Reviewerly/exHarmony huggingface dataset:
Adaptive Network Configuration for Efficient and Accurate Neural Video Inference

Authors:
raw_author_name: Peng Yang
raw_affiliation_string: School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
raw_author_name: Yan Cheng
raw_affiliation_string: School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
raw_author_name: Ning Zhang
raw_affiliation_string: Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, Canada
raw_author_name: Qimin Cheng
raw_affiliation_string: School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
raw_author_name: Li Yu
raw_affiliation_string: School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China

Abstract: Cameras are widely used in many fields, e.g., intelligent transportation, autonomous driving, surveillance, etc. It is thus vital to conduct video analytics in an efficient and accurate manner. However, camera’s built-in capacity is insufficient to support neural network processing, while offloading video streams incurs prohibitive latency and communication cost. In this paper, we find that frame rate, resolution, and neural network inference model, have an intertwined impact on network resource demand. The optimal configuration of these factors also varies with video content feature. To address these challenges, we propose a dynamic configuration update scheme based on predictive video perception using a long short-term memory (LSTM) neural network, to adapt configuration to content changes. This scheme consists of a change detector and a configuration profiler. Through theoretical modeling and analysis, we derive the detection thresholds for both dynamic and stationary video contents, considering the LSTM prediction error. The configuration profiler then updates system by solving an optimization problem, which maximizes the overall utility considering analytics accuracy and resource consumption. Extensive real-world traces-based experiments show that the proposed scheme can save profiling resources by up to 95% while ensuring high accuracy compared with other benchmarks.
Coarse-to-Fine Low-Light Image Enhancement With Light Restoration and Color Refinement

Authors:
raw_author_name: Xu Wu
raw_affiliation_string: Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
raw_author_name: Zhihui Lai
raw_affiliation_string: Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
raw_author_name: Shiqi Yu
raw_affiliation_string: Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
raw_author_name: Jie Zhou
raw_affiliation_string: National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China
raw_author_name: Zhuoqian Liang
raw_affiliation_string: College of Shandong Future Networks Research Institute, Shandong, China
raw_author_name: Linlin Shen
raw_affiliation_string: Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

Abstract: Low-light image enhancement aims to improve the illumination intensity while restoring color information. Despite recent advancements using deep learning methods, they still struggle with over or under-exposure in complex lighting scenes and poor color recovery in dark regions. To address these drawbacks, we propose a novel pipeline (called LRCR-Net) to perform Light Restoration and Color Refinement in a coarse-to-fine manner. In the coarse step, we focus on improving the illumination adaptively while avoiding inappropriate enhancement in the brighter or darker regions. This is achieved by introducing a region-calibrated residual block (RCRB) that balances local and global dependencies among different image regions. In the fine step, we aim to retouch the color of the images enhanced in the coarse step. To achieve this goal, we propose learnable image processing operators (LIPOs), including contrast and saturation operators, to refine the color according to the input images' color and contrast information. The final result is an image with proper illumination and rich color. Experiments on four benchmark datasets (NASA, LIME, MEF, and NPE) show that our model outperforms state-of-the-art methods.
Reinforcement learning with time intervals for temporal knowledge graph reasoning

Authors:
raw_author_name: Ruinan Liu
raw_affiliation_string: College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
raw_author_name: Guisheng Yin
raw_affiliation_string: College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
raw_author_name: Zechao Liu
raw_affiliation_string: College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
raw_author_name: Ye Tian
raw_affiliation_string: Hangzhou Institute of Technology, Xidian University, Xi'an 710000, China

Abstract: Temporal reasoning methods have been successful in temporal knowledge graphs (TKGs) and are widely employed in many downstream application areas. Most existing TKG reasoning models transform time intervals into continuous time snapshots, with each snapshot representing a subgraph of the TKG. Although such processing can produce satisfactory outcomes, it ignores the integrity of a time interval and increases the amount of data. Meanwhile, many previous works focuses on the logic of sequentially occurring facts, disregarding the complex temporal logics of various time intervals. Consequently, we propose a Reinforcement Learning-based Model for Temporal Knowledge Graph Reasoning with Time Intervals (RTTI), which focuses on time-aware multi-hop reasoning arising from complex time intervals. In RTTI, we construct the time learning part to obtain the time embedding of the current path. It simulates the temporal logic with relation historical encoding and compute the time interval between two facts through the temporal logic feature matrix. Furthermore, we propose a new method for representing time intervals that breaks the original time interval embedding method, and express the time interval using median and embedding changes of two timestamps. We evaluate RTTI on four public TKGs for the link prediction task, and experimental results indicate that our approach can still perform well on more complicated tasks. Meanwhile, our method can search for more interpretable paths in the broader space and improve the reasoning ability in sparse spaces.
Feature incremental learning with causality

Authors:
raw_author_name: Haotian Ni
raw_affiliation_string: College of Science, National University of Defense Technology, Changsha, 410073, China
raw_author_name: Shilin Gu
raw_affiliation_string: College of Science, National University of Defense Technology, Changsha, 410073, China
raw_author_name: Ruidong Fan
raw_affiliation_string: College of Science, National University of Defense Technology, Changsha, 410073, China
raw_author_name: Chenping Hou
raw_affiliation_string: College of Science, National University of Defense Technology, Changsha, 410073, China

Abstract: With the emerging of new data collection ways, the features are incremental and accumulated gradually. Due to the expansion of feature spaces, it is more common that there are unknown biases between the distribution of training and testing datasets. It is known as the unknown data selection bias, which belongs to the learning scenario with non-i.i.d samples. The performance of traditional approaches, which need the i.i.d. assumption, will be aggravated seriously. How to design an algorithm to address the problem of data selection bias in this feature incremental scenario is crucial but rarely studied. In this paper, we propose a feature incremental classification algorithm with causality. Firstly, we embed the confounding variable balance algorithm in causal learning into the prediction modeling and utilize the logical regression algorithm with balancing regular terms as a baseline. Then, to satisfy the special requirement of feature increment, we design a new regularizer, which maintains the consistency of the regression coefficients between the data in the current and previous stages. It retains the correlation between the old features and labels. Finally, we propose the Multiple Balancing Logistic Regression model (MBRLR) to jointly optimize the balancing regularizer and weighted logistic regression model with multiple feature sets. We also present theoretical results to show that our proposed algorithm can make precise and stable predictions. Besides, the numerical results also demonstrate that our MBRLR algorithm is superior to other methods.
Deep Contrastive Clustering for Signal Deinterleaving

Authors:
raw_author_name: Shuyuan Yang
raw_affiliation_string: School of Electronic Engineering, Xidian University, Xian, China
raw_author_name: Xinyi Zhao
raw_affiliation_string: School of Electronic Engineering, Xidian University, Xian, China
raw_author_name: Huiling Liu
raw_affiliation_string: School of Electronic Engineering, Xidian University, Xian, China
raw_author_name: Chen Yang
raw_affiliation_string: School of Electronic Engineering, Xidian University, Xian, China
raw_author_name: Tongqing Peng
raw_affiliation_string: School of Electronic Engineering, Xidian University, Xian, China
raw_author_name: Rundong Li
raw_affiliation_string: Electrical Engineering (EE), University of Electronic Science and Technology of China, Chengdu, China
raw_author_name: Feng Zhang
raw_affiliation_string: Information Science Academy, China Electronics Technology Group Corporation, Beijing, China

Abstract: In the complex electromagnetic environment, radar signal deinterleaving (RSD) is a challenging task. In this paper, a deep contrastive clustering algorithm (DCCA) is advanced in a new self-supervised paradigm, for accurate RSD without any prior information of radar emitters. First, a contrastive self-supervised deep attention network (CSDAN) is constructed to learn signal representations, by using self-defined pseudo-labels of augmented signals as supervision. We use CSDAN to learn the differences between different radiation source data and generate deep features suitable for clustering. Three metrics are then used to automatically determine the number of clusters for the subsequent clustering. Extensive experiments are performed on several datasets containing different numbers of emitters. The results show that the proposed DCCA can accurately determine the number of emitters and deinterleave radar pulses. Furthermore, CSDAN can extract discriminative features of emitters with low intra-class similarity and high inter-class similarity
A Novel Deep Belief Network Architecture with Interval Type-2 Fuzzy Set Based Uncertain Parameters Towards Enhanced Learning

Authors:
raw_author_name: Amit K. Shukla
raw_affiliation_string: School of Technology and Innovations, University of Vaasa, Wolffintie 34, FI-65200 Vaasa, Finland
raw_author_name: Pranab K. Muhuri
raw_affiliation_string: Department of Computer Science, South Asian University, Rajpur Road, Maidan Garhi New Delhi, 110068, India

Abstract: This paper proposes a novel Deep Belief Network (DBN) architecture, the ‘Interval Type-2 Fuzzy DBN (IT2FDBN)’, which models the weights and biases with IT2 FSs. Thus, it introduces a novel algorithm for augmented deep leaning, which has the capability to address all the limitations of the classical DBN (CDBN) and T1 fuzzy DBN (T1FDBN). We comparatively evaluate the performance of the IT2FDBN by conducting experiments using the popular MNIST handwritten digit recognition datasets. Additionally, to demonstrate its robustness and generalization capabilities, we also conduct experiments taking two noisy variants of MNIST dataset, viz. the MNIST with AWGN (additive white Gaussian noise) and the MNIST with motion blur. We conduct extensive simulations by considering different combinations of nodes in the hidden layers of the DBN for better model selection. We thoroughly compare the results using well-known performance measures such as root mean square error (RMSE) and Error rate. We show that, in terms of RMSE values and error rates, the proposed IT2FDBN outperforms both T1FDBN and CDBN across all the three datasets. Further, we also provide the results of convergence, runtime-based comparison, and statistical analysis in support of our proposal.
Auto-weighted multi-view clustering with the use of an augmented view

Authors:
raw_author_name: Bing Cai
raw_affiliation_string: School of Computer and Information, Anhui Polytechnic University, Wuhu, 241000, China; School of Computer and Software Engineering, Anhui Institute of Information Technology, Wuhu, 241000, China
raw_author_name: Gui-Fu Lu
raw_affiliation_string: School of Computer and Information, Anhui Polytechnic University, Wuhu, 241000, China
raw_author_name: Yangfan Du
raw_affiliation_string: School of Computer and Information, Anhui Polytechnic University, Wuhu, 241000, China

Abstract: Multi-view clustering is a powerful technique that leverages both consensus and complementary information from multiple perspectives to achieve impressive results. However, existing methods have not fully explored the inherent structural information contained in multi-view data, such as inadequately harnessing the consistency among views or neglecting to explore their high-order correlations. To address these limitations, we propose a novel auto-weighted multi-view clustering (AWMVC) method with the use of an augmented view. The main motivation of AWMVC is that each view naturally embodies consensus information, and concatenating the views maximizes the consistency properties. AWMVC first connects multiple views via feature concatenation to form a newly augmented view. Then, we stack the self-representation matrices of the original views and the newly augmented view, i.e., the concatenated view into a 3rd-order tensor, subject to a low-rank tensor constraint. Additionally, this approach automatically assigns weights to each view and constructs a more reliable affinity matrix. Our method unifies the feature concatenation process, high-order information, and a weighted strategy into one model, and is efficiently optimized through an iterative algorithm. Experimental results on benchmark datasets demonstrate that AWMVC outperforms state-of-the-art methods, highlighting the effectiveness of our approach.
Generative adversarial networks via a composite annealing of noise and diffusion

Authors:
raw_author_name: Kensuke Nakamura
raw_affiliation_string: Computer Science Department, Chung-Ang University, Seoul, Republic of Korea
raw_author_name: Simon Korman
raw_affiliation_string: Department of Computer Science, University of Haifa, Haifa, Israel
raw_author_name: Byung-Woo Hong
raw_affiliation_string: Computer Science Department, Chung-Ang University, Seoul, Republic of Korea

Abstract: Generative adversarial network (GAN) is a framework for generating fake data using a set of real examples. However, GAN is unstable in the training stage. In order to stabilize GANs, the noise injection has been used to enlarge the overlap of the real and fake distributions at the cost of increasing variance. The diffusion process (or data smoothing in its spatial domain) removes fine details in order to capture the structure and important patterns in data but it suppresses the capability of GANs to learn high-frequency information in the training procedure. Based on these observations, we propose a data representation for the GAN training, called noisy scale-space (NSS), that recursively applies the smoothing with a balanced noise to data in order to replace the high-frequency information by random data, leading to a coarse-to-fine training of GANs. We experiment with NSS using DCGAN and StyleGAN2 based on benchmark datasets in which the NSS-based GANs outperforms the state-of-the-arts in most cases.
Traffic sign attack via pinpoint region probability estimation network

Authors:
raw_author_name: Yue Wang
raw_affiliation_string: Shanghai University, Shangda Road 99, 200444, Shanghai, China
raw_author_name: Minjie Liu
raw_affiliation_string: Shanghai University, Shangda Road 99, 200444, Shanghai, China
raw_author_name: Yanli Ren
raw_affiliation_string: Shanghai University, Shangda Road 99, 200444, Shanghai, China
raw_author_name: Xinpeng Zhang
raw_affiliation_string: Shanghai University, Shangda Road 99, 200444, Shanghai, China
raw_author_name: Guorui Feng
raw_affiliation_string: Shanghai University, Shangda Road 99, 200444, Shanghai, China

Abstract: Recent work show that Deep Neural Networks (DNNs) have created great performance in many tasks, but they are vulnerable to adversarial examples which trigger Artificial Intelligence (AI) security risks. Especially in the autonomous driving field, attacking a traffic sign classification network results in a serious consequence. Most existing researches prefer to digital level attacks focusing on smaller or more imperceptible adversarial noise. Given that attacks available to real-world implementation usually emerge in more security-critical scenarios, we propose an adaptively adversarial example generation algorithm for physical attacks in the real-world setting. Taking account of the traffic sign classification, our approach is divided into two steps. The first step is to generate a probability map which precisely predicts the probability of being attacked for each pixel in the input image through the proposed Pinpoint Region Probability Estimation Network (PRPEN) and meanwhile, try to reduce the size of the highlighted area in the map. It can also be regarded as a classification problem in which every pixel has two classes, suitable for attacking or not, including the restrictions on the number of items in the suitable sort. The second one is to remake a mask depending on the probability map and optimize adversarial patches only on what mask decides. Experimental results show that our method achieves almost 100% misclassification rate in several widely used networks with even smaller patches. We also find how to effectively disguise as a target class to mislead the DNN classifiers and improve AI security.
A topic modeling and image classification framework: The Generalized Dirichlet variational autoencoder

Authors:
raw_author_name: Nizar Bouguila
raw_affiliation_string: Concordia University, 1455 De Maisonneuve Blvd. W. Montreal, Quebec, H3G 1M8, Canada

Abstract: Latent Dirichlet allocation model (LDA) has been widely used in topic modeling. Recent works have shown the effectiveness of integrating neural network mechanisms with this generative model for learning text representation. However, one of the significant setbacks of LDA is that it is based on a Dirichlet prior that has a restrictive covariance structure. All its variables are considered to be negatively correlated, which makes the model restrictive. In a practical sense, topics can be positively or negatively correlated. To address this problem, we proposed a generalized Dirichlet variational autoencoder (GD-VAE) for topic modeling. The Generalized Dirichlet (GD) distribution has a more general covariance structure than the Dirichlet distribution because it takes into account both positively and negatively correlated topics in the corpus. Our proposed model leverages rejection sampling variational inference using a reparameterization trick for effective training. GD-VAE compares favorably to recent works on topic models on several benchmark corpora. Experiments show that accounting for topics' positive and negative correlations results in better performance. We further validate the superiority of our proposed framework on two image data sets. GD-VAE demonstrates its significance as an integral part of a classification architecture. For reproducibility and further research purposes, code for this work can be found at https://github.com/hormone03/GD-VAE.
Contrastive clustering with a graph consistency constraint

Authors:
raw_author_name: Yunxiao Zhao
raw_affiliation_string: Institute of Intelligent Information Processing, Shanxi University, Taiyuan, 030006, Shanxi, China
raw_author_name: Liang Bai
raw_affiliation_string: Institute of Intelligent Information Processing, Shanxi University, Taiyuan, 030006, Shanxi, China

Abstract: Compared with classical contrastive learning methods, the performance of contrastive clustering is more easily affected by the quality of positive and negative samples, due to the fact that the clustering assumption requires neighbors of points as their positives. In order to reduce the effect of the uncertainty of positives and negatives on contrastive clustering, we propose a new contrastive clustering algorithm with graph consistency constraint. In this algorithm, a loss of graph consistency is proposed to reduce the impact of false negative samples by comparing the neighbor distribution of positive samples. Furthermore, an incremental training method is designed to control the quality of the selected positives. Extensive experiments show that our algorithm outperforms other deep clustering methods on wide-used benchmark data sets.
MinJoT: Multimodal infusion Joint Training for noise learning in text and multimodal classification problems

Authors:
raw_author_name: Lejian He
raw_affiliation_string: Cornell University, Ithaca, 14850, USA
raw_author_name: Yuchen Xie
raw_affiliation_string: The School of Software Engineering, Xi'an Jiaotong University, Xi'an, 710000, China
raw_author_name: Yuejia Xiang
raw_affiliation_string: Tencent, Shenzhen, 518000, China
raw_author_name: Li Zhu
raw_affiliation_string: The School of Software Engineering, Xi'an Jiaotong University, Xi'an, 710000, China
raw_author_name: Weiping Ding
raw_affiliation_string: School of Information Science and Technology, Nantong University, Nantong, 226019, China

Abstract: Amidst the critical role that high-quality labeled data plays in advancing machine learning, the persistence of noise within widely-used datasets remains a challenge. While noise learning has gained traction within machine learning, particularly in computer vision, its exploration in text and multimodal classification domains has lagged. Furthermore, a comprehensive comparison of noise learning techniques in text and multimodal classification has been lacking, partly due to variations in experimental noise settings across prior studies. Addressing these gaps, this research introduces a pioneering Multimodal Infusion Joint Training (MinJoT) framework featuring a novel co-regularized loss function that seamlessly integrates multimodal information during joint training. This framework notably excels in maintaining model robustness amidst noisy data environments. Adapting established noise learning methods from computer vision to text classification, the study conducts extensive experiments across five English and Chinese textual and multimodal datasets, involving four methods, five noise modes, and seven noise rates. Critically, this work challenges the implicit assumption that widely-used datasets are devoid of noise, revealing that these datasets indeed encompass noise levels ranging from 0.61% to 15.77% which is defined as intrinsic noise in this study. For the first time, the study investigates the impact of intrinsic noise on model performance, categorizing it into distinct levels of ambiguity. To facilitate accurate method comparison, a new dataset, Golden-Chnsenticorp (G-Chnsenticorp), is introduced, carefully crafted to be free of intrinsic noise. This research establishes the inaugural noise learning benchmark for text classification, while simultaneously pioneering the first noise learning framework tailored for multimodal sentiment classification. Through these contributions, the study advances the understanding of noise learning in text and multimodal contexts, providing a novel framework, benchmarks, and insights to propel the field forward.