Reviewerly/exHarmony huggingface dataset:
SibProMQTT: Protection of the MQTT Communication Protocol Against Sybil Attacks Applied for IoT Devices

Internet of Things (IoT) devices require lightweight and fast communication protocol to exchange messages among entities involved in the system. Among several well-known communication protocols, the MQTT is considered as the most lightweight communication protocol for IoT devices. In addition, the MQTT communication protocol can also provide privacy protection due to the existence of the broker connecting publishers and subscribers in an anonymized way without exposing their addresses. However, this anonymity property can lead to Sybil attacks, including message falsification, replay attack, and impersonation attacks to the IoT systems. Therefore, this paper proposes a SibProMQTT scheme that can make the MQTT communication protocol resist against those issues. The SibProMQTT works by using a timestamp and unique session key on each data transmission and eventually encrypting the MQTT payload for avoiding data theft. The experimental results show that our proposed scheme only induces small computational cost addition which is insignificant for the IoT devices. Moreover, security analyses demonstrate that the SibProMQTT can effectively protect the MQTT communication against Sybil attacks as long as the private key cannot be obtained by attackers.
C K Metric and Architecture Smells Relations: Towards Software Quality Assurance

A defect on software can be traced from the generated architecture. These defects are detrimental in keeping the quality of the software including but not limited to security, understandability, and maintainability. A defect in architecture or in usual term an Architecture Smell (AS) presents a threat for developer in maintaining the software quality. Previous study has proven that there is a close correlation between AS and Software Smell which affect certain software quality. Hence, this study focused on finding a direct correlation between AS and object-oriented software quality metrics. Several parameters are tested on software project to determine their AS. Chidamber and Kemerer (CK) metric method is used to evaluate the software quality of software project. Spearman Correlation is used to determine the correlation between the AS and software quality. As a result, it is proven that a direct correlation of AS and software quality is true. To ensure the quality of software, developer could start to keeping track of AS in their project.
SENT2BAYES: A Hybrid Machine Learning Model Combining Word2vec and Multi-Nomial Naive Bayes Classifier for Movie Review Sentiment Analysis in Twitter

Movie review sentiment analysis has gained significant attention due to the vast amount of user-generated content available on social media platforms like Twitter. Accurately analyzing sentiment from such data can provide valuable insights into public opinion and assist in decision-making processes. In the current work, a novel machine learning model, called Sent2Bayes, for movie review sentiment analysis on Twitter is proposed. The proposed model combines the power of Word2Vec embeddings and the Multinomial Naive Bayes classifier to enhance sentiment prediction accuracy. The Sent2Bayes model begins with the creation of Word2Vec embeddings, which capture semantic relationships between words and encode contextual information. These embeddings are then fed into the Multinomial Naive Bayes classifier, which leverages probabilistic modelling to predict sentiment labels for movie reviews. By combining the strength of Word2Vec's semantic understanding and Multi-nomial Naive Bayes' probabilistic approach, Sent2Bayes aims to improve the accuracy and robustness of sentiment analysis on Twitter data. To evaluate the performance of Sent2Bayes, this study conducts an extensive simulation experiments comparing it with existing algorithms commonly used for sentiment analysis. Further, this study employs suitable simulation metrics, including accuracy, precision, recall, and confusion matrix, to measure the model's predictive capabilities and generalization ability. The simulation results demonstrate that Sent2Bayes achieves superior sentiment analysis performance compared to SVM and RNN. The proposed model shows higher accuracy, precision, recall, and confusion matrix across various datasets and test scenarios. The improved performance of Sent2Bayes effectively captures contextual information through Word2Vec embeddings and the robust probabilistic modelling of Multinomial Naive Bayes.
Leveraging the Ensemble Model in Spam detection for Youtube Comments

YouTube (YuTe) spam replies have lately shown an explosive increase, and this research presents a method to identify them. YuTe uses its trojan technology, although it hasn't been effective so far. For this reason, the authors of this research looked at prior research on YuTe spamming remark filtering and ran trials on remark information from four different types of famous Telugu music videos (melody, classical, rock, and duet) to see how well E-GARCH+ARIMA performed as classification techniques. Therefore, this article will use the Hybrid time-series predictive ARIMA (Autoregressive Integrated Moving Average)+ E-GARCH (Exponential Generalized Auto-Regressive Conditional Heteroskedasticity) to anticipate spam identification in Yute music videos. When compared to both standalone simulations and cutting-edge hybrid methods, the aforementioned hybrid ARIMA+E-GARCH paradigm fared better across the board for RMSE, MAE, and MSE.
Brain Age Prediction Using 2D Projections Based on Higher-Order Statistical Moments and Eigenslices from 3D Magnetic Resonance Imaging Volumes

Brain age prediction from 3D MRI volumes using deep learning has recently become a popular research topic, as brain age has been shown to be an important biomarker. Training deep networks can be very computationally demanding for large datasets like the U.K. Biobank (currently 29,035 subjects). In our previous work, it was demonstrated that using a few 2D projections (mean and standard deviation along three axes) instead of each full 3D volume leads to much faster training at the cost of a reduction in prediction accuracy. Here, we investigated if another set of 2D projections, based on higher-order statistical central moments and eigenslices, leads to a higher accuracy. Our results show that higher-order moments do not lead to a higher accuracy, but that eigenslices provide a small improvement. We also show that an ensemble of such models provides further improvement.
On the Problem of Tension Forces Distribution in Cable System of Cable-Driven Parallel Robot

The paper proposes a method for controlling tension forces in statically indeterminable cable-driven systems based on the non-negative least squares method with control of singular or near-singular solutions and a complete search of all possible cable configurations. For cable-driven parallel robots the problem of controlling the cable tension forces is critical, because in the absence of control the cable tension forces are distributed unevenly, which leads to reduced robustness of the system, increased energy consumption and increased deterioration. And in special cases of cable system configuration the tension forces become so great that they lead to cable breaks. At the same time, correction of cable tension force distribution should not lead to significant deviations from the specified position of the mobile platform or, formulating the problem in terms of forces, to violation of kinetostatic equations. Thus, the problem of controlling the tension forces in the cable parallel robot system is solved as a problem of optimizing the tension forces of the cables according to the criteria of minimizing the norm of their vector in the configuration space and minimizing the norm of incoherence of the vector of forces and moments in the operational space of the robot. The developed algorithm is based on the solution of underdetermined systems of linear algebraic equations with finding the minimum least squares norms and subsequent zeroing of negative components of the solution vector. The paper considers examples of the solution of the set problem for the lower cable group of a construction 3D printer based on a cable-driven robot and for a 12-cable system
Reservoir computing utilizing ferroelectric-gate-insulator FETs and capacitors

Since the discovery of HfO2-based ferroelectric films in 2011, ferroelectric-gate-insulator FETs (FeFETs) and metal/ferroelectrics/metal (MFM) capacitors have attracted strong interest from the device research community and have been intensively studied, in particular, for AI computing applications. We have recently proposed and experimentally demonstrated physical reservoir computing utilizing dynamic polarization switching of HfO2-based Si FeFETs and MFM capacitors. This computing operates in a different manner from the static non-volatile property of ferroelectric films, on which most of the ferroelectric AI devices such as a cross-bar CiM array are based. In this presentation, we will introduce the basic concept of reservoir computing using FeFETs and MFM capacitors, the fundamental characteristics, and an example of applications to speech recognition.
Mechanizing Quantum Error Correction Through Entangled Quantum Machine Learning Techniques

Noisy intermediate scale quantum (NISQ) systems are susceptible to errors that culminate in near-one hundred percent data loss. This is due to quantum state fragility and the incredibly high quantum communication error rates caused by decoherence, or quantum noise. As such, stabilizing qubit operational imprecision in quantum information processing is a critical area of research in quantum computing. Adaptive quantum machine learning (QML) methods, like unsupervised and fully entangled quantum generative adversarial networks is one such technology theorized to provide a breakthrough in quantum error suppression. Mechanizing the quantum error detection and correction process with QML provides a path forward from today’s monolithic quantum computers running almost exclusively single-core quantum processing unit (QPU) designs, to the next generation of federated quantum computers using multi-core QPUs. Automating the detection and correction of quantum errors in powerful NISQ devices will pave the way for fault-tolerant quantum computing, making quantum speeds at quantum scale suddenly achievable.
Development of Android Based Mathematics Learning Media for Primary School Students

This research was conducted to develop an android-based mathematics learning media design for second-grade students at Xaverius 2 Primary School Palembang. This research adopted the research procedures outlined by Borg and Gall. The findings of this research are divided into two parts. The first part of the result research pertains to the needs analysis. Based on the need analysis, it is obvious that the development of learning media design to enhance students’ independent learning at home is highly necessary. The second result research relates to the validation of the development of Android-based learning media design. The development of the android-based learning media design falls under the category of validity. Validation testing was conducted through a single assessment, The level of feasibility of Android-based Mathematics Learning Media for Elementary School Students by media experts obtained an overall average of 95.34% in the "Very Feasible" category, by material experts an overall average of 88.6% was obtained in the "Very Feasible" category, and Testing by students obtained an average of 82.47% in the "Very Appropriate" category, so it can be concluded that the Android-based learning media developed is very suitable for use.&#x0D; Keywords: Learning Media, Android-Based Mathematics Learning
Ketergantungan Mahasiswa Universitas Jember Terhadap Artificial Intelligence (AI)

Currently, humanity is entering an era where various technological advances greatly influence their daily lives, especially in the field of education. Media progress can be felt through media that can help humans carry out all their activities, for example looking for entertainment online, studying online, communicating online, and so on. For those who sell, media can be used to assist in the product promotion process which involves online media or social media. Among several forms of development in today's technology is the increasingly powerful artificial intelligence (AI) which is developing significantly and has an impact on the world of education. GPT Chat is an AI that is widely used by students. This research is based on concerns about students who are dependent on using Chat GPT, namely that they may experience a decline in their thinking ability because they are used to using help to do each task. The aim of this research is to increase knowledge about whether some students who often use GPT Chat in their lectures can be said to be dependent on this. The theoretical perspective used in this research is the theory of Structural Functionalism by Talcott Parsons. There are four functional imperatives for all systems, which actions are called the "AGIL" scheme, namely: Adaptation (A) Adaptation, Goal Attainment (G) Achievement of goals, Integration (I) Integration, and Latency (L) Latency or pattern maintenance. In this research, the method used is a qualitative research method with an ethnographic approach and a data collection process in the form of observation, interviews and documentation. The informants studied were 5 people who were students at the University of Jember. The results of the research are that Jember University students know in general about Artificial Intelligence (AI), namely a computer system whose capabilities are equivalent to the human brain and functions to help human work. In this case, students use Artificial Intelligence (AI), especially Chat GPT to help with their assignments. In terms of use, students have never experienced any difficulties. Regarding dependence on the use of Artificial Intelligence (AI), students do not use this technology continuously, but only when they lack inspiration in carrying out assignments. The answers they get are also not always accurate, which sometimes makes students need to add answers other than those from Chat GPT. Then, if you use Artificial Intelligence (AI) technology too often, especially Chat GPT, it can make students even more lazy to think critically.
MODEL KOMUNIKASI REMAJA PEMAIN GAME ONLINE FREE FIRE (STUDI KASUS PADA REMAJA DI RT02 KELURAHAN MANULAI 2 KOTA KUPANG)

The process of adolescent communication in the free fire online game is a two-way communication process carried out by adolescents with their friends and also random in the game, the purpose of this research is to understand the communication process carried out by adolescent free fire online game players to form communication models which describes the relationship between the communication components of the communication process carried out by adolescents when playing the free fire online game. The theory used in this study is the theory of Computer Mediated Communication (CMC). This research is a qualitative research with a qualitative descriptive method. The object of this research is teenagers playing free fire online games at RT 02 Manulai 2 Village, Kupang City. There are three data collection techniques used in this study, namely through direct interview observation and documentation of young online free fire game players. The result of this study is to find a communication model formed from the communication process of free fire online game players, namely the linear communication model and the Schramm interactional communication model. In the Liear Communication Model the communication process only takes place in one direction where there is no feedback in the Linear Communication Model while the Schramm interactional model is a two-way communication process where there is both direct feedback and delayed feedback in the communication process carried out by teenage game players online free fire.
Important Changes to AP Style's Guidance

Nonprofit Communications ReportVolume 22, Issue 1 p. 7-7 Writing Guidelines Important Changes to AP Style's Guidance First published: 06 December 2023 https://doi.org/10.1002/npcr.32317Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume22, Issue1January 2024Pages 7-7 RelatedInformation
An improved Bulgarian natural language processing pipeline

In this paper, we present a language pipeline for processing Bulgarian language data. The pipeline consists of the following steps: tokenization, sentence splitting, part-of-speech tagging, dependency parsing, named entity recognition, lemmatization, and word sense disambiguation. The first two components are based on rules and lists of words specific to the Bulgarian language, while the rest of the components use machine learning algorithms trained on universal dependency data and pretrained word vectors. The pipeline is implemented in the Python library spaCy (https://spacy.io/) and achieves significant results on all the included subtasks. The pipeline is open source and is available on Github (https://github.com/melaniab/spacy-pipeline-bg/) for use by researchers and developers for a variety of natural language processing and text analysis tasks.
AI &amp; Entertainment: The Revolution of Customer Experience

This research paper analyzes the impact of artificial intelligence (AI) in shaping customer experience by revolutionizing the entertainment industry. AI evidently has been one of the key driving forces of the fourth industrial revolution, transforming various domains, notably entertainment. Subsequently, the paper conveys that the application of AI could be either direct or indirect through human computer interaction and enhancement of user experience in entertainment respectively. The primary qualities of AI examined are computer vision (virtual reality and games), natural language processing (chatbots and voice assistants) and personalized recommendations through algorithms. The results of the analysis discover that AI has, in fact, enriched user experience in terms of entertainment, delivering greater convenience and augmented engagement for each individual user. However, like in many other industries, AI has displayed some limitations and challenges to be resolved, which negatively impact user experience in their pursuit for entertainment, despite its tremendous potential to further revolutionize the entertainment industry.
Attribution of Liability for Copyright Infringement by Artificial Intelligence Generated Content

As generative artificial intelligence continues to advance, providing an ever-expanding range of functions and applications for humanity, it has given rise to novel forms of torts, with the potential for more to emerge in the near future. Resolving issues of liability hinges on the identification of the responsible party. However, neither Chinese legislation nor academic views have specified rules or a clear and consistent opinion on the liability attribution issue. This paper aims to address this question. Commencing with the premise that artificial intelligence inherently lacks legal personality, this paper systematically examines all relevant subjects who could potentially bear liability for copyright infringement. The legal foundations for each entitys liability are thoroughly analysed. Meanwhile, this article elucidates the principles of attribution of liability, drawing from established theories of traditional tort law and jurisprudential principles while taking into account the special characteristics of copyright infringement and artificial intelligence. In conclusion, in the absence of a clear consensus on determining the liable party, the principle of correspondence between risks, benefits and responsibilities should guide the attribution of liability for copyright infringement involving generative artificial intelligence. This approach will ensure an equitable distribution of losses, responsible burden-sharing, and comprehensive protection of rights.
Generative AI Processes for 2D Platformer Game Character Design and Animation

AI has the potential to revolutionize the time-consuming and technically complex process of 2D animation production. This paper specifically focuses on creating 2D character animations for platformer games using AI. While AI has made significant contributions to video animations, its application in 2D gaming animation is largely unexplored. Existing AI applications for animation mostly target video animations and lack effective control over randomness. Therefore, this paper explores the role of generative AI in 2D gaming animation, from character design to full animation. Software tools like ChatGPT, Midjourney, Stable Diffusion, and Unity are used to streamline the production process. The research aims to investigate the feasibility and potential of generative AI, with a focus on controlling randomness. By leveraging the unique features of each software, the study aims to enhance the production of 2D game animations. The final output will be an animated character in the Idle state, showcasing the potential of generative AI in 2D gaming animation.
Classification of Acoustic Emission Signal Sources Using Genetic Programming

Genetic Programming (GP) is used to classify the Acoustic Emission (AE) signal to different source of signals. GP has the ability to discover the relationship among the observed data and express them mathematically. GP based technique has an advantage over conventional statistical technique like maximum likelihood estimate, nearest neighbor classifier and etc, because they are distribution free i.e. no a priori knowledge is required about the distribution of data. The data is obtained through an AE test using pulse, pencil and spark signal source on the surface of solid steel block. The signal parameters are measured using AET 5000 system, A set of expert system like rules is extracted from the GP. These rules are used directly to classify the AE signals. Classification of AE signal using genetic programming is found to be effective.
Pandoras Pixel Box: The Rise of AI Art and the Ethical Dilemma of Creativity

The emergence of AI-generated art has sparked a debate on the ethical considerations and social impacts of this new form of creativity. This paper explores the current approaches to AI (Artificial Intelligence) art generators, including the ongoing debate on AI arts creativity and the issues of copyright, ownership, and fair use. Meanwhile, it systematically examines the standard procedures of AI art generators, including machine learning models such as GANs (Generative Adversarial Networks) and text-to-image models. Moreover, the psychological views of art are explored, highlighting the importance of novelty and unexpectedness in determining the relevance of stimuli. The paper presents arguments in favor of and against AI-generated art as a form of creativity, considering factors such as the expression of emotions, the uniqueness of the artwork, and the copyrightability of AI-generated content. The discussion delves into the fair use of copyrighted images in AI-generated art and evaluates the four factors of fair use. The differences in copyright laws internationally are also examined, with some countries recognizing ownership of AI-generated content by programmers. Finally, the paper concludes that AI-generated artwork has the potential to be a positive force in the art world while acknowledging the need for further research and discussion on the legal and ethical implications of AI-generated art.