Reviewerly/exHarmony huggingface dataset:
Soft Sensing Method Based on Online Dynamic Clustering and Self-labeling

Traditional soft sensing methods encounter low prediction accuracy owing to complex process characteristics, such as time-varying and nonlinear behaviors, as well as the scarcity of labeled data. A soft sensing method based on online dynamic clustering and self-labeling (ODCSL) is proposed for data stream mining of industrial processes. This method identifies the process state of the data stream through online clustering analysis, and then adaptively establishes a local Gaussian process regression (GPR) for online prediction. During each prediction run, self-labeling is performed on the recently unlabeled data to provide good pseudo-labeled data, which enhances the prediction of local GPR models. Moreover, when a new sample is available, the clustering results and the GPR models are updated. The effectiveness of the proposed ODCSL method is verified using an industrial fermentation process.
AugRmixAT: A Data Processing and Training Method for Improving Multiple Robustness and Generalization Performance

Deep neural networks are powerful, but they also have short-comings such as their sensitivity to adversarial examples, noise, blur, occlusion, etc. Moreover, ensuring the reliability and robustness of deep neural network models is crucial for their application in safety-critical areas. Much previous work has been proposed to improve specific robustness. However, we find that the specific robustness is often improved at the sacrifice of the additional robustness or generalization ability of the neural network model. In particular, adversarial training methods significantly hurt the generalization performance on unperturbed data when improving adversarial robustness. In this paper, we propose a new data processing and training method, called AugRmixAT, which can simultaneously improve the generalization ability and multiple robustness of neural network models. Finally, we validate the effectiveness of AugRmixAT on the CIFAR-10/100 and Tiny-ImageNet datasets. The experiments demonstrate that AugR-mixAT can improve the model's generalization performance while enhancing the white-box robustness, black-box robustness, common corruption robustness, and partial occlusion robustness.
M<sup>3</sup>Rec: Cross-Modal Context Enhanced Micro-Video Recommendation with Mutual Information Maximization

Personalized recommendation of micro-videos is crucial for many content sharing platforms. Micro-videos typically com-prise fruitful multimodal contents. Existing methods have limitations in learning multimodal representations: (i) They suffer from data sparsity problems as only rely on the inter-action prediction loss to train the whole model. (ii) They do not consider the correlation among multimodal contents into the representation learning. To tackle these limitations, we propose a cross-Modal context enhanced method via Mutual infoMax for Recommendation, termed M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Rec. Specifically, we design the cross-modal graph neural network to inter-change multimodal information to generate modality-aware representations. Then, based on the coupled modality con-text, we design the masked modality prediction (MMP) with three self-supervised objectives to learn the correlation among visual, textual, and acoustic contents empowered by the mu-tual information maximization principle. Finally, we enhance representations via self-supervised pre-training to boost rec-ommendation. Extensive experiments demonstrate that our approach outperforms existing state-of-the-art methods.
The Development of E-Module in the making of LMS by using G-Suite to Improve Indonesian Language Media Literacy

Learning Indonesian during the current COVID-19 pandemic is required to organize the learning process and use and select appropriate learning media. Teachers make efforts to meet these demands by developing competitive and comparative e-modules. E-modules can make it easier for teachers to understand in the making LMS using G-Suite. The development of this pdf e-module can improve Indonesian language media literacy. The e-module was developed using the Research and Development method. Based on the questionnaire results, it is known that scores of 60, 63, and 65 were achieved by one person or 1% each.Furthermore, as many as 13 persons or 18% got a score of 68, 14 persons or 20% got a score of 70, 11 persons or 16% got a score of 73, 9 persons or 13% got a score of 75, 11 persons or 16% got a score of 78, 8 persons or 11% got a score of 80, and 2 persons or 3% earned a score of 83. This shows that the development of the e-module for making LMS using G-Suite to improve Indonesian language media literacy got a good response from the teacher. In terms of the application's usability based on the SUS test, 73 was obtained. Based on material validation, media validation, and usability testing, the e-module for making LMS using G-Suite is a good category for being used as one of the supporting media in the Indonesian language learning process.
Dynamic Obstacle Avoidance Algorithm for Robot Arm Based on Deep Reinforcement Learning

A dynamic obstacle avoidance planning algorithm based on deep reinforcement learning is proposed for rigid manipulators. After the neural network interacts with the environment and learns, it can give real-time action strategies to guide the manipulator to avoid dynamic obstacles. This paper proposes a new state space description method suitable for manipulators and dynamic environments, and designs the corresponding collision detection method and reward value calculation function for this state description method. The test results in the simulation environment demonstrate the effectiveness of the method.
Quality-Oriented Feature Regression for Robust Image Similarity Metric

Full-reference image quality assessment aims to predict the perceptual quality of a distorted image based on its similarity to the pristine reference. In this paper, we propose a robust image similarity metric by fully exploring the representation power of deep learning-based features. A convolutional neu-ral network (CNN) is adopted to extract deep features from multiple scales. We show that such CNN features that con-tain multi -scale visual information are comprehensive and ro-bust enough for quality assessment. We further propose a quality-oriented feature regression (QOFR) module based on the multi-layer perceptron architecture. The QOFR module can efficiently integrate hierarchy CNN features and generate the final quality score. Extensive experiments on the bench-mark datasets demonstrate that our method achieves state-of-the-art performance with outstanding robustness and general-ization ability.
DBCAN: Dual-Branch Cross-Attention Network for Scene Text Recognition

Scene text recognition, especially irregular text recognition, is a challenging task due to the large variance in text appearance. Although some existing methods have achieved state-of-the-art performance with the attention-based encoder-decoder framework, they always perform poorly on some challenging text such as severely curved, blurred, and incomplete-semantic text. To address these issues, we propose a Dual-Branch Cross-Attention Network (DBCAN). Different from the previous methods heavily relying on semantic information, DBCAN can enhance the position clues and learn semantic relations with two separate branches and fuse them by a tailored Cross-Attention Module (CAM). Furthermore, a Convolution-Based 2D Positional Embedding (CBPE) is introduced to describe the 2D spatial dependencies of characters. Extensive experiments demonstrate our DBCAN is more accurate and robust than the previous methods and achieves state-of-the-art performance on several benchmarks, particularly CUTE (93.4%). Our code is made publicly available at https://github.com/GaoXinJian-USTC/DBCAN.
DRL-based Federated Learning Node Selection Algorithm for Mobile Edge Networks

Massive amounts of data have given a huge boost to artificial intelligence for communication networks, for instance, intelligent inspection, power IoT management, but they have also brought problems. The original data generated at the edge of the mobile communication network and imported into the core network not only takes up a lot of bandwidth resources, but also poses a great challenge to the fast and reliable transmission and computing. Traditional cloud-based machine learning methods require data to be centralized in cloud servers or data centers. However, in edge networks, due to limited network resources, direct transmission of centrally learned data will lead to unacceptable communication delays, resulting in low system efficiency, and may lead to serious privacy problems. In order to solve these problems, federal learning technology is attracting people's attention. This paper first analyzes the factors that affect the efficiency of federated learning system, establishes a federated learning system model, then uses DDPG to design and implement a node selection algorithm, the goal is to reduce the federated learning time to the maximum and improve the learning accuracy. Finally, under the condition of different node quality, the simulation experiment verifies that the algorithm can shorten 40% of the model training stability time, thus proving the effectiveness and feasibility of the proposed algorithm, indicating that the federated learning system can effectively select nodes in this way.
Factorial User Modeling with Hierarchical Graph Neural Network for Enhanced Sequential Recommendation

Most sequential recommendation (SR) systems employing graph neural networks (GNNs) only model a user's interaction sequence as a flat graph without hierarchy, overlooking diverse factors in the user's preference. Moreover, the timespan between interacted items is not sufficiently utilized by previous models, restricting SR performance gains. To address these problems, we propose a novel SR system employing a hierarchical graph neural network (HGNN) to model factorial user preferences. Specifically, a timespan-aware sequence graph (TSG) for the target user is first constructed with the timespan among interacted items. Next, all original nodes in TSG are softly clustered into factor nodes, each of which represents a certain factor of the user's preference. At last, all factor nodes' representations are used together to predict SR results. Our extensive experiments upon two datasets justify that our HGNN-based factorial user modeling obtains better SR performance than the state-of-the-art SR models.
Focus by Prior: Deepfake Detection Based on Prior-Attention

Nowadays advanced facial manipulation techniques produce deepfake videos more realistically, which makes deepfake detection more difficult. To capture subtle and intricate artifacts, recent works attempt to enhance low-level textural information by attention-based framework. However, these methods require complex simulated data or extra supervision. Highly dependent on training settings, these methods not only have high training costs but also are prone to overfitting. To address this issue, we propose a novel perspective of deepfake detection via so-called prior-attention. Specifically, we introduce prior textural information, such as edge and noise, to model the attention maps explicitly. Benefiting from these natural “attention maps”, our model significantly enhances discriminative information without additional supervision. Furthermore, we design a Feature Abstraction Block (FAB) to facilitate cross-layer features interaction and insert it into distinct layers of CNN to detect the inconsistencies at multiple spatial levels. Extensive experiments demonstrate that our method achieves performance comparable to state-of-the-art methods.
Feature-Guided Blind Face Restoration with GAN Prior

Blind face restoration (BFR) aims to restore high-quality face images from inputs with complex degradation, which is key to extensive applications. Existing methods usually learn a black-box mapping to achieve the goal, which however often produce over-smoothed results. This work proposes a novel feature-guided framework via leveraging prior from a pre-trained Generative Adversarial Network (GAN) model to recover reasonable textures. Furthermore, we design a feature fusion module to guide the generator with low-level spatial content information in the degraded input, for the sake of holding the consistency on both facial structure and background details. The proposed method can be seamlessly integrated with a learned GAN through a simple yet effective principle to recover realistic results under complex degradation circumstances. Extensive comparisons demonstrate the superiority of our strategy over other state-of-the-art methods in terms of restoration quality and training cost.
DTE-Net: Dual Temporal Excitation Network for Video Violence Recognition

Video-based violence recognition has become a crucial topic owing to the development of surveillance cameras. However, with the extra temporal dimension and no precision range of violent video data, violence recognition is a challenging problem. In this study, we propose a dual temporal excitation network (DTE-Net) consisting of a shift temporal adaptive module (STAM) and a sparse object interaction transformer (SOI-Tr) module. The STAM extracts coarse-grained local and global temporal information by fusing shift module with temporal adaptive modeling module. The SOI-Tr module utilizes important object attention to excite fine-grained global temporal representation reasoning. In addition, we create a multi-class violence (MCV) dataset of video clips extracted from real-world scenes to address the limitation of poorly diversified categories in most existing violence datasets. Finally, we also conduct extensive experiments on five violence datasets, including the MCV, and the results show that our network outperforms state-of-the-art performance.
A Dimensionality Reduction and Reconstruction Method for Data with Multiple Connected Components

In the literature on dimensionality reduction, including Spectral Clustering and Laplacian Eigenmaps, one of the core ideas is to reconstruct data based on similarities between data points, which makes the choice of similarity matrices a key factor on the performance of a dimensionality reduction model. Traditional methods like K-nearest neighbor, ∊-neighbor, and Gaussian Kernel for constructing similarity matrices based on data distribution characteristics have been extensively studied. However, these methods usually focus on only a specific level of the data when considering the similarity between data points, which might result in a great flaw in data reconstruction when data possess hierarchical and multiple groups structure. Specifically, such methods can only characterize the similarity between data within a group, but ignore the similarity between different groups. To overcome this deficiency, this paper proposes a hierarchical way of similarity matrix construction, by introducing strong, weak, and intra- and inter-cluster similarities to describe relations between multiple levels. The proposed method can better adapt to complex data with multiple connected components, and the effectiveness of it is verified in a series of experiments on synthetic and real-world datasets.
A General Data Augmentation Strategy for Siamese Object Tracking

Siamese-based ResNet-driven trackers have made great success in recent years. However, the less discriminative features inhibit the improvements of these trackers. In this work, we discover that a possible reason is the emergence of the gridding artifacts in the deep layers of ResNet-driven trackers. We naively remove the deep layers with gridding artifacts, which reduces parameters by nearly 69% and improves the tracking performance of small objects. Then, we design a parameter-free feature superposition module by adding different samples into the first half samples in each mini-batch iteration in embedding space to increase the semantic information. Further, we introduce an auxiliary loss to reduce the learning difficulty. Finally, the generalization of the feature superposition module and the auxiliary loss are verified by ablation studies. Experiments on challenging benchmarks, including OTB2015, VOT2019, UAV123, LaSOT, and TrackingNet, demonstrate that the proposed method outperforms many SOTA trackers and achieves leading performance.
Toward to Real Low-Resolution Person Re-identification: A New Dataset and Baseline

Person re-identification(re-id) aims at querying and identifying the same target pedestrian in multiple non-overlapping cameras. However, in real scenarios, many person images captured by surveillance cameras tend to have low resolution due to camera hardware, shooting distance, viewing angle, etc. Many existing re-id methods mainly address the crossresolution person re-id problem, i.e., the high-low resolution mismatch problem. In contrast, the problem of matching low-resolution (LR) gallery images and query images with each other has been less studied. In this paper, we address this problem by developing a new gun-ball camera-based person re-id dataset and designing a LR re-id baseline model for this dataset to tackle the LR person matching problem. Extensive experiments validate the effectiveness of the proposed LR baseline model.
A Progressive and Multi-Prior-Guided Network for Image Inpainting

Deep learning techniques have recently made considerable progress in image inpainting by introducing prior knowledge, e.g. texture and structure. However, the existing methods still suffer from artefacts such as distorted texture and abrupt colors due to insufficient consideration of correlation between the prior visual features. In this paper, we propose a novel progressive and multi-prior-guided network (PMPN) for image inpainting inspired by the human painting process, which first constructs the sketch of painting art, then generates the corresponding textures finally fills the colors to the appropriate locations. In particular, to model the global multi-scale contexts during the reconstruction process, we design a bi-directional cross-stage perception module that captures spatial information across branches and stages and guides the model to synthesize a natural and consistent texture. Our proposed PMPN network is evaluated on three publicly available datasets, outperforming the current state-of-the-art models.
Sample-Level and Class-Level Adaptive Training for Face Recognition

Marginal softmax loss function has been widely used for face recognition, where a universal angular margin is added between weight prototypes. However, this method neglects the differences between classes and samples. On class-level, the imbalanced real world training dataset requires different margin for the head and tail classes to equally squeeze each class's feature space. On the sample-level, it's also necessary to assign larger importance for the hard samples during training. In this paper, we address these two issues by combining two strategies: (1) explicitly assign the adaptive margin according to the image quantity so that the margin is enlarged for the tail classes; (2) semantically identify the ‘hard positive, samples and misclassified samples [1] to attach adaptive weights to increase the training emphasis on these samples. Extensive experiments on LFW/CFP/AGEDB and IJB-B/IJB-C show our method's effectiveness.
Combining Virtual Sample Generation Based Data Enhancement and Multi-objective Optimization Based Selective Ensemble for Soft Sensor Modeling

Soft sensor modeling technology realizes the real-time estimation of difficult-to-measure variables by constructing the mathematical model between secondary variables and primary variable. Nevertheless, sufficient and high-quality training samples are difficult to obtain owing to the high cost of data acquisition and low sampling rate. To solve this, a soft sensor modeling method, combining virtual sample generation based data enhancement and multi-objective optimization based selective ensemble (DESE), is proposed. First, a supervised variational autoencoder (SVAE) is constructed by introducing quality variable. Second, a generative model is built through the combination of SVAE and Wasserstein GAN with gradient penalty (WGAN-gp). Third, SV-WGANgp is trained on each sample subset, which is obtained by resampling, and a fixed number of virtual samples are generated. A set of base models is established for the expanded original samples subsequently. Finally, the multi-objective optimization method is utilized to prune these models, which satisfy both accuracy and diversity requirements. After integrating the selected base models, the final prediction results are obtained. Experimental results verify that, compared with the other three popular generation models, DESE significantly improves the prediction performance of soft sensor model by supplementing the original samples.
Finding the Achilles Heel: Progressive Identification Network for Camouflaged Object Detection

Camouflaged object detection (COD) aims to segment objects assimilating into their surroundings. The key challenge for COD is that there are existing high intrinsic similarities between the target object and the background. To solve this challenging problem, we propose the Cascaded Decamouflage Module to progressively improve the prediction map, where each decamouflage module is composed of the region enhancement block and the reverse attention mining block to accurately detect the camouflaged object and obtain complete target objects. In addition, we introduce the classification-based label reweighting to produce the gated label maps as the supervision for assisting the network to capture the most conspicuous region of a camouflaged object and obtain the target object entirely. Extensive experiments on three challenging datasets demonstrate that the proposed model outperforms state-of-the-art methods under different evaluation metrics.
A New IBE Scheme Based on Conjugate Search Problem

With the rapid development of Internet of Vehicles (IoV) technology, its security problem is attracting more and more attention. In this paper, an identity-based encryption scheme based on the conjugate search problem is proposed for the Internet of vehicles. In the scheme, the identity ID of the vehicle is directly used as the public key during communication, and the additional facilities for maintaining the identity certificate are no longer necessary. The scheme is based on the matrix over groupring, which can improve the efficiency of operations, and the scheme reduces the storage space by introducing the circulant matrix. Finally, the security reduction of the scheme to the Conjugate Search Problem based Diffie-Hellman assumption (CSP-DDH) under the random oracle model is proved, and the scheme is semantically secure under the Chosen Plaintext Attack.
Communication-efficient Federated Learning with an Event-triggering Strategy

With the development of artificial intelligence, data has become one of the most important resources. However, the emphasis on data privacy has brought great obstacles to the further development of machine learning algorithms. Federated learning keeps the local data set from being exposed to the outside world by passing model parameters during the training process, so communication cost has become a major bottleneck in the training process of federated learning. In this paper, to reduce useless communications during parameter upload, we propose a new federated learning algorithm with an event-triggering strategy based on model performance. Through this strategy, a certain number of communications have been reduced, so as to reduce the communication cost in training. In order to verify the performance of the proposed algorithm, experiments have been implemented on MNIST. The experimental results show that the proposed algorithm can reduce the communication cost by 38% at the same level of accuracy compared with the federated average. Compared with other event-triggering strategies, it can have less communications at the same level of accuracy.