Reviewerly/exHarmony huggingface dataset:
Real-Time Interval Type-2 Fuzzy Control of an Unmanned Aerial Vehicle with Flexible Cable-Connected Payload

This study presents the design and real-time applications of an Interval Type-2 Fuzzy PID (IT2-FPID) control system on an unmanned aerial vehicle (UAV) with a flexible cable-connected payload in comparison to the PID and Type-1 Fuzzy PID (T1-FPID) counterparts. The IT2-FPID control has significant stability, disturbance rejection, and response time advantages. To prove and show these advantages, the DJI Tello, a commercial UAV, is used with a flexible cable-connected payload to test the robustness of PID, T1-FPID, and IT2-FPID controllers. First, the optimal coefficients of the compared controllers are found using the Big Bang–Big Crunch algorithm via the nonlinear UAV model without the payload. Second, once optimised, the controllers are tested using several scenarios, including disturbing the payload and the coverage path planning area to examine their robustness. Third, the controller performance results are evaluated according to reference achievement and point-based tracking under disturbances. Finally, the superiority of the IT2-FPID controller is shown via simulations and real-time experiments with a better overshoot, a faster settling time, and good properties of disturbance rejection compared with the PID and the T1-FPID controllers.
Situation-Based Connexive Logic

Abstract The aim of this paper is to present a system of modal connexive logic based on a situation semantics. In general, modal connexive logics are extensions of standard modal logics that incorporate Aristotle’s and Boethius’ theses, that is the thesis that a sentence cannot imply its negation and the thesis that a sentence cannot imply a pair of contradictory sentences. A key problem in devising a connexive logic is to come up with a system that is both sufficiently strong to fulfill some specific connexive theses and sufficiently well-motivated from a semantical point of view. The approach proposed here tries to address this problem by defining an appropriate connexive relation in terms of more basic notions. The result is a well-motivated system of modal connexive logic that nicely fits in both with the traditional ideas concerning the connexive conditional and with the current developments in connexive logic.
Clustering of COVID-19 Provinces in Indonesia Using Fuzzy Means Cluster Methods

COVID-19 is a contagious virus that has global pandemic.Indonesia has the highest infected cases of COVID-19 in South-East Asia.The aim of the research is classified provinces in Indonesia using Fuzzy Mean Cluster (FCM) based on positive cases, recovery cases, and death cases of COVID-19.The result using FCM is three clusters.Fist cluster that marked by red zone is DKI Jakarta, third cluster that marked by orange zone is West Java, Central Java, and East Java, second cluster that marked by yellow zone is other provinces in Indonesia.Validity of FCM using Partition Coefficient Index (PCI) is 95,36%.It means that using three clusters is the most optimal.
Validation of cognitive models for subtraction of time involving years and centuries

Years and Centuries are the measurement units used to quantify a longer time duration, while subtraction is the operation required to determine the duration based on two given time points. However, subtraction of time is a difficult skill to be mastered by many elementary students. To identify the root cause of the student's failure in performing subtraction involving the unit of time, we developed and validated the three cognitive models related to this skill by conducting a descriptive study which involved 119 Grade Five students from three Malaysian elementary schools. The cognitive diagnostic assessment developed based on the three cognitive models was used to elicit the participants' responses. Then, Attribute Hierarchy Method and Classical Test Theory were employed to analyse the data. The findings indicated that the hierarchical structures of all cognitive models are supported by the student's responses. The three student-based cognitive models were also highly consistent with the corresponding expert-based cognitive models. The cognitive models developed could guide diagnostic assessment development and diagnostic inference making.
A New Digital Signature Primitive and Its Application in Blockchain

In this paper, we propose a new digital signature primitive, called expander signature, and discuss its application in blockchain. The most promising advantage of expander signature is that a signer can generate all signatures at once using a powerful computer, and stores expander keys personally. Each time the signer wants some of his signatures to be verified, he releases the related expander key. No matter when or where, the signer can do this via a resource-limited device, for example, a personal portable terminal. We formally define the syntax and security of expander signature. Under our precisely defined security model, we give generic constructions of expander signature from both public key infrastructure-based and identity-based signature schemes. The security of our constructed expander signature schemes rigorously depends on the underlying public key signature schemes. The expander keys do not leak any information about the signer’s secret key and the size of the expander keys is constant no matter how many times the expander has been occurred. Finally, we give an application example of expander signature in blockchain.
Fault Attacks on Access Control in Processors: Threat, Formal Analysis and Microarchitectural Mitigation

Process isolation is a key component of the security architecture in any hardware/software system. However, even when implemented correctly and comprehensively at the software (SW) level, process isolation may be compromised by weaknesses of the hardware (HW). Therefore, at the HW level, an exhaustive verification is desirable which provides the needed formal guarantees ensuring the confidentiality and integrity of the microarchitecture. The situation is further exacerbated if the attacker is able to inject faults, a threat requiring additional attention in formal security analysis. In this paper, we consider a threat model where the attacker is able to inject faults and, at the same time, execute user-level programs. We show that this poses a severe security threat even in systems which have been hardened against fault attacks for specific, security-critical system software. For protection against this threat, we present an exhaustive formal verification methodology that provides security guarantees for access control in processors, and demonstrate how such guarantees are sustained in the presence of fault injection. Guaranteeing correct and robust access control is crucial since it is the basis for process isolation in hardware. The proposed approach implicitly models all possible single and multiple bit flips as well as all stuck-at faults. We leverage the results of our formal analysis to augment the system with protection mechanisms that guarantee security w.r.t. the considered threat model. At the example of several open source RISC-V processors, we demonstrate both the scalability of our formal analysis and the efficiency of the generated defenses.
Research on Rapid Identification of Infringement Risk in Financial Technology Data Transactions

Aiming at the problems of low accuracy rate of transaction information mining, high error rate of identification of infringement risk of financial technology data transaction and long identification time in current data transaction infringement risk identification methods, a new rapid identification method of infringement risk in financial technology data transactions is proposed. The entropy of clustering is determined by using coverage density and weighted coverage density to mine the transaction information of financial technology data. The BP algorithm is used to train the T-S fuzzy neural network, and the financial technology data transaction information is input into the trained T-S fuzzy neural network to obtain the quick identification result of the infringement risk of financial technology data transaction. The experimental results show that this method has a high accuracy rate of fintech data transaction information mining, a low error rate of fintech data transaction infringement risk identification, and a short recognition time.
The Future of Web Development and Emerging Technologies

This research paper investigates the future prospects of web development and conducts an analysis of emerging technologies that have the potential to shape the industry in the forthcoming years. The field of web development is characterized by its dynamic nature, marked by constant evolution and the introduction of new technologies. The paper initiates by examining the present state of web development and the trends that are driving significant transformations within the industry. It subsequently conducts a comprehensive exploration of several nascent technologies, including artificial intelligence (AI) and machine learning (ML), serverless architecture, web assembly, progressive web apps, and blockchain.For each of these innovative technologies, the paper assesses their current stage of development, explores potential applications within web development, and evaluates the challenges and opportunities they present. The research paper concludes by emphasizing the significance of keeping pace with emerging technologies to maintain competitiveness in the field of web development. It highlights the necessity for developers to exhibit adaptability and a willingness to acquire new skills as these technologies continue to evolve. By staying informed about the latest advancements and embracing innovation, developers can position themselves for success in the ever-evolving landscape of web development.
An Integrated Optimization-Based Algorithm for Energy Efficiency and Resource Allocation in Heterogeneous Cloud Computing Centers

At a significant moment in the rapid development of cloud technology, large-scale cloud computing centers have emerged. With the emergence of the internet and artificial intelligence, enormous computing resources are required to process data and train machine learning models. The architecture of cloud computing centers involves millions of computing resources, and improper management of these resources can increase operating costs and exert tremendous pressure on the environment. This study proposes an optimized computing resource and energy management algorithm for computing centers with heterogeneous computing resources from the perspective of Green IT. Specifically, this study models the energy consumption at each point in time and the relationship between tasks and also considers the calculation of data backup. This approach will be expanded to optimize decisions for all computing tasks in computing centers based on the sequence of tasks and energy consumption while considering heterogeneous computing resources, energy efficiency, task scheduling, and execution time. By modeling this issue as a highly nonlinear optimization problem and utilizing mathematical programming and Lagrangian relaxation, we propose an optimized energy management algorithm to effectively manage computing resources and create cloud computing centers with high performance and low energy consumption.
Cloud Computing in Web 3.0

The research paper aims to investigate the integration of cloud computing in Web 3.0 and its implications for the future of the internet. It will explore the fundamental characteristics of Web 3.0, emphasizing its decentralized nature and the increased control it gives to users. The paper will also delve into the significance of cloud computing in this context, highlighting its role in providing the necessary infrastructure to support Web 3.0 applications and services. Furthermore, the paper will analyse how cloud computing impacts scalability, interoperability, and decentralization within the Web 3.0 framework. It will examine how cloud computing enables the efficient scaling of resources, facilitates seamless communication and interaction between different platforms and systems, and contributes to the distributed and decentralized nature of Web 3.0.Additionally, the research paper will address the challenges and opportunities associated with integrating cloud computing in Web 3.0. It will identify and discuss potential obstacles that may arise during this integration process, such as security concerns or technical complexities. The paper will propose potential solutions to overcome these challenges and leverage the opportunities that arise from combining cloud computing with the principles of Web 3.0.Overall, the research paper aims to provide a comprehensive analysis of the integration of cloud computing in Web 3.0, exploring its implications for the future of the internet. It will contribute to the understanding of how cloud computing can support the decentralized and user-centric nature of Web 3.0 and offer insights into the challenges and opportunities that this integration presents.
Polish Adaptation of the Igroup Presence Questionnaire

Presence, defined as a psychological state of “being there”, is experienced during an interaction with Immersive Virtual Environments, particularly with Virtual Reality – the most popular type of such environment. Measuring presence is crucial because its level determines the effectiveness of virtual environments. The authors conducted a translation and a study (n = 245) that aimed to provide a Polish version of the Igroup Presence Questionnaire (IPQ-PL). The structure of the questionnaire and relations with other constructs were tested. The results indicate that IPQ-PL may be considered a valid tool for measuring presence and may beused in studies conducted on Polish samples.
Multiple sampling schemes and deep learning improve active learning performance in drug-drug interaction information retrieval analysis from the literature

Drug-drug interaction (DDI) information retrieval (IR) is an important natural language process (NLP) task from the PubMed literature. For the first time, active learning (AL) is studied in DDI IR analysis. DDI IR analysis from PubMed abstracts faces the challenges of relatively small positive DDI samples among overwhelmingly large negative samples. Random negative sampling and positive sampling are purposely designed to improve the efficiency of AL analysis. The consistency of random negative sampling and positive sampling is shown in the paper.PubMed abstracts are divided into two pools. Screened pool contains all abstracts that pass the DDI keywords query in PubMed, while unscreened pool includes all the other abstracts. At a prespecified recall rate of 0.95, DDI IR analysis precision is evaluated and compared. In screened pool IR analysis using supporting vector machine (SVM), similarity sampling plus uncertainty sampling improves the precision over uncertainty sampling, from 0.89 to 0.92 respectively. In the unscreened pool IR analysis, the integrated random negative sampling, positive sampling, and similarity sampling improve the precision over uncertainty sampling along, from 0.72 to 0.81 respectively. When we change the SVM to a deep learning method, all sampling schemes consistently improve DDI AL analysis in both screened pool and unscreened pool. Deep learning has significant improvement of precision over SVM, 0.96 vs. 0.92 in screened pool, and 0.90 vs. 0.81 in the unscreened pool, respectively.By integrating various sampling schemes and deep learning algorithms into AL, the DDI IR analysis from literature is significantly improved. The random negative sampling and positive sampling are highly effective methods in improving AL analysis where the positive and negative samples are extremely imbalanced.
Password-based authentication and the experiences of end users

Passwords are used majorly for end-user authentication in information and communication technology (ICT) systems due to its perceived ease of use. The use for end-user authentication extends through mobile, computers and network-based products and services. But with the attendant issues relating to password hacks, leakages, and theft largely due to weak, reuse and poor password habits of end-users, the call for passwordless authentication as alternative intensifies. All the same, there are missing knowledge of whether these password-based experiences are associated with societal economic status, educational qualification of citizens, their age and gender, technological advancements, and depth of penetration. In line with the above, understanding the experience of end-users in developing economy to ascertain their password-based experience has become of interest to the researchers. This paper aims at measuring the experience of staff and students in University communities within southeastern Nigeria on password-based authentication systems. These communities have population whose age brackets are majorly within the ages of 16 and 60 years; have people with requisite educational qualifications ranging from Diploma to Doctorate degrees and constitutes good number of ICT tools consumers. The survey had 291 respondents, and collected data about age, educational qualifications, and gender from these respondents. It also collected information about their password experience in social media network, online shopping, electronic health care services, and internet banking. Our analysis using SPSS and report by means of descriptive statistics, frequency distribution, and Chi-Square tests showed that account compromise in the geographical area is not common with the respondents reporting good experience with passwords usage. Furthermore, this experience is not in any way related to their age (under 60), and educational qualification. Our experiment did not measure the entropy of end-users' passwords, their password hygiene culture and so cannot relate this experience with the strengths of their passwords nor that of their password hygiene culture. The outcome and recommendations of this research will help inform policy and research direction towards password hygiene culture, management, and the potentials or otherwise of passwordless authentication systems in developing economies.
Bibliometric‐enhanced legal information retrieval: Combining usage and citations as flavors of impact relevance

Abstract Bibliometric‐enhanced information retrieval uses bibliometrics (e.g., citations) to improve ranking algorithms. Using a data‐driven approach, this article describes the development of a bibliometric‐enhanced ranking algorithm for legal information retrieval, and the evaluation thereof. We statistically analyze the correlation between usage of documents and citations over time, using data from a commercial legal search engine. We then propose a bibliometric boost function that combines usage of documents with citation counts. The core of this function is an impact variable based on usage and citations that increases in influence as citations and usage counts become more reliable over time. We evaluate our ranking function by comparing search sessions before and after the introduction of the new ranking in the search engine. Using a cost model applied to 129,571 sessions before and 143,864 sessions after the intervention, we show that our bibliometric‐enhanced ranking algorithm reduces the time of a search session of legal professionals by 2 to 3% on average for use cases other than known‐item retrieval or updating behavior. Given the high hourly tariff of legal professionals and the limited time they can spend on research, this is expected to lead to increased efficiency, especially for users with extremely long search sessions.
A Path Planning Method with a Bidirectional Potential Field Probabilistic Step Size RRT for a Dual Manipulator

The search efficiency of a rapidly exploring random tree (RRT) can be improved by introducing a high-probability goal bias strategy. In the case of multiple complex obstacles, the high-probability goal bias strategy with a fixed step size will fall into a local optimum, which reduces search efficiency. Herein, a bidirectional potential field probabilistic step size rapidly exploring random tree (BPFPS-RRT) was proposed for the path planning of a dual manipulator by introducing a search strategy of a step size with a target angle and random value. The artificial potential field method was introduced, combining the search features with the bidirectional goal bias and the concept of greedy path optimization. According to simulations, taking the main manipulator as an example, compared with goal bias RRT, variable step size RRT, and goal bias bidirectional RRT, the proposed algorithm reduces the search time by 23.53%, 15.45%, and 43.78% and decreases the path length by 19.35%, 18.83%, and 21.38%, respectively. Moreover, taking the slave manipulator as another example, the proposed algorithm reduces the search time by 6.71%, 1.49%, and 46.88% and decreases the path length by 19.88%, 19.39%, and 20.83%, respectively. The proposed algorithm can be adopted to effectively achieve path planning for the dual manipulator.
Shaking Table Design for Testing Earthquake Early Warning Systems

The unpredictability in time of seismic activities and the dependence of tectonic movements on a multitude of factors challenges specialists to identify the most accurate related methods to avoid catastrophes associated with hazards. Early warning systems are critical in reducing negative effects in the case of an earthquake with a magnitude above 5 MW. Their precision is all the better as they corroborate and transmit more information collected from the regional or on-site sensory nodes to a central unit that discloses events and estimates the epicentral location, earthquake magnitude, or ground shaking amplitude. The shaking table is the proper instrument for evaluating an early warning systems’ dynamic response and performance under specific vibration conditions. To this issue, the paper presents a laboratory single-axis shaking table with a small-scale, low-cost design and an accurate displacement control. Experiments based on a suite of 12 real earthquakes provided results with very small errors related to similar models, bearing out the designed shaking table is suitable for early earthquake warning system response testing for high magnitude earthquakes.