Reviewerly/exHarmony huggingface dataset:
Cognitive Aspects of 2D Content Integration and Management in 3D Virtual Reality Spaces

The advent of 2D graphical user interfaces in the 1980s shifted user interactions from line-based terminals to icon-based interfaces. As smartphones emerged in the 2010s, portable 2D graphical interfaces became a reality, liberating users from being confined to a single location when accessing digital services. These transformations have profoundly altered our understanding of digital information systems, with impacts that cannot be easily quantified. Current advancements in virtual and augmented reality (VR/AR), the Internet of Things (IoT), and artificial intelligence (AI) are on the verge of ushering in the next significant leap in cognitive expansion, introducing portable and highly contextual spatial interfaces, also sometimes referred to as Digital Realities (DRs). As a result, users now anticipate the ability to engage with an increasing array and variety of digital content in ways that are more contextualized and tailored to their needs, taking into account factors such as time, location, personalized goals and user-specific histories. In this paper, we aim to give an overview of cognitive aspects relevant to content integration and management specifically in DR environments, and to propose solutions and / or best practices to address them. Our discussion is centered around a paradigm called the Doing When-Seeing (DWS) paradigm, which we propose for the design of Digital Reality interfaces. We demonstrate the applicability of this paradigm to the design of interfaces for creating content, organizing content, and semantically representing and retreiving content within 3D Digital Reality environments.
Increasing the students’ speaking ability in giving suggestions and advice by the using of "whip around" strategy

Speaking is the ability to convey information or expressing to convey everything in their mind orally. Therefore, it is important for students to master speaking in their daily life because through speaking students can express their ideas, feelings, opinions, and knowledge. There were some problems faced by the students in speaking in Second grade of SMP ADHYAKSA namely: the students lacked vocabularies, and the students are not able to deliver their suggestion and advice. Therefore, to overcome the problems, the researcher conducted this research by using “Whip Around” Strategy in second grade of SMP ADHYAKSA. The purpose of the research is to increase the students’ ability in speaking by using whip around Strategy at the second grade of SMP adhyaksa. To achieve the purpose, the researcher conducted CAR Method. The result of the data in Cycle I showed the average of students’ value was 60, the maximum score was 78 and the minimum score was 41. While in Cycle II 20 students (100%), the student got the maximum score was 83 and the minimum score was 75. From the result o the research, it can be concluded that “Whip Around” Strategy increases the students’ speaking ability in second grade of SMP adhyaksa. Finally, the researcher suggested all English teacher to use this strategy in teaching speaking skill because “Whip Around” Strategy is effective to increase the students’ ability in speaking. “Whip Around” Strategy encourages the students’ to express their suggestion and advice orally and engaged the student high motivation during the teaching and learning process.
The Challenges in Teaching Writing through Cooperative Integrated Reading and Composition (CIRC) Strategy

This research aimed to know the challenges in teaching Cooperative Integrated Reading and Composition (CIRC) in teaching writing. The researcher applied a qualitative research design. The instruments of the research were an observation sheet and an interview guide. The participants of the research were two teachers in second grade at SMP Telkom Makassar. The result showed that the implementation of CIRC in teaching writing is good and by the actual procedure, during the process implementation of CIRC had challenges when that strategy was applied to teaching writing skills in the classroom. The teacher must prepare teaching materials that will be discussed in class, the material must be by the needs for implementing this strategy, and the teacher must make time efficient in the class, because the implementation process with the CIRC strategy is applied systematically and the teacher must manage the class in a conducive manner, because of the collaborative implementation of the CIRC strategy. The teachers also modify this strategy in implementing CIRC the teachers apply placement tests before making a group based on characteristics and also group selection that is formed in high, medium, and low-ability students. These indicators include benefits such as strengthening students' public, assisting them in being more critical thinkers, and also increased motivation and writing skills. CIRC can contribute to developing students' abilities to be more creative, independent, collaborative, communicative, and self-confident CIRC can also improve vocabulary mastery, familiarize students with conveying ideas by extending the topic, and build students' self-confidence in the area of writing skill. As a result, they can communicate and improve their writing skill. The other indicator is the difficulties that students face in implementing CIRC, such as taking a long time to apply it in class optimally.
Exploring the Logical Expressiveness of Graph Neural Networks by Establishing a Connection with $$\mathcal {C}_2$$

Graph neural networks (GNNs) have gained widespread application in various real-world scenarios due to their powerful ability to handle graph-structured data. However, the computational power and logical expressiveness of GNNs are still not fully understood. This work explores the logical expressiveness of GNNs from a theoretical view and establishes a connection between them and the fragment of first-order logic, known as $$\mathcal {C}_2$$ , which servers as a logical language for graph data modeling. A recent study proposes a type of GNNs called ACR-GNN, demonstrating that GNNs can mimic the evaluation of unary $$\mathcal {C}_2$$ formulas. Working upon this, we give a variant of GNN architectures capable of handling general $$\mathcal {C}_2$$ formulas. To achieve this, we leverage a mechanism known as message passing to reconstruct GNNs. The proposed GNN variants enable the simultaneous updating of node and node pair features, allowing for the handling of both unary and binary predicates in $$\mathcal {C}_2$$ formulas. We prove that the proposed models possess the same expressiveness as $$\mathcal {C}_2$$ . Through experiments conducted on synthetic and real datasets, we validate that our proposed models outperform both ACR-GNN and a widely-used model, GIN, in the tasks of evaluating $$\mathcal {C}_2$$ formulas.
Invisible Video Watermark Method Based on Maximum Voting and Probabilistic Superposition

Invisible watermarking is an essential measure for media publishers to declare ownership of their content, in the case of minimizing the impact on the viewing experience. In dealing with active attacks such as noise attacks, filtering attacks, geometric attacks, and lossy compression attacks, existing research still has great limitations. In this paper, through probabilistically superposition of "perturbed watermark obtain by maximum voting method" and "determined Bernoulli distribution" to restore the real embedded watermark, is used to deal with complex attack situations. Specifically, the special embedded watermark is obtained by sampling from the n-fold Bernoulli distribution with parameter p. Secondly, the HAAR wavelet transform is performed on the YUV channel of the video fixed-interval image to extract its low-pass component. Then Discrete Cosine Transform is performed to obtain its frequency domain representation. The watermark information is embedded into the frequency domain representation's singular to realize the embedded invisible watermark of video. The watermark bit of every block is determined by the maximum voting method for the disturbed watermark that performs DCT and SVD operations on the low-pass component of YUV channels. At this time, the determined Bernoulli distribution is probabilistically superimposed on the watermark information to guarantee distribution consistency. Finally, mean value operation and cluster processing are performed on the watermark information to reduce volatility. Experiments show that the method proposed in this paper has apparent advantages in solving Invisible video watermarking. With a PSNR of 41 and a corresponding BAR of 0.86, our team achieves 3rd place on the leaderboard of the Invisible Video Watermark Challenge 2023.
Reading Skill : Correlation Between Reading Speed and Reading Comprehension

This article aims to discover whether there is a relationship between students reading speed and students' reading comprehension. The research approach is quantitative in nature, in which the authors use SPSS 22 to calculate students' reading speed and the number of correct answers they have answered. Research participants were 20 students of class XI IPA 2 Madrasah Alyah Negeri Palopo. The research instrument was nrtive text, where students read the text quickly and the researcher counted the time using a stopwatch, then students answered questions based on the text. As a result of the study, the authors found that there is a relationship between reading speed and students' reading comprehension where the Pearson correlation value showed 0.000 or lower than 0.05.
The students’ Readiness for E-Learning during Covid-19 in North Maluku

The objective of this study was to assess the E-learning readiness of the students in Maluku Utara. The present study used descriptive quantitative in design, aimed to examine the level of E-learning readiness among the students. From three bachelor subgroups, participants were selected using stratified random sampling. Students who had attended virtual classrooms for at least one semester during the Covid-19 Pandemic met the selection criteria. The results showed that during Covid-19 pandemic 59% of the students had ready for E-learning 34,2% neutral and 6,8% were not ready. Most of the participants had provided in internet access, devices used were available and the method used during online class were easy to adapt. The results also evidenced that the students supported two factors "Technological adaptation" and "Internet usage", so improving the technological facilities in this regard can enhance the students' readiness for E-learning. Many students around the world have adapted to online education as a result of the COVID-19 pandemic. Additionally, the majority of participants in this study were well-prepared for E-learning, as demonstrated by the study's findings. So, the university can implement virtual education. However, it is necessary to increase the students' motivation to learn by providing them with the required infrastructure and training courses to learn new skills and soft wares.
The Influence of Guess Word Game Media on Mastery of Mufrodat Arabic

For researchers to use media that can foster enthusiasm and understanding in lerarning mufrodat mastery, namely by using the media of guessing games. The data colletion techniques in this study were interviews, observations, dokumentstion, test, and questionnaires. The result of this study are: the application of the word guessing game media to mastery of Arabic mufrodat has a positive impact, namely it can increase students enthusiasm and active role in learning, so that this learning media is suiteble to be implementen in learning Arabic, especially mastery of mufrodat, based on calculation result, the result is 1.078. and d.b = N-1 23-1=22. The T test table using one grup desaign refers to a significant number level of 0,05, namely 1,7171 at a level of 0,05%. Threfore the research shows that there is a significant influence of the influence of the word guessing game media on students Arabic mufrodat mastery.
Direct and Indirect Speech Acts of Apologizing in “Venom Let There Be Carnage” Movie

The objective of this research is to identify direct and indirect apologizing speech acts in the utterances of the characters in the movie "Venom let there be carnage". The data source for this study was taken from a movie entitled "Venom let there be carnage" in which many apologizing speech acts are found in it. The method used is a qualitative method. The theory used to identify apologizing speech acts was put forward by Searle &amp; Vanderveken (1985) while to identify the direct and indirect used by the characters in the film using the theory put forward by Yule (1996). The results of this research found 14 types of apologizing speech acts consisting of 12 direct uses and 2 indirect uses.
Experiencing Visual Blocks for ML: Visual Prototyping of AI Pipelines

We demonstrate Visual Blocks for ML, a visual programming platform that facilitates rapid prototyping of ML-based multimedia applications. As the public version of Rapsai [3], we further integrated large language models and custom APIs into the platform. In this demonstration, we will showcase how to build interactive AI pipelines in a few drag-and-drops, how to perform interactive data augmentation, and how to integrate pipelines into Colabs. In addition, we demonstrate a wide range of community-contributed pipelines in Visual Blocks for ML, covering various aspects including interactive graphics, chains of large language models, computer vision, and multi-modal applications. Finally, we encourage students, designers, and ML practitioners to contribute ML pipelines through https://github.com/google/visualblocks/tree/main/pipelines to inspire creative use cases. Visual Blocks for ML is available at http://visualblocks.withgoogle.com.
Aisen - Web-Based Gaze-Tracking Assistive Communication Interface with Word Cards Generated by LLMs

Aisen is an innovative web-based communication tool that integrates the WebGazer.js library [5] with advanced large language models (LLMs). Designed as an affordable communication solution for those with communication challenges, Aisen facilitates expression through a unique word-selection interface. Rather than using a traditional keyboard, Aisen introduces a two-tiered "word card" system. This system includes a static set of cards tailored to the patient’s specific needs and a "dynamic" set where cards are intelligently generated by LLMs based on user input and preferences. Our research delineated three specific user personas, emphasizing Aisen’s applicability for elderly patients. The platform integrates an eye-tracking mechanism, a gaze-responsive interface, and a word card repository enriched with LLMs. This endeavor highlights the transformative potential of web-enabled eye-tracking and LLMs in enhancing communication for individuals with impairments.
Cybersecurity In The Age Of Artificial Intelligence – Who Should Be Culpable?

The age of the AI has come in hurtling and it has become difficult for law makers to keep pace. At the same time, unbridled by legislations, criminals in the cyber space are using these Artificial Intelligence tools. The instances of ChatGPT’s use in writing academic articles to morphing pictures of people, the degree of seriousness is high. Another incident was the use of morphed pictures of a celebrity couple’s child for disseminating fake news. The lines of reality and morphed is blurring to an extent that the line is very unclear and hence it is essential that proper laws are put in place to identify and assign culpability to the use of AI for criminal activities. Although the AI applications are guided by moral codes, the said moral codes would be guided by the developer’s morals and hence the question of standardized laws, ethics and morals would arise here. While the debate around AI and intellectual property rights have been on the round for some time and Courts in certain jurisdictions have already deliberated on it, the question has shifted to criminal culpability in case of hacking, creation of photos that falsify evidence, publication of pictures of individuals that have been digitally altered via AI, etc. The question that arises is who is in control of the actions of the AI, since the creator of the AI is not always the person who is involved in criminal activities that are affected through the AI. Also, would a standard code of ethics be able to govern the AI’s functioning successfully?
The Development of a Weblog for Learning English Grammar

This research aimed to develop the appropriate weblog for learning English grammar of the twelfth-grade students at SMAN 4 Palopo. Furthermore, the researcher applied Research and Development (R&amp;D) method under the ADDIE model. It consists of five phases: Analysis, Design, Development, Implementation, and Evaluation. This research was located at SMAN 4 Palopo. The subjects in the research were the twelfth-grade students at SMAN 4 Palopo, and 42 students became respondents for the development tryout. This research used several instruments to collect data, i.e., need analysis questionnaires, experts’ judgment questionnaires, and students’ perception questionnaires. The technique of data analysis was a quantitative descriptive analysis technique. This research shows that the developed basic English grammar material through blog learning for the twelfth-grade students at SMAN 4 Palopo was appropriate. The value from the expert’s validation showed that the material expert was 4.33, the language expert was 4.80, and the media/IT expert was 3.44. Thus, the overall mean score for product validity was 4.52 under the excellent category. The result of the questionnaire on students’ perception obtained 4.23 in the excellent category. The appropriate basic English grammar material through blog learning for the twelfth-grade students at SMAN 4 Palopo covers: (1) learning objectives follow students’ necessities; (2) material consists of modal verbs, singular and plural, noun and verb phrases, tenses, direct and indirect speech, passive voice, and articles; (3) presented visually and audio-visually; (4) exciting learning activities and is student-centred such as quizzes, games, and tasks; (5) material’s arrangement consists of a definition, observing activity, grammatical structure, function, and example; (6) an understandable and accessible and covered with a good, attractive, and consistent layout; (7) clear, effective, communicative language and is suitable; (8) material can increase students’ enthusiasm, focus, spirit, confidence, motivation, and activity.
ROAD: Robust Unsupervised Domain Adaptation with Noisy Labels

In recent years, Unsupervised Domain Adaptation (UDA) has emerged as a popular technique for transferring knowledge from a labeled source domain to an unlabeled target domain. However, almost all of the existing approaches implicitly assume that the source domain is correctly labeled, which is expensive or even impossible to satisfy in open-world applications due to ubiquitous imperfect annotations (i.e., noisy labels). In this paper, we reveal that noisy labels interfere with learning from the source domain, thus leading to noisy knowledge being transferred from the source domain to the target domain, termed Dual Noisy Information (DNI). To address this issue, we propose a robust unsupervised domain adaptation framework (ROAD), which prevents the network model from overfitting noisy labels to capture accurate discrimination knowledge for domain adaptation. Specifically, a Robust Adaptive Weighted Learning mechanism (RSWL) is proposed to adaptively assign weights to each sample based on its reliability to enforce the model to focus more on reliable samples and less on unreliable samples, thereby mining robust discrimination knowledge against noisy labels in the source domain. In order to prevent noisy knowledge from misleading domain adaptation, we present a Robust Domain-adapted Prediction Learning mechanism (RDPL) to reduce the weighted decision uncertainty of predictions in the target domain, thus ensuring the accurate knowledge of source domain transfer into the target domain, rather than uncertain knowledge from noise impact. Comprehensive experiments are conducted on three widely-used UDA benchmarks to demonstrate the effectiveness and robustness of our ROAD against noisy labels by comparing it with 13 state-of-the-art methods. Code is available at https://github.com/penghu-cs/ROAD.
GERNS: A Graph Embedding with Repeat-Free Neighborhood Structure for Subgraph Matching Optimization

Subgraph matching is used to determine whether a query graph exists within a target graph, and appears in a lot applications of domains including social sciences, chemistry, biology and database systems. Existing subgraph matching approaches can be broadly categorized into two types: exact matching and approximate matching. Due to allowing for slight variations between the target and query graphs, approximate matching has become a more practical solution with the introduce of graph neural networks (GNN). However, when dealing with large-scale target and query graphs, existing GNN-based approximate matching approaches still have to face the challenge that how to further improve the accuracy and promote the query efficiency. Therefore, we propose the GERNS, a graph embedding with repeat-free neighborhood structure for subgraph matching optimization. Through extracting subgraphs from the target graph based on a specified hop count limit, we incorporate and embed a repeat-free neighborhood structure using a two-layer GNN. Then we generate the relation constrains of subgraphs based on vector order embedding to form the embedding space. Finally, approximate subgraph matching can be realized based on graph embedding. Extensive experiments on both public graph datasets and real-world datasets show the effectiveness of our approach.
LNFGP: Local Node Fusion-Based Graph Partition by Greedy Clustering

Graph partitioning manages large RDF datasets in various applications such as file systems, databases and distributed computing frameworks. Research on graph partitioning can be generally categorized into two types: vertex partitioning and edge partitioning. Due to the independent nature of vertex partitioning, which facilitates easier management and maintenance, vertex partitioning methods have become more practical and popular. However, most existing vertex partitioning methods primarily focus on operating partitions in the original dimensional space, overlooking the control of partition locality from different dimensions. This oversight can adversely affect query efficiency. Therefore, we propose a graph partitioning method based on local node fusion. Based on constructing the co-occurrence matrix to calculate property weight values, we utilize a greedy clustering algorithm to achieve weight-sensitive node fusion. By constructing abstract super-nodes, we achieve a multi-granularity RDF graph that combines regular nodes with super-nodes. By setting a cost threshold, we iteratively apply an edge-cut selection mechanism, ultimately achieving vertex-based graph partitioning and super-node de-fusion. Extensive experiments are conducted on synthetic and real RDF datasets, validating the effectiveness of our proposed method.