Reviewerly/exHarmony huggingface dataset:
AlexNet architecture based convolutional neural network for toxic comments classification

Today online networking has become an indispensable part of life for people all over the world. It is difficult for users to reduce their internet/online communications, as the flow of information increases everyday. While the free flow of information benefits online communications, the high toxicity of online communication is a drawback. Toxic texts are described as disrespectful or insulting messages that make the recipient feel uncomfortable. Deep Learning based Convolutional Neural Networks (CNN) have given exceptional outcomes in Computer Vision Domain, and AlexNet has proven to be the leading architecture in image classification and object detection problems. This article presents a 3-tier CNN architecture that is inspired by the AlexNet model to classify the toxic comments on the Wikipedia forum available in the Google Jigsaw dataset. Fast text-crawl-300d-2 m is used to formulate the pre-trained word embeddings matrix. The Exponential Linear Unit (ELU) activation function is applied in the Convolutional blocks for faster convergence. Dropout is used sufficiently along with different layers of the network to prevent overfitting. From the simulation and subsequent comparative analysis, it is found that the proposed model achieved a decent average accuracy of 98.505% and an average F1 score of 0.79. ROC-AUC score is used as an evaluation parameter. The value of ROC-AUC for the proposed model is approximately 0.9854, which shows that the said model differentiates between the comment classes more accurately.
Detection of Phishing Attacks using Visual Similarity Model

Phishing is a huge problem in the online world, costing businesses and individuals money. Fraudulently replicating an existing online page to trick users into providing their personal, financial, or other transactional information is known as phishing. This type of assault is carried out by creating a copy of the original web page. In this point, hackers are highly proficient at exploiting their expertise and breaking into the systems of others in order to steal their data. Phishing is a type of cybercrime that involves posing as a legitimate business in order to get access to a victim's personal information, such as bank account numbers, credit card numbers, and other sensitive information. Phishing is primarily an internet robbery technique. In order to fool consumers into thinking they are on a legitimate website, a Phishing site looks exactly like the equivalent legal site. It is possible to utilise visual similarity oriented phishing detection approaches to make decisions based on factors such as text content, HTML elements and Cascading Style Sheets (CSS). By comparing the suspected website with its corresponding legal counterpart, this method determines whether or not it is a case of Phishing when there is a high degree of resemblance in features. Deceptive phishing & malware-based phishing are two distinct types of phishing. Various anti-phishing strategies have been devised to keep users secure from attackers. A visual similarity-based method to combat phishing attempts is presented in this work in order to provide readers a better knowledge of the problem, currently selected space, and potential future research directions.
Eco-environment vulnerability assessment using remote sensing approach in East Kalimantan, Indonesia

Indonesia is the world's largest island country blessed with abundant natural resources. Coal is one of the largest natural resources and commodities with an increased production rate annually. On the other hand, coal mining activities in East Kalimantan, Indonesia have been identified as one of the causes of environmental damage. Without proper regulation and comprehensive study, the environmental damage is predicted to increase every year. In order to provide a scientific analysis of the environmental damage prediction and evaluate ecological environmental conditions using remote sensing and geospatial approaches, this study aims to construct Eco-environment Vulnerability Index (EVI) in East Kalimantan and investigate spatial conditions and changes during 2017–2019. Spatial Principal Component Analysis (SPCA) method was applied to construct EVI by integrating satellite imagery and socio-economic data like Gross Regional Domestic Product (GRDP) per capita and population density. The results revealed that most of the areas in East Kalimantan are in good condition as indicated by very low EVI values (less than −0.0078). Meanwhile, very high EVI values (more than 0.4446) were found in the former mining, coastal, and urban areas. These areas have very high EVI because of lack of vegetation, high development of urban areas, and dense population. The mapping results of vulnerable areas are expected to be used as a guideline for governments, communities, and mining companies regarding the impacts exposed by coal mining activities and government plans for new capital city relocation towards the ecological environmental vulnerabilities.
Optimization of Online Course Platform for Piano Preschool Education Based on Internet Cloud Computing System

This article focuses on introducing online piano teaching methods and has developed and implemented a preschool piano education online course platform. The system consists of four parts: backend, WeChat, client, and web page. Backend development uses PHP language and Laravel system framework, WeChat and web development both use JavaScript language and React framework, client development uses Objective-C language, and the system provides internal support for RESTful API, mainly for client, WeChat, and web. The client relies on the existing voice sensors of the research group to recognize and evaluate the performance of the students. The role of the client is to show the students their homework and demonstrate the activities performed by the teacher. The function of the WeChat terminal is to manage student work, user information, and user social interaction functions. The function of the web page is the score management and data analysis functions. Based on the knowledge of network course design, this article studies the design of piano preschool education platform and adds relevant components of the Internet cloud computer system and voice sensor to this platform, which provides great convenience for students to learn piano.
Single Image Super-Resolution Method Based on an Improved Adversarial Generation Network

Super-Resolution (SR) techniques for image restoration have recently been gaining attention due to their excellent performance. For powerful learning abilities, Generative Adversarial Networks (GANs) have been proven to have achieved great success. In this paper, we propose an Enhanced Generative Adversarial Network (EGAN) for improving its effects for a real-time Super-Resolution task. The main content of this paper are as follows: (1) We adopted the Laplacian pyramid framework as a pre-trained module, which is beneficial for providing multiscale features for our input. (2) At each feature block, a convolutional skip-connections network, which may contain some latent information, was significant for the generative model to reconstruct a plausible-looking image. (3) Considering that the edge details usually play an important role in image generation, a perceptual loss function was defined to train and seek the optimal parameters. Quantitative and qualitative evaluations were demonstrated so that our algorithm not only took full advantage of the Convolutional Neural Networks (CNNs) to improve the image quality, but also performed better than other algorithms in speed and performance for real-time Super-Resolution tasks.
Parallel K-clique counting on GPUs

Counting k-cliques in a graph is an important problem in graph analysis with many applications such as community detection and graph partitioning. Counting k-cliques is typically done by traversing search trees starting at each vertex in the graph. Parallelizing k-clique counting has been well-studied on CPUs and many solutions exist. However, there are no performant solutions for k-clique counting on GPUs.
Morphological characteristics of self-assembled aggregate textures using multifractal analysis: Interpretation of Multifractal <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e611" altimg="si4.svg"><mml:mi>τ</mml:mi></mml:math>(q) Using Simulations

In recent years, multifractal analysis has been used in various fields, such as medicine and urban development. In particular, multifractal analysis is expected to offer an understanding of the distribution of the morphology, arrangement, and dispersibility of dispersed particles due to the process of particle self-assembly. However, the numerical values obtained by multifractal analysis have not been quantitatively mapped to the filler arrangement, dispersion, and morphology. In this study, we generate simulated images that mimic self-assembled aggregates and clarify the interpretation of local (micro) and global (macro) changes in the system using multifractal analysis results τ(q). The results show that q < 0 in the τ(q)−q graph can be quantitatively evaluated as macro characteristics (aggregate network structure), whereas q > 0 can be quantitatively evaluated as micro characteristics (aggregate morphology and arrangement). Additionally, the occurrence and signs of percolation can be confirmed using macro- and micro-level internal energies. The same trend is observed from a comparison of a multifractal analysis of actual composite materials with the simulation results. The results of this study can also serve as an indicator in various fields where multifractal analysis is performed.
Development and Applications of Augmented Whale Optimization Algorithm

Metaheuristics are proven solutions for complex optimization problems. Recently, bio-inspired metaheuristics have shown their capabilities for solving complex engineering problems. The Whale Optimization Algorithm is a popular metaheuristic, which is based on the hunting behavior of whale. For some problems, this algorithm suffers from local minima entrapment. To make WOA compatible with a number of challenging problems, two major modifications are proposed in this paper: the first one is opposition-based learning in the initialization phase, while the second is inculcation of Cauchy mutation operator in the position updating phase. The proposed variant is named the Augmented Whale Optimization Algorithm (AWOA) and tested over two benchmark suits, i.e., classical benchmark functions and the latest CEC-2017 benchmark functions for 10 dimension and 30 dimension problems. Various analyses, including convergence property analysis, boxplot analysis and Wilcoxon rank sum test analysis, show that the proposed variant possesses better exploration and exploitation capabilities. Along with this, the application of AWOA has been reported for three real-world problems of various disciplines. The results revealed that the proposed variant exhibits better optimization performance.
Toward Privacy-Preserving Blockchain-Based Electricity Auction for V2G Networks in the Smart Grid

With the development of electric vehicle (EV) technology, EV has become a key component in the future smart grid. Due to the sheer large number of EVs on the road, the emerging vehicle-to-grid (V2G) technology, which allows for two-way electrical flows between EVs and the power grid, is gaining traction. However, establishing a fair and private electricity exchange scheme has gradually become a critical challenge. The emergence of blockchain technology offers a novel approach for resolving this issue. In this study, we overview the opportunities and challenges of blockchain in the smart grid. Then, we provide a privacy-preserving blockchain-based electricity auction scheme for V2G networks in smart grid. In particular, we exploit PS group signatures to keep the privacy of EVs or charging stations and leverage blockchain to provide automated auction execution. With our mechanism, the identity of EV/charging station is conditionally protected. In case of an emergency, the trusted authority (i.e., the group manager) can open the identity. Meanwhile, we present the security analysis to prove our scheme’s security. Finally, we implement the experiment to evaluate efficiency. The experimental results show that our proposal is efficient and suitable for V2G networks.
Harnessing the power of ML and NLP for decision making in education sector from social media data

The active social media users across the globe have passed the 3.8 billion mark. Platforms like Facebook, Reddit, Instagram, Twitter, and more are an ocean full of opinions and views. With over 500 million tweets being generated daily, this enormous volume of data can offer very prominent insights and allow organizations and businesses to make strategic decisions. The COVID-19 pandemic has changed the landscape of learning and education dramatically. With a sudden deviation from the classroom in many parts of the world, some wonder if the adoption of online learning will continue with the outbreak of the post-epidemic epidemic and how such a change could affect the global education market. The crux of the problem is how we need to analyze vast amounts of data efficiently. We chose to employ advanced ML and NLP techniques to analyze the sentiment of the masses on digital learning and important extracting demographic information from them. In this paper, we will make an effort to understand the orientation of the academicians towards the recent online education adoption. We will collect the data from the tweets using the trending tags of COVID-19 and Online classes.
Credit Card Fraud Detection using Deep and Machine Learning

Credit-Card Fraud is indeed a menace in the financial system, with far-reaching consequences. The loss of a credit card or its information is considered Credit Card Fraud. A variety of machine learning techniques can be utilized to detect this fraud. This study demonstrates a variety of classification algorithms. Whether the transaction is a fraud or a real one is difficult to determine without an efficient machine learning algorithm. The models that are utilized in this paper were Logistic Regression, XGBoost, and Multi-Layer Perceptron. Kaggle dataset is used to train and test these models. Accuracy, confusion matrix, Area under ROC Curve, recall, f1 score, and precision were the classification metrics used to judge the performance of the proposed system models. This research work conclude that the Multi-Layer Perceptron [MLP] outperforms XGBoost and Logistic Regression.
QuantumLeap, a Framework for Engineering Gestural User Interfaces based on the Leap Motion Controller

Despite the tremendous progress made for recognizing gestures acquired by various devices, such as the Leap Motion Controller, developing a gestural user interface based on such devices still induces a significant programming and software engineering effort before obtaining a running interactive application. To facilitate this development, we present QuantumLeap, a framework for engineering gestural user interfaces based on the Leap Motion Controller. Its pipeline software architecture can be parameterized to define a workflow among modules for acquiring gestures from the Leap Motion Controller, for segmenting them, recognizing them, and managing their mapping to functions of the application. To demonstrate its practical usage, we implement two gesture-based applications: an image viewer that allows healthcare workers to browse DICOM medical images of their patients without any hygiene issues commonly associated with touch user interfaces and a large-scale application for managing multimedia contents on wall screens. To evaluate the usability of QuantumLeap, seven participants took part in an experiment in which they used QuantumLeap to add a gestural interface to an existing application.
Boosting Monocular 3D Human Pose Estimation With Part Aware Attention

Monocular 3D human pose estimation is challenging due to depth ambiguity. Convolution-based and Graph-Convolution-based methods have been developed to extract 3D information from temporal cues in motion videos. Typically, in the lifting-based methods, most recent works adopt the transformer to model the temporal relationship of 2D keypoint sequences. These previous works usually consider all the joints of a skeleton as a whole and then calculate the temporal attention based on the overall characteristics of the skeleton. Nevertheless, the human skeleton exhibits obvious part-wise inconsistency of motion patterns. It is therefore more appropriate to consider each part's temporal behaviors separately. To deal with such part-wise motion inconsistency, we propose the Part Aware Temporal Attention module to extract the temporal dependency of each part separately. Moreover, the conventional attention mechanism in 3D pose estimation usually calculates attention within a short time interval. This indicates that only the correlation within the temporal context is considered. Whereas, we find that the part-wise structure of the human skeleton is repeating across different periods, actions, and even subjects. Therefore, the part-wise correlation at a distance can be utilized to further boost 3D pose estimation. We thus propose the Part Aware Dictionary Attention module to calculate the attention for the part-wise features of input in a dictionary, which contains multiple 3D skeletons sampled from the training set. Extensive experimental results show that our proposed part aware attention mechanism helps a transformer-based model to achieve state-of-the-art 3D pose estimation performance on two widely used public datasets.
Detection and quantification of anomalies in communication networks based on LSTM-ARIMA combined model

The anomaly detection for communication networks is significant for improve the quality of communication services and network reliability. However, traditional communication monitoring methods lack proactive monitoring and real-time alerts and the prediction effect of a single machine learning model on communication data containing multiple features is not ideal. To solve the problem, A prediction-then-detection anomaly detection method was proposed, and quantitative assessment of network anomalies was developed. Specifically, anomaly-free data was obtained by eliminating outliers, and the long short-term memory (LSTM) and autoregressive integral moving average (ARIMA) were combined via residual weighting to predict the future state of the key performance indicators (KPI) without outliers. Anomalies were identified using the error comparison between the prediction and actual values, and the network condition was quantified using the scoring method. It is observed that the proposed LSTM-ARIMA hybrid model has better prediction effect, which can well represent the performance of KPIs of the future state, and the prediction-then-detection anomaly detection method has excellent performance on both precision and recall.
A Novel Attention-based Global and Local Information Fusion Neural Network for Group Recommendation

Due to the popularity of group activities in social media, group recommendation becomes increasingly significant. It aims to pursue a list of preferred items for a target group. Most deep learning-based methods on group recommendation have focused on learning group representations from single interaction between groups and users. However, these methods may suffer from data sparsity problem. Except for the interaction between groups and users, there also exist other interactions that may enrich group representation, such as the interaction between groups and items. Such interactions, which take place in the range of a group, form a local view of a certain group. In addition to local information, groups with common interests may also show similar tastes on items. Therefore, group representation can be conducted according to the similarity among groups, which forms a global view of a certain group. In this paper, we propose a novel global and local information fusion neural network (GLIF) model for group recommendation. In GLIF, an attentive neural network (ANN) activates rich interactions among groups, users and items with respect to forming a group′s local representation. Moreover, our model also leverages ANN to obtain a group′s global representation based on the similarity among different groups. Then, it fuses global and local representations based on attention mechanism to form a group′s comprehensive representation. Finally, group recommendation is conducted under neural collaborative filtering (NCF) framework. Extensive experiments on three public datasets demonstrate its superiority over the state-of-the-art methods for group recommendation.
Multi-UAV Synchronous Approaching Using Homotopy-Based Trajectory Planning

Synchronous approaching is an important capability for autonomous cooperation of multiple unmanned aerial vehicles (UAVs). In this paper, a homotopy-based trajectory planning method is presented for the multi-UAV synchronous approaching problem. A homotopic trajectory description is employed to construct the trajectory solution space of the UAVs. A novel onion-like homotopy structure is proposed to decouple the performance indexes of the trajectory planning problem. Local trajectory homotopy structures are designed based on the detouring model and the hovering model of UAV. The optimal trajectories for synchronous approaching are searched within the homotopy structures. Simulation results show how synchronous the UAVs are, by using the proposed homotopy-based trajectory planning method.
Under-Display Camera Image Enhancement via Cascaded Curve Estimation

The new trend of full-screen devices encourages manufacturers to position a camera behind a screen, i.e., the newly-defined Under-Display Camera (UDC). Therefore, UDC image restoration has been a new realistic single image enhancement problem. In this work, we propose a curve estimation network operating on the hue (H) and saturation (S) channels to perform adaptive enhancement for degraded images captured by UDCs. The proposed network aims to match the complicated relationship between the images captured by under-display and display-free cameras. To extract effective features, we cascade the proposed curve estimation network with sharing weights, and we introduce a spatial and channel attention module in each curve estimation network to exploit attention-aware features. In addition, we learn the curve estimation network in a semi-supervised manner to alleviate the restriction of the requirement for amounts of labeled images and improve the generalization ability for unseen degraded images in various realistic scenes. The semi-supervised network consists of a supervised branch trained on labeled data and an unsupervised branch trained on unlabeled data. To train the proposed model, we build a new dataset comprised of real-world labeled and unlabeled images. Extensive experiments demonstrate that our proposed algorithm performs favorably against state-of-the-art image enhancement methods for UDC images in terms of accuracy and speed, especially on ultra-high-definition (UHD) images.