title: Visually impaired users on an online social network

abstract: In this paper we present the first large-scale empirical study of how visually impaired people use online social networks, specifically Facebook. We identify a sample of 50K visually impaired users, and study the activities they perform, the content they produce, and the friendship networks they build on Facebook. We find that visually impaired users participate on Facebook (e.g. status updates, comments, likes) as much as the general population, and receive more feedback (i.e., comments and likes) on average on their content. By analyzing the content produced by visually impaired users, we find that they share their experience and issues related to vision impairment. We also identify distinctive patterns in their language and technology use. We also show that, compared to other users, visually impaired users have smaller social networks, but such differences have decreased over time. Our findings have implications for improving the utility and usability of online social networks for visually impaired users.


title: A review of the optical properties of biological tissues

abstract: The known optical properties (absorption, scattering, total attenuation, effective attenuation, and/or anisotropy coefficients) of various biological tissues at a variety of wavelengths are reviewed. The theoretical foundations for most experimental approaches are outlined. Relations between Kubelka-Munk parameters and transport coefficients are listed. The optical properties of aorta, liver, and muscle at 633 nm are discussed in detail. An extensive bibliography is provided. >


title: TANGENT: a novel, 'Surprise me', recommendation algorithm

abstract: Most of recommender systems try to find items that are most relevant to the older choices of a given user. Here we focus on the "surprise me" query: A user may be bored with his/her usual genre of items (e.g., books, movies, hobbies), and may want a recommendation that is related, but off the beaten path, possibly leading to a new genre of books/movies/hobbies. How would we define, as well as automate, this seemingly selfcontradicting request? We introduce TANGENT, a novel recommendation algorithm to solve this problem. The main idea behind TANGENT is to envision the problem as node selection on a graph, giving high scores to nodes that are well connected to the older choices, and at the same time well connected to unrelated choices. The method is carefully designed to be (a) parameter-free (b) effective and (c) fast. We illustrate the benefits of TANGENT with experiments on both synthetic and real data sets. We show that TANGENT makes reasonable, yet surprising, horizon-broadening recommendations. Moreover, it is fast and scalable, since it can easily use existing fast algorithms on graph node proximity.


title: Auditing Security Compliance of the Virtualized Infrastructure in the Cloud: Application to OpenStack

abstract: Cloud service providers typically adopt the multi-tenancy model to optimize resources usage and achieve the promised cost-effectiveness. Sharing resources between different tenants and the underlying complex technology increase the necessity of transparency and accountability. In this regard, auditing security compliance of the provider's infrastructure against standards, regulations and customers' policies takes on an increasing importance in the cloud to boost the trust between the stakeholders. However, virtualization and scalability make compliance verification challenging. In this work, we propose an automated framework that allows auditing the cloud infrastructure from the structural point of view while focusing on virtualization-related security properties and consistency between multiple control layers. Furthermore, to show the feasibility of our approach, we integrate our auditing system into OpenStack, one of the most used cloud infrastructure management systems. To show the scalability and validity of our framework, we present our experimental results on assessing several properties related to auditing inter-layer consistency, virtual machines co-residence, and virtual resources isolation.


title: OntoWiki - An authoring, publication and visualization interface for the Data Web

abstract: OntoWiki is a front-end application for the Semantic Data Web, which was originally developed to support distributed knowledge engineering scenarios. Due to its extensibility it also serves as a development framework for knowledge intensive applications. On the surface, OntoWiki is a generic user interface for arbitrary RDF knowledge graphs. It supports the navigation through RDF knowledge bases using SPARQL-generated lists, tables and trees (e.g. class trees and taxonomies). All resources are automatically represented as hyperlinks and backlinks are created whenever feasible, thus enabling users to easily traverse entire knowledge graphs. Since all collections of resources displayed in OntoWiki are generated by SPARQL queries, they can be further refined by applying additional filters. In order to explore large datasets, a comprehensive statistical data management and visualization method was integrated. We render an architectural overview and explain the navigation, exploration and vi- sualization features provided for RDF based information and derived statistics. To showcase the versatility of OntoWiki and its various deployments in the Linked Data Web, we describe some large-scale use cases in the domains of enterprise data inte- gration, governmental data publication and digital humanities. This article is the first comprehensive presentation of OntoWiki summarizing the advancements after the initial publication in 2006 (4).


ata-driven soft sensor development based on deep learning echnique


title: R (2005) Psychophysical magic: rendering the visible ‘invisible’. Trends Cogn Sci 9: 381–388

abstract: What are the neural correlates of conscious visual awareness? Tackling this question requires contrasting neural correlates of stimulus processing culminating in visual awareness with neural correlates of stimulus processing unaccompanied by awareness. To produce these two neural states, one must be able to erase an otherwise visible stimulus from awareness. This article describes and assesses visual phenomena involving dissociation of physical stimulation and conscious awareness: degraded stimulation, visual masking, visual crowding, bistable figures, binocular rivalry, motioninduced blindness, inattentional blindness, change blindness and attentional blink. No single approach stands above the others, but those producing changing visual awareness despite invariant physical stimulation are clearly preferable. Such phenomena can help lead us ultimately to a comprehensive account of the neural correlates of conscious awareness.


title: GraM: scaling graph computation to the trillions

abstract: G ra M is an efficient and scalable graph engine for a large class of widely used graph algorithms. It is designed to scale up to multicores on a single server, as well as scale out to multiple servers in a cluster, offering significant, often over an order-of-magnitude, improvement over existing distributed graph engines on evaluated graph algorithms. G ra M is also capable of processing graphs that are significantly larger than previously reported. In particular, using 64 servers (1,024 physical cores), it performs a PageRank iteration in 140 seconds on a synthetic graph with over one trillion edges, setting a new milestone for graph engines. G ra M's efficiency and scalability comes from a judicious architectural design that exploits the benefits of multi-core and RDMA. G ra M uses a simple message-passing based scaling architecture for both scaling up and scaling out to expose inherent parallelism. It further benefits from a specially designed multi-core aware RDMA-based communication stack that preserves parallelism in a balanced way and allows overlapping of communication and computation. A high degree of parallelism often comes at the cost of lower efficiency due to resource fragmentation. G ra M is equipped with an adaptive mechanism that evaluates the cost and benefit of parallelism to decide the appropriate configuration. Combined, these mechanisms allow G ra M to scale up and out with high efficiency.


title: gTRICLUSTER: A More General and Effective 3D Clustering Algorithm for Gene-Sample-Time Microarray Data

abstract: Clustering is an important technique in microarray data analysis, and mining three-dimensional (3D) clusters in gene-sample-time (simply GST) microarray data is emerging as a hot research topic in this area. A 3D cluster consists of a subset of genes that are coherent on a subset of samples along a segment of time series. This kind of coherent clusters may contain information for the users to identify useful phenotypes, potential genes related to these phenotypes and their expression rules. TRICLUSTER is the state-of-the-art 3D clustering algorithm for GST microarray data. In this paper, we propose a new algorithm to mine 3D clusters over GST microarray data. We term the new algorithm gTRICLUSTER because it is based on a more general 3D cluster model than the one that TRICLUSTER is based on. gTRICLUSTER can find more biologically meaningful coherent gene clusters than TRICLUSTER can do. It also outperforms TRICLUSTER in robustness to noise. Experimental results on a real-world microarray dataset validate the effectiveness of the proposed new algorithm.


title: CIVSched: Communication-aware Inter-VM Scheduling in Virtual Machine Monitor Based on the Process

abstract: Server consolidation in Cloud Computing makes it possible for multiple servers or desktops to run on one physical server to get high resource utilization, low cost and less energy consumption. However, the scheduler in virtual machine monitor (VMM) is agnostic about the communication behavior between the guest operating systems. It leads to inefficient network communication in consolidated environment. In particular, the CPU resource management has a critical impact on the network latency between co-resident virtual machines (VMs) when there are CPU-bound and I/O-bound workloads existing simultaneously. It brings a negative impact on latency-sensitive VMs. In this paper, we present the design and implementation of CIVSched scheduling to make the VMM aware of the communication behavior between two inter-VMs running on the same virtual platform. CIVSched inspects the network packets transmitted between local domains and find the destination VM and the target process inside that will receive the packets. Then, the destination VM and the target process are preferentially scheduled by VMM scheduler and guest OS scheduler respectively. The cooperation of these two schedulers makes the network packets received by the target application timely. Experimental results show that the CIVSched scheduling can reduce the average response time of network traffic by up to 18% for the highly consolidated environment while keeping the fairness of the VMM scheduler.


title: Multi run ICA and surface EMG based signal processing system for recognising hand gestures

abstract: Hand gesture identification is a complex problem, where more number of muscles will be involved even for a simple hand movement. Surface electromyography (sEMG) is an indicator of muscle activity and related to body movement and posture. In the recent past sEMG had been used with various statistical signal processing technique to identify different hand gestures, but since the hand actions require simultaneous muscle contractions reliability issues exist. Recently Blind source separation (BSS) techniques like Independent Component Analysis (ICA) had been used to tackle this problem. In this paper, a novel method is proposed to enhance the performance of ICA of sEMG by decomposing the signal into components originating from different muscles. First, we use FastICA algorithm to generate random mixing matrix, and the best mixing matrix is chosen based on the highest Signal to interference ratio(SIR) of mixing matrix. Pattern classification of the separated signal is performed in the second step with a back propagation neural network. The proposed model-based approach is able to overcome the ambiguity problems (order and magnitude problem) of BSS methods by selecting an apriori mixing matrix based on known hand muscle anatomy. Testing was conducted using several single shot experiments conducted with seven subjects. The results indicate that the system is able to classify six different hand gestures with 99% accuracy.


title: CSCdroid: Accurately Detect Android Malware via Contribution-Level-Based System Call Categorization

abstract: The detection of the malicious application or malware on Android platform is a very concerned issue. Many studies have demonstrated their effect from static property analysis and dynamic analysis. However, their accuracy and efficacy still cannot satisfy the demand. In this paper, we propose CSCdroid, an accurate malware detection approach for Android via contribution-level-based system call (SC) categorization. Different from existing works, which use all SCs to construct feature vectors so as to determine the security of applications, CSCdroid first introduces a concept named contribution to quantitatively evaluate SCs relevance for malware identification. Based on the contribution level, CSCdroid can categorize SCs into two types, determinate SCs and normal SCs. Eventually, CSCdroid builds a Markov chain by replacing all normal SCs with one specific SC in the SC sequence. Then it constructs the target feature vector from the probability matrix and use the Support Vector Machine (SVM) to detect Android malware. Such way can effectively reduce the state number of Markov chains, and cut down the dimension of the feature vectors into the SVM classifier. Our evaluation confirms our approach possesses the malware detection ability with a high accuracy rate.


Sketch techniques for approximate query processing


title: Selecting a Reference High Resolution for Fingerprint Recognition Using Minutiae and Pores

abstract: High-resolution automated fingerprint recognition systems (AFRSs) offer higher security because they are able to make use of level-3 features, such as pores, that are not available in lower resolution ( <; 500-dpi) images. One of the main parameters affecting the quality of a digital fingerprint image and issues such as cost, interoperability, and performance of an AFRS is the choice of image resolution. In this paper, we identify the optimal resolution for an AFRS using the two most representative fingerprint features: minutiae and pores. We first designed a multiresolution fingerprint acquisition device to collect fingerprint images at multiple resolutions and captured fingerprints at various resolutions but at a fixed image size. We then carried out a theoretical analysis to identify the minimum required resolution for fingerprint recognition using minutiae and pores. After experiments on our collected fingerprint images and applying three requirements for the proportions of minutiae and pores that must be retained in a fingerprint image, we recommend a reference resolution of 800 dpi. Subsequent tests have further confirmed the proposed reference resolution.


title: Abnormal quantitative EEG scores identify patients with complicated idiopathic generalised epilepsy

abstract: Abstract Objective : To investigate the relationship between quantitative EEG (QEEG) scores and "complicating factors" (psychopathology, true pharmacoresistance, neurological symptoms) in idiopathic generalised epilepsy (IGE). Methods : 35 newly referred, newly diagnosed, unmedicated IGE patients were collected in a prospective and random manner. Standard neuro-psychiatric and EEG examination was done. The patients were treated and controlled at regular visits. After 2 years of follow-up, clinical data were summarised and were compared to QEEG results. Clinical target items were neurologic and psychiatric abnormalities, proven pharmacoresistance. Patients with at least one of these items were labelled "complicated", whereas patients without these additional handicap were labelled as "uncomplicated". The 12 QEEG target variables were: Z -transformed absolute power values for three (anterior, central, posterior) brain regions and four frequency bands (1.5–3.5; 3.5–7.5; 7.5–12.5; 12.5–25.0Hz). QEEG scores outside the ±2.5 Z range were accepted as abnormal. The overall QEEG result was classified as normal (0–2 abnormal scores), or pathological (3 or more abnormal scores). Clinical and QEEG results were correlated. Results : All patients with psychopathology showed 4–8 positive pathological scores (power excess not confined to a single cortical region or frequency band). The two patients with pure pharmacoresistance showed pathological negative values (delta power deficit) all over the scalp. Statistically significant ( P Conclusion : Higher degree of cortical dysfunction (as assessed in the clinical setting) is reflected by higher degree of QEEG abnormalities. QEEG analysis can differentiate between IGE patients with or without psychopathology. Forecasting psychopathology may be the practical application of the findings.


title: AnomiGAN: Generative adversarial networks for anonymizing private medical data

abstract: Typical personal medical data contains sensitive information about individuals. Storing or sharing the personal medical data is thus often risky. For example, a short DNA sequence can provide information that can not only identify an individual, but also his or her relatives. Nonetheless, most countries and researchers agree on the necessity of collecting personal medical data. This stems from the fact that medical data, including genomic data, are an indispensable resource for further research and development regarding disease prevention and treatment. To prevent personal medical data from being misused, techniques to reliably preserve sensitive information should be developed for real world application. In this paper, we propose a framework called anonymized generative adversarial networks (AnomiGAN), to improve the maintenance of privacy of personal medical data, while also maintaining high prediction performance. We compared our method to state-of-the-art techniques and observed that our method preserves the same level of privacy as differential privacy (DP), but had better prediction results. We also observed that there is a trade-off between privacy and performance results depending on the degree of preservation of the original data. Here, we provide a mathematical overview of our proposed model and demonstrate its validation using UCI machine learning repository datasets in order to highlight its utility in practice. Experimentally, our approach delivers a better performance compared to that of the DP approach.


title: Semantic Redundancies in Image-Classification Datasets: The 10% You Don't Need

abstract: Large datasets have been crucial to the success of deep learning models in the recent years, which keep performing better as they are trained with more labelled data. While there have been sustained efforts to make these models more data-efficient, the potential benefit of understanding the data itself, is largely untapped. Specifically, focusing on object recognition tasks, we wonder if for common benchmark datasets we can do better than random subsets of the data and find a subset that can generalize on par with the full dataset when trained on. To our knowledge, this is the first result that can find notable redundancies in CIFAR-10 and ImageNet datasets (at least 10%). Interestingly, we observe semantic correlations between required and redundant images. We hope that our findings can motivate further research into identifying additional redundancies and exploiting them for more efficient training or data-collection.


title: Dynamic background modeling and subtraction using spatio-temporal local binary patterns

abstract: Traditional background modeling and subtraction methods have a strong assumption that the scenes are of static structures with limited perturbation. These methods will perform poorly in dynamic scenes. In this paper, we present a solution to this problem. We first extend the local binary patterns from spatial domain to spatio-temporal domain, and present a new online dynamic texture extraction operator, named spatio- temporal local binary patterns (STLBP). Then we present a novel and effective method for dynamic background modeling and subtraction using STLBP. In the proposed method, each pixel is modeled as a group of STLBP dynamic texture histograms which combine spatial texture and temporal motion information together. Compared with traditional methods, experimental results show that the proposed method adapts quickly to the changes of the dynamic background. It achieves accurate detection of moving objects and suppresses most of the false detections for dynamic changes of nature scenes.


title: Software requirement optimization using a multiobjective swarm intelligence evolutionary algorithm

abstract: The selection of the new requirements which should be included in the development of the release of a software product is an important issue for software companies. This problem is known in the literature as the Next Release Problem (NRP). It is an NP-hard problem which simultaneously addresses two apparently contradictory objectives: the total cost of including the selected requirements in the next release of the software package, and the overall satisfaction of a set of customers who have different opinions about the priorities which should be given to the requirements, and also have different levels of importance within the company. Moreover, in the case of managing real instances of the problem, the proposed solutions have to satisfy certain interaction constraints which arise among some requirements. In this paper, the NRP is formulated as a multiobjective optimization problem with two objectives (cost and satisfaction) and three constraints (types of interactions). A multiobjective swarm intelligence metaheuristic is proposed to solve two real instances generated from data provided by experts. Analysis of the results showed that the proposed algorithm can efficiently generate high quality solutions. These were evaluated by comparing them with different proposals (in terms of multiobjective metrics). The results generated by the present approach surpass those generated in other relevant work in the literature (e.g. our technique can obtain a HV of over 60% for the most complex dataset managed, while the other approaches published cannot obtain an HV of more than 40% for the same dataset).


title: Mono-Camera 3D Multi-Object Tracking Using Deep Learning Detections and PMBM Filtering

abstract: Monocular cameras are one of the most commonly used sensors in the automotive industry for autonomous vehicles. One major drawback using a monocular camera is that it only makes observations in the two dimensional image plane and can not directly measure the distance to objects. In this paper, we aim at filling this gap by developing a multi-object tracking algorithm that takes an image as input and produces trajectories of detected objects in a world coordinate system. We solve this by using a deep neural network trained to detect and estimate the distance to objects from a single input image. The detections from a sequence of images are fed in to a state-of-the art Poisson multi-Bernoulli mixture tracking filter. The combination of the learned detector and the PMBM filter results in an algorithm that achieves 3D tracking using only mono-camera images as input. The performance of the algorithm is evaluated both in 3D world coordinates, and 2D image coordinates, using the publicly available KITTI object tracking dataset. The algorithm shows the ability to accurately track objects, correctly handle data associations, even when there is a big overlap of the objects in the image, and is one of the top performing algorithms on the KITTI object tracking benchmark. Furthermore, the algorithm is efficient, running on average close to 20 frames per second.


Detecting Leg Bone Fracture In X-Ray Images


title: Practical Poissonian-Gaussian Noise Modeling and Fitting for Single-Image Raw-Data

abstract: We present a simple and usable noise model for the raw-data of digital imaging sensors. This signal-dependent noise model, which gives the pointwise standard-deviation of the noise as a function of the expectation of the pixel raw-data output, is composed of a Poissonian part, modeling the photon sensing, and Gaussian part, for the remaining stationary disturbances in the output data. We further explicitly take into account the clipping of the data (over- and under-exposure), faithfully reproducing the nonlinear response of the sensor. We propose an algorithm for the fully automatic estimation of the model parameters given a single noisy image. Experiments with synthetic images and with real raw-data from various sensors prove the practical applicability of the method and the accuracy of the proposed model.


title: Coordinated charging of multiple plug-in hybrid electric vehicles in residential distribution grids

abstract: Alternative vehicles based on internal combustion engines (ICE), such as the hybrid electric vehicle (HEV), the plug-in hybrid electric vehicle (PHEV) and the fuel-cell electric vehicle (FCEV), are becoming increasingly popular. HEVs are currently commercially available and PHEVs will be the next phase in the evolution of hybrid and electric vehicles. The batteries of the PHEVs are designed to be charged at home, from a standard outlet in the garage, or on a corporate car park. The electrical consumption for charging PHEVs may take up to 5% of the total electrical consumption in Belgium by 2030. These extra electrical loads have an impact on the distribution grid which is analyzed in terms of power losses and voltage deviations. Firstly, the uncoordinated charging is described where the vehicles are charged immediately when they are plugged in or after a fixed start delay. This uncoordinated power consumption on a local scale can lead to grid problems. Therefore coordinated charging is proposed to minimize the power losses and to maximize the main grid load factor. The optimal charge profile of the PHEVs is computed by minimizing the power losses. The exact forecasting of household loads is not possible, so stochastic programming is introduced.


title: Learning Iteration-wise Generalized Shrinkage–Thresholding Operators for Blind Deconvolution

abstract: Salient edge selection and time-varying regularization are two crucial techniques to guarantee the success of maximum a posteriori (MAP)-based blind deconvolution. However, the existing approaches usually rely on carefully designed regularizers and handcrafted parameter tuning to obtain satisfactory estimation of the blur kernel. Many regularizers exhibit the structure-preserving smoothing capability, but fail to enhance salient edges. In this paper, under the MAP framework, we propose the iteration-wise $\ell _{p}$ -norm regularizers together with data-driven strategy to address these issues. First, we extend the generalized shrinkage–thresholding (GST) operator for $\ell _{p}$ -norm minimization with negative $p$ value, which can sharpen salient edges while suppressing trivial details. Then, the iteration-wise GST parameters are specified to allow dynamical salient edge selection and time-varying regularization. Finally, instead of handcrafted tuning, a principled discriminative learning approach is proposed to learn the iterationwise GST operators from the training dataset. Furthermore, the multi-scale scheme is developed to improve the efficiency of the algorithm. Experimental results show that, negative $p$ value is more effective in estimating the coarse shape of blur kernel at the early stage, and the learned GST operators can be well generalized to other dataset and real world blurry images. Compared with the state-of-the-art methods, our method achieves better deblurring results in terms of both quantitative metrics and visual quality, and it is much faster than the state-of-the-art patch-based blind deconvolution method.


title: Speculative Practices: Utilizing InfoVis to Explore Untapped Literary Collections

abstract: In this paper we exemplify how information visualization supports speculative thinking, hypotheses testing, and preliminary interpretation processes as part of literary research. While InfoVis has become a buzz topic in the digital humanities, skepticism remains about how effectively it integrates into and expands on traditional humanities research approaches. From an InfoVis perspective, we lack case studies that show the specific design challenges that make literary studies and humanities research at large a unique application area for information visualization. We examine these questions through our case study of the Speculative W@nderverse, a visualization tool that was designed to enable the analysis and exploration of an untapped literary collection consisting of thousands of science fiction short stories. We present the results of two empirical studies that involved general-interest readers and literary scholars who used the evolving visualization prototype as part of their research for over a year. Our findings suggest a design space for visualizing literary collections that is defined by (1) their academic and public relevance, (2) the tension between qualitative vs. quantitative methods of interpretation, (3) result- vs. process-driven approaches to InfoVis, and (4) the unique material and visual qualities of cultural collections. Through the Speculative W@nderverse we demonstrate how visualization can bridge these sometimes contradictory perspectives by cultivating curiosity and providing entry points into literary collections while, at the same time, supporting multiple aspects of humanities research processes.


title: Fun in the Philippines: Automatic Identification and Sentiment Analysis of Tourism-Related Tweets

abstract: With the growing use of social media in the Philippines, tourism-related user-generated content is readily available. As a growing hub of tourism and culture, this could be particularly useful to the country. However, a large amount of this data has gone unanalyzed. This study discusses and develops a way that could help bridge that gap using automated tourism-related tweet identification with Support Vector Machines and sentiment analysis with Naive Bayes. F-scores of 0.943 and 0.81 were obtained by these components respectively, with the overall system obtaining an accuracy of 84%. Mapbox was used for visualization, with tweets plotted based on their geolocations and sentiments. This study can be used as a way of gathering tweets from the Philippines, identifying which could be relevant in terms of tourism information and presenting these in a way that could be useful and easy to understand and interpret.


title: Innovative lead discovery strategies for tropical diseases

abstract: Lead discovery is a major bottleneck in the pipeline for novel drugs for tropical diseases such as malaria. Nwaka and Hudson discuss how lead-discovery strategies based on highly integrated partnerships and networks involving academia and industry in industrialized and disease-endemic countries could increase the chances of success.


title: A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation

abstract: Background ::: Recent technological advances in integrated circuits, wireless communications, and physiological sensing allow miniature, lightweight, ultra-low power, intelligent monitoring devices. A number of these devices can be integrated into a Wireless Body Area Network (WBAN), a new enabling technology for health monitoring.


title: Gaining IS Business Value through Big Data Analytics: A Case Study of the Energy Sector

abstract: Following decades of stability and comfortable margins, utility companies today face strong pressure from regulatory bodies and competitors. As a response to the market dynamics, many have initiated a transformation from a “provider” to a service company, yet realize that their customer insights that would be necessary to successfully develop and market new services are sparse. We argue that the required information is contained in consumption data that is available to utility companies. We demonstrate how data analytics and machine learning make sense out of such data and add value to organizations. Using datasets containing annual electricity consumption information of private households, we apply and test in field experiments a Support Vector Machines algorithm that predicts probabilities of individual costumers to sign up on an energy efficiency portal. We show that signup rates can be doubled and argue that classification tools provide customer insights at low cost and at scale.


title: Mining Meaning from Wikipedia

abstract: Wikipedia is a goldmine of information; not just for its many readers, but also for the growing community of researchers who recognize it as a resource of exceptional scale and utility. It represents a vast investment of manual effort and judgment: a huge, constantly evolving tapestry of concepts and relations that is being applied to a host of tasks. ::: This article provides a comprehensive description of this work. It focuses on research that extracts and makes use of the concepts, relations, facts and descriptions found in Wikipedia, and organizes the work into four broad categories: applying Wikipedia to natural language processing; using it to facilitate information retrieval and information extraction; and as a resource for ontology building. The article addresses how Wikipedia is being used as is, how it is being improved and adapted, and how it is being combined with other structures to create entirely new resources. We identify the research groups and individuals involved, and how their work has developed in the last few years. We provide a comprehensive list of the open-source software they have produced.


title: NaLIR: an interactive natural language interface for querying relational databases

abstract: In this demo, we present NaLIR, a generic interactive natural language interface for querying relational databases. NaLIR can accept a logically complex English language sentence as query input. This query is first translated into a SQL query, which may include aggregation, nesting, and various types of joins, among other things, and then evaluated against an RDBMS. In this demonstration, we show that NaLIR, while far from being able to pass the Turing test, is perfectly usable in practice, and able to handle even quite complex queries in a variety of application domains. In addition, we also demonstrate how carefully designed interactive communication can avoid misinterpretation with minimum user burden.


title: Smart wearable body sensors for patient self-assessment and monitoring

abstract: Background ::: Innovations in mobile and electronic healthcare are revolutionizing the involvement of both doctors and patients in the modern healthcare system by extending the capabilities of physiological monitoring devices. Despite significant progress within the monitoring device industry, the widespread integration of this technology into medical practice remains limited. The purpose of this review is to summarize the developments and clinical utility of smart wearable body sensors.


title: MOSAIK - SMART GRID SIMULATION API Toward a semantic based standard for interchanging Smart Grid simulations

abstract: Simulation is an important method to test and evaluate new control mechanisms for future Smart Grids. To generate sound simulation results one has to use validated and established simulation models. In this paper we present the first two out of six layers of our approach to a modular simulation framework, called mosaik. It will allows to specify, compose and simulate Smart Grid scenarios based on the reuse of existing, technologically heterogeneous simulation models. The layers presented here include a syntactic layer defining an interface for Smart Grid simulators and a semantic layer implemented using a domain-specific language that allows to describe the structure and semantics of the simulator that is interfaced.


title: Mori: A Modular Origami Robot

abstract: This paper proposes a new robotic platform based on origami robots and reconfigurable modular robots. The concept combines the advantages of both robot types into a mobile, quasi-two-dimensional, lattice-type reconfigurable modular origami robot, Mori. A detailed description and analysis of the concept is validated by the presentation of a first prototype that incorporates the key functionalities of the proposed system. The modular robot prototype is mobile, can be connected to other modules of its kind, and fold up to create task-specific three-dimensional reconfigurable structures. Three implementations using the prototype in different configurations are presented in the form of individual modules, modular reconfigurable surfaces, and is applied to closed-loop object manipulation. The experiments highlight the capabilities and advantages of the system with respect to modularity, origami-folding, mobility, and versatility.


title: A subjective study on QoE of 360 video for VR communication

abstract: Currently, more and more 360-degree videos (or 360 videos for short) are being provided via the Internet. This kind of videos can render a virtual reality (VR) environment via a head-mounted display (HMD). However, understanding the quality of experience (QoE) of 360 videos is a big challenge because user experience in VR is a very complex phenomenon. In this paper, the QoE of 360 videos is considered in terms of four aspects, namely perceptual quality, presence, acceptability, and cybersickness. Subjective tests are designed to investigate the influences of important factors including encoding parameters, content characteristics, and device types on QoE aspects. In addition, a comparison of perceptual quality and acceptability between VR and non-VR rendering modes, which refer to watching 360 videos with and without using an HMD respectively, is also made in this study. To the best of our knowledge, this is the first study that covers these four QoE aspects and a large number of influence factors.


title: Data Mining in Agriculture on Crop Price Prediction: Techniques and Applications

abstract: crop price analysis, Data mining is emerging as an important research field. In this paper, we will discuss about the applications and techniques of Data mining in agriculture. There are various data mining techniques such as K-Means, K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN) and Support Vector Machines (SVM) which are used for very recent applications of Data Mining techniques. This paper will consider the problem of price prediction of crops. Price Prediction, nowadays, has become very important agricultural problem which is to be solved only based on the available data. Data Mining techniques can be used to solve this problem. This work is based on finding suitable data models that helps in achieving high accuracy and generality for price prediction. For solving this problem, different Data Mining techniques were evaluated on different data sets.


title: Learning to Factor Policies and Action-Value Functions: Factored Action Space Representations for Deep Reinforcement learning

abstract: Deep Reinforcement Learning (DRL) methods have performed well in an increasing numbering of high-dimensional visual decision making domains. Among all such visual decision making problems, those with discrete action spaces often tend to have underlying compositional structure in the said action space. Such action spaces often contain actions such as go left, go up as well as go diagonally up and left (which is a composition of the former two actions). The representations of control policies in such domains have traditionally been modeled without exploiting this inherent compositional structure in the action spaces. We propose a new learning paradigm, Factored Action space Representations (FAR) wherein we decompose a control policy learned using a Deep Reinforcement Learning Algorithm into independent components, analogous to decomposing a vector in terms of some orthogonal basis vectors. This architectural modification of the control policy representation allows the agent to learn about multiple actions simultaneously, while executing only one of them. We demonstrate that FAR yields considerable improvements on top of two DRL algorithms in Atari 2600: FARA3C outperforms A3C (Asynchronous Advantage Actor Critic) in 9 out of 14 tasks and FARAQL outperforms AQL (Asynchronous n-step Q-Learning) in 9 out of 13 tasks.


title: Parental scaffolding as a bootstrapping mechanism for learning grasp affordances and imitation skills

abstract: Parental scaffolding is an important mechanism that speeds up infant sensorimotor development. Infants pay stronger attention to the features of the objects highlighted by parents, and their manipulation skills develop earlier than they would in isolation due to caregivers' support. Parents are known to make modifications in infant-directed actions, which are often called “motionese” 7 . The features that might be associated with motionese are amplification, repetition and simplification in caregivers' movements, which are often accompanied by increased social signalling. In this paper, we extend our previously developed affordances learning framework to enable our hand-arm robot equipped with a range camera to benefit from parental scaffolding and motionese. We first present our results on how parental scaffolding can be used to guide the robot learning and to modify its crude action execution to speed up the learning of complex skills. For this purpose, an interactive human caregiver-infant scenario was realized with our robotic setup. This setup allowed the caregiver's modification of the ongoing reach and grasp movement of the robot via physical interaction. This enabled the caregiver to make the robot grasp the target object, which in turn could be used by the robot to learn the grasping skill. In addition to this, we also show how parental scaffolding can be used in speeding up imitation learning. We present the details of our work that takes the robot beyond simple goal-level imitation, making it a better imitator with the help of motionese.


title: Nanomaterial‐Enabled Stretchable Conductors: Strategies, Materials and Devices

abstract: Stretchable electronics are attracting intensive attention due to their promising applications in many areas where electronic devices undergo large deformation and/or form intimate contact with curvilinear surfaces. On the other hand, a plethora of nanomaterials with outstanding properties have emerged over the past decades. The understanding of nanoscale phenomena, materials, and devices has progressed to a point where substantial strides in nanomaterial-enabled applications become realistic. This review summarizes recent advances in one such application, nanomaterial-enabled stretchable conductors (one of the most important components for stretchable electronics) and related stretchable devices (e.g., capacitive sensors, supercapacitors and electroactive polymer actuators), over the past five years. Focusing on bottom-up synthesized carbon nanomaterials (e.g., carbon nanotubes and graphene) and metal nanomaterials (e.g., metal nanowires and nanoparticles), this review provides fundamental insights into the strategies for developing nanomaterial-enabled highly conductive and stretchable conductors. Finally, some of the challenges and important directions in the area of nanomaterial-enabled stretchable conductors and devices are discussed.


title: A Quaternion Framework for Color Image Smoothing and Segmentation

abstract: In this paper, we present feature/detail preserving models for color image smoothing and segmentation using the Hamiltonian quaternion framework. First, we introduce a novel quaternionic Gabor filter (QGF) which can combine the color channels and the orientations in the image plane. We show that these filters are optimally localized both in the spatial and frequency domains and provide a good approximation to quaternionic quadrature filters. Using the QGFs, we extract the local orientation information in the color images. Second, in order to model this derived orientation information, we propose continuous mixtures of appropriate exponential basis functions and derive analytic expressions for these models. These analytic expressions take the form of spatially varying kernels which, when convolved with a color image or the signed distance function of an evolving contour (placed in the color image), yield a detail preserving smoothing and segmentation, respectively. Several examples on widely used image databases are shown to depict the performance of our algorithms.


title: Organization of the connections between claustrum and cortex in the mouse

abstract: The connections between the claustrum and the cortex in mouse are systematically investigated with adeno-associated virus (AAV), an anterograde viral tracer. We first define the boundary and the three-dimensional structure of the claustrum based on a variety of molecular and anatomical data. From AAV injections into 42 neocortical and allocortical areas, we conclude that most cortical areas send bilateral projections to the claustrum, the majority being denser on the ipsilateral side. This includes prelimbic, infralimbic, medial, ventrolateral and lateral orbital, ventral retrosplenial, dorsal and posterior agranular insular, visceral, temporal association, dorsal and ventral auditory, ectorhinal, perirhinal, lateral entorhinal, and anteromedial, posteromedial, lateroposterior, laterointermediate, and postrhinal visual areas. In contrast, the cingulate and the secondary motor areas send denser projections to the contralateral claustrum than to the ipsilateral one. The gustatory, primary auditory, primary visual, rostrolateral visual, and medial entorhinal cortices send projections only to the ipsilateral claustrum. Primary motor, primary somatosensory and subicular areas barely send projections to either ipsi- or contralateral claustrum. Corticoclaustral projections are organized in a rough topographic manner, with variable projection strengths. We find that the claustrum, in turn, sends widespread projections preferentially to ipsilateral cortical areas with different projection strengths and laminar distribution patterns and to certain contralateral cortical areas. Our quantitative results show that the claustrum has strong reciprocal and bilateral connections with prefrontal and cingulate areas as well as strong reciprocal connections with the ipsilateral temporal and retrohippocampal areas, suggesting that it may play a crucial role in a variety of cognitive processes. J. Comp. Neurol. 525:1317-1346, 2017. © 2016 Wiley Periodicals, Inc.


title: Examining the influence of modularity and knowledge management (KM) on dynamic capabilities: Insights from a call center

abstract: Abstract Modularity in organizations can facilitate the creation and development of dynamic capabilities. Paradoxically, however, modular management can also stifle the strategic potential of such capabilities by conflicting with the horizontal integration of units. We address these issues through an examination of how modular management of information technology (IT), project teams and front-line personnel in concert with knowledge management (KM) interventions influence the creation and development of dynamic capabilities at a large Asia-based call center. Our findings suggest that a full capitalization of the efficiencies created by modularity may be closely linked to the strategic sense making abilities of senior managers to assess the long-term business value of the dominant designs available in the market. Drawing on our analysis we build a modular management-KM-dynamic capabilities model, which highlights the evolution of three different levels of dynamic capabilities and also suggests an inherent complementarity between modular and integrated approaches.


title: Architecting Microservices

abstract: Inspired by Service Oriented Architecture (SOA), and from the convergence of Cloud Computing and Web 2.0, Microservice Architecture (MSA) has recently emerged as an architectural style particularly suitable to the cloud infrastructures. The MSA style is an approach to developing a single application as a suite of small services, each running in its own process and communicating with lightweight mechanisms. Although the set of MSA principles aim for high degree of flexibility, modularity and evolution, adopting, operating and maintaining microservice architectures in practice is challenging and time consuming. This paper reports on a PhD research project addressing three different challenges concerning MSA: (i) the identification of the key properties of microservice architectures, (ii) the identification and investigation on a description language for designing and analyzing architectures, (iii) the identification of the factors that impact the process of migrating existing applications towards MSA. The initial contributions of this project are: (i) a systematic mapping study on architecting microservices performed in order to understand the state of the research and the possible gaps in the area, (ii) an approach for architecture recovery of microservice-based systems named MicroART, and (iii) the implementation of the MicroART first prototype.


title: Review of Machine Learning Algorithms in Differential Expression Analysis

abstract: In biological research machine learning algorithms are part of nearly every analytical process. They are used to identify new insights into biological phenomena, interpret data, provide molecular diagnosis for diseases and develop personalized medicine that will enable future treatments of diseases. In this paper we (1) illustrate the importance of machine learning in the analysis of large scale sequencing data, (2) present an illustrative standardized workflow of the analysis process, (3) perform a Differential Expression (DE) analysis of a publicly available RNA sequencing (RNASeq) data set to demonstrate the capabilities of various algorithms at each step of the workflow, and (4) show a machine learning solution in improving the computing time, storage requirements, and minimize utilization of computer memory in analyses of RNA-Seq datasets. The source code of the analysis pipeline and associated scripts are presented in the paper appendix to allow replication of experiments.


title: An improved bug localization using structured information retrieval and version history

abstract: Locating buggy files is a time consuming and challenging task because defects can deflate from a large variety of sources. So, researchers proposed several automated bug localization techniques where the accuracy can be improved. In this paper, an information retrieval based bug localization technique has been proposed, where buggy files are identified by measuring the similarity between bug report and source code. Besides this, source code structure and frequently changed files are also incorporated to produce a better rank for buggy files. To evaluate the proposed approach, a large-scale experiment on three open source projects, namely SWT, ZXing and Guava has been conducted. The result shows that the proposed approach improves 7% in terms of Mean Reciprocal Rank (MRR) and about 8% for Mean Average Precision (MAP) compared to existing techniques.


title: Tools for the Automatic Generation of Ontology Documentation: A Task-Based Evaluation

abstract: Ontologies are knowledge constructs essential for creation of the Web of Data. Good documentation is required to permit people to understand ontologies and thus employ them correctly, but this is costly to create by tradition authorship methods, and is thus inefficient to create in this way until an ontology has matured into a stable structure. The authors describe three tools, LODE, Parrot and the OWLDoc-based Ontology Browser, that can be used automatically to create documentation from a well-formed OWL ontology at any stage of its development. They contrast their properties and then report on the authors' evaluation of their effectiveness and usability, determined by two task-based user testing sessions.


title: Cultural influences and globally distributed information systems development: experiences from Chinese IT professionals

abstract: Global information systems development has become increasingly prevalent and is facing a variety of challenges, including the challenge of cross-cultural management. However, research on exactly how cross-cultural factors affect global information systems development work is limited, especially with respect to distributed collaborative work between the U.S. and China. This paper draws on the interviews of Chinese IT professionals and discusses three emergent themes relevant to cross-cultural challenges: the complexity of language issues, culture and communication styles and work behaviors, and cultural understandings at different levels. Implications drawn from our findings will provide actionable knowledge to organizational management entities.


title: Mixed MNL Models for Discrete Response

abstract: This paper considers mixed, or random coefficients, multinomial logit (MMNL) models for discrete response, and establishes the following results. Under mild regularity conditions, any discrete choice model derived from random utility maximization has choice probabilities that can be approximated as closely as one pleases by a MMNL model. Practical estimation of a parametric mixing family can be carried out by Maximum Simulated Likelihood Estimation or Method of Simulated Moments, and easily computed instruments are provided that make the latter procedure fairly efficient. The adequacy of a mixing specification can be tested simply as an omitted variable test with appropriately defined artificial variables. An application to a problem of demand for alternative vehicles shows that MMNL provides a flexible and computationally practical approach to discrete response analysis. Copyright © 2000 John Wiley & Sons, Ltd.


title: Planogram Compliance Checking Based on Detection of Recurring Patterns

abstract: In this article, the authors propose a novel method for automatic planogram compliance checking in retail chains that doesn't require product template images for training. Product layout is extracted from an input image by means of unsupervised recurring pattern detection and matched via graph matching, with the expected product layout specified by a planogram to measure the level of compliance. A divide-and-conquer strategy is employed to improve the speed. Specifically, the input image is divided into several regions based on the planogram. Recurring patterns are detected in each region, respectively, and then merged together to estimate the product layout.


RELATIONSHIP BETWEEN DEMOGRAPHIC VARIABLES, ORGANISATIONAL COMMITMENT AND JOB SATISFACTION AND ORGANISATIONAL CITIZENSHIP BEHAVIOUR AMONG PRIMARY SCHOOL TEACHERS, COAST PROVINCE, KENYA


title: Utilizing RNA-Seq data for de novo coexpression network inference

abstract: Motivation: RNA-Seq experiments have shown great potential for transcriptome profiling. While sequencing increases the level of biological detail, integrative data analysis is also important. One avenue is the construction of coexpression networks. Because the capacity of RNA-Seq data for network construction has not been previously evaluated, we constructed a coexpression network using striatal samples, derived its network properties and compared it with microarray-based networks. ::: ::: Results: The RNA-Seq coexpression network displayed scale-free, hierarchical network structure. We detected transcripts groups (modules) with correlated profiles; modules overlap distinct ontology categories. Neuroanatomical data from the Allen Brain Atlas reveal several modules with spatial colocalization. The network was compared with microarray-derived networks; correlations from RNA-Seq data were higher, likely because greater sensitivity and dynamic range. Higher correlations result in higher network connectivity, heterogeneity and centrality. For transcripts present across platforms, network structure appeared largely preserved. From this study, we present the first RNA-Seq data de novo network inference. ::: ::: Contact: [email protected] ::: ::: Supplementary information: Supplementary data are available at Bioinformatics online.


Efficient estimation and stratified sampling


title: Timing of preoperative antibiotic prophylaxis in 54,552 patients and the risk of surgical site infection: A systematic review and meta-analysis

abstract: Abstract The aim of the study was to assess the effect of timing of preoperative surgical antibiotic prophylaxis (SAP) on surgical site infection (SSI) and compare the different timing intervals. The benefit of routine use of SAP prior to surgery has long been recognized. However, the optimal timing has not been defined. For the purpose of developing recommendations for the World Health Organization guideline for SSI prevention, a systematic review and meta-analysis of all relevant evidence was conducted. Major medical databases were searched from 1990 to 2016. The primary outcome was SSI after preoperative-SAP comparing different timing intervals. Adjusted odds ratios (OR) with 95% confidence intervals (CI) were extracted and pooled for each comparison with a random effects model. Fourteen papers with 54,552 patients were included in this review. In a quantitative analysis, there was no significant difference when SAP was administered 120–60 minutes prior to incision compared to administration 60–0 minutes prior to incision. Studies investigating different timing intervals within the last 60 minutes time frame reported contradictive results. The risk of SSI almost doubled when SAP was administered after first incision (OR:1.89; 95%CI:[1.05–3.40]) and was 5 times higher when administered more than 120 minutes prior to incision (OR5.26; 95%CI:[3.29–8.39]). Administration of antibiotic prophylaxis more than 120 minutes before incision or after incision is associated a higher risk of surgical site infections than administration less than 120 minutes before incision. Within this 120-minute time frame prior to incision, no differential effects could be identified. The broadly accepted recommendation to administer prophylaxis within a 60-minute time frame prior to incision could not be substantiated.


title: Using A* for inference in probabilistic classifier chains

abstract: Probabilistic Classifiers Chains (PCC) offers interesting properties to solve multi-label classification tasks due to its ability to estimate the joint probability of the labels. However, PCC presents the major drawback of having a high computational cost in the inference process required to predict new samples. Lately, several approaches have been proposed to overcome this issue, including beam search and an e-Approximate algorithm based on uniform-cost search. Surprisingly, the obvious possibility of using heuristic search has not been considered yet. This paper studies this alternative and proposes an admisible heuristic that, applied in combination with A* algorithm, guarantees, not only optimal predictions in terms of subset 0/1 loss, but also that it always explores less nodes than e-Approximate algorithm. In the experiments reported, the number of nodes explored by our method is less than two times the number of labels for all datasets analyzed. But, the difference in explored nodes must be large enough to compensate the overhead of the heuristic in order to improve prediction time. Thus, our proposal may be a good choice for complex multi-label problems.


title: A Compact Microstrip-Fed Triple Band-Notched UWB Monopole Antenna

abstract: In this letter, a novel low-profile microstrip-fed compact triple band-notched ultrawideband (UWB) antenna is proposed. Notch bands around the 3.3-3.8-GHz WiMAX and 5.15-5.85-GHz WLAN frequencies are obtained by etching out two elliptic single complementary split-ring resonators (ESCSRRs) of different dimensions from the radiating patch of the antenna. Furthermore, by placing two rectangular split-ring resonators near the feedline-patch junction of the antenna, rejection for the 7.9-8.4-GHz X-band frequencies is achieved. Design guidelines for implementing the notch-bands at the desired frequency regions are provided. The match between the simulated and experimental results suggests that the proposed antenna can be a good candidate for application in UWB communication systems.


title: 3D-nonFAR: three-dimensional non-volatile FPGA architecture using phase change memory

abstract: Memories play a key role in FGPAs in the forms of both programming bits and embedded memory blocks. FPGAs using non-volatile memories have been the focus of attention with zero boot-up delay, real-time reconfigurability, and superior energy efficiency. This paper presents a novel three-dimensional (3D) non-volatile FPGA architecture (3D-Non-FAR) using phase change memory (PCM) and 3D die stacking techniques. Basic structures in a conventional FPGA architecture are renovated with PCM, and components are repartitioned and reorganized in 3D-NonFAR to allow an efficient 3D integration of PCM elements. 3D-NonFAR not only preserves the advantages of existing non-volatile FP-GAs, but also provides high integration density, high performance, and bit-level programmability, which enable PCM as a universal memory replacement in FPGAs. Evaluation results show that 3D-NonFAR has smaller footprint, higher performance, and lower power consumption compared with other FPGA counterparts.


title: A Modest Approach to Checking Probabilistic Timed Automata

abstract: Probabilistic timed automata (PTA) combine discrete probabilistic choice, real time and nondeterminism. This paper presents a fully automatic tool for model checking PTA with respect to probabilistic and expected reachability properties. PTA are specified in Modest, a high-level compositional modelling language that includes features such as exception handling, dynamic parallelism and recursion, and thus enables model specification in a convenient fashion. For model checking, we use an integral semantics of time, representing clocks with bounded integer variables. This makes it possible to use the probabilistic model checker PRISM as analysis backend. We describe details of the approach and its implementation, and report results obtained for three different case studies.


title: Drug Therapy of Post-Stroke Aphasia: A Review of Current Evidence

abstract: This review considers the role of drug therapy in the treatment of post-stroke aphasia, the evidence for efficacy of different agents, and the theory-based explanations of drug-related benefits for aphasia rehabilitation. Pharmacological interventions modulating stroke-induced disruption of diverse neurotransmitters may improve language and communication deficits in aphasic patients through facilitation of brain plasticity and long-term potentiation. However, benefits are not evident for all compounds and refinement in clinical trial designs is required. Some pharmacological trials have failed because drug treatment was not combined with speech-language therapy, while other trials combining drugs with intensive model-driven therapies also failed probably because of short-trial duration, inadequate sample selection, or lack of drug action. Preliminary data reveals that combining neuroscience-based intensive aphasia techniques (constraint-induced aphasia therapy) and drugs acting on cholinergic and glutamatergic neurotransmitter systems are associated with better outcomes than other strategies and long-term maintenance of benefits. Although further studies are needed, current state of the evidence suggests that drug therapy may play a key role in the treatment of post-stroke aphasia.


title: Recommending New Items to Ephemeral Groups Using Contextual User Influence

abstract: Group recommender systems help groups of users in finding appropriate items to be enjoyed together. Lots of activities, like watching TV or going to the restaurant, are intrinsically group-based, thus making the group recommendation problem very relevant. In this paper we study ephemeral groups, i.e., groups where the members might be together for the first time. Recent approaches have tackled this issue introducing complex models to be learned offline, making them unable to deal with new items; on the contrary, we propose a group recommender able to manage new items too. In more detail, our technique determines the preference of a group for an item by combining the individual preferences of the group members on the basis of their contextual influence, where the contextual influence represents the ability of an individual, in a given situation, to direct the group's decision. We conducted an extensive experimental evaluation on a TV dataset containing a log of viewings performed by real groups, showing how our approach outperforms the comparable techniques from the literature.


title: Cryptanalysis of the KeeLoq block cipher

abstract: KeeLoq is a block cipher used in numerous widespread passive entry and remote keyless entry systems as well as in various component identification applications. The KeeLoq algorithm has a 64-bit key and operates on 32-bit blocks. It is based on an NLFSR with a nonlinear feedback function of 5 variables. In this paper a key recovery attack with complexity of about 2 steps is proposed (one step is equivalent to a single KeeLoq encryption operation). In our attack we use the techniques of guess-and-determine, slide, and distinguishing attacks. Several real-world applications are vulnerable to the attack. To our best knowledge this is the first paper to describe and cryptanalyze the KeeLoq block cipher.


title: A Biomimetic Underwater Soft Robot Inspired by Cephalopod Mollusc

abstract: This letter presents a biomimetic underwater soft robot design with novel propulsion and maneuvering mechanisms. The robot design took inspirations from cephalopod molluscs. The proposed robot comprises a soft mantle structure with multiple built-in soft actuators, which could deform to provide steering and maneuverability for underwater locomotion. Smart sensors are embedded into the soft mantle to provide real-time feedback to the controller. Underwater thrust is generated by a novel vortex-based soft tail mechanism also inspired by molluscs. The tail mechanism incorporates an electric motor sealed inside a continuum chamber, and rotational motion from the motor is transmitted to the soft-bodied tentacles, which generate a vortex to produce thrust. The resulting soft robot is incorporated in a seamless body, while both propulsion and steering are generated by soft-body deformation. To investigate the novel vortex-based biomimetic propulsion generation mechanism, an analytical model was developed to capture the relation between input motor speed and the resulting thrust force. The entire robot was fabricated using 3-D-printing and soft-material molding. The fabricated robot components and the assembled system were both validated through experiments. The results show that the mollusk-inspired robot could achieve underwater maneuverability by bending the four actuators, and the measured results match well with the proposed analytical mode. The proposed design could easily be repeated by anyone with access to a consumer-grade 3-D printer, enabling a wide range of biomimetic robot enthusiasts to investigate in the new mechanism for underwater propulsion and maneuvering.


title: Replicating Milgram: Would people still obey today?

abstract: The author conducted a partial replication of Stanley Milgram's (1963, 1965, 1974) obedience studies that allowed for useful comparisons with the original investigations while protecting the well-being of participants. Seventy adults participated in a replication of Milgram's Experiment 5 up to the point at which they first heard the learner's verbal protest (150 volts). Because 79% of Milgram's participants who went past this point continued to the end of the shock generator's range, reasonable estimates could be made about what the present participants would have done if allowed to continue. Obedience rates in the 2006 replication were only slightly lower than those Milgram found 45 years earlier. Contrary to expectation, participants who saw a confederate refuse the experimenter's instructions obeyed as often as those who saw no model. Men and women did not differ in their rates of obedience, but there was some evidence that individual differences in empathic concern and desire for control affected participants' responses.


title: Inference in the age of big data: Future perspectives on neuroscience

abstract: Neuroscience is undergoing faster changes than ever before. Over 100 years our field qualitatively described and invasively manipulated single or few organisms to gain anatomical, physiological, and pharmacological insights. In the last 10 years neuroscience spawned quantitative datasets of unprecedented breadth (e.g., microanatomy, synaptic connections, and optogenetic brain-behavior assays) and size (e.g., cognition, brain imaging, and genetics). While growing data availability and information granularity have been amply discussed, we direct attention to a less explored question: How will the unprecedented data richness shape data analysis practices? Statistical reasoning is becoming more important to distill neurobiological knowledge from healthy and pathological brain measurements. We argue that large-scale data analysis will use more statistical models that are non-parametric, generative, and mixing frequentist and Bayesian aspects, while supplementing classical hypothesis testing with out-of-sample predictions.


title: SF-sketch: slim-fat-sketch with GPU assistance

abstract: A sketch is a probabilistic data structure that is used to record frequencies of items in a multi-set. Various types of sketches have been proposed in literature and applied in a variety of fields, such as data stream processing, natural language processing, distributed data sets etc. While several variants of sketches have been proposed in the past, existing sketches still have a significant room for improvement in terms of accuracy. In this paper, we propose a new sketch, called Slim-Fat (SF) sketch, which has a significantly higher accuracy compared to prior art, a much smaller memory footprint, and at the same time achieves the same speed as the best prior sketch. The key idea behind our proposed SF-sketch is to maintain two separate sketches: a small sketch called Slim-subsketch and a large sketch called Fat-subsketch. The Slim-subsketch, stored in the fast memory (SRAM), enables fast and accurate querying. The Fat-subsketch, stored in the relatively slow memory (DRAM), is used to assist the insertion and deletion from Slim-subsketch. We implemented and extensively evaluated SF-sketch along with several prior sketches and compared them side by side. Our experimental results show that SF-sketch outperforms the most commonly used CM-sketch by up to 33.1 times in terms of accuracy. The concise version of our paper will appear in IKDE 2017.


How many discoveries have been lost by ignoring modern statistical methods?


title: Modeling movements in oil, gold, forex and market indices using search volume index and Twitter sentiments

abstract: Study of the forecasting models using large scale microblog discussions and the search behavior data can provide a good insight for better understanding the market movements. In this work we collected a dataset of 2 million tweets and search volume index (SVI from Google) for a period of June 2010 to September 2011. We model a set of comprehensive causative relationships over this dataset for various market securities like equity (Dow Jones Industrial Average-DJIA and NASDAQ-100), commodity markets (oil and gold) and Euro Forex rates. We also investigate the lagged and statistically causative relations of Twitter sentiments developed during active trading days and market inactive days in combination with the search behavior of public before any change in the prices/indices. Our results show extent of lagged significance with high correlation value upto 0.82 between search volumes and gold price in USD. We find weekly accuracy in direction (up and down prediction) uptil 94.3% for DJIA and 90% for NASDAQ-100 with significant reduction in mean average percentage error for all the forecasting models.


title: Modelling Hyperbolic Space: Designing a Computational Context for Learning Non-Euclidean Geometry

abstract: This paper describes and analyses the iterative design and development of a computational context for non-euclidean geometry. Drawing on three episodes from the design process, the paper discusses the epistemological implications associated with interplay between learning hyperbolic geometry and context in which that learning takes place. In particular, it explores the ways in which learners can become designers of the computational context, and the designer can become a learner. The paper concludes with a discussion of the microworld paradigm in relation to what might be called ‘advanced’ mathematics.


title: Effects of Constraint-Induced Movement Therapy on Patients With Chronic Motor Deficits After Stroke : A Replication

abstract: Background and Purpose—Constraint-induced movement therapy (CI therapy) has previously been shown to produce large improvements in actual amount of use of a more affected upper extremity in the “real-world” environment in patients with chronic stroke (ie, >1 year after the event). This work was carried out in an American laboratory. Our aim was to determine whether these results could be replicated in another laboratory located in Germany, operating within the context of a healthcare system in which administration of conventional types of physical therapy is generally more extensive than in the United States. Methods—Fifteen chronic stroke patients were given CI therapy, involving restriction of movement of the intact upper extremity by placing it in a sling for 90% of waking hours for 12 days and training (by shaping) of the more affected extremity for 7 hours on the 8 weekdays during that period. Results—Patients showed a significant and very large degree of improvement from before to after treatment on...


title: Printed circuit board technology inspired stretchable circuits

abstract: In the past 15 years, stretchable electronic circuits have emerged as a new technology in the domain of assembly, interconnections, and sensor circuit technologies. In the meantime, a wide variety of processes using many different materials have been explored in this new field. In the current contribution, we present an approach inspired by conventional rigid and flexible printed circuit board (PCB) technology. Similar to PCBs, standard packaged, rigid components are assembled on copper contact pads using lead-free solder reflow processes. Stretchability is obtained by shaping the copper tracks as horseshoe-shaped meanders. Elastic materials, predominantly polydimethylsiloxanes, are used to embed the conductors and the components, thus serving as a circuit carrier. We describe mechanical modeling, aimed at optimizing the build-up toward maximum mechanical reliability of the structures. Details on the production process, reliability assessment, and a number of functional demonstrators are described.


Manifold Learning with Variational Auto-encoder for Medical Image Analysis


title: A Waste Collection Mechanism based on IoT

abstract: One of the major problems that the developing countries face is the problem of waste management. Not only does waste spread diseases but it also pollutes the water bodies and causes several such issues. Garbage bins at public places are a mattern of concern too. Clearance of bins at the appropriate time is essential. It otherwise creates unhygienic conditions for people as well as repulsiveness to that place and leaving behind bad smell. This paper proposes a smart waste collection system with the help of IoT. Information will be send to municipality center using which the waste can be collected in time. This method is supported by ultrasonic sensor, a weight sensor and MQ gas sensor which are attached to an Arduino Uno microcontroller. When the values read by the sensors reach a threshold, an alert is send to the Municipality server. The real-time progress of the waste collection can be examined with the help of this system. This method will enable waste collection on time and thereby ensure a safer environment.


HELIOS 2014 Team Description Paper


title: AirBag: Boosting Smartphone Resistance to Malware Infection

abstract: Recent years have experienced explosive growth of smartphone sales. Inevitably, the rise in the popularity of smartphones also makes them an attractive target for attacks. In light of these threats, current mobile platform providers have developed various server-side vetting processes to block malicious applications (“apps”). While helpful, they are still far from ideal in achieving their goals. To make matters worse, the presence of alternative (less-regulated) mobile marketplaces also opens up new attack vectors, which necessitate client-side solutions (e.g., mobile anti-virus software) to run on mobile devices. However, existing client-side solutions still exhibit limitations in their capability or deployability. In this paper, we present AirBag, a lightweight OS-level virtualization approach to enhance the popular Android platform and boost our defense capability against mobile malware infection. Assuming a trusted smartphone OS kernel and the fact that untrusted apps will be eventually installed onto users’ phones, AirBag is designed to isolate and prevent them from infecting our normal systems (e.g., corrupting the phone firmware) or stealthily leaking private information. More specifically, by dynamically creating an isolated runtime environment with its own dedicated namespace and virtualized system resources, AirBag not only allows for transparent execution of untrusted apps, but also effectively mediates their access to various system resources or phone functionalities (e.g., SMSs or phone calls). We have implemented a proof-of-concept prototype on three representative mobile devices, i.e., Google Nexus One, Nexus 7, and Samsung Galaxy S III. The evaluation results with a number of untrusted apps, including real-world mobile malware, demonstrate its practicality and effectiveness.


title: A Novel Attack on a FPGA based True Random Number Generator

abstract: True random Number Generators (TRNGs) are important sources of randomness necessary in several cryptographic algorithms and protocols. We describe an attack on a practical Field Programmable Gate Array (FPGA) based TRNG that severely affects the randomness of its output bitstream. In particular, we develop, mathematically analyze and demonstrate an"in-field", post-deployment Hardware Trojan Horse (HTH) insertion technique that exploits the Dynamic Partial Reconfiguration (DPR) capability of modern FPGAs. The attack can be launched from a remote device connected to the FPGA over a standard network connection. The inserted HTH operates by adversely affecting the source of entropy of the TRNG, which results in the probability of zero in the output bitstream to increase to about 0.75 (instead of the ideal value of 0.5). Our experimental results demonstrate that the Trojan is extremely effective, with very low hardware and resource footprint, making its detection very challenging.


title: Path-finding using reinforcement learning and affective states

abstract: During decision making and acting in the environment humans appraise decisions and observations with feelings and emotions. In this paper we propose a framework to incorporate an emotional model into the decision making process of a machine learning agent. We use a hierarchical structure to combine reinforcement learning with a dimensional emotional model. The dimensional model calculates two dimensions representing the actual affective state of the autonomous agent. For the evaluation of this combination, we use a reinforcement learning experiment (called Dyna Maze) in which, the agent has to find an optimal path through a maze. Our first results show that the agent is able to appraise the situation in terms of emotions and react according to them.


Chapter 4 A Guided Tour on the Theory and Practice of State Machine Replication


title: Metaphor in theory and practice: the influence of metaphors on expectations

abstract: The use of metaphors is pervasive in all forms of discourse. This paper is concerned with providing a brief review of the development of metaphor theory,illustrated with some examples of supportive empirical research.These include a canonical study of the way concepts of instruction and education are influenced, a study into the effects of human-computer interface metaphors on computer systems users,and a study designed to assess the impacts of metaphor use on attitudes to internet commerce, particularly on attitudes to the roles played by information technology (IT). The paper provides some contextual background to assist consideration of the effects of metaphors on attitudes and beliefs.In practice, metaphor use may be intentional,unconscious,or a mixture of both, but in any case metaphors can be shown to play powerful roles in the social construction of human reality.


title: Young Adults with Autism Spectrum Disorder Show Early Atypical Neural Activity during Emotional Face Processing

abstract: Social cognition is impaired in Autism Spectrum Disorder (ASD). The ability to perceive and interpret affect is integral to successful social functioning and has an extended developmental course. However, the neural mechanisms underlying emotional face processing in ASD are unclear. Using magnetoencephalography, the present study explored neural activation during implicit emotional face processing in young adults with and without ASD. Twenty-six young adults with ASD and 26 healthy controls were recruited. Participants indicated the location of a scrambled pattern (target) that was presented alongside happy or angry face. Emotion-related activation sources for each emotion were estimated using the Empirical Bayes Beamformer (pcorr<0.001) in SPM12. Emotional faces elicited elevated fusiform, amygdala and anterior insula and reduced ACC activity in adults with ASD relative to controls. Within group comparisons revealed that angry versus happy faces elicited distinct neural activity in typically developing adults; there was no distinction in young adults with ASD. Our data suggest difficulties in affect processing in ASD reflect atypical recruitment of traditional emotional processing areas. These early differences may contribute to difficulties in deriving social reward to faces, ascribing salience to faces, and an immature threat processing system, which collectively could result in deficits in emotional face processing.


title: Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge

abstract: Quantitative analysis of brain tumors is critical for clinical decision making. While manual segmentation is tedious, time consuming and subjective, this task is at the same time very challenging to solve for automatic segmentation methods. In this paper we present our most recent effort on developing a robust segmentation algorithm in the form of a convolutional neural network. Our network architecture was inspired by the popular U-Net and has been carefully modified to maximize brain tumor segmentation performance. We use a dice loss function to cope with class imbalances and use extensive data augmentation to successfully prevent overfitting. Our method beats the current state of the art on BraTS 2015, is one of the leading methods on the BraTS 2017 validation set (dice scores of 0.896, 0.797 and 0.732 for whole tumor, tumor core and enhancing tumor, respectively) and achieves very good Dice scores on the test set (0.858 for whole, 0.775 for core and 0.647 for enhancing tumor). We furthermore take part in the survival prediction subchallenge by training an ensemble of a random forest regressor and multilayer perceptrons on shape features describing the tumor subregions. Our approach achieves 52.6% accuracy, a Spearman correlation coefficient of 0.496 and a mean square error of 209607 on the test set.


title: Clashing over the NFC Secure Element for Platform Leadership in the Mobile Payment Ecosystem

abstract: This paper explores how the control over the Near Field Communication (NFC) secure element could provide platform leadership in the mobile payment ecosystem. During a decade of trials, firms from different industries struggled to launch proximity mobile payment platforms to pay at physical point-of-sales. Mobile payments platforms are complex socio-technical systems that need to combine an array of resources and capabilities from different firms. The implementation of NFC platforms requires to store financial credentials of consumers in a secure element (SE). The industry designed several competing platform architectures for the secure element: SIM-centric, device-centric, SD-centric, and Host Card Emulation (HCE). Each of these alternatives gave more or less control to the different firms involved in the mobile payment ecosystem. For years, firms promoted the architecture which would give them more control. Over the time, this struggle just hindered the emergence of successful mobile payment platforms. The recent emergence of new actors (e.g., Google, Apple, Samsung) raised optimistic prospects for future developments. This paper provides a strategic analysis of the mobile payment ecosystem by studying the relationship between SE architectures and the establishment of platform leadership by incumbents and emerging actors.


title: Multimodal biometric fusion at feature level: Face and palmprint

abstract: Multimodal biometrics has recently attracted substantial interest for its high performance in biometric recognition system. In this paper we introduce multimodal biometrics for face and palmprint images using fusion techniques at the feature level. Gabor based image processing is utilized to extract discriminant features, while principal component analysis (PCA) and linear discriminant analysis (LDA) are used to reduce the dimension of each modality. The output features of LDA are serially combined and classified by a Euclidean distance classifier. The experimental results based on ORL face and Poly-U palmprint databases proved that this fusion technique is able to increase biometric recognition rates compared to that produced by single modal biometrics.


title: Experimental evaluation of the schunk 5-Finger gripping hand for grasping tasks

abstract: In order to perform useful tasks, a service robot needs to manipulate objects in its environment. In this paper, we propose a method for experimental evaluation of the suitability of a robotic hand for grasping tasks in service robotics. The method is applied to the Schunk 5-Finger Gripping Hand, which is a mechatronic gripper designed for service robots. During evaluation, it is shown, that it is able to grasp various common household objects and execute the grasps from the well known Cutkosky grasp taxonomy [1]. The result is, that it is a suitable hand for service robot tasks.


title: Hybrid classifier based life cycle stages analysis for malaria-infected erythrocyte using thin blood smear images

abstract: Malaria, being a life-threatening disease caused by parasites, demands its rapid and accurate diagnosis. In this paper, we develop a computer-assisted malaria-infected life-cycle stages classification based on a hybrid classifier using thin blood smear images. The major issues are: feature extraction, feature selection and classification of erythrocytes infected with different life-cycle stages of malaria. Feature set (134 dimensional features) has been defined by the combination of the proposed features along with the existing features. Features such as prediction error, co-occurrence of linear binary pattern, chrominance channel histogram, R–G color channel difference histogram and Gabor features are the newly proposed features in our system. In the feature selection, a two-stage algorithm utilizing the filter method to rank the feature, along with the incremental feature selection technique, has been analyzed. Moreover, the performance of all the individual classifiers (Naive Bayes, support vector machine, k-nearest neighbors and artificial neural network) is evaluated. Finally, the three individual classifiers are combined to develop a hybrid classifier using different classifier combining techniques. From the experimental results, it may be concluded that hybrid classifier formed by the combination of SVM, k-NN and ANN with majority voting technique provides satisfactory results compared to other individual classifiers as well as other hybrid model. An accuracy of 96.54 ± 0.73% has been achieved on the collected clinical database. The results show an improvement in accuracy (11.62, 6.7, 3.39 and 2.39%) as compared to the state-of-the-art individual classifiers, i.e., Naive Bayes, SVM, k-NN and ANN, respectively.


title: Multidimensional divide-and-conquer

abstract: Most results in the field of algorithm design are single algorithms that solve single problems. In this paper we discuss multidimensional divide-and-conquer , an algorithmic paradigm that can be instantiated in many different ways to yield a number of algorithms and data structures for multidimensional problems. We use this paradigm to give best-known solutions to such problems as the ECDF, maxima, range searching, closest pair, and all nearest neighbor problems. The contributions of the paper are on two levels. On the first level are the particular algorithms and data structures given by applying the paradigm. On the second level is the more novel contribution of this paper: a detailed study of an algorithmic paradigm that is specific enough to be described precisely yet general enough to solve a wide variety of problems.


title: Accuracy of automatic number plate recognition (ANPR) and real world UK number plate problems

abstract: This paper considers real world UK number plates and relates these to ANPR. It considers aspects of the relevant legislation and standards when applying them to real world number plates. The varied manufacturing techniques and varied specifications of component parts are also noted. The varied fixing methodologies and fixing locations are discussed as well as the impact on image capture.


title: Direct-Manipulation Visualization of Deep Networks

abstract: The recent successes of deep learning have led to a wave of interest from non-experts. Gaining an understanding of this technology, however, is difficult. While the theory is important, it is also helpful for novices to develop an intuitive feel for the effect of different hyperparameters and structural variations. We describe TensorFlow Playground, an interactive, open sourced visualization that allows users to experiment via direct manipulation rather than coding, enabling them to quickly build an intuition about neural nets.


title: Real-Time Face Reconstruction from a Single Depth Image

abstract: This paper contributes a real time method for recovering facial shape and expression from a single depth image. The method also estimates an accurate and dense correspondence field between the input depth image and a generic face model. Both outputs are a result of minimizing the error in reconstructing the depth image, achieved by applying a set of identity and expression blend shapes to the model. Traditionally, such a generative approach has shown to be computationally expensive and non-robust because of the non-linear nature of the reconstruction error. To overcome this problem, we use a discriminatively trained prediction pipeline that employs random forests to generate an initial dense but noisy correspondence field. Our method then exploits a fast ICP-like approximation to update these correspondences, allowing us to quickly obtain a robust initial fit of our model. The model parameters are then fine tuned to minimize the true reconstruction error using a stochastic optimization technique. The correspondence field resulting from our hybrid generative-discriminative pipeline is accurate and useful for a variety of applications such as mesh deformation and retexturing. Our method works in real-time on a single depth image i.e. Without temporal tracking, is free from per-user calibration, and works in low-light conditions.


title: Double conical wheels based mobile robot for aerial power distribution lines inspection

abstract: There are several proposals of robots for inspection of overhead power network, and most of them make use of UAVs (Unmanned Aerial Vehicle). Such proposals present disadvantages like, low controllability, small payload capability and short operation range. On this scenario, this work presents a robot that can move over the power line and has as features: three driven wheels, distributed control system and self-configuration frame. The robot can overpass all power line devices taking the advantage of its wheel's shape that provides a self-steering capability. The communications between each motor controller is done by IEEE 802.15.4 wireless communication standard. The robot can be controlled by an electrician to proceeds the inspection from the ground level or in an autonomous way. A prototype was constructed and was tested, in both, laboratory and field environment. The test results shown the effectiveness of the robot proposed.


title: On optimal rule discovery

abstract: In machine learning and data mining, heuristic and association rules are two dominant schemes for rule discovery. Heuristic rule discovery usually produces a small set of accurate rules, but fails to find many globally optimal rules. Association rule discovery generates all rules satisfying some constraints, but yields too many rules and is infeasible when the minimum support is small. Here, we present a unified framework for the discovery of a family of optimal rule sets and characterize the relationships with other rule-discovery schemes such as nonredundant association rule discovery. We theoretically and empirically show that optimal rule discovery is significantly more efficient than association rule discovery independent of data structure and implementation. Optimal rule discovery is an efficient alternative to association rule discovery, especially when the minimum support is low.


title: Cross-lingual RST Discourse Parsing

abstract: Discourse parsing is an integral part of understanding information flow and argumentative structure in documents. Most previous research has focused on inducing and evaluating models from the English RST Discourse Treebank. However, discourse treebanks for other languages exist, including Spanish, German, Basque, Dutch and Brazilian Portuguese. The treebanks share the same underlying linguistic theory, but differ slightly in the way documents are annotated. In this paper, we present (a) a new discourse parser which is simpler, yet competitive (significantly better on 2/3 metrics) to state of the art for English, (b) a harmonization of discourse treebanks across languages, enabling us to present (c) what to the best of our knowledge are the first experiments on cross-lingual discourse parsing.


title: On effective classification of strings with wavelets

abstract: In recent years, the technological advances in mapping genes have made it increasingly easy to store and use a wide variety of biological data. Such data are usually in the form of very long strings for which it is difficult to determine the most relevant features for a classification task. For example, a typical DNA string may be millions of characters long, and there may be thousands of such strings in a database. In many cases, the classification behavior of the data may be hidden in the compositional behavior of certain segments of the string which cannot be easily determined apriori. Another problem which complicates the classification task is that in some cases the classification behavior is reflected in global behavior of the string, whereas in others it is reflected in local patterns. Given the enormous variation in the behavior of the strings over different data sets, it is useful to develop an approach which is sensitive to both the global and local behavior of the strings for the purpose of classification. For this purpose, we will exploit the multi-resolution property of wavelet decomposition in order to create a scheme which can mine classification characteristics at different levels of granularity. The resulting scheme turns out to be very effective in practice on a wide range of problems.


title: Advances in dataflow programming languages

abstract: Many developments have taken place within dataflow programming languages in the past decade. In particular, there has been a great deal of activity and advancement in the field of dataflow visual programming languages. The motivation for this article is to review the content of these recent developments and how they came about. It is supported by an initial review of dataflow programming in the 1970s and 1980s that led to current topics of research. It then discusses how dataflow programming evolved toward a hybrid von Neumann dataflow formulation, and adopted a more coarse-grained approach. Recent trends toward dataflow visual programming languages are then discussed with reference to key graphical dataflow languages and their development environments. Finally, the article details four key open topics in dataflow programming languages.