Reviewerly/exHarmony huggingface dataset:
Iraqi Kurdish Pre-service Teachers and Teacher Educators’ Perceptions on Technological Pedagogical Knowledge and Professional Identity Development

This study intended to investigate pre-service teachers' and teacher educators' perceptions of technological pedagogical knowledge and professional identity development.The research was conducted with 152 English Language Teaching (ELT) pre-service teachers and 73 teacher educators from various universities in Iraqi Kurdistan.Using a mixed-method, the data for this study was collected through semi-structured interviews as well as a Technology, Pedagogy, and Content Knowledge survey questionnaire.Thematic analysis and SPSS 24 were employed for analyzing the interview responses and survey data, respectively.From this data, both preservice teachers and teacher educators perceived a high rate of proficiency in their pedagogical content knowledge as well as familiarity with simple traditional technologies, while they lacked some knowledge of professional technologies.Participants also mostly agreed that pedagogical technologies are effective and affect their professional identity development.However, there were a few participants who believed these technologies were not more effective for their teaching practice and professional identity development.The implications of the study are stated, and some suggestions for further studies in this regard are provided.
Algoritma Multi-Kelas Twin Bounded SVM Untuk Klasifikasi Pola

Pattern recognition is a process of recognizing patterns by using machine learning algorithm. Pattern recognition can be defined as a classification of data based on knowledge that already gained or information extracted from patterns. One method that can be used in pattern classification problem is SVM. In this study we introduced Twin Bounded SVM which is refinement of Twin SVM. The discussion begins with the linear Twin Bounded SVM method to solve a two-class classification problem and followed by an algorithm to solve multi-class classification problem
SCADefender: An Autoencoder-Based Defense for CNN-Based Image Classifiers

Convolutional neural networks (CNNs) have been enormously successful in a variety of image recognition tasks. Robustness is an important metric to evaluate the quality of CNNs. However, recent research shows that CNNs are particularly vulnerable to adversarial attacks. This paper proposes an adversarial defense method to increase the robustness of CNNs, namely, SCADefender. The proposed method trains a reformer on adversarial examples and the training set of a target classifier. The architecture of the reformer is stacked convolutional autoencoder. The adversarial examples are generated by using various adversarial attacks such as untargeted FGSM, untargeted CW [Formula: see text] and untargeted BIS. Given an input image, the trained reformer could remove the adversarial perturbations with a low computational cost. To demonstrate the effectiveness, the proposed method is compared with PuVAE, MagNet, and adversarial training on three well-known datasets including MNIST, Fashion-MNIST, and CIFAR-10. In terms of the average detection rate, the proposed method outperforms other methods. While the proposed method achieves an average detection rate of 97.78% for MNIST, 90.43% for Fashion-MNIST, and 80.64% for CIFAR-10, the comparable methods achieve only 23.69- 86.18% for MNIST, 63.90-79.70% for Fashion-MNIST, and 25.55-77.36% for CIFAR-10.
PELATIHAN MENDELEY DI DAERAH BALONGSARI, KABUPATEN KARAWANG

Abstract--In making meaning requires a representative word choice. Likewise, citations and references are required to be thorough in doing so. Need training that can integrate applications that make work like this more accessible. Mendeley is the application chosen to be applied to the participants in Balongsari village because of the function of this tool which is relatively easy to use by the participants. In this training, the service found that most of the participants, in general, did not know Mendeley's application. However, after the training, the participants knew how to use the Mendeley application. In a more specific sense, Mendeley's training is also a form of plagiarism that may arise due to someone's negligence in writing an article.
Stroke lesion segmentation model based on convolutional fusion and an improved attention mechanism

Ischemic stroke is an acute cerebral blood circulation disorder caused by cerebral artery stenosis or occlusion with high rates of fatality and disability. Magnetic resonance imaging has shown high efficiency in the rapid diagnosis of ischemic stroke. However, the segmentation of stroke lesions is challenging because of their small size and blurred boundaries with normal tissues. Manually segmenting the lesions is subjective, laborious, and time-consuming. Therefore, we propose a segmentation model for stroke lesions that is based on the fusion of convolution and an improved attention mechanism. First, to extract richer context information, we propose a convolutional fusion encoding module that realizes the fusion of two-dimensional convolution and three-dimensional convolution on the contraction path in the encoding end, and enhance features using the convolutional block attention mechanism module. Then, we propose an improved residual-attention gate hybrid decoding module for integrating features with different resolutions focusing on the target region, thus improving the segmentation detail of the edges of small lesions. Lastly, we use a stroke lesion dataset to evaluate its performance in the experiment. The results demonstrate that, compared with the existing models such as TransUNet, SAN-Net, and W-Net, the Dice similarity coefficient of the proposed model exhibits better (0.6193) performance.
A wavelength-multiplexed photonic tensor processor based on Mach-Zehnder modulator

Photonic processors have shown great potential to replace electronic processors due to the high parallelism, low latency, and low power consumption of light. The current direction of integrated optical computing research is focused on realizing vector matrix multiplication in light, which is also called photonic tensor processor. There are two main implementation schemes of photonic tensor processors: coherent and wavelength division multiplexing, and the latter is represented by the micro-ring resonator (MRR) weight bank. However, the MRR is highly sensitive to the manufacturing error, and it takes a long time to calibrate before used for calculation, which makes the realization of a large-scale MRR weight bank a challenge. We propose a novel architecture of wavelength-multiplexed photonic tensor processor based on Mach-Zehnder modulators (MZMs), which can reduce the time and power cost of device calibration We implement a 4×4 optical convolution operator based on the above architecture, which is used to solve the MNIST handwritten digit recognition problem. Prediction accuracy of 91.72% is achieved compared with the accuracy 97.58% achieved by a computer.
How capable are state-of-the-art language models to cope with sarcasm?

Sarcasm detection has established itself as one of the more difficult Natural Language Processing tasks, due to the complex nature of sarcasm. This paper aims to benchmark the performance of state-of-the-art models like BERT, RoBERTa, ALBERT and GPT-3 when faced with this task. The dataset selected is MUStARD, which has increased in popularity in recent years, especially for multimodal tasks, and is one of the most qualitative and data rich dataset. An untuned GPT-3 based model was selected as the baseline and all the other models were fine-tuned using the textual data present in MUStARD, mainly the context and utterance information. The best performer was found to be the GPT-3 fine-tuned model, with an F1 score of 77. This is in line with the reported feats of GPT-3 based models that have popularized in recent months and reaffirms the superiority of GPT-3. Future avenues of research are then presented and explored, and the conclusions are drawn.
DetNetAuth: efficient authentication and integrity protection for deterministic networking

Traditional computer networks provide a best-effort packet delivery service. Packets may be lost or delayed due to network congestion or security threats. Deterministic Networking (DetNet) aims to provide consistent and predictable delivery of data packets in real-time with extremely low data loss rates, bounded latency and jitter. Deploying DetNet in a standard IP based network presents multiple technical challenges and security concerns [1]. This paper presents a security solution that ensures the authentication and integrity protection for the DetNet data plane. We have implemented using the Data Plane Development Kit (DPDK) an efficient computation of the DetNetAuth header - a simplified version of the Authentication header [2] - that will be added after the layer 3 headers. The DetNetAuth header computation process is significantly accelerated compared to the state of the art IPSec Authentication Header (IPSec AH). The optimization technique involves pre-computing intermediate results for the HMAC function and the utilization of solely the layer 3 headers and the secret key for the HMAC computation. Our implementation can be used in networks where deterministic flows are desired, as it provides protection to a wide range of attacks: Replication-related (header manipulation), Spoofing and Replay attacks.
Deploying large private clouds for high availatibily services

In a world where the price of both close-to-home computing such as workstations, GPUs and CPUs, and public cloud infrastructures is increasing, we need to explore a way in which we can deploy private, cost effective cloud infrastructures that can be used for institutions such as research centers and universities. One such private cloud infrastructure provider is OpenStack, a well rounded and modular private cloud system which allows users to manage virtual machines in the same way as a public cloud provider, such as AWS or Google Cloud.This paper explores how an institution can deploy a private cloud solution inside of their existing infrastructure which can provide support for both their users running virtual machines, but also for administrators to migrate their services to a cost-effective highly available infrastructure.
Integrating Blockchain Technology in a Cyber Physical System to Secure Data

Considering the constant growth and evolution of technology, being able to secure data is a challenge. This is why researchers find this a top research topic. Cyber-Physical Systems are complex, large-scale systems that can be found in domains like healthcare, transportation, industrial controls systems or smart grids. Having many components that interact with each other, we can observe here the challenge in maintaining a secure environment for the data as data tampering or data extraction can occur, for example, in the actual transportation of data from one device to another. The main focus of this paper is to give an answer if blockchain technology provides a solution in building a secure network. We give a short review of the related work regarding blockchain integration in large-scale systems like Cyber-Physical Systems. It was also briefly analyzed the layered structure of a blockchain and discussed some solutions for each layer. We then discussed about consensus algorithms for validation and authentication process. In general, consensus algorithms are implemented to assure the validation process of the nodes in a network. In this paper we tried to analyze and propose a model that offers an extra layer of security by adding the authentication step. In this way, the nodes that are validated are also considered trusted nodes in the network.
Upaya meningkatkan hasil belajar siswa melalui penerapan model pembelajaran discovery learning

The application of the Discovery Learning learning model aims to create activity-based learning discoveries that will have an impact on improving student learning outcomes. This type of research is research class action using the Kemmis and Mc Taggart models. This research was conducted at SMK Negeri 2 Merauke, which focuses on mixed class XI, has 11 students in odd semesters. The purpose of this activity to improve learning activities so that they can run effectively according to conditions. Research instruments that used in the form of teacher observation sheets, student observation sheets and student test sheets. Observation sheet for measure the effectiveness of learning activities and test sheets are used to measure student learning outcomes. This research was conducted through 2 cycles. The results of this study is an increase in learning activities students marked by the average percentage of student activity in cycle 1 of 42% (enough) rose to 70% (good) in cycle 2. Student learning activities influence student learning outcomes indicated by the presence of an average percentage of completeness learning outcomes cycle 1 of 0% (low) increased to 72.7% (good) in cycle 2.
Virtual worlds, real technologies: an insight into Metaverse and its principles

The Metaverse is a new concept that aims to revolutionize the way people perceive the time and space in which they live. Thus, through Metaverse, the convergence between the real and the virtual world is achieved on the basis of a combination of augmented reality, virtual reality, and other emerging technologies. The Metaverse has the potential to transform many aspects of society, from entertainment and gaming to education, health, culture, economy business, and social interaction. One of the key benefits of the Metaverse is its ability to connect people from all over the world in a virtual space. This could have a significant impact on the way we work, communicate, and socialize. It could also provide new opportunities for collaboration and innovation, as people from different parts of the world can easily connect and work together. The Metaverse could also have the potential to revolutionize the way we approach education and could open up new opportunities for learners of all ages and backgrounds. It could also provide new opportunities for content creators, such as virtual concerts, performances, and exhibitions. In this paper we intend to present the project “Advanced research in Metaverse and emerging technologies for the digital transformation of society“ and to analyze the most important implications of Metaverse in many aspects of society.
Evolutionary hyper-heuristics for improving global optimization algorithms

In this paper, a new global optimization methodology is proposed. The approach presented is a hyper-heuristic, namely Evolutionary Bald Eagle Search (EBES), based on the Bald Eagle Search (BES) optimization algorithm. Additionally, for comparison, Evolutionary Particle Swarm optimization (EPSO) is demonstrated, which is very similar with EBES in the overall approach, but is based on the well-known PSO heuristic. The main methodology consists in a genetic algorithm that is employed as a high-level method to adjust the hyper-parameters of the low-level heuristics in an online manner in order to maximize the performance of the search for global optima. The proposed hyper-heuristics are compared with the heuristics on which they are based, showing a significant improvement in performance.
Book review: Seda Khadimally (2023). Applications of Machine Learning and Artificial Intelligence in Education. Series: Advances in Educational Technologies and Instructional Design. Information Science Reference.

The book "Applications of Machine Learning and Artificial Intelligence in Education" explores the transformative potential of machine learning and artificial intelligence (AI) in modern education, emphasising the importance of adapting to the needs of 21st-century learners.With the growing demand for innovative technology-moderated tools, traditional learning and instructional practices are evolving into machine-facilitated communication, education, and teaching methodologies.This comprehensive volume delves into various aspects of remote learning, machine learning, deep learning, and AI in contemporary education, covering topics such as data coding, social networking technology, and computational linguistics.The book aims to provide valuable insights for learners interested in deep learning, educators, educational technologists, instructional designers, data evaluators, and special interest groups (SGIs) in the discipline.
FVP: Fourier Visual Prompting for Source-Free Unsupervised Domain Adaptation of Medical Image Segmentation

Medical image segmentation methods normally perform poorly when there is a domain shift between training and testing data. Unsupervised Domain Adaptation (UDA) addresses the domain shift problem by training the model using both labeled data from the source domain and unlabeled data from the target domain. Source-Free UDA (SFUDA) was recently proposed for UDA without requiring the source data during the adaptation, due to data privacy or data transmission issues, which normally adapts the pre-trained deep model in the testing stage. However, in real clinical scenarios of medical image segmentation, the trained model is normally frozen in the testing stage. In this paper, we propose Fourier Visual Prompting (FVP) for SFUDA of medical image segmentation. Inspired by prompting learning in natural language processing, FVP steers the frozen pre-trained model to perform well in the target domain by adding a visual prompt to the input target data. In FVP, the visual prompt is parameterized using only a small amount of low-frequency learnable parameters in the input frequency space, and is learned by minimizing the segmentation loss between the predicted segmentation of the prompted target image and reliable pseudo segmentation label of the target image under the frozen model. To our knowledge, FVP is the first work to apply visual prompts to SFUDA for medical image segmentation. The proposed FVP is validated using three public datasets, and experiments demonstrate that FVP yields better segmentation results, compared with various existing methods.
Congruity approach in the educational process

The issues of improving the quality of education in the context of the growing environmental crisis and the decline in the population health of students require thinking of the lessons within the scope of nature-friendly organization of the educational process, which is especially important in the context of the current pandemic period.Objective of the study is to consider a new type of educational lesson for a university – a “fresh” lesson in the open air, developing a plan for a ‘fresh” lesson in physics.Methodology and research methods: the article reveals the methodological aspects of domestic and similar foreign experience in organizing outdoor training lessons, the prototypes of “fresh” training lessons in the pedagogical heritage of the classics are considered; a sociological survey on the research problem was conducted.Scientific novelty: the concepts of “fresh lesson” and “pedagogical fresh technologies” are substantiated and introduced into scientific circulation, empirical data on the problem under study are obtained, and didactic tools for a “fresh” lesson are developed.Practical significance: practical recommendations are proposed for organizing “fresh” training lessons on the territory of the green zone of an educational institution, recommendations and the development of a training lesson in Physics for undergraduate students of universities, colleges or high school students.Results: the changes in the motivation of students in the conditions of a new type of practical training for a university, college and school - a “fresh” lesson in nature are considered, the importance of “fresh” pedagogical fresh technologies for the health keeping of students and the greening of the educational process is disclosed, the results of an empirical study on implementation of “fresh” training lessons in the educational process of the university.
Course Recommendation Application for Admission to University

Studying in Higher Education institutions has been the dream of many students who completed their post-secondary schools. This study aims to assist the students in their decision for admission to the program courses offered at Universiti Kuala Lumpur (UniKL). A wide variety of technical program courses are offered on UniKL’s twelve campuses. Every campus institute has specific specialisation program courses. Students who wished to study at UniKL usually would have to search for courses offered on various UniKL campuses. Usually, students could not decide which courses suited their personality and future career path. This study proposes a prototype application that assists and recommends the students with their course selection according to their personality and final examination results. The prototype would let the students take a personality test that allows them to understand their personality and proposes a list of UniKL courses that match their personality and examination results. Therefore, the students would have a clear view of the prospective opportunities the program courses have to offer and the direction of their future careers. The career prediction recommendation would let the students target suitable courses that match their examination results and personality.
THE WIND ENERGY POTENTIAL IN COASTAL AREAS OF MERAUKE CITY

Energy transformation to new and renewable energy starts from the green economy, green technology, and green products. The electricity demand in 2060 reached 1942 TWh which was dominated by the industrial and transportation sectors. Electricity demand is supplied by 96% renewable energy and 4% new energy with a total capacity of 708 GW. Merauke city with a geographical location between 137-141 degrees and 5-9 degrees of 46,791.63 km2 is a flat area and has several coastal areas, namely : Lampu Satu beach, Payum beach, and Domande beach with a beach length of 846.36 km. The method used is Topographical analysis, Google map and ESDM one map. Merauke city has two potential areas for wind energy, namely plains with a green spectrum having wind speeds of 4-6 m/s while beaches coastal with a red spectrum wind speed exceeding 6 m/s. For an altitude of 50 meters from the ground at coordinates 141.59621 - 8.04586 degrees, green area with wind speed of 4.27 m/s, area of 0.99 km2 and Musamus University at coordinates 140.47110 - 8.103640 degrees, wind speed 4.97 m/s for an area of 0.99 km2. The point 141.563250 - 8.040420 degrees is placed on the . Lampu satu beach wind speed 8.04 m/s, Domande beach 6.56 m/s, Payum beach wind speed 6.75 m/s