views of sensor panels. EMR stylus systems have been successfully commercialized for high-end devices due to their battery-less, light, and pressure-sensing features [1-2]. However, EMR stylus systems require an extra sensing panel for electro-magnetic coupling. The active stylus system does not require the additional sensing panel but needs a battery located inside the stylus. On the other hand, ECR stylus systems are cost-effective without needing either the additional sensing panel or a built-in battery. Furthermore, ECR stylus systems measure pen pressure by sensing a stylus' resonant frequency change upon pressure without additional circuitry [3]. Due to the external noise injection to the stylus system being more than ten times larger than the pen signal as shown in Fig. 10.4.1, noise immunity is a key performance factor for commercialization. This paper proposes an analog front-end (AFE) for both ECR and active stylus systems with high noise immunity that results in improved signal-to-noise ratio (SNR) by applying a fully differential architecture, adjustable frequency modulation (AFM), and linear-interpolating data reconstruction (LIDR).


title: Review of demand response under smart grid paradigm

abstract: Demand response (DR) has an important role to play in the electricity market for maintaining the balance between supply and demand by introducing load flexibility instead of only adjusting generation levels, at almost all operational time scales. There are many players in the market who benefit from DR, like the TSO, DSOs, retailers and end-customers themselves. The recent advent of smart grid technologies advanced the integration of DR by providing the needed information and communication infrastructure to the existing grid. Available literature on DR talks about the concept and definitions of DR, possible DR models for various region-specific market structures along with few DR implementation experiences in a system with ever increasing levels of loads along with evolution of innovative technologies like renewables, micro-grids, PEVs, etc. In this paper, the available literature on DR is categorized into general concept papers and papers on DR models applicable to the wholesale or retail markets, and are presented in a precise manner.


title: Real-time randomized path planning for robot navigation

abstract: Mobile robots often find themselves in a situation where they must find a trajectory to another position in their environment, subject to constraints posed by obstacles and the robot's capabilities. This poses the problem of planning a path through a continuous domain. Several approaches have been used to address this problem each with some limitations, including state discretizations, planning efficiency, and lack of interleaved execution. Rapidly-exploring random trees (RRTs) are a recently developed algorithm on which fast continuous domain path planners can be based. In this work, we build a path planning system based on RRTs that interleaves planning and execution, first evaluating it in simulation and then applying it to physical robots. Our algorithm, ERRT (execution extended RRT), introduces two novel extensions of previous RRT work, the waypoint cache and adaptive cost search, which improve replanning efficiency and the quality of generated paths. ERRT is successfully applied to a multi-robot system. Results demonstrate that ERRT is improves efficiency and performs competitively with existing heuristic and reactive real-time path planning approaches. ERRT has shown to offer a major step with great potential for path planning in challenging continuous, highly dynamic domains.


Training Fast Twitch Muscle Fibers : Why and How


title: Radioactive decay data: powerful aids in medical diagnosis and therapy, analytical science and other applications

abstract: Abstract Decay data are commonly used to characterise and quantify radioactive material, and provide an important means of understanding the properties and structure of the nucleus. Experimental measurement techniques are reviewed, with the emphasis placed on recent developments that represent a potential quantum leap in advancing our knowledge, particularly by means of γ-ray spectroscopy. A select number of internationally-accepted decay-data evaluations and compilations are also discussed in terms of their contents. Both energy and non-energy related applications require the input of well-defined decay data, and such activities have been reviewed. Various important decay-data issues are assessed, and note taken of any significant requirements for better quantified data.


title: Bacterial Foraging Optimization for Fake Website Detection

abstract: This paper presents an approach to overcome the difficulty and complexity in detecting and predicting phishing websites. Existing system is an intelligent resilient and effective model that is based on using association and classification Data Mining algorithms. These algorithms were used to characterize and identify all the factors and rules in order to classify the phishing website and the relationship that correlate them with each other also compared their performances, accuracy, number of rules generated and speed. Even though the rules generated from the associative classification model showed the relationship between some important characteristics like URL and Domain Identity, and Security and Encryption criteria in the final phishing detection rate, there is no optimal solution. In proposed system we introduced a meta-heuristic algorithm BFOA to get an optimal solution for the search of fake websites. We also compare the proposed algorithm with other existing algorithms, including Ant Colony Optimization, Particle Swarm Optimization.


title: Why we need a granularity concept for user stories

abstract: User stories are a widespread instrument for representing requirements. They describe small user-oriented parts of the system and guide the daily work of developers. Often however, user stories are too coarse, so that misunderstandings or dependencies remain unforeseeable. Granularity of user stories needs to be investigated more, but at the same time is a hard-to-grasp concept. ::: ::: This paper investigates Expected Implementation Duration (EID) of a user story as a characteristic of granularity. We want to find out, whether it is suitable as a quality aspect and can help software teams improve their user stories. ::: ::: We have conducted a study with software engineering practitioners. There, many user stories had a relatively high EID of four or more days. Many developers state to have experienced certain problems to occur more often with such coarse user stories. Our findings emphasize the importance to reflect on granularity when working with user stories.


title: Mutual coupling reduction between microstrip patch antennas

abstract: A compact and simple shape of defected ground structure (DGS) is applied to reduce the mutual coupling between array elements in a microstrip array design. The proposed DGS with its low width is inserted between the adjacent E-plane coupled elements in the array to suppress the surface waves. Parametric studies of DGS parameters for a transmission line loaded with unit cell DGS are presented. A two-element array loaded with this DGS is then shown to provide a relative reduction in mutual coupling of 23 dB as compared to the same structure without the DGS. Simulation and measured results are provided and discussed.


title: Web Theory: An Introduction

abstract: Web Theory is a comprehensive and critical introduction to the theories of the Internet and the World Wide Web. Robert Burnett and P. David Marshall examine the key debates which surround Internet culture: from issues of globalization political economy and regulation, to ideas about communication, identity and aesthetics." "Web Theory explores the shifts in society, culture and the media which have been brought about by the growth of the World Wide Web. It identifies significant readings, Web sites and hypertext archive sources that illustrate the critical discussion about the Internet and it mediates these discussions, indicating key positions within each debate and pointing the reader to key texts.


title: Thermal Safe Power (TSP): Efficient Power Budgeting for Heterogeneous Manycore Systems in Dark Silicon

abstract: Chip manufacturers provide the Thermal Design Power (TDP) for a specific chip. The cooling solution is designed to dissipate this power level. But because TDP is not necessarily the maximum power that can be applied, chips are operated with Dynamic Thermal Management (DTM) techniques. To avoid excessive triggers of DTM, usually, system designers also use TDP as power constraint. However, using a single and constant value as power constraint, e.g., TDP, can result in significant performance losses in homogeneous and heterogeneous manycore systems. Having better power budgeting techniques is a major step towards dealing with the dark silicon problem. This paper presents a new power budget concept, called Thermal Safe Power (TSP), which is an abstraction that provides safe power and power density constraints as a function of the number of simultaneously active cores. Executing cores at any power consumption below TSP ensures that DTM is not triggered. TSP can be computed offline for the worst cases, or online for a particular mapping of cores. TSP can also serve as a fundamental tool for guiding task partitioning and core mapping decisions, specially when core heterogeneity or timing guarantees are involved. Moreover, TSP results in dark silicon estimations which are less pessimistic than estimations using constant power budgets.


title: In-Memory Parallel Processing of Massive Remotely Sensed Data Using an Apache Spark on Hadoop YARN Model

abstract: MapReduce has been widely used in Hadoop for parallel processing larger-scale data for the last decade. However, remote-sensing (RS) algorithms based on the programming model are trapped in dense disk I/O operations and unconstrained network communication, and thus inappropriate for timely processing and analyzing massive, heterogeneous RS data. In this paper, a novel in-memory computing framework called Apache Spark (Spark) is introduced. Through its merits of transferring transformation to in-memory datasets of Spark, the shortages are eliminated. To facilitate implementation and assure high performance of Spark-based algorithms in a complex cloud computing environment, a strip-oriented parallel programming model is proposed. By incorporating strips of RS data with resilient distributed datasets (RDDs) of Spark, all-level parallel RS algorithms can be easily expressed with coarse-grained transformation primitives and BitTorrent-enabled broadcast variables. Additionally, a generic image partition method for Spark-based RS algorithms to efficiently generate differentiable key/value strips from a Hadoop distributed file system (HDFS) is implemented for concealing the heterogeneousness of RS data. Data-intensive multitasking algorithms and iteration-intensive algorithms were evaluated on a Hadoop yet another resource negotiator (YARN) platform. Experiments indicated that our Spark-based parallel algorithms are of great efficiency, a multitasking algorithm took less than 4 h to process more than half a terabyte of RS data on a small YARN cluster, and 9*9 convolution operations against a 909-MB image took less than 260 s. Further, the efficiency of iteration-intensive algorithms is insensitive to image size.


title: The proteasome inhibitor PS-341 inhibits growth, induces apoptosis, and overcomes drug resistance in human multiple myeloma cells.

abstract: Human multiple myeloma (MM) is a presently incurable hematological malignancy, and novel biologically based therapies are urgently needed. Proteasome inhibitors represent a novel potential anticancer therapy. In this study, we demonstrate that the proteasome inhibitor PS-341 directly inhibits proliferation and induces apoptosis of human MM cell lines and freshly isolated patient MM cells; inhibits mitogen-activated protein kinase growth signaling in MM cells; induces apoptosis despite induction of p21 and p27 in both p53 wild-type and p53 mutant MM cells; overcomes drug resistance; adds to the anti-MM activity of dexamethasone; and overcomes the resistance to apoptosis in MM cells conferred by interleukin-6. PS-341 also inhibits the paracrine growth of human MM cells by decreasing their adherence to bone marrow stromal cells (BMSCs) and related nuclear factor kappaB-dependent induction of interleukin-6 secretion in BMSCs, as well as inhibiting proliferation and growth signaling of residual adherent MM cells. These data, therefore, demonstrate that PS-341 both acts directly on MM cells and alters cellular interactions and cytokine secretion in the BM millieu to inhibit tumor cell growth, induce apoptosis, and overcome drug resistance. Given the acceptable animal and human toxicity profile of PS-341, these studies provide the framework for clinical evaluation of PS-341 to improve outcome for patients with this universally fatal hematological malignancy.


title: A Hole-Filling Algorithm for Triangular Meshes Using Local Radial Basis Function

abstract: The french fry food vending machine has a plurality of frozen foodstuff holding compartments in a refrigeration chamber. A metered dispensing rotatable wheel is positioned below the outlet of each holding compartment to, upon activation, dispense a metered quantity of frozen food stuff to a cooking chamber, preferably by the delivery of the foodstuff to a conveyer position beneath a line of metered dispensing rotatable wheels. An oil circulating, filtering, heating and replenishment system is provided to service a deep frying tank with a quantity of Purified hot oil into which frozen food stuff is emerged for a predetermined period of time for cooking.


title: Edge-based split-and-merge superpixel segmentation

abstract: Superpixels are an oversegmentation of an image and popularly used as a preprocessing in many computer vision applications. Many state-of-the-art superpixel segmentation algorithms rely either on minimizing special energy functions or on clustering pixels in the effective distance space. While in this paper, we introduce a novel algorithm to produce superpixels based on the edge map by utilizing a split-andmerge strategy. Firstly, we obtain the initial superpixels with uniform size and shape. Secondly, in the splitting stage, we find all possible splitting contours for each superpixel by overlapping the boundaries of this superpixel with the edge map, and then choose the best one to split it which ensure the superpixels produced by this splitting are dissimilarity in color and similarity in size. Thirdly, in the merging stage, the Bhattacharyya distance between two color histograms in the RGB space for each pair of adjacent superpixels is computed to evaluate their color similarity for merging superpixels. At last, we iterate the split-and-merge steps until no superpixels have changed. Experimental results on the Berkeley Segmentation Dataset (BSD) show that the proposed algorithm can achieve a good performance compared with the state-of-the-art superpixel segmentation algorithms.


title: Knowledge Management Issues in Malaysian Organizations: the Perceptions of Leaders

abstract: Knowledge management is a relatively young topic to other older strategic tools, and it has been attracting the attention of researchers and academics over the past decade or so. In this regard globalization is already in many countries including Malaysia like many other developing countries, Malaysia is also striving to fulfil its vision of becoming a developed nation. It is a fast growing country in terms of both economy and technology. However, obstacles for successful knowledge management initiatives will be there for this country, as well. Thus, exploration of the potential barriers is essential before any Malaysian company embarks on a knowledge management journey. This research has shed light to some of the important issues and challenges with reference to knowledge management in Malaysia by providing a more thorough and clear picture of the knowledge management status amongst Malaysian organizations. This could in turn help top managers and decision makers to develop a more insightful agenda to ensure success for their respective knowledge management initiatives.


title: Rapid stress reduction and anxiolysis among distressed women as a consequence of a three-month intensive yoga program

abstract: Summary Background: Emotional distress is an increasing public health problem and Hatha yoga has been claimed to induce stress reduction and empowerment in practicing subjects. We aimed to evaluate potential effects of Iyengar Hatha yoga on perceived stress and associated psychological outcomes in mentally distressed women. Material/Methods: A controlled prospective non-randomized study was conducted in 24 self-referred female subjects (mean age 37.9±7.3 years) who perceived themselves as emotionally distressed. Subjects were offered participation in one of two subsequential 3-months yoga programs. Group 1 (n=16) participated in the fi rst class, group 2 (n=8) served as a waiting list control. During the yoga course, subjects attended two-weekly 90-min Iyengar yoga classes. Outcome was assessed on entry and after 3 months by Cohen Perceived Stress Scale, State-Trait Anxiety Inventory, Profi le of Mood States, CESD-Depression Scale, Bf-S/Bf-S’ Well-Being Scales, Freiburg Complaint List and ratings of physical well-being. Salivary cortisol levels were measured before and after an evening yoga class in a second sample. Results: Compared to waiting-list, women who participated in the yoga-training demonstrated pronounced and signifi cant improvements in perceived stress (P<0.02), State and Trait Anxiety (P<0.02 and P<0.01, respectively), well-being (P<0.01), vigor (P<0.02), fatigue (P<0.02) and depression (P<0.05). Physical well-being also increased (P<0.01), and those subjects suffering from headache or back pain reported marked pain relief. Salivary cortisol decreased signifi cantly after participation in a yoga class (P<0.05). Conclusions: Women suffering from mental distress participating in a 3-month Iyengar yoga class show significant improvements on measures of stress and psychological outcomes. Further investigation of yoga with respect to prevention and treatment of stress-related disease and of underlying mechanism is warranted.


Ontologies in AI and Law


title: Standard Compliant Hazard and Threat Analysis for the Automotive Domain

abstract: The automotive industry has successfully collaborated to release the ISO 26262 standard for developing safe software for cars. The standard describes in detail how to conduct hazard analysis and risk assessments to determine the necessary safety measures for each feature. However, the standard does not concern threat analysis for malicious attackers or how to select appropriate security countermeasures. We propose the application of ISO 27001 for this purpose and show how it can be applied together with ISO 26262. We show how ISO 26262 documentation can be re-used and enhanced to satisfy the analysis and documentation demands of the ISO 27001 standard. We illustrate our approach based on an electronic steering column lock system.


title: Creating Music and Texts with Flow Machines

abstract: This chapter introduces the vision and the technical challenges of the Flow Machines project. Flow Machines aim at fostering creativity in artistic domains such as music and literature. We first observe that typically, great artists do not output just single artefacts but develop novel, individual styles. Style mirrors an individual’s uniqueness; style makes an artist’s work recognised and recognisable. Artists develop their own style after prolonged periods of imitation and exploration of the style of others. We envision style exploration as the application of existing styles, considered as texture, to arbitrary constraints, considered as structure. The goal of Flow Machines is to assist this process by allowing users to explicitly manipulate styles as computational objects. During interactions with Flow Machines, the user can create artefacts (melodies, texts, orchestrations) by combining styles with arbitrary constraints. Style exploration under user-defined constraints raises complex sequence generation issues that were addressed and solved for the most part during the first half of the project. We illustrate the potential of these techniques for style exploration with three examples.


title: Design of digitally tunable bandpass filter for spectrum sensing application in the TVWS

abstract: This paper presents a digitally tunable bandpass filter(BPF) that can tune its center frequency with constant absolute bandwidth for spectrum sensing applications in the TVWS(TV White Space). The proposed two-pole BPF is implemented using lumped elements and the frequency tuning is realized by PIN diode and varactor diode. Due to the L-C parallel circuit as a J-inverter, constant absolute bandwidth is achieved. 4 PIN diodes in each resonator and the capacitor bank are used to cover the desired frequency tuning range from 480 MHz to 687 MHz with 21 MHz bandwidth. The complete design procedures are described. The fabricated filter has wide tuning range of 42.5% with insertion loss varying from 2.8dB to 4.6dB, while the 1-dB bandwidth of the filter being kept as 21 MHz.


title: A family of OWA operators based on Faulhaber's formulas

abstract: In this paper we develop a new family of Ordered Weighted Averaging (OWA) operators. Weight vector is obtained from a desired orness of the operator. Using Faulhaber's formulas we obtain direct and simple expressions for the weight vector without any iteration loop. With the exception of one weight, the remaining follow a straight line relation. As a result, a fast and robust algorithm is developed. The resulting weight vector is suboptimal according with the Maximum Entropy criterion, but it is very close to the optimal. Comparisons are done with other procedures.


title: Transmit beamforming and power control for cellular wireless systems

abstract: Joint power control and beamforming schemes are proposed for cellular systems where adaptive arrays are used only at base stations. In the uplink, mobile power and receiver diversity combining vectors at the base stations are calculated jointly. The mobile transmitted power is minimized, while the signal-to-interference-and-noise ratio (SINR) at each link is maintained above a threshold. A transmit diversity scheme for the downlink is also proposed where the transmit weight vectors and downlink power allocations are jointly calculated such that the SINR at each mobile is above a target value. The proposed algorithm achieves a feasible solution for the downlink if there is one and minimizes the total transmitted power in the network. In a reciprocal network it can be implemented in a decentralized system, and it does not require global channel response measurements. In a nonreciprocal network, where the uplink and downlink channel responses are different, the proposed transmit beamforming algorithm needs to be implemented in a centralized system, and it requires a knowledge of the downlink channel responses. The performances of these algorithms are compared with previously proposed algorithms through numerical studies.


title: The five‐factor model of personality, work stress and professional quality of life in neonatal intensive care unit nurses

abstract: AIMS ::: The study aim was to determine the direct and indirect relations of the five-factor model of personality traits and work stress with professional quality of life in neonatal nurses. ::: ::: ::: BACKGROUND ::: Neonatal intensive care nursing has positive and negative effects on neonatal nurses' psychological well-being. Although individual and situational factors interact to influence professional quality of life, there have been few studies of these relationships in neonatal nurses. ::: ::: ::: DESIGN ::: A cross-sectional study conducted in 2016. ::: ::: ::: METHODS ::: Self-report questionnaires were used to measure professional quality of life (burnout, secondary traumatic stress and compassion satisfaction), five-factor model of personality traits (neuroticism, agreeableness, extraversion, conscientiousness and openness) and work stress (role ambiguity, role conflict and role overload). ::: ::: ::: RESULTS ::: One hundred and forty (34%) of 405 eligible neonatal nurses provided the data. After controlling for work stress, neuroticism and agreeableness were related to burnout, neuroticism was related to secondary traumatic stress, and extraversion was related to compassion satisfaction. Work stress controlled for personality traits was related to burnout and secondary traumatic stress, but not to compassion satisfaction. Neuroticism moderated the effect of work stress on secondary traumatic stress and agreeableness and openness moderated the effect of work stress on compassion satisfaction. Work stress mediated the effect of neuroticism and extraversion on burnout and the effects of extraversion and conscientiousness on compassion satisfaction. ::: ::: ::: CONCLUSION ::: Strategies to reduce work stress may not lessen burnout and secondary traumatic stress or increase compassion satisfaction in neonatal nurses who are prone to high neuroticism, low agreeableness and low extraversion.


title: Stein Variational Policy Gradient

abstract: Policy gradient methods have been successfully applied to many complex reinforcement learning problems. However, policy gradient methods suffer from high variance, slow convergence, and inefficient exploration. In this work, we introduce a maximum entropy policy optimization framework which explicitly encourages parameter exploration, and show that this framework can be reduced to a Bayesian inference problem. We then propose a novel Stein variational policy gradient method (SVPG) which combines existing policy gradient methods and a repulsive functional to generate a set of diverse but well-behaved policies. SVPG is robust to initialization and can easily be implemented in a parallel manner. On continuous control problems, we find that implementing SVPG on top of REINFORCE and advantage actor-critic algorithms improves both average return and data efficiency.


title: Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++

abstract: Manually labeling datasets with object masks is extremely time consuming. In this work, we follow the idea of Polygon-RNN to produce polygonal annotations of objects interactively using humans-in-the-loop. We introduce several important improvements to the model: 1) we design a new CNN encoder architecture, 2) show how to effectively train the model with Reinforcement Learning, and 3) significantly increase the output resolution using a Graph Neural Network, allowing the model to accurately annotate high-resolution objects in images. Extensive evaluation on the Cityscapes dataset shows that our model, which we refer to as Polygon-RNN++, significantly outperforms the original model in both automatic (10% absolute and 16% relative improvement in mean IoU) and interactive modes (requiring 50% fewer clicks by annotators). We further analyze the cross-domain scenario in which our model is trained on one dataset, and used out of the box on datasets from varying domains. The results show that Polygon-RNN++ exhibits powerful generalization capabilities, achieving significant improvements over existing pixel-wise methods. Using simple online fine-tuning we further achieve a high reduction in annotation time for new datasets, moving a step closer towards an interactive annotation tool to be used in practice.


title: MTGAN: Speaker Verification through Multitasking Triplet Generative Adversarial Networks

abstract: In this paper, we propose an enhanced triplet method that improves the encoding process of embeddings by jointly utilizing generative adversarial mechanism and multitasking optimization. We extend our triplet encoder with Generative Adversarial Networks (GANs) and softmax loss function. GAN is introduced for increasing the generality and diversity of samples, while softmax is for reinforcing features about speakers. For simplification, we term our method Multitasking Triplet Generative Adversarial Networks (MTGAN). Experiment on short utterances demonstrates that MTGAN reduces the verification equal error rate (EER) by 67% (relatively) and 32% (relatively) over conventional i-vector method and state-of-the-art triplet loss method respectively. This effectively indicates that MTGAN outperforms triplet methods in the aspect of expressing the high-level feature of speaker information.


title: Concept Embedded Convolutional Semantic Model for Question Retrieval

abstract: The question retrieval, which aims to find similar questions of a given question, is playing pivotal role in various question answering (QA) systems. This task is quite challenging mainly on three aspects: lexical gap, polysemy and word order. In this paper, we propose a unified framework to simultaneously handle these three problems. We use word combined with corresponding concept information to handle the polysemous problem. The concept embedding and word embedding are learned at the same time from both context-dependent and context-independent view. The lexical gap problem is handled since the semantic information has been encoded into the embedding. Then, we propose to use a high-level feature embedded convolutional semantic model to learn the question embedding by inputting the concept embedding and word embedding without manually labeling training data. The proposed framework nicely represent the hierarchical structures of word information and concept information in sentences with their layer-by-layer composition and pooling. Finally, the framework is trained in a weakly-supervised manner on question answer pairs, which can be directly obtained without manually labeling. Experiments on two real question answering datasets show that the proposed framework can significantly outperform the state-of-the-art solutions.


title: Programmatic assessment: From assessment of learning to assessment for learning

abstract: In assessment a considerable shift in thinking has occurred from assessment of learning to assessment for learning. This has important implications for the conceptual framework from which to approach the issue of assessment, but also with respect to the research agenda. The main conceptual changes pertain to programmes of assessment. This has led to a broadened perspective on the types of construct assessment tries to capture, the way information from various sources is collected and collated, the role of human judgement and the variety of psychometric methods to determine the quality of the assessment. Research into the quality of assessment programmes, how assessment influences learning and teaching, new psychometric models and the role of human judgement is much needed.


title: Mobility management of D2D communication for the 5G cellular network system: A study and result

abstract: Wireless devices connected to the networks have been increasing remarkably for the last couple of decades. Ubiquitous voice and data connection are the key requisite for the next generation of wireless technology. Device to Device communication is widely known as D2D communication which is a new paradigm of cellular communication, and it was initially proposed to boost the network performance. It takes place when two devices communicate directly without taking significant help from the base station. Similar to other wireless communication systems, mobility management for the D2D communication is a big challenge which is yet to explore properly. Mobility management of D2D communications was enlightened in few papers. It should be managed in such a smart way in a cellular network that should enable lower latency, lower power consumptions, less complexity and last but not the least possible uninterrupted data connections which are primarily the requirements of next generations mobile cellular network. This paper extends our algorithm paradigm of mobility management of D2D communication that was proposed and published previously. We come up with the simulation results in this paper that validate our proposed model. However, we also highlight the D2D communications and mobility issues within it.


title: A proof of Golomb's conjecture for the de Bruijn graph

abstract: Abstract Golomb has conjectured that the number of cycles generated by an arbitrary binary recurrence relation does not exceed Z ( n ), the number of cycles generated by the pure cycling register. Lempel gives a conjecture which implies that of Golomb, and he also introduces so called V -sets. We color the vertices of the de Bruijn graph B n in order to find V -sets, and this leads to a proof of Lempel's conjecture.


title: A multidisciplinary survey on discrimination analysis

abstract: Most of the decisions in the today's knowledge society are taken on the basis of historical data by extracting models, patterns, profiles, and rules of human behavior in support of (automated) decision making. There is then the need of developing models, methods and technologies for modelling the processes of discrimination analysis in order to discover and prevent discrimination phenomena. In this respect, discrimination analysis from data should build over the large body of existing legal and economic studies. This paper intends to provide a multi-disciplinary survey of the literature on discrimination data analysis, including methods for data collection, empirical studies, controlled experiments, statistical evidence, and their legal requirements and grounds. We cover the following mainstream research lines: labour economic models, (quasi-)experimental approaches such as auditing and controlled experiments, profiling-based approaches such as racial profiling and credit markets, and the recently blooming research on knowledge discovery approaches.