Reviewerly/exHarmony huggingface dataset:
PENERAPAN MODEL PEMBELAJARAN INDEX CARD MATCH UNTUK MENINGKATKAN MINAT BELAJAR TERHADAP MATERI BILANGAN BULAT PADA PESERTA DIDIK KELAS IV SDI HABI

This study aims to find out the effect of applying the Index Card Match Learning Model in Increasing Student Learning Interest in Integer Material. This research was conducted at SD Inpres Habi Class IV. This study uses qualitative research with a survey method that takes samples from a number of populations and uses a questionnaire as a means of collecting data. The population in this study amounted to 17 students who were all studied. The sampling technique in this study used saturated sampling or sensus sampling, where all members of the population were sampled. This research is Classroom Action Research which is carried out in repeated cycles, where each cycle consists of a series of four activities namely, action planning, action implementation, observation (evaluation), and reflection. Based on the frequency distribution on predetermined criteria regarding students' learning interest in learning mathematics using the Index Card Match model, it shows that 13 students answered in the High category. Therefore, the results of this study concluded that the application of the index card match learning model can increase the learning interest of fourth grade students in integer material at SDI Habi.
Virtual platforms in higher education during the pandemic in Ecuador

The main objective of this paper is to analyze the virtual platforms in Higher Education in times of pandemic in Ecuador, with higher education being one of the sectors within the groups of the fragile area that was exposed to the disease of the COVID -19 pandemic. The methodology used in its approach is qualitative with a non-experimental design of a cross-sectional nature that had an exploratory scope under the analytical-synthetic, inductive-deductive method; the instruments used as a strategy is the use of documentary and bibliographic research. It was determined that the use of virtual platforms in Higher Education is indeed among the groups that will have, according to the projections analyzed, one of the greatest effects on the teaching-learning process of students' online classes after the COVID-19 pandemic. As a relevant conclusion, it is determined that the use of virtual platforms fundamentally helped the educational training process in the transmission of knowledge. Therefore, it is recommended that the higher education community make use of information and communication technologies for the teaching and learning process.
ASA APPLICATION (ARFF SMART APPLICATION) AS LEARNING MEDIA TO IMPROVE THE COMPETENCE OF ARFF PERSONNEL

The ARFF unit is responsible for incidents and accidents at the airport and its surroundings. They have skills such as the use of fire extinguishers, safety equipment operation, and evacuation measures. This preparedness is enhanced through the ARFF Smart Application (ASA) to improve ARFF learning competencies at I Gusti Ngurah Rai Airport. The research used a waterfall research and development (R&amp;D) model with observation as the data collection method. Development involves communication, planning, modeling, construction, and implementation stages. Validation was conducted with a questionnaire according to ISO 25010, measuring the functionality, usability, compatibility, and performance efficiency of the application. Functionality testing involved six testers (3 media experts and 3 material experts), with 92% of the results showing excellent quality. Usability testing involved 37 users with an excellent 95.40% rating in ease of use, learning, and satisfaction. Application compatibility was tested on various Android devices. Performance efficiency testing showed efficient performance in execution time, CPU usage, and memory. ARFF Smart Application fulfills all four aspects of the ISO 25010 standard with an assessment of "very feasible" and is suitable for use in learning by personnel in classroom activities.
THE IMPACT OF CLOSED CIRCUIT TELEVISION ADDITION ON APRON MOVEMENT CONTROL SUPERVISION IN APRON C JUANDA AIRPORT

This Final Project which discusses the impact of Closed Circuit Television Addition on Apron Movement Control Supervision in Apron C Juanda Airport. This is motivated by the limited or almost non-existent number of CCTV cameras in Apron C of Juanda Airport. Juanda Airport is one of the busiest Airports in Indonesia, and with the high volume of flights, the surveillance conducted at the airport needs to be enhanced. Surveillance in a large area like the Apron requires technology to facilitate the activities and CCTV can undoubtedly assist the Apron Movement Control (AMC) in the Airside. However, the current CCTV conditions in Apron C are insufficient to support the supervision adequately. Therefore, there is a need to reconsider the role of CCTV as a supporting facility for supervision in Apron C Juanda Airport. This research employs a quantitative research design using simple regression analysis after instrument testing and prerequisite testing. The research population consists of 40 AMC personnel, and the sampling technique used is consecutive sampling, resulting in a sample size of 3 5 out of 40 AMC personnel. Data collection for this research involves field observations, documentation, and a Google forms-based questionnaire using a likert-scale research instrument, conducted within a predetermined timeframe. The research findings are as follows: (1) There is an impact of the addition of Closed Circuit Television on the Apron Movement Control Supervision at Juanda Airport, (2) There is a 41 % impact of the addition of Closed Circuit Television on the Apron Movement Control supervision at Juanda Airport.
Pengembangan Booklet Learning How To Learn sebagai Layanan Informasi Mengenai Strategi Belajar Kognitif bagi Siswa SMA

This study aims to develop a learning how to learn booklet as an information service regarding cognitive learning strategies for high school students. The type of research used is research and development with research and development (R &amp; D) Borg &amp; Gall methods simplified by the Puslitjaknov Team (2008) which consists of conducting product analysis to be developed, developing initial products, expert validation and revision, trial small-scale field trials and revisions, large-scale field trials and final products. The sampling technique used was non-probability sampling by using quota sampling. The number of samples that will be involved in this study are 20 high school students (SMA) in Banten Province. This research is about product development in the form of a booklet that contains information on cognitive learning strategies that have aspects of 1) Repeating, 2) Elaborating, 3) Organization. To evaluate the product to be made, the researcher uses an evaluation model of alpha test and beta test. By asking material and media experts to assess the feasibility of the product as a way to evaluate, this is called an alpha test. The beta test is a form of product feasibility test based on the prospective user's assessment of the product being developed. After the product was tested for feasibility by material and media experts, then after being tested by students, whether the product developed was feasible or not, to get the results the researcher analyzed the data that had been obtained using the eligibility criteria according to Arikuntio (2009). In the product feasibility test, it was found that the results of the material expert test were 80%, the media expert test results were 87.05% and the user response test results were 84.26%. So from the results of the feasibility test and the respondent's trial, it was found that the product that the researcher developed in the form of a learning how to learn booklet was considered "very feasible".
Don’t Drop the Ball: A Comparison of Throwing Performance Between VR and Real Life

A two-experiment study assessed throwing performance in VR and real life. The first experiment was a direct comparison of a carnival type throwing game in which participants attempted to throw 20 balls into a bucket from distances of 5 and 10 ft. Fifteen participants completed the task in both real life and in an identical VR environment. In the second experiment, the same task was repeated at the 5ft distance only with the addition of a randomized order of three different VR controllers and the real-life condition. Results from both experiments indicate substantial differences between real life and VR performance along with differences between controllers. Significant VR research has stressed enhancing visual quality to better replicate real life. However, the focus needs to shift to examine creating accurate psychomotor interactions to achieve more accurate performance and ultimately training success.
Property Rights in Money (and Cryptocurrencies)

133 Abstract There is a common core of principles that governs all property rights, whether they relate to tangible or intangible goods. The classical principles of property law are applicable to the property law of intangibles. However, classical property law is also specifically designed to deal with physical objects, which raises the question of the extent to which the regulatory complexes of property law can or must be adapted when dealing with incorporeal goods. This is a challenge that can be observed in the discussion on property rights in data. This modern problem has received much attention, while a very similar and classical problem of property law has not been taken into much consideration in this context. The legal tension between a physical storage medium and the incorporeal good contained in it has dominated the property law discussion on money for decades. In fact, the question of whether classical property law is capable of dealing with the assignment of an incorporeal good is an old one, and much experience has been gained in the national property law systems with regard to money. The examination of this classical problem of property law also seems to be an appropriate starting point for the evaluation of newer legislation on property rights in cryptocurrencies. The new discussion on property rights in data and the old discussion on property rights in money have a strong point of contact when it comes to assigning absolute rights to cryptocurrencies.
Black-Box Adversarial Attacks Against Language Model Detector

The Language Model (LM) Detector has gained attention for its remarkable performance in detecting machine-generated texts. It however remains unclear how this detector would perform against different adversarial attacks. In this paper, we aim to address this question by conducting a systematic analysis on the resilience of the LM Detector against eight black-box adversarial attack methods. We also propose a new technique, called StrictPWWS that introduces the semantic similarity constraint into the conventional Probability Weighted Word Saliency (PWWS). Our finding reveals that the selection of a search algorithm helps the attack methods generate better adversarial samples that can bypass the LM Detector. Moreover, tightening linguistic constraints emerges as an effective way to improve the attack success rate. StrictPWWS demonstrates achieving superior performance compared to other adversarial attack methods.
Contagion Effect of COVID-19 - Cerebral venous sinus thrombosis (P1-1.Virtual)

Sunday, April 24May 3, 2022Free AccessContagion Effect of COVID-19 - Cerebral venous sinus thrombosis (P1-1.Virtual)Abhilash Thatikala, Hafsa Bareen Syeda, Shubham Biyani, Owais Syeda, Rucha Bahekar, Sukanthi Kovvuru, Sanjeeva Onteddu, and Krishna NalleballeAuthors Info & AffiliationsMay 3, 2022 issue98 (18_supplement)https://doi.org/10.1212/WNL.98.18_supplement.2987 Letters to the Editor
Efficient Video Retrieval with Advanced Deep Learning Models

Video retrieval is the process of finding specific video content in a large database. This is a crucial challenge in the age of digital multimedia. This article proposes a new approach to video retrieval using advanced deep learning models to extract features and perform retrieval tasks based on those features. Our method combines multiple feature extraction methods, including keyframe extraction, OpenAI CLIP [7] feature extraction, object detection, and automatic speech recognition (ASR). We use BERT [3] embeddings to encode these transcripts and store them in JSON and binary file formats. Our system achieves remarkable results in indexing and retrieving videos based on their visual, audio, textual, and contextual attributes. Our system can also retrieve videos based on either a single text description or multiple text descriptions of a sequence of events. We conducted extensive tests on diverse video data from Ho Chi Minh City AI Challenge 2023 competition organizers to validate the effectiveness of our approach. The results demonstrate that our proposed system is superior to other methods in terms of both retrieval accuracy and speed, making it highly suitable for real-time applications.
Strategies for Autonomous Learning of English as L2 of Pre-Intermediate English Courses at UGC and ESIDE

This research on the autonomous learning of English as a second language (L2) was carried out in conjunction with pre-intermediate English students from La Gran Colombia University (UGC) and the School of Languages and Dialects of the National Army (ESIDE). The main objective was to implement the autonomous learning strategies of English as a L2 in the population sample. Initially, a review of background information related to the theoretical bases was carried out, for which the following categories of analysis were established: Autonomous Learning, Competencies and Abilities, Metacognition and New Technologies of English. The methodology used was mixed with a quantitative predominance under a quasi-experimental design, and we worked with an intentional probabilistic sample. All of the above was carried out through the implementation of instruments such as a competency level pre-test, a semi-structured questionnaire and a post-test, which allowed the results to be contrasted and analyzed. Finally, the results show that more than half of the population obtained academic improvement thanks to the implementation of 27 strategies for autonomous learning of English. Likewise, the adoption of an autonomous culture in their academic and professional lives is evident.
Weaknesses in Emergency Remote Teaching in Higher Education Within the Context of the ODL Learning Component in Turkey

In critical situations caused by crises such as a pandemic, emergency remote teaching (ERT) practices might not be effective because they depend mostly on on-the-spot decision-making. On the other hand, open and distance learning (ODL) has its own dynamics and is a well-planned system. In order to put quality ODL plans into practice in crisis situations, contingency plans, created before any crises, are required. Past crises ought to be examined in order to cope with future crises effectively. This study aims to identify weaknesses in ERT practices in higher education within the context of the learning component of ODL system by focusing on COVID-19 and using it as an example of a past crisis. Exploratory case study was the method used. The study group consisted of 14 faculty and 14 learners from 14 higher education institutions. Qualitative data were collected via semi-structured interviews and documents. The data were analyzed using descriptive and content analysis. Research findings revealed that ERT has many weaknesses in several themes within the context of the learning component of the ODL system; these include teaching method, course structuring, and e-learning materials, among others. In light of the findings, it can be concluded that many factors influence challenges in ERT. Accordingly, to be able to move from ERT to ODL in the next crisis, these weaknesses need to transform into solutions in advance.
Synthbuster: Towards Detection of Diffusion Model Generated Images

Synthetically-generated images are getting increasingly popular. Diffusion models have advanced to the stage where even non-experts can generate photo-realistic images from a simple text prompt. They expand creative horizons but also open a Pandora's box of potential disinformation risks. In this context, the present corpus of synthetic image detection techniques, primarily focusing on older generative models like Generative Adversarial Networks, finds itself ill-equipped to deal with this emerging trend. Recognizing this challenge, we introduce a method specifically designed to detect synthetic images produced by diffusion models. Our approach capitalizes on the inherent frequency artefacts left behind during the diffusion process. Spectral analysis is used to highlight the artefacts in the Fourier transform of a residual image, which are used to distinguish real from fake images. The proposed method can detect diffusion-model-generated images even under mild JPEG compression, and generalizes relatively well to unknown models. By pioneering this novel approach, we aim to fortify forensic methodologies and ignite further research into the detection of AI-generated images.
Classification of knowledge graph completeness measurement techniques

At present, although knowledge graphs have been widely used in various fields such as recommendation systems, question and answer systems, and intelligent search, there are always quality problems such as knowledge omissions and errors. Quality assessment and control, as an important means to ensure the quality of knowledge, can make the applications based on knowledge graphs more complete and more accurate by reasonably assessing the knowledge graphs and fixing and improving the quality problems at the same time. Therefore, as an indispensable part of the knowledge graph construction process, the results of quality assessment and control determine the usefulness of the knowledge graph. Among them, the assessment and enhancement of completeness, as an important part of the assessment and control phase, determine whether the knowledge graph can fully reflect objective phenomena and reveal potential connections among entities. In this paper, we review specific techniques of completeness assessment and classify completeness assessment techniques in terms of closed world assumptions, open world assumptions, and partial completeness assumptions. The purpose of this paper is to further promote the development of knowledge graph quality control and to lay the foundation for subsequent research on the completeness assessment of knowledge graphs by reviewing and classifying completeness assessment techniques.
Multi-Scale Attention Network for Object Detection

Currently, Techniques for detecting objects using deep learning methods are widely used. However, the FPN-based multi-scale feature fusion technology may not fully capture the network's representational capacity. In addition, in the use of attention mechanism, there's a lack of an attention mechanism that can effectively capture long-distance dependency relationships in sequences and adjust the importance of different channels. To address these issues, a novel multi-scale attention algorithm is presented, which incorporates a combination of YOLOv5 and BiFPN structures to replace the original FPN structure. Comparative experiments on different classical algorithms were conducted on the PASCAL VOC2007 and VOC2012 datasets. According to the experimental results, the network equipped with the multi-scale attention mechanism achieves higher detection accuracy. It is important to note that proper nouns and grammar have been translated accurately.
New Approaches to Robust Homogeneous And Clearly Identifiable Cluster Creation

Clustering is a widely used technique in data analysis, but outliers can significantly impact the accuracy of traditional clustering methods. To address this issue, a new clustering technique is proposed that incorporates outliers during clustering. The proposed approach involves using a variable, ($\lambda \gt0)$, to define the cluster radius. Weighted and Unweighted clustering methods are proposed for clustering considering the outliers. Centroids are calculated without using the direct K-means algorithm. Evaluation on standard datasets, such as Fisher Iris, shows improved performance compared to K-means and DBSCAN, with a maximum Silhouette score of 0.568 and an accuracy of 88% even in the presence of outliers.
Experimental Analysis on Breast Cancer Using Random Forest Classifier on Histopathological Images

The rising female death rate can be directly attributed to the prevalence of breast cancer. Due to the lack of resources and the lengthy time required for human diagnosis, an automated diagnosis system for cancer screening is desperately needed. Predicting breast cancer using machine learning might reveal previously unknown traits. In addition, manual detection is arduous work that increases the likelihood of making mistakes and opens the door to misclassification on the part of pathologists. This study introduces a Random Forest Model for the automated identification of breast cancer based on histopathology pictures from the popular Breast Cancer Kaggle dataset, which aims to overcome the aforementioned problems. The study's overarching goal is to learn what characteristics best predict which types of cancer will develop, as well as to spot broader patterns that might guide future model and hyper parameter choices. The aim is to determine the aggressiveness of the breast cancer. A function that can anticipate the incoming input's discrete class has been fitted using machine learning classification methods to do this. Newly generated pictures are used to evaluate the proposed work, with the results being better than those of previous classifiers. Results show that Random Forest Classifier, with an accuracy of 95.67 percent and a reduced error rate, performs better than Deep Convolutional Neural Network.
Rain Removal for Single Image based on DenseNet

Under rainy conditions, computer vision systems will be disturbed by rain, resulting in degraded image quality. So, it is important to remove rain streaks from images and preserve image information meanwhile. For removing rain from a single image, the existing methods, whether based on prior knowledge or neural network, are either excessive or incomplete. In order to effectively remove rain streaks from a single image, the guided filtering is proposed for image pre-processing, the DenseNet is used as the basic network, and the dense blocks with different structures are used to build rain removal network. The experimental results show that, the proposed method can effectively extract the rain streaks residual mapping, remove the rain streaks in the rain image, and better preserve and restore the original background of the image.
Message from the Principal

The main thrust of education is not only providing the right knowledge but also to provide quality real-time learning experience to mould the young minds to become competent engineers. Our institution, M.P.Nachimuthu M.Jaganathan Engineering College is more focused on offering the much needed academic, research and industrial exposure to the students and encourage them to connect with the established industrial and research community. With this objective, I am happy to note that the institution is hosting the “International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS 2023)”. I strongly believe that this conference will be of a great source of knowledge for all the participants.