Reviewerly/exHarmony huggingface dataset:
Selective Wander Join: Fast Progressive Visualizations for Data Joins

Progressive visualization offers a great deal of promise for big data visualization; however, current progressive visualization systems do not allow for continuous interaction. What if users want to see more confident results on a subset of the visualization? This can happen when users are in exploratory analysis mode but want to ask some directed questions of the data as well. In a progressive visualization system, the online aggregation algorithm determines the database sampling rate and resulting convergence rate, not the user. In this paper, we extend a recent method in online aggregation, called Wander Join, that is optimized for queries that join tables, one of the most computationally expensive operations. This extension leverages importance sampling to enable user-driven sampling when data joins are in the query. We applied user interaction techniques that allow the user to view and adjust the convergence rate, providing more transparency and control over the online aggregation process. By leveraging importance sampling, our extension of Wander Join also allows for stratified sampling of groups when there is data distribution skew. We also improve the convergence rate of filtering queries, but with additional overhead costs not needed in the original Wander Join algorithm.
Nonmyopic sensor scheduling for target tracking with emission control

Summary Active sensors obtain the measurements of targets by emitting energy that can be intercepted by enemy surveillance sensors. To satisfy the target tracking requirement and control the whole system emission, we propose a nonmyopic sensor scheduling to minimize the emission cost while maintaining a desired tracking accuracy. The processes of target tracking and emission control are formulated as a partially observable Markov decision process. Then, we translate our scheduling problem to a discrete unconstrained optimization problem, which consists of multistep emission cost and multistep tracking accuracy cost. Furthermore, the cubature Kalman filter is utilized to update the target belief state and predict the multistep tracking accuracy cost, whereas the multistep emission cost is obtained by hidden Markov model filter. Scheduling is implemented efficiently by constructing a decision tree and using a search algorithm, which combines uniform cost search with augmented branch and bound technique. Simulation results demonstrate the effectiveness of our proposed method.
Recommending system for digital educational resources based on learning analysis

In this paper, we propose an online course recommendation system applicable to a distance-learning platform. (LMS) which assists and supports learners in knowledge tests. Most commercial LMS or open source as well as open source software do not include recommendation tools to guide learners during their learning process. Current online training devices, such as MOOCs, enlist thousands of learners, so tutors often feel powerless to build a synthetic representation of learner activity, to send recommendations, remarks to each learner and intervene in appropriate time. In an attempt to meet these needs, we propose an approach that aims to automatically analyze the learners' responses, which determine their level of knowledge. Basis on their answers, our system automatically offers personalized recommendations for the learners. We will then present the results of the implementation of the system developed as part of an experiment we conducted with 10,000 students enrolled on our distance-learning platform. The paper presents the results obtained and the perspectives for more research.
Multiview video quality enhancement without depth information

The past decade has witnessed fast development in multiview 3D video technologies, such as Three-Dimensional Video (3DV), Virtual Reality (VR), and Free Viewpoint Video (FVV). However, large information redundancy and a vast amount of multiview video data needs to be stored or transmitted, which poses a serious problem for multiview video systems. Asymmetric multiview video compression can alleviate this problem by coding views with different qualities. Only several viewpoints are kept with high-quality and other views are highly compressed to low-quality. However, highly compressed views may incur severe quality degradation. Thus, it is necessary to enhance the visual quality of highly compressed views at the decoder side. Exploiting similarities among the multiview images is the key to efficiently reconstruct the multiview compressed views. In this paper, we propose a novel method for multiview quality enhancement, which directly learns an end-to-end mapping between the low-quality and high-quality views and recovers the details of the low-quality view. The mapping process is realized using a deep convolutional neural network (MVENet). MVENet takes a low-quality image of one view and a high-quality image of another view of the same scene as inputs and outputs an enhanced image for the low-quality view. To the best of our knowledge, this is the first work for multiview video enhancement where neither a depth map nor a projected virtual view is required in the enhancement process. Experimental results on both computer graphic and real datasets demonstrate the effectiveness of the proposed approach with a peak signal-to-noise ratio (PSNR) gain of up to 2dB over low-quality compressed views using HEVC and up to 3.7dB over low-quality compressed views using JPEG on the benchmark Cityscapes.
Research on Headlight Technology of Night Vehicle Intelligent Detection Based on Hough Transform

With the advent of the Internet+ era, intelligent transportation has become a hotspot and frontier in the study of traffic development in the world. On the basis of traditional basic transportation facilities, informationization, communic ation and intelligent control are integrated to generate intelligent traffic management applications. It has become an important symbol of the information age and has become an important condition for smart cities. In this paper, based on the difference between night vehicle detection and daytime vehicle detection, a Hough transform night vehicle intelligent detection method is proposed. First, the headlights are extracted by grayscale division, and then the extracted lights are divided into connected domains and extracted to the edge of the lamp; then the circle is detected by Hough transform, and then the radius of the lamp is taken and Positioning of the position in the vehicle; finally, since the normal lights will appear in pairs, the vehicle position is matched to determine the position of the vehicle. The experimental results show that the algorithm has high detection rate for night vehicle detection and low false alarm.
Integrated multiple-attribute decision making and kernel-based mechanism for risk analysis and evaluation

Due to the radical changes in the global economy and internationalization of markets around the world, local corporates are encountering much more severe challenges and uncertainties than ever before. Hence, how to reliably and effectively evaluate whether corporates will exhibit substantial troubl es/difficulties in the near future turns out to be an attractive investigative issue. This study introduces a fusion mechanism that gives decision makers a comprehensive description on a corporate’s operation status so as to prevent a biased judgment from occurring. The introduced mechanism consists of three main procedures: (1) Performance rank determination through the integration of balanced scorecards (BSC) and two-level DEA with a weighting adjusted strategy; (2) Forecasting model construction by combining core vector machine (CVM) with the support vectors (SV)-based online learning strategy; and (3) Knowledge extraction by a rule-based algorithm. The experimental results show that the introduced fusion mechanism (i.e., TDMCR) reduces unnecessary information, gives a more overarching description, satisfactorily predicts a corporate’s operation status, and provides intuitive decision logics for market participators to adjust their investment portfolios for maximizing their profit margins under an anticipated risk level. Examined by real cases, the introduced fusion mechanism is a promising alternative for corporate operating performance forecasting.
Towards a Cybersecurity Investment Assessment method using Primitive Cognitive Network Process

Cybercrime is everywhere in cyberspace. Cybersecurity is a set of technologies, processes and policies to protect the enterprise information asset to prevent business loss assets from the cyber attackers. Planning a sustainable and reliable cybersecurity system is an essential initial step since the large amount of investment on the system has long lasting impact. This paper introduces a multiple criteria decision making method using the Primitive Cognitive Network Process (PCNP) for the cyber-security investment decision making.
Distributed Software Dependency Management Using Blockchain

Contemporary software deployments rely on cloud-based package managers for installation, where existing packages are installed on demand from remote code repositories. Usually frameworks or common utilities, packages increase the code reusability within the ecosystem, whilst keeping the code base small. However, disruptions in the package management services can potentially affect development and deployment workflows. Furthermore, cloud package managers have arguably an ambiguous ownership model and offer limited visibility of packages to the users. This work describes the development of a blockchain-based package control system which is decentralised, reliable, and transparent. Blockchain nodes are installed within the distributed infrastructure to provide immutability, and then a dependency graph is constructed with the help of smart contracts to trace the software provenance. Our system has been successfully tested with 4338 packages from NPM, 950 out of which are the top depended-upon packages.
Extracting multi-view images from multi-focused plenoptic camera

A multi-focused plenoptic camera is a powerful device that can capture a light field (LF), which is interpreted as a set of dense multi-view images. The camera has potential ability such that we can obtain LFs with high spatial/view resolutions and deep depth-of-field. To extract multi-view images, we need a sophisticated rendering process due to the complicated optical system of such cameras. However, there are few researches on this topic, and the only available rendering software to the best of our knowledge could not work well for some camera configurations. We therefore propose an improved rendering method and release our rendering software. Our software can extract multi-view images from a multi-focused plenoptic camera with higher quality than the previous one and work for various camera configurations.
Research on the Application of Blockchain in the Energy Power Industry in China

Blockchain technology has worked over the years and is being successfully applied in finance industry. The characteristics of decentralization, openness, immutability and security provide a natural solution to the distributed autonomy management for diverse energy sources and the transaction between a wide range of participants in the energy power industry. This paper introduces the current development status and the research on the application of blockchain technology in this industry in China from the aspects of demand response, point-to-point transaction and distributed energy resource. Existed problems and suggestions are concluded for the future studies.
A Text Retrieval Algorithm Based on the Hybrid LDA and Word2Vec Model

Text retrieval is a fundamental problem in information retrieval, and it is greatly influenced by the text feature representation. In this paper, we propose a novel text feature representation model, which integrates the LDA and Word2Vec model, and then utilize this model to handle the text retrieval problem. Latent Dirichlet Allocation represents a generative probabilistic model of a corpus, and texts are organized as random mixtures of latent topics. In particular, the proposed algorithm computes the distance between document and topics, and then each document is represented as a feature vector, in which each dimension denotes the distance between this document and a specific topic. To test the effectiveness of the proposed algorithm, several related methods are made performance comparison, and experimental results demonstrate that the proposed solution performs better than other methods, and it can achieve high accuracy for text retrieval.
Deep Learning at Scale

This work presents a novel approach to distributed training of deep neural networks (DNNs) that aims to overcome the issues related to mainstream approaches to data parallel training. Established techniques for data parallel training are discussed from both a parallel computing and deep learning perspective, then a different approach is presented that is meant to allow DNN training to scale while retaining good convergence properties. Moreover, an experimental implementation is presented as well as some preliminary results.
Weight distribution of a subclass of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.gif" overflow="scroll"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">Z</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math>-double cyclic codes

Let m0 and m1 be two odd positive integers. In this paper, the weight distribution of Z2-double cyclic codes of length m0+m1 for a special case are determined explicitly.
A Stochastic Approach to Energy Cost Minimization in Smart-Grid-Enabled Data Center Network

We propose a Lyapunov drift-plus-penalty- (LDPP-) based algorithm to optimize the average power cost for a data center network. In particular, we develop an algorithm to minimize the operational cost using real-time electricity pricing with the integration of green energy resources from the smart grid. The LDPP technique can achieve significant energy cost savings under quality of service (QoS) constraints. Numerical results are presented to evaluate and validate our solution. These results illustrate significant operational/energy cost reductions for a data center network over the conventional approach which optimizes the predicted values of stochastic parameters under a fixed QoS constraint.
Improving Sentiment Analysis using Hybrid Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.
Unknown noise removal via sparse representation model

To remove more complex or unknown noise, we propose a new dictionary learning model by assuming noise as Mixture of Gaussian (MoG) distributions. Since MoG is able to approximate any continuous distributions universally, the proposed method can effectively recover the original image from the corrupted one with various forms of noise. Meanwhile, to solve weighted ℓ2-ℓ0 minimization problems, we further propose modified orthogonal matching pursuit method in sparse coding and extend alternating proximal algorithm to update dictionaries. Experimental results demonstrate that our proposed method is superior to several previous denoising methods in terms of quantitative measures and visual quality.
Monetary-Incentive Competition Between Humans and Robots: Experimental Results

In a controlled experiment, participants ( n=60) competed in a monotonous task with an autonomous robot for real monetary incentives. For each participant, we manipulated the robot's performance and the monetary incentive level across ten rounds. In each round, a participant's performance compared to the robot's would affect their odds in a lottery for the monetary prize. Standard economic theory predicts that people's effort will increase with prize value. Furthermore, recent work in behavioral economics predicts that there will also be a discouragement effect, with stronger robot performance discouraging human effort, and that this effect will increase with prize. We were not able to detect a meaningful effect of monetary prize, but we found a small discouragement effect, with human effort decreasing with increased robot performance, significant at the level. Using per-round subjective indicators, we also found a positive effect of robot performance on its perceived competence, a negative effect on the participants' liking of the robot, and a negative effect on the participants' own competence, all at . These findings shed light on how people may exert work effort and perceive robotic competitors in a human-robot workforce, and could have implications on labor supply decisions and the design of compensation schemes in the workplace.
Camera Obscurer: Generative Art for Design Inspiration

We investigate using generated decorative art as a source of inspiration for design tasks. Using a visual similarity search for image retrieval, the Camera Obscurer app enables rapid searching of tens of thousands of generated abstract images of various types. The seed for a visual similarity search is a given image, and the retrieved generated images share some visual similarity with the seed. Implemented in a hand-held device, the app empowers users to use photos of their surroundings to search through the archive of generated images and other image archives. Being abstract in nature, the retrieved images supplement the seed image rather than replace it, providing different visual stimuli including shapes, colours, textures and juxtapositions, in addition to affording their own interpretations. This approach can therefore be used to provide inspiration for a design task, with the abstract images suggesting new ideas that might give direction to a graphic design project. We describe a crowdsourcing experiment with the app to estimate user confidence in retrieved images, and we describe a pilot study where Camera Obscurer provided inspiration for a design task. These experiments have enabled us to describe future improvements, and to begin to understand sources of visual inspiration for design tasks.
Basic Study on Targeted E-mail Attack Method Using OSINT

In recent years, attackers have easily gained considerable information on companies and individuals using open source intelligence (OSINT), thereby increasing the threat of targeted attacks. In light of such a situation, modeling the synergistic effect of OSINT and targeted attacks will be an effective measure against these attacks. In this paper, we formulate a state transition model that defines the process by which attackers gather a target's information by using OSINT tools. Then we categorize the targeted e-mails that the attackers can generate in each state. The results of the analysis can be used by the victims to estimate the extent of attacks from the contents of the targeted e-mails, and to take appropriate measures.
Stochastic resource allocation

Suboptimal resource utilization among public and private cloud providers prevents them from maximizing their economic potential. Long-term allocated resources are often idle when they might have been subleased for a short period. Alternatively, arbitrary resource overcommitment may lead to unpredictable client performance.
Dynamic Ensemble Selection for Author Verification

Author verification is a fundamental task in authorship analysis and associated with significant applications in humanities, cyber-security, and social media analytics. In some of the relevant studies, there is evidence that heterogeneous ensembles can provide very reliable solutions, better than any individual verification model. However, there is no systematic study of examining the application of ensemble methods in this task. In this paper, we start from a large set of base verification models covering the main paradigms in this area and study how they can be combined to build an accurate ensemble. We propose a simple stacking ensemble as well as a dynamic ensemble selection approach that can use the most reliable base models for each verification case separately. The experimental results in ten benchmark corpora covering multiple languages and genres verify the suitability of ensembles for this task and demonstrate the effectiveness of our method, in some cases improving the best reported results by more than 10%.
Co-clustering from Tensor Data

With the exponential growth of collected data in different fields like recommender system (user, items), text mining (document, term), bioinformatics (individual, gene), co-clustering which is a simultaneous clustering of both dimensions of a data matrix, has become a popular technique. Co-clustering aims to obtain homogeneous blocks leading to an easy simultaneous interpretation of row clusters and column clusters. Many approaches exist, in this paper we rely on the latent block model (LBM) which is flexible allowing to model different types of data matrices. We extend its use to the case of a tensor (3D matrix) data in proposing a Tensor LBM (TLBM) allowing different relations between entities. To show the interest of TLBM, we consider continuous and binary datasets. To estimate the parameters, a variational EM algorithm is developed. Its performances are evaluated on synthetic and real datasets to highlight different possible applications.
Sharp Detection of Event’s Onset in Seismic Signals With Asymmetrical Kaiser Window-Based S-Transform

The S-transform (ST) is a commonly used tool for time-frequency (TF) localization. It uses scalable Gaussian kernel for multiresolution analysis. It is widely used for the analysis of seismic signals. However, the long taper of the Gaussian window results in the degradation of the time resolution at lower frequencies and, hence, lowers its effectiveness in reliable detection of onset of events. This letter proposes an asymmetrical Kaiser window-based ST that provides better time resolution in the forward direction which facilitates sharp detection of events in seismic signals. The inherent optimal energy concentration characteristics of the Kaiser window provide better TF localization compared to existing approaches. The window parameters are also generalized to provide frequency-dependent asymmetry while avoiding unwanted degradation of frequency resolution at higher frequencies. The efficacy of the proposed approach is illustrated by testing it with a synthetic seismic trace and a real noisy seismograph. Simulation results illustrate the improvement in time resolution with the proposed approach as compared to other asymmetrical window-based STs for the detection of event's onset.