Reviewerly/exHarmony huggingface dataset:
ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation

In addition to the unprecedented ability in imaginary creation, large text-to-image models are expected to take customized concepts in image generation. Existing works generally learn such concepts in an optimization-based manner, yet bringing excessive computation or memory burden. In this paper, we instead propose a learning-based encoder, which consists of a global and a local mapping networks for fast and accurate customized text-to-image generation. In specific, the global mapping network projects the hierarchical features of a given image into multiple "new" words in the textual word embedding space, i.e., one primary word for well-editable concept and other auxiliary words to exclude irrelevant disturbances (e.g., background). In the meantime, a local mapping network injects the encoded patch features into cross attention layers to provide omitted details, without sacrificing the editability of primary concepts. We compare our method with existing optimization-based approaches on a variety of user-defined concepts, and demonstrate that our method enables high-fidelity inversion and more robust editability with a significantly faster encoding process. Our code is publicly available at https://github.com/csyxwei/ELITE.
ToonTalker: Cross-Domain Face Reenactment

We target cross-domain face reenactment in this paper, i.e., driving a cartoon image with the video of a real person and vice versa. Recently, many works have focused on one-shot talking face generation to drive a portrait with a real video, i.e., within-domain reenactment. Straightforwardly applying those methods to cross-domain animation will cause inaccurate expression transfer, blur effects, and even apparent artifacts due to the domain shift between cartoon and real faces. Only a few works attempt to settle cross-domain face reenactment. The most related work AnimeCeleb [13] requires constructing a dataset with pose vector and cartoon image pairs by animating 3D characters, which makes it inapplicable anymore if no paired data is available. In this paper, we propose a novel method for cross-domain reenactment without paired data. Specifically, we propose a transformer-based framework to align the motions from different domains into a common latent space where motion transfer is conducted via latent code addition. Two domain-specific motion encoders and two learnable motion base memories are used to capture domain properties. A source query transformer and a driving one are exploited to project domain-specific motion to the canonical space. The edited motion is projected back to the domain of the source with a transformer. Moreover, since no paired data is provided, we propose a novel cross-domain training scheme using data from two domains with the designed analogy constraint. Besides, we contribute a cartoon dataset in Disney style. Extensive evaluations demonstrate the superiority of our method over competing methods.
UniFormerV2: Unlocking the Potential of Image ViTs for Video Understanding

The prolific performances of Vision Transformers (ViTs) in image tasks have prompted research into adapting the image ViTs for video tasks. However, the substantial gap between image and video impedes the spatiotemporal learning of these image-pretrained models. Though video-specialized models like UniFormer can transfer to the video domain more seamlessly, their unique architectures require prolonged image pretraining, limiting the scalability. Given the emergence of powerful open-source image ViTs, we propose unlocking their potential for video understanding with efficient UniFormer designs. We call the resulting model UniFormerV2, since it inherits the concise style of the Uni-Former block, while redesigning local and global relation aggregators that seamlessly integrate advantages from both ViTs and UniFormer. Our UniFormerV2 achieves state-of-the-art performances on 8 popular video benchmarks, including scene-related Kinetics-400/600/700, heterogeneous Moments in Time, temporal-related Something-Something V1/V2, and untrimmed ActivityNet and HACS. It is note-worthy that to the best of our knowledge, UniFormerV2 is the first to elicit 90% top-1 accuracy on Kinetics-400.
4D Panoptic Segmentation as Invariant and Equivariant Field Prediction

In this paper, we develop rotation-equivariant neural networks for 4D panoptic segmentation. 4D panoptic segmentation is a benchmark task for autonomous driving that requires recognizing semantic classes and object instances on the road based on LiDAR scans, as well as assigning temporally consistent IDs to instances across time. We observe that the driving scenario is symmetric to rotations on the ground plane. Therefore, rotation-equivariance could provide better generalization and more robust feature learning. Specifically, we review the object instance clustering strategies and restate the centerness-based approach and the offset-based approach as the prediction of invariant scalar fields and equivariant vector fields. Other subtasks are also unified from this perspective, and different invariant and equivariant layers are designed to facilitate their predictions. Through evaluation on the standard 4D panoptic segmentation benchmark of SemanticKITTI, we show that our equivariant models achieve higher accuracy with lower computational costs compared to their non-equivariant counterparts. Moreover, our method sets the new state-of-the-art performance and achieves 1st place on the SemanticKITTI 4D Panoptic Segmentation leaderboard.
ELFNet: Evidential Local-global Fusion for Stereo Matching

Although existing stereo matching models have achieved continuous improvement, they often face issues related to trustworthiness due to the absence of uncertainty estimation. Additionally, effectively leveraging multi-scale and multi-view knowledge of stereo pairs remains unexplored. In this paper, we introduce the Evidential Local-global Fusion (ELF) framework for stereo matching, which endows both uncertainty estimation and confidence-aware fusion with trustworthy heads. Instead of predicting the disparity map alone, our model estimates an evidential-based disparity considering both aleatoric and epistemic uncertainties. With the normal inverse-Gamma distribution as a bridge, the proposed framework realizes intra evidential fusion of multi-level predictions and inter evidential fusion between cost-volume-based and transformer-based stereo matching. Extensive experimental results show that the proposed framework exploits multi-view information effectively and achieves state-of-the-art overall performance both on accuracy and cross-domain generalization. The codes are available at https://github.com/jimmy19991222/ELFNet.
DomainAdaptor: A Novel Approach to Test-time Adaptation

To deal with the domain shift between training and test samples, current methods have primarily focused on learning generalizable features during training and ignore the specificity of unseen samples that are also critical during the test. In this paper, we investigate a more challenging task that aims to adapt a trained CNN model to unseen domains during the test. To maximumly mine the information in the test data, we propose a unified method called DomainAdaptor for the test-time adaptation, which consists of an AdaMixBN module and a Generalized Entropy Minimization (GEM) loss. Specifically, AdaMixBN addresses the domain shift by adaptively fusing training and test statistics in the normalization layer via a dynamic mixture co-efficient and a statistic transformation operation. To further enhance the adaptation ability of AdaMixBN, we design a GEM loss that extends the Entropy Minimization loss to better exploit the information in the test data. Extensive experiments show that DomainAdaptor consistently outperforms the state-of-the-art methods on four benchmarks. Furthermore, our method brings more remarkable improvement against existing methods on the few-data unseen domain. The code is available at https://github.com/koncle/DomainAdaptor.
Quantum Cryptography for Enhanced Network Security: A Comprehensive Survey of Research, Developments, and Future Directions

With the ever-growing concern for internet security, the field of quantum cryptography emerges as a promising solution for enhancing the security of networking systems. In this paper, 20 notable papers from leading conferences and journals are reviewed and categorized based on their focus on various aspects of quantum cryptography, including key distribution, quantum bit commitment, post-quantum cryptography, and counterfactual quantum key distribution. The paper explores the motivations and challenges of employing quantum cryptography, addressing security and privacy concerns along with existing solutions. Secure key distribution, a critical component in ensuring the confidentiality and integrity of transmitted information over a network, is emphasized in the discussion. The survey examines the potential of quantum cryptography to enable secure key exchange between parties, even when faced with eavesdropping, and other applications of quantum cryptography. Additionally, the paper analyzes the methodologies, findings, and limitations of each reviewed study, pinpointing trends such as the increasing focus on practical implementation of quantum cryptography protocols and the growing interest in post-quantum cryptography research. Furthermore, the survey identifies challenges and open research questions, including the need for more efficient quantum repeater networks, improved security proofs for continuous variable quantum key distribution, and the development of quantum-resistant cryptographic algorithms, showing future directions for the field of quantum cryptography.
From Traditional to Deep: A Survey of Image Forgery Detection Techniques

The detection of image forgeries is a crucial area of study within the discipline of digital image analysis, with the objective of identifying and accurately determining the location of modified regions inside images. The importance of developing strong and dependable forgery detection methods has grown increasingly significant due to the widespread availability of advanced image editing tools and the growing complexity of forgery techniques. This survey paper presents a thorough examination of the comprehensive classification of methods used for detecting image forgeries. The paper considers various factors, including the type of forgery, the detection methodology applied, the evaluation metrics used for performance analysis, and the datasets utilized. The primary objective of this paper is to stimulate further progress in the field by offering valuable insights and up-to-date information on contemporary techniques in forgery detection to researchers in the field.
Impact of Adaptive Educational Game Applications on Improving Student Learning: Efforts to Introduce Nusantara Culture in Indonesia

This study aimed to introduce Nusantara culture based on educational games by adjusting students' learning styles. Culture as an ancestral heritage tradition needs to be preserved by introducing it to the younger gen-eration from an early age. However, the survey results found that less than 26% of student respondents un-derstood Nusantara culture well. Compared to previous research, the model of cultural introduction through adaptive educational games is more fun because it is adapted to the way students learn. This research was carried out using the Design-Based Research (DBR) method through 4 stages of the procedure. The feasibil-ity test and application effectiveness test were carried out on a group of students from several elementary schools in Indonesia, who were taken using a cluster random sampling technique. The results of media design and content validation obtained an average value of 0.76 and 0.82, which means that the media is declared valid. The feasibility test used the System Usability Scale (SUS) with an average value of 80% in the acceptable category. The results of the research obtained a description of the comparison of the final scores of the control class and the experimental class, which was 55 compared to 75. This study concluded that learning media for introducing Indonesian culture based on adaptive educational games had a positive impact by effectively increasing learning outcomes on students' understanding of Indonesian culture. Further development of this game application can be expanded in the application of animation in more depth.
MOORA MCDM based Optimal Machine Learning Regression Techniques for Breast Cancer Prediction

Breast cancer is a serious health issue that affects women all over the globe. For improved treatment outcomes, early breast cancer identification is essential. However, the present diagnostic procedure relies on time-consuming examination, providing difficulties for effective diagnosis.In order to analyze early identification of breast cancer; such a system would be created to make use of modern innovations like artificial intelligence and machine learning algorithms. Eight regression approaches are used in this work to anticipate breast cancer utilizing essential tumor cell data, including MLP, Linear Regression, LGBM Regressor, XGB Regressor, Kernel Ridge, Bayesian Ridge, SVR, and Random Forest Regression. The objective of the study is to choose the best regression model for making precise predictions. The MOORA (Multi Objective Optimization by Ratio Analysis) MCDM (Multi Criteria Decision Making) approach has been used to find out the best optimal ML regression for predicting the incidence of breast cancer. Finally, using the top-ranked two ML regressors, the fitting analysis and prediction accuracy was explored. It has been observed that the XGB Regressor is the best suited for the prediction of breast cancer having an RMSE value of 0.0483. The second most effective ML Regressor is MLP with a RMSE value of 0.0322. The suggested methodology in breast cancer prediction demonstrates the possibility for creating effective self-sustaining diagnostic systems.
The Fundamentals of Metaverse

The word metaverse is resurfacing and becoming viral after Facebook changed itscompany name to Meta. A lot of opportunities are offered by the rise of metaverseconcept. For example, a library can be presented in the form of metaverse so thatvisitors do not need to come physically. Metaverse means beyond the universe or inthis case beyond our universe. Companies around the world including Microsoft,Facebook, Epic Games, Apple, and Nvidia already invested in the metaverse as abusiness opportunity, and the top 3 business sectors are technology, education, andfinance. Most companies also believe there are many benefits of the metaverse that canovercome the current obstacles om this universe, such as removing the physicallimitation to do various activities. This paper will give a fundamental understanding ofthe metaverse and the opportunity of the metaverse in practice and research in thefuture. The result of this paper can be a guide for any scholars and practitioners tounderstand the metaverse starting from the definition to the things that are related to the metaverse.
Too Centralized to Fail A Bitcoin Network Analysis

As the title provocatively suggests, in this article we explore empirically and question on the basis of research outcomes the implications of one of the most distinctive features of Bitcoin as a digital currency: the positively advertised de-centralization of its network and the often derived claim of egalitarianism alleged to the peer-to-peer system. In order to assess degrees and trends of network de-centralization we follow two tracks. First, we analyze a snapshot of BTC transactions taken in October 2020, basing our explorations on a subset of the “crypto_bitcoin” dataset publicly available on Google Cloud Platform and applying some of the more relevant network analysis tools, like degrees and prestige. Then we extend the analysis to the overall Bitcoin system, tracing the structural transformations it has witnessed over time with regard to the hash-rate distribution. Through a longitudinal comparison, we come to show that the number of competitors in the network have decreased over time, reducing the initial outright pluralism of the actors in the system, and gradually melting down into “special nodes”, whose power has grown over time. Such centralization trends, together with the China-centered geographical distribution of the major mining pools, might have had important implications for Bitcoin success as well as they might for its future.
Blockchain-based Food Supply Chains: the role of Smart Contracts

Given blockchain’s ability to record and maintain information in an accessible, verifiable, non-repudiable and tamper-proof manner, this technology appears to provide a secure environment to improve product traceability and provenance throughout the extended supply-chain, which would particularly benefit the agri-food industry. Indeed, the extension and the multi-participant/multi-phase nature of the production cycle that characterize modern agri-food supply chains has made them increasingly difficult to manage, while ensuring that stakeholders are properly informed, that counterfeiting is identified and prevented, that safety standards are met, and that the origin and quality of agri-food products are properly certified. It is a growing opinion, among scholars, that blockchain – as a resilient record-keeping and tracing system - could overcome some of these critical issues; accordingly, smart contracts would be a potentially innovative tool to automatically execute agreements between the various parties involved and, perhaps, help monitor, regulate, and verify each (processing, storage, transfer, etc.) stage that forms the workflow. This paper – building upon the extant academic literature and taking into account Italian policies and legislation on the point – aims to explore the role of smart contracts in food-(block)chain traceability systems, evaluating suitability and critical issues. Finally, the possible contribution of smart contracts - when combined with IoT devices - for more effective (fully or partially automated) controls on food products will be analyzed.
Exploring IoT-Applications: A Survey of Recent Progress, Challenges, and Impact of AI, Blockchain, and Disruptive Technologies

The Internet of Things (IoT) has become a common part of our daily lives. It refers to a network of physical objects, such as home appliances and security systems, that can easily connect to the Internet. By utilizing the internet, data storage, analytics, and sensors, IoT improves our daily needs and offers convenience in our fast-paced modern world. Alongside artificial intelligence, machine learning, and cloud computing, IoT has been a major trend in high-tech. It has great potential in areas like Industry 4.0, Smart Cities, and driverless cars. Advancements in technologies like artificial intelligence, blockchain, 5G, edge AI, and edge computing are driving new trends in IoT. This research paper presents a comprehensive survey of the latest applications of IoT, focusing on cutting-edge developments. The paper also explores the potential of incorporating disruptive technologies like artificial intelligence and blockchain to enhance IoT applications. It covers various aspects related to IoT application areas, including recent developments, significant challenges, and difficulties.
Barking Up the Wrong Tree? Reconsidering Policy Compliance as a Dependent Variable within Behavioral Cybersecurity Research

A rich body of research examines the cybersecurity behavior of employees, with a particular focus on explaining the reasons why employees comply with (or violate) organizational cybersecurity policies. However, we posit that this emphasis on policy compliance is susceptible to several notable limitations that could lead to inaccurate research conclusions. In this commentary, we examine the limitations of using cybersecurity policy compliance as a dependent variable by presenting three assertions: (1) the link between policy compliance and organizational-level outcomes is ambiguous; (2) policies vary widely in terms of their clarity and completeness; and (3) employees have an inconsistent familiarity with their own organization’s cybersecurity policies. Taken together, we suggest that studying compliance with cybersecurity policies reveals only a partial picture of employee behavior. In response, we offer recommendations for future research.
Development of the system of secondary specialized education in the regions of the Arctic zone of the European North of Russia in the 1950s – 1980s

The economic and socio-cultural sphere of the Arctic zone of the European North of Russia was actively developing in the 1950s - 80s. This process has served as a catalyst for increasing the scale of training, including those with secondary specialized education. The article examines the state of the system of secondary special education in the Arctic regions during this period, for the first time in historiography the register of secondary special educational institutions operating in the Arctic zone of the European North of the RSFSR is presented.
SPACE4AI-R: a Runtime Management Tool for AI Applications Component Placement and Resource Scaling in Computing Continua

The recent migration towards Internet of Things determined the rise of a Computing Continuum paradigm where Edge and Cloud resources coordinate to support the execution of Artificial Intelligence (AI) applications, becoming the foundation of use-cases spanning from predictive maintenance to machine vision and healthcare. This generates a fragmented scenario where computing and storage power are distributed among multiple devices with highly heterogeneous capacities. The runtime management of AI applications executed in the Computing Continuum is challenging, and requires ad-hoc solutions. We propose SPACE4AI-R, which combines Random Search and Stochastic Local Search algorithms to cope with workload fluctuations by identifying the minimum-cost reconfiguration of the initial production deployment, while providing performance guarantees across heterogeneous resources including Edge devices and servers, Cloud GPU-based Virtual Machines and Function as a Service solutions. Experimental results prove the efficacy of our tool, yielding up to 60% cost reductions against a static design-time placement, with a maximum execution time under 1.5s in the most complex scenarios.
Ongoing Process of the Integration of Kindergarten and Childcare of Korea

This paper is part of a research project examining young children's musical experiences in a particular setting. An initial focus of the research was to explore children's competence when exposed to a more competent member of the culture, in this instance a skilled musician, and to develop observational and interpretive tools for recording these experiences. Surprised at the children's musical activities and understandings we then broadened our discussion to examine the wider context of the child's right to music in the early years. Using a three-year-old group of children participating in formal music sessions as a focus, a theme of this paper therefore is the relationship between research, policy and practice. This relationship is complex and needs to be unpacked so that underlying assumptions about children and childhood, what children should learn and wider social agendas for education can be discovered. We decided to start this journey by observing daily practice. We argue underlying systems of beliefs and values will be reflected in practice, and obsewations and interpretations of daily experience can assist in determining future research and accompanying policy decisions about support for music in early childhood services.