Reviewerly/exHarmony huggingface dataset:
Multiagent Foundations for Distributed Systems: A Vision

Early works and retrospectives by the researchers who founded the network protocols underlying current distributed systems indicate they were aware of the importance of capturing application meaning but didn’t know how to handle it programmatically. Therefore, those researchers introduced simplifications in the protocols that violated their own principle of the end-to-end argument in systems design. The thesis of this vision paper is the following. First, the above-mentioned simplifications, especially the reliance on reliable, ordered communication protocols such as TCP have run their course. Modern applications demand flexibility that can only be achieved through modeling application meaning, and many applications (such as those based on the Internet of Things) cannot pay TCP’s overhead. Second, the multiagent systems community has developed alternative meaning-based approaches that can provide a new foundation for distributed computing at large.
A Survey on Scalable Resource Allocation in Cloud Computing

The cloud is changing the way of working of small businesses or large enterprises. Utilizing the cloud is becoming trend for everyone as it provides better data storage, flexibility, and security in comparison of the traditional approaches. The cloud provides software’s as well as hardware’s as a resource via the internet through remote servers. Resource allocation is a key factor as it is used to determine the resource utilization, energy efficiency, and feasibility of the data center. The suitable allocation of resources is one of the most important problem in resource optimization as it impacts all the other factors (i.e., energy, cost, time, etc.). This paper provides a comparative study of different issues related to the cloud computing system.
An Efficient Anonymous Authentication Scheme for Medical Services Based on Blockchain

Telemedicine is one of the most rapidly de-veloping areas of health care in recent years. Telemedicine Information Systems (TMIS) enable physicians to pro-vide remote care over the Internet to registered patients anywhere. In this work, we propose an efficient anony-mous authentication scheme between patients and med-ical servers. We combine blockchain technology with biometric technology to form a shared session secret key to protect the privacy of patients through mutual au-thentication between patients and servers. Comprehen-sive comparative measurement shows that our proposed scheme achieves a better experimental performance in both computation and communication efficiencies.
Player Matching in a Persuasive Mobile Exergame: Towards Performance-Driven Collaboration and Adaptivity

Persuasive technologies (PTs) for promoting physical activity (PA) have experienced widespread attention over the years. However, limited knowledge exists on how best to design and match individuals in cooperative or social-based physical activity PTs to achieve the best outcome. It is established that people in the same group influence each other's behaviours. Although few group formation techniques have been proposed in the literature, none of these techniques consider individuals' normal PA levels in the real-world prior to assigning them to groups as a dimension to inform group formation. In this paper, we propose a group formation approach called Performance-based User Matching (PUM) which is based on the hypothesis that assigning users with similar PA levels to the same group (SG) will increase motivation and collective performance, compared to grouping users with dissimilar or mixed PA levels (MG). We empirically validated this approach in an exergaming context by conducting a 6-week field study using a persuasive exergame we developed to investigate and compare the SG and MG paradigms in terms of PA performance (step counts). Our findings show that players in the SG groups significantly increased their PA levels compared to those in the MG groups. Also, players in the SG groups individually and collectively exceeded their baseline steps during the intervention period, compared to those in the MG groups. Our findings hold promises for adaptive user matching and socially-driven persuasive applications design.
Managerial Decisions in Marketing: The Individual Perception of Explainable Artificial Intelligence

This paper examines how marketing managers' perceived explanation quality differs between Artificial Intelligence (AI) and human recommendations. The results of this experimental study extend previous studies on managerial decisions by demonstrating that participants' perception of the explanation quality differed between AI and humans. The explanation behind human advice was perceived as less satisfying, needing more information about the recommendation. In contrast, receiving an AI-generated advice positively changed their perception of the explanation quality. They perceived the explanation as more sufficient without the need for further clarification. These findings contribute to understand the individual perception of the explanation quality of AI-generated recommendations. Including explainability of AI in the decision process is important for building user trust. Thus, this paper is intended to stimulate further research on the use of AI for managerial decisions in marketing, particularly examining the importance of explanations in the decision process to improve AI acceptance.
Hand Gesture Recognition For Sign Language

We have come to know a very genuine issue of sign language recognition, that problem being the issue of two-way communication i.e. between a normal person and deaf/dumb. Current sign language recognition applications lack basic characteristics which are very necessary for the interaction with the environment. Our project is focused on providing a portable and customizable solution for understanding sign language through an android app. The report summarizes the basic concepts and methods in creating this android application that uses gestures recognition to understand American sign language words. The project uses different image processing tools to separate the hand from the rest and then uses pattern recognition techniques for gesture recognition. A complete summary of the results obtained from the various tests performed is also provided to demonstrate the validity of the application.
Diagnostics of Student Teachers’ Pedagogical Thinking Based on a Competence Approach

In the didactics of higher education, the issue of professional thinking development (so-called pedagogical thinking) for students majoring in pedagogics remains insufficiently studied. At the same time, pedagogical thinking, and the methods of its development and diagnostics can provide clear guidelines for teaching individualization and correction mechanisms. These methods should focus on fostering the professional qualities for future teachers. The purpose of the article is to describe the proprietary technology for diagnosing the level of students’ pedagogical thinking - future preschool teachers - using the group expert assessment method. Research methods and techniques. The study was carried out through a competency-based approach, which coordinates the competence-oriented goals of future teachers’ professional training with the level of their pedagogical thinking. The method of the group expert assessment was used to justify the structure, content, and students' pedagogical thinking levels. We involved qualified university instructors and pre-school teachers as experts. Findings. We determined the basic structure of pedagogical thinking and its development levels for these students. This helped to define the choice of teaching aids and targeted assistance to students within individual educational pathways. The article shows the interrelation of components and interdependence of the pedagogical thinking for students with the structural components of the professional and pedagogical competence for a bachelor degree student. The novelty of the research. The authors propose a methodology for diagnosing the development level of students’ pedagogical thinking which is coordinated with the competence-oriented goals of their professional training. The federal-state educational standard of higher education and the Professional standard “Teacher (pedagogical activity in the field of preschool, primary, basic and secondary education)” determines the content of these goals. Practical significance. The findings of the research can be applied by administrative authorities and university instructors for planning individual educational pathways of undergraduate students, as well as of post-graduate students studying the current issues of preschool education.
Impact of Blockchain Technology on the Development of E-Businesses

Blockchain technology is one of the greatest discoveries of technology that has been developed as the driving force in many businesses in recent years. One of the main areas that is increasingly using blockchain technology to facilitate its processes is e-business. Businesses soon recognized an opportunity to employ blockchain technology in different operations by investing time and money in the technology. Since both blockchain and e-business involve transitions, blockchain technology is likely to make major differences in the developments of e-businesses. This paper will focus on understanding the impact of blockchain technology on the development of e-businesses. In addition, since reviewing all areas of e-business may be influenced by blockchain technology, this paper will narrow down the topic to supply chain management. Moreover, it points to future expectations of blockchain technology and how it will impact various sectors.
Sliding mode control of interval type-2 T-S fuzzy systems with redundant channels

This paper investigates the sliding mode control (SMC) of interval type-2 (IT2) T-S fuzzy systems. The measurement outputs are propagated via redundant channels for reducing the probability of packet loss and improving the reliability of data transmission. A key feature for the above problem is that the premise variables and the measurement signals may not be available by the controller, which brings difficulty to stabilize the nonlinear systems. Accordingly, a crucial issue is how to synthesize an implementable SMC law under the redundant channels. To this end, the characteristic of the redundant channels is firstly analyzed and the model of available measurement output signals is established. By employing these available measurements as the premise variables and utilizing the upper and lower bounds of the system membership functions (MFs), new MFs are constructed and the sliding mode controller is synthesized. By introducing some null terms carrying the information of MFs, sufficient conditions are derived in terms of nonlinear matrix inequalities to ensure the stochastically ultimate boundedness of the closed-loop system and the reachability of the specified sliding surface. Besides, a binary genetic algorithm (GA) is introduced to solve the nonlinear criteria via the objective function reflecting the control performance. Finally, a numerical example illustrates the effectiveness of the proposed methods.
Application Research on the cultivation and construction of artificial intelligence innovation and entrepreneurship ability in the era of educational informatization 2.0

With the increasing popularity of educational informatization and the continuous emergence of intelligent learning space, artificial intelligence, as an important part of smart campus, makes teachers' teaching and students' learning more accurate and efficient. As the learning content of innovation and entrepreneurship ability in the new era, students' personalized, convenient and diversified needs continue to highlight. According to the spirit of the informatization 2.0 action plan of the Ministry of education and other documents, the artificial intelligence virtual reality plan is used to assist the classroom teaching of innovation and entrepreneurship ability training. Aiming at “technology integration, discipline integration and teacher-student application integration”, this paper analyzes the problems existing in innovation and Entrepreneurship Education, and puts forward the construction of innovation and entrepreneurship ability training system based on the high-speed development situation of education informatization, Promote the improvement of College Students' innovation and entrepreneurship ability and provide a strong guarantee for colleges and universities to deepen the reform of innovation and entrepreneurship education.
Effect of Employee Independence on Employee Performance

AI Publications is an open access peer review international journal to publish the article in the field of Agriculture, Horticulture, Food science, Rural Development, Environment and Health Research, Chemistry, Mathematics,Physics.
Visualizable or Non-visualizable? Exploring the Visualizability of Concepts in Multi-modal Knowledge Graph

An important task in image-based Multi-modal Knowledge Graph construction is grounding concepts to their corresponding images. However, existing research omits the intrinsic properties of different concepts. Specifically, there are some concepts that can not be characterized visually, such as mind, texture, session cookie and so on. In this work, we define concepts like these as non-visualizable concepts (NVC) and the others like dog that have clear and specific visual representations as visualizable concepts (VC). And, we propose a new task of distinguishing VCs from NVCs, which has rarely been tackled by the existing efforts. To address this problem, we propose a multi-modal classification model combining concept-related features from both texts and images. Due to the lack of enough training samples especially for NVC, we select concepts in ImageNet as the instances for VC, and also propose a webly-supervised method to get a small set of instances for NVC. Based on the small training set, we modify the basic two-step positive-unlabeled learning strategy to train the model. Extensive evaluations demonstrate that our model significantly outperforms a variety of baseline approaches.
A Novel Method of high-Capacity Steganography Technique in Double Precision Images

Purpose of digital image steganography is to hide the existence of secret information into digital images. The spatial domain digital image steganography is flourishing area from the last decades due to its enhanced impressive capability of concealing information. The steganography techniques of information of hiding information into digital images cannot be recognized by human visual systems. In this paper floating points number are being used as it is more redundant and comparatively suitable for time domain steganography for data hiding. The aim of this paper is to have higher capacity and PSNR as compared to previously developed techniques in spatial domain. Here it is being proposed A Novel Method of high-Capacity Steganography Technique in Double Precision Images and this method is based on 64-bit double-precision IEEE754 floating-point number notation. This number system is highly precise and required 64-bit to represent a pixel value in the image. Any change due to data hiding in double precision image pixels is comparatively very less and it increases the capacity and PSNR of the proposed technique.
A Landmark Detection and Iris Prediction Dataset for Gaze Tracking Research

Gaze tracking has become an established technology that enables detecting the position of the eyes and using it for estimating where the user is looking. In the last decades, gaze tracking has been realized primarily with dedicated devices utilizing infrared (IR) sensors, though the requirement of adopting specific hardware has limited gaze tracking and its use in potential large-scale applications. In the last decade, several research groups have pursued the development of gaze tracking solutions based on computer vision and traditional RGB cameras such as webcams embedded in portable computers and mobile devices. Unfortunately, previous studies have shown that gaze tracking systems based on RGB cameras have significantly lower accuracy, are not suitable for tasks that require precise user control, and require further research and development. Recently, TensorFlow released a landmark detection library that predicts the location of key points of the human face, including the position of the eyes. The algorithm outputs approximately 500 features in which each point is represented as a series of coordinates on a three-dimensional space. Although TensorFlow’s landmark detection algorithm could potentially be utilized for gaze tracking tasks, the number and complexity of its features make it unpractical to use it for real-time gaze prediction without further feature extraction and dimensionality reduction. In this paper, we introduce and discuss a dataset designed for stimulating screen-based gaze tracking research aimed at replacing traditional IR devices with standard RGB cameras. Our objective was to label the features estimated by TensorFlow’s landmark detection and iris prediction model with the actual location of the user’s gaze on a screen. To this end, we collected data from 30 users who were involved in gaze tracking tasks. Each sample in our dataset includes all the features of TensorFlow’s landmark detection and iris prediction model and two different labels representing (1) the actual gaze location acquired with a dedicated IR sensor, and (2) a reference point. In this paper, we detail the data collection software and procedure, we describe the dataset, and we discuss its potential use in advancing gaze tracking research.
Current Research Landscape of Machine Learning Algorithms in Online Identity Fraud Prediction and Detection

Online fraud is an ever-growing problem that dates back to the beginning of e-commerce. An online fraudster can utilize many attack vectors and planes; however, identity fraud is one of the most common and detrimental to the victims. This paper will look at the current research landscape of the different machine learning algorithms and approaches used to help predict and detect online identity fraud. By adopting systematic review and meta-analysis protocols, this paper summarizes the types of machine learning algorithms used in online fraud detection and prevention, and highlights the reported effectiveness of these methods through performance measurement indicator analysis. Last, the researchers present the limitations and future research directions based on the results of this study
LINEN ASSESSMENT APPS: Artificial Intelligence Integration to develop Android-based applications that measure the "literacy, numeracy, entrepreneurial mindset" capacity of students in Indonesia

The LINEN Assessment application is an application developed to measure the capacity of students in Indonesia in terms of literacy, numeracy and entrepreneurial mindset. To be able to get an automatic profiling picture and be able to provide more accurate predictions, the application was developed based on artificial intelligence. This study aims to provide an overview of the integration of artificial intelligence that is carried out to be applied in the process of developing the LINEN Assessment application. The research method used is the Neuroresearch research method. Through the three stages in this method, namely exploratory, explanatory and confirmatory research, the research process is carried out to produce a research construct as the basis for compiling a standard and valid LINEN Assessment instrument. The application development stage itself uses the waterfall method, specifically through the agile development method. The results of the study are illustrated by the presence of several diagrams showing the integration process carried out. The description of the LINEN Assessment application also shows that the LINEN Assessment Apps were developed with the concept of self-assessment and diagnostic-assessment to equip students with fundamental skills and competencies that are important for their future. One of these important foundations is when students have basic skills, namely literacy, numeracy and skills that refer to innovative creative power, namely entrepreneurial mindset skills. Students can independently use smartphones to explore their own profiles and explore learning experiences through the LINEN Assessment application.
Sensitivity of Logic Learning Machine for Reliability in Safety-Critical Systems

Nowadays, artificial intelligence (AI) is bursting in many fields, including critical ones, giving rise to reliable AI that means ensuring safety of autonomous decisions. As the false negatives may have a safety impact (e.g., in a mobility scenario, prediction of no collision, but collision in reality), the aim is to push them as close to zero as possible, thus designing “safety regions” in the feature space with statistical zero error. We show here how sensitivity analysis of an explainable AI model drives such statistical assurance. We test and compare the proposed algorithms on two different datasets (physical fatigue and vehicle platooning) and achieve quite different conclusions in terms of achievable performance that strongly depend on the level of noise in the dataset rather than on the algorithms at hand.
Self-Boosted With Dynamic Semi-Supervised Clustering Method for Imbalanced Big Data Classification

Big data plays a major role in the learning, manipulation, and forecasting of information intelligence. Due to the imbalance of data delivery, the learning and retrieval of information from such large datasets can result in limited classification outcomes and wrong decisions. Traditional machine learning classifiers successfully handling the imbalanced datasets still there is inadequacy in overfitting problems, training cost, and sample hardness in classification. In order to forecast a better classification, the research work proposed the novel “Self-Boosted with Dynamic Semi-Supervised Clustering Method”. The method is initially preprocessed by constructing sample blocks using Hybrid Associated Nearest Neighbor heuristic over-sampling to replicate the minority samples and merge each copy with every sub-set of majority samples to remove the overfitting issue thus slightly reduce noise with the imbalanced data. After preprocessing the data, massive data classification requires big data space which leads to large training costs.
Decentralized Multi-UAV Cooperative Search Based on ROS1 and ROS2

The premise of Multi-UAV collaboration is full sharing of information, free from damage to the central node, and any equipment damage, the cluster can continue to carry out tasks. In this paper, a distributed Multi-UAV cooperative search system is designed based on ROS1-ROS2 without a central node. The search system uses ROS1-Mavros as the control layer, ROS2 is used as the collaboration layer, and the task planning layer uses particle swarm planning algorithm. The system combines the stability of ROS1 control with the decentralized feature of ROS2 based on DDS (Data Distribution Service) to quickly discover other members, which realizes stable control and efficient sharing of information between drones. Finally, a simulation with PX4, Gazebo, QgroundControl shows that ROS1 can achieve stable control, and ROS2 can achieve efficient information sharing between drones.