Reviewerly/exHarmony huggingface dataset:
Penerapan Algoritma K-Means Clustering untuk Mengetahui Pola Penerima Beasiswa Bank Indonesia (BI)

Bank Indonesia Scholarships are a type of scholarship sourced from Bank Indonesia for students from selected State Universities, Private Universities, and Polytechnics. From the data on scholarship recipients who have passed the selection from 2020, 2021, 2022 universities in Riau, it is necessary to look for the behavior patterns of scholarship recipien because Bank Indonesia does not yet have a pattern. To find the pattem from scholarship recipients using the method of data mining with K-Means Clustering algorithm. The parameters used are 4, namely study program, semester, GPA, and level. The results of the study using RapidMiner showed that cluster 0 was dominated by students from the Commerce Shipping Management study program, who were in semester 5 and D3 level. Cluster 1 is dominated by students from the Accounting and Management study program, in semester 7, with GPA greater than or equal to 3.51, and S1 level. Cluster 2 is dominated by students from the Nursing study program, in semester 5, with GPA greater than or equal to 3.51, and D3 level. Cluster 3 is dominated by students from the International Relations study program, in semester 7, with GPA greater than or equal to 3.51, and S1 level. Cluster 4 is dominated by students from the Informatics Engineering study program, in semester 5, with GPA greater than or equal to 3.51, and S1 level. It show that the recipients of Bank Indonesia scholarships are dominated by students with high GPA scores or equal to 3.51. In addition, it is also dominated by students who are at the S1 level. Tests were carried out using DBI with k=5 resulting in a validity value of 0.121.
Klasifikasi Jenis Mangga Berdasarkan Tekstur Tulang Daun Menggunakan Metode Learning Vector Quantization (LVQ)

Mango is a fruit plants that has the potential to be developed because it has a high level of genetic diversity. Mangoes vary in shape, size and color of the fruit, indicating a fairly wide genetic diversity. Of the many genetic diversity and types of society, there are still many who cannot distinguish them. This study builds an application to distinguish mango species based on the leaf bone structure where the feature extraction process uses the Learning Vector Quantization (LVQ) method. Then do the merging to produce a specific feature vector, then the classification calculation is carried out using the Euclidean Distance method to identify the type of mango fruit. The results of the study with the amount of training data as many as 6 images and testing data as much as 15 obtained system accuracy, with the calculation results, the remaining clusters are cluster 3 with a centroid value of R = 151.67 G = 145 and B = 153.33. From the test results with 2 scenarios, the mango golek type has low values, namely 50 and 60 because the mango golek type has less brightness than the other 2 types of mango.
Measuring social and psychological factors through home-study setting towards improved academic performance

This study determined the relationship among social and psychological factors through home -study setting of Grades 6 pupils in Liciada Elementary School, in Bustos, Bulacan during the first quarter of School Year 2022-2023. The main objective is to find the implications of social and psychological factors on the achievement of pupils and finding benefits of home-study setting. The method used in the study was t-test and pearson (r). Using descriptive correlational research as research design in the total population of 167 Grade 6 pupils as respondents of the study, the answers to the problems raised in this study were summarized as follows: The number of siblings and socio-economic status has a significant relationship between pupils’ demographic profile and social factor in home study setting with p-values 0.011 and 0.000 which is less than the level of significance at 0.05. Social factor in home-study setting and respondents’ academic achievement has a significant relationship with p-value of 0.001 which was less than the level of significance at 0.05. On the other hand, psychological factor in home study setting and respondents’ academic achievement has no significant relationship with p-value 0.220 which was greater than the level of significance at 0.05. The pupil’s achievement on this present research will be useful to the parents and policymakers in finding the benefits, policy framing, and advantages of home-study.
DETERMINATION OF HEAVY METALS AND TOTAL PETROLEUM HYDROCARBONS IN SOIL SAMPLES AND PLANT LEAVES AROUND OIL REFINERIES LOCATED ON ERBIL-GWER ROAD

Several refinery complexes exist in the Kurdistan Region of Iraq that produce various kinds of petroleum products. During the refining process, different types of gases, metals, hydrocarbons, and liquids are discharged into the environment. This study aimed to determine the concentration of eight heavy metals in soil and plant leaves collected around the refineries along Erbil-Gwer Road on two occasions throughout the study period. Additionally, we aimed to evaluate the levels of Total Petroleum Hydrocarbons (TPH) in the soil samples from the area. The results of the analysis revealed significant differences (P&lt;0.05) in the concentrations of Pb, Ni, Cd, Cr, As, and Mn between the dry and wet seasons in the soil samples. However, no significant differences were observed for Cu and Zn between the two seasons. The levels of metals for both periods were as follows: Mn&gt; Cr&gt; Zn&gt; Ni&gt; Cu&gt; Pb&gt; As&gt; Cd in successive forms. Regarding the plant leaves, significant differences (P&lt;0.05) were found between the study periods for Pb, Cu, Zn, and Cd, whereas no significant differences were observed for the other metals. Furthermore, the concentration of TPH in the soil samples located alongside the refinery effluent stream was higher compared to the other areas.
Deep Blue

Abstract: Deep Blue is a chess machine or in simple language it is a chess engine made by the top programmers of IBM. The purpose of this research is to review the famous Deep Blue Chess engine which defeated Garry Kasparov, the famous World Chess Champion in a six – game match in 1997. Following are the main features of the Deep Blue: A single chip chess engine. A massively parallel system with multiple level of parallelism. A strong emphasis on search extension. Effective use of grandmaster’s game database. This research paper will describe a Deep Blue system and give all the interesting information about this chess engine along with all the other few important Chess engine. Why deep blue only? – Deep blue is the first chess engine who defeated The World Chess Champion Garry Kasparov in 1997
Effects of Pseudo Labels in Pose Estimation Models Using Semi-supervised Learning

This paper aims to analyze the effects of pseudo labels for training pose estimation models using semi-supervised learning. One of the problems in training pose estimation models is the high cost of labeling, which requires keypoint annotation of training data. Pseudo-labels estimated by other pose estimator models are able to improve the performance of another estimator. This paper investigates the trade-off between the number of annotations by humans and pseudo labels given by the model.
Holography in Medicine: Three-Dimensional Visualization for Surgeons

Holography, once the realm of science fiction, has become an emerging technology with significant potential in the field of medicine. This paper explores the applications of holography in medicine, focusing on its role in providing three-dimensional visualization for surgeons. The use of holography in medical imaging, surgical planning, education, and telemedicine is discussed, along with the current challenges and future prospects. By presenting a comprehensive overview of holography's integration into the medical field, this paper aims to shed light on the transformative potential of holographic visualization for surgeons and ultimately enhance patient care.
Research on Water Surface Environment Perception Method Based on Visual and Positional Information Fusion

The water surface environment characterised by complexity and variability, is heavily influenced by weather. To address this problem, this paper proposes a water surface environment perception network based on the fusion of visual and positional information, and proposes an encoder-decoder based semantic segmentation neural network for classifying the pixel points of the input image into three categories: water, sky and environment (obstacles).
DeepMultiple: A Deep Learning Model for RFID-based Multi-object Activity Recognition

Wireless sensing techniques for Human Activity Recognition (HAR) have been widely studied in recent years. At present, research on HAR based on Radio Frequency Identification (RFID) is changing from the tag attachment method to the tag non-attachment method. Affected by multipath, the current solutions in tag non-attachment scenarios mainly focus on single-object activity recognition, which is not suitable for multi-object scenarios. To address these issues, we propose STMultiple, a novel tag non-attachment activity recognition model for multi-object. The model first preprocesses the raw signal with filter and phase calibration, then it applies dilated convolution in the frequency domain to extract multi-object activity features, finally the feature pyramid structure and ProbSparse are used to optimize the vanilla Transformer-Encoder to enhance the activity recognition ability. Extensive experiments show that STMultiple can achieve recognition accuracy of up to 97.93% and down to about 90% in challenging environments ranging from two to five users, which has excellent performance compared to several state-of-the-art methods.
Sequential Masking Imitation Learning for Handling Causal Confusion in Autonomous Driving

Training agents for autonomous driving using imitation learning seems like a promising way since its only requirement is the demonstration from expert drivers. However, causal confusion is a problem existing in imitation learning, which is that with more features offered, an agent may perform even worse. Here, we aim to augment agents’ imitation ability in driving scenarios under sequential setting, by a novel method we proposed: Sequential Masking Imitation Learning(SEMI). First, we train a Vector Quantised-Variational AutoEncoder(VQ-VAE) to encode a sequence of images into a latent representation with discrete codes. After that we deploy several masks on the encoded images, the masks here will randomly hide some semantic objects in the encoded images. Finally, we design the behavior clone network as a predictor of expert action, using an encoded and masked image sequence as input, encouraging the network to make expert-like predictions when some partition of information about the environment is missing. The masking procedure in SEMI helps the imitator identify the contribution of each encoded feature to the expert’s prediction. We demonstrate that this method could alleviate causal confusion in driving simulation by deploying it to the CARLA simulator, and compared it with other methods. Experimental results show that SEMI can effectively reduce confusion in autonomous driving. The agent trained with SEMI method reduces the collision rate by 45 $$ \% $$ compared to methods without masking procedure, and obtain the highest average survival timesteps among competing methods.
The Integration of Augmented Reality in Surgical Procedures

In the abstract, it's crucial to provide a concise yet informative summary of your paper's main findings and contributions. This section should serve as a preview of what readers can expect in the paper. A well-crafted abstract might look something like this:"Advancements in medical technology have paved the way for innovative approaches to surgical procedures. One such advancement is the integration of Augmented Reality (AR), a technology that overlays digital information onto the surgeon's field of view. This paper explores the multifaceted role of AR in surgery, including its applications in visualization enhancement, preoperative planning, intraoperative navigation, and training. We delve into the benefits and challenges associated with AR integration in surgery, supported by real-world case studies. Additionally, this paper discusses the ethical and legal considerations surrounding AR in healthcare. We conclude with insights into future trends and recommendations for healthcare practitioners. The integration of AR in surgical procedures holds great promise for enhancing surgical outcomes and patient care."
Stochastic Resource Allocation with Time Windows

The stochastic resource allocation problem with time windows (SRAPTW) refers to a class of combinatorial optimization problems which are aimed at finding the optimal scheme of assigning resources to given tasks within their time windows. In SRAPTW, the capability of resources to accomplish tasks is quantitatively characterized by probability. The expected allocation scheme should include not only the task-resource pairings but also their allocation time. This paper formulates SRAPTW as a nonlinear mixed 0–1 programming problem with the objective of maximizing the reward of completing specified tasks. Then, a general encoding/decoding method is proposed for the representation of solutions, and several different problem-solving methodologies are presented and compared. Results of computational experiments show that the utilization of SRAPTW-specific knowledge can bring in excellent performance, and a constructive heuristic combining maximal marginal return strategy and maximal probability strategy has remarkable advantages, especially in larger-scale cases.
Wide Flat Minimum Watermarking for Robust Ownership Verification of GANs

We propose a novel multi-bit box-free watermarking method for the protection of Intellectual Property Rights (IPR) of GANs with improved robustness against white-box attacks like fine-tuning, pruning, quantization, and surrogate model attacks. The watermark is embedded by adding an extra watermarking loss term during GAN training, ensuring that the images generated by the GAN contain an invisible watermark that can be retrieved by a pre-trained watermark decoder. In order to improve the robustness against white-box model-level attacks, we make sure that the model converges to a wide flat minimum of the watermarking loss term, in such a way that any modification of the model parameters does not erase the watermark. To do so, we add random noise vectors to the parameters of the generator and require that the watermarking loss term is as invariant as possible with respect to the presence of noise. This procedure forces the generator to converge to a wide flat minimum of the watermarking loss. The proposed method is architectureand dataset-agnostic, thus being applicable to many different generation tasks and models, as well as to CNN-based image processing architectures. We present the results of extensive experiments showing that the presence of the watermark has a negligible impact on the quality of the generated images, and proving the superior robustness of the watermark against model modification and surrogate model attacks.
محددات سلوكيات صنع القرار المهني للأخصائيين الاجتماعيين في خدمات الدعم الاجتماعي والاقتصادي في تركيا

This study aimed to examine the factors that influence the decision-making of social workers through their professional experience. The qualitative research method and case study design were employed in this study. Face-to-face interviews were conducted with a total of twenty participants and the data obtained has been descriptive analyzed. Eight key elements have been identified that have an impact on the participants’ decision-making process. The chief elements of social workers’ included: descriptions, assessment phase, previous experiences, worldviews and ideologies, education, professional experiences and practice, exchanging ideas with their colleagues, and their own institutional and non-institutional expectations. Emotions and culture also form the social worker’s professional decision. The mentioned factors are a rule rather than an exception and these factors make it difficult for the social worker to develop rational decisions. To prevent these effects, the social worker should be aware of the transition between personal and professional values and take the responsibility to deal with them.
Decoding Emotions: Harnessing the Power of Python for Sentiment Analysis in Social Media

Social media usage is increasing tremendously, and it has become a necessity. A person needs to be able to use social media in order to compete in this ever-developing world of technology’s large number of people use social media, some for-entertainment purposes for educational purposes, Some for political, and others for economic purposes. To accommodate this tremendous amount of information that is being disseminated in social media to reflect the views of all these individuals. And all those views (information) have different sentiments echoed in them. To gain some data from the list of information, we need to analyze the feelings of the posts on social media. Sentiment analysis is a powerful tool that utilizes machine learning and natural language processing (NLP) to detect the sentiment - whether it be positive, negative, or neutral - in text. Two primary methods for conducting sentiment analysis are rule-based and automated. Convolutional neural networks (CNNs) and deep learning have been found successful in uncovering meaningful sentiments from texts, allowing for accurate classification of views expressed through written data. By breaking down each step thoughtfully with new ideas, active sentences instead of passive ones, stronger verbs for increased intensity, and synonyms to replace words that could be better used elsewhere, this makes up a successful rewrite of the original text.
The Way to Develop a Project Mindset according to Secondary School Students

The 21st century has brought many changes to our lives. The pandemic and the major changes that followed have affected our daily lives. Many competences have come to the fore that were previously secondary in the world of work. These competences include project thinking and thinking in terms of projects. Project thinking is not a stand-alone concept, but a complex set of competences that encompasses a number of other competences. It includes teamwork, communication, time management, insight, flexibility or even agility and time management. A project approach is nowadays not only needed in the world of work, but many school tasks can be defined as projects, leading students to succeed in real life. The aim of this study is to present the project approach and its different dimensions, based on the perceptions of secondary school students and the results of a questionnaire survey. The paper aims to shed light on the different aspects of the project approach, highlighting the areas and directions that need improvement and which could be the key to the future.
Identifying Human Temperament and Character Type for E-Learning Needs Using a Fuzzy Logic Approach

Today, online training and counseling is becoming increasingly popular. This has been achieved through the use of various successful information and communication technologies and electronic tools - Electronic (ICTE). Therefore, significant changes are needed in the training process. People have different perceptions of information and different types of temperament. That is why the training process should be tailored to the needs of each. The training system should take into account that people have different styles of perceiving information according to the temperament and character type of the student-aplicant. This would reduce online training and increase its quality.Traditional psychometric tests are usually very long and difficult, based on a person’s opinion of themselves and thus, they give different results depending on the person’s mood. Also, depending on the mood and need, the person may be manipulated by possible answers. This is because the answers to such test questions are largely based on ongoing processes in consciousness. So from traditional tests there are other types of tests separately from questionnaires: drawing, colors, geometric figures, pictures, card games, numbers, scenarios, manuscript analysis, drawing and other tests. A special advantage of these types of tests is that they rely more on ongoing processes in the subconscious and are therefore more free to manipulate, while also requiring significantly less time and less dependent on mood.Autors task was to combine several other types of tests and create a free test-short program with the ability to present and process subjective information using modern fuzzy technology, which would allow us to determine the applicant's temperament and character type for e-learning and other needs.
USE OF QUIZIZZ TO IMPROVE ENGLISH VOCABULARY MASTERY AMONG EFL ELEMENTARY SCHOOL STUDENTS IN SETU BABAKAN AREA

This research analyzes whether or not the use of Quizizz is beneficial in expanding the vocabulary of elementary school students. In this inquiry, a quantitative approach to the pre-experiment phase is taken. The purpose of this research is to investigate whether or not the use of Quizizz as a learning aid to boost students' vocabulary is effective. The number of pupils from elementary schools in the area around the Betawi cultural hamlet who took part in the exciting activities that were planned for the course was used to determine the number of samples that were collected. There were a total of twenty children that took part in the activity known as the Fun Course. In order to evaluate the students' ability to respond appropriately to vocabulary questions, the researcher gave them both a pre-test and a post-test. The results of the post-test that were given to students in elementary school suggest that having access to Quizizz makes a substantial difference in improving their overall performance on the exam. On the pre-test, the average score was 69, whereas on the post-test, the average score was 89.8. On the basis of these differences, it is possible to draw the conclusion that the primary students' command of the English language has greatly improved.
The Influence of Problem Characteristics, Regulatory and Non-Regulatory Supporting Capabilities on The Process of Implementing E-Learning Online Learning Policies at Nusa Cendana University

This research aims to analyze the influence of problem characteristic factors, regulatory and non-regulatory carrying capacity on the implementation process of e-learning based online learning policies at Nusa Cendana University. This type of research uses a positivism paradigm with a quantitative approach, where to obtain the truth of the data, direct interviews are conducted with the object under study. The sample used in this research was 467 people who were lecturers and students at Nusa Cendana University. Research data was analyzed using descriptive statistical tests and PLS-SEM inferential statistics. The results obtained show that descriptively the four variables received a good category response with indicators of problem characteristic factors reaching 73.28%, regulatory carrying capacity of 78.27%, non-regulatory carrying capacity of 77.11% and the e-learning implementation process of 75.05%. Hypothesis testing shows that the problem characteristic factor has a significant positive influence (p&lt;0.05) directly at 18.4% and through non-regulatory carrying capacity at 48.3% but through regulatory carrying capacity at only 5.3% it does not have a significant influence (p&gt;0.05) towards the process of implementing e-learning based online learning policies at Nusa Cendana University. The results of the determinant test show that the problem characteristic factors have a significant moderate/moderate influence (p&lt;0.05) through regulatory carrying capacity of 50.2% and through non-regulatory carrying capacity of 53.0% but together/simultaneously they give a strong influence of 75.3% while 24.7% was influenced by other factors outside this research.