Reviewerly/exHarmony huggingface dataset:
Aggregating Heterogeneous Sensor Ontologies with Fuzzy Debate Mechanism

Aiming at enhancing the communication and information security between the next generation of Industrial Internet of Things (Nx-IIoT) sensor networks, it is critical to aggregate heterogeneous sensor data in the sensor ontologies by establishing semantic connections in diverse sensor ontologies. Sensor ontology matching technology is devoted to determining heterogeneous sensor concept pairs in two distinct sensor ontologies, which is an effective method of addressing the heterogeneity problem. The existing matching techniques neglect the relationships among different entity mapping, which makes them unable to make sure of the alignment’s high quality. To get rid of this shortcoming, in this work, a sensor ontology extraction method technology using Fuzzy Debate Mechanism (FDM) is proposed to aggregate the heterogeneous sensor data, which determines the final sensor concept correspondences by carrying out a debating process among different matchers. More than ever, a fuzzy similarity metric is presented to effectively measure two entities’ similarity values by membership function. It first uses the fuzzy membership function to model two entities’ similarity in vector space and then calculate their semantic distance with the cosine function. The testing cases from Bibliographic data which is furnished by the Ontology Alignment Evaluation Initiative (OAEI) and six sensor ontology matching tasks are used to evaluate the performance of our scheme in the experiment. The robustness and effectiveness of the proposed method are proved by comparing it with the advanced ontology matching techniques.
Analysis and control of the fractional chaotic Hopfield neural network

Abstract The fractional Hopfield neural network (HNN) model is studied here analyzing its symmetry, uniqueness of the solution, dissipativity, fixed points etc. A Lyapunov and bifurcation analysis of the system is done for specific as well as variable fractional order. Since a very long time ago, HNN has been carefully studied and applied in various fields. Because of the exceptional non-linearity of the neuron activation function, the HNN system is stoutly non-linear. Chaos control using adaptive SMC considering disturbances and uncertainties is done about randomly chosen points by designing suitable controllers. Numerical simulations performed in MATLAB verify the efficacy of the designed controllers.
An efficient Robust Random Vector Functional Link network for Solar Irradiance, Power and Wind speed prediction

This paper proposes an efficient technique for prediction of solar irradiance, solar power and wind speed at different time intervals (i.e. 5min, 10min and 60min). With the deliberation of historical solar irradiance, power and wind speed data, an ultra-short Prediction model has been established which is known as Robust Regularized Random Vector Functional link (RRVFL) network. This method utilizes a weighted factor in ridge regularized model, for training the samples to assess the weights in output layer. A Huber's cost function has been applied to gain the robustness here. To get the accuracy of the proposed methodology, the test has been carried out with solar and wind for various time intervals in different atmospheric condition. The result shows that the proposed RRVFL method is very superior as compared with other models (i.e. Random vector functional link (RVFL) and Robust Extreme learning machine(R-ELM), etc. Solar and wind data of California, USA has been taken here. The proposed model can be validated in real time scenario by using test bench application and in industries of solar and wind farm.
TLATR: Automatic Topic Labeling Using Automatic (Domain-Specific) Term Recognition

Topic modeling is a probabilistic graphical model for discovering latent topics in text corpora by using multinomial distributions of topics over words.Topic labeling is used to assign meaningful labels for the discovered topics.In this paper, we present a new topic labeling method that uses automatic term recognition to discover and assign relevant labels for each topic, i.e., TLATR (Topic Labeling using Automatic Term Recognition).TLATR uses domain-specific multi-terms that appear in the set of documents belonging to a topic.The multi-term having the highest score as determined by the automatic term recognition algorithm is chosen as the label for that topic.To evaluate TLATR, we use two real, publicly available datasets that contain scientific articles' abstracts.The topic label evaluation is done both automatically and using human annotators.For the automatic evaluation, we use Pointwise Mutual Information, Normalized Pointwise Mutual Information, and document similarity.For human evaluation, we employ the average rating method.Furthermore, we also evaluate the quality of the topic models using the Adjusted Rand Index.To prove that our novel method extracts relevant topic labels, we compare TLATR with two state-of-the-art methods, one supervised and one unsupervised, provided by the NETL Automatic Topic Labelling system.The experimental results show that our method outperforms or provides similar results with both NETL's supervised and unsupervised approaches.
SpanPredict: Extraction of Predictive Document Spans with Neural Attention

In many natural language processing applications, identifying predictive text can be as important as the predictions themselves.When predicting medical diagnoses, for example, identifying predictive content in clinical notes not only enhances interpretability, but also allows unknown, descriptive (i.e., text-based) risk factors to be identified.We here formalize this problem as predictive extraction and address it using a simple mechanism based on linear attention.Our method preserves differentiability, allowing scalable inference via stochastic gradient descent.Further, the model decomposes predictions into a sum of contributions of distinct text spans.Importantly, we require only document labels, not ground-truth spans.Results show that our model identifies semantically-cohesive spans and assigns them scores that agree with human ratings, while preserving classification performance.
Using Linguistic VIKOR and Fuzzy Cognitive Maps to Select Virtual Reality Games Development Project

In the entertainment industry, providing games to players is a main business activity with a long history. There are various kinds of game types such as the computer game, web game, pocket game, mobile game developed for play. Virtual reality is a new technology which can integrate into the computer or mobile for generating virtual reality games. The goal of this study is to develop a framework to evaluate and select the virtual reality games development project. In the proposed framework, Linguistic VIKOR is integrated with fuzzy cognitive maps to select the project. For readers to understand the proposed method, a case study has been introduced to evaluate the project based on the proposed method. Finally, the conclusion and future research are discussed as an ending.
Learning from History for Byzantine Robust Optimization

Byzantine robustness has received significant attention recently given its importance for distributed and federated learning. In spite of this, we identify severe flaws in existing algorithms even when the data across the participants is identically distributed. First, we show realistic examples where current state of the art robust aggregation rules fail to converge even in the absence of any Byzantine attackers. Secondly, we prove that even if the aggregation rules may succeed in limiting the influence of the attackers in a single round, the attackers can couple their attacks across time eventually leading to divergence. To address these issues, we present two surprisingly simple strategies: a new robust iterative clipping procedure, and incorporating worker momentum to overcome time-coupled attacks. This is the first provably robust method for the standard stochastic optimization setting. Our code is open sourced at this https URL.
COIN: COmpression with Implicit Neural representations

We propose a new simple approach for image compression: instead of storing the RGB values for each pixel of an image, we store the weights of a neural network overfitted to the image. Specifically, to encode an image, we fit it with an MLP which maps pixel locations to RGB values. We then quantize and store the weights of this MLP as a code for the image. To decode the image, we simply evaluate the MLP at every pixel location. We found that this simple approach outperforms JPEG at low bit-rates, even without entropy coding or learning a distribution over weights. While our framework is not yet competitive with state of the art compression methods, we show that it has various attractive properties which could make it a viable alternative to other neural data compression approaches.
The Evolving Ecosystem of COVID-19 Contact Tracing Applications

Since the outbreak of the novel coronavirus, COVID-19, there has been significant interest in the use of digital contact tracing as a means of stopping chains of viral transmission, provoking alarm from privacy advocates.Concerning the ethics of this technology, recent studies have predominantly focused on (1) the formation of guidelines for ethical contact tracing, (2) the analysis of specific implementations, or (3) the review of a select number of contact tracing applications and their relevant privacy or ethical implications.In this study, we provide a comprehensive survey of the evolving ecosystem of COVID-19 tracing applications, examining 152 contact tracing applications and assessing the extent to which they comply with existing guidelines for ethical contact tracing.The assessed criteria cover areas including data collection and storage, transparency and consent, and whether the implementation is open source.We find that although many apps released early in the pandemic fell short of best practices, apps released more recently, following the publication of the Apple/Google exposure notification protocol, have tended to be more closely aligned with ethical contact tracing principles.This data set is publicly available at https://www.benjaminlevy.ca/covid-apps and may be updated as the pandemic continues.
Development of micro computer based mobile random number generator with an encryption application

In this study, an equation is derived by changing the parameter values of a chaotic system in the literature and analyzing its chaotic behavior. In the analysis of chaotic behaviors, chaotic system analyses such as phase portraits, sensitivity to initial conditions, Lyapunov exponential spectrum and dimension analysis, bifurcation diagram with Matlab® software were investigated. The derived equations were embedded into a mobile random number generator (RNG). RNG was developed on the Raspberry Pi 3 Model B microcomputer. It has been shown that the obtained random numbers can be securely used in encryption applications by successfully passing the statistical tests NIST 800-22, FIPS 140-1 and ENT. An image encryption application with the generated random numbers was implemented on the Raspberry Pi 3 Model B microcomputer. Finally, the encrypted image was subjected to security tests such as histogram analysis, correlation and entropy coefficients, correlation card, NPCR, UACI utilizing MATLAB software. The performance and reliability of the encryption algorithm were also measured. This paper improves the current state of art as it implements chaotic random number generation algorithm in a small footprint micro computer.
EFL Learner’s Literary Competence Mapping through Reader-Response Writing Assessed using CCEA GCSE Mark Scheme

Abstract: In literary education, literary competence lies in important position as it reflects students’ critical thinking ability to understand text as a whole and connect it to both their experience and social phenomenon. Recently, the research on literary competence focuses on the implementation of reader-response writing; however, it rarely highlights the importance of literary competence mapping through the quantification of the responses. This research aims at revealing the implementation of students’ literary competence mapping by using reader-response writing and The Council for the Curriculum Examinations and Assessment (CCEA) General Certificate of Secondary Education (GCSE) Mark Scheme for literature subject for the assessment. Seven reader response strategies by Beach and Marshal (1991) were used as the framework. Selecting 21 students of English Literature in Prose Analysis class as the research object, this research employed qualitative descriptive method. The result shows, in completing seven reader responses, students provide various answers reflecting their ability to get into the stories. The students mostly find it difficult to describe setting of place and time and their influence to the plot of the story. In assessment process, several adjustments on distributing points of CCEA GCSE Mark Scheme for literature subject. Mark Scheme in each strategy have been made. Almost half of the students are in the band 4 and band 5 meanwhile the rest of them are in band 2 and band 3. This research concludes that reader-response writing and CCEA GCSE Mark Scheme can be alternative to map the students’ literary competence. The research further suggest that the students should be exposed more on the analysis of setting and on the argumentation building on how they should provide sufficient reason and textual evidence. In addition, the lecturer should also emphasize the expected competence level the English Literature students should acquire.
Single-based and Population-Based Metaheuristics Algorithms Performances in Solving NP-hard Problems

Metaheuristics is one of the most well-known field of researches uses to find optimum solution for Non-deterministic polynomial hard problems (NP-Hard), that are difficult to find an optimal solution in a polynomial time. Over time many algorithms have been developed based on the heuristics to solve difficult real-life problems, this paper will introduce Metaheuristic-based algorithms and its classifications, Non-deterministic polynomial hard problems. It also will compare the performance two metaheuristic-based algorithms (Elephant Herding optimization algorithm and Tabu Search) to solve Traveling Salesman Problem (TSP), which is one of the most known problem belongs to Non-deterministic polynomial hard problem and widely used in the performance evaluations for different metaheuristics-based optimization algorithms. the experimental results of the paper compare the results of EHO and TS for solving 10 different problems from the TSPLIB95.
Employee motivation as an indicator of the desire to continue working: evidence from SMEs in Bangladesh

The purpose of this study is to explore how employee motivation factors impact on the employee's desire to keep working in SME firms. This study primarily is based on raw data which are collected from 278 employees of SME firms in Bangladesh. Apart from adopting the quantitative method, the Smart PLS 3.0 and SPSS V23 were utilised for analysing the gathered primary data. Furthermore, we predicted ten hypotheses in this study. Our findings assure that a positive and significant relationship among working conditions, salary and incentive, and job stability with employee motivation is sustained which indicates a direct relationship on the desire of employees to continue working in this sector. This study did not find any conventional relationship between the behaviour of presidents and subordinates with employee motivation. This study further demonstrates that employee motivation does not mediate the relationship between behaviour of presidents and subordinates with the desire to continue working.
Assessing the impact of cloud-based services on the talent management of employees

Purpose Fundamental literature exists on the employment of diverse information technology (IT) innovations relied on utility computing, a little experimental literature exists on cloud computing adoption by medium enterprises. So, this essay aims to analyze the determinants of the acceptance of cloud computing in medium-sized organizations. It also targets the impact of cloud-depended services on employers' talent management in the analysis areas of research reports, hypotheses and frameworks used by them. Design/methodology/approach Cloud computing is a novel and notable development in the network application field with a novel IT viewpoint. The present investigation aims to assist and enrich the scientific argument regarding the phenomenon nominated cloud computing from a managerial perspective. A quantitative study design was utilized to gather and analyze the data. In total, 345 employees from different medium enterprises reported their perspectives via an online survey. Partial least squares was used to evaluate the model of the study. The investigation was performed from April 2020 to November 2020. Findings As expected, the empirical analysis results revealed that cloud computing adaption drivers positively and significantly associated with talent management. The results showed that organizational level is the most important factor in the effectiveness of talent management. Further, this study established a cloud strategy that moderates the relationships between cloud computing adaption drivers and talent management. As a general result, organization managers must implement cloud computing to improve organizational performance, reduce costs, enhance organizational innovation and reduce repetitive work. So, international trade facilitates and helps the country's economy. Research limitations/implications There are still some drawbacks of this analysis that require addressing the findings. Initially, the authors gathered a country's data. Second, by considering multiple significant structures from four aspects, they established the study model. Third, utilizing one-time cross-sectional data, the suggested model was evaluated. As a proposal, future researchers could investigate cloud computing adoption drivers' outcomes within other organizations. Originality/value This research presents a straightforward and easy model for understanding the cloud's effect on employee talent management. By systematizing the unrelated studies on the subject and organizing those contributions into strong managerial literature paradigms, such an aim has been accomplished.
Augmented Reality Using Real-Object Tracking

Augmented reality applications in tourism usually use images as markers, such as brochures or information boards. However, it is still ineffective because users cannot interact directly with real-objects. This study aims to create applications that more interesting and interactive way which can be used by interacting directly with real-object. This research is located in Taman Ayun Temple, one of the tourism objects that are world cultural heritage in Bali. The application is designed with the real-object tracking augmented reality method using the Wikitude Android SDK platform. The application is built in Indonesian and English. The test results show that the percentage of the recognition of 40 objects from various directions is more than 80%, from a distance of 10 to 40 meters is more than 70%, from a good and moderate lighting condition is more than 80%, and recognize similar objects as a different object.
Ableton Live 11

Review| April 01 2021 Ableton Live 11 Ableton Live 11. Ableton AG. URL: http://ableton.com Ethan Hein Ethan Hein ETHAN HEIN is a Doctoral Fellow in Music Education at New York University. He teaches music education, technology, and theory at NYU and the New School. He is coauthor of Electronic Music School: A Contemporary Approach to Teaching Musical Creativity (Oxford University Press, forthcoming in 2021). He worked with the NYU Music Experience Design Lab to develop the Groove Pizza, which has been used by over one million people around the world. Search for other works by this author on: This Site PubMed Google Scholar Journal of the American Musicological Society (2021) 74 (1): 214–225. https://doi.org/10.1525/jams.2021.74.1.214 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Ethan Hein; Ableton Live 11. Journal of the American Musicological Society 1 April 2021; 74 (1): 214–225. doi: https://doi.org/10.1525/jams.2021.74.1.214 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search nav search search input Search input auto suggest search filter All ContentJournal of the American Musicological Society Search Ableton Live is a widely used digital audio workstation (DAW).1 The program’s name refers to its intended use as a performance instrument for electronic musicians, a way to literally play the studio as an instrument in real time. This practice aligns with Brian Eno’s concept of “the studio as compositional tool.”2 Ableton Live’s main user base consists of electronic dance music and hip-hop producers, and it is also becoming common in experimental and academic circles. For example, the composer Morton Subotnick uses Live on stage to loop and process the output of his modular synthesizer. At the New School in New York where I teach, Live is the default DAW among students and faculty alike. This review evaluates Live’s utility for research and teaching in music studies. Some of Live’s useful features are generic to all DAWs: visualizing and playing back audio and MIDI data; segmenting, annotating, and... You do not currently have access to this content.
End-to-end Biomedical Entity Linking with Span-based Dictionary Matching

Disease name recognition and normalization , which is generally called biomedical entity linking, is a fundamental process in biomedical text mining.Recently, neural joint learning of both tasks has been proposed to utilize the mutual benefits.While this approach achieves high performance, disease concepts that do not appear in the training dataset cannot be accurately predicted.This study introduces a novel end-to-end approach that combines span representations with dictionary-matching features to address this problem.Our model handles unseen concepts by referring to a dictionary while maintaining the performance of neural network-based models, in an end-to-end fashion.Experiments using two major datasets demonstrate that our model achieved competitive results with strong baselines, especially for unseen concepts during training.
Lecturers’ Organizational Citizenship Behaviours During COVID-19 Pandemic

Abstract: A limited number of studies indicate that organizational citizenship behaviour (OCB) have the potential to contribute to organizational and individual performance. Educators who have positive behaviours such as helping behaviour can help facilitate the learning process. With the spread of COVID-19 pandemic which has led to the growth of online learning, OCBs among educators is needed in facilitating students’ learning process. This study was conducted to examine the practice of OCB among lecturers from an institution of higher learning who are currently using online learning as the platform of teaching. Based on the input obtained from 136 students, the results found that lecturers are indeed showing high levels of OCB. These students have reported a total of forty behaviours classified as OCB among lecturers. In terms of implications, lecturers’ OCB are seen to be able to support the online learning process which is a new method for most students.&#x0D; &#x0D; Keywords: Academic performance, COVID-19, e-learning, Organizational citizenship behaviours, pandemic.