Reviewerly/exHarmony huggingface dataset:
Cybercrime Awareness among Saudi Nationals: Dataset

The supplementary dataset presented in this paper was used to measure the level of cybersecurity awareness of cybercrime in Saudi Arabia, presented in detail in [1]. The data were collected during the period of August to October of 2019. The dissemination process took place via an online questionnaire. The survey has four main parts: Personal and skill information (10 questions), Cybersecurity Activities (7 questions), Cybercrime Consciousness (8 questions), and Case Reports (6 questions). Two protocols were employed to recruit participants: subject must be of Saudi nationality and older than 18 years old. A combination of purposive and snowball techniques was utilized to collect respondents via university emails from 27 Saudi universities and WhatsApp messages to people meeting the requirements, gathering a total of 1230 responses. The data can be used to inform responsible authorities in Saudi Arabia about their roles in solving anticipated problems, as well as raising the awareness through programs, training, and short courses.
MicroRec: Efficient Recommendation Inference by Hardware and Data Structure Solutions

Deep neural networks are widely used in personalized recommendation systems. Unlike regular DNN inference workloads, recommendation inference is memory-bound due to the many random memory accesses needed to lookup the embedding tables. The inference is also heavily constrained in terms of latency because producing a recommendation for a user must be done in about tens of milliseconds. In this paper, we propose MicroRec, a high-performance inference engine for recommendation systems. MicroRec accelerates recommendation inference by (1) redesigning the data structures involved in the embeddings to reduce the number of lookups needed and (2) taking advantage of the availability of High-Bandwidth Memory (HBM) in FPGA accelerators to tackle the latency by enabling parallel lookups. We have implemented the resulting design on an FPGA board including the embedding lookup step as well as the complete inference process. Compared to the optimized CPU baseline (16 vCPU, AVX2-enabled), MicroRec achieves 13.8~14.7x speedup on embedding lookup alone and 2.5$~5.4x speedup for the entire recommendation inference in terms of throughput. As for latency, CPU-based engines needs milliseconds for inferring a recommendation while MicroRec only takes microseconds, a significant advantage in real-time recommendation systems.
Penerapan Model Discovery Learning Berkolaborasi Google Classroom dan WhatsApp Group untuk Meningkatkan Kompetensi Siswa dalam Menulis Teks Eksplanasi

This study aims to describe the increase in activeness and learning outcomes of writing explanatory text in Indonesian subjects by implementing the Discovery Learning model through the online-based Whatsapp Group assisted by Google Classroom during the Covid-19 pandemic. The method used was classroom action research with a research design using the model of Kemmis and Mc Taggart which consisted of planning, implementing, observing, and reflecting stages. The subjects of this study were students of class VIII A SMP Negeri 3 Bojong, Pekalongan Regency. The technique of collecting data by means of interviews, observation, questionnaires and tests. While the data analysis technique used qualitative and quantitative descriptive analysis. The results of the research in the pre-cycle showed student activeness was 6.25%, in the first cycle it increased to 28.13%, and in the second cycle it increased by 15.62%. From the test results in the pre-cycle the average value was 69, in the first cycle it increased to 73, and in the second cycle it became 76. Students who achieved learning completeness in the pre-cycle 12,%, in the first cycle were 15.63, and in the second cycle it became 84.38%. It can be concluded that with the application of the Discovery Learning learning model through Google Classroom assisted by online-based Whatsapp Group during the Covid-19 pandemic, it can increase the activeness and learning outcomes of competency / explanatory text material for class VIII A students of SMP Negeri 3 Bojong.
APLIKASI DARING QUIZZIZ SEBAGAI SOLUSI PEMBELAJARAN MENYENANGKAN DI MASA PANDEMI

The purpose of this study is to describe alternatives to joy fun online learning using the quizziz application. The research method used is qualitative research. The study was conducted at vocational high school Miftahul Falah, Dawe District, Kudus Regency. Acquisition of data results using indept interviews sent via googleform and key informants to teachers dan students. Based on the results of the study it was concluded that (1) quizziz online learning application is a technology based learning evaluation application that can be accessed online by teachers and students, which is applied during a pandemic in the education sector in order to be able to balance and harmonize learning in school to be always relevant and enjoyable especially in Indonesian language learning content; (2) the existence of the quizziz application provides alternative online learning that can be done without having to face to face directly between the teacher and students and can be accessed outside and in the classroom; (3) quizziz provides advantages for online learning by utilizing learning changes that prioritize learning in the era of the industrial revolution 4.0 using information technology in its delivery, will be more fun, motivating, and attracting the attention of students. Thus, the use of quizziz can make the implementation of online learning no longer make a problem for students and is flexible in accordance with students abilities.
A Rebalancing Framework for Classification of Imbalanced Medical Appointment No-show Data

Abstract Purpose This paper aims to improve the classification performance when the data is imbalanced by applying different sampling techniques available in Machine Learning. Design/methodology/approach The medical appointment no-show dataset is imbalanced, and when classification algorithms are applied directly to the dataset, it is biased towards the majority class, ignoring the minority class. To avoid this issue, multiple sampling techniques such as Random Over Sampling (ROS), Random Under Sampling (RUS), Synthetic Minority Oversampling TEchnique (SMOTE), ADAptive SYNthetic Sampling (ADASYN), Edited Nearest Neighbor (ENN), and Condensed Nearest Neighbor (CNN) are applied in order to make the dataset balanced. The performance is assessed by the Decision Tree classifier with the listed sampling techniques and the best performance is identified. Findings This study focuses on the comparison of the performance metrics of various sampling methods widely used. It is revealed that, compared to other techniques, the Recall is high when ENN is applied CNN and ADASYN have performed equally well on the Imbalanced data. Research limitations The testing was carried out with limited dataset and needs to be tested with a larger dataset. Practical implications This framework will be useful whenever the data is imbalanced in real world scenarios, which ultimately improves the performance. Originality/value This paper uses the rebalancing framework on medical appointment no-show dataset to predict the no-shows and removes the bias towards minority class.
Impact of On Job Training, Training Design and Training Delivery Style on Organizations Performance

Organizations are struggling hard for the success and attainment of competitiveness utilizing skilled human resource. The particular problem discussed in this research is to determine the influence of training on performance of employee and organization within the education sector of Rawalpindi and Islamabad. For getting the primary data about the concepts of people, a survey through questionnaire comprising of 15 questions was carried out. The questionnaires were sent to 300 people, in Rawalpindi/ Islamabad. To depict a good representative of the study, the sample size was chosen randomly. The replies were collected on paper. The independent variables of the study were three in numbers (On job training, Training design, Delivery Style) and Organizational performance is a dependent variable which is being affected by these independent variables by mediation of employee's performance. The data has been tested on SPSS. To find the importance of these variables and to evaluate the results Cronbach’s Alpha, descriptive statistics, correlation, regression and ANOVA were used. The results show that the independent variables training design, delivery style and on the job training has positive and significant relationship with the dependent variable organizational performance by mediating variable employee’s performance and clearly depicts the strong variability among variables
The Complexity of Aggregates over Extractions by Regular Expressions

Regular expressions with capture variables, also known as regex-formulas, extract relations of spans (intervals identified by their start and end indices) from text. In turn, the class of regular document spanners is the closure of the regex formulas under the Relational Algebra. We investigate the computational complexity of querying text by aggregate functions, such as sum, average, and quantile, on top of regular document spanners. To this end, we formally define aggregate functions over regular document spanners and analyze the computational complexity of exact and approximate computation. More precisely, we show that in a restricted case, all studied aggregate functions can be computed in polynomial time. In general, however, even though exact computation is intractable, some aggregates can still be approximated with fully polynomial-time randomized approximation schemes (FPRAS).
Body Composition Analyses Require Compositional Data Analytic (CoDA) Methods

Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
Bitcontracts: Supporting Smart Contracts in Legacy Blockchains

In this paper, our main goal is to design a solution that adds expressive smart contract execution support as a subsystem to existing legacy blockchain systems.The primary usage of our solution is to enhance systems like Bitcoin that have no built-in smart contract capabilities.The secondary usage is to extend the contract execution capabilities of platforms like Ethereum that support contracts but have severe limitations on the complexity of allowed computations. Previous work.Recent research has explored different ways to add contract execution capabilities to blockchains.For instance, Arbitrum [25] and ACE [50] use off-chain execution models, where contract issuers appoint a set of managers who are responsible for executing the contract and communicating the results back to the chain.Hyperledger Fabric [5] uses a similar model in a permissioned setting with an execute-order-validate architecture in which transactions are executed before ordering.The main drawback of such solutions is that they are newly purpose-built systems, and therefore such systems cannot be deployed on legacy systems without modifying the underlying blockchain.Another proposal, FastKitten [16], relies on enclaved execution and collaterals, but only supports short-lived contracts that are restricted to known participants.In addition, such a system cannot tolerate enclave compromise.Recently discovered attacks [11], [45], [29], [13], [44] have shown that TEE compromise is a relevant threat.We discuss the limitations of previous solutions in more detail in Section II-B.Our solution.In this paper, we propose a novel system called Bitcontracts that adds expressive smart contract execution capabilities to legacy cryptocurrencies without requiring protocol changes to the legacy system, and overcomes the main limitations of previous solutions.The starting point of our solution is an off-chain execution model, similar to previous systems like Arbitrum, ACE, or Fabric.In Bitcontracts, the contract issuer appoints a set of service providers that execute the contract's code.The appointed execution set is recorded on the chain together with the contract's code and the contract participants are free to choose if they accept this set.Instead of requiring that all service providers agree on the execution result (as is done in Arbitrum) or trusting the execution environments fully (as is required in FastKitten), we leverage a more flexible quorumbased trust model similar to ACE, where execution results are accepted when t out of n service providers report the same result.Such a model can provide both strong security (up to t -1 service providers can be compromised) and good availability (up to n-t service providers can be unresponsive).
Performance of supply chain management and digitalization of human resource information in SMEs

The aim of this study is to determine the influencing factors of Supply Chain Management and digitalization of human resource information practices in the small and medium enterprises of Oman. Digitalization of human resource information is the adoption of electronic means to the human resource activities of Supply Chain Management. In this system, the whole human resource activities are implemented through electronic means with the support of information technology (IT) infrastructure. To measure the Digitalizationof Human Resource Information Practices and the performance level of Supply Chain Management, two adopted constructs from two different studies were used. The study samples were taken from the SMEs of Oman. Almost 180 employees of different provinces responded to the questionnaires translated into vernacular language through google forms and a few hardcopy surveys were distributed to different locations with the support of SMEs and few entrepreneurs. The study found a significant positive relationship between the Performance of SCM and the Digitalization of Human Resource Information of a prominent variable electronic communication and other variables were found with no significant relationship. The finding of the study acts as a significant contributor to the existing literature on SCM as well as human resource management.
Determining How Social Media Affects Learning English: An Investigation of Mobile Applications Instagram and Snap Chat in TESOL Classroom

The purpose of this research paper was to explore the effects of social media in learning English speaking and reading skills. Although social media platforms like Facebook, Instagram, Twitter, and Snapchat, are mostly used socially and for marketing, they also have a significant impact on learning English. In determining how social media affects learning English, this paper will be focusing on mobile Apps; Snapchat and Instagram. The paper investigates how user attitude, experience, and perception influence the use of Instagram and Snapchat in learning the English language in a classroom. The research was done on different databases, including Academic Search Complete, Education Source, ERIC, Library, Information Science, and Technology Abstracts, and Professional Development Collection. Research shows that learners with social media accounts are less enthusiastic about learning English, even if it means recreational reading. This differs from the learners without user accounts whose attitude of learning English speaking skills is positive. This research paper concludes that, in comparison to Snapchat, Instagram is a more effective social media platform for engaging and learning of the English language. This contributes to a greater understanding of the English language and its culture while providing more practical knowledge and promoting interactions. However, more research is needed to determine the impact of Snapchat on learning English speaking skills.
On Wavelet Decomposition of the Singular Splines

One of the approaches to the problem of approximating functions with a singularity is the creation of an approximating apparatus based on splines with the same feature. For the wavelet decomposition of spline spaces it is important that the property of the embedding of these spaces is associated with embedding grids. The purpose of this paper is to consider ways of constructing spaces of splines with a predefined singularity and obtain their wavelet decomposition. Here the concept of generalized smoothness is used, within which the mentioned singularity is generalized smooth. This approach leads to the construction of a system of embedded spaces on embedded grids. A spline-wavelet decomposition of mentioned spaces is presented. Reconstruction formulas are done
No NLP Task Should be an Island: Multi-disciplinarity for Diversity in News Recommender Systems

Natural Language Processing (NLP) is defined by specific, separate tasks, with each their own literature, benchmark datasets, and definitions. In this position paper, we argue that for a complex problem such as the threat to democracy by non-diverse news recommender systems, it is important to take into account a higher-order, normative goal and its implications. Experts in ethics, political science and media studies have suggested that news recommendation systems could be used to support a deliberative democracy. We reflect on the role of NLP in recommendation systems with this specific goal in mind and show that this theory of democracy helps to identify which NLP tasks and techniques can support this goal, and what work still needs to be done. This leads to recommendations for NLP researchers working on this specific problem as well as researchers working on other complex multidisciplinary problems.
Trajectory Planning For Autonomous Wheeled Mobile Robots With Trailer

This paper presents a dynamic optimization scheme for generating trajectory planning for autonomous wheeled mobile robots with tractor designed to accomplish missions in indoor environments. Such an optimization criterion problem requires a method that can yield a fast execution time and minimum traveling distance that contains geometrics, kinematics, and physical/environment constraints. The main goal is to develop optimal trajectories planning approach of an autonomous wheeled mobile robots with trailer for the execution of predefined tasks in structures environment. The developed approach can be considered as an extension of the Random Profiles Approach used for wheeled mobile robots. The results also illustrate that thanks to its time optimal trajectories planning, our scheme is well adapted to complex tasks as it can get shorter execution time for the autonomous holonomic tractor with a nonholonomic trailer.
MultiHumES: Multilingual Humanitarian Dataset for Extractive Summarization

When responding to a disaster, humanitarian experts must rapidly process large amounts of secondary data sources to derive situational awareness and guide decision-making.While these documents contain valuable information, manually processing them is extremely timeconsuming when an expedient response is necessary.To improve this process, effective summarization models are a valuable tool for humanitarian response experts as they provide digestible overviews of essential information in secondary data.This paper focuses on extractive summarization for the humanitarian response domain and describes and makes public a new multilingual data collection for this purpose.The collection -called MultiHumES -provides multilingual documents coupled with informative snippets that have been annotated by humanitarian analysts over the past four years.We report the performance results of a recent neural networks-based summarization model together with other baselines.We hope that the released data collection can further grow the research on multilingual extractive summarization in the humanitarian response domain.
A hybrid and regenerative model chat robot based on LSTM and attention model

Aiming at the situation that retrieval chat robot relies too much on predefined responses and the training requirements of generative chat robot are too high, a hybrid and regenerative model text chat robot based on LSTM and Attention-model is designed. Due to the retrieval model can only handle scenarios with predefined responses, and a generative model with strong learning ability will produce grammatical errors in certain scenarios. Therefore, firstly,doing text processing based on corpus, and then the retrieval model generates a candidate data set, and the candidate data set is trained by generating model to obtain the final model. The experimental comparison results show that the hybrid and regenerative model chat robot can effectively improve the model response quality compared to the single model chat robot, and accuracy improved by thirty percent.
A Moving Object Detection Method Using Deep Learning-Based Wireless Sensor Networks

Aiming at the problem of real-time detection and location of moving objects, the deep learning algorithm is used to detect moving objects in complex situations. In this paper, based on the deep learning algorithm of wireless sensor networks, a novel target motion detection method is proposed. This method uses the deep learning model to extract visual potential representation features through offline similarity function ranking learning and online model incremental update and uses the hierarchical clustering algorithm to achieve target detection and positioning; the low-precision histogram and high-precision histogram cascade the method which determines the correct position of the target and achieves the purpose of detecting the moving target. In order to verify the advantages and disadvantages of the deep learning algorithm compared with traditional moving object detection methods, a large number of comparative experiments are carried out, and the experimental results were analyzed qualitatively and quantitatively from a statistical perspective. The results show that, compared with the traditional methods, the deep learning algorithm based on the wireless sensor network proposed in this paper is more efficient. The detection and positioning method do not produce the error accumulation phenomenon and has significant advantages and robustness. The moving target can be accurately detected with a small computational cost.
Reuse-centric <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1921" altimg="si32.svg"><mml:mi>k</mml:mi></mml:math>-means configuration

K-means configuration is to find a configuration of k-means (e.g., the number of clusters, feature sets) that maximize some objectives. It is a time-consuming process due to the iterative nature of k-means. This paper proposes reuse-centric k-means configuration to accelerate k-means configuration. It is based on the observation that the explorations of different configurations share lots of common or similar computations. Effectively reusing the computations from prior trials of different configurations could largely shorten the configuration time. To materialize the idea, the paper presents a set of novel techniques, including reuse-based filtering, center reuse, and a two-phase design to capitalize on the reuse opportunities on three levels: validation, number of clusters, and feature sets. Experiments on k-means–based data classification tasks show that reuse-centric k-means configuration can speed up a heuristic search-based configuration process by a factor of 5.8, and a uniform search-based attainment of classification error surfaces by a factor of 9.1. The paper meanwhile provides some important insights on how to effectively apply the acceleration techniques to tap into a full potential.