Reviewerly/exHarmony huggingface dataset:
Social Media Influence on Employee Performance

Purpose: This study investigates the influence of social media platforms such as: Facebook, Linked and Twitter on employee performance. Approach:To show the function and format of working of key variables, an illustrative paradigm was introduced in this study.Paradigm showed that social media affects the knowledge, productivity , and motivational level of employees of Majorel company, Wipro international company and Telus international company.Related data gathered from the selected companies employees in the Dublin office so that real scenarios can be introduced. Findings:The study shows that social media affects the working situation of Majorel company, Wipro international company and Telus international company employees.For exploring key issues, previous studies were thoroughly reviewed and introduced in this study.Primary gathered data was subjected to correlation and regression analysis.Practical implications: Illustrated that using social media by employees affects their skills, knowledge, productivity and motivational level. Originality:The current study determines the social media needs of employees and its usage for optimising their performance.It provides an effective and solid foundation for organisations to use in their upcoming training/coaching program.
The CORONA Model in Improving Students' Scientific Writing Skills: Is it Effective?

Scientific writing is one of the language skills that needs to be developed for students. But in reality, there are still many students who have problems with writing. Based on this phenomenon, it is necessary to present a model in scientific writing. One of the models presented in this study is the CORONA model. This study aims to analyze the validity and effectiveness of the CORONA model. This research was conducted through Focus Group Discussion (FGD) from language education experts. This study applies a weak experiment, which is a pre-experimental design with one group pre-test and post-test design. The research sample consisted of two groups, each of which consisted of 26 students. Prior to learning with the CORONA model, students were given a pre-test on scientific writing skills, as well as after learning was carried out students were given the same test. Data was collected using instrument validity sheets and tests. Data on the validity and reliability of the model were analyzed by expert agreement, while data on students' scientific writing skills were analyzed by pair t-test, n-gain, and two means similarity test. The results showed that: content and construct validity were valid each category with a score of 3.3 and 3.5; and reliable with a score of 85.90% and 85.53%. In addition, the results of the study also showed that there was a significant increase in students' scientific writing skills with the mean n-gain being in the high category and not different for the two groups.
The Effect of Reciprocal Teaching Learning Model and Self-Concept on Reading Comprehension Ability

The reading ability of students in the field is still very worrying. This is reinforced by the low ability of students to understand material or questions that require higher-order thinking skills. The low reading comprehension ability can be seen from the low literacy ability of students based on PISA rankings. This study aims to analyze the effectiveness of the reciprocal teaching model on students' reading comprehension skills in terms of students' self-concept levels. The approach used in this research is quantitative with a quasi-experimental method. The participants of this study were 100 elementary school students who were divided into two groups of 50 students each in the experimental and control groups. Data was collected through reading comprehension tests and observations. Data analysis was performed by two-way ANOVA. The results showed that the Reciprocal Teaching model gave a more significant contribution to students' reading comprehension skills compared to conventional methods, especially for students who had low self-concept. Based on the findings above, it is concluded that the Reciprocal Teaching learning model has an effect on students' reading comprehension skills. Both reciprocal and conventional learning models have the same effect on students who have high self-concept. The implication of this research is that teachers must choose a learning model that can accommodate all students, not only students with high academic abilities, but also students with low academic abilities.
Bikabepo Wabidaka Learning Media to Cultivate Students Character

The use of media in learning in schools is not the same, but must be adapted to the characteristics of each school, including at the elementary school level. One of the learning media that is suitable for use at the elementary school level is the Bikabepo Wabidaka learning media. This research is motivated by the lack of attitudes displayed by elementary school students in the learning process of civic education, so learning media is needed to cultivate the character of these students, one of which is through the Bikabepo Wabidaka media. The purpose of this study is to identify the development of learning media Bikabepo Wabidaka in elementary schools. The research method used is Research &amp; Development using Borg &amp; Gall development procedures which include: potential problems, data collection, product design, design validation, and design revision. The instrument used is a media feasibility validation questionnaire sheet. The results obtained, it can be said that the Bikabepo Wabidaka learning media in civic education lessons that have been developed deserves to be tested further to see the effectiveness in learning and building student character in elementary schools.
The Impact of Collaborative Connectivity on the Risk of Failure Cascades in Collaborative Transport Systems

Vertical collaboration for multi-modal and multi-carrier transportation chains bears great synergy potential, enabling the integration of otherwise isolated proprietary transport networks, and thus facilitating the flow of goods and creating a more efficient and flexible transport system. However, collaboration requires close alignment in the form of data exchange and integration of information systems, which creates dependencies and a risk of disruption through technical failure, cyber attacks, or organizational conflicts. Earlier works has shown that disruption in interdependent networks can propagate and lead to a cascade of failure, which casts doubt on the claim that more collaboration has a solely positive impact. This research aims at identifying and quantifying a trade-off in collaborative transport systems between synergies and vulnerabilities depending on the level of collaborative connectivity. Therefore, a multi-layer network model for collaborative transport systems is coupled with a model for propagation of intended or unintended data disruption, and the impact on network performance under varying levels of collaborative connectivity is observed for two classes of random networks as well as for the European intermodal transport service network. We find that increasing collaborative connectivity does not have a monotone effect on performance, but there is a threshold, beyond which additional collaborations have an adverse impact on the robustness of the system. Below this threshold, more collaborations have a mostly positive impact on performance, since unused synergy potential is high while the risk of disruption causing a cascade is low. Above it, failure cascades become larger and more likely while the marginal added synergies are diminishing. The formation of highly connected cliques of carriers in the collaboration network facilitates the propagation of failure and leads to a lower threshold beyond which vulnerabilities offset synergies.
Decentralizing Privacy: Blockchain Technology as a Solution

Through this paper I have tried to demonstrate a legal validity for blockchain technology under the GDPR as well as the CCPA. The value in blockchain technology is undeniable. It promotes transactional efficiency, and contributes towards transparency, neutrality and security in the digital world. It is also clear that these benefits come at a cost. They have fueled legal tensions that question the validity of their operation entirely (the recent events have certainly not helped in this regard). The challenge is much more significant in the original permissionless blockchain architecture, but the possibility of a permissioned chain does serve as an alternative solution to mitigate this challenge while continuing to support technological innovation.As far as the GDPR is concerned, the consumer is the EU citizen the regulation aims to protect. What should not occur is thorough dismissal of the technology pursuant to stringent and traditional interpretation of the GDPR. This is disproportionate and contrary to business and technological progression.The GDPR or the CCPA does not outlaw blockchain technology. Nor does it make it illegal to buy, sell and trade cryptocurrency. Instead, businesses and big corporations can implement, develop and adapt blockchain technology to innovate. However, in order to do so, it seems prudent to develop such in a permissioned (where responsibility and specific roles can be implemented) manner.Moreover, as the CCPA and the GDPR have a private right of action for breaches, a blockchainâ€™s ability to comply with the CCPA or GDPR will also affect its ability to build trust with consumers. Consumers wish to store their data on secure places; however, they may be sensitive with respect to their personal information and blockchains that can avoid civil penalties and private right of actions will likely save plenty of money and get additional customers. Therefore, complying with the CCPA and the GDPR will be good for businesses who implement blockchain technologies.
Applications of Bayesian Neural Networks in Outlier Detection

Anomaly detection is crucial in a variety of domains, such as fraud detection, disease diagnosis, and equipment defect detection. With the development of deep learning, anomaly detection with Bayesian neural networks (BNNs) becomes a novel research topic in recent years. This article aims to propose a widely applicable method of outlier detection (a category of anomaly detection) using BNNs based on uncertainty measurement. There are three kinds of uncertainties generated in the prediction of BNNs: epistemic uncertainty, aleatoric uncertainty, and (model) misspecification uncertainty. Although the approaches in previous studies are adopted to measure epistemic and aleatoric uncertainty, a new method of utilizing loss functions to quantify misspecification uncertainty is proposed in this article. Then, these three uncertainty sources are merged together by specific combination models to construct total prediction uncertainty. In this study, the key idea is that the observations with high total prediction uncertainty should correspond to outliers in the data. The method of this research is applied to the experiments on Modified National Institute of Standards and Technology (MNIST) dataset and Taxi dataset, respectively. From the results, if the network is appropriately constructed and well-trained and model parameters are carefully tuned, most anomalous images in MNIST dataset and all the abnormal traffic periods in Taxi dataset can be nicely detected. In addition, the performance of this method is compared with the BNN anomaly detection methods proposed before and the classical Local Outlier Factor and Density-Based Spatial Clustering of Applications with Noise methods. This study links the classification of uncertainties in essence with anomaly detection and takes the lead to consider combining different uncertainty sources to reform detection outcomes instead of using only single uncertainty each time.
End-to-End Data

Transferring data across networks is ultimately about delivering data to applications, which means we need to look at how data are represented on different machines. This is often referred to as the presentation layer of networking. There is a range of approaches for making sure that data produced by the sending application can be correctly interpreted by the receiving application, even when they are running on quite different types of computer system. An area that is particularly complex is the representation of multimedia data—audio, images, video—where there are trade-offs to be made between the fidelity of the representation and the number of bits that need to be sent. Multimedia compression has become very sophisticated and we provide an overview of the major approaches, including JPEG and MPEG.
Report on the 1st Early Career Researchers' Roundtable for Information Access Research (ECRs4IR 2022) at CHIIR 2022

The First Early Career Researchers Roundtable for Information Access Research Workshop , in conjunction with the Seventh ACM Conference on Human Information Interaction and Retrieval (CHIIR) 2022, looked into the future of research, collaborations, and self-development to ask the following. Where are the opportunities for researchers in a (post-)pandemic environment, especially for Early Career Researchers (ECRs)? What do we need to do to get there? Which practical implementations can the broader CHIIR community support? The workshop started with an invited talk. Instead of conventional paper presentations, the attendees discussed the lessons learned from working in a pandemic. This report, co-authored by the workshop's organisers and its participants, summarises the discussion. This report aims to provide the broader CHIIR community with feedback on the workshop and foster ideas raised by ECRs to support ECRs. Two primary outcomes are (i) ECRs are often enthusiastic about taking on roles within a community, but formal validation and recognition are needed for their efforts and (ii) that the role of a conference needs to be reevaluated optimising the benefits of attending the event. Date: 14 March 2022. Website: https://sites.google.com/view/ecrs4ir/home.
Report on the 44th European Conference on Information Retrieval (ECIR 2022)

The 44th European Conference on Information Retrieval (ECIR'22) was held in Stavanger, Norway. It represents a landmark, not only for being the northernmost ECIR ever, but also for being the first major IR conference in a hybrid format. This article reports on ECIR'22 from the organizers' perspective, with a particular emphasis on elements of the hybrid setup, with the aim to serve as a reference and guidance for future hybrid conferences. Date: 10--14 April, 2022. Website: https://ecir2022.org.
Fuzzy Dynamic Parameter Adaptation in the Mayfly Algorithm: Implementation of Fuzzy Adaptation and Tests on Benchmark Functions and Neural Networks

Inspired by the flight behavior and mating process of mayflies, the Mayfly algorithm combines the main advantages of swarm intelligence and evolutionary algorithms, resulting in better performance than the particle swarm algorithm. So, we proposed a modification of Mayfly algorithm by applying a fuzzy parameter adapter to be able to apply this to neural network problems. We were able to observe that the fuzzy adapter improves the speed of convergence of the mayfly algorithm and when applied to a neural network for the Mackey glass series, it manages to detect the optimal number of neurons of the hidden layer for the network architecture. However, when using the Mayfly algorithm to optimize the architecture of neural networks, the results do not improve much, so we can deduce that this metaheuristic method is not recommended (for the moment) for this type of optimization, due to the fact that the root mean square error did not get below 0.001 even using the modified Mayfly algorithm with the fuzzy adapter.
Deep Learning-Based Specific Emitter Identification

For various autonomous wireless network deployments, knowledge and analysis of the radio frequency (RF) environment have become essential. One such scenario is, presence of adversaries who are attempting to interrupt the RF communication by hostile behaviors such as jamming or spoofing. Specific emitter identification (SEI) is the associated with signal received from transmitter. In order to learn the specific high-dimensional properties that can be utilized as “fingerprints” for identifying and classifying the transmitters, the SEI takes advantage of the in-phase (I) and quadrature-phase imbalance (i.e., IQ imbalance) found in all transmitters. Machine learning approaches have gained popularity because they can analyze, learn and even anticipate the RF signals and associated properties that define an RF environment. In this paper, a deep learning (DL)-based system was proposed to identify and classify the RF transmitters. The real-time signals (IQ data) are collected from six transmitters: USRP-2900 (universal software radio peripheral), BladeRF, HackRF, FS-i6 transmitter, 3DR 433 MHz Radio Telemetry module, and TS835 5.8 GHz video transmitter. RF signals are received using USRP-2900. Finally, the RF transmitters are classified using convolutional neural network (CNN). The proposed system achieved 98.58% accuracy for three class transmitters and 93.75% accuracy for six class transmitters.
Virtual Machine Allocation Using Genetic-Based Algorithm in Cloud Infrastructure

Virtualization is the essence of cloud computing. It facilitates the providers by virtualizing the storage, servers and other resources which aids in providing the computing services on shared and demand basis via internet. Virtual machine allocation is critical to achieving this goal, which accounts for effective resource usage with minimum expenditure. An efficient allocation results in effective resource utilization thus reducing wastage. This article proposes a genetic-based method exploiting the dual chromosome representation technique. The primary objective is to lessen the imbalance in resource utilization by reducing resource wastage. Another aim is to limit the power usage of the data center by diminishing the active servers’ volume. Lastly, the algorithm is evaluated against other existing work corresponding to several metrics which is further supported by the simulation procedure. The analysis from the result obtained displays a better performance of the algorithm by improving resource wastage, active server’s volume, CPU, and memory utilization.
Data Format Heterogeneity in IoT-Based Ambient Assisted Living: A Survey

Ambient Assisted Living (AAL) has become a significant component of the lives of the elderly in the present decade, allowing them to live independently by assisting their daily activities with automation. Different sensors from various manufacturers with proprietary data formats to detect environmental changes and monitor a person’s health metrics. These data formats are the root cause of the data Heterogeneity issue in AAL and, in turn, contribute to data interoperability challenges. In this paper, we have presented a survey on currently available state-of-the-art solutions to address data heterogeneity challenges in AAL and made a comparative study of suggested methods to overcome the data interoperability.
Schoenberg correspondence for k-(super)positive maps on matrix algebras

We prove a Schoenberg-type correspondence for non-unital semigroups which generalizes an analogous result for unital semigroup proved by Schürmann (in: Quantum probability and applications II, proceedings of a 2nd workshop, Heidelberg/Germany 1984, lecture notes in mathematics, vol 1136, pp 475–492, 1985). It characterizes the generators of semigroups of linear maps on $$M_n(\mathbb {C})$$ which are k-positive, k-superpositive, or k-entanglement breaking. As a corollary we reprove Lindblad, Gorini, Kossakowski, Sudarshan’s theorem (J Math Phys 17:821, 1976; Commun Math Phys 48:119-130, 1976). We present some concrete examples of semigroups of operators and study how their positivity properties can improve with time.
Dinosaur nursery discovered in India

Creating absorbent liquids seemed impossible. But now they are here and they could be surprisingly useful, says Katharine Sanderson
Preface – Special issue: Random dynamics

Stochastics and DynamicsVol. 22, No. 08, 2202004 (2022) No AccessPreface – Special issue: Random dynamicsHongjun Gao, Björn Schmalfuss, and Wei WangHongjun GaoSchool of Mathematics, Southeast University, Nanjing, P. R. China, Björn SchmalfussInstitut für Mathematik, Friedrich-Schiller-Universität Jena, Germany, and Wei WangDepartment of Mathematics, Nanjing University, Nanjing, P. R. ChinaE-mail Address: [email protected]Corresponding author.https://doi.org/10.1142/S021949372202004XCited by:0 Next This article is part of the issue: Special Issue: Random Dynamics Guest Editors: Hongjun Gao, Björn Schmalfuss and Wei Wang AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Dedicated to the 60th birthday of Professor Jinqiao Duan FiguresReferencesRelatedDetails Recommended Vol. 22, No. 08 Metrics History Received 27 December 2022 Accepted 27 December 2022 Published: 26 January 2023 PDF download
Introduction to blockchain in supply chain management

In a volatile, uncertain, complex, and ambiguous world, all businesses are dealing with challenges and believe that only disruptive technologies can help in preparing for the future. Blockchain technology has several applications across various industries while it has tremendous potential to revolutionize supply chain industry whose market is expected to grow rapidly at a rate of 85.9% in the next five years. This chapter provides an overview of blockchain and the various components it is composed of. This chapter guides us on the working of blockchain and the consensus algorithms used to enhance system security. An insight of the features of various platforms on which blockchain applications are built helps organizations in rightly choosing the development platform and thereby saving cost. There are various types of blockchain systems based on the accesses available to the players in the system. This chapter also provides an overview of various applications of blockchain in supply chain management which will lead us to expanding the solution to other potential areas.
Basics of blockchain technology for supply chain operations

Blockchain consists of a series of connected blocks, where the history of transactions can be easily traced via previous blocks to make the system transparent and reliable. Typically, blockchain technology is associated with cryptocurrencies like Bitcoin. End-to-end traceability of the supply chain is in trend now, example a lot of start-ups have done food traceability. There are organic foods and foods for supermarkets that go from the country where they were made to a supermarket in the US. It is a distributed database of transaction records that is authenticated and managed by a computer network from around the world. Bitcoin holds a digital ledger and is one of the most prominent blockchain technologies. Applications, Decentralized Ledger, and Peer-to-Peer Network make up the three core levels of the Blockchain architecture. All transactions within a blockchain are immutable and transparent, as they cannot be edited or deleted. This peer-to-peer innovation has opened up entirely new opportunities and offered a foundation for economic independence.