Reviewerly/exHarmony huggingface dataset:
Recommender System using Distributed Improved Predictive Framework with Matrix Factorization and Random Forest

Online digital marketing achieves their revenue according to their advertisements or sales assignment when companies have the profitable attention for recommending their products to customers via ranking them.Online customers are not able to guarantee that the items delivered through the recommendation by big data are either comprehensive or applicable to their essentials.In the past few years, recommendation frameworks were broadly applied to analyze the massive amount of data.Among those, a Distributed Predictive model with Matrix factorization and random Forest (DPMF) has achieved high efficiency to predict the item ratings.However, it accounts only for user preferences and opinions whereas the other contextual data are necessary for enhancing the efficiency of rating prediction.In this article, a Distributed Improved Predictive model with a Matrix factorization and random Forest (DIPMF) framework is proposed that considers the elements of social context and the dynamic characteristic of every user for every item to enhance the quality of prediction.The primary aim is to combine the information from the preferences, opinions and social context of each user.The social context of users is multiple features of the context such as differences in the current opinion with earlier opinion, behavior, relationship and interaction.Since each user is connected through relations and interactions.At first, the training dataset is split into an optimal amount of splits for accelerating the parallel and distributed training process.Then, the training process is carried out by the DPMF with the Distributed Improved Predictive model with Matrix factorization-Improved variant (DIPMI) to create the representation of every user's preferences, opinions and social contexts in the training set.Further, the prediction of rating is formulated as a regression challenge and solved via the Random Forest (RF) algorithm that predicts the customer's rating behavior with their opinions and social context for every product.Finally, the experiments are conducted on trip advisor and Amazon datasets to evaluate the efficiency of DIPMI and DIPMF compared to the state-of-the-art recommendation frameworks.The findings exhibit that the DIPMF on the trip advisor dataset achieves an average of 0.6826 Root Mean Square Error (RMSE), 0.5925 Mean Absolute Error (MAE), 0.8369 recommendation quality and 0.0023 Confidence Range (CR) 95% compared to the other frameworks.Similarly, the DIPMF on Amazon dataset achieves an average of 0.7591 RMSE, 0.5704 MAE, 0.8298 recommendation quality and 0.0032 CR 95% compared to the other frameworks.
DISTANT LEARNING: PRINCIPLES AND PECULIARITIES

Despite the fact that distance education developed in the 90s of the XX century in most countries, now this type of learning is very relevant because of the events taking place in Ukraine. The aim of this paper is to analyze distance learning with its all peculiarities and features, to compare it with online and virtual learning as it is often associated with online and virtual learning. However, these are different concepts. Distance learning has a more detailed design of the lesson and has a relatively clear structure, such as: setting the lesson, goals, assessment of students’ understanding of the lesson material, the content of the lesson, assessment of mastery of the lesson, time frame of the lesson. Tasks should be clear, useful, and understandable. Distance learning, unlike online one, which has emerged as a result of the spread of the coronavirus pandemic, is more thoughtful and active. It is flexible, teaches independence, and expands the capabilities of both teachers and students. The main difference between distance learning and other types of learning is that online learning is the acquisition of knowledge and skills through gadgets connected to the Internet in the “here and now” mode and this format is a continuation of distance learning. Apps like Face Time, Google Hangouts, Skype, Microsoft Teams, Zoom or What’s App can be used to provide these synchronous connections. Among the advantages of distance learning are individualization of learning topics, accessibility, individual consultation with the teacher, effective feedback, ease of viewing and processing the lesson material and others. Distance learning is a multifaceted concept, because it can be understood as anything – sending tasks, tests and independent work. It can be both synchronous and asynchronous, completely independent, or with the support of the teacher at certain stages. Keywords: distant learning, virtual learning, online learning, synchronous and asynchronous learning, independent work.
Secured Multi-Layer Blockchain Framework for IoT Aggregate Verification

Technologies designed for digital provenance, especially the Internet of Things (IoT) and blockchain, may allow for security, transparency, and traceability in the global supply chain.However, upstream nodes in the supply chain that work for large-scale production suppliers are not considered.In addition, most IoT blockchain systems adopt an ID-based signature scheme that may affect the efficiency of IoT devices.We propose using aggregate verification to improve the security and efficiency of ID-based verification, reduce network traffic on the blockchain, and transfer computing overhead to aggregator nodes.This paper implements a multi-layer blockchain for Agriculture 4.0 supply chain management that has higher efficiency, effectiveness, and security in comparison to conventional blockchains.We design a Multi-Layer Aggregate Verification (MLAV) solution to improve supply chain management with IoT Blockchain for Agriculture 4.0 through the following methods.First, we use a multi-layer IoT blockchain system to reduce Ethereum gas fee.Second, we design an ID-based Aggregate Verification scheme, thereby eliminating the certificate management cost in the traditional Public Key Infrastructure (PKI) and reducing bandwidth and computation time requirements.Third, we implement a three-layer blockchain infrastructure.In Layer 1, IoT devices sense and upload data to the system's database; in Layer 2, smart contracts execute aggregate ID-based signature verification from IoT devices and upload the transactions to the private blockchain; in Layer 3, a batch converts the layer 2 data and uploads its Merkle root to Ethereum, thereby reducing the required gas fee.
IDENTIFYING FACTORS AFFECTING MATHEMATICS SCORE IN THE JUNIOR HIGH SCHOOL STUDENTS DURING COVID-19 PANDEMIC IN SAMARINDA, INDONESIA

This research focuses on identifying factors affecting mathematics scores in junior high school students during the COVID-19 pandemic in Samarinda, Indonesia. We estimate factors of five domains of the mathematics score for e-learning in junior high school students during the COVID-19 pandemic. The factors are sociodemographic (gender and degree level), students’ habits of accessing the Internet outside online learning, tutoring program, learning platforms (video conferencing, online classes, and social media), and skills of digital literacy (internet searching, knowledge assembly, and content evaluation). A total of 160 students in the junior high school in Samarinda were selected accidentally. An online survey was conducted based on a structured questionnaire. The dummy variables multiple linear regression model was used in this research to identify factors affecting mathematics scores in the junior high school students during the COVID-19 pandemic in Samarinda. The novelty of this research is in finding the factors that have a significant positive effect on mathematics scores, namely gender (female), tutoring programs, video conferencing platforms, internet searches, and content evaluation. While the factors that have a significant negative effect on the value of mathematics are the habit of accessing the Internet outside online learning and knowledge assembly. Based on these findings, we recommend the teachers to use video conferencing as the best practice for teaching mathematics. As research implications, both teachers and students can discuss improving the students’ digital literacy skills.
THE RELATIONSHIP BETWEEN INFORMATION SHARING, ABSORPTIVE CAPACITY, SUPPLY CHAIN INNOVATIVENESS AND PERFORMANCE

In today's changing conditions, supply chain provides firms with many advantages in terms of such as cost, speed and quality. In this respect, the investigation of the factors that increase supply chain performance presents important findings to both the literature and practitioners. The aim of this study is to examine the relationships between information sharing, absorptive capacity, supply chain innovativeness, and supply chain performance. For this purpose, data were collected from 194 middle and senior managers in firms in Kocaeli through questionnaires. As a result of the analyzes carried out, it has been determined that information sharing is positively associated with absorptive capacity, but not with supply chain innovativeness. It is also concluded that absorptive capacity is positively related to supply chain innovativeness and supply chain innovativeness is positively related to supply chain performance.
CONTENT PECULIARITIES OF THE ACADEMIC DISCIPLINE «FOREIGN LANGUAGE FOR PROFESSIONAL PURPOSES» (ON THE BASIS OF MA SPECIALITY «PSYCHOLOGY»)

Recent sociological surveys reflect direct dependence of economic growth in European countries on the level of their citizens’ skills of English as the Language of International Communication. Taking this fact into consideration there is an increasing need for capacity building in the sphere of the English language in the learning process of higher educational establishments in general and on the MA level in particular, which will enable all-round growth of every citizen. The relevance of learning English to broaden educational and professional horizons and capacities encourages Ukraine to develop state initiatives aimed at providing labour market with competitive graduates; to promote integration of educational and academic components into international and European communities; to support in-depth and systematic English language learning as a high-priority state policy area. A state educational vision as a predictable result in the sphere of developing English language learning strategy by 2025 is considered the basis for implementing specific recommendations and steps. According to recent projections a concessive adoption of the strategy will likely result in the increase of the English language skills among students and teachers, more attention to and better reputation of English due to high quality methods, free access to educational resources, professional growth of the academic personnel, relevant content of the disciplines taught. The last of the listed actions is of great interest to the developers of the Master courses “Foreign Language for Specific Purposes”. It stems from the fact that Master’s Degree is obtained according to the academic and professional components of educational programmes. Specifically professinal components as a system of essential constituents within the scope of a chosen profession define the anticipated competences an individual must acquire, which results in the content peculiarities of the academic discipline “Foreign Language for Specific Purposes”. Key words: the second educational level, “Foreign Language for Specific Purposes”, psychologists, interdisciplinary approach, competence approach, knowledge, competences, skills.
Facial Recognition Attendance System Using Deep Learning

Abstract: Face authentication is one of the critical unsolved problems in computer science, a lot of time and energy is spent to invent a robust solution for it. Face authentication can play a significant role in security, Biometric verification, auto detection of criminals in a crowd, etc. The paper presents a unique model that authenticates the faces in the image, and securely shares the images. Firstly, the deep learning algorithm identifies all the faces in the image, extracts the invariant features of each face. The invariant feature of the face is studied to compute the emotions. The model captures the friends list of the user and securely shares the image to the known persons in the image.The main purpose of this project is to enhance the relationship between the student and the teaching staff. Due to pandemic every student are forced to attend the online classes, even though some students are tend to bunk the classes by attending those session by use some other else. In order to stop this we developed an windows application to take facial attendance and attending the class. The current old system has a lot of ambiguity that caused inaccurate and inefficient of attendance taking. Many problems arise when the authority is unable to enforce the regulation that exists in the old system. Thus, by means of technology, this project will resolve the flaws existed in the current system while bringing attendance taking to a whole new level by automating most of the tasks. The technology working behind will be the face recognition system. In the old system they use LDA. Although LDA is one of the most common data reduction techniques, it suffers from two main problems: The Small Sample Size (SSS) and linearity problems. We improvised the facial recognition using LBPH – (Local Binary Pattern Histograms) - Local binary patterns (LBP) is a type of visual descriptor used for classification in computer vision. This algorithm is so fast and reliable and the result prediction we have utilized a regression algorithm.
An Embedded Feature Selection Framework for Control

Reducing sensor requirements while keeping optimal control performance is crucial to many industrial control applications to achieve robust, low-cost, and computation-efficient controllers. However, existing feature selection solutions for the typical machine learning domain can hardly be applied in the domain of control with changing dynamics. In this paper, a novel framework, namely the Dual-world embedded Attentive Feature Selection (D-AFS), can efficiently select the most relevant sensors for the system under dynamic control. Rather than the one world used in most Deep Reinforcement Learning (DRL) algorithms, D-AFS has both the real world and its virtual peer with twisted features. By analyzing the DRL's response in two worlds, D-AFS can quantitatively identify respective features' importance towards control. A well-known active flow control problem, cylinder drag reduction, is used for evaluation. Results show that D-AFS successfully finds an optimized five-probes layout with 18.7\% drag reduction than the state-of-the-art solution with 151 probes and 49.2\% reduction than five-probes layout by human experts. We also apply this solution to four OpenAI classical control cases. In all cases, D-AFS achieves the same or better sensor configurations than originally provided solutions. Results highlight, we argued, a new way to achieve efficient and optimal sensor designs for experimental or industrial systems. Our source codes are made publicly available at https://github.com/G-AILab/DAFSFluid.
Review on the parameter settings in harmony search algorithm applied to combinatorial optimization problems

Harmony search algorithm (HSA) is relatively considered as one of the most recent metaheuristic algorithms. HSA is a modern - nature algorithm that simulates the musicians’ natural process of musical improvisation to enhance their instrument’s note to find a state of pleasant (harmony) according to aesthetic standards. Lots of variants of HSA have been suggested to tackle combinatorial optimization problems. They range from hybridizing some components of other metaheuristic approaches (to improve the HSA) to taking some concepts of HSA and utilizing them to improve other metaheuristic methods. This stud y reviews research pertaining to parameter settings of HSA and its applications to efficiently solve hard combinatorial optimization problems.
THE USE OF CONTENT-BASED INSTRUCTION IN TEACHING READING FOR SECOND GRADE OF SENIOR HIGH SCHOOL

The aims of this study were to find out the effectiveness of Content-Based Instruction method in teaching reading for second grade of SMAN 1 Donorojo Jepara and to find out the reading comprehension ability of the second grade of SMAN 1 Donorojo Jepara. The population of this study was from the second grade MIPA which consists of 143 students from 4 classes. The samples of this study were class XI MIPA 2 as control group and class XI MIPA 4 as experimental group. The samples were selected by random sampling technique. The instruments used in this study were reading pre-test and post-test. The data were collected from observation and reading comprehension tests. The data were analyzed statistically in the form of t-test. To find out the significant improvement, the researchers conducted t-test using Paired Sample t-test, it showed that the sig. (2-tailed) of experiment is 0.00 and control group is 0.02. 0.000 &lt; 0.05 and 0.02 &lt; 0.05. There is a significance difference in the result of students' learning scores between the experiment and control group. The students' ability in reading comprehension in second grade of senior high school in SMA Negeri 1 Donorojo Jepara in comprehending English text is poor, after taught using CBI method, the frequency of post-test increased from poor to very good.Teachers may use the CBI method as an effective and innovative teaching method to improve students' reading skill. CBI method is also recommended to be applied in the hybrid classrooms.
Blockchain, AI &amp; Web3.0 in Metaverse of Madness

Abstract: As our world moves forward we have gone from reading books and papers to scrolling through endless instagram feeds. And the metaverse is exactly what the next-gen social media would look like. Anything that we do in the real world e.g. Hanging out with friends, visiting the art gallery, watching a soccer game can now be done in a living, shared, everlasting virtual world. The impact of artificial intelligence and blockchain technology on the metaverse is discussed in this research paper.
Signature and Log-signature for the Study of Empirical Distributions Generated with GANs

&lt;p&gt;In this paper, we bring forward the use of the recently developed Signature Transform as a way to measure the similarity between image distributions and provide detailed acquaintance and extensive evaluations. We are the first to pioneer RMSE and MAE Signature, along with log-signature as an alternative to measure GAN convergence, a problem that has been extensively studied. We are also forerunners to introduce analytical measures based on statistics to study the goodness of fit of the GAN sample distribution that are both efficient and effective. Current GAN measures involve lots of computation normally done at the GPU and are very time consuming. In contrast, we diminish the computation time to the order of seconds and computation is done at the CPU achieving the same level of goodness. Lastly, a PCA adaptive t-SNE approach, which is novel in this context, is also proposed for data visualization.&lt;/p&gt;
FM-YOLO Object Detection Algorithm

Aiming at the problems of YOLOv4 with many parameters and low detection accuracy, a lightweight FM-YOLO (Fused Mobile-You Only Look Once) object detection algorithm is proposed. The algorithm improves the convolution layer of the deep network for feature extraction in two aspects. First, MBConv is used to expand the width of the convolution operation and reduce the amount of model parameters. Secondly, Fused-MBConv in the shallow network solves the problem that the depthwise separable convolution cannot fully utilize GPU acceleration in the shallow network, resulting in slower training speed. The activation function is changed from Mish to SiLU, and dropout is added to avoid network training over-fitting. From the test results of the VOC2007 data set, the parameter of the YOLOv4 algorithm is reduced by 1.4M compared with YOLOv4, and the mAP is increased by 1.45%. This algorithm improves the accuracy of detection while slightly reducing the amount of model parameters.
Remote Data Acquisition and Management Technology of Power Equipment Based on Internet of Things

This paper proposes an architecture scheme for realizing the power equipment data acquisition IoT monitoring network using the latest Internet of Things technology. At the same time, a power equipment data acquisition and monitoring system based on Narrowband Internet of Things (NB-IoT) and exclusive-OR (XOR) encryption algorithms is designed. The system is mainly researched and designed from the aspects of monitoring terminal software and hardware design, data encryption processing, and remote data management cloud platform. This paper installs a big data analysis processor and a network routing processor based on the Internet of Things technology and modifies the system memory, display, and connection circuit to optimize the system hardware equipment. Finally, the system conducts experimental tests to monitor the state of power equipment, such as transmission and distribution towers, power manhole covers, cable tunnels, and distribution line knife gate temperature. The experimental results show that the system can realize low power consumption, comprehensive coverage, low-cost unified access, and centralized management of power equipment status information in different power scenarios. The application of this system dramatically improves the reliability and efficiency of the operation and maintenance of terminal equipment of the power system. The promotion of this system has a great application value.
A Review of Network Security Strategies Employed By the Nigerian Banking Sector (Case Study of Access Bank PLC, Bariga, Lagos, Nigeria)

The security of banking infrastructures viz-a-viz customer funds, investments and deposits are Germaine to successful banking operations. This study reviews computer and network security strategies employed in the banking sector using Access Bank Nigeria PLC, Bariga Branch in Lagos state. Our efforts discovered that some of the strategies put in place to mitigate cyber-attacks include the use of passwords, antivirus, firewalls, encryption, intrusion detection systems and intrusion prevention systems. We posited that the deployment, adoption and utilization of the integrated banking systems used by Nigerian banks provides a certain layer of security that aids banking systems security thus mitigating attacks and ensuring that Nigerian banks rarely experience malicious attacks of any form on their systems. We elucidated some of these strategies and provide insights into their effectiveness. Keywords: Computer Security, Network, Intrusion Detection, Intrusion Prevention System, banking Journal Reference Format: Sarumi, J.a. &amp; Omotosho, O.M. (2022): A Review of Network Security Strategies Employed By the Nigerian Banking Sector (Case Study of Access Bank PLC, Bariga, Lagos, Nigeria). Social Informatics, Business, Politics, L:aw, Environmental Sciences &amp; Technology Journal. Vol. 8, No. 1 Pp 1-10 Article DOI No - dx.doi.org/10.22624/AIMS/SIJ/V8N1P1. www.isteams/socialinformaticsjournal