Reviewerly/exHarmony huggingface dataset:
Automated Siamese Network Design for Image Similarity Computation

Despite the success of Siamese networks in image indexing, face recognition, and signature verification, there has been little research on designing their architectural space compared to convolutional neural networks (CNNs). This work aims to automate the design process of Siamese network architectures and improve their performance in tasks that involve image similarity computing such as indexing and retrieval. To achieve this goal, in contrast with the current literature that focuses on improving the design of the backbone CNN, we use Differentiable Neural Architecture Search (DNAS) to explore the architecture of the Multi-Layer Perceptron (MLP) component of siamese networks, namely the projector and/or predictor heads. The main objective of these MLPs is to enhance the ability of backbone CNNs to learn strong representations from unlabeled data. Using a well-known contrastive learning framework (SimCLR) as a baseline, we show that our approach managed to improve performance on several computer vision tasks such as image classification (ImageNet) and content-based image retrieval (INRIA Holidays).
ÖWF-OD: A Dataset for Object Detection in Archival Film Content

In this paper we propose ÖWF-OD, a new dataset for object detection in archival film content. The dataset enables the evaluation of object detection methods on image data with very different image qualities. 1,000 selected keyframes from 100 hours of video material have been annotated, 4,480 bounding boxes are labeled. In addition to these annotations, image quality measures were calculated for the keyframes in order to assess the influence of image quality on object detection. We evaluate different versions of the YOLO object detector to provide a baseline of object detection results for this dataset. The annotation data and the code to extract the keyframes are provided at https://github.com/TailoredMediaProject/OEWF_ObjectDetection.
Efficient Search with an Interactive Video Retrieval System for Novice Users in IVR4B

In this paper, we present the second release of VideoCLIP, an interactive CLIP-based video retrieval system that participated in the Video Browser Showdown 2023. While we continue to use the underlying architecture to map the content between image and text, we concentrate on improving the user experience for novice users. Specifically, we have implemented three different query modalities and redesigned the user interface in order to adapt to the context of the Interactive Video Retrieval for Beginners (IVR4B) workshop. These modifications ultimately aim to provide newcomers with a simple and efficient user experience to locate the desired videos.
Backtracking Exploration for Reinforcement Learning

Exploration of the behavior policy plays an important role in reinforcement learning as it helps learning algorithms escape local optima. Taking linear value function approximation as an example, exploration directly affects the sampling of states, thereby altering the distribution of states. This distribution is a component of the key matrix, and the magnitude of the smallest eigenvalue of the key matrix is proportional to the convergence speed. However, existing exploration methods are constrained by the MDP chain and require step-by-step backtracking to reach the target policy distribution. This paper breaks the assumption that the action settings of the training environment must be identical to that of the testing environment by introducing state resetting in the training environment and proposes a backtracking exploration algorithm with time window and punishment. This algorithm can be directly combined with existing exploration strategies and value function update rules, and it has the potential to become a new paradigm for the training process in reinforcement learning. Experimental results validate the effectiveness of the proposed algorithm.
DCAC: Reducing Unnecessary Conservatism in Offline-to-online Reinforcement Learning

Recent advancements in offline reinforcement learning (RL) have facilitated the training of powerful agents using fixed datasets exclusively. Despite this, the quality of a dataset plays a critical role in determining an agent’s performance, and high-quality datasets are often scarce. This scarcity necessitates the enhancement of agents through subsequent environmental interactions. Particularly, the state-action distribution shift may exert a potentially detrimental effect on well-initialized policies, thus impeding the straightforward application of off-policy RL algorithms to policies trained offline. Predominant offline-to-online RL approaches are typically founded on conservatism, a characteristic that may inadvertently confine the asymptotic performance. In response, we propose a method referred to as Dynamically Constrained Actor-Critic (DCAC), grounded in the mathematical form of dynamically constrained policy optimization. This innovative method enables judicious adjustments to the constraints on policy optimization in accordance with a specified rule, thus stabilizing the initial online learning stage and reducing undue conservatism that restricts asymptotic performance. Through comprehensive experimentation across diverse locomotion tasks, we have ascertained that our method successfully improves the policies trained offline with various datasets via subsequent online environmental interactions. The empirical results substantiate that our method mitigates the harmful effects of distribution shift and consistently attains superior asymptotic performance in comparison to prior works.
Investigation of Teachers' Sustainable Earthquake Awareness and Earthquake Knowledge Levels

This research aims to examine the relationships between teachers' earthquake knowledge levels and sustainable earthquake awareness levels. A relational survey model with a quantitative research method was chosen for the research. The research group consists of 255 teachers, 166 women, and 89 men, working in private and public institutions in Kocaeli province in the 2022-2023 academic year. The research group was determined by convenient sampling, one of the purposeful sampling methods, and the "Sustainable Earthquake Awareness Scale" and "Earthquake Knowledge Level Scale" were used as data collection tools. The data of the research were analyzed in a particular statistical program using quantitative methods. As a result of the analysis, it was determined that teachers' sustainable earthquake awareness levels did not differ according to their gender and age, and while their earthquake knowledge levels did not differ according to their gender, they did vary according to their age. Additionally, it was observed that there was a positive relationship between teachers' earthquake knowledge levels and sustainable earthquake awareness levels.
Application of Federated Learning to Prediction of Patient Mortality in Vasculitis Disease

In this paper, we propose two methods of application of federated learning to the construction of classifiers for the analysis of data related to predicting the death of patients suffering from the vasculitis. The paper contains results of experiments on medical data obtained from Second Department of Internal Medicine, Collegium Medicum, Jagiellonian University, Krakow, Poland. In order to evaluate the proposed methods, which are trained on data samples, we compared their functionality with the work results of classical classifiers trained on the entire data. It turned out that the quality of classification of federated learning methods is comparable to the quality of classical methods. This means that access to the whole data is not necessary to construct effective classifiers for the considered decision problem.
Preventing Text Data Poisoning Attacks in Federated Machine Learning by an Encrypted Verification Key

Recent studies show significant security problems with most of the Federated Learning models. There is a false assumption that the participant is not the attacker and would not use poisoned data. This vulnerability allows attackers to use polluted data to train their data locally and send the model updates to the edge server for aggregation, which generates an opportunity for data poisoning. In such a setting, it is challenging for an edge server to thoroughly examine the data used for model training and supervise any edge device. This paper evaluates existing vulnerabilities, attacks, and defenses of federated learning, discusses the hazard of data poisoning and backdoor attacks in federated learning, and proposes a robust scheme to prevent any categories of data poisoning attacks on text data. A new two-phase strategy and encryption algorithms allow Federated Learning servers to supervise participants in real-time and eliminate infected participants by adding an encrypted verification scheme to the Federated Learning mode. This paper includes the protocol design of the prevention scheme and presents the experimental results demonstrating this scheme’s effectiveness.
Improving Detection Efficiency: Optimizing Block Size in the Local Outlier Factor (LOF) Algorithm

Detecting outliers in data is essential in various fields, such as finance, healthcare, and many other domains with anomalies. Among well-known outlier detection algorithms, Local Outlier Factor (LOF) is widely used for identifying unusual data points. However, the computational time of LOF significantly increases when dealing with large datasets containing numerical and categorical features. We propose an innovative approach using block size optimisation to speed up the outlier detection process while maintaining high accuracy. By optimizing the block size, we achieve a significant improvement in LOF’s performance without compromising its effectiveness. Experiment results on diverse datasets containing mixed categorical and numerical features demonstrate the effectiveness of our method in accelerating outlier detection while retaining high detection accuracy. This advancement in outlier detection has the potential to improve decision-making processes. It empowers the timely identification of anomalous events, which is significant in critical applications, including cybersecurity.
A New Academic Discipline on Artifacts

Professor Masayuki Matsui from The University of Electro-Communications, Tokyo, proposes that Industrial Engineering and Operational Research (IEOR) can be viewed as the art of his 3M&I-body system3M&I-body system. In earlier years, F.W. TaylorF.W. Taylor had named its trial on shop the “scientific” managementScientific management, and also, N. WienerN. Wiener had named its trial on the similar relation of machine and animal the “cybernetics”Cybernetics in Greece. His study has first focused on artifact bodyArtifact body, and has started at the ellipse structure of white-box type in enterprise body. The pair-methodsPair-methods and findings have led to a new academic disciplineNew academic discipline that contribute to the integration of knowledge and intelligence in nature versus artifacts science. This research facilitates the science and its realization of the middle-initiative and cyber/real worldCyber/real world, including enterprise robots, cloud-coordinated chain management, and has the potential to be applied to the nature versus artifactsNature versus artifacts science, and also, well-beingWell-being/smart cities in the near future.
Indonesian literacy teachers’ efforts to integrate children’s literature in literacy classrooms

Literacy is now an essential part of teaching for Indonesian teachers. This qualitative case study reports on three Indonesian teachers as they integrate the use of children’s literature into their literacy instruction. For 4 months, the teachers’ efforts to use children’s literature were documented through multiple sources such as focus group discussions and interviews, teachers’ reflections, and classroom observational notes. The study uses the reader-response theory framework, especially the notion of teachers as part of the learning context in promoting students’ engagement with a text. The study highlighted some case study teachers’ classroom practices centering on children’s literature, giving access to books, infusing children’s literature in teaching, and using books to discuss difficult topics with students. The findings discussion sheds light on some contributing factors to the teachers’ decision to integrate children’s literature into their classrooms and how they could make such decisions. The discussion points to a larger picture of how these teachers were empowered to make decisions to integrate children’s literature, particularly with diverse themes.
Energy-optimal trajectories for skid-steer rovers

This paper presents the energy-optimal trajectories for skid-steer rovers on hard ground, without obstacles. We obtain 29 trajectory structures that are sufficient to describe minimum-energy motion, which are enumerated and described geometrically; 28 of these structures are composed of sequences of circular arcs and straight lines; there is also a special structure called whirls consisting of different circular arcs. Our analysis identifies that the turns in the trajectory structures (aside from whirls) are all circular arcs of a particular turning radius, R′, the turning radius at which the inner wheels of a skid-steer rover are not commanded to turn. This work demonstrates its paramount importance in energy-optimal path planning. There has been a lack of analytical energy-optimal trajectory generation for skid-steer rovers, and we address this problem by a novel approach. The equivalency theorem presented in this work shows that all minimum-energy solutions follow the same path irrespective of velocity constraints that may or may not be imposed. This non-intuitive result stems from the fact that with this model of the system the total energy is fully parameterized by the geometry of the path alone. With this equivalency in mind, one can choose velocity constraints to enforce constant power consumption, thus transforming the energy-optimal problem into an equivalent time-optimal problem. Pontryagin’s Minimum Principle can then be used to solve the problem. Accordingly, the extremal paths are obtained and enumerated to find the minimum-energy path. Furthermore, our experimental results by using Husky UGV provide the experimental support for the equivalency theorem.
The ‘professional digital competence’: Exploring the perspective of primary music teachers in Spain

The terms ‘Digital competence’ or ‘Professional digital competence’ are frequently used, yet their understanding remains varied and elusive. Moreover, existing research in education heavily focuses on the study of digital competence among students, with comparatively limited attention given to teachers. In this study, we aim to investigate primary music teachers’ perspectives on ‘professional digital competence’ in Spain. This entails understanding their conceptualizations on (1) the term itself, (2) the cultivation of related skills and (3) its practical application with students. Additionally, we seek to compare primary music teachers’ viewpoints with those of teachers from other subjects. Through a qualitative multi-case study involving purposeful sampling of 20 participants, the results suggest that our music teachers, regardless of their self-stated technological skills, possess a limited understanding of the ‘professional digital competence’ and express negative or pessimistic beliefs towards its development and implementation. Furthermore, they feel compelled to use educational technology despite their reservations. Our findings similarly indicate that their beliefs are not meaningfully different from those of non-music teachers in the same context. In discussing the implications, we suggest that these beliefs may lead to stress and anxiety among participants, necessitating support from educational administrators. Additionally, we propose that future research should further investigate both why and how our participants feel/are pressured to use technology, and the particular disadvantages of technology in music education identified in our analysis of their beliefs.
Development of a Textured Picture Book Equipped with Crosswords as a Media for Learning Biology Sub-Material Epithelial Tissue

Purpose of the study: To test the suitability of textured picture books equipped with crossword puzzles as a learning medium for Biology, epithelial tissue sub-material for class XI semester 1, it was assessed by experts and teachers and reviewed based on the effectiveness of its use.&#x0D; Methodology: This research uses the Research and Development (R&amp;D) method with a 4-D development model. The research samples involved were 18 test students and 16 control students. Qualitative data was obtained from advice from material experts and media experts, quantitative data from the results of assessments carried out by media experts, material experts, teachers and students&#x0D; Main Findings: Based on the assessment of media material experts, it has appropriate criteria (very good) with a percentage of 90.9%, the assessment according to media experts is included in the appropriate criteria (very good) with a percentage of 85.5% and the assessment according to teaching teachers is in the appropriate category (very good) with a percentage of 98.48%. The effectiveness of the media on student grades is in the quite effective category with an average test student score of 6.77 compared to an average control student score of 5.70. The media readability test after use in learning is in the very good category with a percentage of 86.1%.&#x0D; Novelty/Originality of this study: The novelty of this research lies in its innovative approach in presenting learning materials, expanding evaluation methods, and its potential to increase students' understanding and involvement in learning epithelial tissue topics.
KARMA VERİ İKİLİLEŞTİRME YÖNTEMİ İLE YENİ BİR RASYONEL SINIFLANDIRMA YAKLAŞIMI

Classification algorithm is a supervised learning technique that is used to identify the category of new observations. However, in some cases, quantitative and qualitative data must be used together. With this approach, we tried to overcome the problems encountered in using quantitative and qualitative data together. In this paper, we model a new classification technique by converting all types of data to binary data because in real world, data are classified in different types such as binary, numeric or categorical. By this way, we develop a more accurate and efficient mixed data binarization approach for multi-attribute data classification problems. First, we determine the classes from available dataset and then we classify the new instances into these predetermined classes by using the new proposed data binarization approach. We show how each step of this algorithm could be performed efficiently with a numeric example. Then, we apply the proposed approach on a well-known iris dataset and our model show promising results and improvements over previous approaches.
Analysis of Reading Skills of Grade 1 Students of SDN 6 Yangapi Through Pop Up Book Media

The aim of the research is to analyze children's reading skills through Pop Up Books. Data was collected using a quantitative descriptive method, which means a type of research that uses quantitative data to describe or explain phenomena or variables that can be measured and calculated. The sample was 34 students, namely 17 men and 17 women. The low reading skills in this class are the reason for researchers to research. Reading skills are classified as good (B) with a calculation result of 75%.
A secure authenticated image encryption scheme based on elliptic curve cryptography

The picture archiving and communication system is a powerful mechanism to store and securely transmit images and reports. The development of a cost-effective encryption algorithm in terms of time and space complexity is needed. Our main goal is to protect the content and integrity of the image being transmitted. We implement an efficient authenticated encryption algorithm based on the Lehmer Random Number Generator, the Sophie Germain Prime Generator and Elliptic Curve Cryptography to transmit the digitally preserved images (including medical) which serves our purpose. Our method achieves randomness along with authentication with the help of random number generator and Elliptic Curve Cryptography which is unique compared to the existing schemes. The performance metrics of the proposed system stay on top of the state-of-the-art techniques that were recently derived. We obtained negative correlation coefficient values of −0.000242, −0.001349, −0.001313 for vertical, horizontal, and diagonal coefficients respectively. This makes our scheme better as it is difficult to find any relationship between the original and encrypted image. On average, our method uses only 10 seconds for encryption using a 521-bit curve.
Impact of Student-Student Interaction Learning Model Faced with Case of Studying IPA on V-Class in the MIS Hayatul Islam Cinangka

of the constraints affecting the learning outcomes of the IPA in students is the lack of variation of the learning model used, then students end up feeling bored and unenthusiastic so that their learning results are interrupted. The study aims to investigate the influence of the Student-Student Interaction (SSI) learning model on the learning outcomes of V-grade students on the IPA students' eyes, using the comparison of the STAD cooperative learning model. (Student Team Achievement Divisions). This research uses a quantitative approach to experimental design with a quasi-experimental design. The study was conducted with a sample of 61 students divided into two, namely 31 experimental classes using the Student-Student Interaction learning model and 30 control class using the STAD learning model (Studant Team Achievement Divisions) in the academic year 2019/2020 at MIS Hayatul Islamiyah. Based on the results of the study using the hypothesis test and nonparametric test Mann Whitney with a 95% confidence rate showed a Sig. (2-tailed) value of 0.044 which means 0.044 &lt; 0.05 so it can be concluded that there is a significant difference to the learning outcome of IPA class V between classes using the learning model SSI (Student-student Interaction) and class using the cooperative learning model STAD type (Student Teams Achievement Divisions).
Multi-stream Point-based model for Blind Geometric Point Cloud Quality Assessment

The evaluation of 3D point cloud quality is a critical component in the development of immersive multimedia systems for real-world applications. While perceptual quality evaluation technics for 2D images and videos have reached high performances, developing robust and efficient blind metrics for point cloud quality assessment is still challenging. In this paper, we propose a no-reference point cloud quality assessment method that evaluates the quality of degraded 3D objects using an end-to-end point-based multi-stream model. To capture the geometric degradation of the point cloud, we incorporate normals, curvatures and geometric coordinates. Then, we divide the distorted object into sub-objects, which are fed to a multi-stream network to extract significant features of the geometric degradation. Afterward, these features are used to predict the quality of each sub-object, and the perceptual quality score of the point cloud is obtained by averaging the quality scores of all sub-objects. Experimental results demonstrate that the proposed model achieves promising performance compared to state-of-the- art full and reduced methods.