Reviewerly/exHarmony huggingface dataset:
Unboxing Employees Perspectives on Factors Affecting Their Compliance to Organizational Information Security Policies

Because information is becoming an increasingly vital aspect of the company, policies to secure it are required. Each stakeholder's norms, duties, and obligations are listed in the Information Security Policy. Organizations still have a tendency to ignore information security policies. The authors of this study seek to determine what factors influence compliance with information security policies. The Awareness and Attitude components are the ones investigated in this study. According to the findings of the research, the factors of awareness and attitude had a substantial beneficial impact on the intention to follow information security policies. The Attitude component was likewise found to have a stronger influence than the Awareness factor. This research employs quantitative techniques, including multiple linear regression analysis with SPSS software.
Federated Analytics Informed Distributed Industrial IoT Learning With Non-IID Data

The increasing concerns of communication overheads and data privacy greatly challenge the gather-and-analyze paradigm of data-driven tasks currently adopted by the industrial IoT deployments. The federated paradigm resolves this challenge by performing tasks collaboratively without uploading the raw data. However, the inherent data heterogeneity (skewness) of diverse industrial IoT data holders significantly degrades the performances of all kinds of federated industrial IoT learning tasks. Quantifying this skewness is non-trivial and cannot be solved by the existing federated learning techniques. In this paper, we propose a Federated skewness Analytics and Client Selection mechanism (FedACS) to quantify the data skewness in a privacy preserving way and use this information to help downstream federated learning tasks. FedACS provably estimates the skewness of the clients using the Hoeffding's inequality based on the distilled insights of edge data in the form of gradient. It then gracefully handles the drifting estimation and robustly selects clients with milder skewness using a novel dueling bandit approach. FedACS gains advantages in privacy preservation, infrastructure reuse, and optimized overheads. Extensive experiments on open datasets demonstrate that FedACS reduces the accuracy degradation by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 78.2%, and accelerates the FL convergence for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim 2.4\times$</tex-math></inline-formula> .
How to Apply Bloom’s Taxonomy to Operator Education in the Way of Human-Machine Cooperation?

Operator education is essential in the process industry to avoid catastrophic accidents caused by human error. The operators of modern industrial plants not only need professional operation skills, more importantly, understand the behavior of advanced machine algorithms, which has played a critical role in daily industrial practice. In this research, a novel operator education paradigm is proposed. First, a model-based industrial intelligence, called shadow operator, is developed, which combines the two-layer industrial model predictive control framework and human-machine cooperation principles. Second, a hierarchical human-machine cooperation model for process operator training based on Bloom's taxonomy is presented, in which Bloom's taxonomy is an educational psychology model. Finally, five identities of shadow operator, namely task performer, cooperation partner, operation advisor, safety supervisor, and trouble maker, based on the proposed hierarchical model are designed to provide dynamic interaction with human operators. An application case in the industrial gas industry is provided to illustrate the effectiveness of the paradigm.
College Students’ Emotion Analysis and Recognition System Based on SVM Model

This paper takes social network text as the research object, and introduces SVM model into emotion analysis and recognition system to effectively improve the accuracy of emotion classification of network text, master the development law of network public opinion, effectively study and judge college students’ social network information interaction behavior, and intervene in high-risk network information interaction behavior in real time, Guide college students to make correct use of network information resources and better grasp the ideological and behavioral state of college students and the law of network information interaction. Establish a fast and efficient crisis early warning and feedback system to make the psychological crisis prevention work achieve early detection, early prevention and early treatment, cultivate students’ psychological capital, strengthen psychological quality education, and effectively improve students’ mental health level.
Is Context All You Need? Non-contextual vs Contextual Multiword Expressions Detection

Effective methods of the detection of multiword expressions are important for many technologies related to Natural Language Processing. Most contemporary methods are based on the sequence labeling scheme, while traditional methods use statistical measures. In our approach, we want to integrate the concepts of those two approaches. In this paper, we present a novel weakly supervised multiword expressions extraction method which focuses on their behaviour in various contexts. Our method uses a lexicon of Polish multiword units as the reference knowledge base and leverages neural language modelling with deep learning architectures. In our approach, we do not need a corpus annotated specifically for the task. The only required components are: a lexicon of multiword units, a large corpus, and a general contextual embeddings model. Compared to the method based on non-contextual embeddings, we obtain gains of 15% points of the macro F1-score for both classes and 30% points of the F1-score for the incorrect multiword expressions. The proposed method can be quite easily applied to other languages.
User Acceptance of Modified Web Page Loading Based on Progressive Streaming

In times of the pandemic, it becomes more evident that our modern society relies heavily on the internet for most areas of life. As websites become more complex and their size increases, the amount of code slows down their loading speed, especially on mobile devices with poor network connectivity. Various improvements exist to optimize the code before and after delivering a web page to a client. However, the delivery and rendering itself was rarely examined. In this paper, we evaluate two new methods of loading and displaying websites faster, namely Text-First and Layout-First. Layout-First reduced the time until first contentful paint (FCP) on average from 281.75 s down to 6.43 s at 32 KB/s, a difference of more than 4.5 min. Text-First reached the FCP on average in 2.15 s at the same network speed. However, our user study revealed that not every technological improvement is well accepted by users. Results showed that users will wait longer if the layout is stable while loading the page. More than 85% of participants preferred the Layout-First method introduced in this paper.
MARF: User-Item Mutual Aware Representation with Feedback

As deep learning (DL) technologies have developed rapidly, many new techniques have become available for recommender systems. Yet, there is very little research addressing how users’ feedback for particular items (such as ratings) can affect recommendations. This feedback can assist in building more fine-grained user profiles, as not all raw clicks will truly reflect a user’s preference. The challenge of encoding such records, which are typically prohibitively long, also prevents research from considering using the whole click history to learn representations. To address these challenges, we propose MARF, a novel model for click prediction. Specifically, we construct fine-grained user representations (by considering both the multiple items browsed, and user’s feedback on them) and item representations (by considering browsing histories from multiple users, and their feedback). Moreover, the flexible up-down strategy is designed to avoid loading incomplete or overloaded historical information by selecting representative users/items based on their feedback records. A comprehensive evaluation on three large scale real-world benchmark datasets, showing that MARF significantly outperforms a variety of state-of-the-art solutions. Furthermore, MARF model is evaluated through an ablation study that validates the contribution of each component. As a final demonstration, we show how MARF can be used for cross-domain recommendation.
On the Generation of Adversarial Samples for Image Quality Assessment

We study the generation of adversarial samples to test, assess, and improve deep learning-based image quality assessment (IQA) algorithms. This is important since social media platforms and other providers rely on IQA models to monitor the content they ingest, and to control the quality of pictures that are shared. Unfortunately, IQA models based on deep learning are vulnerable to adversarial attacks. We created an adversarial sample image generation tool that generates aggressive adversarial samples having good attack success rates. We hope that it can be used to help IQA researchers assess and improve the robustness of IQA.
Towards Mitigating the Eye Gaze Tracking Uncertainty in Virtual Reality

We propose a novel algorithm to evaluate and mitigate the uncertainty of data reported by eye gaze tracking devices embedded in virtual reality head-mounted displays. Our algorithm first is calibrated by leveraging unit quaternions to encode angular differences between reported and ground-truth gaze directions, then interpolates these quaternions for each gaze sample, and finally corrects gaze directions by rotating them using interpolated quaternions. The real part of the interpolated quaternion is used as the certainty factor for the corresponding gaze direction sample. The proposed algorithm is implemented in the VRSciVi Workbench within the ontology-driven SciVi visual analytics platform and can be used to improve the eye gaze tracking quality in different virtual reality applications including the ones for Digital Humanities research. The tests of the proposed algorithm revealed its capability of increasing eye tracking accuracy by 25% and precision by 32% compared with the raw output of the Tobii tracker embedded in the Vive Pro Eye head-mounted display. In addition, the certainty factors calculated help to acknowledge the quality of reported gaze directions in the subsequent data analysis stages. Due to the ontology-driven software generation, the proposed approach enables high-level adaptation to the specifics of the experiments in virtual reality.
An Approach for Enhancing Security of Data over Cloud Using Multilevel Algorithm

Today, users work with many types of data whether it be text, audio, video, or picture file. Storing of such data is of crucial importance. The current trend that provides with easy storage and access to our data is cloud. Cloud computing is a way to manage all such data at a place and access them as and when required. With this storage, it is important that the data stored be secured. Security can be provided by means of encryption and decryption process. Many algorithms are used on cloud to provide security of such data. In this paper, a multilevel approach is proposed by using three levels of encryption. This is achieved by using a combined transposition technique at first level, followed by data encryption standard (DES) at second level and later by Blowfish at third level. With the increase in the number of encryption level, the security of data also increases as it takes more time to crack the algorithm. The analysis of proposed multilevel algorithm is done with DES and Blowfish based on encryption time, decryption time, and memory utilization.
An Intelligent Collaboration Framework of Iot Applications Based on Event Logic Graph

Realizing the dynamic collaboration between Internet of Things (IoT) services is the key to developing intelligent IoT applications. However, heterogeneity, changeable invocation logic, and spatial-temporal constraints of the IoT services are significant challenges. To solve these problems, a framework based on the event logic graph is proposed. It employs the event logic graph to describe the collaboration between IoT services and uses related methods to dynamically select appropriate services according to the spatial-temporal constraint. Specifically, event instances are first recognized from data generated by IoT services with a divide-and-conquer-based strategy. Next, event logic graphs are constructed based on the serial episode mining method. The related event prediction method is established for determining the service invocation logic. And a spatial-temporal constrained mechanism is designed to select the appropriate services. The proposed framework is applied to wheel manufacturing to promote autonomous production and is further compared with related works. The results show the feasibility of the proposed framework and its advantages in adaptability, dynamics, and spatial-temporal relevancy.
Effects of Binning on Logistic Regression-Based Predicted CTR Models

Binning is a process to group number of more or less continuous values into a smaller number of ‘bins’ based on certain criteria which results in handling high volume of data in computationally less expensive time. By adopting the process of binning we basically reduce the cardinality of the data. This paper proposes machine learning-based robust model which can work upon a high cardinality data-set and reproduce bins.
Decision-Based Median-of-Medians Filtering Algorithm to Denoise Salt-and-Pepper Noise in Colour Images

To achieve optimum results for image analysis tasks, the image must be devoid of noise, such as additive white Gaussian noise and salt-and-pepper. These noises corrupt images and render them unusable, hence making denoising techniques crucial for image analysis. One such technique to denoise salt-and-pepper is the median-based filtering algorithm. However, there is always a trade-off between an algorithm’s running time and the quality of its generated output(s). In this paper, a novel salt-and-pepper denoising algorithm is proposed to effectively denoise corrupted images with an excellent run time. The algorithm uses median of medians to estimate median value and mid-valuedecision-median techniques to achieve excellent running time and output. Compared to state-of-the-art denoising algorithms, our proposed algorithm achieved superior values in terms of running time and quality of denoised images. PSNR values of 41.0 and 37.97 were recorded for denoised Lenna images for the proposed algorithm and adaptive frequency median filter (AFMF), respectively, at 10% noise density. The proposed algorithm runs about 4,500 times faster than AFMF when images are corrupted with 10% noise. Restored images also showed that the proposed algorithm is able to handle colour bleeding better than the state-of-the-art impulse noise filters.
Kepemimpinan Situasional Lasa H.S. Perpustakaan Universitas Muhammadiyah Yogyakarta

The University of Muhammadiyah Yogyakarta (UMY) library is led by Lasa H.S. He has served as the Head of Library of UMY Yogyakarta since 2012 until now. So much various achievements on his leadership in the library and UMY librarians. Lasa H.S be come the Head of Library of UMY during these three periods became the focus of this study. This study uses a qualitative descriptive method using data from interviews, literature studies and documentation. The informants of this study were the head of the UMY library and librarian. The results of this study indicate that the leadership of Lasa H.S. included in the situational leadership style, which fulfills 4 (four) indicators, namely telling, selling, participating, and delegating. Lassa H.S. is a leader who has the characteristics of firm, ambitious, tireless in work but still considers the abilities of his subordinates. lassa. H.S. always provides motivation and support to his subordinates and is ready to help if there are things that are not understood by his subordinates.
New developments in relevance theory

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Peran Sistem OPAC Sebagai Katalog Online dalam Efisiensi Penyediaan Informasi di Era Pandemi

Since the COVID-19 outbreak was declared to have hit most countries, various changes in habits and new life orders have been implemented for people in the hemisphere, including Indonesia, in all fields.In an effort to break the chain of spread of the COVID-19 virus, the Indonesian government issued regulations ranging from online school learning to working from home (WFH) for workers.The impact given is felt in all circles and aspects of society.One of them is the library.Activities in the library should be minimized more than usual due to the existing social restriction policies.Libraries must be able to innovate so that the provision of information can continue efficiently even though there are social restrictions that limit the community's space for movement.The online catalog is one means that supports the efficiency of providing information in the library.It is the purpose of this paper to provide an understanding of the role online catalogs play in providing information during the pandemic.The research method used is a qualitative approach.The data collection technique used is a literature study, so that secondary data can be obtained.The results of the study show that online catalogs as information searchers can be a facility that is able to facilitate librarians in providing information as well as users in searching for information availability in the pandemic era where activities outside the home are limited.
Studia Linguistica Hungarica 33 (2021)

Studia Linguistica Hungarica was originally a yearbook of Eötvös Loránd University (ELTE), under the full title of Annales Universitatis Scientiarium Budapestiensis de Rolando Eötvös Nominatae, Sectio linguistica. It formed part of a collection of university yearbooks in various disciplines, and served the purpose of making the results of ELTE-based research in linguistics available to an international audience beyond the iron curtain. The first volume of the yearbook appeared in 1970, and a total of 26 volumes were published by 2005. From 1990, financial problems hindered year-by-year appearance.&#x0D; Throughout this period, Annales was edited by Prof. István Szathmári. The articles were written in a variety of languages including English, German, French, Latin, Russian, Spanish, and others. Thematically, they covered the most diverse fields of research on a wide range of languages.&#x0D; The journal now re-appears with a new title, new editorial and advisory boards, and under very different circumstances. Studia Linguistica Hungarica publishes peer reviewed papers with a thematic focus on Hungarian and a general theoretical and typological orientation. Contributions adopting a usage-based cognitive theoretical perspective are especially, but not exclusively, welcome. The thematic scope of the journal ranges from semantics, syntax, and phonology to pragmatics, text linguistics and stylistics, from both descriptive and historical viewpoints. A single issue is published per year, with papers written in English.
TRANSITIVITY ANALYSIS OF THE VIETNAMESE ECONOMIC CONTRACTS FROM THE PERSPECTIVE OF SYSTEMIC FUNCTIONAL GRAMMAR

The economic contract is a typical type of discourse that depends on the agreement between parties. This paper adopted the theoretical framework of Systemic Functional Grammar (SFG) to carry out a transitivity analysis of the ideational function in the Vietnamese economic contract in order to explore the features of this genre, a type of legal discourse. In the economic contract, there are six processes, among which the material process and existential process are dominantly used while the relational process, mental process, behavioral process, and verbal process, each just accounts for the small percentage. These processes are employed in order to express the parties’ experience through legislation of rights, duties, obligation, remedies that they reach during the negotiation. The dominance of such specific processes reflects some features of legal discourse such as conciseness, clearness, exactness.
Developing Wetland Contextual Interactive Learning Media on Numbers Using Drill and Practice Method

Textbooks used in schools today are available in both print and digital versions. The use of the digital version is not much different from the printed version, which is accessed page by page. The more technology develops, the more learning facilities and infrastructure are developed. Currently the availability of interactive technology that allows students and books to interact with each other. Interactive books can be designed to respond to student activities as a teacher responds to students. The purpose of this study was to develop interactive media on number material for class VII students with a drill and practice approach. The content of teaching materials has wetland content, including information on fruits, fish and handicrafts. This interactive media was developed using HTML, CSS, Javascript, Scratch, Mathjax, JSON, and Firebase technologies. The learning approach uses the drill and practice method. This interactive media was tested to organize online learning during the Covid-19 pandemic. Based on the results of the study, it was found that student learning outcomes showed the overall average score was above the KKM of the subjects, namely the average value of learning outcomes was 75.80 from the KKM of 75.00. The results of teacher and student responses showed a positive response to the developed interactive media.
Novel Block Cipher: Surrounding Elements Technique using Symmetric Key

In recent years, technology advanced rapidly. Therefore, ensuring the security of information transmitted over the Internet is necessary. To increase data security and confidentiality, different encryption algorithms were developed. In this study, to enhance security, a new symmetric key encryption algorithm is proposed by generating key blocks based on random numbers sent from a sender to a receiver. For encryption, the XOR operation is performed on the surrounding elements of the plaintext block with the key block and on the middle elements of the plaintext block with the new elements generated after the previous XOR operation. The proposed encryption technique was evaluated and the results show the efficiency of the proposed technology in generating keys. The proposed technique tested on different data sizes and results obtained that show the effectiveness and efficiency in the time of encryption and decryption.