Reviewerly/exHarmony huggingface dataset:
Blockchain Networks as Knowledge Commons

Researchers interested in blockchains are increasingly attuned to questions of governance, including how blockchains relate to government, the ways blockchains are governed, and ways blockchains can improve prospects for successful self-governance. Our paper joins this research by exploring the implications of the Governing Knowledge Commons (GKC) framework to analyze governance of blockchains. Our novel contributions are making the case that blockchain networks represent knowledge commons governance, in the sense that they rely on collectively-managed technologies to pool and manage distributed information, illustrating the usefulness and novelty of the GCK methodology with an empirical case study of the evolution of Bitcoin, and laying the foundation for a research program using the GKC approach.
A Blockchain-Based Model Migration Approach for Secure and Sustainable Federated Learning in IoT Systems

Model migration can accelerate model convergence during federated learning on the Internet of Things (IoT) devices and reduce training costs by transferring feature extractors from fast to slow devices, which, in turn, enables sustainable computing. However, malicious or lazy devices may migrate the fake models or resist sharing models for their benefit, reducing the desired efficiency and reliability of a federated learning system. To this end, this work presents a blockchain-based model migration approach for resource-constrained IoT systems. The proposed approach aims to achieve secure model migration and speed up model training while minimizing computation cost. We first develop an incentive mechanism considering the economic benefits of fast devices, which breaks the Nash equilibrium established by lazy devices and encourages capable devices to train and share models. Second, we design a clustering-based algorithm for identifying malicious devices and preventing them from defrauding incentives. Third, we use blockchain to ensure trustworthiness in model migration and incentive processes. Blockchain records the interaction between the central server and IoT devices and runs the incentive algorithm without exposing the devices’ private data. Theoretical analysis and experimental results show that the proposed approach can accelerate federated learning rates, reduce model training computation costs to increase sustainability, and resist malicious attacks.
AttentionHTR: Handwritten Text Recognition Based on Attention Encoder-Decoder Networks

This work proposes an attention-based sequence-to-sequence model for handwritten word recognition and explores transfer learning for data-efficient training of HTR systems. To overcome training data scarcity, this work leverages models pre-trained on scene text images as a starting point towards tailoring the handwriting recognition models. ResNet feature extraction and bidirectional LSTM-based sequence modeling stages together form an encoder. The prediction stage consists of a decoder and a content-based attention mechanism. The effectiveness of the proposed end-to-end HTR system has been empirically evaluated on a novel multi-writer dataset Imgur5K and the IAM dataset. The experimental results evaluate the performance of the HTR framework, further supported by an in-depth analysis of the error cases. Source code and pre-trained models are available at GitHub ( https://github.com/dmitrijsk/AttentionHTR ).
Traffic Sign Detection and Recognition in Multiimages Using a Fusion Model With YOLO and VGG Network

The detection and recognition of traffic signs is an important topic in intelligent transportation systems. The automatic detection and recognition of traffic signs during driving is the basis for realizing the unmanned driving. Therefore, the work on the detection and recognition of traffic signs has a potential value and application prospect. In the traditional detection and recognition methods, they often detect and recognize traffic signs image by image. In this case, only the information of the current image is used, and the relationship between the image sequences is not considered. To end this issue, we propose a novel model that can use the relationship in multi-images to detect and recognize traffic signs in a driving video sequence quickly and accurately. The model proposed in this paper is a fusion model based on YOLO-V3 and VGG19 network. Finally, we test this proposed model on a public dataset and compare it to the baseline method, and results show that this proposed model achieves accuracy over 90% and outperforms the baseline method for all types of traffic signs in different conditions. Thus, we can conclude this proposed model is efficient and accurate.
Object Detection for Aerial Images With Feature Enhancement and Soft Label Assignment

Object detection in aerial images, different from general object detection, faces with several challenges such as arbitrary-oriented objects and extremely imbalanced foreground-background distribution. Although some recent proposed aerial object detection methods achieve promising results, they are mainly anchor-based detectors which rely heavily on pre-defined anchor boxes and the final detection performance is sensitive to anchor-related hyper-parameters. In contrast, in this paper, we present an anchor-free detector with feature enhancement and soft label assignment (FSDet) which adopts a simpler design and achieves competitive performance. Specifically, to address the feature misalignment for detecting oriented objects, we propose an oriented feature refinement module to align the features with oriented objects. To alleviate the background issue, we design a class-aware context aggregation module to integrate the intra-class context information and suppress background context. Moreover, we propose a soft label assignment mechanism to measure the weight of training samples within the arbitrary-oriented objects, which can concentrate more on representative items with regard to their potential to detect oriented objects, achieving a more stable optimization during training. Extensive experiments on several datasets suggest that the proposed method is superior to the state-of-the-art methods and achieves a better trade-off between speed and accuracy.
Uncertainty-Driven Spiral Trajectory for Robotic Peg-in-Hole Assembly

The peg-in-hole assembly is an essential robotic task that is widely involved in the industry and daily life. Hole search is a crucial step in peg-in-hole since it aligns the peg and hole where the positional uncertainty is presented. This study introduces a hole search strategy by computing an exploration distribution based on uncertainty analysis. Our method firstly utilizes vision to estimate the hole’s pose. It then performs an uncertainty analysis based on vision estimation and systematic configures to compute the exploration distribution of the hole search. The computed exploration distribution calculates a search trajectory through the distribution and finds the hole. The effectiveness of the search strategy is experimentally verified using a robot manipulator in the peg-in-hole of the optical transceiver, which is a challenging task that has not yet been addressed. The results confirm that the proposed search strategy can achieve a 90 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> success rate on the hole search. Through the rational design of the recovery strategy, our method can secure a 100 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> success rate on the robotic peg-in-hole of apparatus with a small clearance (0.1 mm).
Blockchain-Based Management for Organ Donation and Transplantation

Today&#x2019;s organ donation and transplantation systems pose different requirements and challenges in terms of registration, donor-recipient matching, organ removal, organ delivery, and transplantation with legal, clinical, ethical, and technical constraints. Therefore, an end-to-end organ donation and transplantation system is required to guarantee a fair and efficient process to enhance patient experience and trust. In this paper, we propose a private Ethereum blockchain-based solution to enable organ donation and transplantation management in a manner that is fully decentralized, secure, traceable, auditable, private, and trustworthy. We develop smart contracts and present six algorithms along with their implementation, testing, and validation details. We evaluate the performance of the proposed solution by performing privacy, security, and confidentiality analyses as well as comparing our solution with the existing solutions. We make the smart contract code publicly available on Github.
A Simple Visual-Textual Baseline for Pedestrian Attribute Recognition

Pedestrian attribute recognition (PAR), which aims to identify attributes of the pedestrians captured in video surveillance, is a challenging task due to the poor quality of images and diverse spatial distribution among attributes. Existing methods usually model PAR as a multi-label classification problem and manually map attributes to an ordered list corresponding to the outputs of classifiers or sequential models. However, the inherent textual information among attribute annotations is largely neglected in these visual-only methods. In this paper, we first alleviate this issue by proposing a novel visual-textual baseline (VTB) for PAR which introduces an additional textual modality to explore the textual semantic correlations from attribute annotations by pre-trained textual encoders instead of human definitions. VTB encodes pedestrian images and attribute annotations into visual and textual features respectively, interacts with information across modalities, and predicts recognition results independently to remove the influence of attribute orders. Furthermore, we introduce transformer encoder as the cross-modal fusion module in VTB for sufficient intra-modal and cross-modal correlations exploration. Our method achieves superior performance over most existing visual-only methods on two widely used datasets including RAP and PA-100K, demonstrating the effectiveness of utilizing textual modality to PAR. Our method is expected to serve as a multimodal PAR baseline and inspire new insights for multimodal fusion in future PAR research. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/cxh0519/VTB</uri> .
McEliece Needs a Break – Solving McEliece-1284 and Quasi-Cyclic-2918 with Modern ISD

With the recent shift to post-quantum algorithms it becomes increasingly important to provide precise bit-security estimates for code-based cryptography such as McEliece and quasi-cyclic schemes like BIKE and HQC. While there has been significant progress on information set decoding (ISD) algorithms within the last decade, it is still unclear to which extent this affects current cryptographic security estimates. We provide the first concrete implementations for representation-based ISD, such as May-Meurer-Thomae (MMT) or Becker-Joux-May-Meurer (BJMM), that are parameter-optimized for the McEliece and quasi-cyclic setting. Although MMT and BJMM consume more memory than naive ISD algorithms like Prange, we demonstrate that these algorithms lead to significant speedups for practical cryptanalysis on medium-sized instances (around 60 bit). More concretely, we provide data for the record computations of McEliece-1223 and McEliece-1284 (old record: 1161), and for the quasi-cyclic setting up to code length 2918 (before: 1938). Based on our record computations we extrapolate to the bit-security level of the proposed BIKE, HQC and McEliece parameters in NIST's standardization process. For BIKE/HQC, we also show how to transfer the Decoding-One-Out-of-Many (DOOM) technique to MMT/BJMM. Although we achieve significant DOOM speedups, our estimates confirm the bit-security levels of BIKE and HQC. For the proposed McEliece round-3 192 bit and two out of three 256 bit parameter sets, however, our extrapolation indicates a security level overestimate by roughly 20 and 10 bits, respectively, i.e., the high-security McEliece instantiations may be a bit less secure than desired.
Sustainable Goal Achievement by Digital Revolution During and After Pandemic; How Much One Wins and Losses: A Bird's Eye View for Future Planning

The Digital revolution has entered the public discourse in many countries. The Digital revolution is becoming a key driver of society's transformation. The dynamics and threats of the Digital revolution are essential for the proper implementation of this transformative change. The Digital revolution is also expected to transform the way global economies operate. It will also alter the way we view the Sustainability paradigm. The emergence of the internet of everything (IoE) promises to transform how we live and work. This paper aims to develop a conceptual framework that explains how the IoE may affect business models and create value. In this chapter, the authors discuss the various aspects of the IoE and how it will affect how organizations operate. The Digital revolution is the most critical transformation we need to implement to reach Sustainable Development Goals. Its implementation will require precise science, technology, and innovation roadmaps at all levels. The Digital revolution is a powerful force that can shape the future of sustainable development. It can generate disruptive ideas and solutions but should be handled with caution and care. The chapter also outlines the various conditions that a successful digital transformation will require, such as good governance, prosperity, and social inclusion. It also talks about the social implications of the Digital revolution. Here authors tried to analyze the current implementation activities of significant transformation needed sectors and their synchronization with long-term sustainability goal achievement for the next 50 years.
Application of Distributed Ledger Technology in Distribution Networks

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Postdigital Ecopedagogies: Genealogies, Contradictions, and Possible Futures

This chapter charts some genealogies, challenges, and directions for experimenting with the utopic postdigital ecopedagogies demanded by our present (post)pandemic reality. These are messianic—rather than prophetic—utopias that exist not as proclamations or programmes for a distant future but as potentialities immanent in the irreducible excess of the present. While their roots most clearly emanate from the Freirean-inspired ecopedagogy movement, we conceptualize ecopedagogies instead as educational forms that emerge from, negotiate, debate, produce, resist, and/or overcome the shifting and expansive postdigital ecosystems from and to which we write and think. These are expansive ecosystems of humans, postdigital machines, nonhuman animals, minerals, objects, and more; ecosystems that are overdetermined by new forms of ontological hierarchies and capitalism, imperialism, and settler-colonialism. By charting some of the potential lineages, directions, contradictions, and challenges—and by proposing potential lines of educational praxis—we lay a basis for reinvigorated fields of inquiry that moves beyond the existing postdigital literature on the current pandemic.
Genetic Algorithm-Based Ensemble Hybrid Sparse ELM for Grasp Stability Recognition With Multimodal Tactile Signals

Grasp stability recognition based on tactile perception has attracted increasing attention in the robotics community. In this article, we extract tactile features from multimodal tactile signals and propose a novel ensemble approach named genetic algorithm-based ensemble hybrid sparse extreme learning machine (GA-EHSELM) for the grasp stability recognition task. In contrast to traditional ensemble extreme learning machines (ELMs), the proposed approach takes the sparsity of the random weights assigned to the connection between the input layer and the hidden layer for each base learner into consideration, in order to avoid the problem of overfitting. In addition, The diversity of base learners is increased by constructing two types of sparse ELM (SELM). The random weights of the base SELMs are sampled from two different distributions with a certain probability. Furthermore, we utilize the genetic algorithm (GA) for the optimization of tactile features and the base sparse ELMs. To be specific, GA is employed for feature selection as well as the optimization of the sparsity of the random weights associated with different types of base learners and the number of each type of base learner. The effectiveness of the proposed approach has been demonstrated on a public tactile grasp stability dataset.
Seeing Traffic Paths: Encrypted Traffic Classification With Path Signature Features

Although many network traffic protection methods have been developed to protect user privacy, encrypted traffic can still reveal sensitive user information with sophisticated analysis. In this paper, we propose ETC-PS, a novel encrypted traffic classification method with path signature. We first construct the traffic path with a session packet length sequence to represent the interactions between the client and the server. Then, path transformations are conducted to exhibit its structure and obtain different information. A multiscale path signature is finally computed as a kind of distinctive feature to train the traditional machine learning classifier, which achieves highly robust accuracy and low training overhead. Six publicly available datasets with different traffic types of HTTPS/1, HTTPS/2, QUIC, VPN, non-VPN, Tor, and non-Tor are used to conduct closed-world and open-world evaluations to verify the effectiveness of ETC-PS. The experimental results demonstrate that ETC-PS is superior to the state-of-the-art methods in terms of accuracy, f1 score, time complexity, and stability.
Self-Supervised Masking for Unsupervised Anomaly Detection and Localization

Recently, anomaly detection and localization in multimedia data have received significant attention among the machine learning community.In real-world applications such as medical diagnosis and industrial defect detection, anomalies only present in a fraction of the images.To extend the reconstructionbased anomaly detection architecture to the localized anomalies, we propose a self-supervised learning approach through random masking and then restoring, named Self-Supervised Masking (SSM) for unsupervised anomaly detection and localization.SSM not only enhances the training of the inpainting network but also leads to great improvement in the efficiency of mask prediction at inference.Through random masking, each image is augmented into a diverse set of training triplets, thus enabling the autoencoder to learn to reconstruct with masks of various sizes and shapes during training.To improve the efficiency and effectiveness of anomaly detection and localization at inference, we propose a novel progressive mask refinement approach that progressively uncovers the normal regions and finally locates the anomalous regions.The proposed SSM method outperforms several state-of-the-arts for both anomaly detection and anomaly localization, achieving 98.3% AUC on Retinal-OCT and 93.9% AUC on MVTec AD, respectively.
Solving Robotic Manipulation With Sparse Reward Reinforcement Learning Via Graph-Based Diversity and Proximity

In multigoal reinforcement learning (RL), algorithms usually suffer from inefficiency in the collection of successful experiences in tasks with sparse rewards. By utilizing the ideas of relabeling hindsight experience and curriculum learning, some prior works have greatly improved the sample efficiency in robotic manipulation tasks, such as hindsight experience replay (HER), hindsight goal generation (HGG), graph-based HGG (G-HGG), and curriculum-guided HER (CHER). However, none of these can learn efficiently to solve challenging manipulation tasks with distant goals and obstacles, since they rely either on heuristic or simple distance-guided exploration. In this article, we introduce graph-curriculum-guided HGG (GC-HGG), an extension of CHER and G-HGG, which works by selecting hindsight goals on the basis of graph-based proximity and diversity. We evaluated GC-HGG in four challenging manipulation tasks involving obstacles in both simulations and real-world experiments, in which significant enhancements in both sample efficiency and overall success rates over prior works were demonstrated. Videos and codes can be viewed at this link: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://videoviewsite.wixsite.com/gc-hgg</uri> .
Self-Adaptation Graph Attention Network via Meta-Learning for Machinery Fault Diagnosis With Few Labeled Data

Effective application of fault diagnosis models requires that new fault types can be recognized rapidly after they occur few times, even only one time. To this end, a self-adaptation graph attention network via meta-learning (SGANM) is proposed. Specifically, based on a collected large-scale labeled dataset containing abundant disjoint categories (i.e., any of the categories in the target diagnosis task is not contained), meta-learning is used to train a meta-learner across abundant randomly generated meta-tasks, and the meta-learner can rapidly generalize to the target fault diagnosis task containing only few labeled samples. To full exploitation of the relationships among the samples in the support set and query set of each meta-task, a self-adaptation graph attention network (SGAN) is designed to realize the meta-learner. The effective strategies including spatial-temporal graph-based node initial embedding, two-dimensional edge embedding, and multi-head masked attention mechanism-based embedding propagating make the proposed meta-learner have powerful meta-knowledge learning ability. Experiments are conducted on a benchmark dataset and a dataset collected from a practical experimental platform, and competitive performance has been achieved by the proposed SGANM compared to the other few-shot learning algorithms.
Faster Kyber and Dilithium on the Cortex-M4

This paper presents faster implementations of the lattice-based schemes Dilithium and Kyber on the Cortex-M4. Dilithium is one of three signature finalists in the NIST post-quantum project (NIST PQC), while Kyber is one of four key-encapsulation mechanism (KEM) finalists. Our optimizations affect the core polynomial arithmetic involving number-theoretic transforms in both schemes. Our main contributions are threefold: We present a faster signed Barrett reduction for Kyber, propose to switch to a smaller prime modulus for the polynomial multiplications $$c\mathbf {s}_1$$ and $$c\mathbf {s}_2$$ in the signing procedure of Dilithium, and apply various known optimizations to the polynomial arithmetic in both schemes. Using a smaller prime modulus is particularly interesting as it allows using the Fermat number transform resulting in especially fast code. We outperform the state-of-the-art for both Dilithium and Kyber. For Dilithium, our NTT and iNTT are faster by 5.2% and 5.7%. Switching to a smaller modulus results in speed-up of 33.1%–37.6% for the relevant operations (sum of the base multiplication and iNTT) in the signing procedure. For Kyber, the optimizations results in 15.9%–17.8% faster matrix-vector product which is a core arithmetic operation in Kyber .