Reviewerly/exHarmony huggingface dataset:
Impact of ICT in LIS : Major Shifts and Practices, from Automation to Transformation

In the period of data blast and the spread of computerized data assets, the utilization of ICT in libraries has become inescapable. Viable utilization of ICT in the library assists with working the library and oﬀers the most eﬀective support. The modernization of libraries and data focuses has worked with the exchange and access of data, the accomplishment of objectives, and accordingly, the structure of organizations of libraries and data focuses. This undertaking has achieved signiﬁcant changes in asset improvement, sharing, and use at various levels. Through your membership, you access electronic diaries, CD-ROM data sets, online data sets, online assets, and an assortment of other electronic assets. They join the library consortium and make an advanced library.
India's Inclusive Education : A School Teacher's View of Teaching

Students' activity and eﬀectiveness are stimulated by teaching learning resources, which make the learning process easier, assist in getting higher-quality information, and invigorate students' freedom and decisive reasoning while picking and gathering data. The study's main goal is to look at the relationship between age, teaching experience, yearly pay, and capability, number of instructors in the school, area, the board, orientation, family size, marital status, and caste when it comes to imparting learning content. Education can be deﬁned as a purposeful, conscious or unconscious psychological, sociological, scientiﬁc, and philosophical process that leads to the full development of the individual as well as the greatest advancement of society in such a way that both enjoy the greatest happiness and prosperity. In a nutshell, education is the process of improving an individual in accordance with his or her needs and the demands of society, of which he or she is an integral member. Using verbalism in the classroom has the lowest expectation, whereas direct personal experience will have a greater impact in the classroom. Direct experience activities are more viable than non-project and projected aids. Any innovation increases the student's energy and interest. Video assisted education and computer assisted instruction are methodically customized, taking into account all psychological principles, so that the student's interest in the learning process is piqued regardless of connectivity. New technologies (particularly, the internet) provide teachers with a variety of intriguing tools that can be used to enhance the teaching-learning process. Because of the utility of these tools, it is critical for teachers to have more information on the beneﬁts and potential of employing innovation in the classroom, as well as the outcomes of their use.
ECONOMIC DEVELOPMENT OF THE REPUBLIC OF MOLDOVA BASED ON REMITTANCES

This article examines the effects of remittances on the economic growth of the Republic of Moldova, determined by private consumption. To verify the link between remittances and development in the Republic of Moldova, the study uses a VECM approach. The result of the research is that, for the Republic of Moldova, remittances are an important source for private consumption, which in turn fuels the economic evolution of the country. In order to redirect remittances to investments, the government is implementing various strategies, which do not yet have the expected support of remittance holders.
Supervision of the energy performance of multi-arrays PV plants by means of bi-monthly violin plots

Small PV plants are frequently not equipped with a meteorological station, that is decisive to monitor the energy performance of a PV plant and to detect low-intensity anomalies, as premature ageing, before they become faults. The paper proposes a methodology for PV plant not equipped with a weather station and it is based on a violin plot, that is a statistical tool that integrate information from box plot and histogram. In absence of environment parameters, the monitoring is done, by comparing each other the energy performance of the arrays constituting the PV plant, that should produce the same environment condition affects all the arrays. The methodology is applied to a real 20kWp PV plant, studying the energy performance for different energy datasets.
In Library Science, the Importance of Information Audit

Information audits are a useful information organization tool for confirming, pricing, moving, and legitimizing data assets and organizations. In addition, the audit's postponed outcomes can be used to clarify the obligation that information and information organizations have to make by an affiliation, which is of particular importance for essential authority. The focus point of information audit in library science is the source of inspiration for this instructive designed work. Since, when we talk about audit, everyone agrees that it is more important and will associate it with holding and other financial records in a partnership. In any case, people don't realize that information audit goes through important steps to ensure that the data they receive is accurate and legitimate. Aside from that, we look for any tools or frameworks for information auditing.
A Proposed Technique for Automatic Recognition of Human Activities

While computer vision is widely employed in a wide variety of applications, the precise and efficient identification of human behaviour remains a challenging area in computer vision science. Recent research has concentrated on smaller issues such as approaches for human action recognition of depth data, 3D skeleton data, photographic data, spatiotemporal methods focusing on interest and the identification of human activity. Despite this, no systematic survey of human behaviour appraisal has been conducted. To that end, we present a comprehensive review of methods for identifying human actions, including advances in the hand design of action characteristics in RGB and depth data, established methods for representing deeper learning action-based features, advancements in the methodology for identifying human-object interaction, and prominent present methods of deeper information.
Design and Development of a Content-Based Image Retrieval (CBIR) System for Computing Similarity.

The shared and stored mixed media information is growing, and looking for or retrieving a significant image from a chronicle is a challenging exploration issue. Any image retrieval model's primary objective is to hunt for and mastermind photos that have a visual semantic association with the user's query. The bulk of web indexes on the Internet fetch photos using content-based algorithms that require subtitles as additional information. The user submits a query by inputting some text or keywords that match the file's keywords. )e yield is generated based on keyword matching, and this cycle can obtain insignificant photos. The distinction between human visual understanding and manual naming/commenting is the fundamental reason for producing the irrelevant yield. Any image retrieval framework must meet the fundamental criterion of searching for and sorting comparable photos from the archive with as little human interaction as possible. As implied by the writing, the choice of aesthetic characteristics for any framework is determined by the end user's requirements. Discriminative feature representation is another fundamental requirement for any image retrieval framework. To make the feature more robust and unique in terms of depiction fusion of low-level visual features, a large computational cost is required to obtain more dependable results. Regardless of the case, an ill-advised feature selection can degrade the performance of an image retrieval model. Contrary to conventional ideabased approaches, content-based picture retrieval is incompatible with them. "Content-based" refers to the fact that the hunt evaluates the image's contents rather than its metadata, such as keywords, labels, or depictions associated with the image.
The level of creativity of psychology students: The case of Kokshetau University named after Sh. Ualikhanov

Introduction. The article focuses on creativity, which is viewed as one of the main factors influencing successful professional activity of a psychologist. Creativity is understood as a person’s ability to innovate, which can be traced in emotions, thinking, communication and various types of activity. Creativity occupies an important place in the activity of a psychologist and is a professionally important quality for a psychologist. The formation of creativity begins during the university period, when creative thinking is integrated into educational and professional activities. This makes is relevant to assess the level of creativity in psychology students. The article analyzes the level of creativity of psychology students studying at Kokshetau University named after Sh. Ualikhanov (Kazakhstan). Materials and methods. We used Johnson’s Creativity Questionnaire to assess the following parameters: sensitivity to the problem, fluency of thinking, flexibility of mind, resourcefulness and ingenuity, ability to structure, originality, independence, and self-sufficient behavior. The total indicator of creativity was analyzed. The sample included 35 psychology students aged 18-22. Results. The empirical data showed that psychology students have a high degree of creativity—specifically, 60.0% of students have a high level of creativity, 28.6%, very high, and 11.4%, medium. Students with low and very low creativity levels were not found in the sample. Conclusion. We compared these results with the data regarding creativity in students studying on non-psychology programs presented by other authors, and we found that psychology students have a higher level of creativity. We suggest that these results are explained by the specifics of professional training of psychologists, which creates prerequisites for the development of creativity. The prospects of the study are connected with a detailed verification of this hypothesis.
Analysis of Combination of Parking System with Face Recognition and QR Code using Histogram of Oriented Gradient Method

Security is very important everywhere, including in the campus environment. To provide security and comfort for those who park their vehicles, a parking application is needed that can provide vehicle security while undergoing academic activities on campus. QR code (Quick Response Code) is a technology for converting written data into a two-dimensional code, which is printed on a more compact medium capable of storing various types of data. The most common individual part used to identify a person is the face because it has the unique characteristics of everyone. Histogram of Oriented Gradient (HOG) is a feature extraction used for face identification based on histogram of gradient orientation and gradient magnitude. This application is implemented using the Dlib library for facial recognition. The implementation of this method is expected to improve parking security and provide a record of parked vehicles. The results of testing the implementation of facial recognition methods into android applications show very satisfactory results. With the results of testing the QR code scanning accuracy of 100% and an accuracy of 90% for a 7% damage rate and an accuracy of 85% for a 15% damage rate, and the results of facial recognition testing of 90% on face photos wearing helmets and an accuracy of 92% on photo of face without helmet.
AI Case Study: Develop a Maze Action Game

In this chapter, we are going to look at the AI basics: what it helps us achieve in games and how to implement a range of AI behaviors in FXGL. AI is one of the key areas of game development. Without it, most single-player games would be far less engaging and fun. AI is what gives NPCs and enemies quasi-informed decision-making abilities with the goal to entertain the player. More specifically, we, as game developers, should aim to make the AI sophisticated enough that it can match the player’s skill but shallow enough to give the player an invisible advantage. If designed and implemented well, such an AI agent will generate interesting and challenging interactions with the player but will know when to lose.
Audit of Attendance Information System with Fingerprint Using Cobit 4.1 Framework at PT DSN

The Fingerprint attendance system helps companies control employee working hours more efficiently and improve operational work activities using computerized tools, which can record the attendance list of each company employee. They were conducted following the COBIT Framework 4.1 standards for IT governance. It is necessary to conduct an audit to evaluate the information system's current governance. The research focuses on the sub-domains of AI4, DS1, DS4, DS5, DS10, and ME2. This research uses observation, interview and literature techniques. The data analysis technique used is Maturity level. From the results of the study, it was found that DS5 was at the level of 3.02; DS4 and DS10 were at level 3 (Defined Process); DS1 was at the level of 2.8; AI4 was at level 2.7 (Repeatable but Intuitive); while ME2 is at the level of 1.7 (Initial/ad Hoc). The highest score is on DS5 (Ensure Systems Security) with a value of 3.02, and the lowest is on ME2 (Monitor and Evaluate Internal Control) with a value of 1.7
CekUmpanKlik: an artificial intelligence-based application to detect Indonesian clickbait

This study attempted to deploy a high performing natural language processing model which specifically trained on flagging clickbait Indonesian news headline. The deployed model is accessible from any internet-connected device because it implements representational state transfer application programming interface (RESTful API). The application is useful to avoid clickbait news which often solely purposed to rack money but not delivering trustworthy news. With many online news outlets adopting the click-based advertising, clickbait headline become ubiquitous. Thus, newsworthy articles often cluttered with clickbait news. Leveraging state-of-the-art bidirectional encoder representation from transformers (BERT), a lightweight web application is developed. This study offloaded the computing resources needed to train the model on a separate instance of virtual server and then deployed the trained model on the cloud, while the client-side application only needs to send a request to the API and the cloud server will handle the rest, often known as three-layer architecture. This study designed and developed a web-based application to detect clickbait in Indonesian using IndoBERT as its language model. The application usage and potentials were discussed. The source code and running application are available for public with a performance of mean receiver operating characteristic-area under the curve (ROC-AUC) of 89%.
Reduced hardware requirements of deep neural network for breast cancer diagnosis

Identifying breast cancer utilizing artificial intelligence technologies is valuable and has a great influence on the early detection of diseases. It also can save humanity by giving them a better chance to be treated in the earlier stages of cancer. During the last decade, deep neural networks (DNN) and machine learning (ML) systems have been widely used by almost every segment in medical centers due to their accurate identification and recognition of diseases, especially when trained using many datasets/samples. in this paper, a proposed two hidden layers DNN with a reduction in the number of additions and multiplications in each neuron. The number of bits and binary points of inputs and weights can be changed using the mask configuration on each subsystem to futher reduce the hardware requirements. The DNN was designed using a system generator and implemented using very hardware description language (VHDL). The system achievments outcomes the superior’s accuracy rate of approximately 99.6 percent in distinguishing bengin from malignant tissue. Also, the hardware resources were reduced by 30 percent from works of literature with an error rate of 7e-4 when using the Kintex-7 xc7k325t-3fbg676 board.
Defending against Label-Flipping Attacks in Federated Learning Systems with UMAP

Abstract The importance of computing and storage capacity has increased over time, and the importance of data mining in industrial engineering has become more apparent. Recently, artificial intelligence and machine learning have made significant advancements in industrial engineering. Federated learning is a machine learning technique that aims to solve the problem of distributed computing systems and their applications of data storage while ensuring data privacy. Tolpegin et al. conducted research on data poisoning attacks in a federated learning system, which we have extended to an analysis of the efficiency of Tolpegin's proposed defense technique. We have subsequently compared the efficiency using uniform manifold approximation and projection (UMAP), principal component analysis (PCA), kernel PCA (KPCA), and K-means clustering algorithms. This study confirms that UMAP performs better than PCA, KPCA, and K-means, and provides excellent performance in mitigating data-poisoning attacks.
An Accuracy-Driven Compression Methodology to Derive Efficient Codebook-Based CNNs

Codebook-based optimizations are a class of algorithmic-level transformations able to effectively reduce the computing and memory requirements of Convolutional Neural Networks (CNNs). This approach tightly limits the number of unique weights in each layer, allowing the storage of employed values in codebooks containing a small number of floating-point entries. Then, CNN models are represented as low-bitwidth indexes of such codebooks. This work introduces a novel iterative methodology to find highly beneficial schemes trading off accuracy and model compression in codebook-based CNNs. Our strategy can retrieve non-uniform solutions driven by an accuracy constraint embedded in the optimization loop. Our results indicate that, for a 1% accuracy degradation, our methodology can compress baseline floating-point CNN models up to 19x. Moreover, by reducing the number of memory accesses, our strategy increases energy efficiency and improves inference performance by up to 91%.
Towards making the most of NLP-based device mapping optimization for OpenCL kernels

Nowadays, we are living in an era of extreme device heterogeneity. Despite the high variety of conventional CPU architectures, accelerator devices, such as GPUs and FPGAs, also appear in the foreground exploding the pool of available solutions to execute applications. However, choosing the appropriate device per application needs is an extremely challenging task due to the abstract relationship between hardware and software. Automatic optimization algorithms that are accurate are required to cope with the complexity and variety of current hardware and software. Optimal execution has always relied on time-consuming trial and error approaches. Machine learning (ML) and Natural Language Processing (NLP) has flourished over the last decade with research focusing on deep architectures. In this context, the use of natural language processing techniques to source code in order to conduct autotuning tasks is an emerging field of study.In this paper, we extend the work of Cummins et al., namely Deeptune, that tackles the problem of optimal device selection (CPU or GPU) for accelerated OpenCL kernels. We identify three major limitations of Deeptune and, based on these, we propose four different DNN models that provide enhanced contextual information of source codes. Experimental results show that our proposed methodology surpasses that of Cummins et al. work, providing up to 4% improvement in prediction accuracy.
Preparing students for a digital work-life using student-centred methods

Students are expected to be digital, using digital tools for staying updated on their study programmes, time schedules, and how to find and hand in assignments. The tools that the students need in their future jobs are not necessarily taught, as these tools may be specific to each company. However, in the study programme of Music Production, the use of equipment and digital software is a part of the education.&#x0D; This paper showcases how the students are exposed to tools and work forms that they will use in the music production business. Introductions are provided, and the students can then practice using the tools. The most intensive training is provided in five to six intensive sessions during a study year. These sessions are called “camps”.&#x0D; These camps have a duration of approximately 12 hours each, with the students divided into groups, the purpose of which is to develop and produce song texts and melodies, which at the end of the camp are presented in a plenary session. These intensive learning activities include the extensive use of tools for producing the songs, as well as collaborative work forms, as the students need to work together and learn together in groups.&#x0D; The qualitative investigations from these intensive song writing camps show how the students benefit from the group work, situated learning and collaboration, and present their view on the usefulness of these camps.
Area and Power analysis of a Scalable Primitive Polynomial computation circuit over the field GF(2)

Primitive polynomials are important in hardware realizations for error control codes, cryptography, and other signal processing and communication-based designs. These primitive polynomials over a finite field in association with the core concepts of a particular area of study can be easily implemented on hardware. Researchers have used existing databases of primitive polynomials of various degrees in most cases, generated using a software-based logic. However, not much study has been carried out to implement the logic for computation of primitive polynomials on hardware. This paper describes an implementation of a scalable primitive polynomial circuit. The circuit can compute the primitive polynomial of any degree for a finite field and uses shift registers for the said purpose. This scalable circuit is synthesized on ASIC with a 45 nm CMOS process using Synopsys Design Compiler to assess standard cell area and power. The standard cell area and power consumed by the scalable circuit for various degrees of the primitive polynomial are also analyzed in this paper.
Exploratory evaluation of the C4.5 and Transfer Learning Algorithms for Breast Cancer Classification

Breast cancer is proclaimed to be the most prevalent tumor and classification of the tumor cells into Benign and Malignant ascertains to be beneficial in the preparatory stages for the affected patient to receive the optimal treatment. A contrasting comparison is drawn between two algorithms. Machine Learning makes use of the Transfer Learning technique to attain a higher learning rate and stores the knowledge gained whilst classifying the tumor cells. And the contrasting algorithm C4.5 is used in Data Mining as a Decision Tree Classifier which aids in making the most valuable decision on the given set of data and also proves its use by curtailing the conclusive overfitting rate.