Reviewerly/exHarmony huggingface dataset:
Hyper-Laplacian Regularized Multi-View Subspace Clustering With a New Weighted Tensor Nuclear Norm

In this paper, we present a hyper-Laplacian regularized method WHLR-MSC with a new weighted tensor nuclear norm for multi-view subspace clustering. Specifically, we firstly stack the subspace representation matrices of the different views into a tensor, which neatly captures the higher-order correlations between the different views. Secondly, in order to make all the singular values have different contributions in tensor nuclear norm based on tensor-Singular Value Decomposition (t-SVD), we use weighted tensor nuclear norm to constrain the constructed tensor, which can obtain the class discrimination information of the sample distribution more accurately. Third, from a geometric point of view, the data are usually sampled from a low-dimensional manifold embedded in a high-dimensional ambient space, the WHLR-MSC model uses hyper-Laplacian graph regularization to capture the local geometric structure of the data. An effective algorithm for solving the optimization problem of WHLR-MSC model is proposed. Extensive experiments on five benchmark image datasets show the effectiveness of our proposed WHLR-MSC method.
SGE NET: Video Object Detection with Squeezed GRU and Information Entropy Map

Recently, deep learning based video object detection has attracted more and more attention. Compared with object detection of static images, video object detection is more challenging due to the motion of objects, while providing rich temporal information. The RNN-based algorithm is an effective way to enhance detection performance in videos with temporal information. However, most studies in this area only focus on accuracy while ignoring the calculation cost and the number of parameters.In this paper, we propose an efficient method that combines channel-reduced convolutional GRU (Squeezed – GRU), and Information Entropy map for video object detection (SGE-Net). The experimental results validate the accuracy improvement, computational savings of the Squeezed GRU, and superiority of the information entropy attention mechanism on the classification performance. The mAP has increased by 3.7 contrasted with the baseline, and the number of parameters has decreased from 6.33 million to 0.67 million compared with the standard GRU.
Two-Phase Feature Fusion Network For Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (VI-ReID) is a challenging problem that aims to match pedestrians captured by visible and infrared cameras. Prevailing methods in this field mainly focus on learning sharable feature representations from the last layer of deep convolution neural networks(CNNs). However, due to the large intra-modality variations and cross-modality variations, the last layer’s sharable feature representations are less discriminative. To remedy this, we propose a novel Two-Phase Feature Fusion Network(TFFN) to enhance the discriminative feature learning via feature fusion. Specifically, TFFN contains two fusion modules: (1) Multi-Level Fusion Module(MLFM) that reweights and fuses intra-modality multi-level features to utilize high- and low-level information; (2) Graph-Level Fusion Module (GLFM) that mines and fuses rich mutual information across the two modalities by employing a cross-modality graph attention network. Additionally, for effective fusion, we develop a deep supervision method to enhance the discrimination of pre-fusion features and eliminate noise information. Extensive experiments show that TFFN outperforms the state-of-the-art methods on two mainstream VI-ReID datasets: SYSU-MM01 and RegDB.
WBSum: Workload-based Summaries for RDF/S KBs

Semantic summaries try to extract compact information from the original RDF graph, while reducing its size. State of the art structural semantic summaries, focus primarily on the graph structure of the data, trying to maximize the summary’s utility for a specific purpose, such as indexing, query answering and source selection. In this paper, we present an approach that is able to construct high quality summaries, exploiting a small part of the query workload, maximizing their utility for query answering, i.e. the query coverage. We demonstrate our approach using two real world datasets and the corresponding query workloads and we show that we strictly dominates current state of the art in terms of query coverage.
Nash equilibrium sorting genetic algorithm for simultaneous competitive maximal covering location with multiple players

In this article, a new genetic algorithm (GA), called the Nash equilibrium sorting genetic algorithm (NESGA,) is introduced to identify Nash equilibria for the competitive maximal covering location problem with two and three competitors, which is a combinatorial game theory problem where it is computationally intractable to enumerate all decision options of the competitors. Although GAs are widely used in combinatorial optimization, their applications to non-cooperative, simultaneous games have been limited owing to challenges in guiding the evolutionary search to Nash equilibria, which are not necessarily on the Pareto front of the search space and cannot be found by current multi-objective GAs. A novel fitness assignment strategy is proposed to help the NESGA to converge to multiple Nash equilibria. Computational experiments show that the NESGA can discover multiple Nash equilibria in a single run and outperform other game-theoretic GAs.
Detection of Brute-Force Attacks in End-to-End Encrypted Network Traffic

Network intrusion detection systems (NIDSs) can detect attacks in network traffic. However, the increasing ratio of encrypted connections on the Internet restricts their ability to observe such attacks. This paper proposes a completely passive method that allows to detect brute-force attacks in encrypted traffic without the need to decrypt it. For that, we propose five novel metrics for attack detection which quantify metadata like packet size or packet timing.
Architecture Design and Application of distributed power trading System based on blockchain Asynchronous Consensus

With the deepening of electric power system reformation and the enhancement of distributed energy permeability, distributed energy in power generation and sales side of effective configuration is facing opportunities and challenges. The traditional centralized power trading mode is centralized trading, which has many problems such as low transparency, high cost, low efficiency and unreliable data, so it&#x2019;s unable to adapt to the large-scale distributed power trading scenario. In this context, this paper proposes a distributed power trading mechanism based on blockchain asynchronous consensus mechanism, analyzes the compatibility between blockchain technology and distributed power trading mode, carries out research on the asynchronous consensus mechanism, designs the blockchain-based distributed power trading system architecture and process method and carries out trial application to improve the consensus efficiency of the system consensus. Meanwhile, the mechanism could solve the trust problem in the process of distributed power trading, and ensure the transparency and fairness of distributed power trading.
Impact of Software Bug Report Preprocessing and Vectorization on Bug Assignment Accuracy

Bug handling processes aimed at efficient defect resolution provisioning is an important part of a software development lifecycle and usually has a very formal process definition in modern and professional large software development organizations. Improvements of such process may include automated bug assignment, which is a task of selecting a correct development team for further investigations of a bug report. As bug reports contain lots of natural language descriptions, the bug assignment becomes a non-trivial task, especially in testing of large-scale projects or complex systems. This research focuses on natural language preprocessing and vectorization impact on accuracy of bug report assignment based on real data captured in large software development projects. Experimentation results cover stemming and lemmatization techniques applied for bug description preprocessing and term frequency – inverse document frequency (TF-IDF) parametrization as vectorization method.
Contextualizing toxicity in open source: a qualitative study

In this paper, we study toxic online interactions in issue discussions of open-source communities. Our goal is to qualitatively understand how toxicity impacts an open-source community like GitHub. We are driven by users complaining about toxicity, which leads to burnout and disengagement from the site. We collect a substantial sample of toxic interactions and qualitatively analyze their characteristics to ground future discussions and intervention design.
Decentralized Voting Application Using Blockchain

Blockchain is the latest emerging technology of the future. In this paper, there has been an attempt to present a model of an electronic voting system using blockchain. This proposed system is built using the local blockchain. The concept proposed can be further expanded to build it on a higher level. E-voting system can be used to conduct secure and efficient elections for a large population with minimal efforts. Blockchain concept guarantees the security and protection of the voters’ identity and the votes. This will help in building the trust in the electoral system.
Ship Segmentation via Encoder–Decoder Network With Global Attention in High-Resolution SAR Images

Ship detection in the synthetic aperture radar (SAR) image is of great significance in the fields of military and coastal defense. Most ship detection methods are designed based on the object detection framework, which can only provide the vertices’ coordinates of the bounding box covering the ship targets but cannot provide more detailed contour information. Target segmentation can further explore the shape and edge information of the objects, which can be used as a blazing novel means for automatic object detection. In this letter, a 3-D atrous encoder–decoder neural network with global attention modules (GAM-EDNet) is proposed to achieve ship segmentation in SAR images. The encoder–decoder structure with atrous convolution is developed as the network body to fully exploit the structural information of the ship targets with various sizes. To increase the structural information of the single-polarization SAR images, a 3-D image cube is designed as the input of the GAM-EDNet. A global attention module is proposed to further improve the segmentation performance by integrating the high-level semantic features with the low-level location features. Besides, an SAR ship segmentation dataset (SAR-HR4) is built to evaluate the segmentation performance, and the experimental results show that the proposed GAM-EDNet achieves better performance than other state-of-the-art methods.
Concept for the Southern Campus of the Network University of the Kuzbass Research and Education Center

The article introduces the concept of the New Town Campus as a part of the Network University of the World Class Research and Academic Center «Kuzbass». The research objective is to assess the role of the Campus within the Network University project aimed at improving the efficiency of the local education and managing its resources. The research features the infrastructure solutions for the future New Town Campus and involves the methods of conceptual proposals, functional zoning of urban environment, and urban planning. The New Town as an open, comfortable, and encouraging platform will ensure the world-class development of the business-tourism cluster, eventually turning the Network University into a platform able to compete with global leaders in education, research, and production. The Campus will also create opportunities for the modern intellectual and healthy lifestyle while optimizing the use of territorial resources.
Understanding And Manipulating Neural Net Features Using Sparse Oblique Classification Trees

The widespread deployment of deep nets in practical applications has lead to a growing desire to understand how and why such black-box methods perform prediction. Much work has focused on understanding what part of the input pattern (an image, say) is responsible for a particular class being predicted, and how the input may be manipulated to predict a different class. We focus instead on understanding what internal features computed by the neural net are responsible for a particular class. We achieve this by mimicking part of the net with a decision tree having sparse weight vectors at the nodes. We are able to learn trees that are both highly accurate and interpretable, so they can provide insights into the deep net black box. Further, we show we can easily manipulate the neural net features in order to make the net predict, or not predict, a given class, thus showing that it is possible to carry out adversarial attacks at the level of the features. We demonstrate this robustly in MNIST and ImageNet with LeNet5 and VGG networks.
TSEngine: Enable Efficient Communication Overlay in Distributed Machine Learning in WANs

In recent years, distributed machine learning in WANs (DML-WANs), i.e., collaboratively training a high-quality ML model cross geo-distributed micro-clouds or edge devices, has attracted attention and been widely applied. Compared with cloud-centric training, DML-WANs avoids the high cost of transferring large amounts of raw data to a central cloud and privacy concerns. However, performing DML-WANs still faces challenges. Model synchronization, an essential step of DML-WANs, is accompanied by a lot of model communication cross limited-bandwidth WANs, which generates high communication overhead. Moreover, the parameter server system, which has been widely used, performs model synchronization in a centralized manner, resulting in serious communication in-cast problem. Such communication in-cast further raises the communication overhead, leading to the low efficiency of DML-WANs. To alleviate the communication in-cast, existing researches attempt to build tree-based communication overlays over the parameter server and workers. However, we identify that these approaches can not adapt to the dynamic and heterogeneous network of DML-WANs, resulting in insufficient improvements. This paper proposes TSEngine, an adaptive communication scheduler for efficient communication overlay of the parameter server system in DML-WANs. Its core idea is to dynamically schedule the communication logic over the parameter server and workers based on the active network perception. Specifically, we propose novel communication scheduling protocols for model distribution and model aggregation, respectively. We have implemented TSEngine in a mainstream parameter server system and verified its effectiveness in DML-WANs testbeds.
Development of Interactive Based Powerpoint Learning Media In The Class Iv Students Of Merauke Basic School

This study aims to produce interactive-based PowerPoint learning media on the science content of fourth-grade elementary school students and to determine the potential effects and development of instructional media using interactive-based PowerPoints on the science content of fourth-grade elementary school students. This type of research is a design research type of development study, the subject of this research is the fourth-grade students of SD YPPK Santa Theresia Buti Merauke in the academic year 2019/2020. Data collection techniques in this study were walkthroughs, questionnaires, interviews, and test learning outcomes after using interactive PowerPoint-based learning media. The results of this study are: (1) the research has produced interactive based PowerPoint learning media on science content; (2) the developed learning media has a very potent effect on student learning outcomes and the respondent's attitude to participate in learning is very good; and (3) it is very easy for teachers and students to convey and receive learning about science content.
FA-GAN: Feature-Aware GAN for Text to Image Synthesis

Text-to-image synthesis aims to generate a photo-realistic image from a given natural language description. Previous works have made significant progress with Generative Adversarial Networks (GANs). Nonetheless, it is still hard to generate intact objects or clear textures (Fig 1). To address this issue, we propose Feature-Aware Generative Adversarial Network (FA-GAN) to synthesize a high-quality image by integrating two techniques: a self-supervised discriminator and a feature-aware loss. First, we design a self-supervised discriminator with an auxiliary decoder so that the discriminator can extract better representation. Secondly, we introduce a feature-aware loss to provide the generator more direct supervision by employing the feature representation from the self-supervised discriminator. Experiments on the MSCOCO dataset show that our proposed method significantly advances the state-of-the-art FID score from 28.92 to 24.58.
BRAID: an API recommender supporting implicit user feedback

Efficient application programming interface (API) recommendation is one of the most desired features of modern integrated development environments. A multitude of API recommendation approaches have been proposed. However, most of the currently available API recommenders do not support the effective integration of user feedback into the recommendation loop. In this paper, we present BRAID (Boosting RecommendAtion with Implicit FeeDback), a tool which leverages user feedback, and employs learning-to-rank and active learning techniques to boost recommendation performance. The implementation is based on the VSCode plugin architecture, which provides an integrated user interface. Essentially, BRAID is a general framework which can accommodate existing query-based API recommendation approaches as components. Comparative experiments with strong baselines demonstrate the efficacy of the tool. A video demonstrating the usage of BRAID can be found at https://youtu.be/naD0guvl8sE.
A multi-source entity-level sentiment corpus for the financial domain: the FinLin corpus

We introduce FinLin, a novel corpus containing investor reports, company reports, news articles, and microblogs from StockTwits, targeting multiple entities stemming from the automobile industry and covering a 3-month period. FinLin was annotated with a sentiment score and a relevance score in the range [- 1.0, 1.0] and [0.0, 1.0], respectively. The annotations also include the text spans selected for the sentiment, thus, providing additional insight into the annotators' reasoning. Overall, FinLin aims to complement the current knowledge by providing a novel and publicly available financial sentiment corpus and to foster research on the topic of financial sentiment analysis and potential applications in behavioural science.The online version contains supplementary material available at 10.1007/s10579-021-09555-3.
Table Caption Generation in Scholarly Documents Leveraging Pre-trained Language Models

This paper addresses the problem of generating table captions for scholarly documents, which often require additional information outside the table. To this end, we propose a method of retrieving relevant sentences from the paper body, and feeding the table content as well as the retrieved sentences into pre-trained language models (e.g. T5 and GPT-2) for generating table captions. The contributions of this paper are: (1) discussion on the challenges in table captioning for scholarly documents; (2) development of a dataset DocBank-TB, which is publicly available; and (3) comparison of caption generation methods for scholarly documents with different strategies to retrieve relevant sentences from the paper body. Our experimental results showed that T5 is the better generation model for this task, as it outperformed GPT-2 in BLEU and METEOR implying that the generated text are clearer and more precise. Moreover, inputting relevant sentences matching the row header or whole table is effective.
UAV Image Stitching Using Shape-Preserving Warp Combined With Global Alignment

In this letter, we propose a strategy for unmanned aerial vehicle (UAV) image stitching to generate natural-looking panoramas. Traditional methods using homography to perform alignment cannot account for images with parallax, so they require that the input images should be taken from the same viewpoint or the scene should be near the planar. However, remote sensing images obtained by UAVs usually do not satisfy such an ideal situation, and the stitching results always suffer from artifacts. To overcome these challenges and obtain natural-looking panoramas, a global alignment strategy is proposed to better align the input images. Combined with a shape-preserving warp, the stitching results can achieve better alignment accuracy while maintaining the shape. Meanwhile, locality preserving matching (LPM) is used to eliminate mismatches during feature detection and matching for accurate alignment. In addition, to make the stitching results more natural-looking, we also use multiband blending to eliminate artifacts that may exist in the results due to unmodeled effects. Experiments show that our stitching strategy can effectively improve alignment accuracy and obtain natural-looking results compared to other state-of-the-art methods.
Online Dictionary Learning Based Fault and Cyber Attack Detection for Power Systems

The emerging wide area monitoring systems (WAMS) have brought significant improvements in electric grids' situational awareness. However, the newly introduced system can potentially increase the risk of cyber-attacks, which may be disguised as normal physical disturbances. This paper deals with the event and intrusion detection problem by leveraging a stream data mining classifier (Hoeffding adaptive tree) with semi-supervised learning techniques to distinguish cyber-attacks from regular system perturbations accurately. First, our proposed approach builds a dictionary by learning higher-level features from unlabeled data. Then, the labeled data are represented as sparse linear combinations of learned dictionary atoms. We capitalize on those sparse codes to train the online classifier along with efficient change detectors. We conduct numerical experiments with industrial control systems cyber-attack datasets. We consider five different scenarios: short-circuit faults, line maintenance, remote tripping command injection, relay setting change, as well as false data injection. The data are generated based on a modified IEEE 9-bus system. Simulation results show that our proposed approach outperforms the state-of-the-art method.
Deep Metric Network Via Heterogeneous Semantics for Image Sentiment Analysis

This paper presents a novel method for image sentiment analysis called a deep metric network via heterogeneous semantics (DMNHS). The contribution of the proposed method is introduction of the image captioning into image sentiment analysis to reflect a global impression that cannot be represented by classical visual features extracted from images. In order to consider a sentiment correlation between visual and captioning features, the proposed method newly designs a network to integrate these heterogeneous semantics features (HS features). Furthermore, with consideration of relations among sentiments based on the HS features, the proposed method constructs a sentiment latent space by introducing the center loss concerning relationships between different sentiments and enables the classification of image sentiments. From experimental results, the performance improvement via DMN-HS is confirmed.