Reviewerly/exHarmony huggingface dataset:
Formal Verification of Python Software Transactional Memory Based on Timed Automata

Nowadays Software Transactional Memories (STMs) are used in safety-critical software, such as computational-chemistry simulation programs.To the best of our knowledge, the existing STMs were not developed using rigorous model-driven development process, on the contrary, the majority of proposed STMs are directly implemented in a target programming language and formally verified STMs are proven against more general models.This may result in some key aspects of implementation being omitted or interpreted incorrectly.In this paper, we demonstrate an approach to the formal verification of one particular STM, for the Python language, named Python Software Transactional Memory (PSTM), which is based on a STM design and implementation details.Based on these details, faithful models of a PSTM based system, are developed and verified.The PSTM system components are modeled as timed automata utilizing UPPAAL tool.Finally, it is verified that PSTM satisfies deadlock-freeness, safety, liveness, and reachability properties.
The role of GSCM and GHRM practices on green innovation in Indonesian real estate environmental performance

This study aims to determine the direct effect of green supply chain management (GSCM), green human resource management (GHRM), and green innovation on environmental performance. In addition, this study also investigates the effect of GSCM practices and human resource management on green innovation. The sample number in this study was 100 respondents consisting of owners or managers of real estate companies in Indonesia using a random sampling method. The data is processed using SmartPLS 3.0. The results show that GSCM and GHRM affect green innovation. Furthermore, GSCM, GHRM, and green innovation affect environmental performance.
Tseries: An R Package for Stationarity Tests in Time Series Data

The stationary data tests in time series data are the most common idea to create the model with an autoregressive (AR) model, moving average (MA) model, autoregressive moving average (ARMA) model, and autoregressive integrated moving average (ARIMA) model. In this chapter, we present the tseries package in the R program to investigate stationary data on the time series. This package offers Augmented Dickey-Fuller (ADF), Kwiatkowski-Phillips-Schmidt-Shin (KPSS), and Phillips-Perron (PP) tests. Three tests also provide the statistics test and p-value to decide the acceptance or rejection of the null hypothesis. This chapter also aims to present the calculation of these tests proposes step-by-step and the available coding commands under similar time series data. The performance of these tests considers the probability of type I error and the power of the test. The time series data in this study is simulated from the stationary in term of random walk process and non-stationary in term of MA model. This simulation is also conducted to recommend that the user select the appropriate stationarity tests. The R program employs to simulate and analyzes data on 1,000 replications for several sample sizes. The simulation results showed that the ADF and PP tests could control the best probability of type I error on non-stationary time series data. For the power of the test, ADF and PP tests give the highest power of the test on stationary time series data.
Optimized Implementation and Analysis of CHAM in Quantum Computing

A quantum computer capable of running the Grover search algorithm, which reduces the complexity of brute-force attacks by a square root, has the potential to undermine the security strength of symmetric-key cryptography and hash functions. Recently, studies on quantum approaches have proposed analyzing potential quantum attacks using the Grover search algorithm in conjunction with optimized quantum circuit implementations for symmetric-key cryptography and hash functions. Analyzing quantum attacks on a cipher (i.e., quantum cryptanalysis) and estimating the necessary quantum resources are related to evaluating post-quantum security for the target cryptography algorithms. In this paper, we revisit quantum implementations of CHAM block cipher, an ultra lightweight cipher, with a focus on optimizing the linear operations in its key schedule. We optimized the linear equations of CHAM as matrices by applying novel optimized decomposition techniques. Using the improved CHAM quantum circuits, we estimate the cost of Grover’s key search and evaluate the post-quantum security strength with further reduced costs.
Estimation-of-Distribution Algorithms for Multi-Valued Decision Variables

With apparently all research on estimation-of-distribution algorithms (EDAs) concentrated on pseudo-Boolean optimization and permutation problems, we undertake the first steps towards using EDAs for problems in which the decision variables can take more than two values, but which are not permutation problems. To this aim, we propose a natural way to extend the known univariate EDAs to such variables. Different from a naïve reduction to the binary case, it avoids additional constraints.
A Music Programming Course for Undergraduate Music Conservatory Students: Evaluation and Lessons Learnt

This paper introduces the content and organisation of a music programming course offered to undergraduate Conservatory students in the spring of 2022.A number of evaluation procedures, including pre-and postcourse questionnaires and exercises, and a final assignment have been administered by the teacher.Results indicate an increased confidence in the use of computers and programming, although some aspects of creativity and computational thinking need further revision.The authors examine the course content in light of the results obtained, discuss the followed approach, and make assumptions for the improvement of both course content and assessment methods.
Development and research of a neural network alternate incremental learning algorithm

In this paper, the relevance of developing methods and algorithms for neural network incremental learning is shown. Families of incremental learning techniques are presented. A possibility of using the extreme learning machine for incremental learning is assessed. Experiments show that the extreme learning machine is suitable for incremental learning, but as the number of training examples increases, the neural network becomes unsuitable for further learning. To solve this problem, we propose a neural network incremental learning algorithm that alternately uses the extreme learning machine to correct the only output layer network weights (operation mode) and the backpropagation method (deep learning) to correct all network weights (sleep mode). During the operation mode, the neural network is assumed to produce results or learn from new tasks, optimizing its weights in the sleep mode. The proposed algorithm features the ability for real-time adaption to changing external conditions in the operation mode. The effectiveness of the proposed algorithm is shown by an example of solving the approximation problem. Approximation results after each step of the algorithm are presented. A comparison of the mean square error values when using the extreme learning machine for incremental learning and the developed algorithm of neural network alternate incremental learning is made.
The Effect of Virtual Reality Game Teaching Technology on Students’ Immersion

VR (Virtual Reality) technology as the current cutting-edge technology, is widely used in the field of education, and plays a huge advantage. VR game teaching creates an immersive learning environment so that learners can have a clearer and more intuitive understanding of professional knowledge of various disciplines, enhance their learning enthusiasm and interest in learning, and give full play to subjective initiative of learners. VR technology gradually blurs the boundary between realities and virtual, and experiential have a certain emotional investment in excessively real virtual space, to create a sense of immersion and to effectively cultivate students’ spatial thinking and higher-order thinking ability. A questionnaire on the effect of VR game teaching technology on college students’ immersion was designed, and the effects of four aspects of VR game teaching technology, namely publication rules, role description, game control, and game review on learners’ immersion were explored. Results show that publication rules, role descriptions, and game control in VR game teaching have significant positive effects on college students’ immersion. All samples of different game platforms show a significant degree of immersion (P&lt;0.05), and two VR game platforms, namely wearable VR interaction and VR all-in-one, improve learners’ immersion more significantly. Results of this study have important reference value for integrating VR technology into three-dimensional composition teaching, stimulating students’ sense of participation in game learning, improving VR game teaching effect and improving learning efficiency.
ROSGPT: Next-Generation Human-Robot Interaction with ChatGPT and ROS

This paper presents ROSGPT, an innovative concept that harnesses the capabilities of large language models (LLMs) to significantly advance human-robot interaction. We develop ROSGPT as a ROS2 package that seamlessly integrates ChatGPT with ROS2-based robotic systems. The core idea is to leverage prompt engineering with LLMs, specifically ChatGPT, utilizing its unique properties such as ability eliciting, chain-of-thought, and instruction tuning. The concept employs ontology development to convert unstructured natural language commands into structured robotic instructions specific to the application context through prompt engineering. We capitalize on LLMs' zero-shots and few-shots learning capabilities by eliciting \textit{structured} robotic commands from \textit{unstructured} human language inputs. To demonstrate the feasibility of this concept, we implemented a proof-of-concept that integrates ChatGPT with ROS2, showcasing the transformation of human language instructions into spatial navigation commands for a ROS2-enabled robot. This versatile concept can be easily adapted to various other robotic missions. ROSGPT serves as a new stride towards Artificial General Intelligence (AGI) and paves the way for the robotics and natural language processing communities to collaborate in creating novel, intuitive human-robot interactions. The open-source implementation of ROSGPT on ROS 2 is available on GitHub.
A Tamper-Resistant Broadcasting Scheme for Secure Communication in Internet of Autonomous Vehicles

As increasingly prevalent technologies in autonomous driving, 5G and the Internet of Things (IoT), Internet of autonomous vehicle (IoAV) technology is recognized as a technique that is capable of disruptively changing the way people travel and greatly improving the travel experience. In the IoAV scenarios, information dissemination is inseparable from the interaction between autonomous vehicles and smart infrastructure. However, existing efforts rarely focus on the secrecy, authenticity of interactive data and flexible one-to-many communication between autonomous vehicles. In this paper, we propose a tamper-resistant broadcasting (TRBS) scheme for secure communication, which handles the inefficiencies and insecurity of existing identity-based broadcast signcryption solutions. Not only can our TRBS protect communication data from being illegally accessed, forged, or tampered with by malicious vehicles, but it can also enable efficient and flexible secure information dissemination between autonomous vehicles. We also exhibit strict security proofs and experimental evaluations to demonstrate our TRBS is secure and efficient for real-world applications.
Application and impact of electronic solutions in teaching programming

The market trends that are determining the electronics industry today point to a sharp increase in the use of IoT devices, sensors are collecting data around us, using wireless data transmission technologies to transmit the measured values to cloud-based databases, which are processed with various software.Low-power microcontrollers developed for battery power, which are widely used today, provide sensor data collection and data transfer control.In this article, we present a literature search on the technical IT teaching tools in use today, some of which are inherently educational and popular with students and teachers.We pay attention to the educational principles of technical IT methods.We show with examples how technical IT solutions can provide an appropriate experiential learning opportunity and background in programming education.We focus on teaching methods that use microcontrollers and various sensors to develop programming skills and acquire programming knowledge.By developing both computational and algorithmic thinking, we aim to develop both skills.
Students’ Perception on The Use of Video Recording in Public Speaking Class

Video recording is used in Public Speaking class to cope with the new circumstances and condition of online learning during the spread of Covid-19 disease. This study was conducted to find out students' perceptions towards video recording in the public speaking class. Descriptive qualitative approach was chosen as the research design. There were two kinds of techniques of data collection: questionnaire and interview. The research involved 30 students of class 2021 who have taken the Public Speaking class in their second semester at Pattimura University. Students’perception will be analyzed through three aspects: Psyhological aspects, Academic aspects and Technical aspects. The result of this study showed that most of the students had positive perception on the use of video recording. Students admitted that video recording facilitate a better speaking practice which in turn help them to improve their speaking and they feel that they can achieve the learning competencies (Academic aspects). The students also considered the process of making video recording as fun, and exciting (Psychological Aspect). Students also felt there is no significances difficulty in the process of making the video (Technical aspect). In light of the findings, video recording is considered a favorable and effective media for students in Public Speaking course.
Analisis Kualitas ISO 25010 Aplikasi Artificial Intelligence Troubleshooting Komputer dengan FURPS

Artificial Intellegent Application Computer Troubleshooting is an application developed with the aim of being a tool to understand the damage to the computer. The method used in this test is ISO 25010 on the FURPS concept. The results show that the internal quality of this application has been achieved by meeting the requirements of FURPS. Keyword ISO 25010; Quality Analysis; Artificial Intellegent Aplication; Computer Troubleshooting; FURPS
Information Security Posture to Organize and Communicate the Information Security Governance Program

Information security practice has evolved greatly from being mostly a technical concern to also becoming a concern of executive management. As a result, there are many different frameworks, guidelines and certification programs for information security governance (ISG) and management. The purpose of these standards and certification programs is to help an organization develop a structured approach for governing and managing information security. However, these standards and guidelines are generic and not tailored for any specific organization. These frameworks usually specify “what” should be implemented but not “how”. Additionally, these frameworks do not specify “how” to communicate the information security posture (ISP) to the executive management in a simplistic manner. This paper first defines and conceptualizes the term information security posture, and then proposes a framework on “how” to communicate and organize the ISP. Our contribution complements ISG programs adopted by organizations to give executive management a better understanding and oversight. We argue that describing the ISP of an organization will support well-informed decision-making while ensuring alignment with business objectives.
Integrative Technology for Creating Electronic Educational Resources

To increase the efficiency of using ontologies in the educational process, we have developed the Ontos.xyz resource, which consists of an ontograph editor and a viewer that allows displaying both individual EERs and their collections. The editor allows creating an ontograph that describes the structure of the EER, as well as the ability to assign to each node a context of all types supported by the browser including html-pages, web 2.0 resources, etc. The EER built-in this way allows the integration of data from different sources with the ability to adapt to the conditions of any subject area, regardless of its specifics.&#x0D; Thus, the digital competencies of the EER creators remain relevant, since they do not need to learn new software resources to work with individual ontology concepts (“ontograph nodes”).&#x0D; As a result of our research, we have developed a technology for using the ontological approach to create an EER. For its implementation, the Ontos.xyz resource was developed, which is an ontological graph editor. The ontograph allows for the primary visualization of the EER structure, interpreting its structural elements as nodes (vertices) of the graph, and displaying the logic of transitions between structural elements in the form of directed links. With such an approach, the ontology is some kind of aggregator that ensures the integration of the semantic and technological approaches. Building an EER using the ontological approach requires appropriate technological support in terms of description languages, models, software tools, and systems.&#x0D; The software proposed solution, in contrast to its functional counterparts, focuses on use in the educational process.
Estimating the master stability function from the time series of one oscillator via reservoir computing

The master stability function (MSF) yields the stability of the globally synchronized state of a network of identical oscillators in terms of the eigenvalues of the adjacency matrix. In order to compute the MSF, one must have an accurate model of an uncoupled oscillator, but often such a model does not exist. We present a reservoir computing technique for estimating the MSF given only the time series of a single, uncoupled oscillator. We demonstrate the generality of our technique by considering a variety of coupling configurations of networks consisting of Lorenz oscillators or H{\'e}non maps.
Professor's perception of the use of digital skills and gamification in a Peruvian university

The COVID-19 pandemic has produced profound changes in higher education, with the replacement of face-to-face instruction with distance or remote education, which involves the management of digital skills by teachers and students. The purpose of this research is to analyze the perception of professors with regard to digital skills and the use of gamified strategies in the teaching-learning process in a virtual context. A questionnaire was sent to 81 professors in different areas of knowledge at a Peruvian public university during the first academic semester of 2020. A strong correlation (r≥.700, p=.01) was found between the use of ICT and gamification as a resource for teaching-learning, and a moderate correlation between the knowledge of digital competences and the possibilities of application as part of the instructional strategies. From the results obtained, it is clear that the participants have a positive perception of digital skills and gamified strategies, regardless of academic degree or area of knowledge. This could provide support for the update of those proposals targeting professors in order to improve their digital skills.
Movie Ticket, Popcorn, and Another Movie Next Weekend: Time-Aware Service Sequential Recommendation for User Retention

When a customer sees a movie recommendation, she may buy the ticket right away, which is the immediate feedback that helps improve the recommender system. Alternatively, she may choose to come back later and this long-term feedback is also modeled to promote user retention. However, the long-term feedback comes with non-trivial challenges in understanding user retention: the complicated correlation between current demands and follow-up demands, coupled with the periodicity of services. For instance, before the movie, the customer buys popcorn through the App, which temporally correlates with the initial movie recommendation. Days later, she checks the App for new movies, as a weekly routine. To address this complexity in a more fine-grained revisit modeling, we propose Time Aware Service Sequential Recommendation (TASSR) for user retention, which is equipped with a multi-task design and an In-category TimeSeqBlock module. Large-scale online and offline experiments demonstrate its significant advantages over competitive baselines.
Reporting and Methodological Observations on Prognostic and Diagnostic Machine Learning Studies

Common reporting and methodological patterns were observed from the peer reviews of prognostic and diagnostic machine learning modeling studies submitted to JMIR AI. In this editorial, we summarized some key observations to inform future studies and their reporting.
A Comparative Analysis of Collaborative Filtering Similarity Measurements for Recommendation Systems

Collaborative Filtering (CF) is a widely used technique in recommendation systems to suggest items to users based on their previous interactions with the system. CF involves finding correlations between the preferences of different users and using those correlations to provide recommendations. This technique can be divided into user-based and item-based CF, both of which utilize similarity metrics to generate recommendations. Content-based filtering is another commonly used recommendation technique that analyzes the attributes of items to suggest similar items. To enhance the accuracy of recommendation systems, hybrid algorithms that combine CF and content-based filtering techniques have been developed. These hybrid systems leverage the strengths of both approaches to provide more accurate and personalized recommendations. In conclusion, collaborative filtering is an essential technique in recommendation systems, and the use of various similarity metrics and hybrid techniques can enhance the quality of recommendations.
Traffic Classification of IoT Devices by Utilizing Spike Neural Network Learning Approach

Whenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model based on the Spike Neural Network (SNN) called IoT-Traffic Classification (IoT-TCSNN) to classify IoT devices traffic. The model consists of four phases: data preprocessing, feature extraction, classier and evaluation. The proposed model performance is evaluated according to evaluation metrics: accuracy, precision, recall and F1-score and energy usage in comparison with two models: ML based Support Vector Machine IoT-TCSVM and ML based Deep Neural Network (IoT-TCDNN). The evaluations result has been shown that IoT-TCSNN consumes less energy in contrast to IoT-TCDNN and IoT-TCSVM. Also, it gives high accuracy in comparison with IoT-TCSVM.