Reviewerly/exHarmony huggingface dataset:
An Analysis of Students' Ability in Writing Descriptive Composition in Vocational High School

In writing descriptive composition students still experience deficiencies, where some students still find it difficult to write. The purpose of study is to find out the students' abilities in writing descriptive composition and what that difficulties students face in writing descriptive composition in Vocational High School. This research was conducted by qualitative research using descriptive method. The subject of this research are students of class X OTKP in one of private school in Cirebon. There are 19 students in the class and researcher choses 14 students with a random technique. Furthermore, the researcher used student writing and interviews as instruments. The researcher analyzed the data, and the researcher found some finding in the field. First, it was found that the students' ability in writing descriptive compositions was at a low level. This is supported by the second finding, the results of interviews with students, namely that students' difficulties are the generic structure of descriptive composition which consists of describing objects in detail and grammar. In addition, most students have difficulty in arranging words into sentences and writing the correct spelling of words. In addition, the lack of vocabulary makes them write many repetitions of words in their writing.
Machine Learning-Based Detection of Phishing in COVID-19 Theme-Related Emails and Web Links

During the COVID-19 epidemic phishing dodges increased in frequency mostly the links provided current updates about COVID-19 hence it became easy to trick the victims. Many research studies suggest several solutions to prevent those attacks but still phishing assaults upsurge. There is no only way to perform phishing attacks through web links attackers also perform attacks through electronic mail. This study aims to propose an Effective Model using Ensemble Classifiers to predict phishing using COVID-19-themed emails and Web Links. Our study comprises two types of Datasets. Dataset 1 for web links and Dataset 2 for email. Dataset 1 contains a textual dataset while Dataset 2 contains images that were downloaded from different sources. We select ensemble classifiers including, Random Forest (RF), Ada Boost, Bagging, ExtraTree (ET), and Gradient Boosting (GB). During the analysis, we observed that Dataset 1 achieves the highest accuracy rate as compared to Dataset 2 which is 88.91%. The ET classifier performs with an accuracy rate of 88.91%, a precision rate of 89%, a recall rate of 89%, and an f1 score of 89% which is better as compared to other classifiers over both datasets. Interesting concepts were found during the study.
Multimodal communication in young, multilingual children: learning beyond words <b>Multimodal communication in young, multilingual children: learning beyond words</b> , by Jieun Kiaer, Bristol, Multilingual Matters, 2023, 192 pp., €109.95 (hardback), ISBN 9781800413337

Click to increase image sizeClick to decrease image size AcknowledgmentThe authors express deepest gratitude to Lembaga Pengelola Dana Pendidikan (LPDP/Indonesia Endowment Fund for Education) under the Ministry of Finance of the Republic of Indonesia. The authors also thank Aulia Sholichah Iman Nurchotimah for editing the manuscript.
Disaster Tweets: Analysis from the Metaphor Perspective and Classification Using LLM’s

Nowadays, social networks, specially Twitter (now X), allow the spread of information about all topics; since this platform is completely open, there is little to none restriction on what a user can post, hence, creating a lack of confidence and trust on the information available. However, the information on Twitter sometimes have hidden meanings, as the users use metaphors to define their ideas. This paper analyzes and classifies a set of texts labeled as disaster and non-disaster, where those labeled as non-disaster include metaphorical context, focusing on the metaphorical tweets and their interaction with large language models such as BERT, RoBERTa and DistilBERT. These experiments showed an improvement compared with the state-of-the-art approaches, demonstrating that these models capture proper metaphorical text representations.
IMPLEMENTASI PENGGUNAAN ALAT EDUKASI KARTU BERGAMBAR (FLASH CARD) DALAM MENINGKATKAN KEMAMPUAN BAHASA INGGRIS SISWA

Students' interest in learning English is still low, which means students' English language skills are also low. This is because during the learning process in the classroom, teachers do not use learning media such as educational tools. Educational tools are very important if applied in learning. This is because educational tools can increase students' learning motivation so that learning is more meaningful, innovative and creative. Therefore, the service team carries out service activities regarding the use of educational tools in classroom learning. This service activity aims to increase students' interest in learning and also students' English language skills. This service activity was carried out at MIS Nurul Hidayah Medan. Participants in this activity were class V students consisting of 30 students. This activity will be carried out on September 6 2023. This activity will be carried out in 3 stages. The first stage is to give a pretest to students before carrying out implementation activities using picture card educational tools (flash cards). The second stage is implementing the use of flash cards in English lessons. The third stage is giving a posttest to students after carrying out implementation activities using picture card educational tools (flash cards). The results of this activity were that the student response was very good and students were more interested in learning using the flash card educational tool and there was an increase in student ability obtained from the N-Gain score which was in the medium category. So it can be concluded that the flash card educational tool can improve students' English language skills.
BIAK LANGUAGE GREETINGS AND THEIR IMPLICATIONS FOR LOCAL LANGUAGE LEARNING IN SCHOOLS

The objectives of this research are to describe Biak language greeting words in terms of form and meaning and their implications for local language learning in schools. The method used in this research is descriptive qualitative method. To collect data, the techniques used are observation, listening technique and conversation technique. To analyze the data, the technique used is qualitative analysis technique which includes: 1) Data reduction, namely simplifying the data that has been collected. 2) Presentation of data, namely, presenting data before being simplified through data grouping. 3) Verification, namely checking the accuracy of the data that has been presented. Conclusion, namely drawing conclusions to answer research questions. The results showed: 1) Family greetings: kamam (father), awin (mother), ɛba (brother), ɛknik (younger brother); 2) Kin greetings: mansar (grandfather), insar (grandmother), mɛ (aunt), kabor (nephew), insos (niece), ɛbaya kabor (male cousin), ɛbaya insos (female cousin); 3) Non-relative greetings: /ambɛr beba/ someone who has a position and is older, younger, and so on, /manan wir (community leader), manan wir bɛba (traditional leader). 4) Common greetings: kamam (father), awin (mother), kabor (a man of the same age or younger), insos (a woman of the same age or younger). The implication of this research can be applied in local language learning in schools to better understand and appreciate local culture and grammar.
Visualizing the Cosmos: A Novel Method for Text Recombination with Space News

As the volume of data continues to surge, researchers are confronted with the challenge of extracting meaningful insights from this wealth of information. Despite rapid advancements in Natural Language Processing (NLP) techniques in the AI industry, there remain gaps and opportunities for further exploration, particularly in the realm of data recombination techniques and methods. This paper proposes a novel text recombination method to facilitate the generation of recombined words from a given text. The process commences with a ’stanza’ model, which identifies and compiles Named Entity Recognitions (NERs) into a list. These NERs are then cross-referenced with Wikipedia pages to retrieve relevant information, thereby enhancing entity understanding and analysis. The ensuing step involves preprocessing the output text from the previous stage, generating a list of unique words while eliminating stop words. This preprocessing stage serves to remove noise and focus on meaningful words, laying the groundwork for more effective clustering. To enable clustering, we employ vector embeddings, representing words in a 2-dimensional space, rendering them suitable for clustering techniques. Notably, the proposed method further enhances results by re-clustering words after applying K-Means, thereby identifying the most fitting candidate words for recombination. Comparatively, this method outperforms large language models (LLMs) due to its incorporation of NER information, utilization of Wikipedia pages, and effective preprocessing techniques. Unlike LLMs, which operate as resource-intensive black boxes on static data, this method benefits from real-time information access and knowledge base updates. Furthermore, each stage of the process is visualized to control the progress correctly. Thus, due to the plots of word clusterization, the proposed text recombination approach showed positive results.
Nighttime Thermal Infrared Image Colorization with Feedback-based Object Appearance Learning

Stable imaging in adverse environments (e.g., total darkness) makes thermal infrared (TIR) cameras a prevalent option for night scene perception. However, the low contrast and lack of chromaticity of TIR images are detrimental to human interpretation and subsequent deployment of RGB-based vision algorithms. Therefore, it makes sense to colorize the nighttime TIR images by translating them into the corresponding daytime color images (NTIR2DC). Despite the impressive progress made in the NTIR2DC task, how to improve the translation performance of small object classes is under-explored. To address this problem, we propose a generative adversarial network incorporating feedback-based object appearance learning (FoalGAN). Specifically, an occlusion-aware mixup module and corresponding appearance consistency loss are proposed to reduce the context dependence of object translation. As a representative example of small objects in nighttime street scenes, we illustrate how to enhance the realism of traffic light by designing a traffic light appearance loss. To further improve the appearance learning of small objects, we devise a dual feedback learning strategy to selectively adjust the learning frequency of different samples. In addition, we provide pixel-level annotation for a subset of the Brno dataset, which can facilitate the research of NTIR image understanding under multiple weather conditions. Extensive experiments illustrate that the proposed FoalGAN is not only effective for appearance learning of small objects, but also outperforms other image translation methods in terms of semantic preservation and edge consistency for the NTIR2DC task. Compared with the state-of-the-art NTIR2DC approach, FoalGAN achieves at least 5.4% improvement in semantic consistency and at least 2% lead in edge consistency.
ApproxDup: Developing an Approximate Instruction Duplication Mechanism for Efficient SDC Detection in GPGPUs

Nowadays, Selective instruction Duplication (SelDup) is the typical approach to detect Silent Data Corruption (SDC) in GPGPU. However, owing to the up-to-billions fault sites of parallel GPGPU kernel functions, it usually introduces tremendous overhead to perform Fault Injections (FIs) for obtaining the duplication-candidate instruction set (although can be conducted in parallel). Moreover, current SelDup typically considers all SDCs severe and tends to duplicate more instructions. The nontrivial duplication overhead seriously restricts the deployment of current SelDup on resource-constrained systems (e.g., embedded GPGPUs). To address the above challenges, this paper proposes an Approximate instruction Duplication mechanism (ApproxDup) for efficient SDC detection in GPGPUs. Firstly, to replace the expensive FI-based duplication-candidate instructions identified method, we drive out a Machine Learning-based model (SDC-Predictor) for instruction-wise SDC proneness and severity estimation. Our key insight is that instruction type/functionality and instruction dependency set can efficaciously characterize the instruction-wise SDC proneness in GPGPUs. In contrast, the instruction’s original data magnitude, fault propagation range, and error detected features can distinguish its SDC severity. Secondly, incorporating the concept of approximate computing, we propose ApproxDup that preferentially duplicates Severe-SDC-prone instructions while relaxing the detection of minor/detectable SDCs for traditional SelDup overhead reduction. Experimental results exhibit that ApproxDup can cover 92.51% of Severe SDCs while merely increasing 38% of dynamic instructions, which achieves a better tradeoff between reliability and performance compared with the state-of-the-art SelDup. Furthermore, we discuss the effectiveness of the proposed method on different ML models/applications/GPGPU architectures.
Synthetic Information towards Maximum Posterior Ratio for deep learning on Imbalanced Data

This work explores how class-imbalanced data affects deep learning and proposes a data balancing technique for mitigation by generating more synthetic data for the minority class. In contrast to random-based oversampling techniques, our approach prioritizes balancing the most informative region by finding high entropy samples. This approach is opportunistic and challenging because well-placed synthetic data points can boost machine learning algorithms’ accuracy and efficiency, whereas poorly-placed ones can cause a higher misclassification rate. In this study, we present an algorithm for maximizing the probability of generating a synthetic sample in the correct region of its class by placing it toward maximizing the class posterior ratio. In addition, to preserve data topology, synthetic data are closely generated within each minority sample neighborhood. Overall, experimental results on forty-one datasets show that our technique significantly outperforms experimental methods in terms of boosting deep-learning performance.
Scientometric analysis of 3D printing in libraries, and implications for copyright

3D printing is a process whereby users can create a three-dimensional solid object of any shape by operating a 3D printer using a digital design, thereby creating their own designs or replicate and electronically customize scanned objects. The technology has existed for several decades; however, it was not until around 1990 it really gained momentum (Van Wiele, 2019). In this paper the authors investigated research on 3D printing in libraries and copyright through a scientometric analysis, by searching in Web of Science for libraries, 3D printing and copyright. The data was mapped in WoS-viewer, to show the most important authors, the most cited documents, and the most cited sources. 107 results from WoS Core Collection for “3D printing” AND copyright was found. Also, the authors researched library websites in Norway for guidelines and rules concerning copyright and 3D printing. Norwegian copyright law does not mention 3D printing as such, so how are academic and public libraries teaching or mentoring copyright issues to patrons and students?
Smart Voice Controlled Wheelchair for Physically Disabled People

Abstract: In this groundbreaking study, we introduce a state-of-the-art, user-friendly smart voice-controlled wheelchair designed specifically for individuals with physical disabilities. Leveraging advanced technologies, including the powerful Arduino Uno microcontroller, precise ultrasonic sensors for obstacle detection, and seamless Bluetooth connectivity, our wheelchair system redefines the paradigm of mobility solutions. An intuitive mobile application, available for download on popular platforms such as the Play Store, complements the hardware, enabling effortless control and customization through natural voice commands. This paper meticulously details the design, implementation, and evaluation of our smart voice-controlled wheelchair, emphasizing its innovative features and discussing compelling avenues for future improvements.
Formal Verification of the Burn-to-Claim Blockchain Interoperable Protocol

This paper introduces an abstract blockchain model that employs the Burn-to-Claim cross-blockchain protocol [1]. This multi-level simulator models a virtual environment of nodes running on the Ethereum Virtual Machine (EVM). Developed using the $$CSP\#$$ language [2], it has undergone formal verification with the model checker PAT. Focusing on inter-network operations, our model ( https://github.com/b-pillai/Burn-to-Claim-formal-verification ) examines the properties of correctness, security, and atomicity using PAT. Surprisingly, atomicity, assumed to be inherent in the time-lock mechanism of the Burn-to-Claim protocol, does not always hold. We establish its validity under specific assumptions while confirming the protocol’s correctness and security under the added assumptions.
Dynamic Degradation Intensity Estimation for Adaptive Blind Super-Resolution: A Novel Approach and Benchmark Dataset

Blind Super-Resolution (BlindSR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) images without prior knowledge of the image degradation process. This is a challenging problem in real-world applications, where the degradation can be complex and unknown. Recent unsupervised learning-based BlindSR methods can estimate the image degradation in an unsupervised manner, but they suffer from limited adaptability to different types and intensities of degradation. They tend to capture the average level of degradation across all training samples, resulting in over-smoothing or over-sharpening effects for some images. As a result, the final reconstruction may exhibit the mean effect. Moreover, existing synthetic datasets do not reflect the real-world degradation scenarios, making it difficult to evaluate the performance of BlindSR methods. To address these issues, we propose a novel Degradation Intensity Estimation Module (DIEM) method, which can estimate the pixel-level degradation information of the input image more specifically and use it to guide image reconstruction. Furthermore, we construct a benchmark dataset under real scenarios, which is closer to the real-world BlindSR problem than existing synthetic datasets, and can provide a more reasonable evaluation of BlindSR methods. Extensive experimental results demonstrate that our DIEM-guided BlindSR method can achieve state-of-the-art image reconstruction results. Our code and pre-trained models have been uploaded to GitHub† for validation.
Interactive Dual Network With Adaptive Density Map for Automatic Cell Counting

Cell counting is an essential step in a wide variety of biomedical applications, such as blood examination, semen assessment, and cancer diagnosis. However, microscopic cell counting is conventionally labor-intensive and error-prone for experts, and most of the existing automatic approaches are confined to a specific image type. To address these challenges, we propose a new interactive dual-network framework for automatic and generic cell counting. In this framework, one deep learning model (counter) is trained to regress a density map from a given microscope image. The number of cells in that image can be estimated by performing integration over the regressed density map. Another network (ground truth generator) is employed to dynamically generate suitable ground truth based on the cell samples and the dot annotations to serve as the supervision for training the counter. The interactive process to obtain the optimal model is achieved by jointly training the counter and ground truth generator iteratively. Moreover, we design a hierarchical multi-scale attention-based architecture to act as the counter in the proposed framework. This architecture is crafted to efficiently and effectively process multi-level features, enabling accurate regression of high-quality density maps. Evaluation experiments on three public cell counting datasets demonstrate the superiority of our method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the need for advanced healthcare in the deep learning era. As a routine assessment procedure in healthcare settings, cell counting usually suffers from poor accuracy and inefficiency. We provide a solution to ameliorate the situation by developing a deep learning-based framework for automatic cell counting. After being trained in an end-to-end manner, the dual-network system is able to estimate the number of cells from the given microscopic images more accurately than existing methods. Additionally, this method is robust in various scenarios, such as calculating cell populations in suspension and cells in tissues. In the future, the presented pipeline has the potential to be implemented by biomedical practitioners who are non-expert in programming via wrapping it into a graphical user interface.
Artificial Intelligence and Its Relationship to Teaching School and University Mathematics in Jordan

This study aims to study the scenarios that may arise with the introduction of artificial intelligence in education, especially in mathematics education in Jordanian schools and universities, and what its implications are for the future of schools. The study design was phenomenological, a qualitative approach that looked at the perspectives of participants from various fields. The findings suggest that as AI in education becomes more prevalent, schools and teachers will encounter new products, advantages, and disadvantages. The findings highlight several recommendations for using AI and preventing potential problems and while participants generally seemed to have positive views of AI, there were some negative aspects as well, to which mathematics teachers and academics in particular drew attention in relation to the future of education. While educators view AI as a tool to bring quality and benefit to all in the education sector, they are more focused on the rationale for AI in education and potential future issues.
Spatio-temporal load migration potential of data centers: Evaluation and application

The wide distribution of data centers and the delay tolerance of computing tasks endow data center loads with adjustable characteristics in both temporal and spatial dimensions. Due to the characteristics of abundant spatiotemporal flexibility, data centers can participate in the optimization of power system operation and regulation. To quantify this flexibility, this paper proposes a spatiotemporal load migration potential evaluation model. Meanwhile, a data center energy management strategy is combined with the characteristics of spatiotemporal load migration, which deeply exploits the migration potential through the spatiotemporal redistribution of delay-tolerant tasks, as well as server ON/OFF scheduling and CPU operating frequency scaling across different spatial locations. A case study demonstrates that adopting the proposed approach considering an energy management strategy can effectively improve the load migration potential of data centers. The migration characteristics of data centers have great application prospects in reducing carbon emissions and enhancing operational flexibility.