Reviewerly/exHarmony huggingface dataset:
Retracted: Discussion on Basic Japanese Teaching Mode in Multimedia Network Environment

[This retracts the article DOI: 10.1155/2022/7797540.].
Retracted: Forecast of the Employment Situation of College Graduates Based on the LSTM Neural Network

[This retracts the article DOI: 10.1155/2021/5787355.].
Using Machine Learning to Information Classification Related on Child-Rearing of Infants From Twitter

It is difficult to obtain necessary information accurately from Social Networking Service (SNS) while raising children, and it is thought that there is a certain demand for the development of a system that presents appropriate information to users according to the child&amp;#039;s developmental stage. There are still few examples of research on knowledge extraction that focuses on childcare. This research aims to develop a system that extracts and presents useful knowledge for people who are actually raising children, using texts about childcare posted on Twitter. In many systems numbers in text data are just strings like words and are normalized to zero or simply ignored. In this paper, we created a set of tweet texts and a set of profiles created according to the developmental stages of infants from &amp;quot;0-year-old child&amp;quot; to &amp;quot;6-year-old child&amp;quot;. For each set, we used ML algorithms such as NB (Naive Bayes), LR (Logistic Regression), ANN (Approximate Nearest Neighbor algorithms search), XGboost, RF (random forest), decision trees, and SVM (Support Vector Machine) to compare with BERT, a neural language model, to construct a classification model that predicts numbers from &amp;quot;0&amp;quot; to &amp;quot;6&amp;quot; from sentences. The accuracy rate predicted by the BERT (Bidirectional Encoder Representations from Transformers) classifier was slightly higher than that of the NB, LR, ANN, XGboost, RF, decision trees, and SVM classifiers, indicating that the BERT classification method was better.
Analisis Kesuksesan Sistem Informasi Website Kecamatan Rumbai Menggunakan Model Delone and Mclean

The Rumbai District official website, which has been in use since 2021, still has problems with the confirmation waiting time which is quite long and the community still has to come back to check the required files. The Rumbai District official website is also still static, because there is no interactive menu facility as a form of service to the community. For this reason, it is necessary to measure the success of the Rumbai sub-district official website in terms of system quality, information quality, service quality, use, user satisfaction and benefits and find out what influences the success of the Rumbai sub-district website through the Delone and Mclean models. This is one of the strategies in developing e-government systematically through realistic and measurable stages. In addition, it can provide recommendations to the Rumbai sub-district office, especially the website development team to improve and provide optimal service according to the needs of the Rumbai sub-district community and can increase employee time efficiency in providing services to the community. With the Delone and Mclean model, quality measurements have been carried out from the Rumbai District official website, using 6 variables and 17 indicators. The results obtained are the percentage of success of the Rumbai District official website which is at a success rate of 78.5% and is declared in the "Success" category.&#x0D;
Efficient Sampling of Non Log-Concave Posterior Distributions With Mixture of Noises

This paper focuses on a challenging class of inverse problems that is often encountered in applications. The forward model is a complex non-linear black-box, potentially non-injective, whose outputs cover multiple decades in amplitude. Observations are supposed to be simultaneously damaged by additive and multiplicative noises and censorship. As needed in many applications, the aim of this work is to provide uncertainty quantification on top of parameter estimates. The resulting log-likelihood is intractable and potentially non-log-concave. An adapted Bayesian approach is proposed to provide credibility intervals along with point estimates. An MCMC algorithm is proposed to deal with the multimodal posterior distribution, even in a situation where there is no global Lipschitz constant (or it is very large). It combines two kernels, namely an improved version of (Preconditioned Metropolis Adjusted Langevin) PMALA and a Multiple Try Metropolis (MTM) kernel. Whenever smooth, its gradient admits a Lipschitz constant too large to be exploited in the inference process. This sampler addresses all the challenges induced by the complex form of the likelihood. The proposed method is illustrated on classical test multimodal distributions as well as on a challenging and realistic inverse problem in astronomy.
Architectural Design for Secure Smart Contract Development

As time progresses, the need for more secure applications grows exponentially. The different types of sensitive information that is being transferred virtually has sparked a rise in systems that leverage blockchain. Different sectors are beginning to use this disruptive technology to evaluate the risks and benefits. Sectors like finance, medicine, higher education, and wireless communication have research regarding blockchain. Futhermore, the need for security standards in this area of research is pivotal. In recent past, several attacks on blockchain infrastructures have resulted in hundreds of millions dollars lost and sensitive information compromised. Some of these attacks include DAO attacks, bZx attacks, and Parity Multisignature Wallet Double Attacks which targeted vulnerabilities within smart contracts on the Ethereum network. These attacks exposed the weaknesses of current smart contract development practices which has led to the increase in distrust and adoption of systems that leverage blockchain for its functionality. In this paper, I identify common software vulnerabilities and attacks on blockchain infrastructures, thoroughly detail the smart contract development process and propose a model for ensuring a stronger security standard for future systems leveraging smart contracts. The purpose for proposing a model is to promote trust among end users in the system which is a foundational element for blockchain adoption in the future.
Undergraduate Experiential Coding Courses: Coiling a Software Client [Abstract]

Aim/Purpose. Courses that include development of complex software projects aim to provide career-like experiences prior to a software development-focused capstone. This research shares an international collaborative project (COIL) whereby a remote instructor and their students are the clients for an undergraduate project-focused coding course. Background. This work builds on previous multi-year, Design-Based Research that combined educational best practices in software project development with agile. Prior research incorporated the instructor in the role of both client and Scrum-master and resulted in students experiencing multiple complex software projects prior to the capstone and was conducted in two course formats: synchronous online and in person. There remains the challenge of incorporating a real client. The complexities and challenges of providing real clients in educational settings are well documented; and previous attempts to include real clients in a class setting have been unsuccessful. Involvement in international educational collaborations known as COIL formed the foundation for this exploratory study, as the remote instructor and their students provided the client experience. Methodology. This pilot study examines the experiences of a homogenous and quasi-experimental population consisting of technology students taking a required research course. Validation of the homogenous study is obtained through a small class size, resulting in close interactions between respondents and the instructor, interview-like settings, and immersive data capture. The course modality was Traditional Online, in that students were traditional on campus students with the option to be in the classroom; however, delivery was synchronous-remote. The research involved students enrolled in a technology course in the United States as software developers, working with an instructor and their students who were enrolled in an English class at a German University. Before beginning the project, the instructors met monthly to plan, and to align the project with their individual course outcomes. Throughout the project, COIL communication between the universities was exclusively online. This study builds on previous Design Based Research focused on development and validation of educational tools and techniques combining Scrum, Student Ownership of Learning, guided learning, and a flipped classroom approach. Contribution. This study is among the first to explore COIL as a software client and includes previously modified Scrum practices and tools that can be used to guide complex software projects. This study examines pilot data from an international collaboration where researchers and students have never met in person. It may be useful for institutions that are interested in providing technology students with additional complex software experiences. Findings. {in progress, will be completed spring 2023}
A Strongly Unlinkable Group Signature Scheme with Matching-Based Verifier-Local Revocation for Privacy-Enhancing Crowdsensing

A group signature scheme allows us to anonymously sign a message on behalf of a group. One of important issues in the group signatures is user revocation, and thus lots of revocable group signature (RGS) schemes have been proposed so far. One of the applications suitable to the group signature is privacy-enhancing crowdsensing, where the group signature allows mobile sensing users to be anonymously authenticated to hide the location. In the mobile environment, verifier-local revocation (VLR) type of RGS schemes are suitable, since revocation list (RL) is not needed in the user side. However, in the conventional VLR-RGS schemes, the revocation check in the verifier needs O(R) cryptographic operations for the number R of revoked users. On this background, VLR-RGS schemes with efficient revocation check have been recently proposed, where the revocation check is just (bit-string) matching. However, in the existing schemes, signatures are linkable in the same interval or in the same application-independent task with a public index. The linkability is useful in some scenarios, but users want the unlinkability for the stronger anonymity. In this paper, by introducing a property that at most K unlinkable signatures can be issued by a signer during each interval for a fixed integer K, we propose a VLR-RGS scheme with the revocation token matching. In our scheme, even the signatures during the same interval are unlinkable. Furthermore, since used indexes are hidden, the strong anonymity remains. The overheads are the computational costs of the revocation algorithm and the RL size. We show that the overheads are practical in use cases of crowdsensing.
Communication components for Human Intention Prediction – A Survey

In this review we address the communication components for human intention prediction for Human-robot collaboration (HRC). The HRC is the approach in which human and robot(s) work towards achieving the same goal. The interaction can be both levels physical and cognitive. The traditional settings of the HRC system provides fixed robot program based on waypoints or gestures. It is difficult to predefine the instructions of the situation in complex and variable environment. The understanding of human intention on dynamic basis is crucial for the success of such systems. The core character of co-existence of human and the robot is to understand the dynamic scenes of human intentions. To understand the human intention there is need to understand the components of intention communication. This paper provides comprehensive overview about the understanding the intention as communication components and modelling those components by using machine learning technology in HRC. Multiple ways of communicating intention are possible by using speech, action, gesture, haptic, physiological signals, etc. The article details various approaches to understand the human intention communication aspect particularly in the Human Robot Collaboration setting.
Hybrid Security Approach for Behavioural Privacy of Business Processes in a Cloud Environment

Business processes are surely at the heart of companies but they are also the target of numerous security attacks, especially when using nowadays technologies like Cloud computing. Hence, ensuring business processes’ security is one of the main concerns in both industry and research area. Securing a business process involves securing its three main aspects namely, the informational, the logical (behavioural), and the organisational aspect. While most of the works in the literature focus on the security of the informational aspect, Goettelmann et al. in [9] propose an obfuscation technique that aims to guarantee explicitly the security of the logical aspect of a business process i.e. persevering the privacy of the company’s know-how expressed through the business process. This paper proposes an alternative solution to the obfuscation technique that is based on the strengths of this latter while addressing its identified limits. Our approach combines the ideas of the obfuscation technique and security annotations. The proposed solution is described via a metamodel that exhibits the main concepts it is based on, and a global functional architecture describing the principal steps of the solution, mainly a deployment configuration of the business process, meeting a set of defined constraints (and rules). A comparison between the obfuscation technique and our proposed approach according to security level and deployment costs is also illustrated, through examples.
AI, Cell/Gene Therapy, Precision Medicine Drive Bullish Biotech Investment

GEN EdgeVol. 5, No. 1 TopicsAI, Cell/Gene Therapy, Precision Medicine Drive Bullish Biotech InvestmentSurvey of investors shows continued optimism for life sciences despite VC declines, antitrust and IP concernsAlex PhilippidisAlex PhilippidisSearch for more papers by this authorPublished Online:22 Jun 2023https://doi.org/10.1089/genedge.5.1.90AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 5Issue 1Jan 2023 Information© 2023 by GEN PublishingTo cite this article:Alex Philippidis.AI, Cell/Gene Therapy, Precision Medicine Drive Bullish Biotech Investment.GEN Edge.Jan 2023.460-465.http://doi.org/10.1089/genedge.5.1.90Published in Volume: 5 Issue 1: June 22, 2023PDF download
The Role of Artificial Intelligence in Learning Empowerment for Primary School Students in Republic of Yemen: A survey of Experts' Opinions at Sana'a University

Primary school students in the Republic of Yemen face many problems as a result of the educational system's inability to single out education and adopt the principle of individual differences and the theory of multiple intelligences. This was due to the scarcity of experts specialized in artificial intelligence in Ministry of Education who could have an active role in solving problems. This has led to students' negative attitudes towards learning; hence, this study aimed to identify the role that artificial intelligence could play in empowering students learning and increasing their desire for learning. The study adopted the descriptive approach and the questionnaire was distributed to a sample of 26 experts and specialists in artificial intelligence at Sana'a University in Computer College, the College of Engineering (Communication Department), and the College of Education (Educational Technology Department) to collect data about their opinions of the role that artificial intelligence could play in empowering primary school students learning in the Republic of Yemen. The findings revealed that the degree of artificial intelligence importance in empowering primary school students learning was high at the level of the four dimensions: (smart content, smart systems, student, teacher). The study recommends the establishment of a center for artificial intelligence in the Ministry of Education for qualifying schools, and technical and administrative cadres in artificial intelligence. The study also recommends the Ministry to grant an award for students who produce programs and applications of artificial intelligence, as well as an award for schools that use artificial intelligence in education.
Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks

Graph Convolutional Networks (GCNs) are widely used in a variety of applications, and can be seen as an unstructured version of standard Convolutional Neural Networks (CNNs). As in CNNs, the computational cost of GCNs for large input graphs (such as large point clouds or meshes) can be high and inhibit the use of these networks, especially in environments with low computational resources. To ease these costs, quantization can be applied to GCNs. However, aggressive quantization of the feature maps can lead to a significant degradation in performance. On a different note, Haar wavelet transforms are known to be one of the most effective and efficient approaches to compress signals. Therefore, instead of applying aggressive quantization to feature maps, we propose to utilize Haar wavelet compression and light quantization to reduce the computations and the bandwidth involved with the network. We demonstrate that this approach surpasses aggressive feature quantization by a significant margin, for a variety of problems ranging from node classification to point cloud classification and part and semantic segmentation.
MRML-Movie Recommendation Model with Machine Learning Techniques

In the traditional days people use to visit theaters to watch movies. Some of the reviews used to give some view to visit which movie in what kind of theater and so on. According to the reviews and rating people use to visit the theaters and enjoy the movie. Some movies used to run in theaters for many days like starting with 50, 100 to 365 days and so on. Depending on various suggestions the theater owners use to predict the best movie in several aspects like actors, directors, story, songs and many other factors. In the present days it is very hard to see that a single movie is not in any theater for at least 20 to 50 days. OTT has done a lot for replace the platform for movie lovers. Within 10 to 30 days every movie is entering into the OTT platform as a lot of users are increased for the OTT. Different OTT platforms such as Amazon prime, Netflix, Hot star, Zee cinema, have attracted a lot of movie lovers where they are providing databases from past old movies to the present new movies. New movie rights are brought by many of them in a competitive way. Along with movies they are casting several daily serials, web series and different live streaming also. All the databases are maintained by each of them with high performance servers and attracting the users with reasonable subscription charges.
Online E-Voting Smart System

Abstract: India is the world's biggest vote based system with a populace of more than 10 million; Casting a ballot is the extension between the represented and government. The most recent couple of years have welcomed a re-established center around to the innovation utilized in the democratic cycle. The current democratic framework has numerous security openings, and it is hard to demonstrate even basic security properties about them. A democratic framework that can be demonstrated right has numerous worries. There are a few explanations behind an administration to utilize electronic frameworks are to expand decisions exercises and to lessen the races costs. Still there is some extent of work in electronic democratic framework in light of the fact that it is extremely unlikely of distinguishing proof by the electronic democratic framework if the user is credible and getting electronic democratic machine from reprobates. The proposed framework is to build up a viable democratic machine with high security by utilizing Block-chain innovation to expand security and straight forwardness between the users.
Diagnosis of Melanoma Using Differential Evolution Optimized Artificial Neural Network

Melanoma, a prevalent invasive skin cancer, is treatable in its early stages with timely diagnosis and prompt intervention.However, accurately diagnosing lesions through visual inspection or based on their characteristics remains challenging for physicians.Medical imaging techniques play a crucial role in the rapid and precise prognosis of skin lesions.Our research focuses on analyzing and classifying early-stage melanoma using machine learning techniques.In this paper, we propose a metaheuristic algorithm, Differential Evolution optimized Artificial Neural Network (DEO-ANN), for melanoma diagnosis.Color features are assessed from the Region of Interest (ROI) of the lesion using RGB and opponent color space to enhance classification accuracy.Classification is performed using an artificial neural network trained by a differential evolution algorithm.Simulated output demonstrates that the trained DEO-ANN classifier achieves an Area Under Curve (AUC) of 0.98966 with an accuracy of 94.9% on an ISIC dataset.
Toward a Robust Image Watermarking Method: Exploiting Human Visual System Properties in the Spatial Domain

The need for secure and reliable methods to protect digital content from unauthorized manipulation has become increasingly critical.Although cryptography has been instrumental in addressing digital content security, it is not without shortcomings.In particular, symmetrical cryptographic systems exhibit significant vulnerabilities in encryption key protection, while asymmetric cryptosystems demand high computational time.Consequently, there is an urgent need to explore alternative security solutions to address these limitations.Watermarking has emerged as a promising candidate for ensuring integrity, authenticity, and digital rights protection.In this study, a novel, robust, and informed watermarking approach for color digital images in the spatial domain is proposed, leveraging Local Binary Pattern (LBP) operators for watermark generation.The embedding of the watermark is achieved in the blue channel of the Red, Green, Blue (RGB) image on its corresponding LBP image, owing to the low sensitivity of the blue color in relation to the imperceptibility of the embedded watermark.The proposed approach is evaluated against a diverse range of geometric and non-geometric attacks to demonstrate its reliability.In order to assess the performance of the proposed method in terms of imperceptibility and robustness, watermarked images are subjected to various attacks and analyzed using well-established metrics.The results obtained are highly encouraging, both in terms of the imperceptibility of the embedded watermarks and their robustness against different attack scenarios.