Reviewerly/exHarmony huggingface dataset:
A combined feature-vector based multiple instance learning convolutional neural network in breast cancer classification from histopathological images

Breast cancer, being one of the leading causes of death in women, has a dangerous impact worldwide. Thousands of lives can be saved if the detection of breast cancer at an early stage is possible. Pathologists, after analyzing the morphological characteristics of biopsy samples at different levels of magnification, identify the existence of cancerous tissue. This total diagnostic process depends on the subjective analysis of pathologists and it may lead to some difficulties. In this situation, Computer-Aided Diagnosis (CAD) might be quite useful. The goal of this research is to develop a CAD system to be used as an assistant to the pathologists in making the final decision to diagnose breast cancer accurately. In this paper, our study is conducted on a publicly available dataset called BreakHis in which the histopathological images are categorized into four magnification levels. The concept of Multiple Instance Learning (MIL) is applied in this research. A deep Convolutional Neural Network (CNN) model, with four input paths, is used to take the images at four different magnification levels parallelly. EfficientNet-B0 is applied as the backbone network in our model to classify the histopathological images. Our proposed approach surpassed previous state-of-the-art works by a significant margin in terms of accuracy, precision, recall, F-measure, Matthew’s Correlation Coefficient (MCC), and Area Under the Curve (AUC) when applied to an independent test set.
The “Africa Rising”: An Exploratory Analysis with the Gross Domestic Product Data

The race toward inclusive development has prompted the researchers to reconsider the drivers of economic growth in Africa in view of the term for achieving the Sustainable Development Goals (SDGs). The study examined growth of the 54 African countries using the Generalised Method of Moments (GMM) dynamic system estimators, which previous studies have confirmed to be suitable for growth analyses. The coefficients within the models remain stable even after controlling the effects of Sub-Saharan African countries and Lions ones. As a data source, the main international organisations (UN, WB, IMF) have been used. The covered period is 2010-2019, where the data are more complete. We have used the real per-capita GDP as a dependent variable. The purpose of this research note is an exploration of the determinants of African growth after analysing the recent empirical literature. Our findings show the significant variables are affecting African growth. A more developed institutional and business environment in African countries can to lead a long-run sustainable growth, but this depends on a sound governance.
Analyzing the impact process of banks and private institutions on macroeconomic indicators

Banks are among the main sectors of any country's economy. Nowadays, one of the questions raised about Iran's economy is whether the expansion of banks and nongovernmental credit institutions has led to achieving the goals of increasing competitiveness, efficiency, and overall economic growth.
Machine Learning Chatbot for Sentiment Analysis of Covid-19 Tweets

The various types of social media were increased rapidly, as people’s need to share knowledge between others. In fact, there are various types of social media apps and platforms such as Facebook, Twitter, Reddit, Instagram, and others. Twitter remains one of the most popular social application that people use for sharing their emotional states. However, this has increased particularly during the COVID-19 pandemic. In this paper, we proposed a chatbot for evaluating the sentiment analysis by using machine learning algorithms. The authors used a dataset of tweets from Kaggle’s website, and that includes 41157 tweets that are related to the COVID-19. These tweets were classified and labelled to four categories: Extremely positive, positive, neutral, negative, and extremely negative. In this study, we applied Machine Learning algorithms, Support Vector Machines (SVM), and the Naïve Bayes (NB) algorithms and accordingly, we compared the accuracy between them. In addition to that, the classifiers were evaluated and compared after changing the test split ratio. The result shows that the accuracy performance of SVM algorithm is better than Naïve Bayes algorithm, even though Naïve Bayes perform poorly with low accuracy, but it trained the data faster comparing to SVM.
LEON: Light Weight Edge Detection Network

Deep Convolutional Neural Networks (CNNs) have achieved human-level performance in edge detection. However, there have not been enough studies on how to efficiently utilize the parameters of the neural network in edge detection applications. Therefore, the associated memory and energy costs remain high. In this paper, inspired by Depthwise Separable Convolutions and deformable convolutional networks (Deformable-ConvNet), we aim to address current inefficiencies in edge detection applications. To this end, we propose a new architecture, which we refer to as Lightweight Edge Detection Network (LEON ). The proposed approach is designed to integrate the advantages of the deformable unit and DepthWise Separable convolutions architecture to create a lightweight backbone employed for efficient feature extraction. As we show, we achieve state-of-the-art accuracy while significantly reducing the complexity by carefully choosing proper components for edge detection purposes. Our results on BSDS500 and NYUDv2 demonstrate that LEON outperforms the current lightweight edge detectors while requiring only 500k parameters. It is worth mentioning that we train the network from scratch without using pre- trained weights.
Application of Mineral Resource Assessments to Regional Screening for Potential Environmental Hazards Related to Bedrock Mineral Concentrations

Bedrock geology and mineral resource assessments can provide important information for ecologically based stewardship of land and water. Combining information derived from mineral resource assessments and geoenvironmental mineral deposit models provides a means to rapidly screen a large region for potential for mineral concentrations, to assess potential environmental risk associated with mineralized bedrock and with human disturbances of mineralized bedrock, and to establish priorities for further studies that will allow managers to mitigate or prevent hazardous conditions.
An Unprecedented View of Quantum Computers

Every discussion of quantum computing starts with wave-particle duality, to explain how qubits differ from bits. But what if wave-particle duality were wrong? How would we explain quantum computing then? A little-known science called the Theory of Elementary Waves (TEW) says that quantum particles follow zero-energy waves backwards. Wave-particle duality cannot be true if waves and particles travel in opposite directions. This article proposes the first-ever TEW theory of quantum circuits. Elementary waves emanate from measuring devices and travel backwards through the circuits, whereas qubits move forwards through the wires and gates following those waves backwards. Quantum computers are known to be reversible. After we present that way-of-thinking, we will explain some of the evidence that TEW is valid. There is a mountain of empirical evidence from outside information technology. TEW is a maverick theory, out-of-step with the consensus about how quantum computers work. At first TEW sounds counterintuitive. Its advantage is Occam’s Razor: we present a simpler explanation of quantum circuits. We will present the quantum computer equivalent of saying that before the box is open Schrödinger’s cat is already dead or alive, but not both. Observing the cat simply tells us what was already true before we looked.
Evaluation of the implementation of the 8-week learning recovery curriculum in numeracy of grade 3 learners of Legazpi city

This study sought to evaluate the pretest and posttest results in numeracy of Grade 3 learners of the Schools Division of Legazpi City and determine the effectiveness of the implementation of the 8-Week Learning Recovery Curriculum (LRC) as the numeracy intervention program based on the perception of the Grade 3 teachers. Given that all the Grade 3 learners underwent the intervention program and there was no random assignment of participants, this study qualifies as quasi-experimental research. Methodological triangulation through collection of pretest and post test results, survey, and interview with the Grade 3 teachers is used to establish the credibility of this study. Paired-samples t test is the statistical treatment used to analyze the pretest and posttest results. Descriptive statistics was used to interpret the results of the survey on the level of effectiveness of the intervention program and to capture the percentage of attendance of the learners and teachers. Thematic analysis was used to interpret the qualitative responses. This study found that there is a significant difference between the pretest and posttest results reflective of the t-value of -7.218, p&lt;0.05. This improvement can be attributed to the effective implementation of the numeracy intervention program as perceived by the Grade 3 teacher-respondents. The regular attendance of the learners and teachers also contributed to the effectiveness of the implementation. With this, numeracy intervention programs with similar features as that of the 8-Week LRC can be sustained to continuously address learning gaps until the learners become grade ready in a reasonable time.
Deep Learning and Translation Technology

This chapter discusses deep learning and its application to translation technology. It first explains what deep learning is and how it works, with an introduction to key concepts, such as artificial neural networks, activation functions, loss functions, forward propagation, backpropagation and optimization. It then explores three approaches to machine translation with deep neural networks, which is followed by a discussion of the application of deep neural networks to speech translation, intersemiotic translation and translation memory and a review of noteworthy trends.
Example-Based Machine Translation

This chapter presents an overview of example-based machine translation (EBMT), covering its history, the major issues related to translation examples, and the fundamental stages of translation for an EBMT system. The suitability issue is also discussed, showing the types of translation that are deemed suitable for EBMT and how it interoperates with other MT approaches.
Digitalisasi Pembelajaran Bahasa Indonesia Berintegrasi Pendidikan Karakter

This article aims to analyze the integration of Indonesian language learning in schools that is related to language skills such as listening, speaking, reading, and writing with digitalization of learning that strengthens character education in students. Teachers have an important role in implementing character education to students. In this case, Indonesian language learning can be maximized with the variety of digital learning that can be utilized. In this case, teachers can maximize the basic competencies that can be integrated in strengthening character education can be narrowed down to religious values, national values, togetherness values, independence values, and integrity values. This article focuses on descriptive qualitative methods and literature studies from various relevant references regarding character education that can be integrated with Indonesian language learning at school. The results show that the basic competencies of class XI SMA can be integrated with the focused character education. Then, learning using digital technology can provide new things for teachers, students, and education units. This research focuses on the study of character integration by involving digitalization.
Artificial Intelligence and Translation

Artificial intelligence (AI) is a big field of studies that enables computers and machines to mimic the perception, learning, problem-solving, and decision-making capabilities of humans. More recently, with increasing computing power and data, AI has pushed the boundaries of human-like intelligence. In this chapter, we will discuss how AI can be used to understand text contents to perform translation. The main challenge in AI-based translation comes from the difficulties in understanding the semantics of texts as well as understanding the syntactic structure of texts to translate them. We start from the traditional rule-based machine translation approach to show how human-crafted rules can be used to translate texts and illustrate their limitations. We then move on to statistical machine translation, which learns to translate automatically from text corpus without too much human intervention. After that, with the development of the big data and machine learning technologies, we further discuss the recent deep learning–based approaches in translation and show their strengths. The key component of deep learning is neural networks, which are used to extract text semantics and learn translation patterns from the training data. Importantly, we discuss the widely used attention mechanism, which enables a very deep understanding of the word-level text and sentence relationships to enable precise translation. Finally, we elaborate on the recent advances in language and translation models in the industry and discuss their potential future directions and the current limitations of machine-based translation techniques.
Cloud Computing as a Solution for Security and Privacy Concerns

Abstract: In recent years, cloud computing has emerged as a popular and cost-effective solution for businesses and individuals to store and manage their data. However, the widespread adoption of cloud computing has raised serious concerns about the security and privacy of sensitive information stored in the cloud. This paper aims to explore the various security and privacy issues associated with cloud computing and examines how the technology can be leveraged to address these concerns. Through a comprehensive review of existing literature and case studies, This paper proposes that cloud computing can be an effective solution for security and privacy concerns in the digital age, provided certain best practices are followed by cloud service providers and users.
RANCANGAN SISTEM INFORMASI JASA LAUNDRY

Nonz Eqy Laundry is a service business that is engaged in washing various types of clothes. The transaction data management system laundry that is currently running still uses the method of writing with paper media to record transactions every day. Without a system that can store laundry, data is vulnerable to lose or damage, and errors are still common in reporting. Based on these problems, it is proposed to create a web-based computerized information system that aims to assist in managing transactions and making laundry. The system is built using the Waterfall, consisting of analysis, design, implementation, and system testing stages. The design stages are carried out with application architecture design, Data Flow Diagrams (DFD), and Entity Relationship Diagrams (ERD). The implementation process uses a database and the PHP programming language with h framework Laravel. The final result achieved from this research is that the existence of laundry income reports laundry every month.
Mobile Phone Usage Pattern among Secondary School Children in Some Selected Rural Schools.

Background: Mobile phones are an integral part of our modern life. The use of mobile phone for various purposes (including educational) has increased in recent few years in Bangladesh. The aim of the study was to explore the mobile phone usage pattern among secondary school children in rural schools of Bangladesh.&#x0D; Materials and Methods: This descriptive type cross sectional study was carried among 295 secondary school children by purposive sampling technique from 1st to 31st December, 2019 in different villages of Dhamrai Upazilla, Dhaka. Data were collected by duly pretested a semi-structured questionnaire through face to face interview.&#x0D; Results: The study revealed that, most of the respondents were in class X (36.61%) with female predominance (56.94%). Among them,63.72% were from nuclear family &amp; majority 56.61% of the respondents had family monthly income BDT &gt;20000 taka. 73% respondents were used smart phone. All respondents (100%) were used mobile phone for communicating with relatives &amp; most of the cases (82.37%) reason was kill spare time. Frequently used social media sites was a Messenger (71.52%) followed by You Tube (68.13%) &amp; Facebook (67.11%). 81.35% respondents were using mobile phones at home and 52.88% had no specific time for using a mobile phone. About 82.71% respondents were found monthly expenditure on mobile phone &lt;200 taka.&#x0D; Conclusion: The study findings demand an intensified effort should be made towards creating awareness about the use of mobile phone. The pattern of use revealed extensively on internet browsing. In fact, the usage of mobile phone for educational purpose is quite low and most of them used mobile phone for communicating with relatives. In spite of its various advantages it has some disadvantages also. Misusing of mobile phone increases the disadvantages of this device day by day.&#x0D; AKMMC J 2021; 12(1) : 6-10
On Data Characteristics and Status of the Monitoring System of Domestic Solar Power Generation System

This study entailed investigations on the current status of monitoring systems or platforms that provide important operating data usable as reference data in the design stage of photovoltaic (PV) power generation facilities.Thus, insights into the operating characteristics of such facilities were gained.This survey of the literature identified the features, namely, 'renewable energy cloud platform,' 'renewable energy integrated monitoring system (REMS),' and 'public data portal'; a quantitative analysis was performed using the obtained dataset.The quantitative analysis confirmed that inaccuracies and data errors occasionally occur in individual monitoring systems.Nevertheless, by using a large amount of high-accuracy long-term data, a clear standard for daily operating hours, which can be the most important data in the design of PV power generation facilities, was devised.
Comparation of Discovery Learning Model and Cooperative Model on Writing Procedure Text of Vocational Students

The purpose of this research is to examine how the skills of writing instructional texts and the learning results of Indonesian topics differ for students who are exposed to the Exploration Learning learning model versus those who are exposed to the Collaboration learning model. SMK Negeri 1 Pinrang served as the study site. Using a quasi-experimental technique with a pre- and post-test setup, samples from two groups will be randomly assigned to receive one of several therapies. About 30 pupils make up a typical classroom. We used a random selection method to collect our data. Class XI MM 2 served as the experimental group's implementation of the Discovering Learning model, while class XI MM 3 served as the control group's implementation of the Collaboration model. Final exams, comprised of multiple-choice questions, were administered to pupils in order to collect data for the study. The findings of the data analysis (t test), specifically tcount = 0.476, suggest that when it comes to teaching Indonesian topics, the Exploration Learning model yields superior results to the Collaboration model. After comparing them to a significance threshold of 0.05, we conclude that H0 cannot be supported. Students who engage in the Exploration Learning approach to education have higher learning results (average score of 85.66) than those who engage in the Collaboration approach (average score of 84.83). On average, students who are instructed using the Exploration Learning approach perform better than those whose education is based on the Collaboration approach.
Natural Language Processing

The primary concern of natural language processing is the design and implementation of computational systems for analysing and understanding human languages to automate certain real-life tasks demanding human language abilities. It is typically a multidisciplinary endeavour, drawing on linguistics, computer science, mathematics, and psychology, amongst others, with a particular focus on computational models and algorithms at its core.
Rule-Based Machine Translation

The rule-based method had been dominant in machine translation research for several decades, and it is still functioning in present-day MT systems. Despite its difficulties and problems, this method is gaining new momentum, as the significance of linguistic research has been realized more than ever and the formalization of linguistic knowledge is growing and maturing.
Improved Attacks on (EC)DSA with Nonce Leakage by Lattice Sieving with Predicate

Lattice reduction algorithms have been proved to be one of the most powerful and versatile tools in public key cryptanalysis. In this work, we primarily concentrate on lattice attacks against (EC)DSA with nonce leakage via some sidechannel analysis. Previous works relying on lattice reduction algorithms such as LLL and BKZ will finally lead to the “lattice barrier”: lattice algorithms become infeasible when only fewer nonce is known. Recently, Albrecht and Heninger introduced lattice algorithms augmented with a predicate and broke the lattice barrier (Eurocrypt 2021). We improve their work in several aspects.We first propose a more efficient predicate algorithm which aims to search for the target lattice vector in a large database. Then, we combine sieving with predicate algorithm with the “dimensions for free” and “progressive sieving” techniques to further improve the performance of our attacks. Furthermore, we give a theoretic analysis on how to choose the optimal Kannan embedding factor.As a result, our algorithm outperforms the state-of-the-art lattice attacks for existing records such as 3-bit nonce leakage for a 256-bit curve and 2-bit nonce leakage for a 160-bit curve in terms of running time, sample numbers and success probability. We also break the lattice records on the 384-bit curve with 3-bit nonce leakage and the 256-bit curve with 2-bit nonce leakage which are thought infeasible previously. Finally, we give the first lattice attack against ECDSA with a single-bit nonce leakage, which enables us to break a 112-bit curve with 1-bit nonce leakage in practical time.
Peek into the Black-Box: Interpretable Neural Network using SAT Equations in Side-Channel Analysis

Deep neural networks (DNN) have become a significant threat to the security of cryptographic implementations with regards to side-channel analysis (SCA), as they automatically combine the leakages without any preprocessing needed, leading to a more efficient attack. However, these DNNs for SCA remain mostly black-box algorithms that are very difficult to interpret. Benamira et al. recently proposed an interpretable neural network called Truth Table Deep Convolutional Neural Network (TT-DCNN), which is both expressive and easier to interpret. In particular, a TT-DCNN has a transparent inner structure that can entirely be transformed into SAT equations after training. In this work, we analyze the SAT equations extracted from a TT-DCNN when applied in SCA context, eventually obtaining the rules and decisions that the neural networks learned when retrieving the secret key from the cryptographic primitive (i.e., exact formula). As a result, we can pinpoint the critical rules that the neural network uses to locate the exact Points of Interest (PoIs). We validate our approach first on simulated traces for higher-order masking. However, applying TT-DCNN on real traces is not straightforward. We propose a method to adapt TT-DCNN for application on real SCA traces containing thousands of sample points. Experimental validation is performed on software-based ASCADv1 and hardware-based AES_HD_ext datasets. In addition, TT-DCNN is shown to be able to learn the exact countermeasure in a best-case setting.