Reviewerly/exHarmony huggingface dataset:
THE ROLE OF MULTIMEDIA IN TEACHING AND ENHANCING THE LEARNING MOTIVATION FOR 6TH GRADE STUDENTS IN ENGLISH

The study aims at demonstrating the role of multimedia in teaching and enhancing the learning motivation for 6th grade students in English at the city of Janine schools from teachers’ perspective. It seeks to set the primary media suitable for their learning, and their use in their real-world school context, while developing teacher understanding for learners’ needs in the era or technological advancement. This study identifies the primary media suitable for 6th grade English, as English is a subject students find difficulty in understanding, being their second language. Therefore, the study seeks to answer this following research question: what is the role of multimedia in enhancing the 6th grade students’ motivation to learn English at the schools of Janine City from the teachers’ perspective? The study was conducted under the descriptive analytical method and created a learning community from 6th grade English teachers at the Janine city schools. It took place through purposive sampling with 12 teachers, using the in-person interview tool for the 12-teacher sample at the Janine city schools. This tool dwells on this phenomenon deeply in order to reach the appropriate results and recommendations. Results have shown the importance of multimedia as a factor for positive interaction, teaching engagement and motivation. Therefore, the study recommends that this teaching method should necessarily be incorporated in research, as it has shown effectiveness in achieving an interesting and effective teaching, while enhancing learning motivation, in a way that aligns with the TPACK model
THE DEGREE OF EFFECTIVENESS OF E-LEARNING IN LIGHT OF THE CORONA PANDEMIC FROM THE POINT OF VIEW OF SECONDARY SCHOOL STUDENTS IN THE NORTHERN TRIANGLE AREA IN ISRAEL

This study aimed to identify the effectiveness of e-learning in light of the Corona pandemic from the point of view of secondary school students in the Northern Triangle area in Israel. The study adopted the descriptive analytical approach as it is more suitable for such type of studies, and the study population consists of all secondary school students in the Northern Triangle area in Israel, the study sample consisted of (210) students. The results showed that the degree of effectiveness of e-learning in light of the Corona pandemic in secondary schools in the Northern Triangle area in Israel came with a medium degree, the first of which was the interaction of teachers with e-learning in light of the Corona pandemic with an average (3.92) and a high degree, and then the continuity of e-learning in light of the Corona pandemic with an average arithmetic (3.57) and a medium degree, followed by the interaction of students with e-learning in light of the Corona pandemic with an average arithmetic (3.45) and a medium degree. The results also showed no statistically significant differences attributable to the effect of the gender variable across all areas and total degree. Statistically significant differences attributable to the impact of the specialization variable in all areas and the total degree, and the differences came in favor of the literary specialization. In light of the findings of the study, the researcher recommends a number of recommendations, the most prominent of which are: -The need to provide an appropriate environment for the implementation of the e-learning strategy in secondary schools in the Northern Triangle area in the Israel. -The need to invest in the positive attitudes of students towards the use of e-learning strategy, and work to develop appropriate plans and programs to benefit from these trends. - The need to work on the integration of face-to-face and e-learning to take advantage of the advantages of e-learning.
From obsolete technology to performance instrument: new live presentations of the EMS Synthi 100

The presentation of electronic musical instruments in museum and other institutional contexts prompts a number of questions about the restoration of sound technologies, the reconstruction of musical practices and techniques, and past and present cultures of electronic music, among other topics. Drawing from research into the electronic musical instrument company and sound studio EMS London Ltd, which produced synthesizers and other devices during the 1970s, this paper outlines some examples of how an EMS synthesizer, the Synthi 100, has been restored and re-presented by academic, broadcasting and arts institutions in the last decade. Devised for compositional use in electronic music studios, in recent years the Synthi 100 has been used in concerts and broadcast media as an instrument for live performance. Focusing on some of these events, I ask how live performance is used to draw connections between electronic music’s past and present, as well as to attract new participants and audiences to concerts, exhibitions and broadcasts of electronic music.
Discourse Analysis Of Democracy Criticism In Stand Up Comedy

Awareness of democracy in Indonesia is still a hot topic in every political discussion because it covers various aspects of society, especially in the education and development sectors. This certainly greatly affects the process of running a good and just democracy. This study aims to reveal critica
Development Of Critical Thinking Skill Based Animation Media on Energy Benefits Materials For Class V Kemejing Elementary School

Learning at the elementary school level is a process of interaction between students and educators to achieve learning objectives. Learning at the elementary school level applies thematic learning so that learning requires the use of learning media such as animation media. The use of animated media
Assessing Teaching Practices: A Checklist for Observing Theoretical Classes Taught by "Double-Qualified" Teachers in Chinese Higher Vocational Colleges

The National Implementation Plan for Vocational Education Reform defines"double-qualified"teachers as those who can teach both theory and practice.However,there is a scarcity of comprehensive classroom observation checklists for assessing such teachers in Chinese higher vocational colleges.This stud
Recovering the Quantum Correlations of Biphotons in Noisy Environments via Coherent Energy Redistribution

We propose a denoising scheme for quantum states allowing for an increase of the coincidence-to-accidental ratio (CAR) under different pumping and noise injection settings. The scheme allows for enhanced correlations while maintaining most coincidence counts.
Learning Attention Through Hierarchical Architecture for Visual Object Tracking

Various attention mechanisms have been extensively adopted in visual object tracking networks. However, there search of Siamese networks with convolutional attention mechanisms as the main architecture remains limited. In this letter, we propose a novel Siamese tracker that leverages convolutional attention and a hierarchical architecture. By utilizing the deformable convolution, we expand the receptive field to capture more contextual information. In addition, our hierarchical architecture optimizes the attention by hierarchically adjusting the focus of attention based on the significance of features at different levels. On the other hand, we decompose the step of feature compression during the feature-to-result transformation process and integrate it in segments with the feature fusion process to address the information loss and underfitting issues. Extensive experimental results demonstrate that our tracker achieves state-of-the-art performance on multiple benchmarks including LaSOT, TrackingNet, UAV123, and NFS while maintaining a high processing speed of 40 FPS.
Multi-View Attributed Network Embedding Using Manifold Regularization Preserving Non-Negative Matrix Factorization

Attributed network has more network information, so more and more attention is paid to the embedding of attributed network. A few existing works have considered the node attributes plays a crucial role in the quality of network embedding. They use the non-negative matrix factorization (NMF) method to mine the network information of network structure and node attributes respectively. Considering the reconstruction error of NMF method, the original network information will be lost when the final network embedding is generated. In this paper, we propose a novel multi-view attributed network embedding model with manifold regularization (Mane). The manifold regularization is added to the model to better reflect the Riemann geometry structure of the network in the feature space to enhance the information. And the problem of missing information of NMF is solved. Our approach uses the NMF to get the non-negative coefficient matrix corresponding to network structure and node attributes. Then cooperative regularization and manifold regularization is added to obtain more information in the final network embedding. The model proposed in this paper has been verified by experiments on several real data sets. The result shows that the model is superior to the state-of-the-art algorithm in node classification task.
Equality and Predicate Abstraction

We discussed equality at length in Chap. 11 , but this was before we introduced the machinery of predicate abstraction. Recall that a model is normal if the relation symbol “=” is interpreted to be the equality relation on the domain of the model. The relation is thus the same from world to world. Now it is time to see how equality and predicate abstraction interact. While the basics of what we have to say applies to non-rigid terms generally, things are most easily understood if function symbols are not present, and that is all we discuss formally in this section. Our setting throughout this section can be assumed to be the modal logic K, with varying domains, allowing terms that may not designate, that is, K with the VN conditions. This is the most general setting we have, since all other setups are the result of putting further restrictions on this one.
Parity Shadow Stack: Dynamic Instrumentation based Control Flow Security for IoT Devices

One of the challenges in ensuring the security of Internet of Things (IoT) devices involves preserving control flow integrity (CFI) against return-oriented programming (ROP) attacks. Traditional defense mechanisms, while effective, often require substantial changes to the compiler, thereby limiting their applicability to a broad range of devices. To overcome this challenge, we present the Parity Shadow Stack — a novel, compact shadow stack mechanism that leverages dynamic instrumentation and binary rewriting to achieve similar effects compared to compiler-based methods. Further, this method does not require application source code and can work with legacy applications where only the binary is available. Finally, we demonstrate that it is more flexible as it provides a programming interface to add additional security semantics checks. Through our evaluations, we find that our method introduces a remarkably low overhead, less than 10 percent for medium-size or large-size applications. Therefore, we conclude it provides robust and practical defense against ROP attacks without requiring source code or compiler changes, nor does it require any specialized hardware.
Research on Design of Chaotic Symmetrical Digital Image Encryption System

With the widespread dissemination of digital information, the security encryption of digital images has become particularly critical. This study deeply discusses the chaotic symmetric digital image encryption system based on chaos theory and symmetric encryption technology. By combining the technologies of these two fields, an efficient and safe encryption method is provided for digital images. Experimental evaluations show that the system is capable of effectively encrypting images so that they are visually distinct from the original content. At the same time, the system shows strong resistance to differential attacks, and has a high encryption speed, which is suitable for real-time applications.
Abstracts of the International Conference on Recent Trends in Mathematics and Computer Science 2023

This book presents the abstracts of the selected contributions to the International Conference on Recent Trends in Mathematics and Computer Science 2023 (ICRTMCS-2023), held on 19-21 October 2023 by the Auxilium College of Arts and Science for Women, Regunathapuram, Tamil Nadu, India. ICRTMCS-2023 was a multidisciplinary conference organized with the objective of bringing together eminent academicians, research scholars, and students to exchange ideas, communicate, to discuss research findings and new advances on recent and emerging trends in the field of Mathematics and Computer Science. Moreover, the conference would also enable the participants to explore new fields and gain immense knowledge.
Sharp permutation groups whose point stabilizers are AGL(1,p)

We determine the structure of sharp permutation groups of type [Formula: see text] whose point-stabilizers are isomorphic to [Formula: see text]. It is shown that, up to permutation isomorphism, there is precisely one such permutation group for each odd prime [Formula: see text].
BENCHMARKING THE EXTRACTION OF 3D GEOMETRY FROM UAV IMAGES WITH DEEP LEARNING METHODS

Abstract. 3D reconstruction from single and multi-view stereo images is still an open research topic, despite the high number of solutions proposed in the last decades. The surge of deep learning methods has then stimulated the development of new methods using monocular (MDE, Monocular Depth Estimation), stereoscopic and Multi-View Stereo (MVS) 3D reconstruction, showing promising results, often comparable to or even better than traditional methods. The more recent development of NeRF (Neural Radial Fields) has further triggered the interest for this kind of solution. Most of the proposed approaches, however, focus on terrestrial applications (e.g., autonomous driving or small artefacts 3D reconstructions), while airborne and UAV acquisitions are often overlooked. The recent introduction of new datasets, such as UseGeo has, therefore, given the opportunity to assess how state-of-the-art MDE, MVS and NeRF 3D reconstruction algorithms perform using airborne UAV images, allowing their comparison with LiDAR ground truth. This paper aims to present the results achieved by two MDE, two MVS and two NeRF approaches levering deep learning approaches, trained and tested using the UseGeo dataset. This work allows the comparison with a ground truth showing the current state of the art of these solutions and providing useful indications for their future development and improvement.
Session recommendation based on graph neural networks incorporating graph attention

Session recommendation (RS) is a research focus and hotspot in the field of recommender systems in recent years. Previously, some traditional methods focus on sequence modeling based on user click sequences, but this approach may not be able to fully capture the complex transformation relationships between items. In recent years, graph neural session recommendation introduces session graph topology information to improve the accuracy of item and session feature representation, thus, improving the performance of session recommendation to a certain extent, in addition, this paper also uses graph attention aggregation neighbor nodes to obtain higher-order semantic relationships between items. Based on the item semantic features, the local attention mechanism is used to generate the session features, and finally, based on the item and session semantic features, this paper learns the click probability distribution of different items under a given session by cross-entropy loss. Extensive experiments are conducted on two publicly available and popular datasets, Yoochoose and Diginetica, and the experimental results show that the method has a higher recommendation accuracy.
On the effectiveness of handcrafted features for deepfake video detection

Recent developments in advanced generative deep learning techniques have led to considerable progress in deepfake technology. CNN-based deepfake detection approaches have demonstrated superior performance. The ability to learn meaningful representations generated by convolutional multilayer nonlinear structures is the key to success. However, the black-box nature of such approaches has been a major concern for exploring hidden and complex characteristics as well as potential limitations of CNN-based models. To gain insights into the scope of the deepfake detection task, we investigate the effectiveness of handcrafted feature-based methods for deepfake video detection. First, we experiment with six top-performing handcrafted descriptors to extract the discriminating image features and then train SVMs on the extracted features to learn a suitable model. We also study the effect of selecting specific facial components on the detection performance. Specifically, we consider features extracted from the left eye, right eye, mouth, and entire face. Moreover, we propose a combination of these features and highlight the importance of this combination in terms of detection performance. Experimental results show that the SIFT feature descriptor achieves the best performance on deepfake videos generated by the neural texture technique, with a detection accuracy of 83.50%, which is better than deep learning-based methods. This is in contrast to the conventional understanding that deep learning methods systematically outperform handcrafted feature-based approaches. In addition, the obtained results on the FaceForensics++ dataset highlight the benefit of using some facial components to further boost the detection performance. Moreover, motivated by the effectiveness of the LBPTOP and SIFT in the deepfake detection task, we combined the LBPTOP and SIFT to best characterize the specific spatiotemporal inconsistencies commonly found in fake videos for boosting deepfake detection performance. Finally, we show the strengths and weaknesses of methods based on handcrafted features for deepfake detection and provide directions for future research.
Storage over Rendition. Call for a Sustainable Infrastructure in the Digital Textual Heritage Sector with a Particular Interest in Digital Scholarly Editions

A significant amount of human and pecuniary resources has gone into the production of the long line of digital scholarly editions that within recent decades have sprung to life in Scandinavia, in the Baltic region, as well as in the rest of Europe. Notwithstanding the heritage perspective, the sector is paradoxically characterized by a presentist preoccupation with instant results – first and foremost with the rendition of the given data set. Concerns for long-term data management perspectives – that is: interest in the post-production afterlife of the data – is relatively meagre. This goes for project managers, for project host institutions, and for the research foundations financially supporting the projects. So, despite harmonizing initiatives at production level and pre-edition compilation initiatives, such incentives promote a situation of insulated digital scholarly editions focusing on unique URLs and distinctive qualities of the given material. This hinders project synergy in the production phase. Moreover, it hinders the construction of long-term and sustainable data management solutions. To remedy this situation, we propose a clear division of labour between the tasks of data production, of data rendering, and of data storing. This division should ideally be sought at an institutional level. This will secure the accumulation of know-how in teams refining the respective workflows. In addition, we encourage private and public foundations to bolster this infrastructure by making project compliance a criterion for funding.
Novel Solution for Using Neural Networks for Kidney Boundary Extraction in 2D Ultrasound Data

Background and Objective: Kidney ultrasound (US) imaging is a significant imaging modality for evaluating kidney health and is essential for diagnosis, treatment, surgical intervention planning, and follow-up assessments. Kidney US image segmentation consists of extracting useful objects or regions from the total image, which helps determine tissue organization and improve diagnosis. Thus, obtaining accurate kidney segmentation data is an important first step for precisely diagnosing kidney diseases. However, manual delineation of the kidney in US images is complex and tedious in clinical practice. To overcome these challenges, we developed a novel automatic method for US kidney segmentation. Methods: Our method comprises two cascaded steps for US kidney segmentation. The first step utilizes a coarse segmentation procedure based on a deep fusion learning network to roughly segment each input US kidney image. The second step utilizes a refinement procedure to fine-tune the result of the first step by combining an automatic searching polygon tracking method with a machine learning network. In the machine learning network, a suitable and explainable mathematical formula for kidney contours is denoted by basic parameters. Results: Our method is assessed using 1380 trans-abdominal US kidney images obtained from 115 patients. Based on comprehensive comparisons of different noise levels, our method achieves accurate and robust results for kidney segmentation. We use ablation experiments to assess the significance of each component of the method. Compared with state-of-the-art methods, the evaluation metrics of our method are significantly higher. The Dice similarity coefficient (DSC) of our method is 94.6 ± 3.4%, which is higher than those of recent deep learning and hybrid algorithms (89.4 ± 7.1% and 93.7 ± 3.8%, respectively). Conclusions: We develop a coarse-to-refined architecture for the accurate segmentation of US kidney images. It is important to precisely extract kidney contour features because segmentation errors can cause under-dosing of the target or over-dosing of neighboring normal tissues during US-guided brachytherapy. Hence, our method can be used to increase the rigor of kidney US segmentation.
Potential Biodegradable Sticker Based on Carrageenaan and Chitosan with Addition of Lauric Acid: A Mini Review

Plastic pollution is a significant problem with detrimental effects on the environment and human health. Conventional plastic stickers, widely used in various sectors, contribute to plastic waste accumulation and slow degradation. To address this issue, review has focused on developing biodegradable stickers made from natural polymers such as carrageenan and chitosan. Carrageenan, derived from red seaweed, forms gels and has potential as a biodegradable film due to its stability and high fiber content. Chitosan, derived from crustacean waste, is non-toxic and can act as a stabilizer, thickener, and protective layer in food products. However, chitosan alone has limitations in terms of brittleness and rigidity. Lauric acid, a medium-chain fatty acid found in coconut oil, can be added as a film-forming material to improve the mechanical properties and reduce water vapor permeability. Biodegradable films thickness, tensile strength &amp; Percent elongation, and surface morphology play important roles in their quality characteristics. Biodegradable stickers made from lauric acid-based biodegradable films offer ink resistance, biodegradability, water resistance, and water solubility as key characteristics.