Reviewerly/exHarmony huggingface dataset:
The Existence of Digital Platforms and the Challenges in Enforcement of Indonesian Competition Law

The existence of digital platforms has created a revolutionary change in the competition law system. Its existence as a multisided platform that is conceptually different from conventional economic platforms requires special understanding. The business competition law enforcement authorities see that the multisided character causes the efficiency and consumer welfare approach to be insufficiently applied to business competition law cases involving digital platforms. So, an alternative approach is needed, namely the principle of fairness, to answer challenges in enforcing business competition law.
Forensic Science in Criminalistics in Indonesia: Development and Challenges

Since the birth of forensic science until now it has developed so rapidly. the division of forensic science is so complete that it is able to assist law enforcement in carrying out the task of disclosing criminal cases. The application of forensic science has also undergone standardization that is recognized by the international community, so that in future trials, the use of forensic science can be clearly accounted for. The use of forensic science in Indonesia is the same as following international standards using standards set by the National Accreditation Agency. So that in the end it is professional staff who are needed to manpower the needs of the use of forensic science, at least being able to prevent the perpetrators of crimes from continuing their crimes.
Generic Construction of Public-key Authenticated Encryption with Keyword Search Revisited

Public key authenticated encryption with keyword search (PAEKS) has been proposed, where a sender's secret key is required for encryption, and a trapdoor is associated with not only a keyword but also the sender. This setting allows us to prevent information leakage of keyword from trapdoors. Liu et al. (ASIACCS 2022) proposed a generic construction of PAEKS based on word-independent smooth projective hash functions (SPHFs) and PEKS. In this paper, we propose a new generic construction of PAEKS, which is more efficient than Liu et al.'s in the sense that we only use one SPHF, but Liu et al. used two SPHFs. In addition, for consistency we considered a security model that is stronger than Liu et al.'s. Briefly, Liu et al. considered only keywords even though a trapdoor is associated with not only a keyword but also a sender. Thus, a trapdoor associated with a sender should not work against ciphertexts generated by the secret key of another sender, even if the same keyword is associated. That is, in the previous definitions, there is room for a ciphertext to be searchable even though the sender was not specified when the trapdoor is generated, that violates the authenticity of PAKES. Our consistency definition considers a multi-sender setting and captures this case. In addition, for indistinguishability against chosen keyword attack (IND-CKA) and indistinguishability against inside keyword guessing attack (IND-IKGA), we use a stronger security model defined by Qin et al. (ProvSec 2021), where an adversary is allowed to query challenge keywords to the encryption and trapdoor oracles. We also highlight several issues associated with the Liu et al. construction in terms of hash functions, e.g., their construction does not satisfy the consistency that they claimed to hold.
Inter-Scale Sure-Let Image Restoration with Deep Unrolled Image Prior

This study extends a self-supervised image denoising technique proposed by the authors to a more general image restoration method. The previous work was inspired by Ulyanov’s deep image prior (DIP) method. DIP uses a deep convolutional network as an image prior to generate a restored image from a random one, which brings an advantage of no training data requirement. However, one problem arises that the interpretability is low and it is not trivial to explain the need for the random input. In the previous work, it is shown that Stein’s unbiased risk estimator (SURE) with Monte-Carlo computation can be used to train a denoising network. This approach allows us to interpret the random input requirement. The framework trains an image denoiser instead of an image generator. This work extends the discussion to more general image restoration problem by introducing a deep unrolling approach to reflect the measurement process. The new framework uses the Luisier’s inter-scale linear expansion of the thresholding (LET) for exploiting the cross-correlation among extracted features. Some simulation results of image restoration show the significance of the proposed method.
Segmentation of the Left Ventricle for the Cardiac Phases between End-Diastole and End-Systole

Automatic segmentation of cardiac structures, including the left ventricle (LV), is a crucial step for evaluating cardiac function. While deep learning is the leading approach for this task, the scarcity of labeled data presents a significant challenge. In scenarios with very limited labeled data, even semi-supervised learning methods may not be effective as they rely on the performance of an initially trained network. In this work, we propose a novel method for segmenting the LV contours for unlabeled cardiac phases between end-diastole (ED) and end-systole (ES). Our method leverages temporal coherence from the LV volume-time and shape information from the labeled cardiac phases to transform the ED and ES ground truth labels to the intervening cardiac phases, resulting in a larger set of labeled data. We evaluate our approach using three methods: 1- visual inspection, 2- using our method as data augmentation for training a CNN with limited ground truths, and 3- comparing results with fully supervised segmentation networks. Our method outperforms in all validation methods. Implementation is available at: "https://github.com/behnam-rahmati/LV-label-propagation".
Uncertainty Guided Self-Supervised Monocular Depth Estimation Based on Monte Carlo Method

Depth estimation is an important research topic in the field of computer vision. Recently, self-supervised methods for depth estimation have received significant attention due to their independence from costly annotations. Recently, many methods for constructing uncertainty maps have been proposed to improve the performance of dense prediction tasks, such as semantic segmentation, and deep depth estimation also belongs to this category. This paper employs Monte Carlo methods to generate uncertainty maps that guide self-supervised monocular depth estimation learning. We sample multiple model outputs through the Monte Carlo method and calculate the variance and mean of the multiple output results. The mean is used as a teacher model to provide a supervisory signal, and the variance result is used as an uncertainty map of the supervisory signal. We use this uncertainty map to correct the supervision of depth information for better model performance. In this paper, we tested the challenging dataset KITTI and conducted generalization experiments on the Make3D dataset. The experimental results show that our proposed method has a significant improvement in self-supervised monocular depth estimation.
Attentive Deep K-SVD Network for Patch Correlated Image Denoising

Techniques of dictionary learning and sparse representation are popular in recent study on image denoising, including classic K-SVD and its variants. The extension of K-SVD to its deep structure learned in an end-to-end way shows the state-of-the-art denoising performance with a great computation efficiency. However, we notice that the current learning framework takes images patches as independent samples, which ignores the inherent correlation among the patches. In this paper, we propose a deep K-SVD denoising network with attention mechanism to enhance the correlation within and among the patches. We impose the two-dimensional correlation on the intermediate parameters during the sparse representation procedure to achieve more smoothing and local-structure enhanced image features. Extensive numerical experiments using public data are conducted. The results on two datasets show that the proposed network achieves an average improvement of 0.81dB in peak signal-to-noise ratio (PSNR), 1.66% in the structural similarity (SSIM) and more than 90% in the convergence rate comparing to its counterpart, which demonstrate the efficiency and the competitive performance of our proposed network.
Scapegoat Generation for Privacy Protection from Deepfake

To protect privacy and prevent malicious use of deepfake, current studies propose methods that interfere with the generation process, such as detection and destruction approaches. However, these methods suffer from sub-optimal generalization performance to unseen models and add undesirable noise to the original image. To address these problems, we propose a new problem formulation for deepfake prevention: generating a "scapegoat image" by modifying the style of the original input in a way that is recognizable as an avatar by the user, but impossible to reconstruct the real face. Even in the case of malicious deepfake, the privacy of the users is still protected. To achieve this, we introduce an optimization-based editing method that utilizes GAN inversion to discourage deepfake models from generating similar scapegoats. We validate the effectiveness of our proposed method through quantitative and user studies.
Progressive Multi-View Fusion for 3D Human Pose Estimation

In multi-view 3D human pose estimation (HPE), viewpoint images have large variability due to factors like camera angles and occlusion, making feature extraction and fusion across viewpoints challenging. To address these concerns, we propose a progressive multi-view 3D HPE transformer framework, which achieves effective intra-view pose feature extraction and cross-view fusion by embedding various multi-view fusion methods in the feature extraction process. In order to fully extract spatial semantic features of human joints, we first construct a cross-view spatial fusion module performing spatial feature fusion across adjacent views while mining useful spatial knowledge. To enhance the pose features and alleviate the depth ambiguity problem, we further develop a multi-view spatial-temporal fusion module to extract effective temporal contextual information within the viewpoint and fuse spatial-temporal features across multiple viewpoints. Extensive experiments on two popular 3D HPE benchmarks validate the efficacy and superiority of our method. It outperforms several state-of-the-art methods, effectively alleviates depth ambiguity, and improves 3D pose accuracy without providing camera parameters or complex loss functions.
Discrete-Time Markov Chains: Finite-State

Learn about probability as it is used in computer science with this rigorous, yet highly accessible, undergraduate textbook. Fundamental probability concepts are explained in depth, prerequisite mathematics is summarized, and a wide range of computer science applications is described. Throughout, the material is presented in a “question and answer” style designed to encourage student engagement and understanding. Replete with almost 400 exercises, real-world computer science examples, and covering a wide range of topics from simulation with computer science workloads, to statistical inference, to randomized algorithms, to Markov models and queues, this interactive text is an invaluable learning tool whether your course covers probability with statistics, with stochastic processes, with randomized algorithms, or with simulation. The teaching package includes solutions, lecture slides, and lecture notes for students.
Construction and Application of Agricultural Talent Training Model Based on AHP-KNN Algorithm

At present, the gap of agricultural talents in China is continuously widening, and most enterprises lack agricultural core talents, which has caused great impact on the social economy. To solve this problem, an improved AHP-KNN algorithm is proposed by combining the analytic hierarchy process (AHP) and the optimized K-nearest neighbor algorithm, and an agricultural talent training model is proposed based on this algorithm. The results show that the classification accuracy and classification time of the improved AHP-KNN algorithm are 96.2% and 27.5 seconds, respectively, both of which are superior to the comparison algorithm. The result shows that the classification accuracy of agricultural talents can be improved by using this algorithm. Therefore, the model can be used to classify agricultural talents with the same characteristics into one class, carry out targeted training, and train all-round agricultural talents efficiently and quickly, so as to improve the serious shortage of agricultural talents at present.
The Evolution of Education: Navigating 21st-Century Challenges

Education in the 21st century is undergoing a profound transformation, shaped by a complex interplay of challenges and opportunities. This research paper explores the multifaceted nature of this evolution, delving into key areas that demand our attention and adaptation. We examine the pervasive impact of digital transformation on education, emphasizing the critical need for educators to navigate the digital landscape effectively. Inclusivity stands as another cornerstone, with a focus on accommodating diverse learning styles and abilities. The exploration of innovative pedagogies reveals a departure from traditional one-size-fits-all approaches in favor of methods that encourage active engagement and critical thinking.Furthermore, we investigate the imperative of lifelong learning in a world where skills become obsolete with alarming speed. The importance of global perspectives in education is underscored, as is the pressing issue of student mental health and well-being. The integration of data-driven decision-making offers a glimpse into how analytics can enhance teaching and learning. Lastly, we consider the role of environmental education in preparing future generations to tackle pressing environmental challenges.In conclusion, this research paper underscores the urgency of adapting education to meet the dynamic demands of the 21st century. It serves as a clarion call for educators, policymakers, and stakeholders to collaboratively address these challenges, seizing the opportunities presented by an ever-evolving educational landscape. Only through concerted efforts can we ensure that education remains a powerful force for individual and societal growth in this digital age.
The Role of Social Networks in Economic Development and Entrepreneurship

This research paper aims to explore and analyze the significant role of social networks in driving economic development and fostering entrepreneurship. Social networks, both online and offline, have gained immense prominence in recent years due to their potential to connect individuals, facilitate information sharing, and create conducive environments for entrepreneurial activities. By examining theoretical frameworks, empirical studies, and case examples, this paper highlights how social networks influence various stages of economic development and entrepreneurship, including idea generation, resource mobilization, market access, knowledge dissemination, and innovation. The paper also discusses potential challenges and implications associated with social network-driven economic development and suggests policy considerations to harness the positive effects of social networks for sustainable economic growth.
Healthcare Record Management Using Blockchain

Background. Blockchain has been in popular demand for the last decade or so, but it recently gained recognition with the popularity of Bitcoin. It has been extensively used in fields like finance, government, energy, etc. It is the need of the hour that the technology was utilized in other relevant areas as well like healthcare. Making maximum use of blockchain in the healthcare industry is expected to have numerous significant impacts and positive outcomes. The technology is defined as a decentralized and distributed ledger that can be used to record transactions securely across many computers in a peer-to-peer network, without the need for a third party. Objectives. A block consists essentially of a header and a body. The header of each block contains the hash of the previous block. Among the important features of blockchain is how it is impossible to tamper with and is censorship resistant, giving access to the timestamped transaction while maintaining anonymity at the same time. Methods. The method used is literature survey. We perform a study selection process in four steps. After each steps the number of publications is reduced as unwanted publications are omitted using search strings, inclusion and exclusion criteria. We even conducted a study quality assessment which helps us analysis the quality of findings from the final set of selected papers in answering the research questions. Results. We have summarized the findings from the 10 selected papers from the study selection process according to the research questions. Conclusions. The most prominent feature of blockchain technology is the fact that it removes the need for a centralized third party and makes it possible for two parties to carry out transactions without a centralized authority. It highly improves the transaction speed by removing any type of delay and makes the transaction cheaper as well. This project intends to explore how decentralization makes it easier to maintain transparent and open records for patients to see how, when, and where their information is being used. By employing the programming language Solidity, a smart contract will be created which will be deployed on the Ethereum blockchain. This in turn will further ensure that no recorded data can be deleted. Any data recorded by the doctor in the blockchain can be edited whereas the patients will only have viewing access to the entered medical records. This will not only ensure a high level of security, but also the credibility of the information provided.
Intelligent Vehicle Management: Harnessing Emerging Technologies for Efficiency and Sustainability

Vehicle Management System provides useful features for the users (drivers) to make drive easy and use application smoothly without any friction. In today’s fast-growing digital world of internet, we see often see many conflicts and disputes based on buying and selling old vehicle cause by the lack of transparency. Also, there is huge traffic in metro cities and not able to find parking areas and get fined. So, people are not able to find the available area and if available only some types vehicles can be parked like bikes and cars. There are such more problem related vehicles that exists in real world like connecting with mechanic for breakdown of vehicle. To Solve some of these problems we are intended to make a Vehicle system that helps an drive to make drive easy and reduce dispute.
High-Level K-Nearest Neighbors (HLKNN): A Supervised Machine Learning Model for Classification Analysis

The k-nearest neighbors (KNN) algorithm has been widely used for classification analysis in machine learning. However, it suffers from noise samples that reduce its classification ability and therefore prediction accuracy. This article introduces the high-level k-nearest neighbors (HLKNN) method, a new technique for enhancing the k-nearest neighbors algorithm, which can effectively address the noise problem and contribute to improving the classification performance of KNN. Instead of only considering k neighbors of a given query instance, it also takes into account the neighbors of these neighbors. Experiments were conducted on 32 well-known popular datasets. The results showed that the proposed HLKNN method outperformed the standard KNN method with average accuracy values of 81.01% and 79.76%, respectively. In addition, the experiments demonstrated the superiority of HLKNN over previous KNN variants in terms of the accuracy metric in various datasets.
Review of <i>assessing academic English for higher education admissions</i>

Click to increase image sizeClick to decrease image size Additional informationFundingThis work was supported by the China Social Science Research Foundation [19BYY229].
EDUCATIONAL NETWORK SPACE IN THE CONTEXT OF SOCIAL ENVIRONMENTAL APPROACH

Objective of the study is a socio-philosophical analysis of the educational network space within which the educational process is implemented. The methodological basis of the research is based on an environmental, as well as a systematic approach based on the understanding of network education. Research results. The criteria of the network information society are outlined: technological, informational, socio-economic, spatial, socio-cultural. The concept of educational network space is revealed from the standpoint of the environmental approach, within which three main components are identified: informational, instrumental, social. Prospects of the study are connected with the further socio-philosophical analysis of the educational network space and the formation of a new network type of person.
Use of Gamification to Approach the Algebraic Language and Equations in 1st Grade of Secondary Education

Active methodologies, as innovative agents, are becoming more and more frequent in education, but they are not a majority. However, it is a good resource to adapt the educational system to the current society based on knowledge and information.&#x0D; This article shows a useful and effective tool to connect with the needs and interests of students through an active methodology. The proposal, a gamification (teaching-learning methodology based on the mechanics of games), approaches the students of 1st grade of secondary education to the algebraic language and equations, combining cooperative learning activities.&#x0D; Working through gamification means a higher academic performance, an increase in motivation and the development of a positive attitude allowing, in addition, to attend to diversity in the classroom and the use of ICT promoting collaborative work.&#x0D; Gamification, based on the use of ICT, facilitates the acquisition of knowledge in a more attractive way, favoring their performance.