Reviewerly/exHarmony huggingface dataset:
Sign Language Detection

Abstract: Sign language is a crucial means of communication for individuals with hearing impairments, serving as a rich and expressive form of language. As the world becomes increasingly interconnected, there is a growing need for technology to facilitate effective communication between sign language users and the broader community. Sign language detection, a branch of computer vision and artificial intelligence, plays a pivotal role in bridging these communication gaps. This abstract provides an overview of the key aspects related to sign language detection. The primary focus is on the technological advancements and applications that contribute to the seamless integration of sign language into various aspects of daily life. The paper explores the challenges associated with recognizing and interpreting sign language gestures, considering factors such as variations in signing styles, lighting conditions, and diverse signing communities. The proposed paper delves into the methodologies employed in sign language detection systems, including image and video processing techniques, deep learning algorithms, and sensor technologies. It highlights the significance of dataset diversity and the role of machine learning in enhancing the accuracy and robustness of sign language detection models. Additionally, the paper discusses the potential integration of sign language detection into mainstream devices, applications, and assistive technologies to empower individuals with hearing impairments.
Polygonal Approximation Learning for Convex Object Segmentation in Biomedical Images with Bounding Box Supervision

As a common and critical medical image analysis task, deep learning based biomedical image segmentation is hindered by the dependence on costly fine-grained annotations. To alleviate this data dependence, in this paper, a novel approach, called Polygonal Approximation Learning (PAL), is proposed for convex object instance segmentation with only bounding-box supervision. The key idea behind PAL is that the detection model for convex objects already contains the necessary information for segmenting them since their convex hulls, which can be generated approximately by the intersection of bounding boxes, are equivalent to the masks representing the objects. To extract the essential information from the detection model, a repeated detection approach is employed on biomedical images where various rotation angles are applied and a dice loss with the projection of the rotated detection results is utilized as a supervised signal in training our segmentation model. In biomedical imaging tasks involving convex objects, such as nuclei instance segmentation, PAL outperforms the known models (e.g., BoxInst) that rely solely on box supervision. Furthermore, PAL achieves comparable performance with mask-supervised models including Mask R-CNN and Cascade Mask R-CNN. Interestingly, PAL also demonstrates remarkable performance on non-convex object instance segmentation tasks, for example, surgical instrument and organ instance segmentation. Our code is available at https://github.com/shenmishajing/PAL.
Virtual Educational Collaborative Environments for Low-Cost Mobile VR Headsets

Utilization of virtual reality (VR) technologies in education is becoming more and more common but there are still obstacles to deal with. One of them is a relatively high cost of VR headsets, important for an immersive experience. The solution can be seen in mobile VR headsets, consisting of a relatively cheap holder with lens and a smartphone, providing computing power. In this paper, we present a web-based educational cooperative virtual environment, tailored for mobile VR headsets and results of its evaluation. We also overview contemporary educational VR applications and provide a point of view of local (Slovakia) primary and secondary school teachers on school subject suitable for VR.
Improving Speaker Diarization for Low-Resourced Sarawak Malay Language Conversational Speech Corpus

Speaker diarization plays a vital role in speech transcription involving conversations as it improves the transcribed content's accuracy, comprehension, and usability. By having a speech transcription diarized, the conversation data has a more structured presentation, allowing for a variety of applications that rely on accurate speaker attribution. Even so, speaker diarization is a field that has been less explored for low-resourced languages, as current resources that have been optimized and applied in speaker diarization are mostly for more developed and well-resourced languages, such as English, Spanish or French. In this paper, we propose an approach to using pseudo-labelled speech data to perform self-training on the x-vector models to improve diarization accuracy. The proposed method uses almost 13 hours Sarawak Malay unlabeled conversational speech corpus obtained from the Kalaka: Language Map of Malaysia website for training, as well as 1 hour and 26 minutes of manually labeled Sarawak Malay speech data for testing and evaluation. We demonstrate how speaker diarization models can be fine-tuned with the pseudo-labeled data.
Mathematical Camera Array Optimization for Face 3D Modeling Application

Camera network design is a challenging task for many applications in photogrammetry, biomedical engineering, robotics, and industrial metrology, among other fields. Many driving factors are found in the camera network design including the camera specifications, object of interest, and type of application. One of the interesting applications is 3D face modeling and recognition which involves recognizing an individual based on facial attributes derived from the constructed 3D model. Developers and researchers still face difficulty in reaching the required high level of accuracy and reliability needed for image-based 3D face models. This is caused among many factors by the hardware limitations and imperfection of the cameras and the lack of proficiency in designing the ideal camera-system configuration. Accordingly, for precise measurements, we still need engineering-based techniques to ascertain the specific level of deliverables quality. In this paper, an optimal geometric design methodology of the camera network is presented by investigating different multi-camera system configurations composed of four up to eight cameras. A mathematical nonlinear constrained optimization technique is applied to solve the problem and each camera system configuration is tested for a facial 3D model where a quality assessment is applied to conclude the best configuration. The optimal configuration is found to be a 7-camera array, comprising a pentagon shape enclosing two additional cameras, offering high accuracy. For those who prioritize point density, a 9-camera array with a pentagon and quadrilateral arrangement in the X-Z plane is a viable choice. However, a 5-camera array offers a balance between accuracy and the number of cameras.
The Impact of Automated Evaluation Feedback on Students’ Writing Revision

To make essay scoring more manageable, Automated Writing Evaluation (AWE) programs have been developed and widely used in China. It makes immediate feedback possible and helps students do self-revision instantly. This study attempts to explore the influences exerted by automated scoring feedback on students writing revision and analyze their revision process. with Juku. Pigai AWE system as an example, the research involves 45 non-English major students in Northwestern Polytechnical University and adopts their writing samples to build a small corpus. With careful study of their modification process and detailed analysis of their writing, the results shows that apart from providing holistic ideas on the article, Pigai system provides a specific review from 6 aspects as well. Besides, students can actively respond to the feedbacks and constantly revise their writing according to the feedback points, but mostly in technical details, morphemes, words, and phrases, short sentences. Online modification expands knowledge input, reduces language errors, improves students’ writing quality, and effectively promotes autonomous learning in students’ interaction with machines. However, limitations existed as well, such as lack of effective feedback on the content. Finally, the researcher proposed a “four-step” writing and evaluation model for future improvement.
Automatic Detection of Escherichia coli Bacteria from Tryptic Soy Agar Image Using Deep Learning Method

Escherichia coli is a normal bacterial flora that lives in the human intestine, is harmless and is part of a healthy digestive tract. However, there are several strains of pathogenic Escherichia coli that can cause infections in the digestive tract, namely diarrhea. Diarrheal disease in Indonesia needs treatment and study because most of the diagnoses are still based on clinical diagnosis. Conventional methods used for the detection of Escherichia coli bacteria include culture methods, biochemical tests, and serological tests. This method has the disadvantage of requiring a long time, a large number of samples, and a relatively high error in reading the results. Therefore, the detection process needs to be done automatically using the Faster R-CNN deep learning method. In this research, we used Faster R-CNN with Inception v2 and ResNet-50 architecture and added augmentation and Image Enhancement to the Tryptic Soy Agar image dataset. The test results show that the addition of Image Enhancement greatly affects model performance and the model that has the best performance and is most appropriate to use is the Faster R-CNN ResNet-50 architecture with the addition of Contrast Stretching and Gaussian Filters to the image dataset. This model has 91% accuracy, 90% precision, 95% recall, and 92% F-1 score.
Estimasi Keberhasilan Siswa dalam Pemodelan Data Berbasis Learning Menggunakan Algoritma Support Vector Machine

SMK Negeri 5 Pekanbaru aims to prepare competent graduates who can compete in the global market. The realization of these goals is influenced by student achievement at school. Student achievements determine the ability of students to work in certain fields. Based on observations, it is known that student achievement at SMK Negeri 5 Pekanbaru tend to be low. This is also shown by the data that has been collected through the Curriculum section. Based on the data, there can be extraction using the supervised learning method to make a classification model of student achievements. The supervised learning algorithm used in this research is a Support Vector Machine (SVM). The data used in this study are student's data grade X SMK Negeri 5 Pekanbaru in 2020 totaling 160 data. The classification process is carried out by applying the GridSearch method to find the best kernel to be implemented. Based on the implementation of GridSearch, the kernel to be used is Radial Basis Function (RBF) with Cost (C) and Gamma (?) parameters. Based on 16 experiments with different parameter values, the best classification results are obtained using the value of Cost (C) = 0.1 and the value of Gamma (?) = 0.01, with accuracy values of 94%.
Blockchain-Based Application Security Versus Centralized and Distributed Data Management Systems – A Comparative Study

The current research aims to extend the existing literature on the differentiation of Blockchain Technology (BCT) from Distributed Ledger Technology (DLT) and traditional database management systems (DBMS) on grounds of providing additional security to user applications. Through a qualitative hierarchy methodology, it identifies prominent factors contributing to the same. 31 working professionals across a range of industry types were subjected to a semi-structured interview and the interview transcripts were analysed through NVivo and Rstudio. The findings suggested that the use of BCT in various daily use applications will drastically enhance security over traditional DBMS on grounds of traceability, immutability, transparency, accountability and non-hackability of data. The implementation of BCT over DLT in applications pointed towards enhancement of security too, but considering the areas only when the technical novelty of BCT is needed over the already robust DLT. While some industries would benefit straightaway, some need to strategically set their requirements on grounds of application security and think mindfully before implementing BCT considering the enhanced technical novelty of the same for visibly reaping its benefits.
Exploring Teenage Awareness of Social Media Fraud in Malaysia

Social media platforms have become integral to modern communication, allowing individuals to connect with loved ones and stay abreast of trends. However, recent studies have uncovered the prevalence of fraud within today's social media landscape, highlighting it as a global issue. This research aims to explore into the perceptions of Malaysian teenagers regarding social media fraud. The primary objective of this study is to explore the experiences of Malaysian teenagers in relation to social media fraud, with a focus on identifying the psychological aspects of both teenagers and fraudsters involved. Furthermore, the research seeks to develop effective strategies to reduce and prevent social media fraud among teenagers in Malaysia, while also providing practical recommendations and raising awareness among this demographic. A comprehensive quantitative online survey questionnaire was administered to 385 teenage respondents in Malaysia. The collected data was analysed using IBM SPSS Statistical Package, and an ANOVA test was employed to verify the research hypotheses. The results revealed compelling evidence supporting the hypotheses put forth in this study. Firstly, the analysis indicated that sharing personal information on social media significantly increases the likelihood of teenagers falling victim to fraud (p-value = 0.013). Furthermore, a strong correlation was established between increased social media usage and heightened risk of fraudulent activities (p-value = 0.049), underlining the statistical significance of this relationship. Lastly, the study demonstrated that social media platforms can be exploited as tools for perpetrating fraud (p-value = 0.017), solidifying this assertion with rigorous statistical support. Additionally, the majority of respondents strongly agreed that avoiding suspicious links or websites on social media could effectively reduce the incidence of fraud.
Performance Analysis of The Last Significant Bit (LSB) Method in Steganography for Data Hiding in Image Data

Technology as a means of conveying information can have significant implications for the security and confidentiality of data and information content. When a sender wishes to convey a message to someone and does not want the message to be easily visible, the concealment of this message becomes crucial for the sender. One commonly used method for data concealment is the Least Significant Bit (LSB) method in steganography. The LSB method is preferred for its simplicity, fast process, and greater capacity. Messages are embedded by replacing the least significant bit in the host image with the message bits, with the rule that the number of message bits does not exceed the number of bits in the host image. Based on research findings, the results of steganography are difficult to distinguish from the original content through visual observation. Therefore, it can be concluded that the steganography method is highly effective for concealing a message.
Offensive language in media discussion forums: A pragmatic analysis

Abstract This study intends to contribute to the delimitation of selected offensive language categories based on an analysis of a corpus of contributions to discussion forums in Czech online national newspapers and news platforms called Czech Corpus of Offensive Language (CCOL). It endeavours to study three problematic areas (1) delimitation between the speech acts performed, (ii) lexical realisation of specific properties of the target and (iii) identification and categorisation of implicit offence (e.g. figurative semantic shifts) by exploring contextual cues for the speech act identification, the keywords indicating the properties of the target and the types of semantic shifts in implicit expressions of offence. The findings indicate that annotation systems that do not use context information for the detection of offensive language may face problems with adequate interpretation of the language means under investigation.
LLOD schema for Simplified Offensive Language Taxonomy in multilingual detection and applications

Abstract The goal of the paper is to present a Simplified Offensive Language (SOL) Taxonomy, its application and testing in the Second Annotation Campaign conducted between March-May 2023 on four languages: English, Czech, Lithuanian, and Polish to be verified and located in LLOD. Making reference to the previous Offensive Language taxonomic models proposed mostly by the same COST Action Nexus Linguarum WG 4.1.1 team, the number and variety of the categories underwent the definitional revision, and the present typology was tested in the annotation on the publicly available offensive language datasets of each of the four languages. The results of the annotation are presented and as they are contained within the accepted statistical values on the inter-annotator agreement in the SOL categories and their aspects, we propose this taxonomy as a core ontology which represents the encoding of the supported offensive languages and justify its use on new data in terms of a more universal Linguistic Linked Open Data (LLOD) schema.
Intersections of Open Access and Information Privilege in Higher Education and Beyond

Despite its capacity to reach readers irrespective of affiliation or geographic location, conversations about Open Access (OA) frequently center academic stakeholders in high-income countries. This presentation will examine opportunities for technical services librarians to explore with students some of the inequities of the scholarly communications landscape, including various approaches to and aspects of OA, and to consider the disparate levels of access available to individuals based on institutional affiliation. Because higher education settings afford students a high degree of information privilege, academic librarians face the challenge of teaching students to appreciate the value of information, acknowledge barriers to it, and identify and evaluate the freely available resources and content to which they will have access post-graduation. Although technical services and scholarly communication librarians tend to have fewer teaching responsibilities than those working in public services, our experiences with collection development, licensing, and resource description provide insights to share with students regarding information value and privilege. A case study on student projects which included the editing of Wikipedia entries highlights tensions between institutional privilege and open resources. Teaching students about the costs, processes, and value of information production empowers them to understand their privilege and responsibilities.
Ultra-Fast Photonic-Assisted Spintronics-CMOS Computing

This talk discusses the advantages of leveraging photonics with spintronics devices while using CMOS interfacing circuits, such as high speed, energy-efficient, and higher sensitivity. Three approaches are discussed here, first is the all-optical switching magnetic elements, second is as temperature effect on the system which shows performance improvement, and third is photonic-assisted memristor spin nano-oscillator neural networks which can lead to a futuristic approach for a neuromorphic chip.
Emoji-Inserted Emotional Dialogue Generation with Emoji-Enriched Knowledge Graph

The quality of automatic dialogue systems is getting higher and higher. Most existing works express emotions through plain texts only, which are not enough to represent the true emotion. Emojis, a cenozonic expression, have been used widely on the Internet. There is a huge amount of value hidden in emojis, not only as external knowledge, but also making the expression of emotions more intuitive. In this paper, we endeavor to design an automatic Emojis-inserted open domain emotion controllable dialogue generation system with Emoji-Enriched Knowledge Graph (Emo-EEKG). The model enriches and extends the responses from both textual and non-textual perspectives. From the textual perspective, we construct an Emoji-Enriched Common-sense Knowledge Graph (EE-CKG). Then, the model is encouraged to generate more emotional words by embedding the emotional context knowledge subgraphs. From the non-textual perspective, we design the emoji generator to select emojis and the context judger to classify the sentences. The effectiveness of the proposed approach is verified by experimentally comparing with traditional models on a Twitter dataset. Our model can generate emotional responses with automatic emoji insertion and common-sense.
A Prospective Study for Exploring Saudi EFL Learners’ Strategic Listening Skills through Netflix from Teachers’ Perspectives

The future of technology use in EFL classrooms is an important subject to investigate in future study because Saudi Vision 2030 is a shift toward modernity and openness to the rest of the world; as English is a global language, the effective use of English is essential to achieving this purpose, and it is by developing its skills with a significant emphasis on listening proficiency. The study aimed to explore how Netflix will impact Saudi EFL learners’ strategic listening skills in the future from the teachers’ perspectives. This research’s significance lies in its alignment with the evolving educational paradigms and the increasing reliance on technology for language instruction; the study specifically sought to understand how the incorporation of Netflix might influence learners’ SLS in the future. To achieve this aim, the study involved ninety-six male and female faculty members from Saudi EFL university departments, selected based on their experience in teaching English and familiarity with digital educational tools. A mixed-methods questionnaire was designed and applied to collect the participants’ views on how Netflix will enhance the students’ SLS in the future. The study revealed high expectations about the role of Netflix in promoting learners’ SLS in the future.