Reviewerly/exHarmony huggingface dataset:
OverSegNet: A convolutional encoder–decoder network for image over-segmentation

Efficient and differentiable image over-segmentation is key to superpixel-based research and applications but remains a challenging problem. The paper proposes a fully convolutional deep network, named OverSegNet, for image over-segmentation. OverSegNet consists of an encoder and a decoder, which are designed for the two core parts of over-segmentation, i.e., feature representation and pixel–superpixel association, respectively. To obtain edge-sensitive and noise-insusceptible feature representation, the encoder is endowed with rich over-segmentation-specific convolutional kernels via over-parametrization followed by task-driven neural network search (NAS). The decoder adopts a multi-scale convolutional structure with cross-large-scale connections, to achieve pixel–superpixel association in a coarse-to-fine feed-forward manner while eliminating accumulation errors. We conduct rich ablation studies to verify the effectiveness of the specially designed encoder and decoder. Experiments on both the BSDS500 dataset and NYUv2 dataset show that the proposed OverSegNet is fast, obtains state-of-the-art accuracy and has good generalization ability. Using semantic segmentation and disparity estimation as examples, we also verify the proposed OverSegNet in downstream applications.
SecureBERT: A Domain-Specific Language Model for Cybersecurity

Natural Language Processing (NLP) has recently gained wide attention in cybersecurity, particularly in Cyber Threat Intelligence (CTI) and cyber automation. Increased connection and automation have revolutionized the world’s economic and cultural infrastructures, while they have introduced risks in terms of cyber attacks. CTI is information that helps cybersecurity analysts make intelligent security decisions, that is often delivered in the form of natural language text, which must be transformed to machine readable format through an automated procedure before it can be used for automated security measures. This paper proposes SecureBERT, a cybersecurity language model capable of capturing text connotations in cybersecurity text (e.g., CTI) and therefore successful in automation for many critical cybersecurity tasks that would otherwise rely on human expertise and time-consuming manual efforts. SecureBERT has been trained using a large corpus of cybersecurity text. To make SecureBERT effective not just in retaining general English understanding, but also when applied to text with cybersecurity implications, we developed a customized tokenizer as well as a method to alter pre-trained weights. The SecureBERT is evaluated using the standard Masked Language Model (MLM) test as well as two additional standard NLP tasks. Our evaluation studies show that SecureBERT outperforms existing similar models, confirming its capability for solving crucial NLP tasks in cybersecurity.
Full-field modal identification using reliability-guided frequency-domain-based digital image correlation method based on multi-camera system

A multi-camera array system was established to address the limitations of traditional measurement methods in full-field modal measurement. According to the principle of binocular vision, two cameras are utilized to form a set of three-dimensional measurement subsystems, and the subsystems are calibrated to establish unity in coordinates. To improve computational efficiency, a reliability-guided frequency-domain-based digital image correlation method is proposed to optimize pixel matching and calculation path. This method incorporates the benefits of a reliability-guided strategy and an integer-pixel search method based on the frequency domain to ensure that the seed point with the optimal matching effect in the calculation area is selected. The zero-mean normalized cross-correlation coefficient is used as the criterion for determining reliability. This approach effectively avoids the problem of error transmission and reduces the need for complex operations and repeated searches in the spatial domain, thus improving computational efficiency. The matching time is reduced, the calculation efficiency of displacement signal extraction under large data volume is improved, and the obtained response signal is substituted into the modal identification algorithm to obtain the modal parameters of the structure. Ultimately, the modal test of a solar panel is performed by constructing a four-camera system, which verifies the correctness of the proposed method and has practical engineering significance.
DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Network

Unsupervised approaches for video anomaly detection may not perform as good as supervised approaches. However, learning unknown types of anomalies using an unsupervised approach is more practical than a supervised approach as annotation is an extra burden. In this paper, we use isolation tree-based unsupervised clustering to partition the deep feature space of the video segments. The RGB-stream generates a pseudo anomaly score and the flow stream generates a pseudo dynamicity score of a video segment. These scores are then fused using a majority voting scheme to generate preliminary bags of positive and negative segments. However, these bags may not be accurate as the scores are generated only using the current segment which does not represent the global behavior of a typical anomalous event. We then use a refinement strategy based on a cross-branch feed-forward network designed using a popular I3D network to refine both scores. The bags are then refined through a segment re-mapping strategy. The intuition of adding the dynamicity score of a segment with the anomaly score is to enhance the quality of the evidence. The method has been evaluated on three popular video anomaly datasets, i.e., UCF-Crime, CCTV-Fights, and UBI-Fights. Experimental results reveal that the proposed framework achieves competitive accuracy as compared to the state-of-the-art video anomaly detection methods.
IASA: An IoU-aware tracker with adaptive sample assignment

Most of existing trackers develop tracking in a tracking head network, which is composed of classification branch and regression branch. However, they lack a meaningful exploration of how to define positive and negative samples during training, which can significantly affect tracking performance. Furthermore, they cannot provide a reliable ranking by using classification scores or a combination of classification and regression scores to obtain candidate locations. To address these issues, we propose an intersection over union (IoU) aware tracker with adaptive sample assignment (IASA). The IASA introduces an IoU-aware classification score to achieve a more accurate ranking for candidate tracking locations. We also propose a new loss function, IoU-focal loss, to train the anchor-free tracker IASA to predict the classification scores and introduce a star-shaped box feature representation to refine classification features. To explore the actual content of the training samples, we develop an adaptive sample assignment (ASA) strategy to divide the positive and negative samples according to the statistical characteristics of the sample IoUs. By combining these two proposed components, the IASA tracker treats the tracking task as a classification and a regression problem. It directly finds the candidate tracking location in the classification branch and then regresses the four distances from the location to the four sides of the tracking box. Experimental results show that the proposed IASA can achieve state-of-the-art performance on seven public datasets.
Confederated Learning: Federated Learning With Decentralized Edge Servers

Federated learning (FL) is an emerging machine learning paradigm that allows to accomplish model training without aggregating data at a central server. Most studies on FL consider a centralized framework, in which a single server is endowed with a central authority to coordinate a number of devices to perform model training in an iterative manner. Due to stringent communication and bandwidth constraints, such a centralized framework has limited scalability as the number of devices grows. To address this issue, in this paper, we propose a ConFederated Learning (CFL) framework. The proposed CFL consists of multiple servers, in which each server is connected with an individual set of devices as in the conventional FL framework, and decentralized collaboration is leveraged among servers to make full use of the data dispersed throughout the network. We develop a stochastic alternating direction method of multipliers (ADMM) algorithm for CFL. The proposed algorithm employs a random scheduling policy which randomly selects a subset of devices to access their respective servers at each iteration, thus alleviating the need of uploading a huge amount of information from devices to servers. Theoretical analysis is presented to justify the proposed method. Numerical results show that the proposed method can converge to a decent solution significantly faster than gradient-based FL algorithms, thus boasting a substantial advantage in terms of communication efficiency.
Accelerating Convergence of Federated Learning in MEC with Dynamic Community

Mobile edge computing (MEC) brings computational resources to the edge of network that triggers the paradigm shift of centralized machine learning towards federated learning. Federated learning enables edge nodes to collaboratively train a shared prediction model without sharing data. In MEC, heterogeneous edge nodes may join or leave the training phase during the federated learning process, resulting in slow convergence of dynamic communities and federated learning. In this paper, we propose a fine-grained training strategy for federated learning to accelerate its convergence rate in MEC with dynamic community. Based on multi-agent reinforcement learning, the proposed scheme enables each edge node to adaptively adjust its training strategy (aggregation timing and frequency) according to the network dynamics, while compromising with each other to improve the convergence of federated learning. To further adapt to the dynamic community in MEC, we propose a meta-learning-based scheme where new nodes can learn from other nodes and quickly perform scene migration to further accelerate the convergence of federated learning. Numerical results show that the proposed framework outperforms the benchmarks in terms of convergence speed, learning accuracy, and resource consumption.
Structure-Preserving Graph Representation Learning

Though graph representation learning (GRL) has made significant progress, it is still a challenge to extract and embed the rich topological structure and feature information in an adequate way. Most existing methods focus on local structure and fail to fully incorporate the global topological structure. To this end, we propose a novel Structure-Preserving Graph Representation Learning (SPGRL) method, to fully capture the structure information of graphs. Specifically, to reduce the uncertainty and misinformation of the original graph, we construct a feature graph as a complementary view via k-Nearest Neighbor method. The feature graph can be used to contrast at node-level to capture the local relation. Besides, we retain the global topological structure information by maximizing the mutual information (MI) of the whole graph and feature embeddings, which is theoretically reduced to exchanging the feature embeddings of the feature and the original graphs to reconstruct themselves. Extensive experiments show that our method has quite superior performance on semi-supervised node classification task and excellent robustness under noise perturbation on graph structure or node features. The source code is available at https://github.com/uestc-lese/SPGRL.
Based Complex Key Cryptography: New Secure Image Transmission Method utilizing Confusion and Diffusion

Recently, the transport massive and secure data are fateful and difficult in communication over the IoT. Due to their widespread use and ease of transmission, images are the most frequent and well-liked multimedia data format. Among the various cryptography methods, chaos-based image cryptography is regarded as the one that is best suitable for picture applications due to its sensitivity to the beginning circumstances and control parameters. Conventional image cryptography techniques have employed confusion and diffusion techniques, but the perfect encrypted image has not yet been produced. Secure image transmission is the main goals in proposed method. Therefore, the new chaos method based on confusion and diffusion method is introduced to secure the image transmission. Generally, the confusion technique is used to produce random numbers for the pixel permutation method. While the random matrix is produced using the diffusion approach. The main idea of the proposed method is to idealize the histogram and information entropy of the ciphered image. The experimental results demonstrated that comparing the suggested strategy to benchmark methods stimulates better results. The experimental result showed that the test of the proposed method encourages outcomes against benchmarked methods.
Anonymous Public Key Encryption Under Corruptions

Anonymity of public key encryption (PKE) requires that, in a multi-user scenario, the PKE ciphertexts do not leak information about which public keys are used to generate them. Corruptions are common threats in the multi-user scenario but anonymity of PKE under corruptions is less studied in the literature. In TCC 2020, Benhamouda et al. first provide a formal characterization for anonymity of PKE under a specific type of corruption. However, no known PKE scheme is proved to meet their characterization. To the best of our knowledge, all the PKE application scenarios which require anonymity also require confidentiality. However, in the work by Benhamouda et al., different types of corruptions for anonymity and confidentiality are considered, which can cause security pitfalls. What’s worse, we are not aware of any PKE scheme which can provide both anonymity and confidentiality under the same types of corruptions. In this work, we introduce a new security notion for PKE called ANON-RSO $$_{ {k} }$$ &C security, capturing anonymity under corruptions. We also introduce SIM-RSO $$_{ {k} }$$ &C security which captures confidentiality under the same types of corruptions. We provide a generic framework of constructing PKE scheme which can achieve the above two security goals simultaneously based on a new primitive called key and message non-committing encryption (KM-NCE). Then we give a general construction of KM-NCE utilizing a variant of hash proof system (HPS) called Key-Openable HPS. We also provide Key-Openable HPS instantiations based on the matrix decisional Diffie-Hellman assumption. Therefore, we can obtain various concrete PKE instantiations achieving the two security goals in the standard model with compact ciphertexts. Furthermore, for some PKE instantiation, its security reduction is tight.
&lt;i&gt;F&lt;/i&gt;&lt;sub&gt;10.7&lt;/sub&gt; Index Forecasting Method Based on Area Statistics of Solar Active Regions

太阳10.7cm射电流量（<i>F</i><sub>10.7</sub>）是反映太阳整体活动的重要指标，其主要源头是日面活动区.<i>F</i><sub>10.7</sub>指数与日面活动区具有显著的相关性，且不同面积的活动区与<i>F</i><sub>10.7</sub>并不遵循相同的线性关系.为进一步提高<i>F</i><sub>10.7</sub>预报的准确性，利用日面活动区面积与<i>F</i><sub>10.7</sub>的相关性，依据面积大小分类，提出<i>F</i><sub>10.7</sub>的预报公式并进行验证.采用2008-2018年SWPC （Space Weather Prediction Center）公布的活动区面积数据和CSWFC （Canadan Space Weather Forecast Center）公布的<i>F</i><sub>10.7</sub>实测数据计算预报公式系数，利用高年（2003年）和低年（1997年）的<i>F</i><sub>10.7</sub>预报验证其结果.研究结果表明，预报结果与实测值的相关系数分别为0.9318和0.9295，二者皆优于SWPC同时期的预报结果（相关系数分别为0.9186和0.8771）.本研究首次基于活动区的变化预测了<i>F</i><sub>10.7</sub>，提高了<i>F</i><sub>10.7</sub>预测的准确性.
Image Super-Resolution with Deep Variational Autoencoders

Image super-resolution (SR) techniques are used to generate a high-resolution image from a low-resolution image. Until now, deep generative models such as autoregressive models and Generative Adversarial Networks (GANs) have proven to be effective at modelling high-resolution images. VAE-based models have often been criticised for their feeble generative performance, but with new advancements such as VDVAE, there is now strong evidence that deep VAEs have the potential to outperform current state-of-the-art models for high-resolution image generation. In this paper, we introduce VDVAE-SR, a new model that aims to exploit the most recent deep VAE methodologies to improve upon the results of similar models. VDVAE-SR tackles image super-resolution using transfer learning on pretrained VDVAEs. The presented model is competitive with other state-of-the-art models, having comparable results on image quality metrics.
Auditing Large Language Models: A Three-Layered Approach

The emergence of large language models (LLMs) represents a major advance in artificial intelligence (AI) research. However, the widespread use of LLMs is also coupled with significant ethical and social challenges. Previous research has pointed towards auditing as a promising governance mechanism to help ensure that AI systems are designed and deployed in ways that are ethical, legal, and technically robust. However, existing auditing procedures fail to address the governance challenges posed by LLMs, which are adaptable to a wide range of downstream tasks. To help bridge that gap, we offer three contributions in this article. First, we establish the need to develop new auditing procedures that capture the risks posed by LLMs by analysing the affordances and constraints of existing auditing procedures. Second, we outline a blueprint to audit LLMs in feasible and effective ways by drawing on best practices from IT governance and system engineering. Specifically, we propose a three-layered approach, whereby governance audits, model audits, and application audits complement and inform each other. Finally, we discuss the limitations not only of our three-layered approach but also of the prospect of auditing LLMs at all. Ultimately, this article seeks to expand the methodological toolkit available to technology providers and policymakers who wish to analyse and evaluate LLMs from technical, ethical, and legal perspectives.
IMPaSh: A Novel Domain-Shift Resistant Representation for Colorectal Cancer Tissue Classification

The appearance of histopathology images depends on tissue type, staining and digitization procedure. These vary from source to source and are the potential causes for domain-shift problems. Owing to this problem, despite the great success of deep learning models in computational pathology, a model trained on a specific domain may still perform sub-optimally when we apply them to another domain. To overcome this, we propose a new augmentation called PatchShuffling and a novel self-supervised contrastive learning framework named IMPaSh for pre-training deep learning models. Using these, we obtained a ResNet50 encoder that can extract image representation resistant to domain-shift. We compared our derived representation against those acquired based on other domain-generalization techniques by using them for the cross-domain classification of colorectal tissue images. We show that the proposed method outperforms other traditional histology domain-adaptation and state-of-the-art self-supervised learning methods. Code is available at: https://github.com/trinhvg/IMPash .
Online measurement method for assembly pose of gear structure based on monocular vision

Abstract The gear structure is an important part of the transmission device. The majority of manual methods are currently used to complete the assembly of the large internal gear. Manual assembly is difficult and inefficient. Therefore, an online measurement method for the assembly pose of the gear structure based on monocular vision is proposed. After the critical features of the gear structure have been detected, a duality elimination method based on traversal mapping dots is proposed to obtain the correct solution for the spatial circle pose. Concurrently, the circle pose optimization model is established to enhance pose precision. Then, a new calibration board was designed to complete the hand-eye calibration of the parallel mechanism and camera. Finally, the correction amount required to complete the internal and external teeth assembly is calculated based on the iterative update of the pose measurement method. The experimental results show that the comprehensive accuracy of the pose measurement method exceeds 0.2 mm, the average assembly time is approximately 14 min and the assembly success rate is approximately 97%. It has been realized that simulated gear structure parts can be assembled automatically.
Physical Layer Data Manipulation Attacks on the CAN Bus

The Controller Area Network (CAN) bus standard is the most common in-vehicle network that provides communication between Electronic Control Units (ECUs).CAN messages lack authentication and data integrity protection mechanisms and hence are vulnerable to attacks, such as impersonation and data injection, at the digital level.The physical layer of the bus allows for a one-way change of a given bit to accommodate prioritization; viz. a recessive bit (1) may be changed to a dominant one (0).In this paper, we propose a physical-layer data manipulation attack wherein multiple compromised ECUs collude to cause 0→1 (i.e., dominant to recessive) bit-flips, allowing for arbitrary bit-flips in transmitted messages.The attack is carried out by inducing transient voltages in the CAN bus that are heightened due to the parasitic reactance of the bus and non-ideal properties of the line drivers.Simulation results indicate that, with more than eight compromised ECUs, an attacker can induce a sufficient voltage drop to cause dominant bits to be flipped to recessive ones.
Particle Swarm Optimization for Efficiently Evolving Deep Convolutional Neural Networks Using an Autoencoder-based Encoding Strategy

Deep convolutional neural networks (DCNNs) have achieved surpassing success in the field of computer vision, and a number of elaborately designed networks refresh the performance records in benchmark datasets. Recently, evolutionary neural architecture search (ENAS) has become an emerging area, which could employ an evolutionary computation (EC) technique to automatically construct promising network architectures without human intervention. However, existing algorithms still have limitations: most standard EC approaches cannot process flexible-length architecture representations directly, and most fitness evaluation processes suffer from the exhibitive computational cost and the unreliable prediction results. To overcome these shortcomings, we propose an efficient particle swarm optimization (PSO)-based neural architecture search algorithm to search for appropriate dense blocks on image classification tasks, named EAEPSO. EAEPSO addresses the first limitation by designing an autoencoder to encode variable-length network representations as fixed-length latent vectors, which converts the original search space to a latent space that can facilitate the downstream search. Besides, an efficient and effective hierarchical fitness evaluation method is designed to guide the search process to address the second limitation. The experimental results show that EAEPSO is a very competitive ENAS algorithm that achieves an error rate of 2.74% on CIFAR-10 and 16.17% on CIFAR-100, and reduces the computational cost from hundreds or thousands of GPU-days to only 2.2 and 4 GPU-days, respectively. Further analyses investigate the reduced training data’s effect and confirm the effectiveness of both the proposed autoencoder and the proposed hierarchical fitness evaluation method.