Reviewerly/exHarmony huggingface dataset:
An improved stanley guidance law for large curvature path following of unmanned surface vehicle

In order to enhance the large curvature path following performance of unmanned surface vehicle (USV), an improved Stanley guidance law (ISGL) is proposed. To the best of our knowledge, we are the first to introduce the Stanley guidance law (SGL) to guidance subsystem of USV, and theoretically prove that it can be unified into one form with the line-of-sight (LOS). It is essentially a kind of LOS method with time-varying lookahead distance in the path-tangential reference frame. For the sake of accurately following the path with large curvature, a compensation angle (km/(1+|ye|))Δαm is integrated to improve the original SGL, which can evaluate the path curvature in advance and compensate the desired heading angle. The aim is to improve the heading angle error and make USV turn in advance. In order to verify path following performance of our algorithm, theoretical simulation and lake experiments are launched to compare to extended line-of-sight (ELOS), integral line-of-sight (ILOS) and original SGL algorithms. All the results show that our ISGL algorithm has better path following performance than others.
Privacy-Preserving and Traceable Federated Learning for data sharing in industrial IoT applications

Federated learning enables data owners to jointly train a neural network without sharing their personal data, which makes it possible to share sensitive data generated from various Industrial Internet of Things (IIoT) devices. However, in traditional federated learning, the user directly sends its parameters to the server, which increases the risk of privacy leakage. To solve this problem, several privacy-preserving solutions have been proposed. However, most of them either reduce model accuracy or increase computation and communication overhead. In addition, federated learning is still exposed to the risk of model tampering, which may impair model accuracy. In this paper, we propose PPTFL, a Privacy-Preserving and Traceable Federated Learning framework with efficient performance. Specifically, we first propose a Hierarchical Aggregation Federated Learning (HAFL) to protect privacy with low overhead, which is suitable for IIoT scenarios. Then, we combine federated learning with blockchain and IPFS, which makes the parameters traceable and tamper-proof. The extensive experiments demonstrate the practical performance of PPTFL.
Sentiment-aware multimodal pre-training for multimodal sentiment analysis

Pre-trained models, together with fine-tuning on downstream labeled datasets, have demonstrated great success in various tasks, including multimodal sentiment analysis. However, most most multimodal pre-trained models focus on learning general lexical and/or visual information, while ignoring sentiment signals. To address this problem, we propose a sentiment-aware multimodal pre-training ( SMP ) framework for multimodal sentiment analysis. In particular, we design a cross-modal contrastive learning module based on the interactions between visual and textual information, and introduce additional sentiment-aware pre-training objectives (e,g., fine-grained sentiment labeling) to capture fine-grained sentiment information from sentiment-rich datasets. We adopt two objectives (i.e., masked language modeling and masked auto-encoders) to capture semantic information from text and images. We conduct a series of experiments on sentence-level and target-oriented multimodal sentiment classification tasks, wherein the results of our SMP model exceeds the state-of-the-art results. Additionally, ablation studies and case studies are conducted to verify the effectiveness of our SMP model.
A Study on Different Deep Learning Algorithms Used in Deep Neural Nets: MLP SOM and DBN

Deep learning is a wildly popular topic in machine learning and is structured as a series of nonlinear layers that learns various levels of data representations. Deep learning employs numerous layers to represent data abstractions to implement various computer models. Deep learning approaches like generative, discriminative models and model transfer have transformed information processing. This article proposes a comprehensive review of various deep learning algorithms Multi layer perception, Self-organizing map and deep belief networks algorithms. It first briefly introduces historical and recent state-of-the-art reviews with suitable architectures and implementation steps. Moreover, the various applications of those algorithms in various fields such as wireless networks, Adhoc networks, Mobile ad-hoc and vehicular ad-hoc networks, speech recognition engineering, medical applications, natural language processing, material science and remote sensing applications, etc. are classified.
Autonomous path planning with obstacle avoidance for smart assistive systems

Given the increased interest in smart assistive technologies and autonomous robot vehicles, path planning has emerged as one of the most researched and challenging topics in navigation. Moving to partially known or unknown environment, an assistive navigation system should be able to extract spatiotemporal information and dynamically identify objects and adjust the route. Current approaches typically rely on external services to perform high demanding computations and employ a plethora of overlapping sensors to accurately scan the surrounding environment. This increases their energy demands, size and weight, while incommodes their use in real time applications making their application to wearable assistive systems, such as smart glasses, a challenge. Aiming to provide a comfortable and computationally efficient wearable solution that can be used by human or robotic assistive systems, in this study we propose a novel two-level hierarchical architecture combining global and local path planning. The macroscale navigation involves the construction of the initial global path while the microscale navigation includes the local path planning with obstacle detection and avoidance. The methodology consists of: (i) a novel chaotic ant colony optimization algorithm with fuzzy logic (CACOF) for path construction; (ii) powerful and light weight deep convolutional neural networks for obstacle detection; and (iii) a Bug-like algorithm enhanced with fuzzy rules for obstacle avoidance in case of static objects. A vast experimental evaluation was conducted to test the proposed methodologies in a simulation environment based on the topology of real area. The results proved the computational efficiency and ability of the proposed path planning algorithms to address effectively multi-objective global and path planning problems which make them suitable for real time applications.
Short-term photovoltaic power forecasting method based on convolutional neural network

This research proposes a hybrid model that combines the convolutional neural network (CNN) and the bidirectional long short-term memory network (BiLSTM) to accurately estimate the energy output of a short-term photovoltaic system. Firstly, Pearson correlation analysis is introduced to screen out meteorological factors with high correlation with photovoltaic (PV) power output. Then, a convolutional neural network-bidirectional long short-term memory network (CNN-BiLSTM) combined algorithm is used to extract the characteristics of influencing factors by CNN, and BiLSTM is used for timing prediction. Last but not least, using simulation analysis of data from a particular region in China over the previous two years, the results show that this model reduces training time, improves prediction accuracy, and outperforms the conventional prediction model in terms of the effectiveness of forecasting results, which could also satisfy the demands of the practical application of PV energy generation prediction.
Are we ready for metaverse?

Social virtual reality (VR) has the potential to gradually replace traditional online social media, thanks to recent advances in consumer-grade VR devices and VR technology itself. As the vital foundation for building the Metaverse, social VR has been extensively examined by the computer graphics and HCI communities. However, there has been little systematic study dissecting the network performance of social VR, other than hype in the industry. To fill this critical gap, we conduct an in-depth measurement study of five popular social VR platforms: AltspaceVR, Horizon Worlds, Mozilla Hubs, Rec Room, and VRChat. Our experimental results reveal that all these platforms are still in their early stage and face fundamental technical challenges to realize the grand vision of Metaverse. For example, their throughput, end-to-end latency, and on-device computation resource utilization increase almost linearly with the number of users, leading to potential scalability issues. We identify the platform servers' direct forwarding of avatar data for embodying users without further processing as the main reason for the poor scalability and discuss potential solutions to address this problem. Moreover, while the visual quality of the current avatar embodiment is low and fails to provide a truly immersive experience, improving the avatar embodiment will consume more network bandwidth and further increase computation overhead and latency, making the scalability issues even more pressing.
BANet: Small and multi-object detection with a bidirectional attention network for traffic scenes

Improving the detection accuracy and speed for small and multi-object detection is a hot issue in traffic environments. Despite the substantial advances in object detection algorithms based on deep neural networks, addressing the inaccuracy and low efficiency of small and multi-object detection remains challenging. In this paper, we propose a bidirectional attention network called BANet, which includes multichannel attention (MCA) blocks, alpha-effective intersection-over-union (α-EIoU) loss, and a multiple attention fusion (MAF) module. Each MCA block consists of low-layer, medium-layer, and high-layer features to provide rich base information for feature fusion at the neck module. We introduce MAF to alleviate the spatial location loss and poor semantic performance resulting from the continuous downsampling of the path aggregation feature pyramid network (PAFPNet). Finally, α-EIoU is our regression loss module, which calculates the difference between the predicted box and the ground truth (gt) box. Our study further demonstrates that these strategies yield significant improvements in performance over some existing YOLO detectors. Compared with the performance of YOLOX, BANet demonstrates 0.39%–0.52% [email protected] improvement on the PASCAL VOC 2007 (VOC 07) dataset and 0.55%–2.93% [email protected] improvement on the PASCAL VOC 2012 (VOC 12) dataset. Additionally, 0.3%–1.01% improvement in the [email protected] is achieved on the MS COCO 2017 (COCO 17) dataset, indicating that BANet has a significant effect on multi-object detection. Experiments to determine the approximate number of parameters with YOLOX, show that our strategy not only improves by 7.5 frames per second (FPS) but also reduces the Average forward time by 0.97 ms.
High-speed photonic neuromorphic computing using recurrent optical spectrum slicing neural networks

Neuromorphic Computing implemented in photonic hardware is one of the most promising routes towards achieving machine learning processing at the picosecond scale, with minimum power consumption. In this work, we present a new concept for realizing photonic recurrent neural networks and reservoir computing architectures with the use of recurrent optical spectrum slicing. This is accomplished through simple optical filters placed in an loop, where each filter processes a specific spectral slice of the incoming optical signal. The synaptic weights in our scheme are equivalent to filters central frequencies and bandwidths. This new method for implementing recurrent neural processing in the photonic domain, which we call Recurrent Optical Spectrum Slicing Neural Networks, is numerically evaluated on a demanding, industry-relevant task such as high baud rate optical signal equalization 100 Gbaud, exhibiting ground-breaking performance. The performance enhancement surpasses state-of-the-art digital processing techniques by doubling the reach while minimizing complexity and power consumption by a factor of 10 compared to state-of-the-art solutions. In this respect, ROSS-NNs can pave the way for the implementation of ultra-efficient photonic hardware accelerators tailored for processing high-bandwidth optical signals in optical communication and high-speed imaging applications
Opal: Multimodal Image Generation for News Illustration

Advances in multimodal AI have presented people with powerful ways to create images from text. Recent work has shown that text-to-image generations are able to represent a broad range of subjects and artistic styles. However, finding the right visual language for text prompts is difficult. In this paper, we address this challenge with Opal, a system that produces text-to-image generations for news illustration. Given an article, Opal guides users through a structured search for visual concepts and provides a pipeline allowing users to generate illustrations based on an article's tone, keywords, and related artistic styles. Our evaluation shows that Opal efficiently generates diverse sets of news illustrations, visual assets, and concept ideas. Users with Opal generated two times more usable results than users without. We discuss how structured exploration can help users better understand the capabilities of human AI co-creative systems.
A Multimodal Auxiliary Classification System for Osteosarcoma Histopathological Images Based on Deep Active Learning

Histopathological examination is an important criterion in the clinical diagnosis of osteosarcoma. With the improvement of hardware technology and computing power, pathological image analysis systems based on artificial intelligence have been widely used. However, classifying numerous intricate pathology images by hand is a tiresome task for pathologists. The lack of labeling data makes the system costly and difficult to build. This study constructs a classification assistance system (OHIcsA) based on active learning (AL) and a generative adversarial network (GAN). The system initially uses a small, labeled training set to train the classifier. Then, the most informative samples from the unlabeled images are selected for expert annotation. To retrain the network, the final chosen images are added to the initial labeled dataset. Experiments on real datasets show that our proposed method achieves high classification performance with an AUC value of 0.995 and an accuracy value of 0.989 using a small amount of labeled data. It reduces the cost of building a medical system. Clinical diagnosis can be aided by the system's findings, which can also increase the effectiveness and verifiable accuracy of doctors.
Collaboration enhancing between industry staff and university researchers in international scientific communications system

The article considers the global network space as a set of interacting information systems and as one of the processes in computer technologies development, the content of which is media resources, which are characterized by convergence, digitalization and interactivity. Information and bibliographic environment represent the highest level of international relations integration due to modern technologies. The article proposes a methodology for publication efficiency calculating. As part of the collaboration enhancing between industry staff and university researchers in international scientific communications system considered: role of the organization publication activity, main methods used in scientometrics, as well as topical issues of researchers, such as open identifiers, bibliometric software, new processes, methods and means of scientific communication are described. The main scientific tasks that can be solved with the help of bibliometric indices are highlighted.
Mix-ViT: Mixing attentive vision transformer for ultra-fine-grained visual categorization

Ultra-fine-grained visual categorization (ultra-FGVC) moves down the taxonomy level to classify sub-granularity categories of fine-grained objects. This inevitably poses a challenge, i.e., classifying highly similar objects with limited samples, which impedes the performance of recent advanced vision transformer methods. To that end, this paper introduces Mix-ViT, a novel mixing attentive vision transformer to address the above challenge towards improved ultra-FGVC. The core design is a self-supervised module that mixes the high-level sample tokens and learns to predict whether a token has been substituted after attentively substituting tokens. This drives the model to understand the contextual discriminative details among inter-class samples. Via incorporating such a self-supervised module, the network gains more knowledge from the intrinsic structure of input data and thus improves generalization capability with limited training sample. The proposed Mix-ViT achieves competitive performance on seven publicly available datasets, demonstrating the potential of vision transformer compared to CNN for the first time in addressing the challenging ultra-FGVC tasks. The code is available at https://github.com/Markin-Wang/MixViT
An Architecture for Managing Data Privacy in Healthcare with Blockchain

With the fast development of blockchain technology in the latest years, its application in scenarios that require privacy, such as health area, have become encouraged and widely discussed. This paper presents an architecture to ensure the privacy of health-related data, which are stored and shared within a blockchain network in a decentralized manner, through the use of encryption with the RSA, ECC, and AES algorithms. Evaluation tests were performed to verify the impact of cryptography on the proposed architecture in terms of computational effort, memory usage, and execution time. The results demonstrate an impact mainly on the execution time and on the increase in the computational effort for sending data to the blockchain, which is justifiable considering the privacy and security provided with the architecture and encryption.
A novel UAV path planning approach: Heuristic crossing search and rescue optimization algorithm

Unmanned aerial vehicle (UAV) path planning plays an important role in the flight process of an UAV, which needs an effective algorithm to deal with UAV path planning problem. The search and rescue optimization algorithm (SAR) is easy to implement and has the characteristics of flexible, but it has slow convergence speed and has not been applied to UAV path planning. To address these problems, a heuristic crossing search and rescue optimization algorithm (HC-SAR) is proposed. HC-SAR combines a heuristic crossover strategy with the basic SAR to improve the convergence speed and maintain the population diversity in the optimization process. Furthermore, a real-time path adjustment strategy is proposed to straighten the UAV flight path. In addition, cubic B-spline interpolation is used to smooth the generated path. Comprehensive experiments including two-dimensional and three-dimensional environments for different threat zone are conducted to validate the performance of HC-SAR. The results show that HC-SAR has a high convergence speed and can successfully obtain a safe and efficient path, and it significantly outperforms SAR, differential evolution (DE), ant lion optimizer (ALO), squirrel search algorithm (SSA) and salp swarm algorithm (SSA) in all the cases. These results suggest that the proposed algorithm can effectively serve as an alternative for solving UAV path planning problem.
Long-time simulations for fixed input states on quantum hardware

Abstract Publicly accessible quantum computers open the exciting possibility of experimental dynamical quantum simulations. While rapidly improving, current devices have short coherence times, restricting the viable circuit depth. Despite these limitations, we demonstrate long-time, high fidelity simulations on current hardware. Specifically, we simulate an XY-model spin chain on Rigetti and IBM quantum computers, maintaining a fidelity over 0.9 for 150 times longer than is possible using the iterated Trotter method. Our simulations use an algorithm we call fixed state Variational Fast Forwarding (fsVFF). Recent work has shown an approximate diagonalization of a short time evolution unitary allows a fixed-depth simulation. fsVFF substantially reduces the required resources by only diagonalizing the energy subspace spanned by the initial state, rather than over the total Hilbert space. We further demonstrate the viability of fsVFF through large numerical simulations, and provide an analysis of the noise resilience and scaling of simulation errors.
Self-attention presents low-dimensional knowledge graph embeddings for link prediction

A few models have tried to tackle the link prediction problem, also known as knowledge graph completion, by embedding knowledge graphs in comparably lower dimensions. However, the state-of-the-art results are attained at the cost of considerably increasing the dimensionality of embeddings which causes scalability issues in the case of huge knowledge bases. Transformers have been successfully used recently as powerful encoders for knowledge graphs, but available models still have scalability issues. To address this limitation, we introduce a Transformer-based model to gain expressive low-dimensional embeddings. We utilize a large number of self-attention heads as the key to applying query-dependent projections to capture mutual information between entities and relations. Empirical results on WN18RR and FB15k-237 as standard link prediction benchmarks demonstrate that our model has favorably comparable performance with the current state-of-the-art models. Notably, we yield our promising results with a significant reduction of 66.9% in the dimensionality of embeddings compared to the five best recent state-of-the-art competitors on average.
Approximating functions with multi-features by deep convolutional neural networks

Deep convolutional neural networks (DCNNs) have achieved great empirical success in many fields such as natural language processing, computer vision, and pattern recognition. But there still lacks theoretical understanding of the flexibility and adaptivity of DCNNs in various learning tasks, and the power of DCNNs at feature extraction. We propose a generic DCNN structure consisting of two groups of convolutional layers associated with two downsampling operators, and a fully connected layer, which is determined only by three structural parameters. Our generic DCNNs are capable of extracting various features including not only polynomial features but also general smooth features. We also show that the curse of dimensionality can be circumvented by our DCNNs for target functions of the compositional form with (symmetric) polynomial features, spatially sparse smooth features, and interaction features. These demonstrate the expressive power of our DCNN structure, while the model selection can be relaxed comparing with other deep neural networks since there are only three hyperparameters controlling the architecture to tune.