Reviewerly/exHarmony huggingface dataset:
A Hybrid Convolution Neural Network with Channel Attention Mechanism for Sensor-Based Human Activity Recognition

Abstract Research studies in machine intelligence and ubiquitous computing have become increasingly intrigued by human activity recognition based on wearable sensors. In recent decades, various learning-based approaches have been significantly applied for human activity recognition to determine successful models for identifying human behaviors. Because of the robust feature extraction abilities of deep learning algorithms, their conveniently extracted features acquire excellent detection capability. Nevertheless, most accomplished deep learning approaches have been established for complicated models with several hyperparameters. This paper investigates the present human activity recognition study on deep learning techniques and discusses appropriate recognition strategies. First, we utilized many convolutional neural networks to determine the effective architecture of human activity recognition. Subsequently, we designed a hybrid convolutional neural network that was implanted with a channel attention mechanism to capture deep spatio-temporal characteristics hierarchically and differentiate human movements in everyday life. Investigations on the UCI-HAR and WISDM datasets revealed that our suggested model, which included cross-channel multi-size convolution transformation, surpassed previous deep learning structures with 98.92% and 98.80% accuracy, respectively. The proposed model outperforms state-of-the-art approaches in terms of total accuracy, as indicated by the research findings.
Vectorial bent functions and Linear Codes from Quadratic Forms

Abstract In this paper, we study the vectorial bentness of an arbitrary quadratic form and construct two classes of linear codes of few weights from the quadratic forms. Let $q$ be a prime power, $m$ be a positive integer and $Q:\F_{q^m}\rightarrow\F_q$ be a quadratic form. We first show that $Q$ is a vectorial bent function if and only if $Q$ is non-degenerate and $(q+1)m$ is even (i.e. either $q$ is odd or $m$ is even). Furthermore, if $2\mid (q+1)m$ and $Q(x)= \sum\limits_{i=0}^{m-1} \Tr_{q^m/q}(a_i x^{q^i+1})\ (a_i\neq 0)$, we show that $Q$ is vectorial bent if and only if the associated additive polynomial $L_Q(x)=\sum_i (a_i + a_{m-i}^{q^{i}}) x^{q^i}$ is a permutation polynomial over $\F_{q^m}$. If there is only one $a_i\neq 0$, we recover the constructions of Sidelnikov, Dembowski-Ostrom and Kasami of quadratic vectorial bent functions. We then construct two classes of linear codes $\C'_Q$ and $\C_Q$ over $\F_q$ from $Q$ and completely determine the weight distributions of our codes, showing that they are two-, three- or four-weight codes and contain optimal codes satisfying the Griesmer and Singleton bounds. 2010 Mathematics Subject Classification. 94B05, 15A63, 94A60.
Authorization Scheme for Secure Data Retrieval Using Lls-haval With Gkld-ecc in the Cloud

Abstract The cloud platform is the best choice to provide more space for storing and transmitting data from one location to another in a fast manner for internet users. Nevertheless, while using cloud storage, several risks like data leakage and external attacks arise, especially owing to unauthorized users. Thus, to tackle these problems, this paper proposes Cued Click Points (CCPs) of the fused image with Galois KullbackLeibler Divergence-Elliptic Curve Cryptography(GKLD-ECC) and Log Left Shift-Hash of Variable Length(LLS-HAVAL) for secure data uploading along with retrieval in the cloud. Firstly, the users register their details in the database with the multiple CCPs on the fused image. During registration, the user should login with the provided details for uploading or retrieving data. Next, by employing GKLD-ECC in the cloud, Data Owners (DOs) securely upload data. Afterward, hashed access policy with LSS-HAVAL hash is created based on the features and attributes selected by the Interpolated-Sun Flower Optimization Algorithm (I-SFOA) to enhance the authorization process; also, the hashcode is shared with selected data users. The data can be retrieved successfully if the hashcode gets matched during retrieval. The performance of the proposed framework is verified by achieving a higher security level and lesser time.
SPECIFICS OF IMPLEMENTATION OF THE ASYMMETRIC ENCRYPTION ALGORITHM ON ELLIPTIC CURVES

The modern world is inextricably linked with information technologies. Every year, the Internet expands, and thousands of sites that provide various services have been created. With a stable Internet connection, you can solve most of life's problems without leaving home – from ordering dinner to receiving government services. Nowadays, when educational institutions and businesses have been forced to switch to a remote form of work due to the pandemic, the issue of information protection is more pressing than ever. There are many cryptographic algorithms that have protected our information on the Internet for decades, but as computing power grows, so do the demands on such algorithms. Some of them have not coped with the challenge of time and are no longer used, which is why there is a need to develop new algorithms that use more complex mathematical principles and provide a higher level of security. And there is a whole class of algorithms that use the principles of elliptic curves to provide greater security with relatively lower use of system resources.
IDENTIFICATION AND MANAGEMENT OF RISKS IN THE PROJECT MANAGEMENT OF THE DEVELOPMENT OF SOFTWARE PRODUCTS

Software development is one of the most difficult areas of intellectual activity for forecasting and planning. By their nature, digital products are intangible, and software development projects often involve many stakeholders. Software development processes typically include several stages, including design, documentation, programming, and testing, all of which require a high level of professional, technological, and managerial knowledge. Due to the complex nature of digital projects, from the very beginning of the implementation of any software project, it is necessary to take into account and manage a wide range of risks, which are one of the most important factors affecting the success of a digital project. The work analyzes and classifies project risks, conducts a qualitative and quantitative identification of the main risks for the selected subject area, the Crawford card method was used to work with project risks, the risks were ranked, a responsibility matrix for the project was created, the workload of the project resources was analyzed, and the project was optimized.
The Attitudes of Secondary School Teachers toward the Utilization of Microcontroller in The Classroom

The goal of this study was to determine the level of smart electronic device microcontroller development boards were used as teaching tools (BBM) in Penang secondary schools for the teaching and learning of the subject of design and technology (RBT).The effectiveness of using microcontroller development boards as instructional aids and tools (BBM) based on digital technology might enhance the quality of instruction and learning in electronic design provided by RBT teachers.As a result, this article describes the conclusions of the attitudes of RBT teachers towards fuel usage that were based on a microcontroller development board.To conduct the study, 128 RBT teachers who teach electronic design to Form 2 and Form 3 students in secondary schools were given questionnaires.Statistical Package for the Social Sciences (SPSS) Version 26 software was used to perform a descriptive analysis of the data to determine the frequency distribution, percentage, and mean comparison.T-tests were also run to determine whether there was a gender difference.With an average score of 3.93, the results demonstrate that Form 2 and Form 3 RBT teachers had a moderate attitude regarding the employment of microcontroller boards.The t-test results, however, revealed no gender differences between the RBT teachers' use of the microcontroller board.The same research topics were recommended to be broadened to include students in the future and explore the relationship between teachers' attitudes and the use of microcontrollers from a behavioral perspective in Malaysian classrooms.
Biomonitoring of heavy metal signatures in urbanized contaminated ecosystems of Southern India: A case study using raptor, <i>Athene brama</i>

Background: Heavy metals remain tenacious in the environment which further leads to emerging bioaccumulation, rising through the food chain. Birds are great environmental sentinels that can spot even the smallest changes. The aim of this study was to determine the quantity of heavy metals, specifically Ni, copper (Cu), aluminum (Al), titanium (Ti), and Se, in the feathers, liver, kidney, muscle, and bones of spotted owlets (Athene brama), which were opportunistically collected from a variety of contaminated ecosystems in the urbanized landscapes of Visakhapatnam on India’s East Coast. Methods: Opportunistic sample collection was done followed by necropsy. Following standard protocols, organic samples were digested by adding 10 mL HNO3 , 5 mL of HClO4 , and 2 mL of H2 O2 to eliminate the excess organic matter. Results: Based on the GIS data, raptors were shown to occur in production landscapes and mostly in urban spaces, and currently, are under several anthropogenic threats. The findings showed that the sequence of heavy metal concentrations was Ti&gt;Al&gt;Cu&gt;Ni&gt;Se, but the tissues of birds in which metals accumulated are in the following order: liver&gt;kidney&gt;bones&gt;muscle. The tissues of A. brama showed the signs of significant concentrations of Ti and Al. The results obtained were analyzed using Kruskal-Wallis non-parametric test. Conclusion: Heavy metal studies on various matrices of the environment have been performed over time with certain metals either neglected or not being documented properly. The main finding of this study is the first report of Ti and Al in bird species from India as there is a lacuna in this context.
Vertical Fragmentation Impacts in the Performance and Management Database Health Information System (Comparative Study of Fragmentation Vertically Algorithm)

The cost and the speedness of data access on a query depend on the numbers of attributes. In fact the query function will search for data from various attributes in the database table, whereas not all the attributes are needed on the accessed tables. To avoid the not necessary attributes, Vertical Fragmentation methods can be used. The purpose of this research aims to compare the vertical fragmentation of database tables using Bond Energy algorithm and Graph-Based Vertical Partitioning algorithm to create a distributed database. The results of both algorithms will determine which attributes will be avoided. Then, the results of the two algorithms are compared and evaluated using Partition Evaluator to show the amount of the access data costs that are used, so to find which is the better algorithm to be applied. Based on some experimental results, GBVP algorithm showed a better performance with higher Partition Evaluator value respectively at 4,98; 10,41; 17,95; 4,64; and 14,07 as well as the lower value of execution time respectively at 0,003; 0,002; 0,002; 0,002; 0,003 and 0,003.
A Novel Automated Requirements Prioritization and Selection Model Based on Requirements Weights and Stakeholder Importance

This paper proposes a novel approach to select and prioritize the requirement items in the requirement document. We use an automated requirement method that collects requirements from different stakeholders groups and categories. The new approach is based on assigning an importance factor to each stakeholder group and an acceptance weight for each requirement item. The importance factor and the acceptance weight for each requirement are used to decide whether to include or exclude this requirement item based on a proposed formula. This method is validated by using a case study based on a set of examples, and shows a significant improvement in software requirements process.
Utilizing OCR to Retrieve Text from Identity Documents

Abstract: Nowadays, automatic information extraction can offer a big boost in efficiency, accuracy, and speed in all those business processes where data capture from documents plays an important role. Feeding enterprise information systems with data coming from documents often requires manual data entry that is a long, tedious, and error-prone job. Therefore, automating data entry can save a lot of time and speedup the execution of business processes allowing employees to focus on core and more valuable aspects of their daily activities. Organizations in all industries may require to process myriads of documents with a variety of formats and contents. Such documents are often digitized as images and then converted to text format. This work addresses the text recognition task for identity documents. In particular, identity documents are paramount in business processes related to customer subscription and on boarding where users frequently submit photos taken by smartphones or poor-quality scanned images that are blurred, unclear and with very complex framing angles can be bring off by this process. The text recognition method present in this work is based on transfer learning and Scene Text Recognition (STR) networks. In this project, user can explore how to extract text from a image. In addition, a method to convert multiple images into editable string format with in single click.
A Web Application for Medicine, Food, Books &amp; Cloth Collection and Donation

Abstract: The increase within the quantity of wastage in terms of Medicines, food, books, clothes make the necessity in terms of donation. There are many those that donates food, clothes and books etc. manually by vising completely different places on their own so as to resolve this crisis of needed things wastage in our country. Donations like Medicines, food, clothes and books once given with correct organization &amp; correct management will save many lives and might ease the suffering of the poor and impoverished ones. The “Altruistic ones” acts as a connecting link between the donors and also the NGOs. Considering the number of wastages goes in a very year, donation of unused medicines together with alternative donations like food, books and clothes appear a decent thanks to get out of it because the world is growing quick with the new technologies individuals also are adapting it. Through our web-based application donor from anyplace will present the items at one platform.
Study Materials Providers Application

Abstract: The present Covid-19 situation pandemic forces studentsto attend their classesthrough online. This becomes adifficult task for Students to listen classes as they are facing several distractions.It also leads to Lack of communication between Students and teachers. Even students can’t take notesproperly. Also there will be a library in college which helps thestudents to prepare for their Exams. This Project helps Teachersto upload theirsubject related notes, videos orBooksin the App. This project can help the students in a better way than other apps So Students will feel so easy to study for their exam and also helps to gain Knowledge regarding the Subject. It reduces the teacher’s work as well. Also the app provides the students with live chat and chat assistant feature
Effectiveness of DRTA Strategy in Improving the Reading Skills

This study aimed to evaluate the reading level of the grade 7 learners before and after the conduct of intervention and to determine the effectiveness of Directed Reading-Thinking Activity (DRTA). The reading level was measured using the Philippine Informal Reading Inventory (Phil-IRI) tool where word recognition and comprehension are the variables used. On word recognition, mispronunciation, repetition, substitution, omission, insertion, reversal and transposition are considered miscues while the comprehension level was determined by the scores in the comprehension questions. Reading level is determined by their level in word recognition and comprehension taken from the Phil-IRI formula. The researcher used experimental group of respondents in teaching reading using the DRTA. The respondents are the grade 7 learners of Mindanao State University-Maguindanao who experienced modular learning for the last two school years in elementary, 2020 – 2022. The study was conducted in September to October 2022. The instruments are the reading selection consisting of 134 words and the 10 comprehension questions. Results revealed that there is a significant difference on respondents’ reading level in terms of word recognition with the t-value -8.388 and comprehension with t-value -14.357 after the use of Directed Reading-Thinking Activity (DRTA). It is therefore concluded that DRTA has a positive effect in improving the reading skills of the grade VII.
Internet of Things and Pervasive Computing in Computer-Aided Interactive English Teaching

The environment for training and recognition in Chinese speech recognition under computer-aided design may vary due to the difference of channel and background noise.When the trained model cannot well represent the test data, the recognition rate of the system will drop sharply.The computer-aided design method focuses on using a small amount of Chinese voice data to improve the performance of the system in the test environment.In this paper, we choose the BiLSTM CRF word separation model under deep learning as the improved benchmark model, and combine the Bert language pre-training module to enhance the performance of Chinese word separation.Combining the deep learning sample transfer learning theory and the improved sampling strategy, an adaptive translation model for intelligent Chinese domain is constructed.The experimental results show that Bert Chinese word segmentation model is superior to other word segmentation models in different data sets and has the best word segmentation performance, which can provide reliable support for the application experiment of this model.The test results show that this method can achieve high speech recognition accuracy and good application results.
Analysis of Chinese Speech Adaptive Translation Model Combining Deep Learning Technology and CAD Technology

The environment for training and recognition in Chinese speech recognition under computer-aided design may vary due to the difference of channel and background noise.When the trained model cannot well represent the test data, the recognition rate of the system will drop sharply.The computer-aided design method focuses on using a small amount of Chinese voice data to improve the performance of the system in the test environment.In this paper, we choose the BiLSTM CRF word separation model under deep learning as the improved benchmark model, and combine the Bert language pre-training module to enhance the performance of Chinese word separation.Combining the deep learning sample transfer learning theory and the improved sampling strategy, an adaptive translation model for intelligent Chinese domain is constructed.The experimental results show that Bert Chinese word segmentation model is superior to other word segmentation models in different data sets and has the best word segmentation performance, which can provide reliable support for the application experiment of this model.The test results show that this method can achieve high speech recognition accuracy and good application results.
Factorization of the normalization of the Nash blowup of order $n$ of $\mathcal{A}_{n}$ by the minimal resolution

We show that the normalization of the Nash blowup of order $n$ of the toric surface singularity $\mathcal{A}{n}$ can be factorized by the minimal resolution of $\mathcal{A}{n}$. The result is obtained using the combinatorial description of these objects.
Explaining Variations in Cryptocurrency Ownership: A Cross-country Study

This study investigates how cryptocurrency ownership varies across countries using a set of variables that reflect a country's overall economic circumstances and long-term outlook. Despite the nascent stage of the cryptocurrency market and the concomitant deficiency of the data on cryptocurrency, a multiple linear regression is fitted as an incipient step towards more advanced future studies. Our empirical findings show that cryptocurrency ownership is more likely to grow in a country with (1) a high human capital stock such as citizens' health and education and (2) a rich endowment of legal property rights. It was also inferred from the regression results that cryptocurrency could be an effective hedge against inflation to some extent.
Evolutionary language learning

This essay discusses the development of a language learning program for the local community on the island of San Cristóbal in the Galapagos Islands. Second and third language skills are very important to residents of the Galapagos due to the islands’ dependency on the tourism sector. In pursuit of UN Sustainable Development Goal Four, Quality Education, the Department of Foreign Languages has implemented formal English and French as a Foreign Language education programs for children, adolescents and adults on San Cristóbal. USFQ’s Language Department in the Galapagos is small, but has used bespoke course design based around communicative functions to meet the needs of different groups within the local community.
LANGUAGE LEARNING STRATEGIES AND SOCIAL MEDIA USE AS DETERMINANTS OF ENGLISH LANGUAGE SKILLS SELF-EFFICACY

Self-efficacy in English language is characterized as the ability to communicate in English through the mastery of reading, writing, speaking, and listening skills. It is a factor that appears to influence student achievement; however, it is likely that students do not have many opportunities to use English in their daily lives and have a limited understanding of the language. This study aimed to ascertain whether English Language Strategies and Social Media Use could significantly determine Self-efficacy in English language skills among Senior High School students in Davao City. Utilizing quantitative descriptive-correlational research, the study involved 250 senior high school students in Davao City, Philippines from five schools that offer Grade 11 and Grade 12 curriculum in school year 2021-2022. The results revealed that the overall senior high school students’ Language Learning Strategies, Social Media Use and Self-efficacy in English language skills were high which means that the variables under study were evident. Moreover, it was found that Language Learning Strategies and Social Media Use were significant determinants of Self-efficacy in English language skills. KEYWORDS:English language self-efficacy; language learning strategies; social media use
Undivided Attention: Are Intermediate Layers Necessary for BERT?

In recent times, BERT-based models have been extremely successful in solving a variety of natural language processing (NLP) tasks such as reading comprehension, natural language inference, sentiment analysis, etc. All BERT-based architectures have a self-attention block followed by a block of intermediate layers as the basic building component. However, a strong justification for the inclusion of these intermediate layers remains missing in the literature. In this work we investigate the importance of intermediate layers on the overall network performance of downstream tasks. We show that reducing the number of intermediate layers and modifying the architecture for BERT-BASE results in minimal loss in fine-tuning accuracy for downstream tasks while decreasing the number of parameters and training time of the model. Additionally, we use centered kernel alignment and probing linear classifiers to gain insight into our architectural modifications and justify that removal of intermediate layers has little impact on the fine-tuned accuracy.