Reviewerly/exHarmony huggingface dataset:
Scrutinizing and Appraising the Usages of Cryptographic API

Developing and maintaining an appropriate series of safety regulations that balance the abuse of cryptographic APIs is a daunting task as cryptographic APIs are continually changing with new primeval and cryptographic settings, rendering current versions balanced. We are proposing a new approach to eliminating security patches from thousands of code changes in order to resolve this challenge. Our approach involves (i) detecting program modifications that sometimes cause security fixes, (ii) an abstraction that filters trivial code changes (such as refactoring), and (iii) a cluster analysis that recognizes similarities between semantine program modifications and helps to obtain safety laws. We used our approach to the Java Crypto API and demonstrated that it is effective: (i) effectively filter changes in non-modification code (more than 99% of all changes) without removing them from our abstraction, and (ii) over 80 percent of code changes are security fixes that define security rules. We have established 13 rules, including new ones, based on our findings, that are not supported by existing security checks. CCS COCEPTS: Security and privacy → Systems security; Cryptanalysis and other attacks; Software security engineering;
Chaotic image encryption algorithm based on R-order Chebyshev composite map

Aiming at the problems of limited parameter range of traditional one-dimensional mapping, narrow chaotic range, and insufficient complexity, this paper proposes a compound mapping based on R-order Chebyshev. The Lyapunov exponent, uniformity and complexity analysis of the R-order compound Chebyshev map are performed, and the NIST randomness test is performed on it to verify its good randomness. The R-order Chebyshev composite map is applied to the scrambling and diffusion stages of the image encryption system. Through comparative research with other encryption algorithms, the image encryption algorithm proposed in this paper has high security and can effectively resist common attacks
Administration importance into education management

In this article, the study of education management and its significance as an area of research between 2000 and today are addressed. The article analyzed the importance of research evaluations and proposed research recommendations as a technique for assessment of historical patterns, present problems, effective leadership and management of education. The study examined the main influences of education management and leadership in research, education management as a humanist, moral and non-scientific effort, and available analytical resources for research, and evaluated various methods ofphilosophy and analytics in agreement with an agreed collection of academic steps. Progress in educational leadership and administration has risen significantly. Researchers utilizing fresh academic experience are accountable for promoting disciplined test methods.
Distributed Gradient Optimization Algorithms

In this chapter, we will elaborate on state-of-the-art gradient optimization algorithms designed for distributed training of machine learning models. We classify these methods by their targeted machine learning models, including but not limited to generalized linear models, deep learning models, and tree models. For each category of machine learning model, we briefly describe their concepts and principles and then introduce the existing gradient optimization algorithms that can be adopted.
Retraction: Securing Connected &amp; Autonomous Vehicles: Challenges posed by Adversarial Machine Learning and the way forward (J. Phys.: Conf. Ser. 1916 012110)

This article (and all articles in the proceedings volume relating to the same conference) has been retracted by IOP Publishing following an extensive investigation in line with the COPE guidelines. This investigation has uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation. IOP Publishing respectfully requests that readers consider all work within this volume potentially unreliable, as the volume has not been through a credible peer review process. IOP Publishing regrets that our usual quality checks did not identify these issues before publication, and have since put additional measures in place to try to prevent these issues from reoccurring. IOP Publishing wishes to credit anonymous whistleblowers and the Problematic Paper Screener [1] for bringing some of the above issues to our attention, prompting us to investigate further. [1] Cabanac G, Labbé C and Magazinov A 2021 arXiv: 2107.06751v1 Retraction published: 23 February 2022
Mining Maximal Co-location Patterns Based on the Count-Ordered Instances-Tree in Spatial Databases

Looking for the spatial co-location that appears frequently in nearby space is widely used in many areas, including mobile phone services and traffic management.To achieve this goal, the SGCT algorithm improves other algorithms which use tables to discover candidate sets.It uses an undirected graph to mine candidates of the maximal co-location patterns first, then uses a condensed-tree structure to store instance cliques of candidates.However, as the amount of data grows, the SGCT algorithm may store large number of nodes in the process of generating the tree.In this paper, we propose a new strategy which will consider the number of instances of each event.We propose a Count-Ordered Instances-tree to record candidates of relation sets.From our experimental results, we show that our approach needs shorter time and costs less storage space than the SGCT algorithm.
06 - PREDICTORS OF ADHERENCE TO REMOTE WEB-BASED SYMPTOM MONITORING INTERVENTION

Unique Patients (N=6933) Encounters (N=20986) Age Mean (SD) 61.3 (12.7) 61.1 (12.7)Age Brackets Unknown 3 (0.0%) 6 (0.0%) <40 y.o.374 (5.4%) 1211 (5.8%) 40-60 y.o.2654 (38.3%) 7958 (37.9%) >60 y.o.3902 (56.3%) 11811 (56.3%)Gender Male 2880 (41.5%) 10001 (47.7%)Female 3929 (56.7%)
Detection of lung cancer metastases in lymph nodes using a multiple instance learning approach

Computational tools in pathology become more and more widespread, however, development of such tools usually needs large amounts of data. Furthermore, producing the required annotations can be tedious for pathologists. Previous approaches were able to omit the need for pixel-wise annotations and instead rely on global slide labels. Furthermore, a smart selection of relevant tiles within whole slide images reduces the amount of data needed for training. Such technique is feasible for end-to-end learning. In this paper, a weakly supervised learning algorithm was trained on 668 whole slide images of lymph nodes from lung cancer patients with a nodal disease stage of either N1 or N2. Systematic experiments were designed to explore less complex deep learning models. We evaluated our study on different numbers of representative tiles for each slide. The best performing model scored 84% and 0.903 on accuracy and AUC respectively on a small amount of training data.
Assessing the Impact of Immersive Virtual Reality on Objective Learning Outcomes Based on Presence, Immersion, and Interactivity

Sweeping the digital realm in the past few decades, immersive virtual reality (IVR) has attracted much attention from industry and academia. A promising tool for teaching and training, this powerful medium offers access to experiences and places otherwise inaccessible to learners for reasons including finance, time, and safety. Among the factors that influence the IVR experience, presence, immersion, and interactivity are elements directly related to users’ cognitive and affective interactions in IVR. As the rapid development of technology continues to enhance presence, immersion, and interactivity in IVR, the mechanism by which these components promote learning remains underexplored. In this thematic review, we examined how presence, immersion, and interactivity have been defined in relevant studies as well as what scales were employed to measure them. We also reviewed the work of scholars investigating the relation between these elements and objective learning outcomes. Despite the absence of a definitive conclusion stating that enhancing the presence, immersion, and interaction of IVR promotes learning, this chapter nevertheless offers a rich context to review the impacts of these three elements on learning outcomes.
The nature of moral rights in Turkish copyright law

Since their inception as a legal ground for the protection of authors, five broad categories of moral rights can be identified: (i) the right of attribution-paternity (droit de paternité),1 (ii) the right of disclosure (droit de divulgation),2 (iii) the right of respect or right of integrity (droit à l’intégrité),3 (iv) the right of retraction (droit au retrait et droit au repentir),4 and (v) the right of access (droit d’accès).5 These rights are now widely recognized in varying degrees in 162 different jurisdictions throughout the world.6 Certain moral rights—the right of attribution and the right of integrity—have been given international recognition in Article 6bis of the Berne Convention.7 In 1996, for the first time, the WIPO Performances and Phonograms Treaty (WPPT) introduced international protection for certain moral rights in favour of performers.8...
Retraction: Fire Detection System Using Machine Learning (J. Phys.: Conf. Ser. 1916 012209)

This article (and all articles in the proceedings volume relating to the same conference) has been retracted by IOP Publishing following an extensive investigation in line with the COPE guidelines. This investigation has uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation. IOP Publishing respectfully requests that readers consider all work within this volume potentially unreliable, as the volume has not been through a credible peer review process. IOP Publishing regrets that our usual quality checks did not identify these issues before publication, and have since put additional measures in place to try to prevent these issues from reoccurring. IOP Publishing wishes to credit anonymous whistleblowers and the Problematic Paper Screener [1] for bringing some of the above issues to our attention, prompting us to investigate further. [1] Cabanac G, Labbé C and Magazinov A 2021 arXiv: 2107.06751v1 Retraction published: 23 February 2022
Distributed Machine Learning Systems

In recent decades, many distributed machine learning systems have been designed in both academia and industry. They are proposed for different purposes and adopt different architectures. Some are general systems that can run different kinds of ML models through delicate systematic abstractions, while others focus on a specific class of ML models through highly specific optimizations. Besides, machine learning systems are constructed over different underlying infrastructures, e.g., new hardware (GPU/FPGA/RDMA), cloud environment, and databases. In this chapter, we will describe a broad range of machine learning systems in terms of motivations, architectures, functionalities, pros, and cons.
Generalized Net Model of Library Activities Using Intuitionistic Fuzzy Estimations

In the current research work a Generalized net (GN) model of the library processes is discussed. The simulation of the GN model presents the stages of the work activities in library. The proposed Generalized net model of library can be used for monitoring the working processes. In the last step of the GN model intuitionistic fuzzy evaluations for book and intuitionistic fuzzy estimations of the library are presented. They will help to analyze the trends in renting books in the library.
Peer review declaration

All papers published in this volume of Journal of Physics: Conference Series have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review: Double-blind • Conference submission management system: https://icosta2021.unimed.ac.id/registration/ • Number of submissions received: 265 • Number of submissions sent for review: 164 • Number of submissions accepted: 154 • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100): 58.49 • Average number of reviews per paper: 2 • Total number of reviewers involved: 68 • Any additional info on review process: All articles have followed very strict stages starting from the initial process of submitting abstracts, reviewing abstracts, submitting full papers, checking for scope conformity, checking strict international standards, reviewing processes by reputable reviewers to decisions by the conference editor team. Review criteria: 1. Technical Criteria 2. Plagiarism 3. Quality Criteria 4. Presentation Criteria 5. Based on reviewer and editor comments the authors are instructed to revise the accepted papers and instructed to submit the revised paper for JPCS. • Contact person for queries: Name : Dr. Saronom Silaban, M.Pd Affiliation: Secretary of the Quality Assurance of Postgraduate School of Universitas Negeri Medan Email : saronomsilaban@unimed.ac.id
Shape investigations of structures formed by the self-assembly of aromatic amino acids using the density-based spatial clustering of applications with noise algorithm

Tyrosine, tryptophan, and phenylalanine are important aromatic amino acids for human health. If they are not properly metabolized, severe rare mental or metabolic diseases can emerge, many of which are not researched enough due to economic priorities. In our previous simulations, all three of these amino acids are discovered to be self-organizing and to have complex aggregations at different temperatures. Two of these essential stable formations are observed during our simulations: tubular-like and spherical-like structures. In this study, we develop and implement a clustering analyzing algorithm using density-based spatial clustering of applications with noise (DBSCAN) to measure the shapes of the formed structures by the self-assembly processes of these amino acids. We present the results in quantitative and qualitative ways. To the best of our knowledge, the geometric shapes of the formed structures by the self-assembly processes of these amino acids are not measured quantitatively in the literature. Analytical measurements and comparisons of these aggregations might help us to identify the self-aggregations quickly at early stages in our simulations and hence provide us with more opportunity to experiment with different parameters of the molecular simulations (like temperature, mixture rates, and density). We first implement the DBSCAN method to identify the main self-aggregation cluster and then we develop and implement two algorithms to measure the shapes of the formed structures by the self-assembly processes of these amino acids. The measurements, which are completely in line with our simulation results, are presented in quantitative and qualitative ways.
Organizing project-based learning related to local production in Khoai Chau district, Hung Yen province with the application of information technology to develop students’ collaborative competency

Project-based learning related to local production is a teaching method that students learn and apply knowledge to solve a complex and practical problem. Students often work in groups, which is very suitable to develop students’ collaborative competency. This article presents two learning projects related to local production in Khoai Chau District, Hung Yen province, in teaching Organic Chemistry in grade 12. In which, the application of information technology is presented in the steps of organizing project-based learning. The proposed projects and lesson plans were pedagogically experimented with 87 students from 2 campuses of Khoai Chau High School in Hung Yen province; the learning results and the development of students' collaborative competency were evaluated. The analysis of the results confirmed the feasibility and effectiveness of the proposed contents.
A Framework for Object Detection using Deep-Reinforcement Machine Learning

Machine learning area enable the utilization of Deep learning algorithm and neural networks (DNNs) with Reinforcement Learning. Reinforcement learning and DL both is region of AI, it’s an efficient tool towards structuring artificially intelligent systems and solving sequential deciding problems. Reinforcement learning (RL) deals with the history of moves; Reinforcement learning problems are often resolve by an agent often denoted as (A) it has privilege to make decisions during a situation to optimize a given problem by collective rewards. Ability to structure sizable amount of attributes make deep learning an efficient tool for unstructured data. Comparing multiple deep learning algorithms may be a major issue thanks to the character of the training process and therefore the narrow scope of datasets tested in algorithmic prisons. Our research proposed a framework which exposed that reinforcement learning techniques in combination with Deep learning techniques learn functional representations for sorting problems with high dimensional unprocessed data. The faster RCNN model typically founds objects in faster way saving resources like computation, processing, and storage. But still object detection technique typically require high computation power and large memory and processor building it hard to run on resource constrained devices (RCD) for detecting an object during real time without an efficient and high computing machine.
Removal of Outliers and Missing Values in Diabetes Dataset Using Ensemble Method

Missing data imputation is an ongoing and crucial research topic in data mining. There may be many missing values in large dataset. However, there are few methods used solely for downstream analyses, with a few prediction tools, which definitely do need a full descriptor value matrix. We propose and assess a looping imputation method called ensemble based on few imputation methods. By calculating an average over a lot of regression trees which are unpruned, the ensemble method intrinsically constitutes a multiple imputation scheme. Using bagging estimate, boosting estimate and stacking estimate of the ensemble method, we are able to estimate the imputation error. Evaluation is finished on molecular descriptor datasets generated from a diverse choice of pharmaceutical fields with artificially delivered missing values ranging from 10 to 30%. The experimental end result demonstrate that missing values have an amazing impact at the effectiveness of imputation strategies and our approach ensemble is sturdier to missing values than the alternative ten imputation strategies used as benchmark. Additionally, the ensemble method exhibits appealing computational performance and can address high-dimensional data.
An Interactive Visual System for Data Analytics of Social Media

With the development of the Internet, Big Data analysis of social media has become a hot research topic in recent years. However, a challenging problem in social media analysis is how to intelligently detect event information from massive media data and how to help users quickly understand event content. Therefore, we propose a method to extract important time periods of events from media data to analyze the media event information, and develop an interactive visual system based on the “5W” principle. The system provides an interactive analysis platform for exploring media Big Data (e.g., Twitter data). The system uses a dynamic topic model to extract topics, a Naive Bayesian classifier to distinguish emotions, and explores events based on the evolution of time, topics, and emotions. In terms of visualization, the system allows users to add some annotations based on segmentation of complex information and highlight important events by adding different story cards to the timeline so that users can get a quick overview of events. Users can also interactively modify the story cards during the exploration process. Finally, several case studies show that our system can effectively reduce the time required to understand social media data and allow users to quickly explore the full picture of an event through an interactive approach.
Security and Privacy Provocation of Data in Cloud Computing

Cloud computing is an on-demand resource sharing paradigm that serves users through Internet. The resource sharing extends from data to computing infrastructure resources. The term “cloud computing” generally refers to data centres with sufficient infrastructure accessed by various users over the Internet. Besides the benefits of cloud computing, data security is a significant concern and it is highly vulnerable to multiple attacks. Different applications and domains utilize cloud computing services to store their sensitive data in cloud servers. The specific data that were targeted by the attackers are the banking account information, organization information, etc. The sensitive information in cloud computing encompasses a wide range of domains, including personal and official information about an organisation, which, when breached, causes the entire system to fail. Numerous data security attacks occur worldwide. A few of the attacks that contributed to the system’s collapse is Yahoo’s, which has the dubious distinction of having experienced the largest major data breach in attack history, compromising over 3 million compromised accounts. This attracts the efforts of researchers and cybersecurity professionals to concentrate much on the security and privacy of sensitive data in cloud computing.
Enriching E Commerce Fraud Detection by using Machine Learning

As there has been a proliferation of the internet platform, it has been increasingly getting affordable for a lot of individuals. The rise has been instrumental in achieving several services including the E-commerce platform. This has led to an unprecedented increase in the amount of fraud that is being committed on this platform. The fraud that is being committed on the E-commerce platforms is very different from the frauds committed on other platforms online. Numerous researches have been performed to combat the evils of credit card frauds and money laundering rings. But there is a severe lack of research on the fraud that is committed on the E-commerce platform. Therefore, this research paper defines an innovative approach for the identification of fraud on E-commerce platforms through the implementation of machine learning approaches. The presented technique utilizes Linear Clustering, Entropy Estimation and Frequent itemset mining in addition to the inclusion of Artificial Neural Networks, Hypergraph formation and Fuzzy classification. The implementation of this system will give more security for E-commerce platform-based transactions by identifying fraudulent activities with better efficiency. The methodology has been tested extensively through rigorous experimentation to evaluate the performance metrics which yielded significantly positive results.