Reviewerly/exHarmony huggingface dataset:
The impact of perceived integrity of government agencies on trust in the police and the mediating effect of trust in government agencies

Looking at previous studies on factors affecting trust in police in Korea, we can see that most of them are limited to variables related to police activities (fairness, effectiveness, etc.). However, the police is one of the government agencies under the Ministry of Public Administration and Security, and improving police activities or citizens' awareness of police activities alone may not be enough to increase trust in the police. Previous overseas studies have gone further to identify factors influencing trust in the police through comparative studies using country-level variables. The purpose of this study is to determine the impact of citizens' perception of the integrity of government agencies on trust in the police and to verify the mediating effect of trust in government agencies in that influence relationship. Accordingly, the Korea Institute of Public Administration conducted a quantitative statistical analysis based on the results of a survey collected as part of the 2021 “Study on Governance and the Level of Trust in Korean Society.” As a result of the analysis, trust in government agencies was found to have a significant partial mediating effect between the integrity of government agencies and trust in the police. These research results suggest that there is a need to more broadly discuss improving trust in the police in order to elicit police cooperation as an essential element of community police activities, which is the basis of the modern police activity paradigm. Therefore, in follow-up research, related influencing factors should be explored through a review of overseas previous research, and scale development and validation work to apply it domestically should be prioritized.
Pengembangan Bahan Ajar E-Komik Menggunakan Web Pixton Pada Tema 3 Materi Sistem Pencernaan Manusia

E-comic teaching materials are one of the teacher's tools in the learning process to convey material to students by combining the use of technology and story illustrations as an effort to innovate in the development of teaching materials. This study aims to develop e-comic teaching materials on theme 3 of human digestive system material, using the Research and Development (R&amp;D) method and the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation). This research was conducted at SDN Pajajaran Bogor class V which consisted of 26 students as respondents. The results of this study indicate that the development of e-comic teaching material products gets very feasible criteria, based on the results of validator assessments and student responses. This is based on the percentage results of media experts 94.66%, linguists 97.33%, material experts 96% and the results of the student response questionnaire 93.07%. Based on this, it can be concluded that the e-comic teaching material product using pixton web on theme 3 of human digestive system material developed is suitable for use as teaching material.
Enhancing the Quantum Correlation of Biphotons via Coherent Energy Redistribution

Towards meeting the strict demands of practical quantum networks, we leverage coherent energy redistribution for noise-tolerant quantum signal processing. We demonstrate the enhancement of noisy biphoton coincidence-to-accidental ratios by up to 3.8 times.
MacSR: Macroblock-aware Lightweight Video Super-Resolution

SummaryThe mobile video quality can be improved by video super-resolution (SR) especially when bandwidth is limited. To achieve real-time SR, the latest work, ClassSR (CVPR 19), divides frames into equal-size image blocks (IBs), and different-complexity SR models are used respectively to reduce the computational burden.
Training needs for kindergarten teachers from their point of view in Al-Marj

The study aimed to introduce the training needs of kindergarten teachers in the field of cognitive and personal competencies. The sample consisted of (50) female kindergarten teachers in the 11 accredited private schools in Al-Marj, and a performance was prepared by Prof. JajanJuma Muhammad and M. AhlamAdeeb Dawood included (20) items in the field of cognitive competencies and (15) items in the field of personal competencies, and in front of each paragraph three alternatives were applied to the members of the sample after making sure of its sincerity and stability. The data were statistically processed using the weighted weighted mean and the results showed that there is a real need for training and high proportions in the field of cognitive competencies in his briefing using various strategies to evaluate the child and knowledge of methods of studying the child's behavior and familiarity with the evaluation methods of the kindergarten child and determine the goals and activities of the activities and clarify them and how to use the computer, and take note of aspects The growth of children and its manifestations and the identification of modern scientific sources in child-learing and knowledge of methods of innovation and renewal. And knowing the methods of studying behavior and the skill of using modern technologies. As for the field of personal competencies, which is represented in the use of various strategies and knowledge of how to develop positive relationships with colleagues, how to develop the trust of parents in their children, informing the child of love and taking into account individual differences between children, identifying methods of pleasant conversation with children and understanding the needs of children Their desires, and the skill of the art of writing reports on the child's familiarity with methods of maintaining personal calm and knowing the methods of effective communication with others.
Exploring the Fundamental Units of Semantic Representation of Data Using Heterogeneous Variable Network in Data Ecosystems

The value creation achieved through the exchange, distribution, and collaboration of data among different organizations has garnered significant attention as a new source of innovation. The mathematical treatment of the meaning of data helps measure its “quality” to formulate evaluation criteria for data exchange between stakeholders with distinct background knowledge in data ecosystems. This study examines the structure of data morphemes, the fundamental units of semantic representation of data, by conducting network and association analyses of variables present in metadata from diverse fields. Network analysis identifies the globally sparse and locally dense characteristics of variable co-occurrence networks and highlights essential relationships and core variables. Key findings include the discovery of “depth,” “sediment/rock,” and “sample code/label” as both universal variables and crucial nodes between datasets used in the experiment. Association analysis reveals vital variable pairs, such as “age” and “ring width” or “latitude” and “longitude.” This research may provide a understanding of the structure and meaningful representation of data, facilitating smooth data exchange and utilization practices among stakeholders with different domains, purposes of data use, and background knowledge in data ecosystems.
GesturePitch: A Smart Presentation System Utilizing Hand Gestures

Traditional method of handling presentations is using devices like mouse and keyboard. Later on it is improvised with new technique based on gesture recognition. These gesture-controlled presentation systems have limitations, such as the requirement of appropriate lighting conditions and backgrounds, additional hardware devices, and powerful processors. These systems may also face challenges in accurately detecting the presenter's gestures in the presence of obstructions. Hence system is designed called GesturePitch. This system addresses the limitations of existing systems by providing real-time background replacement and eliminating the need for additional hardware devices, allowing the system to run on standard processors. The system includes a threshold feature that enables the presenter to set a distance beyond which the system will not detect any hands, making it easier for the system to identify the presenter's hand even in the presence of obstructions. OpenCV and TensorFlow libraries and packages such as mediaPipe, SelfSegmentation, HandDetection module, etc. are used to implement the system. By addressing these limitations, GesturePitch provides a more accessible approach to controlling presentations and allows individuals with disabilities to deliver presentations without assistance, promoting inclusivity and diversity in professional and educational settings. Additionally, it promotes inclusivity and diversity by allowing people with different abilities to participate in professional and educational settings.
Transformation of Text to Decode Morse Code and its Detection using Eye Blinks

There are approximately 22 million people in India who suffer from various forms of disabilities. This software enables individuals to communicate using their eye blinks through a method known as Morse code. The author utilized the open-CV method to detect patterns of human eye blinking. The eye aspect ratio formula is employed to detect eye blink patterns. Additionally, an interface has been developed using tkinter to facilitate the detection of Morse code from eye blinks. Our approach in this field focuses on using eye blinks in real-time to generate Morse code, leveraging a webcam and employing three machine learning techniques for predictive strength. Currently, we propose a free software solution that employs open-source computer vision to interpret eye blinks and translate messages sent in Morse code (comprising dots and dashes) into understand-able English. Thus, this system serves as a substitute for augmentative and alternative communication (AAC) devices. The primary objective of this paper is to create a simple and affordable system that aids individuals with speech disorders and facilitates the transmission of private messages, particularly in settings such as the military. The concepts of eye blinks, facial landmarks, eye blink detection, and Morse code are utilized to accomplish the core task of capturing values represented by dots and dashes.
Contrastive Multi-view Learning for Graph Stucture Learning

Graph Structure Learning (GSL), with its ability to simultaneously optimize the graph structure and learn the suitable parameters of Graph Neural Networks (GNN), has attracted considerable attention. Although existing methods are capable of learning optimal graph structure from single or multiple information sources, they exhibit certain limitations such as disregarding intricate relational information in the original graph structure and bias introduced by a small number of labels. In this study, we introduce a novel contrastive multi-view method for graph structure learning, named CMVGSL, which estimates graph structure suited for GNN properties from a broader range of perspectives. Specifically, we extract a k truss subgraph as an augmented view for contrastive learning. The representations learned from both semi-supervised and contrastive learning are utilized to construct observations of the optimal graph, and then the estimated graph structure is obtained by employing a Bayesian inference-based method. Furthermore, we design a post-processing method to impose sparsity and internode distance constraints on the estimated graph. The joint optimization of graph structure and GNN parameters is achieved through multiple iterations. Extensive experimental results on benchmark datasets with different homophily demonstrate the significant effectiveness of our proposed CMVGSL.
Generating large-scale human flow datasets from measured pedestrian movement data and simulation

Appropriate evacuation guidance for specific events and facilities can be safely and rapidly achieved by controlling gates and emergency exits. However, in the case of large-scale evacuations, such as those in cities, it is difficult to obtain an optimal control method because of the complexity of the entire city. Understanding changes in pedestrian movement and urban characteristics requires an urgent need to build a dataset based on simulation results from various scenarios. In this study, we discuss the requirements for datasets in various studies, such as agent simulation, machine learning, and artificial intelligence.
Speeding Up Unsupervised Learning Through Early Stopping

Ever-increasing amounts of data make labeling of datasets uneconomic. Unsupervised learning, specifically the K-Means family of clustering algorithms, provide a pragmatic way of dealing with sizable quantities of unlabeled data. The bisecting K-Means algorithm with refinement has been shown to outperform the regular K-Means in both clustering speed and quality. Past works on bisecting K-Means revealed that limiting the iterations of the K-Means algorithm is a promising approach to speed up calculations while still maintaining clustering quality as measured by the sum of square errors (SSE). Particular works from the past employed the centroid difference as an iteration-stopping condition when using the bisecting K-Means algorithm or some forms of the fuzzy C-Means algorithm. In this paper, we show that the centroid difference is affected by the dataset and leads to unreliable clustering quality. We propose a single-parameter criterion for stopping the K-Means iterations. The parameter of the criterion employs SSE, and its selection manages the desired final clustering quality. Moreover, our method is easy to implement and does not carry the dataset-dependent issues of the centroid-difference-based stopping condition. To examine the effectiveness of our method, we performed various experiments under diverse parameter values and using multiple datasets. Our studies tested the effects of our parameter on clustering speed and quality. All the experiments performed used the no-membership change as the benchmark stopping condition. The early-stopping method results reveal speed-up factors of over 200, with an increase of SSE of only about three percent compared with our benchmark. In addition to this, we also applied early stopping to the automatic determination of optimal cluster numbers using the Calinski-Harabasz and Ray-Turi Indices. The results obtained reveal that optimal clustering can be obtained with impactful speed-up factors and minimal loss of cluster quality. Our experimental findings provide a promising path for the early-stopping criterion due to its computational reduction, preservation of clustering quality, ease of use, and broad dataset applicability. Sample code associated with this paper can be found at the following link, https://github.com/adocaj/code.
One-GPT: A One-Class Deep Fusion Model for Machine-Generated Text Detection

On the brink of the one-year anniversary since the public release of ChatGPT, scholarly research has directed their interest toward detection methodologies for machine-generated text. Different models have been proposed, including feature-based classification and detection approaches, as well as deep learning architectures, with a small portion of them integrating contextual information to enhance accurate predictions. Moreover, detection approaches explored thus far have focused primarily on English datasets, with limited attention given to the examination of similar methods in other languages. As a result, the applicability and efficacy of these methods in linguistically diverse contexts remains underexplored. In this paper, we present a one-class deep fusion model that considers both contextual text features derived from word embeddings and linguistic features to detect machine-generated texts in English and Spanish. Experimental results indicated that our model outperformed popular baseline one-class learning models in the detection task, presenting higher accuracy scores in the English dataset. Results are discussed in comparison to competing classifiers as well as the language biases found in detection models.
Label Freedom: Stable Diffusion for Remote Sensing Image Semantic Segmentation Data Generation

Remote sensing image semantic segmentation of land use, benefitted from the development of deep learning and consequently made considerable progress in terms of inferencing accuracy and speed. However, the effective training of semantic segmentation models for remote sensing imagery necessitates extensively detailed pixel-level annotations, and gathering such data is both time-intensive and laborious. Thus, this study implemented low-rank adaptation on a stable diffusion algorithm to learn the distribution of the pixel-level annotations in case of the LoveDA dataset. Consequently, the annotation-image pairs were used to train the remote sensing image generator based on stable diffusion guided by ControlNet. We proposed a stable diffusion based approach, which can generate image-annotation pairs from scratch. The generated annotation and image pairs achieved a high accuracy of 0.520 mean intersection-over-union on LoveDA dataset, which is close to the original data training result of 0.539 mIoU. Furthermore, the mixed training using generated and original data achieved 0.542 mIoU, thereby demonstrating the data augmentation function of our approach. This study provided a solution for the high-cost pixel-level annotation issue, and thus, exhibited the potential of artificial intelligence generated content.
Humor Level Recognition Based on Prompt Learning and Contrastive Learning

Humor recognition is one of the hot topics in the field of natural language processing in recent years. Most existing methods focus on whether something is humorous, while there is less research on humor level recognition. This article proposes a method based on prompt learning and contrastive learning for humor level recognition. Firstly, the context with prompt learning templates and the context without prompt learning templates are input into a pre-trained language model separately. Then, during the fine-tuning stage, a prompt learning strategy based on P-tuning is used to learn semantic information related to humor levels in the text. Subsequently, a contrastive learning loss function is introduced to increase the distance between the vector representations of different classes and reduce the classification difficulty of strong humor and weak humor samples. Finally, a multi-task learning strategy is used to simultaneously perform prompt learning, contrastive learning, and sentence binary classification tasks. Experiments on the Reddit public humor dataset show that the model’s accuracy on the corpus improves by 1.1% compared to the previous best results. Experimental results indicate that the model proposed in this article can effectively recognize humor levels.
A Scalable Approach to Aligning Natural Language and Knowledge Graph Representations: Batched Information Guided Optimal Transport

The triples of a knowledge graph (KG), comprised of a subject-object pair and a predicate linking the two, share common structural properties to natural language (NL) sentences. Recent advances in data compression via neural networks, specifically low-dimensional embeddings, have been applied to both data domains (KG and NL), yet little work has been done to explore how these two different representations may be linked together in an attempt to align the knowledge of one domain to another. In this work, we develop an unsupervised deep learning methodology for such alignment using tools of optimal transport and the Wasserstein distances between the respective representations. Due to the size and complexities of each embedding space, we introduce a novel process to improve scalability and reduce training time we call Batched Information Guided Optimal Transport (BIG-OT). We experiment on two datasets (NYT-Freebase and Wikidata) without using the supervision of aligned pairs and show that our solution can outperform the current benchmarks while only operating on sub-samples of each respective space, thus providing gains in algorithmic efficiency. Our algorithm scales to Big Data use cases regardless of the baseline embedding methodologies utilized, and we additionally show a simple procedure to improve those embeddings again leads to performance gains.
An Automated Framework for Hematologists Level Leukemia Detection Using Blood Cell Images

Leukemia stands a form of cancer that affects the bone marrow besides blood, and it can be difficult to see in its early stages. But cutting-edge machine learning techniques, notably deep learning, have shown potential in powering the diagnosis of leukemia by scrutinizing tiny blood cell pictures. The present research recommends using the EfficientNet design, a cutting-edge deep learning model, to detect leukemia sickness. The model achieves great accuracy and specificity when distinguishing leukemia cells since it was trained using a large collection of blood cell images. The recommended technique might boost the efficacy besides precision of leukemia diagnosis. The accuracy of model is as good as 99.263, providing improved outcomes for patients.
Investigating Visual Analytics against Terrorist Financing in Dark Web Marketplaces

This paper addresses the growing issue of terrorists utilizing the Internet, and particularly the Dark Web market places, with the purpose of fundraising for their illegal activities. It proposes the Visual Analytics (VA) system, an advanced AI-powered tool, in an effort to combat cross-border financing associated with terrorism. The tools focus on semantic concept detection and large-scale visual data indexing, and the ultimate goal is to familiarize end-users, practitioners, and law enforcement investigators with these technologies. This paper discusses prior works concerning the presentation and visualization of Deep Learning results to users, outlines the main objectives, and provides fundamental usage instructions for the VA system.
Impact of Toss on the Result of IPL Matches Using Machine Learning

Cricket is by far the most popular sport in India and is played virtually everywhere. The Indian Premier League (IPL) is a professional men's Twenty20 cricket tournament contested by 10 clubs based in 10 distinct Indian cities. In 2007, the Board of Control for Cricket in India (BCCI) founded the division. Cricket match results are largely influenced by a number of key variables, including the toss, home field advantage, previous results, the location, the team's current form, and the players. However, we focused on the most important factor because it has the greatest impact on all other factors. The primary objective of this paper is to determine whether a team's decision to win the coin toss affects its odds of winning the game. If so, by what percentage does it change? We have conducted a thorough research on the winning percentage of the team that won the toss using a variety of algorithms. The supervised machine learning techniques k-nearest neighbors (KNN), Naive Bayes, Decision trees, support vector machines (SVM), and random forests have all been implemented. We found that KNN had the best accuracy, at 76%, while Naive Bayes had the lowest accuracy, at 60%. According to the findings of this study, the team that wins the coin toss has about 80% of the odds of winning the game since they get to choose the criteria based on their strengths and side advantages. Additionally, other cricket formats include the One-Day International (ODI), Champion League and many more tournaments.