Reviewerly/exHarmony huggingface dataset:
On Trust, Blockchain, and Reputation Systems

Trust management in distributed systems has always been a topic of active interest in the research community to understand how to foster and manage aspects. In this sense, Distributed Ledger Technologies (DLT) and, among them, Blockchains (BC), emerge as an alternative for shifting trust assumptions between users to the protocol that regulates the interaction, fostering trust in distributed systems. Especially reputation management systems have enabled several applications to be revisited as an application running based on an underlying distributed system. Thus, a clear understanding of major properties, threats and vulnerabilities, and challenges of reputation systems based on different types of DLT and BC (i.e., permissioned and permissionless) are key to determine their usefulness and optimization potentials. In this sense, a use case of a BC-based reputation system within the context of cooperative network defenses illustrates such benefits and drawbacks of exploiting DLTs for reputation systems.
Insights of Neural Representations in Multi-Banded and Multi-Channel Convolutional Transformers for End-to-End ASR

End-to-End automatic speech recognition (ASR) models aim to learn generalised representations of speech. Popular approaches for End-to-End solutions have involved utilising extremely large amounts of data and large models to im-prove recognition performance. However, it is not clear if these models are generalising the training data or memorising the data. This paper combines the power of a mixture of experts (MoE) models, which is referred to as multi-band, multi-channel, with a popular model for ASR, the CNN-transformer, to capture longer-term dependencies without increasing the computational complexity of training. The goal is to investigate how the transformer models adapt to these different input representations of the same data. No external language models were used to remove the impact of external language models during inference. Although the proposed multi-band transformer shows performance gain, the main finding of this paper is to show the adaptive memo-risation nature of transformers and the neural representations of transformer embedding. Using the statistical correlation index SVCCA, comparative discussion of the neural repre-sentations of the proposed model and transformer approach is provided, with key insights into the distinct learned structures.
Multi-Robot Path Planning in Narrow Warehouse Environments with Fast Feasibility Heuristics

In this paper, we present a novel method for solving the multi-robot path planning problem in narrow warehouse environments. Robots may have to be operated in close proximity to each other in narrow lanes between pod clusters, which challenges robot-robot collision avoidance. This motivates us to consider an efficient algorithm to generate collision-free robot paths in narrow warehouse environments. The algorithm builds a two-stage scheme based on integer programming: (i) multi-robot path planning under one-way constraints; and (ii) fast feasibility heuristics. The developed algorithm has two advantages: (i) the proposed scheme successfully avoids the situation where the robots collide in narrow lanes; and (ii) by using the fast feasibility heuristics which replaces the integer programming solver's built-in feasibility heuristics, the feasibility heuristics' running time can be effectively reduced while producing better initial feasible solutions for the integer programming model solving process. Compared with the existing optimal, sub-optimal, and polynomial-complexity algorithms, our developed algorithm can leverage the advantage of integer programming to effectively reach a balance between running time and optimality in narrow warehouse environments. This point is demonstrated in the simulations.
Real-Time Ocean Small Target Detection Based on Improved YOLOX Network

In recent years, target recognition and detection technology, which is an important research direction in the field of computer vision, is widely used in human life. The technology has been relatively mature for the recognition of medium and large targets such as people and objects on land. However, due to some conditions, it is relatively rare in the marine field. The main reason for the analysis is that the ocean environment is complex, and many related factors, such as waves and light, can affect it. At the same time, the small targets are usually vague with little information. As a result, the feature expression ability is weak, and very few effective features can be extracted in the process of feature extraction. This problem has aroused the great attention. Based on the existing YOLOX neural network structure, this paper proposes a new neural network structure CBAM-YOLOX. The experiment results show that the AP value of CBAM-YOLOX is 2.94% higher than that of YOLOX, which effectively improves the low positioning accuracy and missing detection of ocean small targets. That has great significance to the application in the ocean field.
Interactive Multi-Robot Aerial Cinematography Through Hemispherical Manifold Coverage

This paper presents a distributed interactive framework to provide high-level position instructions for multi-robot aerial cinematography based on coverage over a hemisphere. The control strategy based on optimization of the coverage functional and geometric relationships over a hemisphere is presented. It enables multiple Unmanned Aerial Vehicles (UAVs) to coordinate their motion while tracking a dynamic (real or virtual) target, and can accommodate high-level human inputs to influence UAV concentration. In this framework, each UAV uses local information combined with exogenous inputs to determine its motion. The two inputs to the system, i.e., the predicted trajectory of the target and user-defined aesthetic preferences, are agnostic to the size of the multi-robot system (MRS). The proposed framework is validated using the PX4 SITL Autopilot simulator in Gazebo, and the scalability of the framework is verified via simulations.
3D Human Pose Estimation Using Möbius Graph Convolutional Networks

3D human pose estimation is fundamental to understanding human behavior. Recently, promising results have been achieved by graph convolutional networks (GCNs), which achieve state-of-the-art performance and provide rather light-weight architectures. However, a major limitation of GCNs is their inability to encode all the transformations between joints explicitly. To address this issue, we propose a novel spectral GCN using the Möbius transformation (MöbiusGCN). In particular, this allows us to directly and explicitly encode the transformation between joints, resulting in a significantly more compact representation. Compared to even the lightest architectures so far, our novel approach requires 90– $$98\%$$ fewer parameters, i.e. our lightest MöbiusGCN uses only $$0.042\text {M}$$ trainable parameters. Besides the drastic parameter reduction, explicitly encoding the transformation of joints also enables us to achieve state-of-the-art results. We evaluate our approach on the two challenging pose estimation benchmarks, Human3.6M and MPI-INF-3DHP, demonstrating both state-of-the-art results and the generalization capabilities of MöbiusGCN.
Understanding and mitigating multi-sided exposure bias in recommender systems

Masoud Mansoury is a postdoctoral researcher at Amsterdam Machine Learning Lab at University of Amsterdam, Netherlands. He is also a member of Discovery Lab collaborating with Data Science team at Elsevier Company in the area of recommender systems. Masoud received his PhD in Computer and Information Science from Eindhoven University of Technology, Netherlands, in 2021. He has published his research works in top conferences such as FAccT, RecSys, and CIKM. His research interests include recommender systems, algorithmic bias, and contextual bandits. This research conducted by Masoud Mansoury investigated the impact of unfair recommendations on different actors in the system and proposed solutions to tackle the unfairness of recommendations. The solutions were a rating transformation technique that works as a pre-processing step before recommendation generation and a general graph-based solution that works as a post-processing approach after recommendation generation for mitigating the multi-sided exposure bias in the recommendation results. For evaluation, he introduced several metrics for measuring the exposure fairness for items and suppliers, and showed that the proposed metrics better capture the fairness properties in the recommendation results. Extensive experiments on different publicly-available datasets confirmed the superiority of the proposed solutions in improving the exposure fairness for items and suppliers.
Facilitating Conversational Interaction in Natural Language Interfaces for Visualization

Natural language (NL) toolkits enable visualization developers, who may not have a background in natural language processing (NLP), to create natural language interfaces (NLIs) for end-users to flexibly specify and interact with visualizations. However, these toolkits currently only support one-off utterances, with minimal capability to facilitate a multi-turn dialog between the user and the system. Developing NLIs with such conversational interaction capabilities remains a challenging task, requiring implementations of low-level NLP techniques to process a new query as an intent to follow-up on an older query. We extend an existing Python-based toolkit, NL4DV, that processes an NL query about a tabular dataset and returns an analytic specification containing data attributes, analytic tasks, and relevant visualizations, modeled as a JSON object. Specifically, NL4DV now enables developers to facilitate multiple simultaneous conversations about a dataset and resolve associated ambiguities, augmenting new conversational information into the output JSON object. We demonstrate these capabilities through three examples: (1) an NLI to learn aspects of the Vega-Lite grammar, (2) a mind mapping application to create free-flowing conversations, and (3) a chatbot to answer questions and resolve ambiguities.
The Feasibility of the CRYSTALS-Kyber Scheme for Smart Metering Systems

This paper investigates the feasibility of a quantum-secure Key Encapsulation Mechanism (KEM) in hardware constrained Smart Meter (SM) equipment. In this sense, the Cryptographic Suite for Algebraic Lattices (CRYSTALS)-Kyber scheme, the new standard for post-quantum Public-Key Encryption (PKE) systems chosen by the post-quantum cryptography (PQC) standardization process, is scrutinized and implemented in a System-on-a-Chip (SoC) Field Programmable Gate Array (FPGA) device. Experimental results show that the proposed hardware/software co-design implementation of the CRYSTALS-Kyber scheme in an SoC FPGA device reduces its execution time by around 70% compared to its software implementation. Moreover, the hardware resource analysis shows that the proposed hardware/software co-design implementation of the CRYSTALS-Kyber scheme in an SoC device is feasible for SM equipment.
Research on the Performance of different Convolutional Neural Network Models on small Datasets

In recent image processing field, as the core of artificial intelligence, the application of machine learning methods has always occupied a very important position. The emergence of convolutional neural networks has undoubtedly played a revolutionary role. As typical models of CNN, resnet, lenet, alexnet models have all achieved great success in the work of processing large image data sets. In this paper, this paper carried out the attribute classification and recognition experiment of 800 Pokémon images using the above three complex models to explore the training effect of complex machine learning models under small data set. Besides, this analyzed the relationship between the training effect of complex models and the size of the dataset.
Graph Laplacian Regularization based on the Differences of Neighboring Pixels for Conditional Convolutions for Instance Segmentation

We propose a simple and effective regularization method for instance segmentation, GLRDN-L2 (Graph Laplacian Regularization based on Differences of Neighboring Pixels). Instance segmentation is a challenging task in computer vision. For many years, ROI-based methods such as Mask R-CNN have dominantly presented the top performances; however, the recently proposed CondInst, which employs dynamic FCNs as a mask head and performs instance-aware mask prediction, outperforms Mask R-CNN. To our best knowledge, all methods optimize a model based on pixel-wise losses such as Dice Loss. Even with the results of high-resolution masks, there are problems such as blurred boundaries and hollows in the instances. We assume that these problems are due to the spatial structure and contextual information contained in the relationships between neighboring pixels not being incorporated well in the model. To address these problems, we propose a regularization that penalizes the errors in the spatial structure with a graph composed of the differences between neighboring pixels. We compare models trained with and without our regularization in CondInst to validate the effect of a natural extension that adds differentiation to the loss function and demonstrate performance improvement on both COCO and Cityscapes datasets.
CTR: Contrastive Training Recognition Classifier for Few-Shot Open-World Recognition

AI-enabled systems in security, autonomous systems, safety, and healthcare do not only need to effectively detect Out-of-Distribution (OoD) samples, but also to recognize Objects of Concern (OoC), e.g. multiple thorax diseases, efficiently with few-shots. Detecting OoD samples is crucial, because reporting an out-of-domain input as abnormal is better than falsely classifying it. Data samples, during inference, are not confined to a finite labelled set, and thus closed-set approaches are limiting, as they misclassify OoD inputs, and they may assign them high prediction confidence. Furthermore, although anomaly detection is possible, recognizing new OoC fast using only few-shot samples remains challenging. There is a lack of methodologies for joint anomaly detection and few-shot OoC classification. Our contribution is the development of a framework for joint few-shot OoC detection and classification and anomaly detection in the unknown previously-unseen, in the wild, environment, which is known as Open-World Recognition (OWR). We propose a novel methodology, the data distribution boundary Contrastive Training Recognition (CTR) classifier for few-shot OWR. CTR takes advantage of labels and classes to learn the normal (and few-shot abnormal) data better, to more accurately detect OoD. The proposed model: (i) reduces failures to detect anomalies in health- and safety-critical applications for avoiding unfavourable consequences, (ii) decreases false alarms, and (iii) improves performance overall. Our framework differs from existing approaches because: (a) anomaly and OoC detection are combined, which has several benefits, including improved OoD performance, (b) the performance, accuracy, and robustness of OoD and few-shot OoC detection are improved by strengthening the estimation of the normal class distribution at the boundary of its support, self-generating samples and setting them as abnormal, and (c) the knowledge base of models is also augmented by learning class-incrementally, alleviating forgetting. CTR outperforms baselines in several settings, including on the SVHN, CIFAR-FS, and BSCD-FS ChestX and ISIC datasets.
Physical Layer Authentication Based on Integrated Semi-Supervised Learning in Wireless Networks for Dynamic Industrial Scenarios

Supervised machine learning algorithms are not enough to establish a low-complexity, lightweight, and high-security authentication mechanism in wireless communication systems for industrial scenarios where only the physical layer channel information of legitimate nodes is known. Therefore, this paper establishes a new physical layer authentication scheme based on an integrated semi-supervised algorithm around the positive-unlabeled (PU) learning and bootstrap aggregating (bagging) strategy, which can accurately distinguish legal nodes from illegal nodes in unmarked channel information when only the channel information label of legal nodes is known. According to the characteristics of the high dimension of industrial scene data, the characteristics of the original physical layer channel information data are extracted. Using the characteristics of amplitude, phase, carrier frequency offset (i.e. phase change), and variance, the high-dimensional complex channel information matrix is transformed into a seven-dimensional feature set, which not only reduces the dimension but also retains the original complex information. Finally, the effectiveness of feature extraction was verified by using public data set collected by the National Institute of Standards and Technology (NIST) in a real industrial scene. Simulation results show that the authentication strategy proposed in this paper could accurately distinguish legal nodes from illegal nodes with unmarked channel information in dynamic industrial scenarios.
An Approach to Generate Music using Genetic Algorithm

Music is a very vast domain with various notes, chords, and octaves. Music theories are very complex in nature and are difficult to understand by a layman, in this research paper the aim was to implement Genetic algorithm for generating fresh and unique music generation which is independent of human intervention. The work will automate the music generation capabilities. The algorithm works on different music parameters like pitch, frequency, beats, and notes in order to generate more relatable and interesting melodies by understanding the relationship between different given parameters. The algorithm can generate music melodies with constant rating analysis of the music generated by the algorithm. Each analysis phase helps in better and synchronized music generation. The algorithm takes inspiration from the theory of Natural selection.
A Blockchain and Edge Computing Based Public Audit Scheme for Cloud Storage

Cloud data management has significant benefits for data storage and processing. However, it also brings some security concerns, such as the integrity of data and the reliability of online verification. To solve this problem, this paper proposes a blockchain and edge computing based public audit scheme, which is different from the traditional third-part auditor and the common blockchain-based two-party audit scheme. Specifically, this public audit scheme consists of data owners, edge computing servers, and cloud service providers. According to privacy data, a data owner generates lightweight verification labels and homomorphic hashes, and updates them on the blockchain. A completeness proof based on homomorphic hashes is computed by edge computing servers. In addition, the proposed scheme theoretically is capable of achieving 100% audit confidence. Se-curity analysis shows that it can avoid the fraud of cloud server provider and data owner in the audit process, and has a strong ability to resist malicious attacks. Experimental results show that the proposed scheme outperforms the existing scheme in the computational cost of completeness verification.
$$\mathrm {CT^2}$$: Colorization Transformer via Color Tokens

Automatic image colorization is an ill-posed problem with multi-modal uncertainty, and there remains two main challenges with previous methods: incorrect semantic colors and under-saturation. In this paper, we propose an end-to-end transformer-based model to overcome these challenges. Benefited from the long-range context extraction of transformer and our holistic architecture, our method could colorize images with more diverse colors. Besides, we introduce color tokens into our approach and treat the colorization task as a classification problem, which increases the saturation of results. We also propose a series of modules to make image features interact with color tokens, and restrict the range of possible color candidates, which makes our results visually pleasing and reasonable. In addition, our method does not require any additional external priors, which ensures its well generalization capability. Extensive experiments and user studies demonstrate that our method achieves superior performance than previous works.
Error-Mitigation-Aided Optimization of Parameterized Quantum Circuits: Convergence Analysis

Variational quantum algorithms (VQAs) offer the most promising path to obtaining quantum advantages via noisy intermediate-scale quantum (NISQ) processors. Such systems leverage classical optimization to tune the parameters of a parameterized quantum circuit (PQC). The goal is minimizing a cost function that depends on measurement outputs obtained from the PQC. Optimization is typically implemented via stochastic gradient descent (SGD). On NISQ computers, gate noise due to imperfections and decoherence affects the stochastic gradient estimates by introducing a bias. Quantum error mitigation (QEM) techniques can reduce the estimation bias without requiring any increase in the number of qubits, but they in turn cause an increase in the variance of the gradient estimates. This work studies the impact of quantum gate noise on the convergence of SGD for the variational eigensolver (VQE), a fundamental instance of VQAs. The main goal is ascertaining conditions under which QEM can enhance the performance of SGD for VQEs. It is shown that quantum gate noise induces a nonzero error-floor on the convergence error of SGD (evaluated with respect to a reference noiseless PQC), which depends on the number of noisy gates, the strength of the noise, as well as the eigenspectrum of the observable being measured and minimized. In contrast, with QEM, any arbitrarily small error can be obtained. Furthermore, for error levels attainable with or without QEM, QEM can reduce the number of required iterations, but only as long as the quantum noise level is sufficiently large, and a sufficiently large number of measurements is allowed at each SGD iteration. Numerical examples for a max-cut problem corroborate the main theoretical findings.
A Comparative Study on Sign Language Recognition Methods

The process by which two or more people exchange their information, views, and opinions on different situations and on different topics is known as communication. The need for a common platform for humans to communicate with one another has played an essential role in the evolution of human civilization ever since its start. Human beings communicate with each other using several verbal and non-verbal cues. Non-verbal communication involves the use of signs and hand gestures. Hard-of-hearing individuals can communicate only using non-verbal cues and they rely on others to communicate with them using hand gestures as well. Systematic sign language systems, for instance, the American Sign Language, the Indian Sign Language and several such similar sign language systems have been developed to aid the specially abled individuals. Sign Language recognition (SLR) is a process that uses technology to recognize what the specially-abled person is saying by identifying the signs and hand gestures and returning the output in the form of text or speech. In this survey, a comparative analysis is done based on the various techniques and algorithms used and implemented to carry out the process of Sign language Recognition (SLR).