Reviewerly/exHarmony huggingface dataset:
Practical Course for Creating Stereoscopic VTuber

The authors conduct a practical class for third-year students in the Information Media Studies Department, focused on creating stereoscopic CG content. They focus on virtual YouTubers (VTubers), that has been gaining popularity on the internet. Despite the current VTubers being created as 3D models, they lack actual stereoscopic capabilities. VTuber characters can feel more lifelike upon introduction of stereoscopic visuals, allowing creators to expand their ideas and unlocking significant potential in the metaverse era. In this study, each student captures video footage of their upper body using their laptop's web camera. Then, they combine various freely available software tools online to create stereoscopic VTuber videos without the need for special equipment. The students produce content using techniques, such as anaglyph videos (using red-blue glasses), lenticular lenses, and fly's eye lenses for autostereoscopic viewing. They also add audio to the VTuber content, and at the end of the exercise, they hold a “Stereoscopic VTuber” presentation, where mutual evaluations are conducted. Results of the student surveys indicate a positive outcome in terms of their increased interest in related technologies and enhanced creative motivation. This practical course holds significant values as an education that combines information media art, system-oriented thinking, and AI technologies.
Table Cape VR Education Technology Project: An Immersive Learning Experience of Walking with Giants

This paper profiles the Table Cape VR Education Technology Project titled "Walking with Giants," which was designed to offer an immersive learning experience for community storytelling. Table Cape is located in Wynyard, a town in rural Northwest Tasmania, Australia; it is an extinct volcano renowned for its rich tourism and heritage values. As part of a wider education technology and digital tourism initiative, Walking with Giants provides educators, students, and community members with the opportunity to visualise the high-fidelity 3D scanned terrain of Table Cape in virtual reality. Currently, the project is being exhibited at the Wonders of Wynyard Exhibition and Visitor Information Centre - a joint initiative involving creators and researchers from the University of Tasmania, industry partners from Business Northwest, and the Waratah Wynyard Council. In this paper, we describe the technical aspects of this digital technologies project and discuss the specialised knowledge-based requirements for teaching and learning incorporating such resources.
Mixed Reality as Empathy Machine. Case Study in Universal Design Course

Empathy has become a central focus in design and is clearly evident in various frameworks, such as universal design, inclusive design, and human-centered design. However, among numerous stakeholders, designers, and engineers are the ones who particularly require an expansion of their empathetic understanding. The emergence of immersive technologies has the potential to facilitate the development of empathy in individuals. Extended reality (XR) combines virtual reality with the real world, enabling the simulation of various physical states, health conditions, and limitations of the human body. This paper introduces three distinct XR scenarios that immerse potential users in the experiences of people with special needs. The elaborated tasks encompass aspects like mobility impairments, pregnancy, and challenges faced by the elderly. All exercises take place in a familiar supermarket environment, as shopping is a common, everyday activity for most people. The XR application is designed for the Oculus Quest 2 platform and is complemented by external equipment such as a geriatric suit, pregnancy belly simulator, or wheelchair. The proposed simulations were assessed and validated by 42 students from the Lodz University of Technology taking the Universal Design course. The testing process comprised several stages, which included an empathy pre-test, the execution of the XR experience, an empathy post-test, and a brief interview. Additionally, we registered the time for exercises and monitored users’ movement activity during the experience.
Training with, about, for Metaverse: A Mixed Methods Research on Training Pre-Service Teachers as Metaverse-Certified Practitioners

This mixed-method research aims to examine transformative affordances of the metaverse for pre-service teachers and investigate support mechanisms for technology-enhanced teacher training. Specifically, 40 pre-service teachers were involved in training &lt;i&gt;with, about, for&lt;/i&gt; metaverse (TM) to convert high-stakes TOEIC test items into virtual content. Upon completion of the TM, participants were eligible to apply for nationally accredited private metaverse certificates to mark them as certified practitioners. To capture their multifaceted TM experience, this research employed surveys based on UTAUT (Unified Theory of Acceptance and Use of Technology) 2 constructs, as well as reflective journals and advanced analytics. The collected data were analyzed using quantitative techniques (&lt;i&gt;t&lt;/i&gt;-tests, multiple regression analysis, and structural equation modeling) and qualitative methods (sentiment and content analysis) to offer a more comprehensive understanding of the technological training for pre-service teachers. The findings revealed that the overall reception of the TM experience among pre-service teachers was highly positive. Notable improvements were observed in facilitating conditions, effort expectancy, and hedonic motivation. Textmining results also revealed how the TM framework transformed pre-service teachers’ perspectives on teaching, testing, and learning. Based on the findings, the study contributes to the ongoing dialogue on effective technology integration in education.
Boosting Facial Landmark Detection via Self-supervised and Semi-supervised Learning

Keypoint detection is one of the main focused fields in computer vision with various applications. Traditional fully-supervised deep learning methods currently dominate the field with impressive accuracy, but typically require careful, expensive, and laborious effort for keypoint annotations. To tackle this problem, recent semi-supervised methods have emerged and shown great potential in utilizing a large amount of available unlabeled data. In this work, we explore a novel semi-supervised keypoint detection method, aiming to reduce the annotations required while maintaining the accuracy of traditional fully-supervised methods. We further augment the method by integrating it with a robust backbone network that has been pre-trained through self-supervised learning, thereby enabling better utilization of unlabeled data. Experimental results on three different datasets show that models trained using our semi-supervised method outperform their fully-supervised counterparts in accuracy despite using the same amount of labeled data. Additionally, under specific settings, our method can match the performance of existing semi-supervised methods even when using a reduced set of labeled data.
IQAGA: Image Quality Assessment-Driven Learning with GAN-Based Dataset Augmentation for Cross-Domain Person Re-Identification

Person re-identification (reID) is the task of matching images of the same person across different cameras or domains. It has many applications in security, surveillance, and biometrics. However, supervised learning-based person reID faces the challenge of domain shift, which means that the performance of a model trained on a specific domain (source domain) may degrade when testing on another domain (target domain) with different distributions, backgrounds, and lighting conditions. To enhance the generalization of person reID models, we propose a new approach consisting of three components: GAN-based data augmentation, cross-domain learning, and evaluation modules. Particularly, Generative Adversarial Network (GAN) approaches are used first to generate synthetic data from real source data by diversifying the environmental condition of the dataset. We then propose a cross-domain learning approach powered by image quality assessment (IQA) to reduce the impact of low-quality images in the combined source data, including synthetic and real source data. The extensive experiments evaluate the superiority of our proposed method over state-of-the-art methods on two famous person reID benchmarks, namely DukeMTMC-reID and Market-1501.
On the Value of Code Embedding and Imbalanced Learning Approaches for Software Defect Prediction

Automated software defect prediction aims to identify and estimate the likelihood of defects in software source code elements, seeking to enhance software quality while reducing testing costs. Previous research on software defect prediction primarily concentrated on investigating design-related features such as source code complexity and object-oriented design metrics for the purpose of classifying program elements into two categories: (i) defective and (ii) non-detective. Nevertheless, the majority of these studies have relied solely on hand-crafted software metrics, neglecting the valuable asset of source code instruction, which can play a pivotal role in detecting bugs. This study leverages the use of source code embedding techniques to extract essential information from program elements through a convolutional neural network. The likelihood of a source file element (e.g., class or method) being defective is established through the utilization of a fully connected network that incorporates both source code features and design-related attributes. Additionally, we explore specific imbalanced learning strategies to address the skewed defect data distribution issue. To assess the effectiveness of our proposed approach, we conducted experiments on the publicly available dataset, namely PROMISE. The empirical results consistently showcase the superior performance of our method, as it effectively predicts defective source files, outperforming other state-of-the-art models.
Scoring model for NFT evaluation

The non-fungible token (NFT) market has experienced significant growth in recent years. Similar to real estate and artwork, NFT is illiquid and it does not have a market price. Thus, there arises a need for a comprehensive scoring system to analyze the value of NFTs. Using a single score is often inadequate, as NFT value is influenced by numerous factors and is assessed based on personal objectives. Addressing the issue, this study explores critical factors influencing NFT value and develops three scores to rank NFTs including rarity, return on investment (ROI), and reputation score. The scores serve to evaluate both NFTs and NFT collections. (i) The rarity score evaluates the level of scarcity, and it applies to NFTs within a collection. (ii) The reputation score evaluates the interest of communities in NFT projects, considering the number of followers and the interaction rate; the score is for NFT collections. (iii) The ROI score assesses the profit generated by NFTs; it applies to both NFTs and NFT collections. Our empirical results show that a well-distributed rarity score of NFTs enhances their demand and profit; high-rarity NFTs are typically associated with a low number of sale transactions. NFT collections with high ROI and high reputation generally draw more attention and yield a positive return. In addition to the three scores, this paper presents a system to collect and process a huge amount of blockchain and social network data for NFT evaluation.
A Monte Carlo Tree Search with Ant Colony Optimization for Inter-domain Path Computation Problem

Over the past few years, the Hierarchical Path Computation Element (h-PCE) architecture has been advocated for handling packet routing in multi-domain networks. However, this architecture may not be able to effectively deal with large-scale networks, especially as the number of network components increases. To address this challenge, the Inter-Domain Path Computation problem under the Domain Uniqueness constraint (IDPC-DU) was proposed to enhance the h-PCE architecture. The IDPC-DU limits the search space by finding paths that pass through each domain only once. Although the constraint can help increase the search process's efficiency significantly, IDPC-DU is still an NP-hard problem. So far, some algorithms have been proposed to solve the problem, but either the encoding method is complex, or their search easily falls into the local optima. To overcome the drawbacks, we propose a two-level approach combining Monte Carlo Tree Search (MCTS) and Ant Colony Optimization (ACO). In the proposed algorithm, we represent the problem's search space in the form of a search tree, using MCTS, then ACO algorithm is used to find a good solution on that tree. Moreover, a pruning technique is integrated into ACO to reduce the solution space. The algorithm is tested on many benchmark datasets. The experimental results show that the proposed algorithm outperforms all other algorithms in most cases.
Enhancing Video Retrieval with Robust CLIP-Based Multimodal System

In the rapidly evolving landscape of multimedia data, the need for efficient content-based video retrieval has become increasingly vital. To tackle this challenge, we introduce an interactive video retrieval system designed to retrieve data from vast online video collections efficiently. Our solution encompasses rich textual to visual descriptions, advanced human detection capabilities, and a novel Sketch-Text retrieval mechanism, rendering the search process comprehensive and precise. At its core, the system leverages the Contrastive Language-Image Pretraining (CLIP) model, renowned for its proficiency in bridging the gap between visual and textual data. Our user-friendly web application allows users to create queries, explore top results, find similar images, preview short video clips, and select and export pertinent data, enhancing the effectiveness and accessibility of content-based video retrieval.
Entangled topologies for quanvolutional neural networks in quantum image processing

Image classification and image processing are critical tasks that form the basis for problems in computer vision. Recently, numerous classification techniques based on quantum machine learning have been proposed, such as quanvolutional neural network (QNN) - a hybrid quantum-classical model which has the potential to process high-resolution images and outperform current image processing techniques. In this article, we investigate the use of entangled topologies in QNN to extract features more efficiently. We also propose a training strategy for the quantum part of the QNN model. The results show that with a valid hyperparameter, our QNN achieves higher accuracy than CNN with a significantly smaller number of parameters.
Contextual Language Model and Transfer Learning for Reentrancy Vulnerability Detection in Smart Contracts

The proliferation of smart contracts on blockchain technology has led to several security vulnerabilities, causing significant financial losses and instability in the contract layer. Existing machine learning-based static analysis tools have limited detection accuracy, even for known vulnerabilities. In this study, we propose a novel deep learning-based model combined with attention mechanisms for identifying security vulnerabilities in smart contracts. Our experiments on two large datasets (SmartBugs Wild and Slither Audited Smart Contracts) demonstrate that our approach successfully achieves a 90% detection accuracy in identifying smart contract reentrancy attacks (e.g. performing better than other existing state-of-the-art deep learning-based approaches). In addition, this work also establishes the practical application of deep learning-based technology in smart contract reentrancy vulnerability detection, which can promote future research in this domain.
Domain Adaptation in Nested Named Entity Recognition From Scientific Articles in Agriculture

In the realm of digital agriculture, the ability to make timely, profitable, and actionable decisions depends on agronomists using agricultural data and related cultivated data, including text sources such as news articles, farm notes, and agricultural scientific reports. Named entity recognition (NER) and agricultural entity recognition (AGER) facilitate semantic understanding, enabling precise identification, categorization of farming components, and knowledge discovery. However, current approaches to agricultural entity recognition encounter limitations due to limited resources. Moreover, the necessity to identify nested named entities emerges from the complexities inherent in the agricultural domain. Relevant information often traverses multiple interconnected elements rather than residing as isolated entities. For instance, comprehending a target farming practice might necessitate pinpointing the crop, the associated nutrients, or diseases—each constituting a nested entity within a broader context. Consequently, agricultural entity recognition from unstructured text gives high importance to information retrieval and knowledge construction within this domain. This study constructs the SAGRI dataset, incorporating a novel tagset for AGER that encompasses prevalent agricultural and scientific concepts, methodically established through annotation. This tagset enables the extraction of domain-independent concepts from scientific article abstracts. This study also introduces a cutting-edge deep learning baseline with an advanced Triaffine attention mechanism for robust entity extraction. Additionally, it presents a pioneering few-shot learning strategy that optimizes cross-domain categorization, mainly when dealing with scarce training data. Notably, this strategy achieves high F1 scores compared to the baseline, underscoring its potential to curtail required training data considerably.
Locally Differentially Private and Fair Key-Value Aggregation

In the era of Big Data, the ability to extract meaningful insights from vast datasets while maintaining individual privacy has become an increasingly complex challenge. Recent years have witnessed the development of various locally differentially private data aggregation schemes which allow an untrusted data collector to derive meaningful statistics from user data while maintaining strong privacy guarantee for individual users. As a fundamental data type in NoSQL databases, key-value data has two important statistics of interest, the frequency of each key and the corresponding mean value. Current locally differentially private key-value aggregation schemes primarily rely on uniform sampling for mean estimation, i.e., a single key-value pair is selected randomly from each user's key-value set. This approach, however, results in high mean estimation accuracy for frequent keys and low accuracy for infrequent ones. To tackle this problem, this paper presents the design and evaluation of Adaptive, a novel locally differentially private and fair key-value aggregation scheme that can deliver uniformly high mean estimation accuracy across different keys. In the first phase, we utilize a portion of the privacy budget to estimate the frequency of each key. Subsequently, based on the key frequencies estimated in the first phase, we employ non-uniform random sampling for mean estimation, which enables higher probability sampling of values associated with low-frequency keys. Comprehensive theoretical analysis and simulation studies confirm the superiority of Adaptive over previous solutions.
Improve the Quantum Approximate Optimization Algorithm with Genetic Algorithm

The Quantum Approximate Optimization Algorithm (QAOA) is a variational quantum optimization technique used for solving combinatorial optimization problems. However, in constrained binary optimization, QAOA's reliance on equal initial probabilities for all solutions can lead to suboptimal outcomes. To enhance the performance of QAOA in this context, we propose a novel approach that combines QAOA with genetic algorithms. In this hybrid approach, the results obtained from QAOA serve as the initial population for a genetic algorithm. We apply this methodology to address the k-heaviest subgraph problem, a critical challenge in quantum computing research. Our experiments, conducted on benchmark datasets, demonstrate a significant improvement in solution quality.
Anomaly Event Retrieval System from TV News and Surveillance Cameras

In an era defined by the proliferation of digital content and a growing reliance on video data, the need for effective anomaly detection systems has never been more pressing. This paper introduces a sophisticated system architecture designed to address the complex challenges associated with acquiring, processing, and presenting anomaly videos. At its core, our architecture prioritizes openness and modularity, allowing for seamless upgrades and customization. This approach ensures adaptability to evolving technology trends and user preferences. We emphasize the crucial aspects of component interfacing and user interaction, highlighting the integration of feedback mechanisms for ongoing system refinement. Additionally, we contribute significantly to the research community by extending an established anomaly event dataset using proven methods and techniques. This extension enhances the dataset's breadth and depth, providing a valuable resource for training and evaluating anomaly event retrieval systems. Our paper presents a forward-looking system architecture poised to meet the demands of anomaly video detection while also enriching the available resources for anomaly event research. Subsequent sections will delve into architecture components and methodologies, showcasing its potential to revolutionize modern anomaly detection systems.
Stratified Ranking for Dense Passage Retrieval

Dense passage retrieval has recently boosted the performance of involved systems such as question answering or search engine. On this problem, prior works trained a dense retriever by learning to rank, i.e. ranking relevant/positive passages higher than irrelevant/negative ones. In this paper, we propose a Stratified Ranking approach for Dense Passage Retrieval (SR-DPR), which performs three-way ranking instead of the typical two-way positive-negative ranking. SR-DPR is concerned with three relevance levels, which are positive, hard negative and random/easy negative. We train SR-DPR model by minimizing a contrastive negative log-likelihood (NLL) loss involved with these three relevance levels, which is a finer-grained version of the N-pair loss [16]. To efficiently implement SR-DPR, we designed three data pipelines, each pipeline is used to learn the contrast between two out of three relevance levels. SR-DPR outperforms the strong baseline DPR [5] by 0.6-1.5% retrieval accuracy on Natural questions [6] dataset and 3-6% of that on Zalo Legal Text Retrieval dataset. SR-DPR also gives competitive results compared with current state-of-the-art methods without requiring complicated training regime or intensive hardware resources. The idea of stratified ranking of SR-DPR is not restricted to the scope of dense passage retrieval but can be applied in any contrastive learning problem. We conducted detailed ablation studies to give insights into SR-DPR's behavior.