Reviewerly/exHarmony huggingface dataset:
The Effects of Bubble Map and Tree Map Method in CEFR Reading Comprehension

The effective method of teaching CEFR reading comprehension makes students to have better exposure and gain confidence in Malaysian secondary schools. The ultimate goal of this study is to investigate whether the use of Bubble Map and Tree Map methods enhances students’ learning of Multiple-Choice Questions (MCQ) in the CEFR reading comprehension. This quantitative study employed a quasi-experimental design. The sample of this study consisted of 105 Form One students (13 years old) from three different schools (school A, B and C) from Petaling Jaya, Selangor. The sample were chosen as intact groups. The Experimental Group 1 (EG1) from school A was taught using Bubble Map, Experimental Group 2 (EG2) from school B was taught using Tree Map and the Control Group (CG) from school C was taught using conventional method. The pre-test and post-test were used as instruments to collect the data for this study. The quantitative data was analyzed using SPSS program for Windows version 26. The MANCOVA test and Tukey HSD were used to analyze the data. The findings demonstrated that EG1 (using Bubble Map) significantly outperformed EG2 and CG in their CEFR reading comprehension. The results also indicated that EG2 (using Tree Map) performed significantly better than CG (using conventional method). This study has essential pedagogical implication because the Bubble Map and Tree Map methods had positive effects in enhancing students’ CEFR reading comprehension. As such, teachers can use Bubble Map and Tree Map methods as an alternative method to teach CEFR reading comprehension in the ESL classroom.
Comparing the Effects of Graphic Organisers and Conventional Method on Students’ Writing Skills

Graphic Organisers help students make valuable connections by cataloguing and patterning ideas in writing. This study aims to investigate whether there is any difference in the overall mean writing scores between two groups: the Experimental Group (using Graphic Organisers) and the Control Group (using the Conventional Method) in the post-test. This study employs a quasi-experimental design, with quantitative data used for data analysis. Bubble maps and tree maps were the Graphic Organisers used by the Experimental Group in learning ESL writing skills. The study consisted of 120 Form Four students (16 years old) from two schools in the District of Hulu Langat, Selangor. The sample consisted of 60 students in each group (Experimental Group and Control Group). The experiment lasted for eight weeks. A pre-test was conducted prior to the intervention, and the post-test was carried out at the end of the experiment. The instruments (pre-test and post-test) consisted of three types of writing tasks: email writing, directed writing, and extended writing. ANCOVA was used to analyse the data obtained from the pre-test and post-test. The results showed that the Experimental Group significantly outperformed the Control Group in their overall writing performance, particularly in the areas of content, communicative achievement, organization, and language. The Control Group had difficulty in their overall and the four areas of writing because the Conventional Method did not help them improve their performance. This study has pivotal pedagogical implications as it demonstrates that Graphic Organisers (Bubble maps and Tree maps) facilitate students in their ESL writing.
Morphological Hierarchies: A Unifying Framework with New Trees

Morphological hierarchies constitute a rich and powerful family of graph-based structures that can be used for image modeling, processing and analysis. In this article, we focus on an important subfamily of morphological hierarchies, namely the trees that model partial partitions of the image support. This subfamily includes in particular the component-tree and the tree of shapes. In this context, we provide some new graph-based structures (one directed acyclic graph and three trees): the graph of valued shapes, the tree of valued shapes, the complete tree of shapes and the topological tree of shapes. These new objects create a continuum between the two notions of component-tree and tree of shapes. In particular, they allow to establish that these two trees (together with a third notion of adjacency tree generally considered in topological image analysis) can be defined and handled in a unified framework. In addition, this framework enables to enrich the component-tree with additional information, leading on the one hand to a topological description of grey-level images that relies on the same paradigm as persistent homology, and on the other hand to the proposal of a topological version of tree of shapes. This article provides a theoretical analysis of these new morphological hierarchies and their links with the usual ones. It also proposes an algorithmic description of two ways of building these new morphological hierarchies, and a discussion on the links that exist between these morphological hierarchies and certain topological invariants and descriptors.
Research on emotion recognition method of police dogs based on deep learning

Background: At present, artificial intelligence has been widely used in fields such as facial recognition. Since facial expression is the most direct feature of emotion, it can be used to predict the emotional state of police dogs by recognizing facial expression. In our study, we developed and experimentally analyzed an improved method to predict the emotions of police dogs through deep learning to recognize their expressions. Methods: We sifted through and analyzed public dog data sets on the Internet. Finally, it was decided to use the dog data set published by Stanford University, and the categories of dogs were accurately divided and marked manually. Emotion recognition and experimental analysis of police dogs were carried out by using group recombination residual network. Results: Through experimental analysis and comparison with existing methods, the accuracy of this method is 6% higher than that of the method based on residual network, and the accuracy is up to 89%. In addition, the number and size of the model parameters are reduced. Conclusion: Through experimental analysis and comparison with other mainstream identification networks, this method has a good advantage in the number of network parameters. Compared with other methods, the residual network based on group recombination can significantly improve the accuracy of emotion recognition for police dogs.
Toward a deep CNN and RS‐GOA framework for vehicle detection, traffic flow estimation, and optimal path selection from surveillance videos

Summary Several autonomous traffic monitoring systems have been created as a result of the growing number of vehicles in urban areas. Traffic surveillance systems that use roadside cameras, in particular, are becoming widely used for traffic management. For an efficient traffic control and vehicle navigation system, accurate traffic flow information must be obtained based on the vehicles detected in surveillance videos. However, vehicles of various scales are difficult to spot in traffic surveillance videos due to the presence of barricades, other vehicles, and the impact of poor lighting. Also, adverse weather conditions like snow, fog, and heavy rain diminish the visual quality of the surveillance footage. This paper proposes multi‐scale dense nested deep CNN (MSDN‐DCNN) and regional search grasshopper optimization algorithm (RS‐GOA) framework to accurately detect the vehicles, estimate the traffic flow, and find the optimal path with less travel time. First, the surveillance videos are pre‐processed, which includes frame conversion, redundancy removal, and image enhancement. The pre‐processed frames are given as input to the MSDN‐DCNN for multi‐scale vehicle detection. The detected results are used for vehicle counting and estimating the traffic flow. Finally, the optimal path is chosen based on the traffic flow information by using the RS‐GOA algorithm. The performance of the proposed method is compared with the existing vehicle detection and path selection techniques. The results illustrate that the proposed Deep CNN‐RS‐GOA framework has improved performance with high detection accuracy (91.03%), high speed (53.9 fps), less running time (1,000 ms), less travel time, and faster convergence.
Plato’s Philosophy and Cloud Computing System with a Cognitive Approach

This study presents Plato’s metaphysical world under the interpretation of today’s cloud computing technology. In this sense, the world of ideas in Plato’s philosophy is defined as a cloud system and some cognitive descriptions are made in this direction. Hence, in this study, ancient philosophy is described as a base of today’s technology. With this study, The Platon's philosophy is also interpreted as a technological reflection of the philosophy and human thinking system.
SAP Integration Suite

The chapter provides an overview of SAP Integration Suite introduction, emphasising its important capabilities, including Cloud Integration, API Management, Open Connectors, and others. The chapter addresses SAP's security procedures and emphasises how important security is for cloud-based solutions. Additionally, it provides a detailed tutorial for creating a trial account and gaining access to the suite.
Microsoft breach raises stakes for vendors and users

Headline INT: Microsoft breach ups stakes for vendors and users
Hardware Implementation of Compact Reconfigurable AES/SM4 Encryption Circuit Against Differential Power Attact

In order to resist differential power attacks (DPA) effectively, a compact implementation of the whole masked reconfigurable AES/SM4 encryption circuit is proposed. Firstly, the general design of whole masked reconfigurable AES/SM4 encryption circuit is introduced. Secondly, the detailed design of m
Pengaruh Penggunaan Media Manipulatif terhadap Kemampuan Berhitung Permulaan Siswa Kelas 2 Sekolah Dasar

Counting is often considered a difficult and unpleasant subject, it is necessary to use manipulative media in order to make learning to count more interactive, interesting and easily understood by students. This study aims to analyze the effect of the use of manipulative media on the numeracy skills of grade 2 students at SD Negeri 54/I Bajubang. This study uses a qualitative method with a type of phenomenological research. The research was conducted from March to April 2023. The subjects in this study totaled 21 students, all students were grade 2 students at SD Negeri 54/I Bajubang. The research data is in the form of descriptions, the data collection instruments used are observation sheets, oral tests and documentation. The data analysis technique used in this study is a comparison technique. Prior to the use of manipulative media in learning, each student only mastered a few indicators of initial numeracy skills. After using manipulative media in learning, each student is able to master all indicators of initial numeracy skills. This shows that manipulative media can improve students' ability to understand learning to count. Manipulative media in learning fosters students' motivation and interest in learning.
Application of Convolutional Neural Network (CNN) Method in Fluctuations Pattern

In the acquisition of amplitude data, the inaccuracy of a signal with the occurrence of an unstable peak value of the amplitude in the data is called a fluctuation. This study uses High-High Fluctuation (HHF) signal data from the acquisition of Multi-Spectral Capacitive Sensors (MSCS) with Hydrogen Dioxide (H2O) and Hydrogen Dioxide (H2O) objects mixed with Sodium Hydroxide (NaOH) that have been organized into a matrix form. The data acquisition results in previous studies have several parts that are difficult to distinguish with the naked eye. The method used in this study applies the CNN method for image recognition of signal fluctuations of type HHF from H2O and H2O mixed with NaOH, using the architecture known as AlexNet. Then, the H2O and H2O data groups mixed with NaOH were grouped into training data groups of 280 image data for each data type, and 70 image data for test data for both groups. During the training stage, the number of epochs used is 20. However, by the time the number of epochs reaches 15, the accuracy rate is already high, reaching 98%. Furthermore, at the testing stage, the CNN program can correctly recognize the entire 70 image data for both materials, achieving perfect recognition for the total amount of the two materials.
DEVELOPMENT INVENTORI SYSTEM METODE TREND MOMENT UNTUK OPTIMALISASI BERBASIS RFID

In the warehouse department or warehouse in companies including PT. Semarang Packing Indonesia has a service system that still has not maximized the current technological developments that are growing rapidly. Included in the warehouse inventory system which often occurs is not available stock and excessive stock. This causes the inventory function in the warehouse not to work optimally.&#x0D; From these problems,. This system includes controlling inventory in the warehouse for purchases so as to reduce excessive or insufficient stock so that the stockter inventory is controlled, as well as the data input process that utilizes RFID so as to speed up the time in the data input process. This system uses the PHP and MYSQL programming languages as the database and utilizes the PHP Framework, namely CodeIgniter, in making this website. With a web-based system, it makes it easier for employees to process data in a timely manner.
Corpus Database Management Design for Chinese-Portuguese Bidirectional Parallel Corpora

As deep learning techniques continue to mature, machine translation (MT) is gaining popularity among translators. However, the accuracy of machine translation depends not only on the size of the parallel corpus but also on the quality of the parallel corpus. The management of these massive parallel corpora is often unaware due to the lack of tools. As a result, many conflicting and confusing parallel corpora are trained together to influence the MT engines. Therefore, this study proposes a novel parallel corpus database design aimed at assisting data management efforts. After a series of experimental tests, our proposed database design can effectively generate domain-specific MT models with better BiLingual Evaluation Understudy (BLEU) values than other models. Furthermore, this database design helps to analyze, validate, and evaluate the quality of parallel corpora in database engines.
Indian and Australian University Students’ Acceptance of Using Accessible, Web-Based, and Smartphone-Delivered Augmented Reality in Tertiary Learning: A Cross-Country Analysis

Accessible and equitable education is a national priority recently highlighted by the Indian and Australian Governments. New developments in web-based architecture allow augmented reality (AR) lessons to be delivered via smartphone. Although educational technology is commonplace in the Australian curriculum, it is unclear if Indian tertiary students would be welcoming towards web-based mobile learning due to a historically slower uptake and only recent availability of connected devices in their educational system. This study evaluated feedback after using a web-deployed AR smartphone-based application across both Australia (70 participants) and India (100 participants) to see if this technology can assist in capacity building on a global scale. From thematic analyses on the provided feedback, it was identified that Australian students were more focused on the benefits received from the educational technology. In contrast, Indian students were far less critical of the embedded lesson, and more interested in the prospect of introducing the specific technology into their curricula. The data suggests that a rollout of web-based mobile AR for learning in countries more digitally-native should likely prioritise the content within it. Alternatively, for countries recently-introduced to educational technology, such as India, a rollout should focus on embedding the technology itself first. Although there is a risk of learners being distracted by the technology, smartphone web-based AR presents an excellent option to equitably provide a modern, innovative intervention, regardless of wealth, location, or status.
An optimized enhanced-multi learner approach towards speaker identification based on single-sound segments

Abstract Speaker Identification (SI) is the task of identifying an unknown speaker of an utterance by comparing the voice biometrics of the unknown speaker with previously stored and known speaker models. Although deep learning algorithms have been successful in different speech and speaker recognition systems, they are computationally expensive and require considerable run-time resources. This paper approaches this issue by proposing an optimized text-independent SI system based on convolutional neural networks (CNNs) that not only delivers accuracies on par with state-of-the-art benchmarks but also demands significantly fewer trainable parameters. The proposed system integrates an Enhanced Multi-Active Learner framework, which distributes the complexity of the learning task among an array of learners, with a novel SI approach in which speakers are identified based on a single sound segment of voice biometrics. Here, experiments were conducted with all 1881 VoxCeleb 1 and TIMIT speakers, and results were compared with the SI systems reported in the literature that were assessed on the same speakers’ data. Results indicate that first, the proposed system outperformed the benchmark systems’ performances by delivering up to 2.43% better top-1 accuracy, and second, it reduced the number of deep learning trainable parameters by up to 95%. The proposed SI could bring offline, large-scale speaker identification to low-end computing machines without specific deep learning hardware and make the technology more affordable.
QuOTeS: Query-Oriented Technical Summarization

When writing an academic paper, researchers often spend considerable time reviewing and summarizing papers to extract relevant citations and data to compose the Introduction and Related Work sections. To address this problem, we propose QuOTeS, an interactive system designed to retrieve sentences related to a summary of the research from a collection of potential references and hence assist in the composition of new papers. QuOTeS integrates techniques from Query-Focused Extractive Summarization and High-Recall Information Retrieval to provide Interactive Query-Focused Summarization of scientific documents. To measure the performance of our system, we carried out a comprehensive user study where participants uploaded papers related to their research and evaluated the system in terms of its usability and the quality of the summaries it produces. The results show that QuOTeS provides a positive user experience and consistently provides query-focused summaries that are relevant, concise, and complete. We share the code of our system and the novel Query-Focused Summarization dataset collected during our experiments at https://github.com/jarobyte91/quotes .
Research on Lightweight Based on SSD Fatigue Driving Detection Algorithm

In this paper, an improved single shot multibox detector detection algorithm is proposed for the insufficient real-time performance of the driver fatigue driving detection model and the poor detection effect in complex lighting scenes. The algorithm replaces the SSD backbone feature extraction network VGG16 with ShuffleNet to make the model lightweight. Add the lightweight attention mechanism network SENet (Squeeze-and-Excitation Network) to the shallow features of the model to improve the detection accuracy of small targets in the model.
A Character-Level Document Key Information Extraction Method with Contrastive Learning

Key information extraction (KIE) from documents has become a major area of focus in the field of natural language processing. However, practical applications often involve documents that contain visual elements, such as icons, tables, and images, which complicates the process of information extraction. Many of current methods require large pre-trained language models or multi-modal data inputs, leading to demanding requirements for the quality of the data-set and extensive training times. Furthermore, KIE datasets frequently suffer from out-of-vocabulary (OOV) issues. To address these challenges, this paper proposes a document KIE method based on the encoder-decoder model. To effectively handle the OOV problem, we use a character-level CNN to encode document information. We also introduce a label feedback mechanism in the decoder to provide the label embedding back to the encoder for predicting adjacent fields. Additionally, we propose a similarity module based on contrastive learning to address the problem of content diversity. Our method requires only text inputs, has fewer parameters, but still achieves comparable results with state-of-the-art methods on the document KIE task.
TBM-GAN: Synthetic Document Generation with Degraded Background

Deep document enhancement models often suffer in real world applications due to limited annotation and bias in training data. Moreover, generative models are often prone to spectral bias towards certain frequencies. The background (noisy) texture is usually harder to learn as it is composed from different frequency regions. In this work, we propose TBM-GAN, a generative adversarial network based framework to synthesise realistic handwritten documents with degraded background. In addition to the spatial information, TBM-GAN also incorporates the frequency information in its loss function to focus on complex noisy texture. Overall, we develop an automated pipeline for TBM-GAN and train it with artificially annotated data from publicly available resources. The pipeline provides both text-label and corresponding pixel-level annotation. We evaluate the quality of synthetic images in the downstream task of OCR. In text images with historical noisy background, we observe an $$11\%$$ reduction in the character error rate when the OCR is trained with synthetic data from TBM-GAN.
Application of Google Workspace in Mathematical Training of Future Specialists in the Field of Information Technology

Education in the field of information technology in Ukraine should introduce a special innovative approach in the training of specialists in computer science and information technology. Problems of personality-oriented and competence-based models of education in the process of training future specialists in information specialties become topical. All professionals in the field of information technology must have specific personality traits and professional competencies formed in the studying different disciplines. Educational process has to result the formation of both hard and soft skills of students. Future IT specialists require a deep knowledge of the basic mathematical theoretical foundations and possession of the skills of applying mathematics. The study of the educational process in multinational academic groups seems to us quite interesting, since it has some peculiarities. The aim of this article is the consideration of new learning technologies and the features of their implementation in the context of distance education. We study methodological problems of collaborative approach in online teaching of mathematical disciplines applying Google Workspace for Education tools.