Reviewerly/exHarmony huggingface dataset:
The Dilemma of Teaching in the Digital Era: Artificial Intelligence. Risks And Challenges for Education

In the digital era, a new dilemma is beginning to take shape in the mind of every teacher: Is teaching still necessary? The question has been a concern since 1990, when Tim Berners-Lee, a researcher at the Centre Européen de Rechérches Nucleaires in Geneva, laid the foundations for the World Wide Web (www). Shortly after the emergence of the World Wide Web, desired information could be accessed with a click, which contributed to a rapid decline in the frequency of visits to libraries, second-hand bookshops, or reading clubs. The issue of AI is viewed here from the perspective of the dangers observed, including addiction, the impact on personal data security, the increase in the digital divide, and the loss of various abilities or competencies. It can be seen that "This generation will no longer have their heads filled, they will navigate the net, cheerful and inventive, sheltered from wars and poverty" (Blais, Gauchet, and Ottavi, 2019). From a pedagogical perspective, our concern is to outline possible directions of action that can help us effectively cope with the challenges posed by the use of AI in teaching-learning activities.
Sentiment Classification of Diabetes-Related Tweets Using Transformer-Based Deep Learning Approach

It is estimated that 1 out of 77 people in India are formally diagnosed with diabetes, which is alarming. Nowadays, people use social media platforms such as Facebook and Twitter to express their beliefs, feelings, and concerns on any topic, including healthcare. It is interesting and highly useful to analyze this humongous data to unearth latent patterns for further analysis and decision-making. This study aimed at (1) analyzing the sentiment of the diabetes-related tweets from India and (2) unearthing latent themes of discussion about diabetes in India from Twitter. We collected tweets from India using the keywords - diabetes, diabetes mellitus, and hyperglycemia. We identified the sentiments of retrieved tweets using a deep learning sentiment classifier followed by a topic modeling approach to analyze the major themes of discussion. The results show that the majority of the sentiments were positive, but still, there are concerns regarding diabetes risk and the alarming rate of diabetes in India. Our topic modeling results showed some interesting patterns from the discussions, such as diabetes advisory, diabetes risk, diabetes warning, and diabetes control.
Social Media Hate Speech Detection Using Machine Learning Approach

Humanity has profited enormously from the interchange of information and the expanding use of social media but it has also raised a number of challenges, such as the persistence of hate speech. This growing problem on social media platforms, latterly studies used a different type of point engineering system and machine literacy algorithms to automatically descry hate comments on numerous data. As we know, several studies have been done so far and compared several point engineering strategies with machine literacy algorithms to discover which strategy is the most productive. This investigation aims to examine the performance of multiple engineering approaches with five machine literacy algorithms. The data sets contain the class orders hate speech, not hate speech and offensive comments independently. These social media posts are split into these two groups. To recognize the particular traits of hate speech text messages, the appropriate n-gram feature sets are extracted. The n-gram TF-IDF weights provide the foundation for these feature models. The main aspiration of this research work is to analyze, and resolve the above problem and compare algorithms and features used in machine learning to automatically detect hate speech and specified them like labeling into various classes like hate speech, offensive, and neither, etc. After using different classifiers, “Random Forest” has come up with better accuracy, precision, and recall compared to SVM (Support Vector Machine), Naive Bayes, Logistic Regression, Ada Boost, and Gradient boost algorithms. This system achieved an accuracy of 90.26% using a Random Forest. The experimental result showed that the “Random Forest” provided the best all-around accuracy from the model that has been made and it is more accurate than compare to other work done in recent times on this. So, the result obtain from the model, based on the resulting intensity of the comments can be extracted.
Five Phases Algorithm: A Novel Meta-heuristic Algorithm and Its Application on Economic Load Dispatch Problem

&lt;p&gt;A new meta-heuristic algorithm named the five phases algorithm (FPA) is presented in this paper. The proposed method is inspired by the five phases theory in traditional Chinese thought. FPA updates agents based on the generating and overcoming strategy as well as learning strategy from the agent with the same label. FPA has a simple structure but excellent performance. It also does not have any predefined control parameters, only two general parameters including population size and terminal condition are required. This provides flexibility to users to solve different optimization problems. For global optimization, 10 test functions from the CEC2019 test suite are used to evaluate the performance of FPA. The experimental results confirm that FPA is better than the 6 state-of-the-art algorithms including particle swarm optimization (PSO), grey wolf optimizer (GWO), multi-verse optimizer (MVO), differential evolution (DE), backtracking search algorithm (BSA), and slime mould algorithm (SMA). Furthermore, FPA is applied to solve the Economic Load Dispatch (ELD) from the real power system problem. The experiments give that the minimum cost of power system operation obtained by the proposed FPA is more competitive than the 14 counterparts. The source codes of this algorithm can be found in https://ww2.mathworks.cn/matlabcentral/fileexchange/118215-five-phases-algorithm-fpa.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Intelligent Decision Support System for Assessing Works with Free Response based on Production Model

Works with free response are usually used in training, as well as in engineering education. These works assessment faces a lot of challenges, especially in conditions of online learning. The use of criteria system underlies the formation of a score for work with free response. In the case of works like essays the assessment can be made without the participation of expert-tutor by means of automated essay scoring systems. In the other cases his engagement is required, and the assessment takes place usually manually with the use of a rubric. This procedure is very long and labour-intensive for tutors, particularly with the large number of works, which are to be assessed as quickly as possible. The challenge is further amplified by iterativeness of assessment procedure which is typical, for example, to term papers, productive and pre-diploma practice reports actively used in engineering education when students are as close as possible to real tasks implementation. Another question arises, because work assessment is carried out without consideration for work correction characteristics, that is why a formed score is inaccurate. A student work with free response assessment model based on production model that takes into account the efficiency of work correction made by student, and whose application allows to ease the formation of a score for work with free response, is proposed in the paper. The assessment model is implemented in an intelligent decision support system, which was used in assessing pre-diploma practice reports in Ufa State Aviation Technical University during online learning.
Improved Recommender System for Kid's Hobby Prediction using different Machine Learning Techniques

A recommendation system plays a vital role in the social media world, and it is used in many applications which provide recommendations based on user preferences. A recommendation is nothing but a choice-making system. The utilization of recommender systems is increasing daily as many choice-based challenges potentially influence users. Now a day's, parents are worried about their kids regarding the future. Most parents need help to identify their kid's interests and force their interests against kids. So this prediction will be helpful to society. There are many types of recommendation systems with different applications that have been successfully adopted. This work needs to specify the age limits for kids, and it's preferable for below ten years old kids. The dataset is prepared based on different parameters. Fourteen parameters have been identified, and the result is three possible classifications. The first classification is 1) academics, 2) arts, and 3) sports. The academics indicate the kid's interest in science with a mathematics background. A total of 1600 dataset has been used for training and modelling the different algorithm. Azure and auto-learn tools are used for processing the data, and n number of classification algorithms are used. In azure Multiclass Logistic Regression produced the highest result of 93.21%. In auto-sklearn algorithms, Liblinear SVC (support vector classification) produced 93% of the result.
Refining Codes for Locality Sensitive Hashing

Learning to hash is of particular interest in information retrieval for large-scale data due to its high efficiency and effectiveness. Most studies in hashing concentrate on constructing new hashing models, but rarely touch the correlation and redundancy between hash bits derived. In this article, we first introduce a general schema of hash bit reduction to derive compact and informative binary codes for hashing techniques. Further, we take locality sensitive hashing, one of the most widely-used hashing methods, as an example and propose a novel and two-stage binary code refinement method under the reduction schema. Specifically, the proposed method includes two stages, i.e., bit evaluation and bit refinement. The former stage aims to initially extract a small portion of informative hash bits in terms of their importance and quality evaluated by bit balance and similarity preservation. Then, the representation capabilities of the reduced hash bits are strengthened further by refining their binary values. The purpose of refinement is to lessen the correlations and redundancies between the reduced bits, making themselves more discriminative. The experimental results on three widely-used data collections confirm the effectiveness of the proposed bit reduction method and its superiority over the state-of-the-art hashing methods, as well as a bit selection method.
Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python

The herein proposed Python package pflacco provides a set of numerical features to characterize single-objective continuous and constrained optimization problems. Thereby, pflacco addresses two major challenges in the area optimization. Firstly, it provides the means to develop an understanding of a given problem instance, which is crucial for designing, selecting, or configuring optimization algorithms in general. Secondly, these numerical features can be utilized in the research streams of automated algorithm selection and configuration. While the majority of these landscape features is already available in the R package flacco, our Python implementation offers these tools to an even wider audience and thereby promotes research interests and novel avenues in the area of optimization.
Accelerating Adaptive Parallel Tempering with FPGA-based p-bits

Special-purpose hardware to solve optimization problems formulated as Ising models has generated great excitement recently. Despite a large diversity in hardware, most solvers employ standard variations of the classical (simulated) annealing (CA) algorithm. Here, we show how powerful replica-based Parallel Tempering (PT) algorithms can significantly outperform CA, using FPGA-based probabilistic computers. Using a massively parallel (graph-colored) architecture, we implement the Adaptive PT (APT) algorithm, generating problem-dependent temperature profiles to equalize replica swap probabilities. We benchmark our p-computer against analytical results from classical Ising theory and use our machine to solve spin-glass instances formulated as hard optimization problems. APT outperforms heuristic choices of temperature profiles used in conventional PT and a replica-based version of CA. Our machine provides 6,000X speedup over optimized CPU, with orders of magnitude further speedup projected for scaled implementations. The developed co-design techniques may be useful for a broad range of Ising machines beyond p-computers.
Multi-UAV-Assisted Federated Learning for Energy-Aware Distributed Edge Training

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has largely extended the border and capacity of artificial intelligence of things (AIoT) by providing a key element for enabling flexible distributed data inputs, computing capacity, and high mobility. To enhance data privacy for AIoT applications, federated learning (FL) is becoming a potential solution to perform training tasks locally on distributed IoT devices. However, with the limited onboard resources and battery capacity of each UAV node, optimization is required to achieve a large-scale and high-precision FL scheme. In this work, an optimized multi-UAV-assisted FL framework is designed, where regular IoT devices are in charge of performing training tasks, and multiple UAVs are leveraged to execute local and global aggregation tasks. An online resource allocation (ORA) algorithm is proposed to minimize the training latency by jointly deciding the selection decisions of clients and a global aggregation server. By leveraging the Lyapunov optimization technique, virtual energy queues are studied to depict the energy deficit. With the help of the actor-critic learning framework, a deep reinforcement learning (DRL) scheme is designed to improve per-round training performance. A deep neural network (DNN)-based actor module is designed to derive client selection decisions, and a critic module is proposed through a conventional optimization method to evaluate the obtained selection decisions. Moreover, a greedy scheme is developed to find the optimal global aggregation server. Finally, extensive simulation results demonstrate that the proposed ORA algorithm can achieve optimal training latency and energy consumption under various system settings.
Revolution of Artificial Intelligence and IoT in Healthcare: A Keyword Co-occurrence Network Analysis Using CiteSpace

The current revolution in information and communication technologies (ICT) has completely altered the healthcare industry. Researchers have exploited the capabilities of ICT to reduce the impact of the ongoing global health pandemic. Specifically, artificial intelligence and Internet of Things (IoT) are significantly employed in the healthcare sector to mitigate the challenges posed by COVID-19. The purpose of this paper is to conduct a keyword co-occurrence network analysis of published scholarly literature on the AIoT-enabled healthcare domain. It conducts a keyword co-occurrence network analysis to deduce thematic clusters, emerging research areas, research hotspots, and technological trends using the visualization tool CiteSpace. The results of this research revealed that India is the most prominent country in this knowledge domain. Similarly, COVID-19 and smart homes are the major research areas in the IoT-enabled healthcare field. Furthermore, blockchain technology, remote monitoring, and 5G technology are the most recent technologies that are significantly employed with IoT to improve the quality of healthcare services.
Association Rules Generation for Injuries in National Football League (NFL)

Injury is a very common occurrence in sports; due to the amount of stress, the body undergoes and the number of accidents and concussions that happen in contact sports. We have chosen a dataset from National Football League which is a contact sport which has a lot of injuries due to the above stated reasons. Various data mining techniques have been implemented using WEKA to analyze these said injuries between the years of 2012 and 2014. The main point of concern would be to analyze concussion as to which could be life threatening, using Apriori algorithm, EM cluster algorithm, and visualization technique.
Nickel Grade Inversion of Lateritic Nickel Ore Using WorldView-3 Data Incorporating Geospatial Location Information: A Case Study of North Konawe, Indonesia

The North Konawe region in Indonesia, known for its lateritic nickel (Ni) deposits, holds significant potential for obtaining Ni resources. However, the complex topographic conditions of this area pose challenges. Exploring the application of remote sensing technology to reveal the spectral response mechanism of Ni grade from high-precision multispectral data and inversion of Ni grade represents a novel direction in future Ni resource exploration. Traditional remote sensing inversion methods solely consider the spectral characteristics of sample data and ignore vital geospatial location information. As a result, efficiently obtaining regional details of target substance content over large areas has become challenging. The introduction of the geographically weighted regression (GWR) method offers an opportunity for fine-grained Ni grade inversion based on remote sensing. This study focused on the E and K blocks within the lateritic Ni mining area in North Konawe. Through utilizing the WordView-3 multispectral data which exhibits immense potential in quantitative remote sensing inversion studies, GWR was employed to integrate spectral features and spatial information. The goal was to reveal the correlation between multispectral remote sensing data and Ni grade. The obtained results were then compared and analyzed with multiple linear regression (MLR) and back propagation neural network (BPNN) models. The findings revealed that GWR achieved the highest coefficient of determination R2 of 0.96, surpassing MLR and BPNN values of 0.05 and 0.17, respectively. Additionally, GWR exhibited the lowest root mean square error of 0.04, which was lower than those of MLR and BPNN with the values of 0.25 and 0.23, respectively. These results confirmed the enhanced stability and accuracy of the GWR method compared to MLR and BPNN. Furthermore, GWR effectively mapped the spatial distribution trends of Ni grades in the study area, providing evidence of the method’s effectiveness in Ni grade inversion. The study also delved into the inversion effect of the GWR method in areas with varying weathering crust thickness and vegetation cover. The research revealed that higher values of weathering crust thickness negatively impacted the inversion effect. However, the influence mechanism of vegetation cover on Ni grade inversion necessitated further investigation. These results served as a significant demonstration of the remote sensing inversion of mineral resource grades in similar areas. They provided valuable insights for future exploration and decision-making processes.
Content Analysis of Persuasion Principles in Mobile Instant Message Phishing

The popularity of Mobile Instant Messaging (MIM) Applications (apps) presents cybercriminals with a new venue for sending deceptive messages, known as ‘Phishing’. MIM apps often lack technical safeguards to shield users from these messages. The first step towards developing anti-phishing solutions to identify phishing messages in any attack vector is understanding the nature of the attacks. However, such understanding is lacking for MIM-enabled phishing. This study provides insights into how phishers apply persuasion principles in MIM phishing. Using the deductive content analysis method and Cialdini’s six principles of persuasion, this study identified and analysed 67 examples of real-world MIM phishing attacks from various online sources. Each phishing example was coded to identify the persuasion techniques used and how they were applied. Findings reveal that the principles of social proof, liking, and authority were most widely used in MIM phishing, followed by scarcity and reciprocity. Furthermore, most of the phishing examples contained three persuasion principles, most often a combination of authority, liking, and social proof. These findings provide insights into how phishers execute phishing in MIM apps and provide a theoretical foundation for future research on the psychological aspects of phishing in MIM apps and the development of anti-phishing solutions to identity phishing in MIM.
Blockchain in Oil and Gas Supply Chain: A Literature Review from User Security and Privacy Perspective

Blockchain’s influence extends beyond finance, impacting diverse sectors such as real estate, oil and gas, and education. This extensive reach stems from blockchain’s intrinsic ability to reliably manage digital transactions and supply chains. Within the oil and gas sector, the merger of blockchain with supply chain management and data handling is a notable trend. The supply chain encompasses several operations: extraction, transportation, trading, and distribution of resources. Unfortunately, the current supply chain structure misses critical features such as transparency, traceability, flexible trading, and secure data storage - all of which blockchain can provide. Nevertheless, it is essential to investigate blockchain’s security and privacy in the oil and gas industry. Such scrutiny enables the smooth, secure, and usable execution of transactions. For this purpose, we reviewed 124 peer-reviewed academic publications, conducting an in-depth analysis of 21 among them. We classified the articles by their relevance to various phases of the supply chain flow: upstream, midstream, downstream, and data management. Despite blockchain’s potential to address existing security and privacy voids in the supply chain, there is a significant lack of practical implementation of blockchain integration in oil and gas operations. This deficiency substantially challenges the transition from conventional methods to a blockchain-centric approach.
Addressing cold start in recommender systems with neural networks: a literature survey

Filtering information on the Internet and recommending the right choices is more than important for Internet users and various businesses that offer products and services. Although recommender systems do this work efficiently, problems such as Cold Start often appear when new users or items enter the system. The traditional methods of recommender systems, collaborative filtering and content–based techniques, do not offer an optimized solution to this problem. The integration of neural networks in recommender systems offers a new approach to solving cold start. Whether using the feature of extracting hidden data, or using deep learning algorithms with more layers, the accuracy of recommendations and predictions has increased significantly. We have analyzed 40 papers that approached solving the cold start problem using neural networks. We have researched how neural networks are integrated into recommender systems, what they are used for, which neural network algorithms have shown to be more efficient in solving the cold start problem, and which algorithms have increased the accuracy of the recommendation. We aim to answer these questions with other subquestions related to types of cold start such as item or user cold start and warm, partial, or strict cold start.