Reviewerly/exHarmony huggingface dataset:
DEVELOPMENT OF ANDROID-BASED PETA (PENJELAJAHAN MATEMATIKA) LEARNING MEDIA ON SPEED LEARNING MATERIALS AT A FIFTH-GRADE ELEMENTARY SCHOOL

This paper describes the developmental process of PETA (Penjelajahan Matematika) learning media based on the Android application on speed learning materials at a fifth-grade elementary school, determines the feasibility of the application through the validation process of experts, and finds out the compatibility of applications in several devices with different Android versions. The research uses a research and Development (RnD) method with the ADDIE model (Analysis, Design, Development, Implementation, Evaluation). The results of the research are (1) the development of the PETA application is relevant to the needs of the mathematics learning process on speed learning material at class V SD, (2) the design of the PETA application is adopted from the concept of exploring games with various forms of presentation, (3) the PETA application design that has been made is developed into file.apk., (4) the PETA application has the feasibility of the content in the very feasible category with a percentage of 97%, the feasibility of the media in the very feasible category with a percentage of 92.75%, and already fulfilled the standard of portability on adaptability and installability aspects with a testing percentage of 100%, and (5) the final product of the PETA application is developed based on the evaluation results at the testing stage of functional suitability and portability by making minor improvements to the instruction aspects of the use and the additional menu of an "additional information" to make it easier for users to get information about latest updates of the PETA application.
Exploring EFL Students Difficulties and Strategies in Learning TOEFL ITP of Structure and Written Expression

Nowadays the TOEFL test is very important. The TOEFL test is not only to measure English proficiency, but also for the sake of scholarship requirements, and as a requirement to graduate from college. This study explores the difficulties of EFL students in learning the TOEFL ITP of Structure and Written Expression (SWE) and how EFL students overcome difficulties in learning the TOEFL ITP of SWE. This study uses a narrative inquiry design. The data used in this study is qualitative data obtained from information provided by students through questionnaires and interviews. The results of the study show that there are several difficulties for students in learning TOEFL ITP, namely sentences with multiple clauses, sentences with inverted subjects and verbs, problems with prepositions, problems with the form of the verb, sentences with one clause, problems with subject/verb agreement. The way students overcome difficulties in the TOEFL ITP of SWE is to re-learn what has been taught by the tutor, reopen the module, learn from several other references besides the reference given by the tutor, learn from youtube, re-learn with classmates who have mastered the material. , do the practice questions independently. The findings in this research are expected to give an empirical contribution to TOEFL teachers to pay attention to students' difficulties in learning TOEFL ITP, especially SWE.
Examination of the Bi-LSTM Based 5G-OFDM Wireless Network Over Rayleigh Fading Channel Conditions

Fifth generation (5G) wireless networks’ system performance is dependent on having perfect knowledge of the channel state information (CSI). Deep learning (DL) has helped improve both the end-to-end reliability of 5G and beyond fifth generation (B5G) networks and the computational complexity of these networks. This work uses the Bi-linear long short-term memory (Bi-LSTM) scheme to examine the overall performance of the 5G orthogonal frequency division multiplexing (OFDM) technology. The least squares (LS) channel estimation scheme is a famous scheme employed to estimate the fading channel coefficients due to their lower complexity without the prior CSI. However, this scheme has an exceedingly high CSI error. Using pilot symbols (PS) and loss functions, this work has proposed the Bi-LSTM 5G OFDM estimators to improve the channel estimation obtained by the LS approach. All simulation analysis uses convex optimization (CO) software (CVX software) and stochastic gradient descent (SGD). When combined with many PS (72) and a cross-entropy loss function, the proposed Bi-LSTM outperforms the long-short-term memory (LSTM) cross-entropy, LS, and minimum mean square error (MMSE) estimators in low, medium, and high signal-to-noise ratio (SNR) regimes. The computational and training times of Bi-LSTM and LSTM DL estimators are also compared. Because of its DNN design, it can evaluate massive datasets, find hidden statistical patterns and characteristics, establish underlying relationships, and transfer what it has learnt to other contexts. Statistical analysis of the bit error rate (BER) reveals that Bi-LSTM outperforms the MMSE in terms of accurate channel prediction.
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For the emergence of legal consequences, in addition to real factual circumstances, legally permissible assumptions about factual circumstances - legal presumptions and fictions - are of great importance. Inadequate study of the characteristics and correlation of presumptions and fictions causes difficulties in law-making and law-enforcement practice. The difficulty in distinguishing these two phenomena is due to the fact that they are quite similar and closely related. The purpose of this article is to investigate the characteristics and correlation of legal fictions and presumptions. In order to achieve the set goal, the following main tasks were identified: ➢ to study the nature of legal presumptions and fictions, highlighting their commonalities and features, ➢ to study the relationship between legal presumptions and fictions, ➢ to develop of proposals aimed at fixing presumptions and fictions in legal acts and improving their application in practice. The subject of the study was examined with the use of general and special scientific methods, such as dogmatic, comparative legal, logical and legal modelling. As a result of the conducted research, a number of conclusions were made, the main contents of which are as follows: Legal presumption and fiction perform similar functions in the process of legal regulation. Their existence is due to the need to overcome the gaps in the legislation, to eliminate uncertainty in public relations. The meaning of the existence of legal fictions and presumptions is to create, with the help of an assumption, a legal consequence that would normally result from an event or action, they replace legal facts in the service of overcoming uncertainty. Fiction and presumption are beyond reality. They do not exist in the real world, but they are an integral part of the legal reality. At the same time, the analysis of their relationship has shown that, despite existing similarities, these normative generalizations are different. A presumption is rebuttable, and the assumption to be asserted is probable, while a fiction is irrefutable, and a proposition defined by a fiction is false. Presumption is inductive in nature, in the form of incomplete induction, and fiction is deductive. The purpose of using fiction is to recognize as true a assumption that is consciously inconsistent with reality, which does not pursue the goal of establishing the truth, its main purpose is to regulate those relations that cannot be regulated in any other way. Presumptions establish a necessary legal fact based on the probability of its existence. Based on the results of the research was proposed to supplement the Law of the Republic of Armenia on Normative Legal Acts with a new article on the use of legal presumptions and fictions. The proposed addition will have a guiding significance from the point of view of modeling legal norms. With that addition, it will be possible to subject the legislative and sub-legislative field to systematic and comprehensive regulation in terms of the application of legal presumptions and fictions.
Application of distributed solar photovoltaic power generation in highway field

Natural resources are the most critical material basis for national economic and social development, and the key guarantee for improving national viability. With the development of national economy and the progress of society, people’s awareness of the importance of resources is also growing, and the issue of resources has begun to become an important topic of common concern for governments around the world. At a time when non-renewable resources such as coal and natural gas are in urgent need, and energy shortage has gradually become a problem endangering global economic development, more and more countries have begun to actively implement the “sunshine plan”, that is, to develop and utilize sunlight resources to find new impetus for economic and social development. Solar energy resource is a very important resource, which is inexhaustible, non-polluting, and relatively cheap and can be used at will. Vigorously developing and using solar energy is the most effective way to solve the shortage of resources and achieve sustainable economic development. Therefore, the application in the highway field is very necessary to promote the construction of distributed photovoltaic power generation system.
Efficient and reliable data extraction in radiation oncology using python programming language: A pilot study

In recent years, data science approaches have entered health-care systems such as radiology, pathology, and radiation oncology. In our pilot study, we developed an automated data mining approach to extract data from a treatment planning system (TPS) with high speed, maximum accuracy, and little human interaction. We compared the amount of time required for manual data extraction versus the automated data mining technique.A Python programming script was created to extract specified parameters and features pertaining to patients and treatment (a total of 25 features) from TPS. We successfully implemented automation in data mining, utilizing the application programming interface environment provided by the external beam radiation therapy equipment provider for the whole group of patients who were accepted for treatment.This in-house Python-based script extracted selected features for 427 patients in 0.28 ± 0.03 min with 100% accuracy at an astonishing rate of 0.04 s/plan. Comparatively, manual extraction of 25 parameters took an average of 4.5 ± 0.33 min/plan, along with associated transcriptional and transpositional errors and missing data information. This new approach turned out to be 6850 times faster than the conventional approach. Manual feature extraction time increased by a factor of nearly 2.5 if we doubled the number of features extracted, whereas for the Python script, it increased by a factor of just 1.15.We conclude that our in-house developed Python script can extract plan data from TPS at a far higher speed (>6000 times) and with the best possible accuracy compared to manual data extraction.
Reducing Water Related Risks in the Lower Danube Through Nature Based Solution Design: A Stakeholder Participatory Process

Abstract This chapter describes the research conducted in the Lower Danube case study to understand the role of natural assurance schemes in complex natural, economic and social contexts. In order to improve the accuracy, replicability and the scalability of the natural assurance scheme to be developed and potentially implemented, we have concentrated our efforts on the Dabuleni-Potelu-Corabia (DPC) enclosure, a land reclamation area exposed to water-related risks. NAIAD Project proposed the implementation of a wetland restoration project by designing a Nature Based Solution (NBS) for dealing with flood risk. The implementation in the Romanian case study aimed at providing support to facilitate both local collaboration between the different stakeholders and the engagement of these local stakeholders in decision-making and policy setting, by integrating their (local) knowledge with the results of scientific models. The entire process aimed to design intervention scenarios based on NBS implementation and the key co-benefits to be produced, followed by the assessment of its effectiveness. Furthermore, to assess the DPC enclosure vulnerability to water-related hazards, we performed an economic assessment based on the literature review and the development of a GIS infra-territorial indicator methodology. The GIS-based indicators can support the lack of insurance data, with the objective to define the most exposed areas. The literature review together with the GIS analysis were able to provide a reliable cost estimation of the damage related to destroyed and affected dwellings. This indicator should be a tool to help the decision-making process to assess the case study vulnerability and to select the more appropriate NBS.
A Long Short Term Memory Machine Learning Technique for Covid-19 Patient Count Prediction

The Corona-virus H19 pandemic is quickly spreading throughout the globe. Every three to four times, waves occur and have a major effect on people’s lives. Other illnesses including covid disorders are misdiagnosed in this setting. There is no reliable statistics on the total number of covid patients in the nation, and no system exists to track them. This prevents the patients from receiving the necessary care and treatment. The number of patients in a given dataset may be determined with more precision using AI methods. In this article, we show how to forecast how many patients will be included in the Covid-19 database by using an adaptive method. Python spyder is used to run the simulation.
Residual Steganography: Embedding Secret Data in Images using Residual Networks

All the residing image steganography methods depicts the problem of degradation, low capacity. In order to overcome this problem, we introduce an encoder decoder based residual network along with convolutional neural network to conceal one image into another. This paper presents a novel approach to image steganography in the residual domain. Traditional image steganography techniques typically involve embedding information directly into the image data, which can often lead to noticeable artifacts or degradation of the image quality. To evaluate the effectiveness of our approach, we conducted a series of experiments using a large dataset of natural images. Our results show that our approach is able to conceal a significant amount of secret data with minimal impact on the visual quality of the image. Moreover, our method is robust against various steganalysis techniques, making it suitable for secure communication applications. Overall, our proposed approach represents a promising direction for image steganography in the residual domain.
Skin Segmentation and SVM for Identification and Spotlighting of Hand Gesture for ISLR System

People who have hearing loss utilise sign language to communicate. It enables gestures and spoken language to communicate with one another by connecting letters, words, and phrases. The hearing-impaired community could benefit from a machine that can translate spoken language into sign language English, allowing them to interact with the general public. They will be helped to improve their skills and become more conscious of what they can do to advance humanity. In this study, we attempted to create an automated system that could identify sign language in challenging environments. It is possible to extract the part of the signer’s hand that matches their skin tone from a video of them signing. From the hand image, Extractive and categorical features that can identify the sign are used. Support vector machine is used for the categorization. India is diverse in terms of religion, culture, and language. There is no recognised sign language in India. There are numerous ISL dialects with verbal variations spoken in India. There are numerous varieties of Sign language, even in Kerala, a small state. In order to recognise the signs unique to our region, we are working to construct a SLR system.
Underwater Image Enhancement using Convolutional Block Attention Module

Underwater images include poor contrast, fuzzy features, and colour distortion due to light scattering, refraction and absorption by unwanted dust particles in water. This research demonstrates that assigning the appropriate receptive field size context depending on the traversal scope of the color channel can result in a significant performance boost for the objective of underwater image enhancement. It’s critical to reduce non-uniform multiple contextual elements, and also boost the model’s representational potential. So to dynamically modify the learnt multi-contextual characteristics, we included an attentive skip method. The suggested framework is improved via pixel wise and feature based cost functions. Experiments and comparisons with existing deep learning models and conventional approaches validate the framework for underwater image enhancement. The proposed framework is superior according to comparison results.
A Support Vector Machine Learning Technique for Detection of Phishing Websites

In the field of cyber attacks, phishing is considered to be one of the pioneers. Phishing sites are websites that look and sound like legitimate ones but are really scams. For the purpose of duping the target into thinking its real, they are manufactured. The phishing scams of today are more complex and dangerous than ever before. Phishing website detection may be achieved by using machine and deep learning approaches based on artificial intelligence. It is possible to use the ML classification algorithm for phishing website detection. This paper presents a support vector machine learning technique for the detection of phishing websites. Simulation is performed using spyder IDE. Simulation results provide better accuracy.
Cybercrime And Senior Citizens In India – A Comparative Study Of Legal Frameworks Within Cyberspace Globally

Cyber space regulation is that arena which is growing very rapidly, due to multidisciplinary approach in tackling innovation, technological updation, persistent cybercrimes emerging from interactions within cyberspace. This doctrinal paper is a critical study and analysis of challenges faced by stakeholders, jurisprudence of crimes committed within cyberspace, effect on vulnerable group ‘senior citizens’ as potential victims since emergence of Information Communication Technology. Aimed to study international treaties, national policies of QUAD members, statutes, community help programs, emerging new theories of cyberspace jurisprudence, adopted by netizens, challenges that are being faced by all the stakeholders to influence cyberspace, socio economic legal disabilities within a sections of society, principles or prepositions thus found, important facets of cybercrime comprehensively to contribute in the development of Cyber Security legislations and Regulatory Framework of India, for a secure safe cyberspace as severity of the issues, challenges in cyber victimization against senior’s can permeate legal gaps as previous research efforts have largely revolved around women and children, while neglecting them. Victimization in cyberspace is pushing seniors further away from use of technology confidently so as to kick off original plans for equalitarianism as envisioned in our constitution.
Communication

In this chapter, we will look at how the PIC can communicate with different devices, such as another PIC, some extra EEPROM, a terminal device, a Real-Time Clock (RTC), and many others. There are three main methods for communicating with these devices that PICs use and they are
Etiquette of female weblog

The issue of gender influencing the language has long been discussed in linguistics. But a great number of researches are focused on making a list of speech means, the use of which must reflect the specifics of female and male communicative types. From our viewpoint it is more fruitful to make a complex study of speech behavior in a single society united by some signs, outlining typical communicative scenarios. In this relation it is methodically correct to outline the etiquette line and to consider its peculiarities to determine the specifics of female speech behavior in the web. As an empiric material we used female weblogs as a unique deflection of the traditional genre of personal diary. The weblog is different from its offline version by the initial orientation on the wide (occasional, it is better to say) audience. Gradually the author forms a range of regular readers — subscribers who leave comments under each publication (or post) in the log. Inside each log along with typical for all female weblogs etiquette rules there exist own ones, due to the peculiarities of speech and non-speech self-presentation of the weblog author. As the study shows, it is typical for female speech behavior to strictly follow the selected rituals, to find violations of etiquette and to struggle with such violations, to long to psychological comfort as a main communicative value, to follow topical unity inside a single dialogue cycle, emotional dynamic which leads to the appearance of specific aggressive forms in speech — catfights, discussion of private life, entering the private sphere.
Context parameters retrieval framework from electronic healthcare record through biomedical NLP for clinical support

This paper presents architecture for extracting context features from electronic health records (EHR) to design a clinical support system using transfer learning for natural language processing (NLP). The system is trained to provide a supporting summary to the medical practioners on the basis of the ICD 9 codes and respective symptoms of the patient. The BioALBERT model has been trained over biomedical corpora and the proposed model uses improvised parameter sharing techniques and requires less physical memory. The theoretical analysis of the proposed system is supported by the experimental analysis. MIMIC-III database has been used to fine train the proposed models and to assess the efficiency and efficacy of the proposed work. This study introduced a context-aware approach for extracting useful context from EHR, which can be used to acquire a basic understanding of the treatment path.
ANALISIS PENGELOMPOKAN BANK UMUM KONVENSIONAL DI INDONESIA DENGAN METODE K-MEANS CLUSTERING

The purpose of this study is to determine the grouping of conventional commercial banks in Indonesia which include healthy, unhealthy and unhealthy bank clusters. Data analysis was performed using the K-Means Clustering methodBased on the data analysis, the results are (1) there are three conventional commercial banks which are categorized as healthy bank groups or clusters, namely: Bank Rakyat Indonesia, Bank Mandiri and Bank Central Asia; (2) there are nine conventional commercial banks that fall into the category of unhealthy bank groups or clusters, namely: West Java and Banten Banks, BTPN Bank, CIMB Niaga Bank, Danamon Bank, DBS Indonesia Bank, Maybank Indonesia Bank, OCBC NISP Bank, Panin Bank and Permata Bank; (3) there are 32 conventional commercial banks which are categorized as unhealthy bank groups or clusters, namely: Bali BPD Bank, Yogyakarta Special Region BPD Bank, DKI Bank, Central Java Bank, East Java Bank, West Kalimantan Bank, East Kalimantan Bank, Lampung Bank , West Sumatra Bank, Papua Bank, South Sulawesi and West Sulawesi Bank, North Sulawesi Bank, North Sumatra Bank, ANZ Indonesia Bank, Bumi Arta Bank, Capital Indonesia Bank, CCB Indonesia Bank, Commonwealth Bank, CTBC Bank Indonesia, Ganesha Bank, Bank HSBC Indonesia, Bank ICBC Indonesia, Bank Ina Perdana, Bank KB Bukopin, Bank KEB Hana Indonesia, Bank Mega, Bank Multiarta Sentosa, Bank Resona Perdania, Bank of America, Bank of China, Citibank and Standard Chartered.