Reviewerly/exHarmony huggingface dataset:
Entity and relation collaborative extraction approach based on multi-head attention and gated mechanism

Entity and relation extraction has been widely studied in natural language processing, and some joint methods have been proposed in recent years. However, existing studies still suffer from two problems. Firstly, the token space information has been fully utilized in those studies, while the label space information is underutilized. However, a few preliminary works have proven that the label space information could contribute to this task. Secondly, the performance of relevant entities detection is still unsatisfactory in entity and relation extraction tasks. In this paper, a new model GANCE (Gated and Attentive Network Collaborative Extracting) is proposed to address these problems. Firstly, GANCE exploits the label space information by applying a gating mechanism, which could improve the performance of the relation extraction. Then, two multi-head attention modules are designed to update the token and token-label fusion representation. In this way, the relevant entities detection could be solved. Experimental results demonstrate that GANCE has better accuracy than several competitive approaches in terms of entity recognition and relation extraction on the CoNLL04 dataset at 90.32% and 73.59%, respectively. Moreover, the F1 score of relation extraction increased by 1.24% over existing approaches in the ADE dataset.
Two fast variance-reduced proximal gradient algorithms for SMVIPs-Stochastic Mixed Variational Inequality Problems with suitable applications to stochastic network games and traffic assignment problems

In this paper, we propose two proximal gradient algorithms with variance reduction for stochastic mixed variational inequality problems. One is a proximal extragradient algorithm and another is a proximal forward–backward–forward algorithm. Under the monotonicity assumption on the mapping F and other moderate conditions, we derive some asymptotic convergence properties and O(1/k) convergence rate in terms of the restricted gap function values for the proposed algorithms. Furthermore, under the bounded metric subregularity condition, we investigate the linear convergence rate and oracle complexity bounds for the proposed algorithms when the sample-size increases at a geometric rate. If the sample-size increases at a polynomial rate of ⌈k+1⌉−s with s>0, the mean-squared distance of the iterates to the solution set decays at a corresponding polynomial rate, while the iterations and oracle complexities to obtain an ε-solution are O(1/ε1/s) and O(1/ε1+1/s) respectively. Finally, some numerical experiments on stochastic network games and traffic assignment problems indicate that the proposed algorithms are efficient.
A word embedding technique for sentiment analysis of social media to understand the relationship between Islamophobic incidents and media portrayal of Muslim communities

Islamophobia is a sentiment against the Muslim community; recently, atrocities towards Muslim communities witnessed this sentiment globally. This research investigates the correlation between how news stories covered by mainstream news channels impede the hate speech/Islamophobic sentiment. To examine the objective mentioned above, we shortlisted thirteen mainstream news channels and the ten most widely reported Islamophobic incidents across the globe for experimentation. Transcripts of the news stories are scraped along with their comments, likes, dislikes, and recommended videos as the users' responses. We used a word embedding technique for sentiment analysis, e.g., Islamophobic or not, three textual variables, video titles, video transcripts, and comments. This sentiment analysis helped to compute metric variables. The I-score represents the extent of portrayals of Muslims in a particular news story. The next step is to calculate the canonical correlation between video transcripts and their respective responses, explaining the relationship between news portrayal and hate speech. This study provides empirical evidence of how news stories can promote Islamophobic sentiments and eventually atrocities towards Muslim communities. It also provides the implicit impact of reporting news stories that may impact hate speech and crime against specific communities.
AdaBoost Support Vector Machine Method for Human Activity Recognition

Human activity recognition research is being implemented more and more as technology advances in computer vision. Many fields require activity recognition technology, such as theft detection or online exam cheating detection. One method that is widely used is AdaBoost. This study proposes the AdaBoost Support Vector Machine Method, a combination of the AdaBoost Method and the Support Vector Machine. The evaluation uses datasets for human activity recognition and compares them with other machine learning algorithms. The results obtained indicate that the proposed method has the highest performance compared to the tested algorithms. The highest accuracy in this study was 96.06%. It shows that SVM as an AdaBoost component is proven to be able to improve the performance of AdaBoost.
Bidirectional Multiscale Refinement Network for Crisp Edge Detection

With the development of deep convolution neural networks (CNNs), contour detection has made great progress. Some contour detectors based on CNNs have better performance than human beings on standard benchmarks. However, it is easier for CNNs to learn the similar features of adjacent pixels, and the number of background pixels and edge pixels in the input training sample is highly imbalanced. Therefore, the prediction edge by the edge detector based on CNNs is thick and requires post-processing to obtain crisp edges. Accordingly, we introduce a novel parallel attention model and a novel loss function that combines cross-entropy and dice loss through the use of adaptive coefficients, and propose a novel bidirectional multiscale refinement network (BMRN) that stacks multiple refinement modules in order to achieve richer feature representation. The experimental results show that our method has better performance than the state-of-the-art on BSDS500 (ODS F-score of 0.828), NYUDv2 depth datasets (ODS F-score of 0.778) and Multi-Cue dataset (ODS F-score 0.905(0.002)).
Approximating Viewership of Streaming T.V Programs Using Social Media Sentiment Analysis

Approximating viewership of a program is essential for broadcast networks and cable (TNT, AMC, and HBO) as they profit by selling advertising space during programming (airing of the show). With the advent and increased popularity of streaming services such as Netflix and Amazon Prime Video, the model for providing television content to viewers has fundamentally changed. However, this change in the delivery model for television programs has also made it more challenging to determine the actual viewership of television programs on streaming services. Because television programs are no longer aired in pre-determined timeslots, viewership shares cannot be determined. To make matters even more complicated, streaming services like Netflix and Amazon Prime Video are proprietary platforms. They have traditionally kept viewership numbers closely guarded and have not released them to the public. This paper proposes a methodology to approximate the viewership of streaming television shows. This is achieved by exploiting data from social networks, i.e., publicly available information and sentiment score derived from sentiment analysis on tweets published about television programs, using Random Forest classifier. The proposed methodology achieved 85% accuracy in predicting the viewership of the streaming shows.
Who Leaves Malicious Comments on Online News? An Empirical Study in Korea

Because online news comments have a strong influence on the reader’s perception of public opinion, there is a call for efforts to reduce the adverse impact of online news comments, particularly malicious ones. Although many online news platforms currently use technology to detect malicious comments automatically, there is still a technical limit in identifying malicious comments. To improve detection accuracy, it is necessary to understand not only malicious comments but also malicious commenters. Despite the importance of understanding malicious commenters, there is little empirical research on their characteristics. This study aims to understand the characteristics of malicious commenters and develop a prediction model based on their features using real data of users and commenting activities from Naver, a leading Internet news portal in Korea. This study found that the demographic characteristics of malicious commenters tend to be those of males and older people. In terms of commenting activities, the online news commenters who leave more comments per news article and per day, delete more comments, and leave longer comments tend to be malicious commenters.
An autonomous navigation approach for unmanned vehicle in outdoor unstructured terrain with dynamic and negative obstacles

Abstract At present, the study on autonomous unmanned ground vehicle navigation in an unstructured environment is still facing great challenges and is of great significance in scenarios where search and rescue robots, planetary exploration robots, and agricultural robots are needed. In this paper, we proposed an autonomous navigation method for unstructured environments based on terrain constraints. Efficient path search and trajectory optimization on octree map are proposed to generate trajectories, which can effectively avoid various obstacles in off-road environments, such as dynamic obstacles and negative obstacles, to reach the specified destination. We have conducted empirical experiments in both simulated and real environments, and the results show that our approach achieved superior performance in dynamic obstacle avoidance tasks and mapless navigation tasks compared to the traditional 2-dimensional or 2.5-dimensional navigation methods.
Joint Emergency Data and Service Evacuation in Cloud Data Centers Against Early Warning Disasters

As important network infrastructures to support data storage and service delivery for worldwide users, cloud data centers are facing great threaten by frequent disasters around the world and thus the survivability of cloud data centers becomes a critical issue. Since both data and service evacuations are desired at the same time under a real disaster scenario, this paper studies a joint design of them to fight against disasters. We consider a disaster that can present an early warning time before it really affects cloud data centers, and by exploiting the intrinsic interplay between data and service evacuations and efficiently utilizing the early warning time we propose a joint data and service evacuation scheme for emergency protection. We first formulate the joint design as two optimal Integer Linear Program (ILP) models. Notice that the protection process is highly time-sensitive due to the early warning time constraint, two time-efficient heuristics are then designed by carefully selecting evacuated services and candidate evacuation nodes to achieve a better sharing of network resources between data backup and service migration. Extensive numerical results demonstrate the efficiency of the proposed scheme on improving survivability of data and services in cloud data centers. With a set of given resource and early warning time constraints, this work can guide data center operators to achieve a tradeoff between data backup and service migration.
A Multi-station Signal Sorting Method Based on TDOA Grid Clustering

As the electromagnetic environment of the modern battlefield becomes more complex and the radar technology develops rapidly, radar signal sorting has faced a lot of difficulties. A new method of signal sorting based on multistation cooperative and TDOA grid clustering is proposed in this letter. This new method estimates TDOA from pulse sequences received by several stations by TDOA grid clustering. Compared to other pulse description words (PDW) such as PRI, CF, DOA and other parameters, TDOA is a more stable and easily obtained parameter. Therefore, this method can solve the problem that the traditional methods have poor effects on complex signals. Theoretical analysis and simulations show that this method has strong robustness and wide applicability, it shows high accuracy for complex pulse sorting and has a good sorting effect in case of a high amount of losing pulses.
Fully Unsupervised Machine Translation Using Context-Aware Word Translation and Denoising Autoencoder

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GPU-Accelerated Adaptive Dictionary Learning and Sparse Representations for Multispectral Image Super-resolution

Recently, single image super-resolution (SISR) based on sparse representations has been gaining much attention from the research community in the field of remote sensing. In this paper, a fast SISR reconstruction framework is developed for multispectral remote sensing (MSRS) images based on adaptive dictionary learning and sparse representations. It consists of two major parts: first, a novel super-resolution approach is developed for MSRS using sparse coding and adaptive dictionary learning. High-frequency features present in the input low-resolution MS image are extracted by using Butterworth low-pass, difference of Gaussian (DoG), and Sobel filters in horizontal and vertical directions. The proposed feature extraction method reveals the edges and other detailed information present in the MS image effectively. Secondly, massively parallel algorithms are designed for adaptive dictionary learning and sparse reconstruction using the Compute Unified Device Architecture (CUDA)-enabled General Purpose-Graphics Processing Unit (GP-GPU) programming model. The proposed method GP-GPU implementation not only gives better results in terms of visual quality and objective fidelity criteria, but also significantly reduces the computation time compared to its CPU counterparts to achieve near-real time operating speed.
The Impact of Automation and Knowledge Workers on Employees’ Outcomes: Mediating Role of Knowledge Transfer

While there are existing empirical reviews regarding the relation between knowledge workers (KW) and knowledge transfer (KT), no study has examined the association of automation and knowledge transfer. The study describes knowledge workers as employees embedded with special abilities and having the main role of transfering their abilities to other workers in the organization. Additionally, automation (AUT) is described in the study as a technological process embedded with tacit knowledge. Therefore, using knowledge worker productivity theory and the technology acceptance model, this study aims to recommend and test a research model which examines the mediating role of knowledge transfer between automation, knowledge workers, and employee outcomes (employee creativity (EC) and innovative performance (IP)). The study evaluates the significant influence of knowledge transfer on innovative performance and creativity of other employees within the organization. This study applied a judgmental non-probability sampling research strategy to gather data from employees at an industrial firm in Japan by administering a questionnaire via Google Form. The data were analyzed with partial least squares based on structural equation modeling aimed at testing the predictive power and relationships of the model estimates. The results found support for the mediating role of knowledge transfer between automation and employee creativity as well as between automation and innovative performance. However, the study found partial support that knowledge transfer mediates the relationship between knowledge worker and employee creativity, as well as knowledge worker and innovative performance. In the same, automation and knowledge workers have significant impacts on knowledge transfer. Knowledge transfer has a significant impact on employee creativity and innovative performance.
HashWalk: An efficient node classification method based on clique-compressed graph embedding

• Based on homophily and co-citation hypothesis, cliques reflect structural features of a graph. • Considering a clique as a sample unit improves the quality of random walk . • Compressing graph reduces computational cost in graph embedding. • Compressing graph by consistent cliques preserves structural information. In recent years, random walk based embedding has become a popular method for node classification. However, current methods still require a huge computational cost to obtain the representation of a large number of nodes. In addition, walking methods cannot adapt well to diverse network structures. Hence, this paper proposes HashWalk to generate a clique-compressed graph that can be used in random walk based embedding for node classification. Specifically, HashWalk compresses cliques into single nodes, and these single nodes are able to inherit neighbors of cliques. As a result, HashWalk can significantly reduce the number of training nodes and computational cost. Besides, HashWalk uses the random walk mapping method to obtain walking sequences of the clique-compressed graph, which makes random walk adapt to network structure. The experimental results prove that HashWalk provides faster time efficiency and lower space complexity while ensuring the accuracy. In summary, this paper provides a fast and effective method using clique-compressed graph embedding for node classification.
The Construction of Movie Recommendation System Based on Python

A movie recommendation system has been implemented based on python, B / S framework and MySQL database. Firstly, crawler technology is used to crawl the movie information of Douban, and then the crawled movie data is stored in MySQL. In the system, users can view, search and collect the movie information. According to the user's search history, the user-based collaborative filtering algorithm is used to recommend movies that may be of interest to users. Through the design of these functional modules, the whole movie recommendation system is realized.
SpartaPlex: A deterministic algorithm with linear scalability for massively parallel global optimization of very large-scale problems

SpartaPlex is a novel black-box optimization algorithm that yields superior results to state-of-the-art optimizers under tight function evaluation budgets. SpartaPlex is compared with 11 state-of-the-art optimization algorithms on 24 n-dimensional and 6 application-based benchmark problems including mechanical, structural, and antenna design optimization. Using identical computing resources, SpartaPlex finds superior objective function solutions and, as dimensionality increases, executes at least an order of magnitude faster than prior state-of-the-art. SpartaPlex is additionally evaluated for scalability using a compute cluster with 46,080 cores across 16 Graphical Processing Units. Tested in up to 100,000 dimensions, SpartaPlex demonstrates linear scalability in time with respect to dimensionality and computational resources.
Social Media Opinion Mining Based on Bangla Public Post of Facebook

Social media holds the freedom to express anyone as they are. Still, people fail to follow community standards and cross the boundary of self-limit, hurting other people, sometimes leading to cyberbullying. Social media mining is the frontier where researchers contend with ensuring safe cyberspace with the help of robust information retrieval and data mining techniques. In this paper, we are aiming towards achieving such a goal for Bangla language-spoken people. We have created a corpus that contains 11006 Bangla comments from Facebook, analyzed them demographically, annotated them to create robust classifiers to classify these comments as positive, negative, and neutral polarity. We have decomposed these polarities to further sentiments based on contents of the text varying from wishful thinking to gender-based hate speech. Our multiclass classification algorithm, consisting of TF-IDF vectorizer alongside uni-gram, bi-gram, and tri-gram followed by MNB, MNB, and KNN, gives 82.60%, 82.33%, and 79.63% accuracy, respectively.
Matrix approach to verification of finite multi-potential games

This paper studies the verification of multi-potential games with a given partition, and proposes a kind of multi-potential equation based on semi-tensor product of matrices. Firstly, the basis of each player's potential function is constructed, based on which, a new formula is established for the calculation of each player's potential function. Secondly, a new potential equation is proposed for the verification of potential games via each player's potential function. Using the new potential equation, a kind of multi-potential equation is constructed for the verification of multi-potential games with a given partition. It is proved that a finite game is a multi-potential game with a given partition if and only if the multi-potential equation has solution. In addition, all possible potential functions are obtained for each group of the partition. An illustrative example is worked out to show the effectiveness of the obtained new results.
Introduction to the Special Issue on Open Innovation in Science

KEYWORDS: Open innovation in sciencescientific researchopenness in sciencecollaboration in scienceinter- and transdisciplinary research
Comparative performance analysis of Fuzzy Logic and Particle Swarm Optimization (PSO) techniques for image quality improvement: With special emphasis to old and distorted folk paintings

Cultural artwork has always held an important place in the society. These artworks hold within themselves a treasure trove of information about one's society/community, their way of living and their history and many more things. However such artworks are not properly maintained and need to be carefully preserved. Preserving them digitally can give them a long life and the digital image processing techniques can be used to restore or enhance the quality of degraded artworks. This paper presents a comparison of two such techniques, PSO (Particle Swarm Optimization) and Fuzzy logic technique for enhancement of the distorted images. Image parameters like PSNR (Peak-to-Signal Noise Ratio), SSIM (Structure Similarity Index), AMBE (Absolute Mean Brightness Error), r (Pearson Correlation Coefficient), NMSE (Normalized Mean Square Error), IEF (Image Enhancement Factor) and CPP (Contrast per Pixel) have been tabulated to compare the performance of both the techniques. MATLAB software has been used for implementation of the algorithms and calculations of the parameters.