Reviewerly/exHarmony huggingface dataset:
A Filter-APOSD approach for feature selection and linguistic knowledge discovery

The development of natural language processing promotes the progress of general linguistic studies. Based on the selected features and the extracted rules for word sense disambiguation (WSD), some valuable knowledge of the relations between linguistic features and word sense classes may be discovered, which may provide theoretical and practical evidence and references for lexical semantic study and natural language processing. However, many available approaches of feature selection for WSD are in the end to end operation, they can only select the optimal features for WSD, but not provide the rules for WSD, which makes knowledge discovery impossible. Therefore, a new Filter-Attribute partial ordered structure diagram (Filter-APOSD) approach is proposed in this article to fulfill both feature selection and knowledge discovery. The new approach is a combination of a Filter approach and an Attribute Partial Ordered Structure Diagram (APOSD) approach. The Filter approach is designed and used for filtering the simplest rules for WSD, and the APOSD approach is used to provide the complementary rules for WSD and visualize the structure of the datasets for knowledge discovery. The features occurring in the final rule set are selected as the optimal features. The proposed approach is verified by the benchmark data set from the SemEval-2007 preposition sense disambiguation corpus with around as the target word for WSD. The test result shows that the accuracy of WSD of around is greatly improved comparing with the one by the state of the art, and 17 out of 22 features are finally selected and ranked according to their contribution to the WSD, and some knowledge on the relations between the word senses and the selected features is discovered.
A real‐time decoupling trajectory planning method for on‐road autonomous driving

Abstract Trajectory planning is a core technology for autonomous vehicle directlyreflecting the driving safety and efficiency. In this paper, a spatial‐speed decoupled planning method is studied for real‐time trajectory generation in the on‐road environment. The proposed approach mainly includes two parts: optimization‐based lateral planning and selection‐based longitudinal speed planning. The optimization‐based lateral planning is employed to generate an optimal collision‐free and smooth path by solving a quadratic programming problem. Specifically, the lateral planning first constructs a safe corridor by integrating obstacles and initial risky corridor together in the Frénet frame. The safe corridor is useful for quadratic programming problem formulation. The selecting‐based longitudinal speed planning is proposed to generate a suitable and continuous velocity trajectory. The novel speed selector considering four cases is designed to select a more suitable reference velocity in different lane situations. The continuous velocity trajectory is obtained by solving piecewise continuous quartic polynomial. As a result, the combination of the spatial path and the velocity trajectory is the final planning result. The proposed algorithm is tested extensively in a simulation environment. Experimental results demonstrate that the proposed algorithm has good real‐time property and practical validity.
Future Proposals for E-Learning at Conventional Tertiary Institutions as they Move on Past the COVID Experience

As the COVID-19 pandemic was spreading rapidly throughout the world, the most widespread reaction in many countries to curtail the disease was lockdown. As a result, educational institutions had to find an alternative to face-to-face learning. The most obvious solution was e-learning. Conventional tertiary institutions with little virtual learning experience had to deal with an unprecedented challenge. This study delineates practices that can be suggested for conventional institutions of tertiary education that may be planning to acquire more e-learning experience in the post-COVID era so that they may be better prepared to move on beyond traditional classroom teaching. The qualitative research method was utilized to carry out this study. The data collected for the study comprised mainly two sources: the existing literature that tackles the issue of how tertiary institutions around the world shifted to e-learning during the pandemic, and an analysis by the researchers of COVID-related circulars that were issued by the Saudi Ministry of Education and by Prince Sattam bin Abdulaziz University. The review of related literature and the analysis of the circulars enabled the researchers to produce a number of suggestions aimed at improving the e-learning experience of conventional tertiary institutions. The researchers followed five steps in their literature review: (1) pinpointing search terms and then creating a search strategy and implementing it, (2) sorting the studies gathered, excluding duplicates and studies deemed irrelevant, and decreeing standards for deciding what to keep and what to rule out, (3) evaluating the studies in the light of those standards, (4) obtaining data, and (5) analysing data. This study does not recommend that conventional tertiary institutions become 100% online, but it does suggest that those institutions should make some learning content digitally accessible, build community partnerships, encourage self-study skills among students, help students change their learning style from passive to active, and revisit their e-assessment practices. This paper also stresses the importance of giving further support to teaching staff, sheds light on how institution buildings can reopen smoothly as the restrictions ease, discusses what factors influence the outcomes of e-learning, and tackles the issue of student dropouts during the pandemic. This study concludes by outlining the important notion of adopting blended learning and developing e-learning programs on an international scale.
Digital Transformation of the Russian Historical Education

The modern Russian education system is on the path of digital modernization, which is accompanied by a paradigm shift in the choice of models of educational institutions, approaches to the management of the educational process, modification of learning conditions, innovations within the methodological arsenal of teachers, modern digital technologies are being introduced. Digital transformation or digitalization of education, along with computerization, individualization, etc., is one of the key trends in the development of the education sector. Undoubtedly, for the successful digital transformation of the educational system Russia needs to solve many tasks, both global (logistics, upgrading professional competencies, stimulating the development of educational IT technologies, etc.) and local. Among the local, but no less significant, tasks is the search for digital learning tools that can improve the effectiveness of the educational environment. It is necessary to answer the question: how is it possible to train qualified specialists basing on digital educational tools? In this article, the authors consider the use of machine learning technology in successful educational practices, as well as highlight “digital products” tested by teachers of the Department of History of Astrakhan State University. Digital transformation of education is considered by the authors of the article in relation to the economic development of the country, The Caspian region and the Astrakhan region, which characterizes the features of the “state order” in the educational sphere.
Improving fidelity in video streaming experimentation on testbeds with a CDN

Video streaming is the leading network traffic on the Internet, yet there are few tools to run high fidelity experiments with video streaming traffic on network emulation-based testbeds. In this paper, we present a framework to enable higher fidelity and principled experimentation with 36 different video streaming traffic scenario combinations that can be configured and deployed on a notional CDN and data metrics infrastructure. This framework can be used to further study and experiment with adaptive bitrate algorithms and other AI/ML solutions for video delivery.
Implementation security system using motorcycle fingerprint identification and notification Telegram

A motorcycle security system using fingerprint recognition and Telegram notification is a solution to solve motorcycle safety problems and reduce motorcycle loss cases by using fingerprint sensors attached to the owner’s motorcycle and Telegram application as a monitor connected to the motorcycle. The microcontroller used is the Wemos D1 mini connected to the Telegram application as a communication line between the user and the motorcycle. The sensor used is the fingerprint sensor. There are differences in data obtained from the fingerprint sensor response results and the response of the Telegram notification system by testing fingerprints that have been registered and that are not registered on the fingerprint sensor with the measurement results using the stopwatch. Measurement differences on the fingerprint sensor have errors with an average time dispute of 0.27 seconds. Measurement differences in Telegram notifications have errors with an average dispute time of 1 second. Hygiene conditions on the fingerprint sensor influence the difference in reading duration. If there is fingerprint oil before it, then the reading tends to be slow. The difference in measurements obtained is small enough that it can be used as a reference.
Hyperspectral Core-Logging for Past Primary Productivity Assessment

Past primary productivity is tracked in lake sediments to reconstruct environmental changes. However, the resolution of the routinely used destructive techniques is not suitable for the analysis of a large number of samples due to cost. Non-destructive analysis of chlorophyll-a performed by hyperspectral imagery enables the quick determination of indices of past primary productivity. Eighteen indices used in paleo-environmental reconstruction were inventoried to define the best index capable of tracking chlorophyll-a by this technique. All the indices were applied to hyperspectral data measured on the sediment of Lake Bresson, in which detrital organic matter input is likely to skew chlorophyll-a identification, and the results were compared with concentrations measured by a routinely used destructive analysis. The 18 indices all produced a different result and only three indices were positively correlated with chlorophyll-a concentrations (n = 28, p &lt; 0.0001). The detrital organic matter impacts chlorophyll-a characterization and shows the bias produced by the sediment matrix variations. Moreover, artificial modification of the sediment matrix revealed an impact of the mineral phase. To tackle this issue, the indices are normalized by two proxies of the sediment components. This new approach reduces the impact of detrital organic matter, hence the sediment matrix variations also reduce the normalization of the chlorophyll-a indices by a specific proxy of the mineral phase. These results identify the impact of local geochemical features that limit past primary productivity assessment and show the necessity of characterizing the sediment composition prior to tracking the chlorophyll-a by hyperspectral imaging.
Deep Learning Model Analysis and Web-Based Implementation of Cryptocurrency Prediction

Cryptocurrency is a digital asset designed by cryptography, such as Secure Hash Algorithm 2 (SHA-2) and Message Digest 5 (MD5). Cryptocurrency uses Blockchain technology to ensure security, transparency, ease of locating, and unchangeability. This makes cryptocurrency very popular in many sectors, especially in the financial industry. Although, the uncertainty and the dynamic change of cryptocurrency price make the risk for investment in this digital asset high. This is the reason why studies about cryptocurrency price prediction became popular globally. This study intended to predict cryptocurrency prices using hybrid GRU LSTM than setting up the epoch to get the most accurate prediction model. The researcher would make a web-based application that can be used by the public, especially those involved in cryptocurrency investment. The result was a web-based application that could predict the price of cryptocurrency for the next few days, which had been validated using data from the previous 7 days, 14 days, 30 days, 60 days, and 90 days.
Traffic Lights Detection and Recognition with New Benchmark Datasets Using Deep Learning and TensorFlow Object Detection API

Today, traffic lights are widely used in places with high vehicle traffic. Especially in autonomous vehicles, fast and high accuracy detection and recognition of traffic lights are critical. Machine learning methods are generally used to do this. Deep learning models give more successful results than machine learning methods in detecting the exact location of traffic lights in different climatic conditions. In this study, Faster R-CNN Inception v2 deep learning model was trained and tested on two different datasets that we prepared and published publicly under variable traffic and climatic conditions in Turkey. Successful results were obtained with fewer data by using the Transfer Learning method with the help of TensorFlow Object Detection API in the training of the model. It has been shown that the datasets we have prepared can be developed considering the conditions in other countries and successful results will be obtained.
Visuals

This chapter reviews the types, use, production, accessibility and efficacy of data visuals contained in IPCC assessments and special reports, drawing upon available published literature. Visuals of different types are key to the communication of IPCC assessments. They have been subject to academic interest among social and cognitive scientists. Furthermore, wider societal interest in the IPCC has increased, especially since the publication of its Fifth Assessment Report. In response, the IPCC has revisited its approach to communication including visuals, which has resulted in a greater professionalisation of its visualisations — involving information designers and cognitive scientists — and in new forms of co-production between authors and users.
Multimedia Data Secure Transmission: A Review

Encryption is a technique of encoding data so that they can only be recognized by authorized receivers. More interactive media information is communicated in the medical, business, and military fields because of the rapid advances in various multimedia transmission and networking technologies, which may contain sensitive information that must be kept hidden from public users. Advanced encryption standards (AES) and data encryption standards (DES) are widely used encryption algorithms for text data. However, they are not appropriate for video data. To ensure that this information cannot be accessed by attackers, the demand for efficient video-protection techniques has been raised. This article provides multimedia design requirements to maintain a secure multimedia system occupied with a threat model for detecting and ranking the potential risks facing a multimedia system. the risks exposed to multimedia security and their impacts on users are typically described according to the textual description and also an overview of the current state-of-the-art video-encryption schemes are presented and their performance parameters have been examined. The relationship between encryption algorithms and compression techniques is also discussed and various multimedia applications have been presented in this paper; Additionally, as the synchronisation of real-time continuous streams is necessary for the interchange of these streams in multimedia conferencing services, multiple synchronisation strategies have been given in this study along with video synchronisation challenges.
Metacognitive strategy to enhance students' reading text-ability: case study in the vocational high school

This study aims to determine: the process of implementing metacognitive strategies in learning reading skills. The case study research method is used in this study. The results of this study indicate that the implementation of metacognitive strategies applies three stages, namely: 1) self-planning, 2) self-monitoring, and 3) self-evaluation. These stages are carried out by creating a pleasant relationship with students through discussion. The students were highly motivated by the learning process and five students showed enthusiasm, achievement, awareness, and independence in learning, especially in reading skills.
A Transformer for SAG: What Does it Grade?

Automatic short-answer grading aims to predict human grades for short free-text answers to test questions, in order to support or replace human grading. Despite active research, there is to date no wide-spread use of ASAG in real-world teaching. One reason is a lack of transparency of popular methods like Transformer-based deep neural networks, which means that students and teachers cannot know how much to trust automated grading. We probe one such model using the adversarial attack paradigm to better understand their reliance on syntactic and semantic information in the student answers, and their vulnerability to the (easily manipulated) answer length. We find that the model is, reassuringly, likely to reject answers with missing syntactic and semantic information, but that it picks up on the correlation between answer length and correctness in standard training. Thus, real-world applications have to safeguard against exploitation of answer length.
A Prototype For Quantum Database in Hybrid Quantum

&lt;p&gt;This study proposes a prototype And a possibility to converge both classical database and fully quantum database. This study mostly identifies the gap between this classical database and quantum database and it proposes a hypothesis and prototype which can be implemented in future products. It is a way that can be used in future industrial product development on hybrid quantum computers. The existing concept used to consider oracle as a black box this study opens up the possibility for the quantum industry to develop the QASAM module so that we can develop a fully quantum database instead of using a classical database as BlackBox. As the Toffoli gate is basically an effective NAND gate it is possible to run any algorithm theoretically in quantum computers. So we will propose a logical design for memory management for the quantum database, security enhancement model, Quantum Recovery Manager &amp; automatic storage management model and more for the quantum database which will ensure the quantum advantages. In this study, we will also explain the Quantum Vector Database as well as the possibility of improvement in duality quantum computing. It opens up a new scope, possibilities and research area in a new approach for quantum database and duality quantum computing.&lt;/p&gt;
Investigation of Malaysian ESL Students’ Learning Experience of the Smartphone Applications for English Language Learning

This qualitative study investigated the use of language learning smartphone applications (Apps) among Malaysian undergraduates to enhance their English language learning. Students’ interviews were collected and analysed based on Braun and Clarke's thematic analysis. The emerging themes revealed that the participants were convinced that the language learning smartphone Apps were able to enhance their English language learning. Participants found that the Apps helped them to improve their vocabulary, listening, speaking, reading and writing skills. The study offers the trends and usage patterns of English language smartphone Apps among Malaysian ESL learners. Some of the features in these Apps that promote English language learning are gamification, repetition of certain language skills, extended learning space and interactivity. Meanwhile, students also highlighted their concerns related to limited knowledge offered by the Apps, distraction, application fee and the Internet connection. The findings of the study can assist researchers, practitioners and educators to facilitate and guide students on the use of language learning smartphone Apps in educational practices.
Improving Moth-Flame Optimization Algorithm by using Slime-Mould Algorithm

The MFO algorithm is one of the modern optimization algorithms based on swarm intelligence, and the SMA algorithm is also one of the latest algorithms in the same field and has the advantages of fast convergence, high convergence accuracy, robust and robust. In this research paper, we introduce an optimized algorithm for MFO based on the SMA algorithm to get better performance using the features in the two algorithms, and two different algorithms are proposed in this field. The two predicted new algorithms were tested with standard test functions and the results were encouraging compared to the standard algorithms.
Mining Evaluation of Sulfur Deposits in Al-Mishraq Area Using Geostatistical Method

The area of the study is located in northern Iraq on the western side opposite the confluence of the Tigris River with the upper Zab tributary, about 45 km southeast of the city of Mosul within the Hamrin-Makhoul zone belonging to the foothills zone within the unstable area. The area contains rocks of many formations ranging in age from the Lower Miocene to the Quaternary, and includes the formations of the Euphrates, Fatha, and Quaternary deposits. Sulfur deposits are present in the Fatha formation within the productive bed which contains three major sulfur-bearing horizons or zones separated by several heterogeneous layers of marly claystone and dolomite limestone of different thicknesses. The formation of sulfur due to the processes of reducing gypsum or anhydrite with the availability of hydrocarbons and the action of anaerobic bacteria, where hydrogen sulfide gas H2S is produced, which is later oxidized by groundwater, forming sulfur ore with the help of other factors represented by tectonic and hydrogeological factors.&#x0D; The mineral reserves of sulfur deposits were recalculated and estimated using the geostatistical method, as it is one of the modern applications adopted in the Rockwork-14 program, which takes into account the location of each model in relation to the other within the mineral deposits extensions and even outside the boundaries of those extensions, which gives high accuracy and reliability about the amount of reserves depending on the group Among the important criteria, such as thickness, concentration of the ore, and density, the reserve of sulfur in the Mishraq sulfur field-1 was estimated at 121,230,554,68 tons.&#x0D; The validity and suitability of using the open mine method for extracting sulfur in the study area was discussed based on a decisive and important factor, which is the stripping ratio, which determines the type of appropriate extractive method, and it was equal to 6 for the entire study area, a large value that exceeded the maximum permissible limit of 5. This method is not economically feasible due to the high extractive cost and the presence of sulfur at different depths ranging between 80-230 m as well as the presence of environmental and geological problems that hinder the use of this method in sulfur extraction.&#x0D; The study was based on the data obtained for a group of (9) exploratory wells that were drilled in the region.