Reviewerly/exHarmony huggingface dataset:
Reimagining Art: Virart, a Platform Using Virtual Reality for Creative Expression and Cultural Engagement

Virart is an immersive experience that showcase the potential of Virtual Reality in recreating artistic works. By leveraging the capabilities of VR, users can explore and interact with digital recreations of J ´ ulio Maria dos Reis Pereira artistic creations. The VR environment enhances the sense of presence and enables viewers to explore the art from different angles, offering a deeper level of engagement compared to traditional mediums. By showcasing the possibilities of virtual reality in recreating artistic works, Virart opens new avenues for artists, art enthusiasts, and the general public to appreciate and engage with art in innovative ways. It demonstrates the potential of virtual reality technology in preserving and promoting cultural heritage, allowing art to be experienced beyond physical limitations
IoT Framework in a Blockchain dependent Cloud Environment

Due to characteristics like simplicity of use and improved Internet accessibility, the IoT and its utilizationsis growing in a rapid extent currently. Researchers can increase the effectiveness of IoT-dependent frameworks by integrating more cutting-edge technologies, such as blockchain and cloud computing. IoT applications may now be accessible from any location and at any time thanks to these improvements. However, a database is necessary to store the application's data. As it offers remote services like storage, processing, and analysis, cloud infrastructure is a perfect choice for storing IoT-based applications. These programmes' primary flaw is their inability to guarantee data confidentiality and privacy. With attributes such as immutability, transparency and decentralized framework, blockchain technology aids in solving these shortcomings. The objective of research work IoT framework in a blockchain. The simulation work has been performed in cloud environment. This article makes a proposal for an IoT application that uses the cloud and blockchain to help detect an attacker by effective surveillance. Important benefit for this application is that it can function in environments where it is difficult for humans to monitor, and the data is maintained in a secure environment using blockchain technology. The data privacy, security and portability have been enhanced. The proposed system can be used to monitor and record illegal activities. Finding presents that hybrid design used in this study enhances the system's portability and data privacy.
Improving Speaking and Listening Skills: An Educational Eco-system for Foreign Languages Teaching in Higher Education

The digital age is marked not only with new tools in education but also with new technologies. Nowadays, the majority of educational institutions have accepted new digital approaches to education and adapted their academic programmes to digital transformations. Different digital technologies are based on the “connectivism approach” to education, which embraces such new things as Mobile micro-learning, BYOD technology (Bring Your Own Device) and Gamification. The combination of different messengers and apps in training leads to the introduction of a new term in education - “eco-system”, which results in the emergence of such terms as “eco-teaching” and “eco-learning”. The authors of this study hypothesized that eco-teaching, involving the use of several different messengers and social networks for teaching speaking and listening, can generally increase the level of English language proficiency of students. To prove this hypothesis, the authors conducted an experiment in Peter the Great St. Petersburg Polytechnic University in the autumn term of 2022 with a total of 184 first-year students. The students of the experimental division, improving speaking and listening skills, were supposed to use game mechanics in different messengers. The control division students were not supposed to use any electronic devices or/and online educational resources. After analyzing the data obtained, the authors came to the conclusion that the academic results of the experimental division students are much better than those of the control group students. Based on the conducted research, the authors consider the introduction of eco-teaching with game elements in universities where foreign languages are studied to be very promising.
Assessement of the Impact of Digital Economy on Revenue Generation in Rwanda (Case Study: Musanze)

The digital economy has emerged as a transformative force in many developing countries, including Rwanda. This study aims to assess the impact of the digital economy on revenue generation in Musanze, Rwanda, by focusing on three key objectives, the study employs a mixed-methods approach, combining quantitative data analysis and qualitative interviews collected using Kobo toolbox with relevant Stakeholders. R-Studio was used to generate descriptive analysis as well inferential statistics for models. The research evaluates the revenue generation trends in Musanze the adoption of digital technologies. Additionally, it investigates the extent to which the cashless economy has influenced revenue collection processes, the growth of e-commerce sales, and the implications of ICT infrastructure taxation on revenue generation. The findings of this study provide valuable insights into the evolving landscape of revenue generation in Rwanda’s digital economy, specifically in the Musanze region. This study found that there is a positive relationship between adoption of cashless economy and revenue generation with the p-value of 0.014, the adoption of E-commerce sales has increased the revenue since the p-value was 0.004 and finally utilization of adequate ICT infrastructure has increased revenue with p-value 0.03. It aims to inform policymakers, businesses, and the government on the potential benefits and challenges associated with the digital transformation in revenue collection. Ultimately, this research contributes to the broader understanding of how the digital economy can shape fiscal policies and revenue collection strategies in emerging economies like Rwanda.
EpilogueParts of a Universal Tree

Extract The “universal tree” of Sri Sabhapati Swami’s literature and teachings on Śivarājayoga beckons us to take a closer look at its many parts, from its translocal leaves down to its local roots. His literature remained quietly relegated to local Tamil religious spheres and diffused in translocal occult milieus for almost a century and a half, as our journey from the first chapter to the seventh has demonstrated. While it did not have the widespread success that Sabhapati had perhaps initially envisaged, engaging his body of work now has unique advantages from a scholarly point of view. First, its history at both the translocal and local levels remains discrete and trackable relative to many other modern traditions of yoga that became “globalized,” so to speak, in the middle to late twentieth century. The contents of Sabhapati’s literature and visual diagrams, like a time capsule of sorts gathering dust in library archives and private bookcases, offers much additional perspective on the intersection between social and religious concerns as well as on how yoga fit into daily life and discourses. Their detailed descriptions also afford scholars and interested readers in South Asia and beyond an unprecedented opportunity to better access and understand the religious history of a vast and understudied network of locations and places (e.g., Velachery, Sathuragiri, Courtallam, Papanasam, Pothigai Malai, among many others) that were considered important sacred sites prior to the colonial period and up to the present day. In other words, further examination of the life and teachings of this yogi (or Jñānaguru Yogī, to use one of his titles) continues to reveal so much more even outside of just this single figure, including an interconnected web of personal relationships and publication history, translocal flows and interactions with occultists and other known personalities, sacred places and temples, and philosophical, religious, and scientific engagement. If this book has focused on Śivarājayoga as the main connecting thread given its importance to Sabhapati himself, it is by no means the only aspect of this yogi worthy of further research. In concluding, however, I will cite two overarching scholarly assumptions among many that this research at once complicates and enriches.
KNVQA: A Benchmark for evaluation knowledge-based VQA

Within the multimodal field, large vision-language models (LVLMs) have made significant progress due to their strong perception and reasoning capabilities in the visual and language systems. However, LVLMs are still plagued by the two critical issues of object hallucination and factual accuracy, which limit the practicality of LVLMs in different scenarios. Furthermore, previous evaluation methods focus more on the comprehension and reasoning of language content but lack a comprehensive evaluation of multimodal interactions, thereby resulting in potential limitations. To this end, we propose a novel KNVQA-Eval, which is devoted to knowledge-based VQA task evaluation to reflect the factuality of multimodal LVLMs. To ensure the robustness and scalability of the evaluation, we develop a new KNVQA dataset by incorporating human judgment and perception, aiming to evaluate the accuracy of standard answers relative to AI-generated answers in knowledge-based VQA. This work not only comprehensively evaluates the contextual information of LVLMs using reliable human annotations, but also further analyzes the fine-grained capabilities of current methods to reveal potential avenues for subsequent optimization of LVLMs-based estimators. Our proposed VQA-Eval and corresponding dataset KNVQA will facilitate the development of automatic evaluation tools with the advantages of low cost, privacy protection, and reproducibility. Our code will be released upon publication.
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation

Scene segmentation via unsupervised domain adaptation (UDA) enables the transfer of knowledge acquired from source synthetic data to real-world target data, which largely reduces the need for manual pixel-level annotations in the target domain. To facilitate domain-invariant feature learning, existing methods typically mix data from both the source domain and target domain by simply copying and pasting the pixels. Such vanilla methods are usually sub-optimal since they do not take into account how well the mixed layouts correspond to real-world scenarios. Real-world scenarios are with an inherent layout. We observe that semantic categories, such as sidewalks, buildings, and sky, display relatively consistent depth distributions, and could be clearly distinguished in a depth map. Based on such observation, we propose a depth-aware framework to explicitly leverage depth estimation to mix the categories and facilitate the two complementary tasks, i.e., segmentation and depth learning in an end-to-end manner. In particular, the framework contains a Depth-guided Contextual Filter (DCF) forndata augmentation and a cross-task encoder for contextual learning. DCF simulates the real-world layouts, while the cross-task encoder further adaptively fuses the complementing features between two tasks. Besides, it is worth noting that several public datasets do not provide depth annotation. Therefore, we leverage the off-the-shelf depth estimation network to generate the pseudo depth. Extensive experiments show that our proposed methods, even with pseudo depth, achieve competitive performance on two widely-used bench-marks, i.e. 77.7 mIoU on GTA to Cityscapes and 69.3 mIoU on Synthia to Cityscapes.
Fair Polylog-Approximate Low-Cost Hierarchical Clustering

Research in fair machine learning, and particularly clustering, has been crucial in recent years given the many ethical controversies that modern intelligent systems have posed. Ahmadian et al. [2020] established the study of fairness in \textit{hierarchical} clustering, a stronger, more structured variant of its well-known flat counterpart, though their proposed algorithm that optimizes for Dasgupta's [2016] famous cost function was highly theoretical. Knittel et al. [2023] then proposed the first practical fair approximation for cost, however they were unable to break the polynomial-approximate barrier they posed as a hurdle of interest. We break this barrier, proposing the first truly polylogarithmic-approximate low-cost fair hierarchical clustering, thus greatly bridging the gap between the best fair and vanilla hierarchical clustering approximations.
Two Views Are Better than One: Monocular 3D Pose Estimation with Multiview Consistency

Deducing a 3D human pose from a single 2D image or 2D keypoints is inherently challenging, given the fundamental ambiguity wherein multiple 3D poses can correspond to the same 2D representation. The acquisition of 3D data, while invaluable for resolving pose ambiguity, is expensive and requires an intricate setup, often restricting its applicability to controlled lab environments. We improve performance of monocular human pose estimation models using multiview data for fine-tuning. We propose a novel loss function, multiview consistency, to enable adding additional training data with only 2D supervision. This loss enforces that the inferred 3D pose from one view aligns with the inferred 3D pose from another view under similarity transformations. Our consistency loss substantially improves performance for fine-tuning with no available 3D data. Our experiments demonstrate that two views offset by 90 degrees are enough to obtain good performance, with only marginal improvements by adding more views. Thus, we enable the acquisition of domain-specific data by capturing activities with off-the-shelf cameras, eliminating the need for elaborate calibration procedures. This research introduces new possibilities for domain adaptation in 3D pose estimation, providing a practical and cost-effective solution to customize models for specific applications. The used dataset, featuring additional views, will be made publicly available.
Multi-Objective Optimization via Wasserstein-Fisher-Rao Gradient Flow

Multi-objective optimization (MOO) aims to optimize multiple, possibly conflicting objectives with widespread applications. We introduce a novel interacting particle method for MOO inspired by molecular dynamics simulations. Our approach combines overdamped Langevin and birth-death dynamics, incorporating a "dominance potential" to steer particles toward global Pareto optimality. In contrast to previous methods, our method is able to relocate dominated particles, making it particularly adept at managing Pareto fronts of complicated geometries. Our method is also theoretically grounded as a Wasserstein-Fisher-Rao gradient flow with convergence guarantees. Extensive experiments confirm that our approach outperforms state-of-the-art methods on challenging synthetic and real-world datasets.
ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and Quantization

Parameter-efficient fine-tuning (PEFT) techniques make it possible to efficiently adapt a language model to create "expert" models that specialize to new tasks or domains. Recent techniques in model merging and compositional generalization leverage these expert models by dynamically composing modules to improve zero/few-shot generalization. Despite the efficiency of PEFT methods, the size of expert models can make it onerous to retrieve expert models per query over high-latency networks like the Internet or serve multiple experts on a single GPU. To address these issues, we present ComPEFT, a novel method for compressing fine-tuning residuals (task vectors) of PEFT based models. ComPEFT employs sparsification and ternary quantization to reduce the size of the PEFT module without performing any additional retraining while preserving or enhancing model performance. In extensive evaluation across T5, T0, and LLaMA-based models with 200M - 65B parameters, ComPEFT achieves compression ratios of 8x - 50x. In particular, we show that ComPEFT improves with scale - stronger models exhibit higher compressibility and better performance. For example, we show that ComPEFT applied to LLaMA outperforms QLoRA by 4.16% on MMLU with a storage size reduction of up to 26x. In addition, we show that the compressed experts produced by ComPEFT maintain few-shot compositional generalization capabilities, facilitate efficient communication and computation, and exhibit enhanced performance when merged. Lastly, we provide an analysis of different method components, compare it with other PEFT methods, and test ComPEFT's efficacy for compressing the residual of full-finetuning. Our code is available at https://github.com/prateeky2806/compeft.
Large Language Models in Education: Vision and Opportunities

With the rapid development of artificial intelligence technology, large language models (LLMs) have become a hot research topic. Education plays an important role in human social development and progress. Traditional education faces challenges such as individual student differences, insufficient allocation of teaching resources, and assessment of teaching effectiveness. Therefore, the applications of LLMs in the field of digital/smart education have broad prospects. The research on educational large models (EduLLMs) is constantly evolving, providing new methods and approaches to achieve personalized learning, intelligent tutoring, and educational assessment goals, thereby improving the quality of education and the learning experience. This article aims to investigate and summarize the application of LLMs in smart education. It first introduces the research background and motivation of LLMs and explains the essence of LLMs. It then discusses the relationship between digital education and EduLLMs and summarizes the current research status of educational large models. The main contributions are the systematic summary and vision of the research background, motivation, and application of large models for education (LLM4Edu). By reviewing existing research, this article provides guidance and insights for educators, researchers, and policy-makers to gain a deep understanding of the potential and challenges of LLM4Edu. It further provides guidance for further advancing the development and application of LLM4Edu, while still facing technical, ethical, and practical challenges requiring further research and exploration.
Control of open quantum systems via dynamical invariants

In this work, we confront the challenge of controlling quantum systems that are influenced by their environment, utilizing the theory of dynamical invariants. Our strategy involves a reverse engineering method for formulating control protocols like Shortcuts to Adiabaticity (STA), tailored to be resilient against environmental noise and dissipation. This technique offers two main advantages compared to other quantum control methods: firstly, it incorporates the time-varying aspect of the dissipation factor in the master equation, which arises from driving the system's Hamiltonian (the control fields). Secondly, our method eliminates the need for iterative propagation of the system state, a process that is typically resource-intensive. The efficacy and practicality of our approach are demonstrated through the application to two fundamental models: a two-level quantum system and a quantum harmonic oscillator, each interacting with a thermal bath.
Towards Improving Document Understanding: An Exploration on Text-Grounding via MLLMs

In the field of document understanding, significant advances have been made in the fine-tuning of Multimodal Large Language Models (MLLMs) with instruction-following data. Nevertheless, the potential of text-grounding capability within text-rich scenarios remains underexplored. In this paper, we present a text-grounding document understanding model, termed TGDoc, which addresses this deficiency by enhancing MLLMs with the ability to discern the spatial positioning of text within images. Empirical evidence suggests that text-grounding improves the model's interpretation of textual content, thereby elevating its proficiency in comprehending text-rich images. Specifically, we compile a dataset containing 99K PowerPoint presentations sourced from the internet. We formulate instruction tuning tasks including text detection, recognition, and spotting to facilitate the cohesive alignment between the visual encoder and large language model. Moreover, we curate a collection of text-rich images and prompt the text-only GPT-4 to generate 12K high-quality conversations, featuring textual locations within text-rich scenarios. By integrating text location data into the instructions, TGDoc is adept at discerning text locations during the visual question process. Extensive experiments demonstrate that our method achieves state-of-the-art performance across multiple text-rich benchmarks, validating the effectiveness of our method.
Test-time Adaptive Vision-and-Language Navigation

Vision-and-Language Navigation (VLN) has witnessed significant advancements in recent years, largely attributed to meticulously curated datasets and proficiently trained models. Nevertheless, when tested in diverse environments, the trained models inevitably encounter significant shifts in data distribution, highlighting that relying solely on pre-trained and fixed navigation models is insufficient. To enhance models' generalization ability, test-time adaptation (TTA) demonstrates significant potential in the computer vision field by leveraging unlabeled test samples for model updates. However, simply applying existing TTA methods to the VLN task cannot well handle the adaptability-stability dilemma of VLN models, i.e., frequent updates can result in drastic changes in model parameters, while occasional updates can make the models ill-equipped to handle dynamically changing environments. Therefore, we propose a Fast-Slow Test-Time Adaptation (FSTTA) approach for VLN by performing decomposition-accumulation analysis for both gradients and parameters in a unified framework. Specifically, in the fast update phase, gradients generated during the recent multi-step navigation process are decomposed into components with varying levels of consistency. Then, these components are adaptively accumulated to pinpoint a concordant direction for fast model adaptation. In the slow update phase, historically recorded parameters are gathered, and a similar decomposition-accumulation analysis is conducted to revert the model to a stable state. Extensive experiments show that our method obtains impressive performance gains on four popular benchmarks.