Reviewerly/exHarmony huggingface dataset:
Differential Effects Of Mother Tongue And English Language As A Medium Of Instruction On Pupil’s Numeracy SKILLS

This study explores the differential effects of the mother tongue and English language as mediums of instruction on pupil’s numeracy skills. The objective is to determine if there are significant differences in posttest gains between a control group taught in the mother tongue (Tagalog) and an experimental group taught in English. The researcher also aims to identify the factors and challenges faced by teachers using both languages as mediums of instruction. Using a mixed-method design, the researcher collected quantitative and qualitative data. Sixty pupils each were randomly selected for the control and experimental groups from Laiya Elementary School. Pretest and posttest scores were analyzed for the 120 Grade 3 pupils. The results revealed that the experimental group taught in English achieved significantly higher post-test results compared to the control group taught in the mother tongue. Qualitative data was obtained through interviews with Grade 3 and Grade 4 teachers, highlighting factors such as poor study habits, lack of interest and motivation, limited parental involvement, and low reading comprehension levels as barriers to improving numeracy skills. Furthermore, teachers faced challenges when using both languages, with Grade 3 teachers struggling to translate mathematical terms into the mother tongue and Grade 4 teachers finding it difficult to assess pupils’ prior knowledge due to their strong exposure to the mother tongue. Transitioning between languages, especially in the first quarter, created confusion and hesitation among pupils. Limited English vocabulary acquired during primary years further hindered understanding and comprehension. To enhance numeracy skills, teachers emphasized the need for motivation, additional activities, integration of numeracy across subjects, improved reading comprehension, parental participation, varied teaching methods, and a conducive learning environment. The study highlights the importance of selecting an appropriate language, establishing a language-rich environment, incorporating cultural relevance, and integrating language and mathematics instruction to foster deep understanding, critical thinking skills, and confidence in students as proficient mathematicians.
Realizing impossibilities

Abstract It is common in epistemic modal logic to model the epistemic states of agents via box operators in the normal logic S5. However, this approach treats agents as logically omniscient by requiring their knowledge to be closed under classical logical consequence. A promising way of avoiding logical omniscience consists in extending epistemic models with impossible states, that is states, where complex formulas are not evaluated recursively. However, this approach faces the dual problem of logical ignorance by modeling agents as not even minimally logically competent. In this paper I will outline an epistemic logic that combines impossible states with dynamic realization modalities akin to the dynamic announcement operators from public announcement logic (PAL). I will show that this epistemic logic avoids both the problem of logical omniscience and the problem of logical ignorance. Furthermore, I prove that so-called successful updates in my logic can be characterized in the same way as in PAL and that a similar logic due to Johan van Benthem can be simulated in my logic. Finally, I will compare my epistemic logic with a similar one, which has recently been advanced by JC Bjerring and Mattias Skipper.
Comparing The Effectiveness Of Distance Learning And Onsite Learning In Pre-Medical Course

Distance learning has experienced a significant surge in popularity due to the increasing digitalization of education. This study explores the effectiveness of distance learning compared to on-site learning in the context of pre-medical course students (grade 12). The research investigates students from the math science class and utilizes an online questionnaire in conjunction with the final results of the Thai Professional Aptitude Test (TPAT). Crucially, the research reveals that distance learning students tend to outperform their on-site counterparts in TPAT exams, although the difference is not overwhelmingly significant. This study contributes valuable insights into the growing impact of distance learning and its potential benefits for pre-medical course students. Further research and analysis in this domain could provide educators and institutions with valuable guidelines for optimizing educational approaches in the digital era.
TIR‐YOLO‐ADAS: A thermal infrared object detection framework for advanced driver assistance systems

Abstract An object detection framework using thermal infrared (TIR) cameras is proposed to meet the needs of an advanced driver assistance system (ADAS) operating at night‐time and in low‐visibility conditions. The proposed detection framework, referred to as TIR‐YOLO‐ADAS, is an improvement of YOLOX for TIR object detection in ADAS. First, to address the disadvantages of TIR objects, the part of the attention mechanism is designed to enhance the discriminative ability of feature maps in the spatial and channel dimensions. Second, a focal loss function is used as the confidence loss function to enable the framework to focus on detection tasks of difficult, misclassified targets in the process of network training. The results of the ablation experiment on the Forward‐looking infrared (FLIR) thermal ADAS dataset indicate that the proposed framework significantly improves the performance of TIR object detection. Comparative experimental results further show that TIR‐YOLO‐ADAS performs favourably when compared with three representative detection algorithms. To evaluate the practicality and feasibility of the proposed framework in various applications, a qualitative assessment in real road scenarios was conducted. The experimental results confirm that the proposed framework performs promisingly and could be integrated into vehicle platforms as an ADAS module.
Multilevel contrast strategy for unpaired image-to-image translation

Contrastive learning for unpaired image-to-image translation utilizes adversarial loss to ensure the realism of generated images in the target domain and incorporates pixel-wise contrastive loss to maximize the correlation between them. However, existing methods only contrast pixel-wise features, ignoring higher-level features, and the pixel-wise contrast is imperfect, which leads to poorer perceptual and visual results. In order to alleviate these problems, we propose an effective multilevel contrast strategy for unpaired image-to-image translation (MLCUT), which contrasts features at three levels to generate more harmonious and realistic images. Specifically, we strengthen the pixel-wise level contrast and introduce the contrasts of plane and voxel-wise levels. On the one hand, MLCUT enhances training effectiveness by picking over hard negative keys for each query at the pixel-wise level. On the other hand, we strengthen the learning preferences of generators on features of objects rather than backgrounds by contrasting the plane-wise discriminative matrices in adversarial loss. Furthermore, by contrasting voxel-wise global semantic vectors, MLCUT effectively improves the realism of generated images and avoids mode collapse. Qualitative and quantitative experiments demonstrate that our method effectively improves performance in perception and vision.
Oral Discourse In English Of Second Language Teachers: An Analysis

This study primarily aimed to analyze the discourse competence of teachers in English as a Second Language (ESL) classrooms in the Biliran Division. Employing the mixed-methods research design, this research elicited data from the 20 secondary teachers in English as a Second Language (ESL) classrooms in Biliran Division during the school year 2021-2022. The findings reveal that the most common cohesive ties used by the English teachers were: reference (rephrasing, restatement); ellipsis (omission of word/s); substitution (the use of one); discourse markers (the use of alright, so, okay); and discourse connectives (the use of so). Moreover, the syntactic errors committed by English teachers in the Biliran Division are mostly on subject-verb agreement, use of prepositions, code-switching, pronunciation, and plurality. Based on the overall observation and analysis of the participants’ utterances, the researcher concluded that the teachers in English as a Second Language (ESL) Classrooms in Biliran Division used linguistic features as their immediate recourse in times of vocabulary loss and grammar confusion. In line with this, the school administrators may invest and strengthen the language learning development programs for ESL teachers because it is through them that the schools train and develop the minds of the next generation.
UAV path planning based on a dual-strategy ant colony optimization algorithm

With the rapid development of modern communication and automatic control technologies, unmanned aerial vehicles (UAVs) have increasingly gained importance in both military and civilian domains. Path planning, a critical aspect for achieving autonomous aerial navigation, has consistently been a focal point in UAV research. However, traditional ant colony algorithms need to be improved for the drawbacks of susceptibility to local optima and weak convergence capabilities. Consequently, a novel path planning methodology is proposed based on a dual-strategy ant colony algorithm. In detail, an improved state transition probability rule is introduced, redefining ant movement rules by integrating the state transition strategy of deterministic selection during the iterative process. Additionally, heuristic information on adjacent node distance and mountain height is added to further improve the search efficiency of the algorithm. Then, a new dynamically adjusted pheromone update strategy is proposed. The update strategy is continuously adjusted during the iteration process, which is beneficial to the algorithm’s global search in the early stage and accelerated convergence in the later stage, preventing the algorithm from falling into local optimality and improving its convergence. Based on the above improvements, a new variation of ant colony optimization (ACO) called dual-strategy ACO algorithm is formed. Experimental results prove that dual-strategy ACO has superior global search capabilities and convergence characteristics from four key aspects: path length, fitness values, iteration number, and running time.
Digital Cultural Heritage, Cybersecurity, and the Human Factor

Abstract Digital heritage, with its intellectual and cultural identity value, constitutes a significant asset. This also makes it an important asset in the cyber market. Cultural heritage institutions, recognising the vital role of digital assets, confront various information security risks and must adopt cyber threat related strategies to counter potential cyber-attacks. This paper first investigates the susceptibility of digital heritage objects to cyber threats by examining their distinctive characteristics. The study subsequently poses research enquiries concerning the cybersecurity proficiency and general awareness of cultural heritage professionals. A conducted survey in Croatia among these professionals analyses their information and computer practices related to the storage and preservation of digital cultural heritage. Findings reveal that while overall cybersecurity awareness is commendable, there exists a deficiency in cybersecurity and data protection training among cultural heritage professionals, even at the basic level. Additionally, the study highlights insufficient technical support and equipment for cybersecurity within cultural heritage institutions.
Lossless medical image compression and noise removal using deep learning models

With the rapid expansion of the medical industry and advancements in medical sensors, mobile computing, and connectivity, data storage is increased by collecting, processing, and uploading medical data from remote sites for remote medical collaboration and diagnosis. In medical imaging, image reduction is essential for reducing data storage. To balance a good compression ratio (CR), greater image quality after decoding, and minimum processing time is the main problem of the compression technique in medical imaging. Due to increasing the CR, the loss of information in the decoded image impacts major issues in processing medical images. This research chapter presents a new lossless image compression and noise removal scheme using deep learning (DL) models. It focuses on compressing the data without loss of information using deep neural networks and an entropy coding scheme and takes minimum computation time to process the noise removal using different types of DL models. This scheme&apos;s performance is measured using the standard performance metrics of CR%, mean square error (MSE), and peak signal-to-noise ratio (PSNR), and its results are compared with JPEG and JPEG-2000.
An optical encryption method based on double-threshold correspondence imaging

Abstract An optical cryptosystem called double-threshold correspondence imaging (DTCI) encryption method is proposed. In DTCI encryption, two thresholds are used to select the larger and smaller bucket detector values, and the object image can be recovered only by averaging the speckle patterns corresponding to these selected bucket detector values. Compared with the traditional computational ghost imaging encryption, the ciphertext of DTCI encryption is a group of serial numbers rather than all bucket detector values, which not only reduces the volume of ciphertext but also enhances the security. Furthermore, the average operation replaces the second-order correlation operation in the decryption process, simplifying the decryption process and reducing the time cost. And the feasibility and high security of this method are verified by computer simulations.
Mediation of archival information through the Web

This article aims to contribute to an analysis of the level of the Websites of the Portuguese District Archives and equivalent (ADs), an assessment based on the perspectives of users and archive professionals, namely on aspects of the functionality, accessibility and interaction of these websites, thus rectifying an existing gap in the literature on this subject. For this purpose, the Quadripolar Method was adopted, complemented by criteria based on two central references: CONARQ and DLGAB. The process began with a review of the existing literature on Web mediation in about 24 ADs and equivalent in Portugal (Aveiro, Beja, Braga and University of Minho, Bragança, Castelo Branco, University of Coimbra, Guarda, Alfredo Pimenta – Guimarães, Évora, Leiria, Lisbon, Portalegre, Porto, Santarém, Setúbal, Viana do Castelo, Vila Real, Viseu, Madeira, Angra do Heroísmo, Ponta Delgada, Horta, Torre Tombo) and a methodology of action was implemented consisting of online surveys sent to 595 users. The objective of this study was to diagnose the level of interactivity of ADs based on the perceptions of the users surveyed, in order to know to what extent the Internet brings new potentialities in the mediation of ADs' information.
Breast Cancer Detection Based DenseNet with Attention Model in Mammogram Images

Breast cancer has become a very interesting topic due to the massive number of deaths among women across the world. Radiologists can diagnose breast cancer faster and more accurately because of advances in the computer-aided diagnosis (CAD) system. In this paper, we presented a new breast cancer detection system based on the integration of self attention model in the pre-trained deep neural networks DenseNet. First, we extracted automatic high-level features from breast images using DenseNet extraction layers, and thereafter attention model was applied to focus the treatment on the relevant parts of the region of interest. The experiments were conducted on a multi-class Mammographic Image Analysis Society (MIAS) database, including three classes of breast cancer images. We achieved the accuracy of 0.9939 when applying both transfer learning, data augmentation, and self attention mechanism.
Social Recommendation Using Deep Auto-encoder and Confidence Aware Sentiment Analysis

The development of online social networks has attracted increasing interest in social recommendation. On the other hand, recommender systems based on deep learning and sentiment analysis techniques are currently widely used to solve the problem of data sparsity. However, only a few attempts have been made in social-based recommender systems. This article focuses on this issue and proposes a novel hybrid approach named CASA-SR (Confidence Aware Sentiment Analysis-based Deep Social Recommendation). Our approach exploits sentiment analysis by detecting fake reviews and combines predictions generated by collaborative and content-based filtering. A neural architecture has been adopted using an auto-encoder and a multilayer perceptron neural network. Moreover, our approach integrates social information, including users’ trust (credibility and similarity degrees). Experimental results conducted on different datasets showed significant improvements in recommendation performance according to the state-of-the-art work.
Execution Planning for Aggregated Search in the Web of Data: A Free-Metadata Approach

An aggregate search system in the web of data makes it possible to search for pieces of information that may not exist entirely in a single source. It provides a unified query interface allowing access to several data sources. One of the major concerns while setting up an aggregate search system is adopting an adequate execution planning strategy, indeed, thanks to execution planning, the system optimizes network traffic while collecting fragments of data, avoids unnecessary executions, and reduces the waiting time for the user. Execution planning strategies in the state of the art often include using metadata or checking the existence of resources before execution. In this paper, we present a solution for execution planning in aggregated search systems without using metadata or consulting the sources. This solution aims to optimize network traffic by avoiding duplicate results and exaggerated size of intermediate data, our method is based only on the analysis of the input query.
Hand Gesture Recognition in Indian Sign Language Using Deep Learning

Sign languages are important for the deaf and hard-of-hearing communities, as they provide a means of communication and expression. However, many people outside of the deaf community are not familiar with sign languages, which can lead to communication barriers and exclusion. Each country and culture have its own sign language, and some countries have multiple sign languages. Indian Sign Language (ISL) is a visual language used by the deaf and hard-of-hearing community in India. It is a complete language, with its own grammar and syntax, and is used to convey information through hand gestures, facial expressions, and body language. Over time, ISL has evolved into its own distinct language, with regional variations and dialects. Recognizing hand gestures in sign languages is a challenging task due to the high variability in hand shapes, movements, and orientations. ISL uses a combination of one-handed and two-handed gestures, which makes it fundamentally different from other common sign languages like American Sign Language (ASL). This paper aims to address the communication gap between specially abled (deaf) people who can only express themselves through the Indian sign language and those who do not understand it, thereby improving accessibility and communication for sign language users. This is achieved by using and implementing Convolutional Neural Networks on our self-made dataset. This is a necessary step, as none of the existing datasets fulfills the need for real-world images. We have achieved 0.0178 loss and 99% accuracy on our dataset.
Can designers take the driver’s seat? A new human-centered process to design with data and machine learning

Machine Learning (ML) is increasingly becoming a crucial asset across diverse industries. However, designers lack human-centered processes to envision and develop innovative solutions enabled by ML. By engaging in a Research-through-Design activity, we outline a new design process to generate human-centered adaptive systems enabled by data and ML. We describe and discuss the possibilities and limits of designing with ML, the need to concurrently address user experience and ML aspects, and the implications of their mutual influence. We argue that designers can envision and design human-centered ML-enabled systems if they acquire fundamental ML knowledge, although certain tasks necessitate close collaboration with ML experts. We discuss how uncertainty and risk of failure characterize the outlined process and may limit its applicability. The proposed process serves as a foundational framework for future research in human-centered design innovation through data and ML.
Blockchain Ethics

There is no question about the innovation force and the economic potential of blockchain technology. As the basis for new currencies, financial services, and smart contracts, blockchain technology can be seen as the fifth disruptive computing paradigm, after mainframes, personal computers, the Internet, and mobile devices. However, there are questions about its ethical implications, which have the potential to also impact the economic success of blockchain technology. This article aims to provide ethical guidance on blockchain technology. In order to reach this goal, the focus of the ethical analysis will first concentrate on the unique characteristics of blockchain technology compared to other technology-based innovations. The unique nucleus of blockchain technology can be defined as a move from the trust in people to a trust in math, as a move from an internet of information to an internet of value, or—as I would propose—a shift from an intermediated network to an immediate network. Second, the ethical opportunities (e.g., transparency, participation, global access to services) and risks (e.g., ecological impact, lack of legal monitoring and enforcement) associated with this unique nucleus of blockchain technology will be discussed. Third, an outlook on possible concrete solutions will be provided.