Reviewerly/exHarmony huggingface dataset:
Conditional Statements

In this chapter, we explain conditional statements used in C programming. A conditional statement is used when a decision is to be made considering a Boolean expression called a condition. A condition may depend only on a single factor, or it can be the combination of a number of factors.
A CNN Ensemble Approach to Provide Non-combat and Support Roles Training Deaf and Dumb Individuals using Hand Signs

Appointing deaf and dumb individuals in military services is very challenging, as it is very difficult process to provide them training. Machine learning model is a useful tool for providing effective non-combat and support roles training to deaf and dumb individuals through hand signs interpreters. We have developed a CNN Ensemble approach to recognize and classify various hand signs used in during training sessions. This can help interpreters to quickly and accurately interpret the commands given by military trainers, making training more efficient and effective. We have made a comprehensive dataset for this research purpose. We have designed this dataset for 35 common commands used by military trainers during training. For each of command class we have taken 160 images. So our dataset consists of total 5600 images. To design our final model, we have proposed two Convolutional Neural Networks model. Then we have imported VGG19 using TensorFlow and Keras libraries. We have applied these three models on our dataset. To get better performance we have also applied Voting Ensemble model on these three models. We have also combined these three models to make an Average CNN model and applied several classifier models such as SVM, RF, KNN and DT on that model. As we have noticed, Voting Ensemble model performed better than all these models with highest accuracy 94.20%. We believe that our proposed models will bring a great impact in defence sector.
Resource Optimization based Virtual Machine Allocation Technique in Cloud Computing Domain

Cloud computing allows the clients to add or delete the dynamic resource pooling of massive capacity without adding the extra infrastructure to the client site. One of the vital challenges to delivering this feature required an efficient virtual machine allocation policy. The virtual machine allocation involves a large number of variables, and rapidly increasing the demand for resources makes it an NP-complete problem. Many heuristics and meta-heuristic-based dynamic resource allocation mechanisms are designed to generate the optimal VM allocation sequence considering the multifariousness Quality-of-Services (QoS) requirements in the cloud environment. However, most existing VM allocation policies failed to balance various Quality of Service (QoS) parameters. This work elaborated on the VM allocation problem and proposed an enhanced Genetic Algorithm (GA) to solve the optimal resource schedule to allocate requested tasks on a given number of virtual machines efficiently in cloud computing while considering various QoS parameters. By achieving the balance between the performance parameters, our developed algorithm makes ensures that the resource utilization reaches the maximum limit. Our extensive simulation experiment on the proposed algorithm is performed using the CloudSim simulation toolkit, and the comparison results prove the effectiveness against the energy and makespan parameters.
Writer Identification from Offline Handwriting Images in Urdu Script with Dense-Net: A Deep Learning Approach

Handwriting serves as a valuable tool in various organizational processes, such as recruitment, interviewing, team-building, counseling, and career planning. Even identical twins with identical genes exhibit distinct handwriting styles. Leveraging this uniqueness, systems that can classify handwriting can determine the identity, age, handedness, nationality, and other details of the writer, making it an effective and reliable indicator of personality and behavior. Writer identification systems, for instance, can identify writers based on their distinct handwriting patterns. Manual extraction of handwriting features from a large dataset was once nearly impossible, but thanks to deep learning algorithms, it is now feasible to automatically extract features from documents and accurately predict the author's identity. However, there is a dearth of such systems for Urdu script, as evidenced by the literature survey. Therefore, in this paper, we propose a CNN-based model, Dense-Net, as a writer identification system trained and tested on an offline Urdu handwritten dataset contributed by 418 Urdu writers. Our model achieved an impressive overall accuracy of 99.20%, outperforming all existing methods for writer identification systems.
Classification of Breast Cancer using a Novel Neural Network-based Architecture

Breast cancer is currently one of the deadliest types of cancer, and the death rate has considerably grown as a result of a lack of knowledge about the disease, its symptoms, and prevention techniques. Therefore, early detection at a nearly stage is crucial and vital in order to limit the progression of cancer. Malignant and benign breast cancer are the two further subtypes. In order to categorise the different forms of breast cancer, an automated system with logistic regression and neural network is proposed. The classification of the breast cancer data utilises the DNN with various levels of processing. The Digital Database for Screening Mammography (DDMS) dataset was used in the proposed study on the Kaggle platform. The dataset was divided into various train-test splits. On the basis of accuracy, ROC AUC cuve the system’s performance is evaluated. The outcome demonstrates that proposed-2 outperforms in a comparable sense with a training accuracy of 98.99% and testing accuracy of 98.83%.
A Survey Paper on Browser Extensions to Detect Web Attacks

Browser extensions that have been specifically designed to detect online attacks are able to add a layer of security for users as they browse the internet. Their popularity can be attributed to the ever-increasing need for personalized browsing experiences and the rising demand for enhanced security measures to combat web-based threats. Recently, web attacks and their mitigation have become a trending topic in modern research. Acquiring knowledge about how attacks are mitigated with the assistance of browser extensions would enable researchers to better understand the results of their scientific investigations and thus enhance reproducibility. A comprehensive review of the literature was conducted to assess the current best practices for detecting browser attacks using web extensions. This review provides an overview of the available extensions used for identifying attacks. Our analysis primarily focuses on OWASP attacks that are currently being detected while also highlighting the limitations of existing extensions in the market and their technical approaches, which can be categorized as either traditional or machine learning-based models.
Exploring Multilingual Indian Twitter Sentiment Analysis: A Comparative Study

Sentiment analysis is a valuable method for analyzing texts and understanding the opinions, attitudes, and emotions expressed toward different subjects. Its application to large-scale data, particularly on social media platforms like Twitter or Facebook, offers valuable insights. However, Twitter data presents unique challenges due to its complex and noisy nature, as well as its intricate syntactic and semantic structures. Moreover, analyzing sentiment becomes even more difficult when dealing with multimodal Twitter data in Indian languages. In order to address these obstacles, it is essential to create a robust framework capable of effectively processing Twitter content across diverse languages and encodings. To ascertain the most optimal methodology, we carry out extensive assessments employing multiple variations of multilingual and single-language models, ultimately relying on the evaluation outcomes to guide our selection process. Our aim is to create a comprehensive model capable of effectively characterizing all aspects of Twitter data. Through a comprehensive examination of the data, we gain a deeper understanding of the sentiments expressed across various languages used on Twitter in India. These results offer valuable insights into the diverse range of emotions, opinions, and attitudes expressed by Indian Twitter users, enabling a more nuanced understanding of public sentiment in this multilingual context.
Propositional Logics for the Lawvere Quantale

Lawvere showed that generalised metric spaces are categories enriched over $[0, \infty]$, the quantale of the positive extended reals. The statement of enrichment is a quantitative analogue of being a preorder. Towards seeking a logic for quantitative metric reasoning, we investigate three $[0,\infty]$-valued propositional logics over the Lawvere quantale. The basic logical connectives shared by all three logics are those that can be interpreted in any quantale, viz finite conjunctions and disjunctions, tensor (addition for the Lawvere quantale) and linear implication (here a truncated subtraction); to these we add, in turn, the constant $1$ to express integer values, and scalar multiplication by a non-negative real to express general affine combinations. Quantitative equational logic can be interpreted in the third logic if we allow inference systems instead of axiomatic systems. For each of these logics we develop a natural deduction system which we prove to be decidably complete w.r.t. the quantale-valued semantics. The heart of the completeness proof makes use of the Motzkin transposition theorem. Consistency is also decidable; the proof makes use of Fourier-Motzkin elimination of linear inequalities. Strong completeness does not hold in general, even (as is known) for theories over finitely-many propositional variables; indeed even an approximate form of strong completeness in the sense of Pavelka or Ben Yaacov -- provability up to arbitrary precision -- does not hold. However, we can show it for theories axiomatized by a (not necessarily finite) set of judgements in normal form over a finite set of propositional variables when we restrict to models that do not map variables to $\infty$; the proof uses Hurwicz's general form of the Farkas' Lemma.
Strengthening Sustainable Development Goals – SDG 9 Concerning Flooding in Malaysia

The Sustainable Development Goals (SDGs), commonly known as the Global Goals, were approved by the United Nations in 2015 as an international call to eradicate poverty, safeguard the environment, and guarantee that by 2030, everyone may live in peace and prosperity. The 17 SDGs are interconnected; they acknowledge that actions in one area will impact others and that sustainable development must balance social, economic, and environmental sustainability. SDG 9 aims to develop resilient infrastructure, advance sustainable industry, and support innovation. This is because economies with a diverse industrial sector and resilient infrastructure suffered less harm and recovered faster. Malaysia experiences flood disasters more frequently than any other ASEAN nation, coming in second after Indonesia. According to a special report on flood impact in Malaysia by the Department of Statistics, overall losses for public assets and infrastructure recorded Rm 2 billion in 2021 and Rm 232.7 million in 2022. Hence, this data reveals that public assets and infrastructure record the highest losses due to flood occurrences in Malaysia. The paper identifies the resilient infrastructure currently existing in Malaysia in response to flooding. The research implements a qualitative approach by examining secondary sources such as studies, print, and online sources. In addition, open-ended questionnaires were used to interview key stakeholders to better understand the situation. This study summarizes that flood-resilient infrastructures require further implementation to safeguard people and the environment. Given the frequency and severity of flooding in Malaysia, it is necessary to concentrate more on SDG 9. Further research is recommended to explore the issues in the implementation of SDG 9 in Malaysia.
The prediction of anyons: Its history and wider implications

Prediction of “anyons,” often attributed exclusively to Wilczek, came first from Leinaas and Myrheim in 1977, and independently from Goldin, Menikoff, and Sharp in 1980-81. In 2020, experimentalists successfully created anyonic excitations. This paper discusses why the possibility of quantum particles in two-dimensional space with intermediate exchange statistics eluded physicists for so long after bosons and fermions were understood. The history suggests ideas for the preparation of future researchers. I conclude by addressing failures to attribute scientific achievements accurately, both inadvertent and intentional. Such practices disproportionately hurt women and minorities in physics.
Problematika Penerapan Accrual Accounting Pada Lembaga Keuangan Syariah

Today, there is much debate about the basic assumptions that should be used in the financial statements of Islamic entities, namely the accrual basis and cash basis. This study will reveal that the accrual basis which is widely applied in the preparation of financial statements turns out to have many gaps to manipulate data, so that it will cause earnings management to arise. The unpopular cash basis applied in the preparation of financial reports is more able to reveal real conditions, income and expenses. This study also discusses the reasons for using the accrual basis in the framework for preparing and presenting Islamic financial statements based on PSAK 101 whereby Islamic entities must prepare financial statements on an accrual basis. This research will also show the many problems that arise in the application of accrual accounting in Islamic financial institutions today.
DIFFERENCE IN THE COMPRESSIVE STRENGTH BETWEEN THE SURFACE AND THE CENTER OF CONCRETE MEMBERS IN A NUCLEAR REACTOR BUILDING AFTER LONG-TERM USE

A research project on the aging concrete of Hamaoka nuclear power plant (NPP) Unit 1―it is currently decommissioning―, was started in 2016. The objective of this project is to create a concrete database of material properties, and employ non-destructive evaluation (NDE) method to construct a better soundness method. For this purpose, approximately 200 core samples were obtained from various concrete structural members, exceed 1.0m owing to seismic performance requirements. It was that the compressive strength of thick members showed a convex-shaped trend, indicating that the center of the cross-section was larger than the surface (Ref. [1]). In ordinary environmental conditions, the average compressive strength of all specimens obtained from seismicresistant 1.0 ~ 1.5m thick walls is approximately 10% larger than the average compressive strength of surface element regardless of these surface coatings.
A LARGE-SCALE MODIFICATION WORK OF SUPPRESSION CHAMBER AND INTERNAL STRUCTURES AT ONAGAWA NPP

The suppression chamber and its internal structures, that is a vent system consisted of vent pipe, vent header, and downcomer at Onagawa Nuclear Power Plant Unit 2 of Tohoku Electric Power Co., Inc., were subjected to a significant increase in the acceleration response level applying to the seismic design due to the enforcement of the new regulatory standards in 2013. In order to ensure seismic resistance to the acceleration response level, a large-scale modification work of the components was required in addition to the upgrading of the evaluation method.
THE STUDY ON AN AUTOMATIC LAYOUT METHOD FOR EQUIPMENT IN NUCLEAR POWER PLANT

Within the development of the technology related to nuclear power plant, more and more attention have been paid on the design efficiency and reasonability. However, at present, the layout design of equipment in a typical nuclear power plant has always been carried out relying heavily on engineering experience, which is limited in the searching for candidate schemes and time-consuming. Thus, we propose an automatic layout method to realize the effective arrangement of equipment in nuclear power plant, in which some engineering concerns can be taken into account.
Effectiveness of a Service Robot Referencing Joruri Puppet in Attracting Visitors at a Large-Scale Exhibition and Measurement Using 2D-LiDAR

Our goal is to create a service robot that can effectively express itself by incorporating the choreography that have been inherited throughout the ages. In this paper, we use a robot based on Joruri puppet with integrating 2D-LiDAR, and verify its effectiveness in attracting visitors at a large exhibition. Then, we will attempt to measure the effectiveness, the color of the kimono, the effect of sound, and visitors' movements such as taking pictures. At the same time, we verify the possibility of using 2D-LiDAR for measurement.
Human Gender and Age Estimator Using Local and Global Features with Machine Learning

The study focuses on face processing and seeks to provide a solid framework for face, age, and gender recognition. Although most previous methods rely on a visual descriptor that frequently captures a single property of picture areas, our approach incorporates various feature types, HOG, and LBP descriptors. To do this, feature selection generates more accurate and comprehensive face descriptions. Following the computation of descriptors, a compact collection of features is selected, allowing face image processing for age and gender analysis. The experimental findings show that our age and gender outperform previously suggested approaches tested on three demanding datasets, including face objects with varying expressions and degrees of lighting. This accomplishment indicates the usefulness of our suggested technique and enables us to attain more precision than current state-of-the-art technologies.
When Supply Chain Security Meets Blockchain: Applications and Challenges

Supply chain security has always been an important issue concerned by enterprises and consumers. In the traditional supply chain system, security issues such as low transparency of transactions, low security of data storage, and difficulty in guaranteeing product safety have affected the healthy and orderly development of the supply chain. In recent years, as an emerging technology, blockchain has received more and more attention due to its characteristics, such as distributed storage and tamper-proof data. Several works have now started to study how blockchain technology can be applied to ensure supply chain security. In this paper, we first provide brief introductions to blockchain and the supply chain. We organize and summarize the existing literature on the application of blockchain in supply chain security management, which mainly includes logistics security, data security, and financial security. We also discuss the advantages and challenges of the application of blockchain technology in the supply chain.
“Why Did You Say That?”: Understanding Explainability in Conversational AI Systems for Older Adults with Mild Cognitive Impairment (MCI)

As Conversational AI systems evolve, their user base widens to encompass individuals with varying cognitive abilities, including older adults facing cognitive challenges like Mild Cognitive Impairment (MCI). Current systems, like smart speakers, struggle to provide effective explanations for their decisions or responses. This paper argues that the expectations and requirements for AI explanations for older adults with MCI differ significantly from conventional Explainable AI (XAI) research goals. Drawing from our ongoing research involving older adults with MCI and their interactions with the Google Home Hub, we highlight breakdowns in conversational flow when older adults seek explanations. Based on our experience, we conclude with recommendations for HCI researchers to adopt a more human-centered approach as we move towards developing the next generation of AI systems.
Fundamentals of cloud computing: a study guide

The study guide reveals the basic concepts of cloud computing technologies. The process of deploying a cloud-oriented environment in secondary and higher education institutions using Google Workspace services is considered. The guide discusses the capabilities of platforms that allow you to deploy an academic cloud according to the "infrastructure as a service" model. A series of laboratory exercises has been developed to help students master the practical skills of managing cloud services. For students of pedagogical speciality "014.09. Secondary Education (Informatics)".
Kinesthetic and Visual Feedback in Online Vocal Lessons

The purpose of the research is to analyze the possibilities and impact of kinesthetic and visual feedback on the quality of online vocal lessons. Research methodology. The article uses the following methods: experimental, retrospective and observation. The scientific novelty of the study of kinesthetic and visual feedback in online vocal lessons is to examine the effectiveness of these two methods of providing feedback in the online environment. Such a study can help to establish scientifically based approaches to the use of kinesthetic and visual feedback in online classes and provide a basis for further development and improvement of the methodology of vocal pedagogy. Conclusions. Kinesthetic and visual feedback allows students to feel their bodies and voices, which helps to identify problematic moments and shortcomings in technique. With these sensations in mind, students can work on correcting incorrect movements, controlling their breathing, and improving their technique. In addition, kinesthetic and visual feedback can serve as a source of motivation and encourage students to exercise self-discipline. Through conscious performance and seeing their progress on video, students can feel motivated to continuously improve their technique and achieve their goals in vocal performance. Thus, kinesthetic and visual feedback contributes to effective online vocal training, the development of technique, consciousness and communication between student and teacher, as well as encourages motivation and self-discipline.
Review of the Round Table – Presentation of the collective monograph “From Comparative to Intercultural Philosophy”

On March 21, 2023, at the Institute of Philosophy of the Russian Academy of Sciences, Department of Oriental Philosophies held an online interuniversity round table dedicated to the presentation of the collective monograph “From Comparative to Intercultural Phi­losophy” published in the academic series “Comparative Philosophy” at the end of 2022. The broadcast was attended by 22 participants, among whom there were the authors of the articles included in the book, as well as Russian and foreign colleagues, scientists and university professors. There were 6 keynote speakers and 2 invited guests. The meet­ing discussed the monograph, its strengths and weaknesses, relevance, prospects, possible directions for further research, expectations, as well as how it is possible to use the results of this work in teaching.