Reviewerly/exHarmony huggingface dataset:
Heuristic self-organization of knowledge representation and development: analysis in the context of explainable artificial intelligence

From the analysis of the main theoretical provisions of heuristic self-organization systems and logical models, it follows that according to O. G. Ivakhnenko's systems of heuristic self-organization, the first task is to determine the factors content “that determine the essence of different images”. These are the images that characterize the objects of a particular subject area. After determining the composition and content of these images, the next problem is solved, namely, the problem of “generating the new successful heuristic”, which in content is a solution that leads to increased accuracy. Note that we are talking about improving the accuracy of solving the problem of data processing. It follows from the above mentioned that heuristic self-organization systems are data processing systems. This allows the multiplicity of heuristics. Heuristics in content correspond to the logical rules applied in heuristic self-organization systems. The main provisions of the heuristic self-organization system theory were developed by O. G. Ivakhnenko in the eighties of the last century, but they remain unnoticed to this day. At this time, the task is to explain why the neural network makes such a decision and not another. Based on this, the concept of “explainability of artificial intelligence” was introduced for artificial intelligence. It is the content of heuristics that forms the structure of the neural network in the form of logical rules and determines the logic of the decision made. It is established that the derivation rule, which is the basis for constructing artificial neural networks, is an abductive rule, which, unfortunately, does not meet the fourth heuristic and does not meet the definition of intelligence: intelligence is the ability to measure things. Unfortunately, none of the neural networks can measure things. From the analysis of the basic rules content of inference, it follows that the dialectical method of inference is general (generating) for the basic logical methods of inference. The difference lies in the composition and content of the middle member of the triangular relationship, namely, in the form of the element combination of the relationship: the transition from one concept to another. The explainability of artificial intelligence refers to the laws of the structure and activity of artificial neural networks. But modern theories of artificial neural networks ignore the existence of logical rules (heuristics), which were established by O. G. Ivakhnenko. After all, only knowing the rules based on which problems are solved, it is possible to check the correctness of the decision, but not by searching for such rules. The three hypotheses about the explainability of artificial intelligence and the theory of machine identification can be further defined as statements or theorems and strictly proved.
A Joint Framework for Explainable Recommendation with Knowledge Reasoning and Graph Representation

With the development of recommendation systems (RSs), researchers are no longer only satisfied with the recommendation results, but also put forward requirements for the recommendation reasons, which helps improve user experience and discover system defects. Recently, some methods develop knowledge graph reasoning via reinforcement learning for explainable recommendation. Different from traditional RSs, these methods generate corresponding paths reasoned from KG to achieve explicit explainability while providing recommended items. But they suffer from a limitation of the fixed representations that are pre-trained on the KG, which leads to a gap between KG representation and explainable recommendation. To tackle this issue, we propose a joint framework for explainable recommendation with knowledge reasoning and graph representation. A sub-graph is constructed from the paths generated through knowledge reasoning and utilized to optimize the KG representations. In this way, knowledge reasoning and graph representation are optimized jointly and form a positive regulation system. Besides, due to more than one candidate in the step of knowledge reasoning, an attention mechanism is also employed to capture the preference. Extensive experiments are conducted on public real-world datasets to show the superior performance of the proposed method. Moreover, the results of the online A/B test on the large-scale Meituan Waimai (MTWM) KG consistently show our method brings benefits to the industry.
Design of Power Manipulator for Hot Cell Facility

A power manipulator (SDL-01) robot system was designed for radioactive hot cell facility which contained radioactive aerosol. The system consisted of a gantry robot with telescope and a six degree of freedom (6-DOF) robotic arm with a replaceable gripper. The manipulator allowed the execution of high forces and handle heavy objects. It was designed to withstand an accumulative radiation dose of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> Gy ( H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> O ) [ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">60</sup> Co] [1], the electrical drive system must be redesigned and radiation harden. The early power manipulator operated remotely by using speed control mode, we designed a radiation resistant position control servo system for SDL-01.
The Logical Pattern of Argument: A Case Study of National University Debating Championship

In academic field, argument is an essential factor of understanding statements. In this regard, the present paper aims to analyse National University Debate Contest (NUDC) 2016 using Toulmin model argumentative structure and its relation with the implied meaning from National University Debate Contest. This paper employs descriptive qualitative analysis to examine the logical pattern of arguments through linguistic expressions that occurred in the debate. The data were taken from NUDC in Mercu Buana University focusing on (part 1) the statement of the government proponent (Bina Nusantara University) and the opposite speaker (Brawijaya University). The data were analysed using the Toulmin model of logical argument. The findings showed that the government opposite speaker very often made the rebuttal claim by showing the weaknesses from the government side; while the government proponent speaker has claimed almost in every pattern of argument proposed by Toulmin model. The paper concludes that both speakers’ patterns of argument have utilized Toulmin model such as claims, data, qualifiers, rebutting conditions, and warrants. Even though there are so many grammatical mistake and unwell-organized structure, but the logical structure can be analysed using Toulmin model.
RV-TEE-Based Trustworthy Secure Shell Deployment: An Empirical Evaluation.

Incorrect cryptographic protocol implementation and malware attacks targeting its runtime may lead to insecure execution even if the protocol design has been proven safe.This research focuses on adapting a runtime-verification-centric trusted execution environment (RV-TEE) solution to a cryptographic protocol deployment -particularly that of the Secure Shell Protocol (SSH).We aim to show that through a concrete realization of RV-TEE, which is neither tied to specific CPU mode nor requires the consequential operating system support, SSH execution can be rendered trustworthy.We provide: (i) An RV-TEE setup for a popular SSH implementation based on a widely-adopted RV tool, and a USB-connected hardware security module (ii) An overview of the property extraction process through a methodical analysis of the SSH protocol specifications (iii) Security vulnerabilities identified as a result of RV-TEE adoption (iv) An overhead analysis delineating what SSH applications can benefit from our proposed setup in a practical manner.
Demand Characteristics and Training Trend of Engineering Science and Technology Personnel Based on Education for Sustainable Development

From the Agenda for the 21st Century to the Education 2030 Framework for Action, the international community continues to promote education for sustainable development (ESD) and to integrate, reshape, and innovate the existing education systems.The concept of ESD has played a leading role in the reform of international engineering education in the 21st century and is increasingly becoming an important strategic choice to improve the quality of engineering education.In this study, we explore the historical evolution of ESD and analyze the demand characteristics of engineering science and technology personnel supporting ESD in view of 2030 goals; the analysis focuses on the following aspects: new requirements of training objectives and key abilities based on ESD, analysis of the International Engineering Alliance Graduate Attributes and Professional Competencies framework, and China's engineering education reform practice.On the basis of the concept of ESD, we summarize the existing challenges in the training of engineering scientific and technological personnel in China, specifically regarding training orientation, training process design, and student assessment and evaluation.The research indicates that China's promotion of ESD for 2030 should strengthen top-level design to exert policy synergy, promote international cooperation to integrate global high-quality resources, optimize professional layout to maximize the advantages of professional clusters, improve curriculum teaching for training excellent engineering personnel, and strengthen accreditation while improving the quality standards system, thereby promoting the continuous improvement of the quality of engineering science and technology personnel that meet the sustainable development goals.
Unsupervised Tree Extraction in Embedding Spaces for Taxonomy Induction

Exposing latent structure (graph, tree...) of data is a major challenge to deal with the web of data. Today’s embedding techniques incorporate any data source (noisy graphs, item similarities, plain text) into continuous vector spaces that are typically used as input to classifier. In this work, we are dealing with the opposite task: finding structures (taxonomies) from embedded data. We provide an original unsupervised methodology for taxonomy induction by directly searching for graph structures preserving pairwise distances between items. Contrary to the state-of-the-art (SOTA), our approach does not require to train classifiers; it is also more versatile as it can be applied to any embedding (eg. word embedding, similarity embedding like space-time local embedding...). On standard benchmarks and metrics, our approach yields SOTA performance. As another contribution, we propose better evaluation metrics for taxonomy induction, leveraging graph kernel similarities and edit distance, showing that the structures of our predicted taxonomies are significantly closer to the ground-truth than SOTA solutions.
A Study of Autonomous Landing of UAV for Mobile Platform

Robot cooperation is the key to search and rescue (SAR) missions, which usually occur in complex situations affected by different types of disasters, and such situations usually require UAVs to cooperate with ground unmanned vehicles (UGVs) to provide valuable information, but achieving autonomous landing when return is urgently needed has been a research hotspot and difficulty in the field of mobile robotics. In this paper, an autonomous landing strategy for UAVs on mobile platforms is proposed to solve the above problems, and the specific research includes: (1) the construction of an autonomous landing platform and the design of cooperative target are completed considering the rapidity and stability of landing; (2) a method that can detect and track moving targets in real-time is proposed for the problem of moving target position movement; (3) the simulation scene and the actual scene of target identification is conducted accordingly. The experimental results show that the recognition of the target in both the simulation scene and the real scene is rapid and accurate.
HEAT WAVE AND ITS IMPACT IN NORTHEASTERN NIGERIA: A CASE STUDY OF YOBE STATE, NIGERIA

Heatwaves are significant natural disaster that is caused primarily by extreme temperatures. The extreme heat events associated with heatwaves often cause severe health complications and deaths. In view of the environmental hazards associated with heatwaves, this study investigated the impact of heatwaves in Yobe State, northeast Nigeria. Temperature and humidity data of thirty years (1991-2020) for the hot season (March-June), were obtained from the Nigerian Meteorological Agency (NiMet) Abuja. Time series analysis and basic statistical tools were used to analysis the data which was used to evaluate the trend of heatwaves and their impact in the study area. The results of the study showed that 2010 was extremely hot with an average maximum temperature anomaly of 2.18, while 2013 and 2015 have an average maximum temperature anomaly of 1.93 respectively, and were very hot. The trend in the average maximum temperature anomaly of the study area showed a continuous increase in extreme heat for about a decade (2009-2020). The positive temperature trend and the average maximum temperature anomaly showed that the intensity of heatwaves in the study area will increase in the future as a result of global warming caused by the continuous emission of anthropogenic greenhouse gases. The results also showed that the heatwaves were exacerbated by the drought which occurred between 2010 and 2014. The annual heat index which range from 29.4oC - 34oC in the study area showed that the heatwaves have impacted both human and animals negatively in the area.
Design Level Class Decomposition using the Threshold-based Hierarchical Agglomerative Clustering

Refactoring activity is essential to maintain the quality of a software's internal structure.It decays as the impact of software changes and evolution.Class decomposition is one of the refactoring processes in maintaining internal quality.Mostly, the refactoring process is done at the level of source code.Shifting from source code level to design level is necessary as a quick step to refactoring and close to the requirement.The design artifact has a higher abstraction level than the source code and has limited information.The challenge is to define new metrics needed in class decomposition using the design artifact's information.Syntactic and semantic information from the design artifact provides valuable data for the decomposition process.Class decomposition can be done at the level of design artifact (class diagram) using syntactic and semantic information.The dynamic threshold-based Hierarchical Agglomerative Clustering produces a more specific cluster that is considered to produce a single responsibility class.
Impact of Blockchain technology in Healthcare

The current healthcare systems are facing many issues in terms of data management, data sharing, information security and patient privacy, data immutability, trust, and transparency. In addition, the multiple existing healthcare systems are centralized which complicates the healthcare professionals, patients in managing their data and causes several problems. Blockchain technology as a decentralized peer-to-peer network has the power to digitalize and transform the manner that the data are managed in the healthcare industry, in this regard, is one such domain that might benefit from Blockchain technology in different manners. This paper aims to improve a review of recent works on Blockchain-based healthcare applications.
Breast Ultrasound Image Segmentation to Detect Tumor by Using Level Sets

Common cancer among the women is breast cancer that develops in either the lobules or the ducts of the breast. Identifying the tumor shapes in ultrasound images is still a challenging job because of speckle noise, poor contrast, and image intensity variations. A multiphase level set strategy is proposed in this research to efficiently segment the ultrasound image. Speckle noise of ultrasound images is reduced by using speckle reducing anisotropic diffusion (SRAD) filter. This proposed model demonstrates that it outperforms the Chan-Vese (CV) method and handles noisy, low contrast images better. This proposed approach is more robust to intensity inhomogeneities. Experiments show that the suggested method extracts more precise tumor boundaries than the CV method. This proposed approach is validated with different performance measure metrics such as Jaccard coefficient, Dice coefficient, and Hausdorff distance.
The evaluation method for English MOOC quality based on grounded theory

In order to overcome the poor rationality of the weight distribution of the existing English MOOC quality evaluation indicators, a grounded theory-based method of English MOOC quality evaluation is proposed. This method constructs the evaluation framework of English MOOC quality and determines the overall goal of teaching. Based on the selected MOOC quality evaluation indexes, the weight value of each evaluation index is determined by AHP, and the comprehensive evaluation indexes of each level are evaluated by fuzzy algorithm. The evaluation results of English MOOC quality are obtained by integration and calculation. The evaluation of English MOOC quality based on grounded theory is realised price. The experimental results show that the maximum value of the weight distribution of the proposed MOOC quality evaluation method can reach 29.45, and the quality evaluation result U >= 90, which shows that the quality effect is excellent and has better evaluation effect.
A Novel Fast Color Image Encryption Algorithm based on 2D-Hybrid Maps

Objectives: To design a fast and efficient color image encryption technique using 2D Duffing, Henon and Tinkerbell maps.Methods: The presented work employs the confusion-diffusion-confusion structure for encryption.Pixellevel scrambling is undertaken in the first phase of confusion, and the scrambled image is diffused using the Exclusive-OR operation.Finally, bitlevel permutation is used for improved security.This work makes use of the 44 samples from the USC-SIPI image database.Findings: The presented color image encryption technology's performance is quantitatively compared with two recent techniques and observed that the proposed work reduces time by 81.2%.Novelty: This work employs a confusion-diffusion-confusion framework which increases algorithmic security by 47.8%.Further, a novel key generation scheme is designed to generate dynamic and input image-sensitive keys.Applications/Improvements: The simple Exclusive-OR operation-based diffusion that is devised significantly minimizes encryption time, thereby making it ideal for real-time applications.
Augmented remote operating system for scaling in smart mining applications: Quality of experience aspects

Remote operation and Augmented Telepresence are fields of interest for novel industrial applications in e.g., construction and mining.In this study, we report on an ongoing investigation of the Quality of Experience aspects of an Augmented Telepresence system for remote operation.The system can achieve view augmentation with selective content removal and Novel Perspective view generation.Two formal subjective studies have been performed with test participants scoring their experience while using the system with different levels of view augmentation.The participants also gave free-form feedback on the system and their experiences.The first experiment focused on the effects of in-view augmentations and interface distributions on wall patterns perception.The second one focused on the effects of augmentations on the depth and 3D environment understanding.The participants' feedback from experiment 1 showed that the majority of participants preferred to use the original camera views and the Disocclusion Augmentation view instead of the Novel Perspective views.Moreover, the Disocclusion Augmentation, that was shown in combination with other views seemed beneficial.When the views were isolated in experiment 2, the impact of the Disocclusion Augmentation view was found to be lower than the Novel Perspective views.
STRATEGIES AND TECHNIQUES OF LSRW SKILLS IN ACQUISITION OF ENGLISH LANGUAGE FOR SECONDARY SCHOOL STUDENTS

It is natural that the demand for communication is high in this ever-changing world. Language plays a crucial role in communication and English is no doubt the foremost and most important tool of communication all over the world. Students in India are exposed to their language studies right from their primary level. English is taught only as a second language in India and there is a great difference between the city-bred children and the rural children in adapting to English language. The city-bred children are exposed to a lot of methods of learning English with ample facilities and special training in schools to improve their Listening, Speaking, Reading and Writing skills which are the basic skills for learning any language whereas rural students have only limited exposure to learn English. The main objective of this research paper is to acquire a satisfactory understanding in terms of bringing about improvements in the LSRW skills of students. It is essential for the educators to put into operation, the teachinglearning methods and instructional strategies in such a manner that would impart not only sufficient information in terms of academic concepts, but also in terms of LSRW skills. One needs to pay attention on a continuous basis in bringing about progressions in these skills. Developments in these skills would help in enriching the overall quality of lives of the individuals.
Analysis of Role of Cloud Computing in Providing Internet Banking Services: Case Study Bank Melli Iran

In this paper, the cloud-computing model proposed by Davis has been used to explain the main parameters of cloud computing by the use of information systems. This paper investigates influential factors in decision to use cloud computing among employees of the national bank information technology organization based on the cloud-computing model. To analyze and investigate the use of cloud computing in national bank services, the designed questionnaire was distributed among 230 experts and acquainted with the information technology industry in the national bank information technology organization; which resulted in the questionnaire’s Cronbach's alpha 0.84. After conducting exploratory and confirmatory factor analysis with regard to analytical coefficients, utility factors were proved to have the most critical influence on the decision of using cloud computing in the electronic banking system. The findings of this research can be considered as guidelines used by national bank managers to employ cloud computing technology.
Efficient Dual-Process Cognitive Recommender Balancing Accuracy and Diversity

In this paper, we propose a dual-process cognitive recommendation system for sequential recommendations. The framework includes an intuitive representation module (System 1) and an inference module (System 2). System 1 is designed to understand the user’s historical interaction sequences with external knowledge graph. System 2 is built to make recommendations by reinforcement learning to consider long-term returns and diversity. We demonstrate the performance of our method on a wide range of recommendation datasets. Experiments show significant improvement over the state-of-the-art models regarding both relevance and diversity.