Reviewerly/exHarmony huggingface dataset:
Automated Freight Brokerage Based on Multi-Agent Semantic Web Service Composition

Semantic web technologies and multi-agent system are expected to affect brokers’ role in freight transportation. Services provided by freight brokers can be seen as automated service composition problems. This problem is popular, and many research results published. Major elements of this problem are described and presented. The service composition in the freight domain resulted in multiple papers with a strong focus on optimization. By focusing on interoperability the ontology-based approach is taken and based on this baseline extensible solution architecture is presented.
Structure of Context Recognition in Computer-Based Systems for Transportation Management

The process of context recognition in dynamically changing transportation processes is quite complex. The complexity arises from many aspects of understanding the concept of “context”. When developing computer-based systems, it is important to define what is included and how the concept of context is understood. Of course, a fairly wide range of ICT infrastructure components can be integrated into the digitization of contextual data. Quite a variety of devices and software can be included to help capture environmental data: different wireless channels, heterogeneous wireless sensor networks (WNSs), different flow management and e-document management systems, and various monitoring systems. Heterogeneity arises when we want to include different sensors and different means of communication. Vehicles can also be equipped with a wide variety of specialized apparatus and systems. Roads are equipped with special tools and road infrastructure software components. One of the goals of this part of the study is the desire to convey a wide range of context recognition processes. Artificial intelligence (AI) methods can be used for context recognition and such components form the basis of intelligent service systems. The provision of smart services should be based on a wide range of management needs in freight transport processes. How primary data sources are incorporated into all possible interconnected infrastructures is analysed in this section.
Effective Selection of Entities From Heterogeneous and Large Resources Using a Cooperative Neuro-Fuzzy System

This paper focuses on using the cooperative neuro-fuzzy system for the effective and customised selection of entities from large and heterogeneous resources by presenting a general architecture. An experiment is carried out with the fast-moving consumer goods to prove the utility of the architecture. It is observed that most consumers go for the frequent purchase of fast-moving consumer items. Further, various brands, costs, discounts, schemes, quantities, and reviews might make it challenging. Hence, such decisions need to be intelligent and practically feasible in terms of time and effort. The paper discusses neural networks to categorise the entities, type-1 &amp; 2 fuzzy membership functions with rules, training sets, and graphical views of the fuzzy rules and the experiment details. Besides the generic approach and experiment, the paper also discusses the work done so far with their limitations and applications in other domains. At the end, the paper presents the limitations and possible future enhancements.
Introduction to Research Area

This chapter presents research background and research areas that are going to be investigated in this monograph. The authors revise the dynamism of the environment which is important for operational freight transportation. Also, herein the separate attention is given to the ongoing digitalization initiatives and their new opportunities for transport management. Later on, in the chapter some ongoing researches in their area and the overview of European Commission focus directions are presented. Further, the development of contextual systems and the discussion on context meaning are introduced. Finally, the chapter ends with the presentation of research object.
Agent-Based Simulation of Multi-robot Soil Compaction Mapping

Soil compaction, an increase in soil density and decrease in porosity, has a negative effect on crop yields, and damaging environmental impacts. Mapping soil compaction at a high resolution is an important step in enabling precision agriculture practices to address these issues. Autonomous ground-based robotic approaches using proximal sensing have been proposed as alternatives to time-consuming and costly manual soil sampling. Soil compaction has high spatial variance, which can be challenging to capture in a limited time window. A multi-robot system can parallelise the sampling process and reduce the overall sampling time. Multi-robot soil sampling is critically underexplored in literature, and requires selection of methods to efficiently coordinate the sampling. This paper presents a simulation of multi-agent spatial sampling, extending the Mesa agent-based simulation framework, with general applicability, but demonstrated here as a testbed for different methodologies of multi-robot soil compaction mapping. To reduce the necessary number of samples for accurate mapping, while maximising information gained per sample, a dynamic sampling strategy, informed by kriging variance from kriging interpolation of sampled soil compaction values, has been implemented. This is enhanced by task clustering and insertion heuristics for task queuing. Results from the evaluation trials show the suitability of sequential single item auctions in this highly dynamic environment, and high interpolation accuracy resulting from our dynamic sampling, with avenues for improvements in this bespoke sampling methodology in future work.
Face Detection-Induced Access Control System via Large Margin Metric Learning

With the development of science and technology and the acceleration of economic integration, identity authentication has become the most basic element in cyberspace and the basis of the whole information security system. Biometric recognition technology is an important technology in the process of identity authentication. Among them, face recognition technology has been favored by researchers, social applications, and users in the field of identity authentication by virtue of its inherent advantages such as ease of use and insensitivity. In this paper, a face recognition-based access control system is established with the help of large margin metric learning. First, a face library is input into a deep neural network to extract representation features. Second, the deep representation features are used to learn a large margin metric learning model. Third, the face image is captured by a digital camera to input into large margin metric learning model for identifying the person. The experimental results show that the proposed system can accurately identify most of the persons.
Correlation Expert Tuning System for Performance Acceleration

One configuration can not fit all workloads and diverse resources limitations in modern databases. Auto-tuning methods based on reinforcement learning (RL) normally depend on the exhaustive offline training process with a huge amount of performance measurements, which includes large inefficient knobs combinations under a trial-and-error method. The most time-consuming part of the process is not the RL network training but the performance measurements for acquiring the reward values of target goals like higher throughput or lower latency. In other words, the whole process nearly could be considered as a zero-knowledge method without any experience or rules to constrain it. So we propose a correlation expert tuning system (CXTuning) for acceleration, which contains a correlation knowledge model to remove unnecessary training costs and a multi-instance mechanism (MIM) to support fine-grained tuning for diverse workloads. The models define the importance and correlations among these configuration knobs for the user's specified target. But knobs-based optimization should not be the final destination for auto-tuning. Furthermore, we import an abstracted architectural optimization method into CXTuning as a part of the progressive expert knowledge tuning (PEKT) algorithm. Experiments show that CXTuning can effectively reduce the training time and achieve extra performance promotion compared with the state-of-the-art auto-tuning method.
THE INFLUENCE OF CONTRACTS CC (CREATIVE COMMON) ON INTELLECTUAL AND LEGAL RELATIONS OF A CROSS-BORDER NATURE

The practice of exchanging works free from traditional legal disclaimers aims to mark both ideological and systemic distance from the exclusive proprietary regime of copyright. Positive public engagement in creative action is a defining feature of a transformative culture in the digital realm that encourages creative collaboration among multiple people, without any constraints in space or time. IP regimes are contrary to this practice. This article will address the intellectual property issues that arise in connection with a transformative culture. Take the example of Creative Commons licenses and their interaction with internationally recognized intellectual property rights. We conclude that the chilling implications of this legal uncertainty surrounding IP enforcement may be detrimental to copyright in general, but that IP rights can still represent a strong defense mechanism against today's risks associated with digitally transformative creativity.
Applications of supervised machine learning techniques with the goal of medical analysis and prediction: A case study of breast cancer

Breast cancer is the most prevalent cancers in women, and it is the second leading cause of death from cancer in women after lung cancer. Diagnosis of breast cancer is a time-consuming process, and early prognosis can play a significant role in the treatment and increases survival rates. In this work, principal features are identified, and eight supervised machine learning classifiers are compared toward the goal of breast cancer prediction. The standard Wisconsin Breast Cancer Diagnosis dataset is extracted, cleaned, and preprocessed first, and modern data visualization tools are exercised to grasp the essential but covert patterns. Feature selection and dimensionality reduction techniques are applied next to trim out nonessential attributes. The correlations between the selected attributes and the target attribute are thoroughly investigated before applying the classifiers. The performances of these algorithms are compared with standard performance parameters like accuracy, precision, recall, and F1-score. The Random Forest model performed the best with an average accuracy of 97.5%, and the Naïve Bayes model found to be the worst with an average accuracy of 91%. The XGBoost algorithm worked equally good with both attribute selection techniques.
HISTORIC EVOLUTION AND FUTURE OF THE METAVERSE

The Metaverse as a world or set of simulated worlds is acquiring a high importance in recent times. The adoption of the name Meta by a social network and the vision of its CEO to offer a virtual world in it is, together with large technological investments, probably the clearest example of this expansion of the concept. This article reviews the current state of the Metaverse and shows the applicability in different fields of the immersive worlds that could be considered Metaverses, as well as a reflection of the possible short-term evolution of the Metaverse concept. Finally, it describes various considerations, both technological and social, that may result from the mass adoption of the Metaverse. Keywords: Metaverse, virtual reality, simulation, virtualization, digitization
Effect of button layout on the exploration and learning of robot operation using an unfamiliar controller

Robots are becoming increasingly accessible to both experts and non-experts. Therefore, establishing a method for learning robot operations that can be easily mastered by non-experts is important. With this in mind, we aimed to develop a method that facilitates skill acquisition for non-experts that operate robots. As a first step, this study examined the effects of button layout on the exploration and learning of robot operations. A humanoid robot was operated using an unfamiliar tablet-based user interface to achieve the task of shifting the robot's posture to the desired posture: single-foot-standing. The process in which participants found and repeated sequences of commands to achieve the shift task was observed. Four types of button layouts were prepared: normal, random, name appears after the first success (NAFS), and change to normal controller after the first success (CNFS). The normal layout roughly matched the position of the robot's joints, whereas the random layout was randomly assigned, and no information was displayed on each button. Before completing the shift task, a random layout was provided in the NAFS and CNFS layouts. After the first success, the corresponding joint information was displayed in the NAFS layout, whereas the layout was changed to a normal one in the CNFS layout. In total, 51 participants used the normal layout, 7 participants used the random layout, 25 participants used the NAFS layout, and 24 participants used the CNFS layout. The results indicate that providing a random layout during the exploration process (before the first success) is preferable for effective exploration and learning. However, during the learning process (after the first success), providing the relationship between joint movements and buttons in a visual manner is better without changing the button layout from that used in the exploration process.
VR Distance Judgments are Affected by the Amount of Pre-Experiment Blind Walking

Many studies have found people can accurately judge distances in the real world while they underestimate distances in virtual reality (VR). This discrepancy negatively impacts some VR applications. Direct blind walking is a popular method of measuring distance judgments where participants view a target and then walk to it while blindfolded. To ensure that participants are comfortable with blindfolded walking, researchers often require participants to practice blind walking beforehand. We call this practice ”pre-experiment blind walking” (PEBW). Few studies report details about their PEBW procedure, and little research has been conducted on how PEBW might affect subsequent distance judgments. This between-participant study varied the amount of the PEBW and had participants perform distance judgments in VR. The results show that a longer PEBW causes less distance underestimation. This work demonstrates the importance of clearly reporting PEBW procedures and suggests that a consistent procedure may be necessary to reliably compare direct blind walking research studies.
IoT Security in Smart University Systems

Through new intelligent, interactive, and most importantly, interconnected services, the connected campus would therefore allow for better control of building operating costs, especially in Smart Universities. Smart University campuses are the subject of current research, movements and advances in the nation. Therefore, transforming the university into a smart campus can provide several benefits to students, faculty, and staff by enhancing their experiences. Indeed, for a student, a campus should primarily facilitate meetings, socialization, and collaboration, before being a technological showcase. It is about optimizing the use of facilities according to the real needs of students, teachers, and visitors, to enhance the quality of life on campus on campus. Thus, students expect to have Internet access everywhere as they would at home, including on campus and in the residence halls. To support tech-savvy, always-connected students, universities must provide modern campus experiences that are convenient, secure, and personalized. However, transforming the university into a Smart Campus presents several challenges, such as security being a crucial challenge. In our article, we have studied the different technologies to make the university smart, made a detailed study of various types of attacks, and the solutions to deal with these attacks. The analysis of the proposed solutions showed us that the security mechanisms of traditional computer systems, are not effective for intelligent systems based on IoT. So the combination of Blockchain (BC) and Explainable Artificial Intelligence will be a solid security solution to secure IoT systems within Smart Universities.
Big Data Mining Using K-Means and DBSCAN Clustering Techniques

The World Wide Web industry generates big and complex data such as web server log files. Many data mining techniques can be used to analyze log files to extract knowledge and valuable information for both organizations and web developers. Large amounts of heterogeneous data are generated by websites, performing effective analysis on these data and transforming them into useful information using the existing traditional techniques is a challenging process. Therefore, this paper aims to analyze and cluster the log file data to get useful information that helps understand the users' behavior. A variety of data mining techniques were used to address the problem; three steps of data pre-processing were applied, namely the cleaning of data, the identification of users, and the identification of sessions. Results obtained after pre-processing phase showed that the data quality will improve when the number of records reduced by (51.45%). The density-based spatial clustering of applications with noise (DBSCAN) and the K-means algorithm were used to develop clustering algorithms. Density-based clustering with three clusters outperformed the K-Means algorithm with three clusters in terms of accuracy.
Hybrid Generalized Normal Distribution Optimization with Sine Cosine Algorithm for Global Optimization

This paper proposes two hybrid versions of the generalized normal distribution optimization (GNDO) and sine cosine algorithm (SCA) for global optimization. The proposed hybrid methods combine the excellent characteristics of the GNDO and SCA algorithms to enhance the exploration and exploitation behaviors. Moreover, an additional weight parameter is introduced to further improve the search ability of the hybrid methods. The proposed methods are tested with 23 mathematical optimization problems. Our results reveal that the proposed hybrid method was very competitive compared to the other metaheuristic algorithms.
Analysis of Cybersecurity Risks and Their Mitigation for Work-From-Home Tools and Techniques

This chapter is about analyzing cybersecurity risks and their mitigation for work-from-home considering the COVID-19 situation, concerning the tools and techniques being used to run the organization’s operations. This chapter will help you understand the utilizations of online stages as a home office, and it will clear all the issues that emerge during this pandemic circumstance. Everything is discussed in insights concerning the dangers of tools and applications, which has picked up the business's goal shockingly. This is the worst pandemic that ever happened to humanity because every company and department suffers from this tragedy. Moreover, cybercriminals are constantly looking for new attack vectors. Already they have attempted to exploit the servers of many corona research centers. Also, they have strived to take over video conferencing platforms like zoom. Alleviation of those all-outsider applications and their answers are covered in this study work, which will assist you to make your home environment and your home office safe.
Exploiting Online Services to Enable Anonymous and Confidential Messaging

Messaging services are usually provided within social network platforms and allow these platforms to collect additional information about users, such as what time, for how long, with whom, and where a user communicates. This information allows the identification of users and is available to the messaging service provider even when communication is encrypted end-to-end. Thus, a gap still exists for alternative messaging services that enable anonymous and confidential communication and that are independent of a specific online service. Online services can still be used to support this messaging service, but in a way that enables users to communicate anonymously and without the knowledge and scrutiny of the online services. In this paper, we propose messaging using steganography and online services to support anonymous and confidential communication. In the proposed messaging service, only the sender and the receiver are aware of the existence of the exchanged data, even if the online services used or other third parties have access to the exchanged secret data containers. This work reviews the viability of using existing online services to support the proposed messaging service. Moreover, a proof-of-concept of the proposed message service is implemented and tested using two online services acting as proxies in the exchange of encrypted information disguised within images and links to those images. The obtained results confirm the viability of such a messaging service.
Matching Teaching Content and Strategy of Practical Document Writing and Processing Courses Based on Wisdom Education

Writing ability is an important carrier to measure the comprehensive quality of college students, but the current situation of practical document writing and document processing courses in higher vocational colleges is worrying. In terms of school management, managers of higher vocational colleges try their best to meet the teaching and training needs of professional courses, but few managers pay attention to the course of practical writing and document processing. Therefore, in view of this phenomenon, this paper aims to study the matching of teaching contents and strategies of practical document writing and document processing courses under the background of wisdom education. This paper proposes a support vector machine algorithm based on artificial intelligence and a collaborative filtering recommendation algorithm based on cloud computing. Smart education is education informatization, which refers to the process of comprehensively and deeply using modern information technology in the field of education to promote the reform and development of education. Its technical characteristics are digitization, networking, intelligence, and multimedia. The experimental results of this paper show that under the new smart classroom teaching mode, 21 students are very satisfied, accounting for 46.7%, and 12 students are satisfied, accounting for 26.6%. Only 4 students were dissatisfied, representing a percentage of 8.9%. This fully shows that the teaching mode of the new smart classroom is more popular with students and is more conducive to the improvement of students’ learning efficiency. Therefore, the research on the background of wisdom is very meaningful.
Reality of Time Series Learning Data Collection

This chapter deals with reality of time series learning data collection in the real world. These include generating learning data with pseudo-step responses, artificial augmentation of learning data collection, and generating of learning data with emulators.