Reviewerly/exHarmony huggingface dataset:
Zero Initialized Active Learning with Spectral Clustering using Hungarian Method

Supervised machine learning tasks often require a large number of labeled training data to set up a model, and then prediction - for example the classification - is carried out based on this model. Nowadays tremendous amount of data is available on the web or in data warehouses, although only a portion of those data is annotated and the labeling process can be tedious, expensive and time consuming. Active learning tries to overcome this problem by reducing the labeling cost through allowing the learning system to iteratively select the data from which it learns. In special case of active learning, the process starts from zero initialized scenario, where the labeled training dataset is empty, and therefore only unsupervised methods can be performed. In this paper a novel query strategy framework is presented for this problem, called Clustering Based Balanced Sampling Framework (CBBSF), which is not only select the initial labeled training dataset, but uniformly selects the items among the categories to get a balanced labeled training dataset. The framework includes an assignment technique to implicitly determine the class membership probabilities. Assignment solution is updated during CBBSF iterations, hence it simulates supervised machine learning more accurately as the process progresses. The proposed Spectral Clustering Based Sampling (SCBS) query startegy realizes the CBBSF framework, and therefore it is applicable in the special zero initialized situation. This selection approach uses ClusterGAN (Clustering using Generative Adversarial Networks) integrated in the spectral clustering algorithm and then it selects an unlabeled instance depending on the class membership probabilities. Global and local versions of SCBS were developed, furthermore, most confident and minimal entropy measures were calculated, thus four different SCBS variants were examined in total. Experimental evaluation was conducted on the MNIST dataset, and the results showed that SCBS outperforms the state-of-the-art zero initialized active learning query strategies.
Real-World Thermal Image Super-Resolution

Thermal cameras are used in various domains where the vision of RGB cameras is limited. Thermographic imaging enables the visualizations of objects beyond the visible range, which enables its use in many applications like autonomous cars, nightly footage, military, or surveillance. However, the high cost of manufacturing this type of camera limits the spatial resolution that it can provide. Real-World Super-Resolution (RWSR) is a topic that can be used to solve this problem by using image processing techniques that enhance the quality of a real-world image by reconstructing lost high-frequency information. This work adapts an existing RWSR framework that is designed to super-resolve real-world RGB images. This framework estimates the degradation parameters needed to generate realistic Low-resolution (LR) and High-resolution (HR) image pairs, then the SR model learns the mapping between the LR and HR domains using the constructed image pairs and applies this mapping to new LR thermal images. The experiments results show a clear improvement in the perceptual quality in terms of clarity and sharpness, which surpasses the performance of the current SotA method for thermal image SR.
Credit Card Fraud Detection Using Local Outlier Factor &amp; Isolation Forest Algorithms: A Complete Analysis

Credit card fraud problem is one of the soared issues in present. A huge economical damage has severely impacted the cusses who are using and connected with the credit card and to tackle it down, one of the most powerful fraud detection technique is the Machine Learning, and for study or research work a vital number of historical data is present by which this article examines various machine learning fraud detection techniques and compares them using performance measures such as accuracy and precision. This planned system also increases the accuracy of credit card fraud detection. Furthermore, the proposed system uses a learning classification algorithm to classify the data and will pop-up the technique that which will be great for detecting the frauds among the global and anthropoids can save loss of millions of dollars due to established system.
An Overview of Blockchain Technology for Identity and Access Management in the IoT Ecosystem

With the rapid growth in the usage of Smart Devices for real-time applications, the Internet of Things (Iot) has become the primary requirement for deploying a smart environment. A smart device is a physical object that can communicate, collect, and process a massive amount of real-time data, including device/user private data published on the web. The data in the IoT network is easily modifiable and re-configurable by malicious users/devices. As a result, the chances of private data leakages are high. Preserving the privacy of user/device data is a challenging task in an IoT ecosystem. Several techniques have been proposed in the past; one of the trending and efficient solution architecture lies in Blockchain Technology. This paper presents a review of Blockchain-based privacy-preserving techniques for user/device identity. Blockchain type, Consensus algorithm, Cryptographic process, advantages, and disadvantages have been focused on enhancing understandability. To conclude, key challenges are identified and discussed to defend against private data leakage and also efficient blockchain type and consensus algorithms are identified.
&lt;P Class="Elsarticletitle"&gt;Bpti: Bilingual (Arabic/English) Printed Text Images Dataset for Recognition Research&lt;O:P&gt;&lt;/O:P&gt;&lt;/P&gt;

Datasets of text images are important for optical text recognition systems. Such datasets can be used to enhance performance and recognition rates. In this research work, we present a bilingual dataset consists of Arabic/English text images to address the lack of availability of bilingual text databases. The presented dataset consists of 97812 text images, which are categorized into two groups; Scanned page and digitized line images. Images of the two forms are written with 10 fonts and four sizes, and prepared/scanned with four dpi resolutions. The dataset preparation process includes text collection, text editing, image construction, and image processing. The dataset can be used in optical text recognition, optical font recognition, language identification, and segmentation. Different text recognition and language identification experiments have been conducted using images of the dataset and Hidden Markov Model classifier. For the digitized images recognition experiments, the best-achieved recognition correctness is 99.98% and the best accuracy is 99.98%. The font that has the highest recognition rates was Tahoma. For the scanned images recognition experiments, Tahoma has also shown the highest performance with 99.04% for correctness and 98.98% for accuracy. For the language identification experiments, Tahoma has shown the performance with 99.98% for word-language identification rate.
A Gap between Digital and Physical World - Interactive and Immersive Activity Platforms

This paper examines the recently emerging concepts such as AR/VR,Metaverses, and Video Games ( and culture of games in broader) to identify a gap inbetween their relationships and intersections. The paper explores what are the newaspects of such interactions and demonstrates the untapped gaps in between thesesubjects currently. This new gap is named IIAP (Immersive and Interactive ActivityPlatform) which suggests a new and contemporary mode of immersion, interaction,and activity concerning topics such as economies, entertainment, social interactions,generativity, and variety of experiences and activities.Â The paper demonstrates that the primordial forms of IIAPs are already beingdeveloped and used by an immense number of users in unique ways and activelyshaping our society by exemplifying platforms such as Minecraft, Terraria, etc., andthe works resulting. These platforms, according to the arguments, pave a way to anew type of living and existence.
Cryptanalysis of the Rank Preserving Signature

In code-based cryptography, the rank metric usually allows one to have smaller keys and signatures than the traditional Hamming metric. Recently, a new rank-based signature was proposed: Durandal []. It relies on the use of proofs of knowledge, namely the Schnorr-Lyubashevsky approach. The authors of the Rank Preserving Signature (RPS) [] built upon this approach and proposed even smaller keys and signatures than Durandal.In this paper, we describe attacks against the RPS scheme which break all sets of parameters proposed in [].More precisely, our attacks enable us to forge valid signatures in \(2^{68}\) and \(2^{47}\) operations for sets of parameters whose claimed securities are, respectively, 128 and 192 bits. In addition to this, we give a quantum adaptation of our attack which yields an attack on the last two sets of parameters given in [].Overall, our attacks highlight weaknesses of the RPS scheme and give new constraints when designing new parameter sets.In order to describe the complexities of our attacks, this paper contains theoretical arguments together with experimental results for which we give the source code of our programs.
Security Defense Model of Web Page Design and Evaluation of Online Shopping Platform Based on Block Chain

Block chain technology is a decentralized, transparent and open, fair and equitable Internet technology that can be used in online shopping platforms to promote transactions, provide certification, and improve operational capabilities. But at the same time, various security issues in web design and evaluation also follow, and it is necessary to build a security defense model based on the block chain-based web shopping platform web design and evaluation. The purpose of this article is to study the security defense model of the web page design and evaluation of the block chain-based online shopping platform. This paper analyzes the security problems faced by the web page design and evaluation of online shopping platforms, and combines the requirements of web security defense, and summarizes the overall network environment and the deployment location of the system modules required for the realization of the security defense model. This paper selects a set of different numbers of test samples to test the system's detection time, block proliferation time, detection accuracy rate and false alarm rate performance. Experimental data shows that the accuracy rate is up to 99%, and the average false alarm rate is 5.3%. This shows that the design of this model meets the design expectations and has a good security defense.
Sentiment Analysis of the Papuan Movement on Twitter Using Naïve Bayes Algorithm

Issue on Papua has long been one of highlighted matters for the Indonesian government. Discussions on such issue have been continuously going on not only in the government level but also in the general public. Social media, in this case Twitter, becomes one of the platforms where people’s expressing their view on this issue. This paper focuses on sentiment analysis to compare three related keywords namely "Papua Merdeka (Free Papua)", "Papua bagian Indonesia (Papua part of Indonesia)", and "Otsus Papua" using Twitter data to determine the classification of tweets accurately, whether positive, negative, or neutral. Sentiment analysis of tweets from Twitter uses the Naïve Bayes Multinomial algorithm. This paper provides an analysis of how the community reacts and their opinions on the data obtained, comparisons of public opinion via Twitter social media from 2018 to 30 September 2021 for consideration, and the extent to which the Indonesian government has tried to maximize development and improve services for the people in the Papua region.
PENERAPAN MODEL PEMBELAJARAN QUANTUM TEACHING UNTUK MENINGKATKAN HASIL BELAJAR IPA TEMA LINGKUNGAN SAHABAT KITA PADA SISWA KELAS V SDN GIRIMARGO 1 TAHUN AJARAN 2020/2021

&lt;p&gt;&lt;em&gt;&lt;em&gt;The study aimed: (1) to describe the steps in applying Quantum Teaching, (2) to improve natural science learning outcomes through the application of the Quantum Teaching, and (3) to analyze the obstacles and solutions in applying Quantum Teaching. It was a collaborative classroom action research (CAR) conducted in three cycles. The subjects were teachers and students. Data collection techniques used observation, interviews, and tests. Data analysis techniques included data reduction, data presentation, and drawing conclusions. The results showed that: (1) the six steps to the application of Quantum Teaching were Grow, Experience, Name, Demonstrate, Repeat, Celebrate; (2) the application of Quantum Teaching improved natural science learning outcomes in theme “environment of our friends”. The increasing of student’s learning outcomes were 82.61% in the first cycle, 85.66% in the second cycle, and 86.08% in the third cycle; (3) the obstacles were: the teacher had not implemented steps: Name and celebrate, students were less confident, students did not conduct discussions, students were passive, students made some noises, learning materials were inadequate. The solutions were: the teacher applied Quantum Teaching steps appropriately, the teacher motivated the students to be more confident, the teacher guided the students in discussion, the teacher reprimanded students who made noises, the teacher prepared the learning materials before class began. It concludes that the application of Quantum Teaching improves natural science learning outcomes in theme “environment of our friends” to fifth grade students of SDN Girimargo 1 in academic year of 2020/2021.&lt;/em&gt;&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;&lt;em&gt;&lt;strong&gt;&lt;em&gt;Keywords&lt;/em&gt;&lt;/strong&gt;&lt;em&gt;: Quantum Teaching, learning outcomes, natural science&lt;/em&gt;&lt;/em&gt;&lt;/em&gt;&lt;/p&gt;
Low-dissipated Power Design for the Optical Blass Matrix Architecture

A low-dissipated power design is proposed for the optical Blass matrix architecture. The wasted dissipation power is reduced to the Extinction Ratio (ER) level, which means a 99.63% decrease compared with the existing method.
A Differential Privacy Mechanism Combining Randomized Response with Laplace Distribution

By combining randomized response with Laplace distribution, it obtains a new kind of differential privacy mechanism called Pseudo-Laplace mechanism. By analyzing the privacy and data availability of the mechanism, as well as an optimization problem that provides better privacy protection than the Laplace mechanism based solely on the Laplace distribution (traditional Laplace mechanism) under the same data availability, and the instantiations of the mechanism obtained in the approximate optimization algorithm, it finds that the mechanism could have almost the same privacy protection level in the middle and high privacy protection region (approximately ε < 5 ) and better privacy protection level in the low privacy protection region (approximately ε ≥5 ) than the traditional Laplace mechanism under the same data availability. It has important practical implications for many practical applications where data availability is prioritized while better user privacy is protected.
HRFA: Don’t Ignore Strangers with Different Views

Review-based recommender suffers from the sparsity of reviews: only a few users leave substantial comments in the real world. As a result, some recent methods resort to supplementary reviews written by similar users, which only leverage homogeneous preferences. However, users holding different views could also supply valuable information with heterogeneous preferences. In this paper, we propose a recommendation model for rating prediction, named Heterogeneous Review-based Recommendation via Four-way Attention (HRFA). To take advantage of the heterogeneous preferences, the supplementary reviews in HRFA are redefined as reviews from all users with common purchase history, no matter whether they give similar ratings. Specially, we integrate the heterogeneous preferences into the one semantic space via introducing a similarity projection based on rating difference. Experiments conducted on five datasets demonstrate that our model achieves higher rating prediction accuracy than other baselines.
Identifying User Relationship on WeChat Money-Gifting Network

With the proliferation of online social networks, the identification or classification of real-life relationship between users has been very useful for many applications such as financial fraud detection. In real life, usually people with different relationships would present gifts with special meanings to each other on different dates. In many Asian cultures, especially in Chinese culture, the red packet is a traditional form of monetary gift. With the rapid development of the Internet, people gradually began to give electronic red packets instead of paper ones as the means of money gifting on social network platforms. As motivated, in this paper we advocate a novel approach that exploits users’ red packet interactions for users relationship identification on WeChat, one of the largest social platforms in China. Specifically, we analyze the WeChat red packets network, identify the real-life relationship types between users through mining the semantic information of the amount and sending time of each red packet. In order to better capture the red packet gifting behaviors between users for relationship identification, on one hand, we construct an Amount-Date Graph and apply the graph embedding method to learn embeddings of the amount and sending date of each red packet. On the other hand, we propose a novel sequential model, Cross & Attention Sequence Model (CASM), which explicitly learns the interactions between the latent semantic information of each red packet’s amount and sending date in the red packets sequence between two users. To validate our approach, we conduct comprehensive experiments on a real-world WeChat Users Red Packets dataset that involves 8 kinds of real-life relationships. The experiments show that our proposed approach performs significantly better than baselines and achieves 81.70 percent prediction accuracy.
District Health Information System 2 (DHIS2) Data Utilization and its Determinants Among Performance Monitoring Team in Public Health Facilities of Harari Region and Dire Dawa City Administration, Eastern Ethiopia: Cross-Sectional Study Design

Data utilization is one of the key functions of the Health Information System. It enables performance monitoring team for evidence based decision making to improve quality of health service delivery. This can be supported by the use of District Health Information System 2 platform which is expected to increase the utilization of health data. However, little is known about the use of health data from District Health system 2 and its determinants especially among performance monitoring team working at facility level since its implementation.The study aimed to assess data utilization from District Health Information System 2 and its determinants among performance monitoring team in public health facilities of Harari Region and Dire Dawa City Administration.The study utilized an institutional-based cross-sectional study design. A stratified sampling technique was used to select a total of 220 participants. Data was entered using Epi-Data version 3.1 and exported to STATA version 14.2 for analysis. The binary logistic regression model was applied to identify determinants of District Health Information System version 2 data utilization. Statistical significance was declared at P<0.05.Overall utilization of data from District Health Information System 2 was about 45% (95%, CI= [39%, 50%]). Favorable attitude (AOR=3, 95% CI: 1.90 – 8.62), good perceived self-competence (AOR=2.9, 95% CI: 1.14 – 7.38), good perceived data quality (AOR=4.4, 95% CI: 1.76 – 10.9), and getting supportive supervision (AOR=4.3, 95% CI: 1.48-12.45) were significantly associated with data utilization from District Health Information System 2.The overall utilization of data was below national recommendation. Attitude, perceived self-competence and perceived data quality and supportive supervision were determinants of data utilization from District Health Information System 2 among performance monitoring team.
Automatic Goal Discovery in Subgoal Monte Carlo Tree Search

Monte Carlo Tree Search (MCTS) is a heuristic search algorithm that can play a wide range of games without requiring any domain-specific knowledge. However, MCTS tends to struggle in very complicated games due to an exponentially increasing branching factor. A promising solution for this problem is to focus the search only on a small fraction of states. Subgoal Monte Carlo Tree Search (S-MCTS) achieves this by using a predefined subgoal-predicate that detects promising states called subgoals. However, not only does this make S-MCTS domain-dependent, but also it is often difficult to define a good predicate. In this paper, we propose using quality diversity (QD) algorithms to detect subgoals in real-time. Furthermore, we show how integrating QD-algorithms into S-MCTS significantly improves its performance in the Physical Travelling Salesmen Problem without requiring any domain-specific knowledge.
Elimination of Information Redundancy of Hyperspectral Raster Images Technology Enhancement

The work is a continuation of the authors' research on the problem of adaptive compression of raster hyperspectral images of Earth remote sensing. In the first part of the article, the authors give an overview of the current state of affairs in the processing of images of remote sensing of the Earth, the characteristic properties of raster hyperspectral images in the context of the prospects for lossy compression, the problems of the effectiveness of existing compression methods of this type of graphic documents are indicated. Further, the article highlights the issues of increasing the efficiency of methods for eliminating information redundancy of raster hyperspectral images of Earth remote sensing. The problems of designing and creating parallel methods and algorithms for the compression of raster hyperspectral ERS images are considered. A method for the development of a parallel algorithm for constructing a system of local homogeneous "well-adapted" basis functions for raster hyperspectral images, based on the Chebyshev approximation for systems using the CUDA graphics processor, is proposed.
Analyzing inconsistencies in the Tor consensus

Every distributed system needs some way to list its current participants. The Tor networks consensus is one way of tackling this challenge. But creating a shared list of participants and their properties without a central authority is a challenging task, especially if the system is constantly targeted by nation state attackers. This work carefully examines the Tor consensuses created in the last two years, identifies weaknesses that did already impact users, and proposes improvements to strengthen the Tor consensus in the future. Our results show undocumented voting behavior by directory authorities and suspicious groups of relays that try to conceal the fact that they are all operated by the same entity.