Reviewerly/exHarmony huggingface dataset:
Delving Into the Security &amp; Privacy of the Metaverse Matrix

In the burgeoning Metaverse, an intricate digital realm sculpted by the convergence of physical virtual reality and enduring virtual spaces, there lies unprecedented potential for revolutionary social and economic interactions. However, despite its growing influence in our daily lives, there exists a notable research gap: a comprehensive understanding of the security and privacy facets inherent to the Metaverse. This study endeavors to bridge this gap by examining the pivotal dimensions of security and privacy within this nascent domain. As the distinctions between physical and digital realms fade, comprehending and addressing vulnerabilities becomes vital to preempt threats and uphold individual freedoms. Our findings elucidate not only the continuous threats, such as unauthorized data breaches and virtual reality hardware susceptibilities but also spotlight promising countermeasures. The utility of blockchain for decentralized identity protection and zero-knowledge proofs for enhancing transactional privacy without excessive data exposure are exemplary solutions. Additionally, the emergence of AI-driven security offers the potential for defenses that adapt to shifting threats. Yet, relying exclusively on technological innovations is insufficient. The Metaverse's enduring success and safety rest on a symbiotic relationship among technology, regulatory frameworks, and informed user participation. This requires developers to be proactive in integrating security, regulators to formulate and enforce robust standards, and users to stay vigilant. To conclude, the Metaverse, with its transformative potential, can only flourish within a trustworthy and secure environment. It necessitates a collaborative effort from all stakeholders to foster a Metaverse that truly enriches, empowers, and connects users in this expansive digital landscape.
Robust Low-Dimensional Representation of Tensor under F -norm Metric

Tensor principal component analysis is widely adopted in the low-dimensional representation of high-dimensional tensor data, such as time-space trajectories and color images. In the traditional methods, multilinear principal component analysis (MPCA) with the squared F -norm is not robust to the noise-corrupted data that widely exists in realistic scenarios. Besides, although L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -norm based schemes can mitigate the influence of the outliers and noise-corrupted data, the reconstruction error is not taken into consideration. To tackle the above problems, a robust MPCA algorithm is proposed to boost the robustness of low-dimensional representation by using F -norm to measure the projection distance. The proposed method not only retains the rotation invariance attribute of F -norm, but also concurrently maximizes the projection distance and minimizes the reconstruction error under F -norm metric. Finally, experiments demonstrate that this robust scheme improves the robustness of low-dimensional representation for tensor data under interference of noise-corrupted data.
Efficient Transfer Learning Methods Using Parameterization in Few-Shot Speech Recognition

Fine-tuning self-supervised models is an effective transfer learning method for speech tasks, exhibiting exceptional performance in low-resource speech recognition scenarios. However, self-supervised pre-trained models contain a large number of parameters, resulting in high fine-tuning costs. When adapters are employed during the fine-tuning process of pre-trained models for specific tasks, they substantially reduce the number of adjustable parameters while minimizing performance degradation. The recent introduction of adapter methods in the field of speech recognition research has yielded significant achievements. To further reduce the number of fine-tuning parameters and improve the adaptability of adapters to specific tasks, this paper first introduces the Houlsby adapter into the Wav2Vec2.0 model and then adopts the LN-Tuning method for joint participation in downstream task fine-tuning, resulting in only 2.4% of the model’s parameters being involved in the fine-tuning process. Experimental results demonstrate that the methods employed in this paper outperform previous adapter methods in terms of performance, with only a marginal difference of 0.4% compared to fine-tuning alone. Furthermore, these techniques facilitate more efficient model training and have yielded promising outcomes on other self-supervised models.
Data Analysis for Machine Sound Detection: Challenges, Methods, and Future Trends

With the rapid development of industrial production automation, the smooth operation of mechanical equipment is crucial for industrial production. This paper describes the wide range of applications of sound signals in the detection of abnormalities in machinery and equipment. It also discusses methods for sound data analysis, including data visualization, regression analysis and cluster analysis. These methods help to understand the characteristics of sound data, establish relationships between sounds and machine states, and identify patterns of abnormal sounds. Sound data analytics has a wide range of promising applications in industry, including improving productivity, reducing maintenance costs, and enabling predictive maintenance. However, sound data analytics also faces a number of challenges, including issues of data ethics, environmental noise, data quality and model complexity. Addressing these challenges requires interdisciplinary collaboration and continuous innovation. In the future, sound data analytics will evolve towards automation, real-time processing, deep learning applications and cross-domain integration. According to ethics and regulations, it will become more important to ensure the legal and ethical use of sound data. The future of sound data analytics will be full of opportunities and promises to bring innovative and sustainable advancements in industry and healthcare.
Exploring the Effective Model of Teaching English Speaking Practiced at University

Model applied by the lecturer depends on the student's ability to accept the subjects being taught and the student's ability, so it takes the phases of the model used to influence students to be able to speak English well. The purposes of the research are to discover model of teaching speaking practiced at university, students’ perceptions toward the model, the impact of the model, and kinds of interaction used in model of teaching speaking. The researcher employed qualitative methods for the study. The data resources for the research chosen through random sampling was used lecturer from Bosowa University of Makassar, which also included observation and interviews. The result of this study confirms that the model of teaching speaking practiced in Bosowa university was the mastery learning model. This model can be divided into five phases namely: Orientation, Presentation, Structures Practice, Guided Practice, and Independent Practice. The impacts of speaking model are active responsive such as students active to practice their English, students hard working to practice their speaking such as personality practice and group discussion, students have high motivation to practice their English such as prepare core point of speaking exercises, students have good self –confident such as the students are not shy to practice their English, and student have speaking fluency in practicing speaking. Types of classroom interaction used lectures in teaching speaking are closeness interaction, virtual interaction. The impact of the teaching speaking model is active responses, high student motivation, self-confidence, and speaking fluency.
Unity-Engine-Based E-Module to Increase Learning Results on Online Learning

The development of technology is currently moving very fast. These developments affect student learning in schools. However, the teachers have not used it properly, so learning is not in accordance with the needs of students. This is also due to the limited teaching materials used. This research aims to develop the teaching materials of the unity-based E-module and its utilization. The stages started from analysis, design, development, and implementation. The instruments were used to determine the feasibility of validation sheets and questionnaires. The results of the study indicate that teaching materials are feasible to use. The assessment of media experts indicates this gets a percentage score of 88%, the assessments of material experts get a percentage score of 75%, the assessments from linguists get a percentage score of 86%, and the assessments by teachers get a percentage score of 95%. Based on the explanation of the research results above, it can be concluded that the teaching materials of Unity engine-based energy E-Module and its utilization can be used in the learning process.
An Adaptive Fractal Image Steganography Using Mandelbrot and Linear Congruent Generator

Despite the advancements that occurred in the field of technology, information security (i.e., IS) is still deemed important and critical topic. It is still especially deemed so during the transfer process. In this research, a new approach is proposed for hiding information through the use of iterated function systems (i.e., IFS) from Fractals. This approach employs the main feature of fractals that concentrate on the idea that hackers who seek to find the hidden data shall not be able of locating it. Therefore, there is a need to carry out a decoding process in the aim of revering the conversion for securing the transmitted information. In this research, the secure information is hidden inside a fractal Mandelbrot image using the Linear Congruent Generator (i.e., LCG). Regarding the proposed system, it generates the fractal image through the use of the predefined knowledge gained from the hider site that works as a host for different types of secret messages. The knowledge that comes from the key of image dimensions, parameters of Mandelbrot, LCG key, and key agreement of cryptography method, which makes Stego-image analyses of hidden data unacceptable without the correct knowledge. Based on the results that are obtained through carrying out experiments showed the proposed method meets all the requirements for steganography. Such requirements include: the ones related to capacity, visual appearance, undetectability, robustness against extraction (i.e., security), and hit the highest capacities with a visual appearance of high quality.
The Writers’ Workshop Impact to the Writing of English Foreign Language (EFL) Learner in Indonesia

The research delved into assessing the impact of the Writers' Workshop approach on the English writing proficiency of students exhibiting diverse writing abilities. The Writers' Workshop is an instructional method that prioritizes cultivating students' writing skills through a sequence of imaginative and reflective activities. Within this approach, students are empowered to become active writers, granted the freedom to explore their ideas, craft their own manuscripts, and engage in collaborative efforts with their peers. In Riau, Indonesia, thirty-five students (19 females and 16 males) participated in English writing instruction grounded in the Writers' Workshop framework. This intervention occurred once a week for a continuous sixteen-week period. As part of the evaluation, students were tasked with composing an opinion essay as both a pre-test and post-test to gauge their writing proficiency. Notably, there was a significant improvement in the writing results across all groups, each displaying distinctive outcomes. The advanced group exhibited more pronounced benefits compared to the intermediate and novice groups, evident in the content of their essays. This disparity underscores the effectiveness of the Writers' Workshop approach, particularly in catering to varying skill levels. The positive strides observed in the students' writing abilities signify the potential of this method to foster holistic development in English writing skills, emphasizing the importance of tailored approaches in heterogeneous learning environments.
Implementasi Algoritma Learning Vector Quantization Dalam Aplikasi Pengenalan Pola

Along with the development of technology, a lot of technology has emerged and aims to make computers intelligent so that they can mimic the daily work of humans. One of the jobs that is often done is typing documents from a book or article. If the typed document is not too long, it will not cause problems. However, if there are quite a lot of documents to be typed, the operator will need a relatively long time to complete the job. This work can be streamlined by utilizing Artificial Intelligence, namely the Learning Vector Quantization (LVQ) algorithm. LVQ is used because it can learn, calculate distances and separate clusters from the patterns it has learned. This application is implemented using the Microsoft Visual Basic 6 programming language. The result is that the application can segment image characters, and carry out the training process as well as the process of recognizing patterns or shapes of characters, which in turn can recognize each character in a sentence. As well as displaying the steps in the training process and the recognition process, so that it can help learning the LVQ algorithm, especially in character recognition.
Builder’s Searching and Recommendations System using Mobile-Based Application and GPS

In the current digital era, information technology-based applications are rapidly evolving and becoming increasingly essential in everyday life. One type of application that is gaining popularity among the public is recommendation applications, especially those for finding handyworkers. Airmadidi is one of the cities in Indonesia with many boarding houses offering various facilities and prices. However, for newcomers seeking handyworkers in the city, finding the right one for their needs can be a challenging and exhausting task. Therefore, there is a need for a recommendation and handyworker search application system that can assist seekers in addressing issues in their boarding houses or homes according to their specific requirements. This application system will gather information about available handyworkers in Airmadidi and provide recommendations based on users' preferences and needs.The aim of this research is to design a recommendation and handyworker search application in Airmadidi using an appropriate software development method supported by the latest technology. This application system is expected to help users easily and efficiently find handyworkers that match their needs. The research methodology employed in this writing includes interviews, literature reviews, and system design using the SDLC (Software Development Life Cycle) method with a spiral model. The stages of the spiral model With the existence of this recommendation and handyworker search application system, searching for handyworkers in Airmadidi is expected to become easier, more efficient, and effective for seekers. Additionally, this research can serve as a reference for similar studies in other areas.
Action-Item-Driven Summarization of Long Meeting Transcripts

The increased prevalence of online meetings has significantly enhanced the practicality of a model that can automatically generate the summary of a given meeting. This paper introduces a novel and effective approach to automate the generation of meeting summaries. Current approaches to this problem generate general and basic summaries, considering the meeting simply as a long dialogue. However, our novel algorithms can generate abstractive meeting summaries that are driven by the action items contained in the meeting transcript. This is done by recursively generating summaries and employing our action-item extraction algorithm for each section of the meeting in parallel. All of these sectional summaries are then combined and summarized together to create a coherent and action-item-driven summary. In addition, this paper introduces three novel methods for dividing up long transcripts into topic-based sections to improve the time efficiency of our algorithm, as well as to resolve the issue of large language models (LLMs) forgetting long-term dependencies. Our pipeline achieved a BERTScore of 64.98 across the AMI corpus, which is an approximately 4.98% increase from the current state-of-the-art result produced by a fine-tuned BART (Bidirectional and Auto-Regressive Transformers) model.
Exact Controllability of Discrete-Time Stochastic System with Multiplicative Noise

This paper is concerned with the exact controllability of discrete-time stochastic system which is one of the basic problems of modern control theory. Though the exact controllability of continuous-time system governed by Ito stochastic differential equations has been well studied in S. Peng, Progress in Natural Science, 1994, the counterpart of the discrete-time case is still open due to the adaptiveness constraint of the controllers and the solvability challenging of stochastic difference equation with terminal value. The main contribution in this paper is to present both the Gramian matrix criterion and the Rank criterion for the exact controllability of discrete-time stochastic system. The novelty lies in the transformation of the forward stochastic difference equation into a novel backward one.
LARP: Language-Agent Role Play for Open-World Games

Language agents have shown impressive problem-solving skills within defined settings and brief timelines. Yet, with the ever-evolving complexities of open-world simulations, there's a pressing need for agents that can flexibly adapt to complex environments and consistently maintain a long-term memory to ensure coherent actions. To bridge the gap between language agents and open-world games, we introduce Language Agent for Role-Playing (LARP), which includes a cognitive architecture that encompasses memory processing and a decision-making assistant, an environment interaction module with a feedback-driven learnable action space, and a postprocessing method that promotes the alignment of various personalities. The LARP framework refines interactions between users and agents, predefined with unique backgrounds and personalities, ultimately enhancing the gaming experience in open-world contexts. Furthermore, it highlights the diverse uses of language models in a range of areas such as entertainment, education, and various simulation scenarios. The project page is released at https://miao-ai-lab.github.io/LARP/.
The Tyranny of Possibilities in the Design of Task-Oriented LLM Systems: A Scoping Survey

This scoping survey focuses on our current understanding of the design space for task-oriented LLM systems and elaborates on definitions and relationships among the available design parameters. The paper begins by defining a minimal task-oriented LLM system and exploring the design space of such systems through a thought experiment contemplating the performance of diverse LLM system configurations (involving single LLMs, single LLM-based agents, and multiple LLM-based agent systems) on a complex software development task and hypothesizes the results. We discuss a pattern in our results and formulate them into three conjectures. While these conjectures may be partly based on faulty assumptions, they provide a starting point for future research. The paper then surveys a select few design parameters: covering and organizing research in LLM augmentation, prompting techniques, and uncertainty estimation, and discussing their significance. The paper notes the lack of focus on computational and energy efficiency in evaluating research in these areas. Our survey findings provide a basis for developing the concept of linear and non-linear contexts, which we define and use to enable an agent-centric projection of prompting techniques providing a lens through which prompting techniques can be viewed as multi-agent systems. The paper discusses the implications of this lens, for the cross-pollination of research between LLM prompting and LLM-based multi-agent systems; and also, for the generation of synthetic training data based on existing prompting techniques in research. In all, the scoping survey presents seven conjectures that can help guide future research efforts.
Towards Faithful Explanations for Text Classification with Robustness Improvement and Explanation Guided Training

Feature attribution methods highlight the important input tokens as explanations to model predictions, which have been widely applied to deep neural networks towards trustworthy AI. However, recent works show that explanations provided by these methods face challenges of being faithful and robust. In this paper, we propose a method with Robustness improvement and Explanation Guided training towards more faithful EXplanations (REGEX) for text classification. First, we improve model robustness by input gradient regularization technique and virtual adversarial training. Secondly, we use salient ranking to mask noisy tokens and maximize the similarity between model attention and feature attribution, which can be seen as a self-training procedure without importing other external information. We conduct extensive experiments on six datasets with five attribution methods, and also evaluate the faithfulness in the out-of-domain setting. The results show that REGEX improves fidelity metrics of explanations in all settings and further achieves consistent gains based on two randomization tests. Moreover, we show that using highlight explanations produced by REGEX to train select-then-predict models results in comparable task performance to the end-to-end method.
A Tool for the Procedural Generation of Shaders using Interactive Evolutionary Algorithms

We present a tool for exploring the design space of shaders using an interactive evolutionary algorithm integrated with the Unity editor, a well-known commercial tool for video game development. Our framework leverages the underlying graph-based representation of recent shader editors and interactive evolution to allow designers to explore several visual options starting from an existing shader. Our framework encodes the graph representation of a current shader as a chromosome used to seed the evolution of a shader population. It applies graph-based recombination and mutation with a set of heuristics to create feasible shaders. The framework is an extension of the Unity editor; thus, designers with little knowledge of evolutionary computation (and shader programming) can interact with the underlying evolutionary engine using the same visual interface used for working on game scenes.
A clean-label graph backdoor attack method in node classification task

Backdoor attacks in the traditional graph neural networks (GNNs) field are easily detectable due to the dilemma of confusing labels. To explore the backdoor vulnerability of GNNs and create a more stealthy backdoor attack method, a clean-label graph backdoor attack method(CGBA) in the node classification task is proposed in this paper. Differently from existing backdoor attack methods, CGBA requires neither modification of node labels nor graph structure. Specifically, to solve the problem of inconsistency between the contents and labels of the samples, CGBA selects poisoning samples in a specific target class and uses the label of sample as the target label (i.e., clean-label) after injecting triggers into the target samples. To guarantee the similarity of neighboring nodes, the raw features of the nodes are elaborately picked as triggers to further improve the concealment of the triggers. Extensive experiments results show the effectiveness of our method. When the poisoning rate is 0.04, CGBA can achieve an average attack success rate of 87.8%, 98.9%, 89.1%, and 98.5%, respectively.