Reviewerly/exHarmony huggingface dataset:
A Cognitive Framework in Learning English Progressive Tense

Dealing with the English language and the skills of using it is still the focus of interest for many researchers, teachers and workers in the field of education. Cognitive grammar theory founded by Langacker (1987) is one of the prominent theories in this field. Iraqi students face a problem in mastering and understanding the use of the English language tenses. There are a lot of researches that have been conducted in the Iraqi context to improve the level of Iraqi students and to benefit from cognitive theory such as analysing the polysemy of English preposition. This research is an empirical study in which 85 students from the College of Education for Women at the University of Baghdad, English department participated in this experiment. The data were collected through two tests, pre and post, and the SPSS statistical editor was used to measure the extent of improvement in the participants' performance, in addition to a focus group discussion and questionnaire before and after the experiment. The results showed an improvement in the students’ achievement. This indicates that the cognitive grammar theory has positive contributions in improving the understanding, assimilation and use of tenses in the English as a foreign language.
Quantum learning unravels quantum system

A quantum computer has a decisive advantage in analyzing quantum experiment results.
recommenderlab: An R Framework for Developing and Testing Recommendation Algorithms

Algorithms that create recommendations based on observed data have significant commercial value for online retailers and many other industries. Recommender systems have a significant research community, and studying such systems is part of most modern data science curricula. While there is an abundance of software that implements recommendation algorithms, there is little in terms of supporting recommender system research and education. This paper describes the open-source software recommenderlab which was created with supporting research and education in mind. The package can be directly installed in R or downloaded from https://github.com/mhahsler/recommenderlab.
VLUE: A Multi-Task Benchmark for Evaluating Vision-Language Models

Recent advances in vision-language pre-training (VLP) have demonstrated impressive performance in a range of vision-language (VL) tasks. However, there exist several challenges for measuring the community's progress in building general multi-modal intelligence. First, most of the downstream VL datasets are annotated using raw images that are already seen during pre-training, which may result in an overestimation of current VLP models' generalization ability. Second, recent VLP work mainly focuses on absolute performance but overlooks the efficiency-performance trade-off, which is also an important indicator for measuring progress. To this end, we introduce the Vision-Language Understanding Evaluation (VLUE) benchmark, a multi-task multi-dimension benchmark for evaluating the generalization capabilities and the efficiency-performance trade-off (``Pareto SOTA'') of VLP models. We demonstrate that there is a sizable generalization gap for all VLP models when testing on out-of-distribution test sets annotated on images from a more diverse distribution that spreads across cultures. Moreover, we find that measuring the efficiency-performance trade-off of VLP models leads to complementary insights for several design choices of VLP. We release the VLUE benchmark to promote research on building vision-language models that generalize well to more diverse images and concepts unseen during pre-training, and are practical in terms of efficiency-performance trade-off.
FDGNN: Fully Dynamic Graph Neural Network

Dynamic Graph Neural Networks recently became more and more important as graphs from many scientific fields, ranging from mathematics, biology, social sciences, and physics to computer science, are dynamic by nature. While temporal changes (dynamics) play an essential role in many real-world applications, most of the models in the literature on Graph Neural Networks (GNN) process static graphs. The few GNN models on dynamic graphs only consider exceptional cases of dynamics, e.g., node attribute-dynamic graphs or structure-dynamic graphs limited to additions or changes to the graph's edges, etc. Therefore, we present a novel Fully Dynamic Graph Neural Network (FDGNN) that can handle fully-dynamic graphs in continuous time. The proposed method provides a node and an edge embedding that includes their activity to address added and deleted nodes or edges, and possible attributes. Furthermore, the embeddings specify Temporal Point Processes for each event to encode the distributions of the structure- and attribute-related incoming graph events. In addition, our model can be updated efficiently by considering single events for local retraining.
Monocular Depth Estimation from a Single Infrared Image

Thermal infrared imaging is attracting much attention due to its strength against illuminance variation. However, because of the spectral difference between thermal infrared images and RGB images, the existing research on self-supervised monocular depth estimation has performance limitations. Therefore, in this study, we propose a novel Self-Guided Framework using a Pseudolabel predicted from RGB images. Our proposed framework, which solves the problem of appearance matching loss in the existing framework, transfers the high accuracy of Pseudolabel to the thermal depth estimation network by comparing low- and high-level pixels. Furthermore, we propose Patch-NetVLAD Loss, which strengthens local detail and global context information in the depth map from thermal infrared imaging by comparing locally global patch-level descriptors. Finally, we introduce an Image Matching Loss to estimate a more accurate depth map in a thermal depth network by enhancing the performance of the Pseudolabel. We demonstrate that the proposed framework shows significant performance improvement even when applied to various depth networks in the KAIST Multispectral Dataset.
Differential Trail Search in Cryptographic Primitives with Big-Circle Chi:

Proving upper bounds for the expected differential probability (DP) of differential trails is a standard requirement when proposing a new symmetric primitive. In the case of cryptographic primitives with a bit-oriented round function, such as Keccak, Xoodoo and Subterranean, computer assistance is required in order to prove strong upper bounds on the probability of differential trails. The techniques described in the literature make use of the fact that the non-linear step of the round function is an S-box layer. In the case of Keccak and Xoodoo, the S-boxes are instances of the chi mapping operating on l-bit circles with l equal to 5 and 3 respectively. In that case the differential propagation properties of the non-linear layer can be evaluated efficiently by the use of pre-computed difference distribution tables.Subterranean 2.0 is a recently proposed cipher suite that has exceptionally good energy-efficiency when implemented in hardware (ASIC and FPGA). The non-linear step of its round function is also based on the chi mapping, but operating on an l = 257-bit circle, comprising all the state bits. This making the brute-force approach proposed and used for Keccak and Xoodoo infeasible to apply. Difference propagation through the chi mapping from input to output can be treated using linear algebra thanks to the fact that chi has algebraic degree 2. However, difference propagation from output to input is problematic for big-circle chi. In this paper, we tackle this problem, and present new techniques for the analysis of difference propagation for big-circle chi.We implemented these techniques in a dedicated program to perform differential trail search in Subterranean. Thanks to this, we confirm the maximum DP of 3-round trails found by the designers, we determine the maximum DP of 4-round trails and we improve the upper bounds for the DP of trails over 5, 6, 7 and 8 rounds.
CIPP Evaluation Model and Its Effect on E-Learning

One indicator of the quality of educational institutions is the use of information technology in teaching and learning activities. E-learning is fundamentally a teaching and learning activity using internet-based technology. This research aims to evaluate teaching and learning activities using e-learning. This study utilized the CIPP (Context, Input, Process, Product) evaluation model related to e-learning. The analysis technique used was multiple linear regression using R Software to see how much effect context and Input had on the learning process and how much Context and Input had on the learning product. Based on the study results, knowledge and understanding of the Context component were of more significant concern since 63 (44.68%) of 141 cadets stated ‘poor’. The average score of knowledge and understanding of the Input component was 41, which was in score interval 33 – 42, included in the good category. Moreover, the average score of knowledge and understanding of the Process component was 82, which was in score interval 65 - 84, included in the good category. Importantly, knowledge and understanding of the Product component were of greater concern since 69 (48.94%) of 141 cadets showed poor results. The effect size of Context and Input components on the process was 0.5479 or 54.79%, while the remaining 45.21% was affected by other factors not examined in this study. The input component had a significantly greater effect on the Process component with a significance level of 0.001 than the Context component. Moreover, the effect size of Context and Input components on the Product was 0.3303 or 33.03%, while the remaining 66.97% was affected by other factors not examined in this study. The context component had a significantly greater effect on the Product with a significance level of 0.001 than the Input component.
OmniXAI: A Library for Explainable AI

We introduce OmniXAI (short for Omni eXplainable AI), an open-source Python library of eXplainable AI (XAI), which offers omni-way explainable AI capabilities and various interpretable machine learning techniques to address the pain points of understanding and interpreting the decisions made by machine learning (ML) in practice. OmniXAI aims to be a one-stop comprehensive library that makes explainable AI easy for data scientists, ML researchers and practitioners who need explanation for various types of data, models and explanation methods at different stages of ML process (data exploration, feature engineering, model development, evaluation, and decision-making, etc). In particular, our library includes a rich family of explanation methods integrated in a unified interface, which supports multiple data types (tabular data, images, texts, time-series), multiple types of ML models (traditional ML in Scikit-learn and deep learning models in PyTorch/TensorFlow), and a range of diverse explanation methods including "model-specific" and "model-agnostic" ones (such as feature-attribution explanation, counterfactual explanation, gradient-based explanation, etc). For practitioners, the library provides an easy-to-use unified interface to generate the explanations for their applications by only writing a few lines of codes, and also a GUI dashboard for visualization of different explanations for more insights about decisions. In this technical report, we present OmniXAI's design principles, system architectures, and major functionalities, and also demonstrate several example use cases across different types of data, tasks, and models.
Penerapan Metode Agile Scrum Pada Rancangan SisIAM4

Development of web based information systems is grown very rapidly, one of which can help the process of processing information and data, to improve performance in various fields, including in the academic. This research aims to design a web-based academic information system in schools to make it easier for Muhammadiyah 4 High School to manage student values, as well as improve students in seeing grades and schedules online. In making this website using the Hypertext Preprocessor (PHP) programming language, MySQL as a database and CodeIgniter framework, and Visual Studio Code as a medium for writing coding. The method used in making this application is Agile software development with a Scrum model and application testing using the White box testing method. With the creation of this information system is expected to help schools in managing academic data.
Bio-inspired BAS: Run-time Path-planning And The Control of Differential Mobile Robot

Trajectory tracking and obstacle avoidance lies at the heart of autonomous navigation for mobile robots. In this paper, a control architecture for trajectory tracking while avoiding obstacles and controller tuning is proposed for a differential drive mobile robot (DMR). The framework of optimization algorithm is inspired by the food search behavior of beetles using their antennae. Path planning and controller tuning remain computationally demanding tasks despite of the proposed algorithms existing today. Our bio inspired approach unifies these two problems by minimizing the respective cost functions and solving the optimization problem efficiently. Trajectory tracking problem is based on the difference of the current and next pose of the robot while obstacle avoidance is achieved on the principle of maximizing the minimum distance between the robot and obstacle in the path of the robot. The proposed architecture is simulated in V-REP environment using MATLAB. Simulation results have verified that beetle antennae search can successfully plan and track the reference path by tuning the PID controller efficiently.
Integrasi Sistem ERP, Arus Informasi Dan Kualitas Informasi

This paper aims to provide empirical evidence regarding the relationship between the level of integration of ERP systems and the quality of information perceived by managers directly and indirectly. The quality of information is affected by the presence of specific features of information flow, defined as information processing capacity, meeting intensity, and information sharing. This research was conducted by approaching structural equation modeling (SEM) with a test tool using partial least square (PLS). Empirical results show that the level of integration of ERP systems has a positive effect on information flow variables and information flow positively affects the quality of information. Other results also show that the level of integration of ERP systems can positively and indirectly affect the quality of information obtained by managers mediated by the flow of information. Managers can benefit from this research in supporting decision making to achieve the optimal level of integration of ERP systems used to improve the quality of information within the company.
Teachers’ Work In The Conditions Of The Covid-19 Pandemic: Legal Regulation

The COVID-19 Coronavirus infection has affected all spheres of society.The educational environment has also been affected by the pandemic.The modification of the sphere of education caused by the coronavirus infection occurred too quickly.None of the participants in the educational process was ready for the changes that had arisen.The article analyses the problems of regulating the work of the teaching staff in introducing distance learning in the Russian education system.The authors identified and analysed the advantages and disadvantages of distance learning, which differ from the usual off-line learning and are characterized by direct interaction of students with the teaching staff.As a result of the analysis of the identified features, the conclusions are drawn.Despite the existing positive features, distance learning does not cease to be inferior to the traditional one.In addition, the sudden transition of the conventional educational environment to the online format has revealed the factual unpreparedness of the teaching staff for it.At the same time, many significant elements of the labour status of the teaching staff were left without proper and high-quality legislative regulation.The authors propose methods and techniques of competent legal regulation of the labour activity of the teaching staff in the online format of the educational process.
Faster Service with Less Resource: A Resource Efficient Blockchain Framework for Edge Computing

Abstract By integrating edge computing with blockchain technology, traceable and immutable services can be provided to address the distrust issue between edge devices. However, the contradiction between the computing and storage requirements of blockchain deployment and the constrained resources of edge devices is the greatest obstacle standing in the way. This makes edge devices have to consume expensive costs to deploy a blockchain system for trusted collaboration. To address the above problem, this article proposes a resource-efficient blockchain framework for edge computing. First, we propose a cloud-edge collaboration consensus protocol called the Proof-of-Lottery (PoL) that completely offloads the computing requirement of consensus protocol execution from edge devices to the cloud. By introducing double-chain structure and dynamic difficulty target mechanism, PoL can significantly improve the throughput and latency of the blockchain transaction while remains decentralization and security. Then, the framework division mechanism based on edge-terminal consensus zone (ETCZ) is proposed to decrease the minimum storage requirement for resource constrained edge devices. The multi-ETCZ PoL protocol is also designed for consensus in the multi-ETCZ blockchain framework. Finally, controlled experiments are tested to demonstrate that our framework can significantly reduce the resource requirements for edge devices on blockchain deployment while achieving high-throughput and low-latency services.
The effects of illness script method on clinical reasoning of undergraduate nursing students: A quasi-experimental study

The improvement of clinical reasoning in nursing students is a benchmark for students' abilities in carrying out nursing care. Disease script-based learning can help students recognize the information. The purpose of this study was to determine the effect of illness scripts on the clinical reasoning ability of nursing students. The research design used was a quasi-experimental approach with a non-equivalent approach with a control group. The sampling technique used consecutive techniques. The number of samples in each group is 35 respondents, data collection using Clinical Reasoning Assessment Tools. Data analysis using Wilcoxon Signed Rank test. The results of this study indicate that there is an increase in the clinical reasoning ability of nursing students in the experimental group from the average value of 40.6 to 50.3. Most of the respondents in the experimental group had clinical reasoning at the intermediate learner level. The results of the Wilcoxon test showed that the Z-count value in the experimental group was 5.092 with a p-value = &lt;0.001. It can be concluded that there is an effect of illness scripts method on the clinical reasoning ability of nursing students.
The Wave Function Collapse Algorithm and its Application to AI Navigation

For so long, game developers have had to hand place waypoints, leading to linear paths being taken by Artificial Intelligence (AI) which move between them. These paths often loop, and if they don’t, they only serve to move an AI from point A to point B in the same way time and time again. A more dynamic solution is needed, and the Wave Function Collapse Algorithm is suited to provide that. This solution was implemented in the Unity engine using C#. By utilizing the provided Wave Function Collapse Algorithm template, waypoints were able to be substituted for game objects. When implemented in this manner, the Wave Function Collapse Algorithm places waypoints within a defined area allowing an AI navigator to maneuver between them. Should a waypoint be inaccessible, the waypoints will regenerate. Waypoints, upon the map regenerating, are removed so that any of those colliding waypoints cannot be accessed by the AI. This creates a dynamic path for an AI to traverse. The AI only moves between active waypoints and the map is able to regenerate so that the paths laid out do not succumb to the same pitfalls that traditionally plotted paths do. The next logical step is to apply the Wave Function Collapse Algorithm to a generated map. By allowing the Wave Function Collapse Algorithm to plot waypoints dynamically and continuously, different applications can utilize this to allow an AI to traverse a map in a unique way.
Operational Gaps in Implementing the COVID-19 Case Investigation and Contact Tracing in Madhesh Province of Nepal, May–July 2021

In Nepal, case investigation and contact tracing (CICT) was adopted as an important public health measure to reduce COVID-19 transmission. In this study, we assessed the performance of CICT in Madhesh Province of Nepal against national benchmarks, using routine programmatic data reported by district CICT teams. Between May and July 2021, 17,943 COVID-19 cases were declared in the province, among which case investigation was performed for 30% (95% CI: 29.6-31.0%) within 24 h (against 80% benchmark). As a result of case investigations, 6067 contacts were identified (3 contacts per 10 cases), of which 40% were traced and tested for SARS-CoV-2 infection (against 100% benchmark). About 60% of the contacts tested positive. At most 14% (95% CI: 13.1% to 14.9%) of traced contacts underwent a 14-day follow-up assessment (against 100% benchmark). We found the performance of the CICT program in Madhesh Province to be sub-optimal and call for corrective measures to strengthen CICT in the province and the country at large. Similar studies with wider geographical scope and longer time frames are needed to identify and address deficiencies in data recording and reporting systems for COVID-19, in low- and middle-income countries like Nepal and others.
Wireless Technology is Easy to Use

Wireless networking is the connection of computers, digital communication devices, network equipment, and various other devices via radio waves. It is applied in places where the wired infrastructure cannot be installed or the price of introducing such a structure is too high. In addition, it has some features that are a great advantage over wired networking, such as customer mobility, easy expandability, and fast and low-cost temporary networking. Wireless technology allows us mobility and ease of use, but most users do not think about security. Users are insufficiently informed about the dangers of the Internet. Many of them do not pay attention to that and access important data such as bank accounts, e-mail, and any other contents that must be preserved and hidden. Today, there are more and more malicious actions, where hackers use various methods and technologies to attack users' accounts, bypassing all protections. Today, the issue of security is one of the priorities for every Internet user. Due to its characteristics, wireless communication is exposed to attacks due to the way they are sent, and there is a possibility of intercepting information.
The Role of Data in an Emerging Research Community:

Open science data benefit society by facilitating convergence across domains that are examining the same scientific problem. While cross-disciplinary data sharing and reuse is essential to the research done by convergent communities, so far little is known about the role data play in how these communities interact. An understanding of the role of data in these collaborations can help us identify and meet the needs of emerging research communities which may predict the next challenges faced by science. This paper represents an exploratory study of one emerging community, the environmental health community, examining how environmental health research groups form, collaborate, and share data. Five key insights about the role of data in emerging research communities are identified and suggestions are made for further research.