Reviewerly/exHarmony huggingface dataset:
Effect of Transformational Leadership on Employees’ Innovativeness and Job Satisfaction in Kuwait Private Sector

Literature on leadership identifies that current transformational leadership styles are related to job satisfaction by varying degrees. Several studies have indicated that transformational leadership results in higher levels of job satisfaction. In consideration of the current Kuwait business market, the overall aim of the study was to investigate the relationship between transformational leadership styles and employees’ innovativeness and job satisfaction levels within the Kuwait private sector. A quantitative study was conducted to accomplish the objectives of the study. Questionnaires were administered to employees within the selected target population in the Kuwait private sector. The data received were analysed using statistical packages. The results of the study show that there are statistically significant relationships between overall transactional leadership, employees’ innovativeness and job satisfaction, overall transformational leadership and job satisfaction. This study is one of the limited research focusing on Kuwait private sector to help enhance the understanding of how best to manage and retain the employees in the private sector. These findings are also useful for private sector in the Gulf region to maintain the work-life balance of affective commitments, leading to positive in-role performances.
Generalized Variational Continual Learning

Continual learning deals with training models on new tasks and datasets in an online fashion. One strand of research has used probabilistic regularization for continual learning, with two of the main approaches in this vein being Online Elastic Weight Consolidation (Online EWC) and Variational Continual Learning (VCL). VCL employs variational inference, which in other settings has been improved empirically by applying likelihood-tempering. We show that applying this modification to VCL recovers Online EWC as a limiting case, allowing for interpolation between the two approaches. We term the general algorithm Generalized VCL (GVCL). In order to mitigate the observed overpruning effect of VI, we take inspiration from a common multi-task architecture, neural networks with task-specific FiLM layers, and find that this addition leads to significant performance gains, specifically for variational methods. In the small-data regime, GVCL strongly outperforms existing baselines. In larger datasets, GVCL with FiLM layers outperforms or is competitive with existing baselines in terms of accuracy, whilst also providing significantly better calibration.
British Muslims Caught Amidst FOGs—A Discourse Analysis of Religious Advice and Authority

This paper discusses the symbolic capital found within Islamic documents that were circulated in the UK during the COVID-19 outbreak. Specifically, the work explores “fatwas” and “other” similar documents as well as “guidance” documents (referred to as FOGs) that were disseminated in March–April 2020 on the internet and social media platforms for British Muslim consumption. We confine our materials to FOGs produced only in English. Our study takes its cue from the notion that the existence of a variety of documents created a sense of foggy ambiguity for British Muslims in matters of religious practice. From a linguistic angle, the study seeks to identify (a) the underlying reasons behind the titling of the documents; and (b) the construction of discourses in the documents. Our corpus-assisted critical discourse analysis (CA-CDA) found noticeable patterns that hold symbolic capital in the fatwa register. We also found that producers of “other” documents imitate the fatwa register in an attempt to strengthen the symbolic capital of their documents. Accordingly, fatwas act as the most authoritative documents in religious matters and are written by senior religious representatives of the Muslim community, whereas guidance documents were found to be most authoritative in health matters. The findings raise questions regarding the manner in which religious instruction may be disseminated in emergency situations. Based on this study, a call for the standardisation and unification of these diverse and sometimes contradicting religious publications may be worth considering.
SRCM: A Semi Formal Requirements Representation Model Enabling System Visualisation and Quality Checking

Requirements engineering is pivotal to the successful development of any given system.The core artifact for such phase is the requirements specification document.Requirements can be specified in informal, semiformal, and formal notations.The majority of the requirements across many fields and domains are written natural language.However, natural language is inherently ambiguous and imprecise and the requirements cannot be automatically validated.Formal notations on the other hand enable automated testing and validation but is only comprehensible by experts and requires rewriting the requirements.Semi-formal notations strikes a good balance between comprehension and checking for several systems.However, the majority of the existing representation models mandates the requirements to be (re)written to adhere to certain templates.They also do not support automated checking.In this paper, we present SRCM -a semi-formal requirements representation model based on a comprehensive requirements capturing model (RCM) that does not enforce much limitations on how the requirements can be written.We also provide an automated approach to construct SRCM from RCM.In addition to providing a unified visualisation of the system entities and relations between the requirements key components, SRCM also enables automated quality checking on the requirements.
Deteksi Objek Menggunakan Metode Single Shot Multibox Detector Pada Alat Bantu Tongkat Tunanetra Berbasis Kamera

Blind people have difficulty in navigating due to the limited sensing they are capable of. In this research, we design a stick tool that can distinguish objects in the form of humans, animals and inanimate object based on camera. Processing is carried out with the Raspberry Pi with a webcam camera as input and indicators in the form of a buzzer and vibrator. The feature extraction process is carried out by deep learning using the tensorflow library and image processing using the Single Shot MultiBox Detector (SSD) method. Tests were carried out on human objects, animals (cats), and inanimate objects (chairs and tables) for indoor and outdoor conditions and obtained an accuracy of 92%, a sensitivity of 83%, and a specificity of 100%.
Tropical convective cloud growth models for hydrometeorological disaster mitigation in Indonesia

Heavy rain in Semarang on 1 January 2020 caused flooding. The existence of heavy rain cannot be separated from the dynamics of weather parameters which are closely related to the convection process and cloud formation. In this paper research has been conducted using Weather Research and Forecasting (WRF), to analyze the growth patterns of convective clouds as an effort to mitigate the initial events of extreme rainfall disasters. Several weather parameters that support cloud growth are very significant, namely the parameter values ​​that support the convection process to form convective clouds (Cumulonimbus). The results of the model show that there has been extreme rainfall which can be used as initial mitigation of hydrometeorological disasters.
ACCOUNTING INFORMATION SYSTEM FOR PURCHASING, SALES, AND SERVICE (CASE STUDY OF PT. GM MOTOR PONTIANAK)

PT. GM Motor Pontianak is a company engaged in the sale and service of motorbikes. Service transaction data processing and motorbike sales are still managed in a simple way, namely recording transactions into a ledger and copied to Microsoft Excel for recapitulation or report presentation. The main problem that becomes an obstacle for the company is that the admin as a transaction data processor often makes mistakes and delays in making a recapitulation of monthly sales transactions, as well as errors in making financial reports. Therefore, this study discusses the design of an accounting information system that provides facilities to two users, namely the Admin and the Director. The admin can manage account data, spare part brands, spare part data, mechanical data, tariff data, account data, spare part purchase transactions, spare part sales transactions, service transactions, sales, and expense transactions. The director can process user data, access spare part stock reports, purchase reports, sales reports, service reports, mechanical incentive reports, general journal reports, general ledger reports, trial balance reports, and income statements. The system built is expected to help PT. GM Motor Pontianak in managing purchases, sales, and service transactions and producing financial reports in accordance with financial accounting standards.
Visualizing Data Using Augmented Head Up Displays in Surgery

Several issues, circumstances and hurdles regarding the existing and the possible vulnerabilities in the medical field i.e. surgical field specifically has been a matter of concerned and discussion since years. The risk and disadvantages that might come along during surgery is unavoidable and there are evidences of numerous surgical accidents leading towards death. To mitigate these vulnerabilities, augmented wearable devices were introduced. One can see lots of improvements and success in the field of surgery with the help of these wearable devices. However, shortcomings and limitations still exist in even with these devices. The main objective of this research paper is to point out the specific issues that comes along with the use of these wearable devices and analyses the root cause behind this. Numerous journals, articles and papers were thoroughly read, analyzed and evaluated in order to find the possible solution. Different kinds of issues were figured out on the references of the journals out of them some were complicated. I have collected lots of information and data to analyses the importance and the magic these devices have done so far in the field of surgery. Along with the achievements, I have figured out some difficult issues and circumstances as well. After analyzing all these papers, we came to the conclusion that there are lots of works researchers are still doing for the upliftment and betterment of these devices in the field of medicine.
IDENTIFIKASI KESULITAN MAHASISWA DALAM PENYUSUNAN SKRIPSI PRODI PGSD UNIVERSITAS MATARAM

This qualitative research aims to identify the difficulties experienced by students in completing their paper assignments. This research was conducted on 144 students and alumni of PGSD University of Mataram. This research was conducted during the Covid-19 pandemic, so the research was conducted online. Data collection tools in this study were questionnaires and interviews. The research data analysis was conducted qualitatively. Based on the results of questionnaires and interviews conducted, several factors were identified that cause student to experience difficulties in completing paper, including individuals themselves, student agencies, families, and pandemic conditions. Based on the research results obtained, it takes several efforts that must be made by the institution in solving the problem of paper preparation, including strengthening cooperation between thesis supervisors, academic supervisors, validator lecturers, and completing and adding reference sources in institutional libraries.
A DNA Cryptographic Solution for Secured Image and Text Encryption

In recent days, DNA cryptography is gaining more popularity for providing better security to image and text data.This paper presents a DNA based cryptographic solution for image and textual information.Image encryption involves scrambling at pixel and bit levels based on hyperchaotic sequences.Both image and text encryption involves basic DNA encoding rules, key combination, and conversion of data into binary and other forms.This new DNA cryptographic approach adds more dynamicity and randomness, making the cipher and keys harder to break.The proposed image encryption technique presents better results for various parameters, like Image Histogram, Correlation co-efficient, Information Entropy, Number of Pixels Change Rate (NPCR), and Unified Average Changing Intensity (UACI), Key Space, and Sensitivity compared with existing approaches.Improved time and space complexity, random key generation for text encryption prove that DNA cryptography can be a better security solution for new applications.
Review of Taylor, Sharma, Martin, and Jameson’s Data Justice and COVID-19: Global Perspectives

Cinnamon reviews Data Justice and COVID-19: Global Perspectives edited by Linnet Taylor, Gargi Sharma, Aaron Martin and Shazade Jameson.
Side-Channel Leakage Detection with One-Way Analysis of Variance

Side-channel analysis (SCA) is usually used for security evaluation to test the side-channel vulnerability of a cryptographic device. However, in practice, an analyser may need to cope with enormous amounts of side-channel measurement data to extract valuable information for SCA. Under the circumstances, side-channel leakage detection can be used to identify leakage points which contain secret information and therefore improve the efficiency of security assessment. This investigation proposes a new black-box leakage detection approach on the basis of the one-way analysis of variance (ANOVA). In accordance with the relevance between leakage points and inputs of a cryptographic algorithm, the proposed method divides side-channel samples into multiple classes and tests the difference among these classes by taking advantage of the one-way ANOVA. Afterwards, leakage points and nonleakage points can be distinguished by determining whether the null hypothesis is accepted. Further, we extend our proposed method to multichannel leakage detection. In particular, a new SCA attack with a <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>F</mi> </math> -statistic-based distinguisher is capable of developing if the input of the leakage detection approach is replaced by a sensitive intermediate variable. Practical experiments show the effectiveness of the proposed methods.
Academic Mobility Assessment: the Strengths and Limitations of Different Approaches

The article discusses methodological approaches to measuring academic mobility when a researcher changes his place of work within the academic environment. Researchers’ academic mobility is a factor of their productivity, it provides knowledge transfer, renews scientific ideas, and in general contributes to the active development of higher education and science. However, in Russia the phenomenon of academic mobility is not studied sufficiently and demands an estimation of both mobility as a whole, and its factors, and its consequences. There are not many data sources on mobility, and getting corresponding information is often problematic – these factors make it difficult to empirically measure researchers’ academic mobility. This review might be the first step towards filling the gap in empirical research on academic mobility in Russia. The author suggests a detailed analysis of academic mobility assessment methodologies, including surveys, CVs, bibliometric and combined approaches. The article summarizes the experience of practical approaches application, identifies advantages and disadvantages of each of them, and provides their comparative analysis. The author comes to the conclusion that in the absence of a unique approach to measuring the academic mobility, the bibliometric approach, which provides large representative samplings, research results reproducibility, and greater possibilities of mobility factors assessment, best meets the goals of modern research on causality of academic mobility. This paper should help those who study science and higher education in selecting the most suitable approach to academic mobility measurement and in considering a number of nuances of each approach as practically applied in the research planning process.
A GMM supervector approach for spoken Indian language identification for mismatch utterance length

Gaussian mixture model-universal background model (GMM UBM) supervectors are used to identify spoken Indian languages. The supervectors are calculated from short-time MFCC, its first and sec derivatives. The UBM builds a generalized Indian language model, and mean adaptation transforms it to a duration normalized language-specific GMM. Multi-class support vector machine and artificial neural network classifiers are used to identify language labels from the supervectors. Experimental evaluations are performed using 30 sec speech utterances from nine Indian languages comprised five Indo-Aryan and four Dravidian languages, extracted from all India radio broadcast news data-set. Eight smaller duration data-sets were manually derived to study the effect of training and test duration mismatch. In mismatch conditions, identification accuracy decreases with a decrease in test and train utterance duration. Investigations showed that the 32-mixture model with ANN classifier has optimal performance.
Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language

Current NLP datasets targeting ambiguity can be solved by a native speaker with relative ease.We present Cryptonite, a large-scale dataset based on cryptic crosswords, which is both linguistically complex and naturally sourced.Each example in Cryptonite is a cryptic clue, a short phrase or sentence with a misleading surface reading, whose solving requires disambiguating semantic, syntactic, and phonetic wordplays, as well as world knowledge.Cryptic clues pose a challenge even for experienced solvers, though top-tier experts can solve them with almost 100% accuracy.Cryptonite is a challenging task for current models; fine-tuning T5-Large on 470k cryptic clues achieves only 7.6% accuracy, on par with the accuracy of a rule-based clue solver (8.6%).
An Efficient Query Optimizer with Materialized Intermediate Views in Distributed and Cloud Environment

In cloud computing environment hardware resources required for the execution of query using distributed relational database system are scaled up or scaled down according to the query workload performance. Complex queries require large scale of resources in order to complete their execution efficiently. The large scale of resource requirements can be reduced by minimizing query execution time that maximizes resource utilization and decreases payment overhead of customers. Complex queries or batch queries contain some common subexpressions. If these common subexpressions evaluated once and their results are cached, they can be used for execution of further queries. In this research, we have come up with an algorithm for query optimization, which aims at storing intermediate results of the queries and use these by-products for execution of future queries. Extensive experiments have been carried out with the help of simulation model to test the algorithm efficiency
Research on Android Multi-classification Based on Text

Abstract In recent years, more and more malicious applications have appeared on mobile application platforms, and they are often disguised as social, communication, and game applications. If we classify applications by category when detecting malware, which can improve the accuracy of malware detection. Classification of applications’ categories requires a large number of high-quality samples, but labels of applications’ categories are vary widely in different app stores, and samples of the same function type cannot be obtained quickly and efficiently. This thesis proposes a method for constructing a multi-classification model by using the text content of application description information, and guiding the classification of application by the category of application description. This method collects the description of an application in different app stores, predicts the category of the description through the classification model, and obtains the application category by voting. The model is based on CNN and RNN, and its F1-score is about 3% higher than the text classification model such as textCNN, LSTM. Its training prediction time and memory consumption are only 6% higher than that of textCNN and LSTM models. We named it CRNN, this thesis constructs a data set that can be used for application classification. The data set is classified using application description to obtain each application description and its category.
Implementasi Metode K-Means pada Hasil Produksi Daging Jenis Ternak

Meat production results should have good quantity and quality. To increase meat production, of course it is necessary to look at healthy types of livestock. Meat continues to increase in line with the increase in population, community income, education, standard of living and awareness of the nutritional value of animal production. The need for livestock meat production is one of the driving factors for the economy in Indonesia. This research can provide and input to the local government which is the leading producer of meat for the type of livestock in North Sumatra province and as a basis for making policies to increase meat production for other provinces. The method used in this research is the K-Means Algorithm. Where K-Means is one of the Algorithms in Data Mining that can be used to group data clusters. So that the data from 33 districts / cities will be divided into 2 clusters where cluster 1 is the high group, while cluster 2 is the low group. The results obtained from the study show that the results of manual calculation Algorithms and Microsoft Excel data have the same value, namely high cluster 1 and low cluster 32, and entering Microsoft Excel calculations into rapidminer has the same value as well
PENGEMBANGAN BAHAN AJAR EKONOMI BERBASIS PROBLEM BASED LEARNING DALAM BENTUK BUKU SAKU DIGITAL

Teaching materials are educational resources for transferring knowledge, but the ineffectiveness of teaching materials can be an inhibitors in achieving the learning objectives. The research on development digital pocket books based on Problem Based Learning (PBL) aimed to explain the level of feasibility; analyzed effectiveness through learning outcomes; as well as knowing the practicality of using digital pocket books in the learning process. Type of the research was a R&amp;D (Research and Development) used the ADDIE model by Dick &amp; Carry. The research procedure consisted of five stages, analysis, design, development, implementation, and evaluation. The research subjects were 20 students of class XI Science. Data collection techniques through interviews, questionnaires, and tests. Data analysis techniques are (1) expert analysis and validation; (2) student questionnaire responses; and (3) gain score calculation. The results showed that the digital pocket book "very feasible" was used by getting a percentage score of eligibility of 83.77% and practicality of 88%, as well as getting a gain score of 0.64 with the criteria of "moderate". Based on these results, PBL-based digital pocket books are declared very feasible and practical to use during the learning process because it can increase the learning outcomes. Keywords : Teaching Materials, Digital Pocket Book, Economic, Problem Based Learning
Time-Memory Analysis of Parallel Collision Search Algorithms

Parallel versions of collision search algorithms require a significant amount of memory to store a proportion of the points computed by the pseudo-random walks. Implementations available in the literature use a hash table to store these points and allow fast memory access. We provide theoretical evidence that memory is an important factor in determining the runtime of this method. We propose to replace the traditional hash table by a simple structure, inspired by radix trees, which saves space and provides fast look-up and insertion. In the case of many-collision search algorithms, our variant has a constant-factor improved runtime. We give benchmarks that show the linear parallel performance of the attack on elliptic curves discrete logarithms and improved running times for meet-in-the-middle applications.