Reviewerly/exHarmony huggingface dataset:
A study of stereo matching algorithm on low texture and depth discontinuity regions

&lt;p&gt;This article studies the performance of the proposed stereo matchingalgorithm on complex regions. These regions are areas with very limitedinformation for the matching process which are low texture, and depthdiscontinuity regions. In this study, each algorithm uses different matchingcost computation (MCC) techniques, but for cost aggregation (CA), disparityoptimization (DO) and disparity refinement (DR), the technique remains thesame. The MCC areabsolute difference(AD), the combination ofabsolutedifference and gradient matching(AD+GM) andcensus transform(CT).Then, for CA, DO and DR, they areminimum spanning tree(MST),winnertake all(WTA) andbilateral filter(BF), respectively. The results are presentedand discussed in this article. Hence, thru this study the robust method can beestimated at the MCC stage.&lt;/p&gt;
Impact blockchain technology on traditional electronic payment system

&lt;span&gt;Every moment there is a new idea and a new project that creates new technologies to make life easier and faster and to invest resources better. In this research, talk about a new technology that is considered a revolution in the age of the Internet, which in turn is the offspring of the new generation of the internet (5G), which is the blockchain technology, which began to be completed and appear to us from 2017, and it is not like other technologies, but in essence depends on changing many of the concepts that we are accustomed to in Communication, exchange and sharing via the Internet, it will change it in the same way as open-source software and in the same way as the Linux system, which, when it appeared, became the main focus for programmers and the development of software technologies, and this is the case with blockchain technology, this technology depends on the user and the customer only. There is no third party or responsible party. The user is responsible here.&lt;/span&gt;
Neural Drone Racer Mentored by Classical Controllers

The autonomous drone race has driven the development of various approaches to agile flight involving perception, planning and control. The latter is most effective when using external information such as high-frequency drone position. On the other hand, deep learning-based methods propose to train an artificial pilot to learn to associate sensory data with flight commands using only visual information. In this paper, we propose an approach for developing a neural drone racer based on the mentoring of three classical flight controllers combined with visual information. We performed a comparison of the three trained models. Our results demonstrated an improvement in navigation behaviour and increased the speed to complete a racetrack by a factor of 7 compared to that reported in the state of the art.
The Effect of Reading Interest and Vocabulary Mastery on Students’ Ability to Comprehend Narrative Text at State Senior High Schools in Cirebon

The present study was conducted to respond to the students’ low ability to comprehend narrative text. This was likely due to their lack of reading interest and vocabulary mastery. Therefore, the study was aimed to find out the effect of reading interest and vocabulary mastery collectively on the ability of comprehending narrative text. The research question, then, formulated as: How can the students’ lack of reading interest and vocabulary mastery affect their ability to comprehend narrative text? Students’ reading interest in Indonesia is very low. This is because students today still ignore the importance of reading. The low reading interest in Indonesia are caused by various factors, such as environment, online game, and social media. Students’ vocabulary mastery in reading will affect their ability in comprehending and understanding the writer’s message through the writer’s writing. The research design used was classroom action. The subjects were first-year level students from state senior high school of 2, 3 and 4 Cirebon. The research method used was multiple regression technique. Questionnaires were administered to 60 participants. As a sample, the research used proportional random sampling technique. The data were collected using a questionnaire and a quiz then analyzed statistically using descriptive statistics such as mean, median, standard deviation, and standard inferential. It was aimed to find correlation coefficient with t testing using SPSS 20. The result of the implementation showed that: (1) there was a significant effect of both reading interest and vocabulary mastery on students’ ability to comprehend narrative text at state senior high schools in Cirebon. It was showed by the point of Sig = 0.000 &lt; 0.05 and Fcount = 14.928. (2). There was a significant effect of reading interest on students’ ability to comprehend narrative text at state senior high schools in Cirebon. It was showed by the point of Sig = 0.005 &lt; 0.05 and tcount = 2.932. (3). There was a significant effect of vocabulary mastery on students’ ability to comprehend narrative text at senior high schools in Cirebon. It was showed by the point of Sig = 0.033 &lt; 0.05 and tcount = 2.181.&#x0D; Keywords: reading interest; vocabulary mastery; narrative text
Programmable Photonic Solver for Computationally Complex Problems

Computationally complex problems such as the subset sum problem (SSP) are difficult to solve on the electronic computer when the problem size scales up. Optical computing enables the acceleration of complex problems due to its low latency and multiple free degrees of parallelism. Here, we thoroughly exploit the programmability in silicon photonic circuits and construct a programmable microring resonator mesh to implement the solution of two types of computationally complex problems: the SSP and stochastic process simulation. We experimentally accomplish the solving of multiple different sets, which shows the great versatility and scalability of the SSP solver. The knapsack problems are also demonstrated as an example of practical optimization problems derived from SSP. Besides, two-state discrete time stochastic processes are also experimentally accomplished with our solver chip. Our proposed photonic solver shows the potential application of optical NP-complete problem-solving and quantum computing.
Detection of AI-Created Images Using Pixel-Wise Feature Extraction and Convolutional Neural Networks

Generative AI has gained enormous interest nowadays due to new applications like ChatGPT, DALL E, Stable Diffusion, and Deepfake. In particular, DALL E, Stable Diffusion, and others (Adobe Firefly, ImagineArt, etc.) can create images from a text prompt and are even able to create photorealistic images. Due to this fact, intense research has been performed to create new image forensics applications able to distinguish between real captured images and videos and artificial ones. Detecting forgeries made with Deepfake is one of the most researched issues. This paper is about another kind of forgery detection. The purpose of this research is to detect photorealistic AI-created images versus real photos coming from a physical camera. Id est, making a binary decision over an image, asking whether it is artificially or naturally created. Artificial images do not need to try to represent any real object, person, or place. For this purpose, techniques that perform a pixel-level feature extraction are used. The first one is Photo Response Non-Uniformity (PRNU). PRNU is a special noise due to imperfections on the camera sensor that is used for source camera identification. The underlying idea is that AI images will have a different PRNU pattern. The second one is error level analysis (ELA). This is another type of feature extraction traditionally used for detecting image editing. ELA is being used nowadays by photographers for the manual detection of AI-created images. Both kinds of features are used to train convolutional neural networks to differentiate between AI images and real photographs. Good results are obtained, achieving accuracy rates of over 95%. Both extraction methods are carefully assessed by computing precision/recall and F1-score measurements.
The Hash base Apriori Technique for Association Rule Mining and Data Sanitization

Under different circumstances, private information is exposed, and it must be sanitised before even being shared to address privacy issues. Data mining techniques can collect large amounts of data in a short amount of time. The information gathered by the powerful machine learning techniques may identify the most sensitive content, which pertains to an individual or organization. The degree of sensitivity of data belonging to a business or an agency might vary. Only approved individuals and organizations have access to this information. As a result, using access limitations to confirm the security of complicated data is not a complete operation. It can have an impact on the utility of a data mining solution, and the user may be able to re-identify sensitive data. To introduce instruments to find a mechanism for the security of confidential information. Finding ways to secure confidential data by developing data mining tools and procedures that can be applied to databases, despite the fact that this diminishes the data mining results' trust worthiness. We proposed a data sanitization strategy in this article that uses a frequent itemset classification approach with a modified apriori algorithm. The problem is to maintain intelligence information for vital arrangements while preventing the numerous exposures of company rule mining at the same time. Data sanitization strategy is used to perform a thorough investigation of numerous sequential pattern algorithms for ensuring the privacy of large amounts of data. Our research shows that our approach is efficient, scalable, and provides meaningful correction when compared to other methods used in existing systems.&#x0D; Keywords: Association Rule Mining, Classification, Frequent itemset, data privacy, data hiding, rule generation, support, confidence and transactional dataset
Comparing Transformer-Based Machine Translation Models for Low-Resource Languages of Colombia and Mexico

This paper offers a comparative analysis of two state-of-the-art machine translation models for Spanish to Indigenous languages of Colombia and Mexico, with the aim of investigating their effectiveness and limitations under low-resource conditions. Our methodology involved aligning verse pairs text using the Bible for twelve Indigenous languages and constructing parallel datasets for evaluation using BLEU and ROUGE metrics. The results demonstrate that transformer-based models can deliver competitive performance in translating from Spanish to Indigenous languages with minimal configuration. In particular, we found the Opus-based model obtained the best performance in 11 of the languages in the test set but, the Fairseq model performs competitively in scenarios where training data is more scarce. Additionally, we provide a comprehensive analysis of the findings, including insights into the strengths and limitations of the models. Finally, we suggest potential directions for future research in low-resource language translation, specifically in the context of Latin American indigenous languages.
The human touch in a digital era: Culture, technology and capabilities at precursor assurance

While technological advancements have reshaped numerous industries, the human factor remains paramount, providing unmatched advisory expertise. A central element of the storyline in this paper is the company Precursor Assurance. This local Singaporean accounting firm exemplifies how technological advancements, and the involvement of humans are seamlessly integrated. With KG Tan as its leader, the company promotes a work environment that values transparency, respect, and trust while emphasising grit and flexibility. The case depicts a scenario wherein technology amplifies human potentials in accounting rather than supplanting them.
Contact Tracing Using ML

At the time of Cororna, we are undergoing a dangerous situation due to wide spread of “Corona Virus”.If a person is found to be infected with the Corona Virus,, then It is critical to find the persons got contacted with the infected person. This can be done by Manual methods or numerical methods. Technology can be used for Contact tracing is a process that produces more efficient and accurate results than when the procedure is done manually. Contact Tracing is onetype of the processes to identify the infected persons using machine learning. It can help to reduce the infected persons and can fasten the process of treating people who have been infected with a disease. We can save a lot of lives this way. To find the movement of infected person, we use GPS data set that contains time and location of a person. The location data contains longitude and latitude coordinates.
Secure And Efficient Access Control Over Blockchain PHR Cloud Storage System

A novel architecture of blockchain-based secured electronic health record (EHR) management system is presented in this paper. Electronic health data record-keeping in cloud-based storage systems always pose a threat to information security. Intruders can delete EHR of patients, giving benefits to insurance companies or hiding medical malpractices. A secured EHR management system is required that would essentially solve such issues. The blockchain is an emerging technology that can be adapted to develop a secured and an efficient data management system. Storing and sharing health records through electronic systems pose security risks. However, establishing a new blockchain based system replacing the existing system is expensive. In our proposed architecture, we introduced an integration mechanism, named as the blockchain handshaker, between the existing cloud based EHR management system and public blockchain network to develop a secured health record management system. A blockchain-based protocol that secures health records, addressing all of the main security and complementary properties defined in current regulations. A prototype to provide evidence on the feasibility of the proposed concept is presented in this paper. The performance assessments demonstrate the eﬃciency of our proposed solution in terms of computation, communication, and storage.
Analisis Kesulitan Belajar Daring Mahasiswa Tadris Matematika Pada Materi Aljabar

This study aims to describe the various difficulties faced by students during brave learning in abstract algebra courses. The research subjects were 4 semesters VI students of Tadris Mathematics at IAIN Fattahul Muluk Papua who teach Abstract Algebra courses. Data collection techniques use observation and interviews of the think-aloud type which make it possible to vilify information through thinking subjects during the learning process. The results of the research show that the difficulties faced by each student vary. However, in general, the difficulties faced by students are understanding the material when using the WhatsApp, Google Classroom, and Zoom Cloud Meeting applications. Comprehension through the WhatsApp Group is less effective because the learning outcomes obtained by students are still low when compared to Google Classroom and Zoom Cloud Meetings.
Privacy-Preserving Smart Contracts for Confidential Transactions Using Dual-Mode Broadcast Encryption

Blockchain-based smart legal contract, as a legally binding executable contract, has attracted extensive attentions in trade finance. However, since the contract is deployed on open and transparent blockchain network, all transaction data are publicly visible, which brings to privacy disclosure problem. Aiming at this problem, we introduce an improved architecture of smart legal contract with privacy protection, involving contract development, deployment, and execution. In this architecture, the sensitive data of transaction are declared and protected in the form of contract terms. These terms allow the compiler to link predefined cryptographic algorithms into smart contract programs, and then to generate executable contract code. Furthermore, as predefined cryptographic algorithms, we construct a new dual-mode identity-based broadcast encryption (DM-IBBE) scheme to meet specific-purpose or generic-purpose privacy by using selective encryption mode or exclusive encryption mode, respectively. We proved that our DM-IBBE scheme is semantically secure under the decisional Diffie–Hellman assumption. In addition, our experimental results show that the proposed scheme can satisfy the privacy requirements of transaction, and it is practicable and easy-to-develop for introducing privacy preserving mechanisms into smart legal contract languages.
Secure and Privacy-Preserving Decentralized Federated Learning for Personalized Recommendations in Consumer Electronics using Blockchain and Homomorphic Encryption

Over the past few years, personalized recommendations have emerged as a fundamental component of the consumer electronics sector. The rise of decentralized federated learning has expanded the horizons of personalized recommendations, offering significant potential. Nonetheless, the utilization of confidential data from diverse clients raises legitimate concerns regarding privacy and security. In response to these challenges, we present an innovative framework for secure and privacy-preserving decentralized federated learning, tailored to personalized recommendations within the consumer electronics sector. Our approach strives to facilitate the collective contribution of data from multiple clients to the learning process while safeguarding their privacy. To accomplish this, we harness the power of homomorphic encryption, ensuring that clients’ data remains encrypted and impervious to prying eyes. Additionally, we leverage blockchain technology to establish a secure, decentralized foundation for data exchange and management. Through the utilization of blockchain, we empower clients to validate the integrity of the learning process, guarantee system transparency, and thwart any malicious attempts at result manipulation. Our framework is rigorously assessed using real-world consumer electronics data, highlighting its capacity to provide a secure, decentralized, and privacy-centric solution for personalized recommendations. This approach not only enriches the user experience but also offers robust safeguards for sensitive data.
THE CHALLENGES IN TEACHING WRITING BY USING E-LEARNING AT MAN 1 TANJUNG JABUNG TIMUR

The purpose of this study is to determine the difficulties that the teachers at MAN 1 Tanjung Jabung Barat face when attempting to teach writing utilizing online learning. Case study methodology was applied throughout the course of this investigation. The researcher gathered data for this study by conducting interviews, conducting observations, and documenting the findings. The data was evaluated by first simplifying it, then displaying it, and finally drawing conclusions from it. Based on the findings of the data collection, the researcher determined that adopting E-learning to teach writing presents various obstacles for teachers. The use of the e-learning system is based on the subject teacher's position that implementation e-learning is less effective in learning to write, which is one of the obstacles. Other obstacles include students' lack of motivation to understand learning to write, differences in the level of understanding among students in the same class, and a lack of support from parents.
MORPHOLOGICAL ERRORS IN STUDENTS’ WRITING RECOUNT TEXT OF JUNIOR HIGH SCHOOL 18 BATANGHARI

The aim of this study was to find out morphological errors made by students of SMP Negeri 18 Batanghari in writing recount text. This is descriptive qualitative research, the data were collected by documentation and analysed by Surface Strategy Taxonomy. This research revaled that there are four types of errors with the total number 58 errors made by the students in writing recount text. Omission error which consists of three categories of omission errors i.e. omission of plural, auxiliary verb, gerund, with the total number of errors are 8 or 13,56%, addition error includes four categories of addition errors i.e. addition of gerund, plural, pronoun, noun, with the total number of errors are 8 or 13,56%, misformation error that consist of misformation error of tense with the total number of errors are 38 or 66,10%, and the last misordering error that consist of misordering of spelling with the total number of errors are 4 or 6,78%.
STUDENT WORKSHEET (LEMBAR KERJA PESERTA DIDIK/LKPD) TO TEACH READING FOR EIGHTH GRADE STUDENTS AT STATE JUNIOR HIGH SCHOOL (SMPN) 22 JAMBI CITY

This study aimed to design LKPD for eighth-grade students at SMPN 22 Jambi City. This study employed a Research and Development (R&amp;D) design. This study produced an LKPD for the reading skill of eighth-grade students. The process of designing the LKPD employed the ADDIE model: Analysis, Design, Develop, Implementation, and Evaluation. A material expert and a design expert validated the LKPD. After the experts validated the LKPD, the teacher also assessed it. After the product was validated and revised, it was given to students to know their responses to using the LKPD. This product has obtained a validity score of 89,23% from the material expert and 91,67% from the design expert. The validation score from the teacher was 94,67%. A score of 89% was obtained from the students’ responses. It means that the LKPD was accepted as an instructional medium to teach reading.
STUDENTS’ DIFFICULTIES IN READING COMPREHENSION AT THE EIGHT GRADE STUDENTS OF SMPN 22 KOTA JAMBI

This research aimed to analyze students' difficulties in reading comprehension at SMPN 22 Jambi. This is a qualitative research. The technique of data collection used in this research is observation and interviews. The subjects in this study were students of class VIII G at SMPN 22 Jambi. The results of this study indicate that the difficulties faced by class VIII G students in reading comprehension are four difficulties. Firstly, students have difficulty in understanding complexity of grammar. Secondly, the students has difficulty in knowing certain vocabulary when understanding reading. Thirdly, students have difficulty in using reading strategies, and the last one the students have difficulty in concentrations.
CLASSROOM MANAGEMENT APPLIED BY ENGLISH TEACHER AT NINTH GRADE IN THE STATE JUNIOR HIGH SCHOOL 7 JAMBI

The purpose of this study was to explore the classroom management techniques utilized by the English teacher at the ninth grade level at the state junior high school 7 in Jambi. The descriptive qualitative research approach was utilized in this study, and the methods of data collection were observation, interviewing, and documentation. During the pre-activity phase, the teacher ensured that the students were prepared for the activity by ensuring that they were comfortable. The instructor also organized the seating for the students, ensured that the whiteboard and any other necessary equipment was in the appropriate location, and managed the students by providing a different manner of instruction at each meeting. During the process of learning, the instructor has the ability to foster an environment that is conducive to learning and can construct things that are constructive in order to achieve the best possible results from the learning process.