Reviewerly/exHarmony huggingface dataset:
Automating the extraction of information from a historical text and building a linked data model for the domain of ecology and conservation science

Data heterogeneity is a pressing issue and is further compounded if we have to deal with data from textual documents. The unstructured nature of such documents implies that collating, comparing and analysing the information contained therein can be a challenging task. Automating these processes can help to unleash insightful knowledge that otherwise remains buried in them. Moreover, integrating the extracted information from the documents with other related information can help to make more information-rich queries. In this context, the paper presents a comprehensive review of text extraction and data integration techniques to enable this automation process in an ecological context. The paper investigates into extracting valuable floristic information from a historical Botany journal. The purpose behind this extraction is to bring to light relevant pieces of information contained within the document. In addition, the paper also explores the need to integrate the extracted information together with other related information from disparate sources. All the information is then rendered into a query-able form in order to make unified queries. Hence, the paper makes use of a combination of Machine Learning, Natural Language Processing and Semantic Web techniques to achieve this. The proposed approach is demonstrated through the information extracted from the journal and the information-rich queries made through the integration process. The paper shows that the approach has a merit in extracting relevant information from the journal, discusses how the machine learning models have been designed to classify complex information and also gives a measure of their performance. The paper also shows that the approach has a merit in query time in regard to querying floristic information from a multi-source linked data model.
Securing ADS-B data transmissions using blockchain: a comprehensive survey and analysis

Purpose The purpose of this paper is to study the various solutions and recommendations provided by researchers in applying the blockchain concept to different problems in aviation industry. It will discuss and highlight the specific approaches that leverages blockchain to mitigate the automatic dependent surveillance-broadcast (ADS-B) security issues. Furthermore, it introduces an innovative design and method to secure ADS-B data using a tamper-resistant distributed public ledger of authenticated flight plans and validates the position and other parameters of the associated aircraft identifier against the flight paths/routes that are stored in the blockchain ledger. Design/methodology/approach ADS-B is the key technology that is mandated by Federal Aviation Administration in USA by 2020. However, ADS-B data is neither encrypted nor authenticated. This paper proposes a novel solution using blockchain to secure the ADS-B data communications and in-depth analysis of existing solutions covering the following aspects: classification of various possible attacks on ADS-B. Presents various solutions proposed by different researchers regarding use of blockchain in aviation industry. Discuss a new solution to secure ADS-B using blockchain. Discuss the high-level architectural framework of the proposed and patented solution. Finally, presents the conclusions and future work scope. Findings While the main intention of this paper is to bring together all the existing solutions using blockchain to secure aviation and ADS-B data at one place, the proposed novel solution could contribute to maintaining security and privacy for aircrafts flying in the airspace at any point in time. Continuously securing the ADS-B data transmissions based upon the filed flight plans in real time can provide a mechanism to identify spoofed aircraft messages and communicate the same to ground stations for authentication of existence of such a malicious aircraft. Thus, this solution also differs from all the existing ones. Practical implications As aviation industry is in its infancy stage in implementing blockchain-based solutions, practical implementation of the proposed concept might take longer. Originality/value This paper is a comprehensive survey and review paper. To the best of the authors’ knowledge, this is the first of its kind that presents various use cases for usage of blockchain in aviation industry along with a detailed review of existing proposed or implemented solutions using blockchain to secure ADS-B data. This can serve as an invaluable reference for the future researchers on this topic in both industry and academia.
Higher Order Moments Dynamics for Some Multimode Quantum Master Equations

We derive Heisenberg equations for arbitrary high order moments of creation and annihilation operators in the case of the quantum master equation with a multimode generator which is quadratic in creation and annihilation operators and obtain their solutions. Based on them we also derive similar equations for the case of the quantum master equation, which occur after averaging the dynamics with a quadratic generator with respect to the classical Poisson process. This allows us to show that dynamics of arbitrary finite-order moments of creation and annihilation operators is fully defined by finite number of linear differential equations in this case.
Random Algorithm and Skill Evaluation System Based on the Combing of Construction Mechanism of Higher Vocational Professional Group

At present, the employment situation in China is very severe, and there are both recruitment and employment difficulties in the society. There are many reasons for this problem, among which, the incompatibility between talents training and social demand in higher vocational colleges is one of the main reasons, and it is necessary for higher vocational colleges to effectively evaluate students’ skills training with the real demand. Based on this, this paper studies the study of stochastic algorithm and skill evaluation system based on the combing of the construction mechanism of higher vocational professional clusters and builds a higher vocational skill evaluation system based on a simple analysis of the skill evaluation system and related evaluation algorithms in higher vocational institutions. Principal component analysis was selected to realize the quantitative processing of skill evaluation indexes, and random forest algorithm was used to realize skill evaluation. The simulated annealing algorithm is introduced to realize parameter selection, parameter optimization, and weight setting, and experiments are designed to analyze the performance of the algorithm. The simulation results show that the random forest algorithm is applied to skill evaluation with high accuracy, small error, and better generalization ability.
Optimizing Data Centre Energy Efficiency via Event Driven Deep Reinforcement Learning

[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2022.3157145]
Research on Injury Causes and Prevention Effect of College Rowing Athletes Based on Multiple Regression and Residual Algorithm

Rowing competition in colleges and universities is an international competition, and it is also a favorite competition for college students. However, in the course of rowing competition, the stability of athletes' injuries often occurs, which is difficult to solve effectively. Aiming at the problem that the loss of athletes in rowing competition in colleges and universities cannot be accurately prevented, this paper puts forward a multiple regression prevention effect model and makes a comprehensive analysis combined with complex reasons. Through the integration of multiple regression and residual analysis, we can better find out the influencing factors, aiming at finding out the causes of athletes' injuries and putting forward corresponding countermeasures. First of all, analyze the causes of loss, establish a framework of injury prevention for college rowers, and the overall diagnosis framework is reasonable. Then, according to the "University Rowing Prevention and Control Standards" divided into various prevention measures, through the comprehensive prevention and control measure mechanism to get the cause of injury, finally, the optimal combination of various control measures forms a control system. The results of MATLAB show that the combination of multiple regression and residual analysis can improve the accuracy of athletes' injury prevention and treatment, make the accuracy more than 90%, shorten the diagnosis time less than 10 minutes, and meet the requirements of athletes' injury diagnosis under normal rowing competition.
Evaluating the effect of knowledge risks on sustainability: the mediating role of organizational performance

Purpose The purpose of this paper is to examine the links between knowledge risks, organizational performance and knowledge-intensive firms (KIFs)' sustainability. Design/methodology/approach A questionnaire was administered with a sample of 427 respondents from Lebanon. The gathered data were analyzed using SEM approach. Findings The empirical evidence confirms the potential role of knowledge risks in reducing the sustainability of firms. Furthermore, organizational performance was revealed to partially mediate the relationship between knowledge risks and sustainability. Practical implications The study's findings inspire managers of KIFs to use effective knowledge management practices to mitigate potential knowledge risks. Originality/value Knowledge risks and knowledge risk management are still unexplored in the literature. This paper is a pioneering study that advances the knowledge management field by emphasizing the significance of knowledge risks and their influence on the performance and sustainability of KIFs in Lebanon, a country with a culture distinct from that of Western contexts.
Early Neolithic Ceramic Complex of the Settlement of Mergen 6 in the Lower Ishim: technological aspect

The article presents the results of a study on the technologies making of Early Neolithic ceramics from the settlement of Mergen 6 – a reference site in the development of issues of the Early Neolithic in the forest-steppe zone of the Tobol-Ishim interﬂuve. Based on morphological characteristics, the researchers divided the ceramic complex of the settlement into four groups – the frst and second groups correspond to the traditions of the Boborykino and Koshkino cultures, the third group includes vessels with sculptural ornamentation as relief bands – the «relief-band» group, the fourth is represented by vessels with mixed ornamental features – «syncretic» group. The study of the ceramic collection was carried out by the author according to the method of A.A. Bobrinsky. The features of ancient potters' skills at the stages of selection of initial plastic raw materials were revealed, as well as composition of molding masses, the surface treatment of products and fring them. Based on the data obtained, the issue of the emergence of pottery technology in the region under consideration is discussed.
Retrieving API Knowledge from Tutorials and Stack Overflow Based on Natural Language Queries

When encountering unfamiliar APIs, developers tend to seek help from API tutorials and Stack Overflow (SO). API tutorials help developers understand the API knowledge in a general context, while SO often explains the API knowledge in a specific programming task. Thus, tutorials and SO posts together can provide more API knowledge. However, it is non-trivial to retrieve API knowledge from both API tutorials and SO posts based on natural language queries. Two major problems are irrelevant API knowledge in two different resources and the lexical gap between the queries and documents. In this article, we regard a fragment in tutorials and a Question and Answering (Q&amp;A) pair in SO as a knowledge item (KI). We generate ⟨ API, FRA ⟩ pairs (FRA stands for fragment) from tutorial fragments and APIs and build ⟨ API, QA ⟩ pairs based on heuristic rules of SO posts. We fuse ⟨ API, FRA ⟩ pairs and ⟨ API, QA ⟩ pairs to generate API knowledge (AK for short) datasets, where each data item is an ⟨ API, KI ⟩ pair. We propose a novel approach, called PLAN, to automatically retrieve API knowledge from both API tutorials and SO posts based on natural language queries. PLAN contains three main stages: (1) API knowledge modeling, (2) query mapping, and (3) API knowledge retrieving. It first utilizes a deep-transfer-metric-learning-based relevance identification (DTML) model to effectively find relevant ⟨ API, KI ⟩ pairs containing two different knowledge items (⟨ API, QA ⟩ pairs and ⟨ API, FRA ⟩ pairs) simultaneously. Then, PLAN generates several potential APIs as a way to reduce the lexical gap between the query and ⟨ API, KI ⟩ pairs. According to potential APIs, we can select relevant ⟨ API, KI ⟩ pairs to generate potential results. Finally, PLAN returns a list of ranked ⟨ API, KI ⟩ pairs that are related to the query. We evaluate the effectiveness of PLAN with 270 queries on Java and Android AK datasets containing 10,072 ⟨ API, KI ⟩ pairs. Our experimental results show that PLAN is effective and outperforms the state-of-the-art approaches. Our user study further confirms the effectiveness of PLAN in locating useful API knowledge.
Marketing Intelligence Dimensions As an Innovative Approach for Customer Retention through the Intermediate Role of Customer Experience

This study aims to investigate the effect of marketing intelligence-dimensions (market intelligence, competitor intelligence, customer intelligence and product intelligence) on customer retention through the intermediating role of customer experience. A quantitative approach was used to collect data from four -and five-star hotels in Jordan using a questionnaire designed to achieve the study objectives. A simple random sampling methodology was used for this purpose, where a total of (307) questionnaires were valid for analysis out of (350) questionnaires distributed randomly. SPSS v23 and Smart-PLS 3.2.7 were used to answer the study questions and test the study hypotheses. The study results revealed that there is a significant positive direct effect of marketing-intelligence dimensions on customer experience and a significant positive effect of customer experience on customer retention. In addition, there is a significant positive indirect effect of marketing-intelligence dimensions on customer retention through customer experience. The study concludes that four -and five- star hotels have to utilize innovative tools to acquire information about the external environment, including the market, competitors, customers and products to survive in the competitive market environment.
Dilated Context Integrated Network with Cross-Modal Consensus for Temporal Emotion Localization in Videos

Understanding human emotions is a crucial ability for intelligent robots to provide better human-robot interactions. The existing works are limited to trimmed video-level emotion classification, failing to locate the temporal window corresponding to the emotion. In this paper, we introduce a new task, named Temporal Emotion Localization in videos (TEL), which aims to detect human emotions and localize their corresponding temporal boundaries in untrimmed videos with aligned subtitles. TEL presents three unique challenges compared to temporal action localization: 1) The emotions have extremely varied temporal dynamics; 2) The emotion cues are embedded in both appearances and complex plots; 3) The fine-grained temporal annotations are complicated and labor-intensive. To address the first two challenges, we propose a novel dilated context integrated network with a coarse-fine two-stream architecture. The coarse stream captures varied temporal dynamics by modeling multi-granularity temporal contexts. The fine stream achieves complex plots understanding by reasoning the dependency between the multi-granularity temporal contexts from the coarse stream and adaptively integrates them into fine-grained video segment features. To address the third challenge, we introduce a cross-modal consensus learning paradigm, which leverages the inherent semantic consensus between the aligned video and subtitle to achieve weakly-supervised learning. We contribute a new testing set with 3,000 manually-annotated temporal boundaries so that future research on the TEL problem can be quantitatively evaluated. Extensive experiments show the effectiveness of our approach on temporal emotion localization. The repository of this work is at https://github.com/YYJMJC/TemporalEmotion-Localization-in-Videos.
Low Latency Live Streaming Implementation in DASH and HLS

Low latency live streaming over HTTP using Dynamic Adaptive Streaming over HTTP (LL-DASH) and HTTP Live Streaming (LL- HLS) has emerged as a new way to deliver live content with an respectable video quality and short end-to-end latency. Satisfying these requirements while maintaining viewer experience in practice is challenging, and adopting conventional adaptive bitrate (ABR) schemes directly to do so will not work. Therefore, recent solutions including LoL+, L2A, Stallion, and Llama re-think conventional ABR schemes to support low-latency scenarios. These solutions have been integrated with dash.js [9] that supports LL-DASH. However, their performance in LL-HLS remains in question. To bridge this gap, we implement and integrate existing LL-DASH ABR schemes in the hls.js video player [18] which supports LL-HLS. Moreover, a series of real-world trace-driven experiments have been conducted to check their efficiency under various network conditions including a comparison with results achieved for LL-DASH in dash.js. Our version of hls.js is publicly available at [3] and a demo at [4].
Socioeconomic conditions and academic performance in higher education in Colombia during the pandemic

This article studies the relationship between the socioeconomic conditions of higher education students in Colombia and their academic performance during the pandemic. The household’s socioeconomic conditions are approximated by the education level of the parents, their occupation and the possession of assets. A multiple regression model tests the effect of these variables on academic performance before and during the pandemic. Results suggest that before the pandemic, the mother’s graduate education and household technology assets showed a positive impact on test score. Mixed effects of parents’ occupations by gender were also found. During the pandemic, the effect of the mother’s education remained the same, and the effect of technological assets, in-person education and high-quality accredited establishment increased.
Proof of learning: Two Novel Consensus mechanisms for data validation using Blockchain Technology in Water Distribution System

This paper proposes an architecture of a data validation system for Water distribution system (WDS) utilising machine learning as two consensus mechanisms instead of the typical consensus mechanism based on the hashing function. The two consensus mechanisms are called Proof-of-single-learning (PoSL) and Proof-of-multiple-learning (PoML) in which the data is validated based on the learning. These two novel methods are compared with the other five hashing-based consensus mechanisms: Proof-of-Work (PoW), Proof-of-Trust (PoT), Proof-of-Vote (PoV), Proof-of-Assignment (PoA), and proof-of-Authentication (PoAuth) for evaluation. Five case studies of WDS are applied and three performance metrics, and two data conversion methods are utilised. Throughput, latency and operations per transaction (OpT are investigated to evaluate the proposed system.
Virtual Machine and Container Live Migration Algorithms for Energy Optimization of Data Centre in Cloud Environment: A Research Review

The growing pace of cloud computing technology needs to optimize the energy consumption of the data centres. Virtualization is one such technology that helps in live migration of Virtual Machines and Containers and thus help reduction in energy consumed. An effort is made in this paper to explore different approaches for energy optimized live migration using Virtual Machine and Container. Majority of the work has been carried out by researchers considering CPU utilization as the parameter to optimize the energy consumption and some works have used other parameters like memory, disk space, application execution time for active server along with CPU utilization. The survey also depicts majority of the work are implemented using CloudSim and also there is more scope for developing solutions for optimal migration in Virtual Machine and Containers.
An efficient and low complex model for optimal RBM features with weighted score-based ensemble multi-disease prediction

Multi-disease prediction is regarded as the capacity to simultaneously identify various diseases that are expected to be affected an individual at a certain period. These multiple diseases are seemed to be at various progression levels and need to be detected in the patient at the time of clinical visits. Diverse studies in the literature have included the predictive models for particular diseases yet, it is unable to notice humans with multiple diseases since humans are mostly suffered not only from a single disease but also from multiple diseases. Hence, this article aims to implement a novel multi-disease prediction model using an ensemble learning approach with deep features. The required data for the multi-disease prediction is collected from the standard datasets. Then, the collected data are given into the "Deep Belief Network (DBN)" approach, where the features are obtained from the RBM layers. These RBM features are tuned with the help of Deviation-based Hybrid Grasshopper Barnacles Mating Optimization (D-HGBMO) for improving the prediction performance. The optimized RBM features are considered in the ensemble learning model named Ensemble, in which the multi-disease prediction is performed with "Deep Neural Network (DNN), Extreme Learning Machine (ELM), and Long Short Term Memory." The predicted score from three classifiers is used in the optimized weighted score and thresholding-based final prediction using the same D-HGBMO for determining the accurate multi-disease prediction results. The experimental results show the effective performance of the proposed model by comparing it with the existing classifiers with the help of different quantitative measures.
Visualization Method of Key Knowledge Points of Nursing Teaching Management System Based on SOM Algorithm and Biomedical Diagnosis

The traditional nursing teaching knowledge point recommendation algorithm based on collaborative filtering is difficult to deal with the problem of data sparsity, while the traditional recommendation algorithm based on matrix decomposition has poor scalability in dealing with high-dimensional data, and their recommendation results are only determined according to the prediction score, resulting in low recommendation accuracy. In view of this, a nursing teaching knowledge point recommendation method based on a SOM neural network and ranking factor decomposition machine is proposed. Firstly, the SOM neural network is used to cluster users based on users’ academic background information, then the partial order relationship of nursing teaching knowledge points is constructed by using users’ explicit and implicit web access behavior, and finally, the factor decomposition machine is used as the ranking function to classify users’ academic background web access behavior, borrowing nursing teaching introduction text, and other characteristic information were modeled, and the peer-to-peer ranking learning algorithm was used to accurately recommend nursing teaching knowledge points. Experimental results show that the proposed method can effectively alleviate the problem of data sparsity and improve the accuracy and efficiency of recommendations.