Reviewerly/exHarmony huggingface dataset:
Boosting English Reading Proficiency: The Efficacy of the KWL Learning Strategy

In the contemporary globalized society, addressing the challenges of low academic performance and limited English reading comprehension is paramount. This study investigates the potential of the KWL (Know-Want-Learned) learning strategy, a technique posited to enhance student outcomes, in bridging the observed gap between theoretical pedagogies and practical interventions. Utilizing a quasi-experimental design, diverse students from varied grades and backgrounds were divided into two groups: one exposed to the KWL technique and another receiving traditional instruction. Standardized tests were employed to measure outcomes. Notably, students undergoing KWL-based instruction exhibited significant advancements in academic performance and English reading comprehension. These findings underscore the value of integrating the KWL strategy into educational curricula. While the results are promising, further studies are warranted to understand the long-term effects and the strategy's adaptability across diverse educational settings and demographics. In essence, this research not only highlights the pressing educational challenges but also introduces an evidence-backed solution for substantial improvement.
Semiotic Structuring in Movie Narrative Generation

In this paper we apply, in a novel way, our ongoing research work on the interactive composition of narratives based on semiotic relations. To the two basic components of interactive systems, namely, a software tool and a user interface, we add a third component – AI agents, understood as an upgraded rendition of software agents. Our semiotic relations approach considers four ways of composing new narratives from existing narratives. Along the horizontal syntagmatic axis one can form the new narrative by combining two or more previous narratives. Along the vertical paradigmatic axis, the new narrative may emerge as a similar version, which imitates the previous one in a different context. Along the depth meronymic axis, the hierarchic narrative levels, such as event and scene, are explored, allowing to zoom in and out in the composition process. Lastly, the antithetic consideration, rather than adding a dimension, aims at some form of reversal, through the adoption of opposite values. A fully operational prototype is described, with ChatGPT operating as the main AI agent component. To run the experiments, we concentrated on movie narratives.
Image Acquisition by Image Retrieval with Color Aesthetics

The objective of this work is to obtained aesthetically pleasing images related based on the location information. To achieve this, we develop a system that can acquire photos using the geographic information and cloud data by image retrieval. This system comprises an insertion module and a search module. The insertion module recognizes the weather conditions, reads the image information and stores the image data in the cloud database. On the other hand, the search module reads the location information of the onboard sensors, searches for images with the similar location from the cloud database using the proposed optimal image selection algorithm. The search module then provides multiple photos with similar geographic information, selects the best image, and provides feedback suggestions. In addition to the software development, we also implement the proposed system on a hardware device that can directly retrieve the outdoor images for display and storage. The experiments with real scenes have demonstrated the feasibility of the proposed system.
Speckle Noises Reduction and Coherence Enhancement In Ultrasound Fetal Images

Ultrasound is an imaging method that uses high-frequency sound waves to produce images of structures within your body.The images can provide collectible information for determining and treating a variety of diseases and conditions. The existence of noise in ultrasound images can lead to improper identification of a disease or problem or its cause. It is crucial to restoring the quality of the images to extract information from the ultrasound images. These observations explain the speckle noises present within the ultrasound medical images and also the filters for de-speckling. Speckle is a granular ’noise’ that deteriorate the quality of medical images. The performances of these algorithms are put forward for speckle noise. A review is carried out by using Lee filter, Frost filter, Kuan filter, Median Filter, SRAD(Speckle Reducing Anisotropic Diffusion) and Weiner filter. The study shows the performance and comparisons of different filters with their PSNR (Peak signal to noise ratio) and MSE (Mean Square Error).
SGGNet<sup>2</sup>: Speech-Scene Graph Grounding Network for Speech-guided Navigation

The spoken language serves as an accessible and efficient interface, enabling non-experts and disabled users to interact with complex assistant robots. However, accurately grounding language utterances gives a significant challenge due to the acoustic variability in speakers’ voices and environmental noise. In this work, we propose a novel speech-scene graph grounding network (SGGNet2) that robustly grounds spoken utterances by leveraging the acoustic similarity between correctly recognized and misrecognized words obtained from automatic speech recognition (ASR) systems. To incorporate the acoustic similarity, we extend our previous grounding model, the scene-graph-based grounding network (SGGNet), with the ASR model from NVIDIA NeMo. We accomplish this by feeding the latent vector of speech pronunciations into the BERT-based grounding network within SGGNet. We evaluate the effectiveness of using latent vectors of speech commands in grounding through qualitative and quantitative studies. We also demonstrate the capability of SGGNet2 in a speech-based navigation task using a real quadruped robot, RBQ-3, from Rainbow Robotics.
Mathematics and science teacher educators experiences of using the WhatsApp platform as a tool for supporting teaching during the Covid-19 pandemic

This article problematises the media used for delivery of curriculum at the onset of lockdowns due to the outbreak of the Covid-19 pandemic, which resulted in a sudden cessation of all face-to-face teaching and learning at a South African university. The article reports on how the otherwise neutral WhatsApp social media platform was appropriated to become a serious teaching tool for mathematics and science pre-service teacher educators. The study draws predominantly on connectivism learning theory to understand how WhatsApp was used to continue the teaching of mathematics and science during the Covid-19 lockdowns. The sample consisted of 10 mathematics and science teacher educators from whom data were collected through authentic conversations and questionnaires. The article reports on the affordances and constraints the WhatsApp platform presented, as well as how it reshaped the teaching of mathematics and science. Findings indicate that teacher educators transformed the use of WhatsApp from a social media tool to a tool for supporting teaching. However, being a social tool, WhatsApp does have affordances and constraints when used for teaching. The affordances include ease of access and ease of use, while constraints include the risk of students not possessing smartphones and intrusion into the personal lives of teacher educators.
THE EFFECTIVENESS OF USING VIDEO BLOGS (VLOGS) TO IMPROVE SENIOR HIGH SCHOOL STUDENTS' SPEAKING ABILITY

This study aimed to examine the effectiveness of utilizing video blogs (vlogs) as a means to enhance the speaking ability of senior high school students. Employing a quantitative approach with a quasi-experimental design, the research involved two classes comprising 37 students each. The participants were assigned randomly to either the experimental group, which received the vlog treatment, or the control group, which did not. Both groups underwent pre-test and post-test assessments to evaluate their speaking ability. The findings from the paired sample t-test demonstrated a significant improvement in the speaking ability of the experimental group (p &lt; .05), indicating the positive impact of vlogs. Furthermore, effect size analysis indicated a substantial effect of vlogs on enhancing students' speaking skills, with an effect size of 0.37. In conclusion, this research provides support for the use of vlogs as an effective tool for improving the speaking ability of senior high school students.
Analysis of the evolving factors of social media users’ emotions and behaviors: a longitudinal study from China’s COVID-19 opening policy period

Abstract The outbreak of the COVID-19 pandemic has triggered citizen panic and social crises worldwide. The Chinese government was the first to implement strict prevention and control policies. However, in December 2022, the Chinese government suddenly changed its prevention and control policies and completely opened up. This led to a large-scale infection of the epidemic in a short period of time, which will cause unknown social impacts. This study collected 500+ epidemic-related hotspots and 200,000+ data from November 1, 2022, to March 1, 2023. Using a sentiment classification method based on pre-trained neural network models, we conducted inductive analysis and a summary of high-frequency words of various emotions. This study focuses on the inflection point of the emotional evolution of social media users and the evolution of “hot topic searches” events and emotional behavioral factors after the sudden open policy. Our research results show that, first of all, the positive emotions of social media users are divided into 4 inflection points and 5 time periods, and the negative emotions are divided into 3 inflection points and 4 time periods. Behavioral factors are different at each stage of each emotion. And the evolution patterns of positive emotions and negative emotions are also different. Secondly, the evolution of behavioral elements deserves more attention. Continue to pay attention: The treatment of diseases, the recovery of personal health, the promotion of festive atmosphere, and the reduction of publicity on the harm of “new crown sequelae and second infections” are the behavioral concerns that affect users’ emotional changes. Finally, it is necessary to change the “hot topic searches” event by guiding the user’s behavioral focus to control the inflection point of the user’s emotion. This study helps governments and institutions understand the dynamic impact of epidemic policy changes on social media users, thereby promoting policy formulation and better coping with social crises.
Participation of Persons with Disabilities in the Disaster Preparedness Program in Kepuharjo Village

Persons with disabilities face a higher risk during disasters. Socioeconomic and physical vulnerabilities make them more vulnerable to disasters. Unfortunately, persons with disabilities tend to be overlooked in emergency preparedness systems. This fact causes persons with disabilities lack an understanding of disasters and how to overcome them. This community service program is conducted in Kepuharjo Village, a village prone to the “Merapi” volcano disaster. The programs implemented ensure that persons with disabilities are subjects in disaster preparedness. Activities are carried out by making innovations by realizing inclusive disaster risk reduction. The result of the innovation programs is the management information systems, disability data, and village disability groups. There is the increasing knowledge of village governments, disaster resilient village teams, persons with disabilities to obtain information and about disasters; there is standard operating inclusive evacuation and rescue procedure; there is advocacy for the availability of accessible refugee barracks. The village already had a policy and allocated a budget for providing protection and fulfilling the rights of persons with disabilities. The sister village also has an inclusive disaster preparedness program.
Query Recommendation System in Social Networks

Recommender Systems are software which provides suggestions to the user according to his or her interest. These suggestions are related to supporting users in making their decisions, for example, what to search, what to buy, what to listen, etc. Recommender systems are very important in online stores where there are a lot of items to buy. These recommender systems help user to find things according to their interest and buy them. There are a lot of techniques proposed for recommendation and used in commercial environments. People are thought to trust suggestions from friends more than those from websites that are similar to them [2]. As a result, it is helpful to feed a recommender system with the friends' ratings. However, social media sharing websites' recommender systems have several difficulties, such as ranking the information from the user's friends as well, finding information from other sources in comparison to the user's immediate friends, and using metadata and context links for suggestion. In this research, an architecture based on profile-based crawling of social media sharing websites is proposed for query recommendation.
Perspectives on Place

This publication, the third in the three-volume Perspectives series, explores the many ways in which materials, methodologies, and technologies make and define place through an expansive geographical, temporal, and theoretical range of artworks, sites, and subjects.
Welcome to the Tangle: An Empirical Analysis of IOTA Ledger in the Chrysalis Stage

The Tangle, the distributed ledger of IOTA, is built on a directed acyclic graph (DAG) where the nodes represent messages, and the edges correspond to reference relations. This feature of the Tangle makes it suitable for analysis using network science methods. While previous research on the Tangle has mainly focused on stability, security, scalability, and the forthcoming IOTA Coordicide update, there is limited empirical research on the evolution of the Tangle, particularly during the Chrysalis stage (IOTA 1.5). To address this gap in the literature, our study aims to develop workflows for collecting IOTA Tangle data and transaction data in the Chrysalis stage, conduct a comprehensive analysis to examine the features of messages and reconstruct the Tangle graph to study its characteristics from a network science viewpoint. Our study is the first to illuminate activities and information transferred on the Tangle, as well as its graph characteristics during the Chrysalis stage, which can enhance our comprehension of its functionality and contribute to the improvement of the Tangle’s performance in the upcoming upgrades.
Application and Safety Management of Computer Electronic Information Engineering Technology

With the continuous development of science and technology, electronic engineering technology pays more and more attention to the application of computers in this field, and the application of electronic information engineering technology is also increasing. However, it also makes him more and more risky, and the information may be stolen and changed randomly. Especially since the 21st century, the development of information technology has been quite rapid. Among all kinds of science and technology, computer and electronic engineering technology also occupies a more important position, which has brought about earth-shaking changes in people's life and work style and promoted social development to a certain extent. Under this background, there are inevitably hidden dangers in information security, which also hinders the progress of science and technology and the development of society to some extent. Based on this situation, we must strengthen our electronic information engineering prevention technology to ensure everyone's privacy. This paper first introduces the application of electronic information engineering technology, and then discusses its security risks and related measures to realize the security management of electronic information engineering technology.
On Interval Perturbation of the α-Universal Triple I Algorithm for Unified Interval Implications

In the fuzzy field, Zadeh put forward the CRI (compositional rule of inference) algorithm to deal with fuzzy reasoning. Later, Wang put forward the triple I algorithm, which is logically more complete than CRI. Thenceforward, Tang found that the harsh conditions of the triple identical implication operators lead to low system performance, so the α-universal triple I (α-UTI) algorithm is proposed. In actual applications, the quality of fuzzy inference algorithm directly is determined by the robustness of inference algorithm. This study researches the interval robustness of the α-UTI algorithm under the interval-valued environment. The interval perturbation performance of the α-UTI algorithm is respectively revealed for the R-interval implication, the triangular modulus interval implication, the S-interval implication and the QL-interval implication. It is observed that the α-UTI algorithm has good interval perturbation performance for single rule or multi-rules, in which unified interval implications are employed.
On Simple Perturbation of the α-Universal Triple I Algorithm via Specific Interval Implication Operators

Fuzzy reasoning aims to describe things with fuzzy and uncertain concepts and convert them into information that computers can process. Wang thought that the inference algorithm based on Compositional Rule of Inference (CRI) had certain defective nature and proposed the triple I algorithm with fully implication operator. Later, the α-universal triple I (α-UTI) algorithm was proposed. As an important part of the fuzzy inference algorithm, the implication operator directly affects the robustness of the inference algorithm, that is, affects the quality of the inference algorithm. Besides, the quality of fuzzy reasoning algorithm can be partly determined by the robustness of reasoning algorithm. In this paper, the ordinary implication operator is extended to a more general interval implication operator. And the simple perturbation of the α-UTI algorithm is studied for some specific interval implication operators under the interval-valued environment. It is observed that the α-UTI algorithm possesses good simple perturbation performance for single rule or multi-rules, in which specific interval implications are used.
Two-Stage Model-Based Predicting PV Generation with the Conjugation of IoT Sensor Data

This paper proposes a novel short-term photovoltaic voltage (PV) prediction scheme using IoT sensor data with the two-stage neural network model. It is efficient to use environmental data provided by the meteorological agency to predict future PV generation. However, such environmental data represent the average value of the wide area, and there is a limitation in detecting environmental changes in the specific area where the solar panel is installed. In order to solve such issues, it is essential to establish IoT sensor data to detect environmental changes in the specific area. However, most conventional research focuses only on the efficiency of IoT sensor data without taking into account the timing of data acquisition from the sensors. In real-world scenarios, IoT sensor data is not available precisely when needed for predictions. Therefore, it is necessary to predict the IoT data first and then use it to forecast PV generation. In this paper, we propose a two-stage model to achieve high-accuracy prediction results. In the first stage, we use predicted environmental data to access IoT sensor data in the desired future time point. In the second stage, the predicted IoT sensors and environmental data are used to predict PV generation. Here, we determine the appropriate prediction scheme at each stage by analyzing the model characteristics to increase prediction accuracy. In addition, we show that the proposed prediction scheme could increase prediction accuracy by more than 12% compared to the baseline scheme that only uses a meteorological agency to predict PV generation.
The Law(s) Applicable to Central Bank Digital Currencies

This contribution is of a prospective nature, as Central Bank Digital Currencies (CBDC) are mostly still either at the stage of reflection for their implementation or at the stage of experimentation. After defining the concept of CBDC, the chapter reviews the main conflict of laws rules applicable to money and international payment. It then attempts to apply these traditional solutions to this future new form of currency, namely CBDC. The syllogistic approach proves not to be very fruitful, however, especially when the traditional solutions to conflict of laws raised by money were based on a material analysis of money. Thus, while the lex monetae principle seems to have a bright future ahead of it, the lex loci solutionis principle seems to have almost no future in relation to CBDCs. The issuance of CBDCs and the pattern chosen by the monetary authorities to decide on their international use show that a new range of questions such as the law applicable to the protection of data, must be addressed before actually launching CBDCs on a multinational scale.
Research on Deep Learning-Based SAR Image Denoising Algorithm

Synthetic Aperture Radar (SAR) has the advantages of high penetration, high resolution, all-weather, and all-time, but due to the special imaging mechanism of SAR images, there are a large number of coherent speckle noises in the images. If not processed, it will greatly limit the subsequent use of data. In order to improve the use of SAR images, noise removal processing is needed. In order to choose the appropriate noise removal algorithm, this paper compares the performance of three noise removal algorithms: multiplication model, enhanced Lee-sigma filter, and CNN trained on SAR images (SAR-CNN). The results show that the multiplication model has better noise removal ability and ability to extract effective information.
NEED ANALYSIS FOR DEVELOPING PARAGRAPH WRITING MATERIALS BASED ON PROBLEM-BASED LEARNING MODEL FOR INDONESIAN EFL LEARNERS

This study aims to explore models of Paragraph Writing materials based on problem-based learning needed by students and lecturers. A descriptive qualitative method design is employed. It involves thirty- three students and two instructors of the English Department of a private university in Mataram West Lombok, Indonesia who attend paragraph writing class in the academic year 2023-2024. To collect the data, questionnaires, and interviews were used. The finding revealed that students and lecturers preferred teaching materials with understanding part of the paragraph, unity and coherence, and paragraph organization. The topic of paragraph writing material on vocabulary, subject-verb agreement, and part of speech. Learning exercises provide in individual and group, learning exercises which focus on writing topic sentences, supporting sentences and concluding sentences. Evaluation from both theoretical and practical elements, to understand the concept of problem-based learning. To conclude, designing a model of teaching materials for paragraph writing materials based on problem-based learning is needed to conduct to keep up with updated insightful needs. By giving information on the needs of students and educators, this study has shown the capacity to bridge the gap and also recommendation to conduct need analysis regularly for designing Paragraph Writing Material. Keywords: Paragraph writing material, problem-based learning, EFL learners