Reviewerly/exHarmony huggingface dataset:
Quantum2FA: Efficient Quantum-Resistant Two-Factor Authentication Scheme for Mobile Devices

Smart-card based password authentication has been the most widely used two-factor authentication (2FA) mechanism for security-critical applications (e.g., e-Health, smart grid and e-Commerce) in the past decades, and it is likely to hold its status in the foreseeable future. Hundreds of this type of 2FA schemes have been proposed, yet to our knowledge, most of them are built on the intractability of conventional hard problems (e.g., discrete logarithm problems and integer factoring problems) which are no longer hard in the quantum era. With the recent advancements in quantum computing, the design of secure and efficient smart-card based password authentication schemes against quantum attacks is becoming increasingly urgent. However, it is not as simple as it seems, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">how to design such a quantum-resistant 2FA scheme is challenging due to the demanding security requirements and the resource-constrained nature of mobile devices</i> . In this work, we take the first step towards this issue by proposing Quantum2FA, a practical quantum-resistant smart-card-based password authentication scheme that employs Alkim <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ’s lattice-based key exchange and Wang-Wang’s “fuzzy-verifier + honeywords” technique (IEEE TDSC’18). Particularly, Quantum2FA can thwart the newly revealed key-reuse attack (ACISP’18, CT-RSA’19) against lattice-based key exchange schemes in two aspects: signal leakage attacks and key mismatch attacks. Specifically, it restricts the necessary conditions (i.e., the attacker must be the initiator of the key exchange) for an adversary to analyze the signal; It introduces honeywords to detect the key mismatches between the smart card and the server, and thus smart card loss attack can be thwarted. We formally prove the security of Quantum2FA under the random oracle model and demonstrate its efficiency through experiments on a 32 MHz 8-bit AVR Embedded Processor. Comparison results show that Quantum2FA is not only more secure but also offers better computation efficiency than the state-of-the-art conventional 2FA schemes.
Migration-Based Moth-Flame Optimization Algorithm

Moth–flame optimization (MFO) is a prominent swarm intelligence algorithm that demonstrates sufficient efficiency in tackling various optimization tasks. However, MFO cannot provide competitive results for complex optimization problems. The algorithm sinks into the local optimum due to the rapid dropping of population diversity and poor exploration. Hence, in this article, a migration-based moth–flame optimization (M-MFO) algorithm is proposed to address the mentioned issues. In M-MFO, the main focus is on improving the position of unlucky moths by migrating them stochastically in the early iterations using a random migration (RM) operator, maintaining the solution diversification by storing new qualified solutions separately in a guiding archive, and, finally, exploiting around the positions saved in the guiding archive using a guided migration (GM) operator. The dimensionally aware switch between these two operators guarantees the convergence of the population toward the promising zones. The proposed M-MFO was evaluated on the CEC 2018 benchmark suite on dimension 30 and compared against seven well-known variants of MFO, including LMFO, WCMFO, CMFO, CLSGMFO, LGCMFO, SMFO, and ODSFMFO. Then, the top four latest high-performing variants were considered for the main experiments with different dimensions, 30, 50, and 100. The experimental evaluations proved that the M-MFO provides sufficient exploration ability and population diversity maintenance by employing migration strategy and guiding archive. In addition, the statistical results analyzed by the Friedman test proved that the M-MFO demonstrates competitive performance compared to the contender algorithms used in the experiments.
NFT marketing: How marketers can use nonfungible tokens in their campaigns

Nonfungible tokens (NFTs) are a record of ownership of primarily digital media, where the NFT is stored on a blockchain. According to the 2021 Gartner Hype Cycle for Key Technologies, NFTs may significantly transform marketing functions. Marketing managers wishing to adopt NFTs therefore need to know something about the marketing implications. This article explains NFTs in broad terms and discusses the marketing implications using a modified AIDA hierarchy. These implications can give marketing managers and executives guidelines on how to persuade consumers to purchase NFTs owing to their unique characteristics, such as scarcity, nonfungibility, proven authenticity, proof of ownership, royalties, and direct distribution infrastructure.
NFTs: Applications and Challenges

Before the widespread application of blockchain-based technologies, the mechanisms in place for verifying ownership of digital assets and thus, means of securing them remained susceptible to tampering that translated into significant losses. Decades of research and advancements in blockchain led to the development of Non-Fungible Tokens (NFTs), which are tokens that represent digital assets and have proof of ownership embedded. The novel characteristic of each token being unique and distinctive from another has strengthened the security of assets and reinforced unique ownership. This cutting-edge technology continues to grow and capture the attention of the masses as more applications of NFTs are identified with time. This research aims to present a comprehensive overview of NFT and its underlying core technologies, namely blockchain and Ethereum. Further, numerous platforms for buying and selling NFTs are presented along with the applications of NFTs across various sectors including education, fashion, sports, and digital art. Moreover, the paper highlights the key challenges in adaptation of NFT technology from the perspective of security, privacy, environmental impact, ownership, governance, and property rights.
Improvement of K-means Cluster Quality by Post Processing Resulted Clusters

A big volume of new data is generated in every moment of the day by different devices and domains as social network, mobile and desktop devices, financial transaction, online websites, different search engines and a lot of smart home devices. The generated data is diversified and can be structured or unstructured. Clustering is the process of categorizing a dataset in groups of records that are similar and are called clusters, the grouping process being performed using a specific criterion. The K-means clustering algorithm is still popular after many years. Different versions of the K-means algorithm emerged along the time and were focused on improving the K-means algorithm by performing some preprocessing steps or by reducing the number of iterations having as a final objective the improvement of the processing time. This paper presents a way of improving the resulted clusters generated by the K-means algorithm by post processing the resulted clusters with a supervised learning algorithm. The proposed approach is focused on improving the quality of the resulting clusters and not on reducing the processing time.
HiNet: Deep Image Hiding by Invertible Network

Image hiding aims to hide a secret image into a cover image in an imperceptible way, and then recover the secret image perfectly at the receiver end. Capacity, invisibility and security are three primary challenges in image hiding task. This paper proposes a novel invertible neural network (INN) based framework, HiNet, to simultaneously overcome the three challenges in image hiding. For large capacity, we propose an inverse learning mechanism by simultaneously learning the image concealing and revealing processes. Our method is able to achieve the concealing of a full-size secret image into a cover image with the same size. For high invisibility, instead of pixel domain hiding, we propose to hide the secret information in wavelet domain. Furthermore, we propose a new low-frequency wavelet loss to constrain that secret information is hidden in high-frequency wavelet subbands, which significantly improves the hiding security. Experimental results show that our HiNet significantly outperforms other state-of-the-art image hiding methods, with more than 10 dB PSNR improvement in secret image recovery on ImageNet, COCO and DIV2K datasets. Codes are available at https://github.com/TomTomTommi/HiNet.
Self-Calibrating Neural Radiance Fields

In this work, we propose a camera self-calibration algorithm for generic cameras with arbitrary non-linear distortions. We jointly learn the geometry of the scene and the accurate camera parameters without any calibration objects. Our camera model consists of a pinhole model, a fourth order radial distortion, and a generic noise model that can learn arbitrary non-linear camera distortions. While traditional self-calibration algorithms mostly rely on geometric constraints, we additionally incorporate photometric consistency. This requires learning the geometry of the scene, and we use Neural Radiance Fields (NeRF). We also propose a new geometric loss function, viz., projected ray distance loss, to incorporate geometric consistency for complex non-linear camera models. We validate our approach on standard real image datasets and demonstrate that our model can learn the camera intrinsics and extrinsics (pose) from scratch without COLMAP initialization. Also, we show that learning accurate camera models in a differentiable manner allows us to improve PSNR over baselines. Our module is an easy-to-use plugin that can be applied to NeRF variants to improve performance. The code and data are currently available at https://github.com/POSTECH-CVLab/SCNeRF
RL/DRL Meets Vehicular Task Offloading Using Edge and Vehicular Cloudlet: A Survey

The last two decades have seen a clear trend toward crafting intelligent vehicles based on the significant advances in communication and computing paradigms, which provide a safer, stress-free, and more enjoyable driving experience. Moreover, emerging applications and services necessitate massive volumes of data, real-time data processing, and ultrareliable and low-latency communication (URLLC). However, the computing capability of current intelligent vehicles is minimal, making it challenging to meet the delay-sensitive and computation-intensive demand of such applications. In this situation, vehicular task/computation offloading toward the edge cloud (EC) and vehicular cloudlet (VC) seems to be a promising solution to improve the network’s performance and applications’ Quality of Service (QoS). At the same time, artificial intelligence (AI) has dramatically changed people’s lives. Especially for vehicular task offloading applications, AI achieves state-of-the-art performance in various vehicular environments. Motivated by the outstanding performance of integrating reinforcement learning (RL)/deep RL (DRL) to the vehicular task offloading systems, we present a survey on various RL/DRL techniques applied to vehicular task offloading. Precisely, we classify the vehicular task offloading works into two main categories: 1) RL/ DRL solutions leveraging EC and 2) RL/DRL solutions using VC computing. Moreover, the EC section-based RL/DRL solutions are further subcategorized into multiaccess edge computing (MEC) server, nearby vehicles, and hybrid MEC (HMEC). To the best of our knowledge, we are the first to cover RL/DRL-based vehicular task offloading. Also, we provide lessons learned and open research challenges in this field and discuss the possible trend for future research.
A trust transitivity model of small and medium-sized manufacturing enterprises under blockchain-based supply chain finance

Small and medium-sized manufacturing enterprises (SMMEs) have been facing the financing contradiction of “strong demand” and “weak trust” for a long time, and financing constraints are closely related to the production efficiency of manufacturing enterprises. Therefore, how to solve the financing problem has become one of the most concerned issues for the development of SMMEs. After years of development, supply chain finance (SCF) has become an important way of financing for SMMEs. Under the traditional supply chain finance system, Tier 1-supplier in the manufacturing industry chain can make use of the high credit of core enterprises for financing, but the high credit of core enterprises cannot be transmitted to Tier 2 to Tier N-suppliers in the long tail market. In recent years, the innovative development of supply chain finance driven by blockchain technology has played a positive role in overcoming the lack of trust and has spawned a multi-level supply chain finance system. In this context, the paper proposes to establish a trust transitivity model based on the electronic payment voucher (EPV) issued by the blockchain, using the intuitionistic fuzzy set theory, and comprehensively consider direct trust and indirect trust. In trust aggregation, the dynamic weight allocation strategy is introduced to play the role of incentive and punishment for SMMEs. This study aims to provide a new idea for the trust evaluation of SMMEs by building a trust transitivity model, so as to give full play to the value-added and transmission effect of the manufacturing industry chain, and relieve the financing difficulties for SMMEs. Finally, the study further verified the rationality of the model through the analysis of numerical examples.
A new chaotic Lévy flight distribution optimization algorithm for solving constrained engineering problems

Abstract This work proposed a new metaheuristic dubbed as Chaotic Lévy flight distribution (CLFD) algorithm, to address physical world engineering optimization problems that incorporate the chaotic maps in the elementary Lévy flight distribution (LFD). Hybridization aims to increase the LFD rate of convergence while also providing a problem‐free optimization approach. The proposed methodology is investigated for five case studies of constrained optimization issues followed by shape optimization of structural design. The outcomes from the CFLD algorithm are further contrasted with its fundamental version and other distinguished recently introduced algorithms. The computational analysis illustrates the dominance of CLFD over other considered optimizers. Moreover, the present investigation shows that CLFD is a robust technique that can efficiently find optimal mechanical design problems with a proper chaotic map selection.
An Artificial Intelligence enabled Smart Industrial Automation System based on Internet of Things Assistance

Several applications adapt the smart technologies to the existing versions to make the application more comfortable to its consumers, in which the technologies such as Internet of Things (IoT), Artificial Intelligence and so on. The adaptation of such technologies provides a huge support to the organizations and individuals to attain the commercial and non-commercial needs in good manner. The logic of Internet of Things provides a great support to automate the organizations, industries and even a residential buildings in proper way using such powerful technology advancements as well as it provides a way to the users to operate and monitor the respective place in global perception without any range restrictions. In this paper, the logic of Internet of Things is applied to the industrial automation needs and attain the benefits from that in correct manner. This is done with the abilities of Artificial Intelligence as well and the proposed logic is named as Artificial Intelligence Assisted Network Paradigm (AIANP). The proposed AIANP provides a huge support to automation industry to control and monitor the industrial environment without any security lackings and delay by means of associating the AI logic in its approach. This is achieved by means of associating the internet enabled services to the industry to operate the machineries accordingly. In literature lots of IoT based automation approaches are available but all are strucked up with certain limitations and lackings, so that a new approach is required to resolve the issues over conventional automation technologies. This paper provides a robust industrial automation abilities to the users to control and monitor the industry in proper way using proposed Artificial Intelligence Assisted Network Paradigm. And the resulting section provides the proper proof for the proposed approach efficiency and security effectiveness in graphical manner.
A federated graph neural network framework for privacy-preserving personalization

Abstract Graph neural network (GNN) is effective in modeling high-order interactions and has been widely used in various personalized applications such as recommendation. However, mainstream personalization methods rely on centralized GNN learning on global graphs, which have considerable privacy risks due to the privacy-sensitive nature of user data. Here, we present a federated GNN framework named FedPerGNN for both effective and privacy-preserving personalization. Through a privacy-preserving model update method, we can collaboratively train GNN models based on decentralized graphs inferred from local data. To further exploit graph information beyond local interactions, we introduce a privacy-preserving graph expansion protocol to incorporate high-order information under privacy protection. Experimental results on six datasets for personalization in different scenarios show that FedPerGNN achieves 4.0% ~ 9.6% lower errors than the state-of-the-art federated personalization methods under good privacy protection. FedPerGNN provides a promising direction to mining decentralized graph data in a privacy-preserving manner for responsible and intelligent personalization.
Fast Fine-Grained Image Classification via Weakly Supervised Discriminative Localization

Fine-grained image classification is to recognize hundreds of subcategories in each basic-level category. Existing methods employ discriminative localization to find the key distinctions among subcategories. However, they generally have two limitations: (1) Discriminative localization relies on region proposal methods to hypothesize the locations of discriminative regions, which are time-consuming. (2) The training of discriminative localization depends on object or part annotations, which are heavily labor-consuming. It is highly challenging to address the two key limitations simultaneously, and existing methods only focus on one of them. Therefore, we propose a weakly supervised discriminative localization approach (WSDL) for fast fine-grained image classification to address the two limitations at the same time, and its main advantages are: (1) n-pathway end-to-end discriminative localization network is designed to improve classification speed, which simultaneously localizes multiple different discriminative regions for one image to boost classification accuracy, and shares full-image convolutional features generated by region proposal network to accelerate the process of generating region proposals as well as reduce the computation of convolutional operation. (2) Multi-level attention guided localization learning is proposed to localize discriminative regions with different focuses automatically, without using object and part annotations, avoiding the labor consumption. Different level attentions focus on different characteristics of the image, which are complementary and boost the classification accuracy. Both are jointly employed to simultaneously improve classification speed and eliminate dependence on object and part annotations. Compared with state-of-the-art methods on 2 widely-used fine-grained image classification datasets, our WSDL approach achieves the best performance.
Improving individual predictions: Machine learning approaches for detecting and attacking heterogeneity in schizophrenia (and other psychiatric diseases)

Psychiatric diseases are very heterogeneous both in clinical manifestation and etiology. With the recent rise of using machine learning techniques to attempt to diagnose and prognose these disorders, the issue of heterogeneity becomes increasingly important. With the growing interest in personalized medicine, it becomes even more important to not only classify someone as a patient with a certain disorder, its treatment needs a more precise definition of the underlying neurobiology, since different biological origins of the same disease may require (very) different treatments. We review the possible contributions that machine learning techniques could make to explore the heterogeneous nature of psychiatric disorders with a focus on schizophrenia. First we will review how heterogeneity shows up and how machine learning, or multivariate pattern recognition methods in general, can be used to discover it. Secondly, we will discuss the possible uses of these techniques to attack heterogeneity, leading to improved predictions and understanding of the neurobiological background of the disorder.
BLIQUE-TMI: Blind Quality Evaluator for Tone-Mapped Images Based on Local and Global Feature Analyses

High dynamic range (HDR) image, which has a powerful capacity to represent the wide dynamic range of real-world scenes, has been receiving attention from both academic and industrial communities. Although HDR imaging devices have become prevalent, the display devices for HDR images are still limited. To facilitate the visualization of HDR images in standard low dynamic range displays, many different tone mapping operators (TMOs) have been developed. To create a fair comparison of different TMOs, this paper proposes a BLInd QUality Evaluator to blindly predict the quality of Tone-Mapped Images (BLIQUE-TMI) without accessing the corresponding HDR versions. BLIQUE-TMI measures the quality of TMIs by considering the following aspects: 1) visual information; 2) local structure; and 3) naturalness. To be specific, quality-aware features related to the former two aspects are extracted in a local manner based on sparse representation, while quality-aware features related to the third aspect are derived based on global statistics modeling in both intensity and color domains. All the extracted local and global quality-aware features constitute a final feature vector. An emergent machine learning technique, i.e., extreme learning machine, is adopted to learn a quality predictor from feature space to quality space. The superiority of BLIQUE-TMI to several leading blind IQA metrics is well demonstrated on two benchmark databases.
Hybrid Software Development Approaches in Practice: A European Perspective

The surveyed companies applied hybrid development approaches to specific projects even when company-wide policies for process usage existed. These approaches emerged from the evolution of different work practices and were consistently used regardless of company size or industry sector.