Reviewerly/exHarmony huggingface dataset:
BTDEC: Blockchain-Based Trible Data Elliptic Curve Cryptosystem with Fine-Grained Access Control for Personal Data

In an AI-driven era, data the board is reliant on security confirmation and open commerce.A standard datasharing organization stage is important in the current datasharing courses of action, and clients transmit their information to a cloud server for limitation and dispersion.Customers, on the other hand, would lose control of their data the instant it was sent off the server, making security and insurance a major worry.Even though data encryption and access control are regarded as cutting-edge innovations for storing individual data on cloud servers, they only go so far.Regardless, it continues to depend heavily on an external source of validity, the Cloud Service Provider (CSP).To tackle this challenge, they combined blockchain, 3DES ciphertext technology, ECC, and the Interplanetary File System (IPFS).This research focuses on BTDEC, a Blockchain-based Trible Data Elliptic Curve Crypto System for Personal Data.The data holder encrypts the sharing data and saves it on IPFS in this customer-driven way, boosting the decentralization of the arrangement.The standardized data area and unscrambling key will be coupled utilizing 3DES with ECC, and the data owner will disseminate his data-related information and send on keys to data customers using blockchain, according to the built-up confirmation method.The data may only be downloaded and interpreted by the data client whose credits fulfill the confirmation conditions.BTDEC enables the data owner to deny a particular data client at the individual dimension without affecting others, providing him fine-grained network access over his data.When obtaining data, the ciphertext phrase search is almost usually utilized to secure the data customer's security.They investigated BTDEC's security and recreated our technology on the EOS blockchain, proving the concept's validity.Meanwhile, they investigated the limitation and overhead and determined that BTDEC performed well.
A Symmetric, Probabilistic, Non-Circuit Based Fully Homomorphic Encryption Scheme

Traditional encryption allows encrypted data to be decrypted before any computation could be performed on such data.This approach could compromise the security of the data when an untrusted party is involved in the computation.To be able to work on data in its encrypted form, a homomorphic encryption approach is recommended.Homomorphic encryption allows computation to be done on data that has been encrypted and yields the same results that would have been obtained if the computation had been performed on the unencrypted form of the data.Most of the Homomorphic encryption (HE) algorithms are deterministic.These deterministic algorithms produce the same ciphertext for a given data on different occasions.This could allow an adversary to easily predict a plaintext from a ciphertext.Probabilistic algorithms, however, resolve the aforementioned challenge of deterministic algorithms.A probabilistic encryption algorithm ensures different ciphertexts for the same plaintext on different occasions.Another challenge of most homomorphic encryption schemes is the way data is encrypted.Most algorithms encrypt data bit-by-bit (i.e.circuitbased).Circuit-based encryption makes the encryption and decryption complex, thereby increasing the running time.To reduce the running time, Non-Circuit based encryption and decryption are preferred.Here, numeric data need not be converted to binary before any encryption is done.To ensure a very secure, efficient but simpler HE scheme, the authors have offered a fully homomorphic encryption (FHE) scheme that is Probabilistic, Non-Circuit based, and uses symmetric keys.Results from the experiment conducted show that the proposed scheme is faster than Fully Homomorphic Encryption over the Integer (DGHV), A simple Fully Homomorphic Encryption Scheme Available in Cloud Computing (SDC), and Fully Homomorphic Encryption by Prime Modular Operation (SAM) schemes.The proposed scheme has a time complexity of O(log(n 2 )) and consumes less memory space.Even though HE schemes are naturally slow, the less memory space consumed by the proposed scheme and the time complexity of O (log(n 2 )), makes the proposed scheme suitable for real-life implementation such as auction, electronic voting, and in other applications that make use of private data.
Learning Continuous Graph Structure with Bilevel Programming for Graph Neural Networks

Learning graph structure for graph neural networks (GNNs) is crucial to facilitate the GNN-based downstream learning tasks. It is challenging due to the non-differentiable discrete graph structure and lack of ground-truth. In this paper, we address these problems and propose a novel graph structure learning framework for GNNs. Firstly, we directly model the continuous graph structure with dual-normalization, which implicitly imposes sparse constraint and reduces the influence of noisy edges. Secondly, we formulate the whole training process as a bilevel programming problem, where the inner objective is to optimize the GNNs given learned graphs, while the outer objective is to optimize the graph structure to minimize the generalization error of downstream task. Moreover, for bilevel optimization, we propose an improved Neumann-IFT algorithm to obtain an approximate solution, which is more stable and accurate than existing optimization methods. Besides, it makes the bilevel optimization process memory-efficient and scalable to large graphs. Experiments on node classification and scene graph generation show that our method can outperform related methods, especially with noisy graphs.
A Comparison of Weighted Least Square and Quantile Regression for Solving Heteroscedasticity in Simple Linear Regression

Regression analysis is the study of the relationship between dependent variable and one or more independent variables. One of the important assumption that must be fulfilled to get the regression coefficient estimator Best Linear Unbiased Estimator (BLUE) is homoscedasticity. If the homoscedasticity assumption is violated then it is called heteroscedasticity. The consequences of heteroscedasticity are the estimator remain linear and unbiased, but it can cause estimator haven‘t a minimum variance so the estimator is no longer BLUE. The purpose of this study is to analyze and resolve the violation of heteroscedasticity assumption with Weighted Least Square(WLS) and Quantile Regression. Based on the results of the comparison between WLS and Quantile Regression obtained the most precise method used to overcome heteroscedasticity in this research is the WLS method because it produces that is greater (98%).
Deep Learning, a Not so Magical Problem Solver: A Case Study with Predicting the Complexity of Breast Cancer Cases

Using guideline-based clinical decision support systems (CDSSs) has improved clinical practice, especially during multidisciplinary tumour boards (MTBs) in cancer patient management. However, MTBs have been reported to be overcrowded, with limited time to discuss all cases. Complex breast cancer cases that need further MTB discussions should have priority in the organization of MTBs. In order to optimize MTB workflow, we attempted to predict complex cases defined as non-compliant cases despite the use of the decision support system OncoDoc. After previously obtaining insufficient performance with machine learning algorithms, we tested Multi Layer Perceptron for classification, compared various samplers to compensate data imbalance combined with cross- validation, and optimized all models with hyperparameter tuning and feature selection with no improvement and lacklustre results (F1-score: 31.4%).
Analisis Faktor Kesulitan Belajar Siswa dalam Mata Pelajaran Ekonomi Pada Pembelajaran Daring di Masa Pandemi Covid-19

This study aims to confirm and analyze the causative factor of learning difficulties on online learning during Covid-19. The population in the research is 107 students of SMA Negeri 4 Sungai Penuh and the sample is 74 students obtained using accidental sampling technique. The data is collected with questionnaire from 74 respondents who doing online learning during covid-19. The data will be analyzed using descriptive and confirmatory factor analysis. The result of the analysis show that motive factor, school environmental factors, the peer factor, family factor, community factor, study habits factor, motive factor, mass media are the factors that influences the causative of learning difficulties on online learning during Covid-19. From the results of the research analysis, recommended to students, school and families can work together in order online learning to run well.Keywords : learning difficuties, causative factors, online learning, covid-19
An Overview of Hybrid, Digital and Virtual Library

A digital library is a combination of textual, numeric, scanned photos, graphics, audio, and video recordings that allows consumers to easily retrieve information from a digital collection. Recent advancements in computer store and processor, communication technologies, e-products, networking, and internet use have resulted in a radical shift in the way libraries and their services operate. Current study discusses a functioning collection of textbooks, documents, newspapers, and audiovisual resources stored and arranged in a library for anyone to read or borrow. Information and Communications Technology (ICT)has had a significant influence on libraries, and it has altered the traditional library idea in which print and paper materials are the primary components of the system. Libraries are transforming into digital libraries in order to fulfill the massive information explosion and rising demand for information. Due to the digitization of library materials and the rapid advancement of technology, a new sort of library has emerged: the virtual library. Most of us are often perplexed by library jargon. In this work, we attempt to clarify the language used in these libraries in a professional manner. Such libraries will increase the efficiency of education in the coming eras.
Pengembangan aplikasi “SIMBA” (Social studies instructional media based android) untuk mata pelajaran IPS kelas VII SMP

Media is an important component in multidisciplinary social studies learning that must be presented in an integrated method. This study aimed to design learning media "SIMBA" (Social Studies Instructional Media Based on Android) and to analyze the feasibility based on an assessment by experts and students. The stages in this research and development were carried out by adopting 4D development, namely: define, design, develop, and disseminate. Due to limitations, the research was only carried out at the development stage, The research data is a feasibility assessment using a Likert scale from expert and VII grade students at SMP Negeri 21 Malang were collected using a questionnaire. The data were then analyzed by using the percentage descriptive technique. The results of the development were learning media which contains six menu components, namely: the competency and objectives, learning materials, our surroundings, videos, quizzes, and the developer menu. The results of the assessment from experts are “Very Appropriate”. While the students showed similar things which means the learning media "SIMBA" was feasible and could meet the needs of students in the learning process. Based on the weaknesses of this study, it is necessary to develop similar applications in other subjects that are capable of producing offline applications and can be accessed by teachers and students in the future.
ALIH KODE DAN CAMPUR KODE PADA INTERAKSI GURU DENGAN SISWA DALAM PEMBELAJARAN BAHASA INDONESIA

This study aims to describe the form of code switching and code mixing as well as the factors that influence the occurrence of code switching and code mixing events in teacher and student interactions during the teaching and learning process in the classroom. This research is included in qualitative descriptive research. The data in this study were obtained by using the listening method and the following techniques, namely tapping technique, free-involvement-free listening (SBLC) technique, recording technique and note-taking technique. The research data were then analyzed using qualitative descriptive analysis techniques based on sociolinguistic theory. Checking the validity of the data obtained in this study using a triangulation test. The results of the study found that there were 22 utterances of code switching and code mixing found in the teaching and learning process in Indonesian language learning, namely: (1) Internal code switching was 10 utterances and external code switching was 1 utterance. (2) Mixing the internal code of 5 utterances and mixing the external code of 6 utterances. It can be concluded that internal code switching is more dominantly used during teaching and learning interactions in learning Indonesian
Gender recognition from unconstrained selfie images: a convolutional neural network approach

&lt;p&gt;Human gender recognition is an essential demographic tool. This is reflected in forensic science, surveillance systems and targeted marketing applications. This research was always driven using standard face images and hand-crafted features. Such way has achieved good results, however, the reliability of the facial images had a great effect on the robustness of extracted features, where any small change in the query facial image could change the results. Nevertheless, the performance of current techniques in unconstrained environments is still inefficient, especially when contrasted against recent breakthroughs in different computer vision research. This paper introduces a novel technique for human gender recognition from non-standard selfie images using deep learning approaches. Selfie photos are uncontrolled partial or full-frontal body images that are usually taken by people themselves in real-life environment. As far as we know this is the first paper of its kind to identify gender from selfie photos, using deep learning approach. The experimental results on the selfie dataset emphasizes the proposed technique effectiveness in recognizing gender from such images with 89% accuracy. The performance is further consolidated by testing on numerous benchmark datasets that are widely used in the field, namely: Adience, LFW, FERET, NIVE, Caltech WebFaces and&lt;br /&gt;CAS-PEAL-R1.&lt;/p&gt;
Self-supervision based Semantic Alignment for Unsupervised Domain Adaptation

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)Self-supervision based Semantic Alignment for Unsupervised Domain AdaptationRonghang Zhu and Sheng LiRonghang Zhu and Sheng Lipp.1 - 9Chapter DOI:https://doi.org/10.1137/1.9781611977172.1PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Unsupervised domain adaptation aims to learn domain-invariant features across domains to transfer knowledge from a well-labeled source domain to an unlabeled target domain. Recently, some unsupervised domain adaptation methods focus on semantically aligning data distributions with pseudo-labels of the target domain. However, semantic alignment based on pseudo-labels has potential risks, e.g., inaccurate pseudo-labeling from classifier, and error accumulation from pseudo-label bias. To alleviate these risks, we propose a novel self-supervision based semantic alignment (S3A) approach for unsupervised domain adaptation, which can jointly incorporate the source alignment and cross-domain target alignment for better semantic alignment across domains. S3A consists of a two-stage semantic alignment procedure with self-supervision. One is to capture the discriminative structure of source domain by aligning source data to source class prototypes, and the other is to match each target data to its neighbor in source domain with self-supervision. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our proposed method, compared with the representative adversarial learning and self-supervised learning based unsupervised domain adaptation methods. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-717-2 https://doi.org/10.1137/1.9781611977172Book Series Name:ProceedingsBook Code:PRDT22Book Pages:1-737Key words:Unsupervised Domain Adaptation, Self-Supervised Learning, Transfer Learning
A Small-Step Operational Semantics for GP 2

The operational semantics of a programming language is said to be small-step if each transition step is an atomic computation step in the language.A semantics with this property faithfully corresponds to the implementation of the language.The previous semantics of the graph programming language GP 2 is not fully small-step because the loop and branching commands are defined in big-step style.In this paper, we present a truly small-step operational semantics for GP 2 which, in particular, accurately models diverging computations.To obtain small-step definitions of all commands, we equip the transition relation with a stack of host graphs and associated operations.We prove that the new semantics is non-blocking in that every computation either diverges or eventually produces a result graph or the failure state.We also show the finite nondeterminism property, viz.that each configuration has only a finite number of direct successors.The previous semantics of GP 2 is neither non-blocking nor does it have the finite nondeterminism property.We also show that, for a program and a graph that terminate, both semantics are equivalent, and that the old semantics can be simulated with the new one.
Human Posture Estimation

With the development of the artificial intelligence field, automatic drive, and machine learning, both did well in their field. Human posture estimation could help people in many cases, as an imitation of the human body, entertainment, sports, and other field have had some achievement, also shows the importance of human posture estimation for the future. In 2013, Toshev introduced Deep Pose into human posture estimation, and the study of human body posture estimation began to shift from traditional methods to Deep learning. CNN was firstly proposed to be applied to the detection of human joints and transform the estimation of human body posture into the regression problem of joints. Secondly, CPM mainly uses Heatmap to represent joints' position and position constraint relationship, which can fully consider the spatial position relationship between various joints. Then in the process of top to bottom and bottom to top in Hourglass, through convolution and pooling, the image's resolution is adjusted to the best, and the information of the image is integrated. CPN also uses the top-down method to detect the body frame first and detect the human body nodes in the body frame. RefineNet can correct the results with large errors. HRNET is the follow-up to Simple Baseline, which enables multi-scale fusion through information exchange across resolution subnetworks. We discussed a lot about human posture. Deep learning has led to the rapid development of academy and technology. The development of technology will improve our future analysis on human detection data, for example, detect our daily walking state, or in different occasions through images and other data to analyze the characteristics of the human posture. This will help us develop in the future, which will have a crucial impact on our development.
Differential phase measurement based on synchronous phase shift determination

Based on synchronous phase shift determination, we propose a differential phase measurement method for differential interference contrast (DIC) microscopy. An on-line phase shift measurement device is used to generate carrier interferograms and determine the phase shift of DIC images. Then the differential phase can be extracted with the least-squares phase-shifting algorithm. In addition to realizing on-line, dynamic, real-time, synchronous and high precision phase shift measurement, the proposed method also can reconstruct the phase of the specimen by using the phase-integral algorithm. The differential phase measurement method reveals obvious advantages in error compensation, anti-interference, and noise suppression. Both simulation analysis and experimental result demonstrate that using the proposed method, the accuracy of phase shift measurement is higher than 0.007 rad. Very accurate phase reconstructions were obtained with both polystyrene microspheres and human vascular endothelial.
The Adoption and Use of Digital Literacy among Selected Libraries in Tertiary Colleges in Tanzania

This study chiefly aimed at assessing the adoption and use of digital literacy among libraries in selected tertiary colleges. Two tertiary college were selected as cases studies, namely, Tanzania Public Service College (TPSC) and Dar es Salaam Maritime Institute (DMI).The study aimed at examining the status of digital literacy adoption, determining the use of digital literacy on their day-to-day activities, and identifying challenges of digital literacy adoption and use among libraries in selected colleges. A sample of 98 library users were selected as respondents. Data was collected through questionnaire and face-to-face interview. Quantitative data was analyzed using Statistical Package for the Social Sciences, while content analysis was used for qualitative data. The findings show that majority of the respondents were aware of the adoption and use of Digital Literacy, but faced challenges such as technophobia, lack of enough and supported instructions on digital literacy, poor power supply to support internet, and insufficient digital literacy facilities. In fact, the majority of respondents had adopted and used digital literacy for less than a year. The use of digital literacy has a number of benefits such as easy and quick access of information, retrieval of library information in cataloguing and classification, as well as accessing different databases to acquire relevant data and information. The study recommends that the government and top management of institutions or colleges should make available more funds to support libraries in the adoption and use of digital literacy, allow librarians to take short courses to enhance adoption and use of digital literacy, purchase more digital literacy facilities, purchase a strong standby generator, and accommodate digital literacy programmes in college curricula.
PENGEMBANGAN MEDIA PEMBELAJARAN MATEMATIKA BERBASIS ANDROID PADA MATERI TRIGONOMETRI

The purpose of this research is to produce an android application that is valid, practical, and effective which is used as a medium for learning mathematics. This type of research is research development using ADDIE (analysis, design, development, implementation, and evaluation) research method. At the analysis stage, the thing to do is analyze the characteristics of students and analyze the curriculum through interviews. From the results of interviews, it is known that the characteristics of students are influenced by their abilities, there are 2 groups of students' abilities, namely high and low ability students, and the applicable curriculum is the 2013 curriculum. At the design stage, product design is done, namely the collection of materials, images, questions, and flowchart creation. Validation results from media experts and material experts get an average value of 4.0 and 4.1 validators respectively so that the media is said to be valid. In the implementation stage, limited trials were conducted with using the test of learning results and student response questionnaires. At the time of pre-test obtained an average of 66.7 with unfinished criteria, while the average result of post-test is 85.6 with good criteria. Based on the assessment of the questionnaire responses, students obtained an average of 4,326 with very good criteria. This study shows that the learning media is valid, practical, effective.