Reviewerly/exHarmony huggingface dataset:
Energy efficient task scheduling based on deep reinforcement learning in cloud environment: A specialized review

Authors:
raw_author_name: Siti Nuraishah Agos Jawaddi
raw_affiliation_string: School of Computing Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA (UiTM), Shah Alam, 40450, Selangor, Malaysia
raw_author_name: Azlan Ismail
raw_affiliation_string: School of Computing Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA (UiTM), Shah Alam, 40450, Selangor, Malaysia

Abstract: The expanding scale of cloud data centers and the diversification of user services have led to an increase in energy consumption and greenhouse gas emissions, resulting in long-term detrimental effects on the environment. To address this issue, scheduling techniques that reduce energy usage have become a hot topic in cloud computing and cluster management. The Deep Reinforcement Learning (DRL) approach, which combines the advantages of Deep Learning and Reinforcement Learning, has shown promise in resolving scheduling problems in cloud computing. However, reviews of the literature on task scheduling that employ DRL techniques for reducing energy consumption are limited. In this paper, we survey and analyze energy consumption models used for scheduling goals, provide an overview of the DRL algorithms used in the literature, and quantitatively compare the model differences of Markov Decision Process elements. We also summarize the experimental platforms, datasets, and neural network structures used in the DRL algorithm. Finally, we analyze the research gap in DRL-based task scheduling and discuss existing challenges as well as future directions from various aspects. This paper contributes to the correlation perspective on the task scheduling problem with the DRL approach and provides a reference for in-depth research on the direction of DRL-based task scheduling research. Our findings suggest that DRL-based scheduling techniques can significantly reduce energy consumption in cloud data centers, making them a promising area for further investigation.
SRMADNet: Swin ResUnet3+-based mammogram image segmentation and heuristic adopted multi-scale attention based DenseNet for breast cancer detection

Authors:
raw_author_name: Kalyani Ghuge
raw_affiliation_string: School of Computing Science and Engineering, VIT Bhopal University, Kothrikalan, Sehore 466114, Madhya Pradesh, India

Abstract: Breast tumor is the second wide largest mortality rate of all the other cancers that are found in women and it is also thought to be the main factor in a high death rate. Mammographic pictures are mostly examined by the trained and expert radiologists to identify the abnormalities in the breast such as microcalcifications and masses. A correct diagnosis and early detection can aid in the fight against this form of dangerous disease. Breast cancer identification and categorization have been found to benefit greatly from the segmentation of mammography lesions. Segmentation can help with both shape-related feature extraction and precise lesion localization. Therefore, specialists implemented the latest image segmentation for mammogram images and the identification architecture for the identification of breast cancers, most importantly for the women. The wanted mammogram pictures are collected from traditional global databases. The gathered pictures are fed into the segmentation process, where the images are segmented employing Swin ResUnet3+. Thus, the mammogram segmented pictures are forwarded to the identification stage, where Adaptive Multi-scale Attention-based Densenet with Extreme Learning Machine (AMAD-ELM) model is implemented to identify the breast tumor effectively from the mammogram pictures. The parameters from AMAD-ELM are optimized through the suggested Position-based Improved invasive Weed and Crisscross Optimization (PIWCO). The effectiveness of the recommended breast tumor identification system with the help of deep learning is analyzed through various existing models to show its effectiveness.
Machine Translation for Historical Research: A Case Study of Aramaic-Ancient Hebrew Translations

Authors:
raw_author_name: Chaya Liebeskind
raw_affiliation_string: Department of Computer Science, Jerusalem College of Technology, Jerusalem, Israel
raw_author_name: Shmuel Liebeskind
raw_affiliation_string: Department of Data Mining, Jerusalem College of Technology, Jerusalem, Israel
raw_author_name: Dan Bouhnik
raw_affiliation_string: Department of Computer Science, Jerusalem College of Technology, Jerusalem, Israel

Abstract: In this article, by the ability to translate Aramaic to another spoken languages, we investigated machine translation in a cultural heritage domain for two primary purposes: evaluating the quality of ancient translations and preserving Aramaic (an endangered language). First, we detailed the construction of a publicly available Biblical parallel Aramaic-Hebrew corpus based on two ancient (early 2 nd to late 4 th century) Hebrew-Aramaic translations: Targum Onkelus and Targum Jonathan. Then using the statistical machine translation approach, which in our use case significantly outperforms neural machine translation, we validated the excepted high quality of the translations. The trained model failed to translate Aramaic texts of other dialects. However, when we trained the same statistical machine translation model on another Aramaic-Hebrew corpus of a different dialect (Zohar, 13 th century), a very high translation score was achieved. We examined an additional important cultural heritage source of Aramaic texts, the Babylonian Talmud (early 3 rd to late 5 th century). Since we do not have a parallel Aramaic-Hebrew corpus of the Talmud, we used the model trained on the Bible corpus for translation. We performed an analysis of the results and suggest some potential promising future research.
Variable sample-size operator extrapolation algorithm for stochastic mixed variational inequalities

Authors:
raw_author_name: Zhen-Ping Yang
raw_affiliation_string: School of Mathematics, Jiaying University, Meizhou 514015, China
raw_author_name: Yong Zhao
raw_affiliation_string: College of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing 400074, China

Abstract: In this paper, we present a variable sample-size operator extrapolation algorithm for solving a class of stochastic mixed variational inequalities. One distinctive feature of our algorithm is that it updates a single search sequence by solving a prox-mapping subproblem and computing an evaluation of the expected mapping at each iteration and hence it may significantly reduce computation load. In particular, the iteration sequence generated by our algorithm always belongs to the feasible region. We show that, under some moderate conditions, the proposed algorithm can achieve O(1/T) ergodic convergence rate in terms of the expected restricted gap function, where T denotes the number of iterations. We derive some results related to the convergence rate of the Bregman distance between iterates and solutions, the iteration complexity, and the oracle complexity for the proposed algorithm when the sample size increases at a geometric rate. We also investigate the sublinear convergence rate in terms of the residual function under the generalized monotonicity condition. Numerical experiments on stochastic network game, stochastic sparse traffic assignment problems and sparse classification problem indicate that the proposed algorithm is promising compared with some existing algorithms.
A non-regularization self-supervised Retinex approach to low-light image enhancement with parameterized illumination estimation

Authors:
raw_author_name: Zunjin Zhao
raw_affiliation_string: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China
raw_author_name: Daming Shi
raw_affiliation_string: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China
raw_author_name: Hexiu Lin
raw_affiliation_string: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China; School of Games and Creative Technology, University for the Creative Arts, UK
raw_author_name: Guoqing Zhou
raw_affiliation_string: Shenzhen Zuya Technology Co., Ltd., Shenzhen, China

Abstract: In current Retinex-based low-light image enhancement (LLIE) methods, fine-tuning regularization parameters for Retinex decomposition and illumination estimation can be cumbersome. To address this, we present a novel non-regularization self-supervised Retinex approach for illumination estimation. Our contributions are twofold: First, we introduce a self-supervised method that incorporates edge-aware smoothness properties in bilateral learning, eliminating the need for regularization terms and simplifying parameter adjustments. Second, to enforce smoothness constraints on the estimated bilateral grid, we propose a bilateral grid parameterization network. This network employs a generative encoder to parameterize the bilateral grid of illumination and a trainable slicing layer guided by a map, reconstructing the grid into an illumination map. Despite the absence of regularization terms, our model excels in generating piece-wise smooth illumination, resulting in enhanced naturalness and improved contrast in images. Our model offers exceptional flexibility by eliminating the need for additional regularization terms and parameter fine-tuning. Moreover, it does not depend on external datasets for training, overcoming dataset collection challenges. Extensive experiments, comparing our model with eight state-of-the-art methods across five public available datasets, unequivocally demonstrate our model's state-of-the-art performance based on key metrics such as NIQE, NIQMC, and CPCQI. These results reaffirm the effectiveness of our approach in low-light image enhancement. Code will be available at: https://github.com/zhaozunjin/NeurBR.
Conditional feature generation for transductive open-set recognition via dual-space consistent sampling

Authors:
raw_author_name: Jiayin Sun
raw_affiliation_string: National Laboratory of Pattern Recognition, CASIA, Beijing, 100190, China; School of Artificial Intelligence, UCAS, Beijing, 100049, China
raw_author_name: Qiulei Dong
raw_affiliation_string: Center for Excellence in Brain Science and Intelligence Technology, CAS, Beijing, 100190, China; National Laboratory of Pattern Recognition, CASIA, Beijing, 100190, China; School of Artificial Intelligence, UCAS, Beijing, 100049, China

Abstract: Open-set recognition (OSR) aims to simultaneously detect unknown-class samples and classify known-class samples. Most of the existing OSR methods are inductive methods, which generally suffer from the domain shift problem that the learned model from the known-class domain might be unsuitable for the unknown-class domain. Addressing this problem, inspired by the success of transductive learning for alleviating the domain shift problem in many other visual tasks, we propose an Iterative Transductive OSR framework, called IT-OSR, which implements three explored modules iteratively, including a reliability sampling module, a feature generation module, and a baseline update module. Specifically at the initialization stage, a baseline method, which could be an arbitrary inductive OSR method, is used for assigning pseudo labels to the test samples. At the iteration stage, based on the consistency of the assigned pseudo labels between the output/logit space and the latent feature space of the baseline method, a dual-space consistent sampling approach is presented in the reliability sampling module for sampling some reliable ones from the test samples. Then in the feature generation module, a conditional dual-adversarial generative network is designed to generate discriminative features of both known and unknown classes. This generative network employs two discriminators for implementing fake/real and known/unknown-class discriminations respectively. And it is trained by jointly utilizing the test samples with their pseudo labels selected in the reliability sampling module and the labeled training samples. Finally in the baseline update module, the above baseline method is updated/re-trained for sample re-prediction by jointly utilizing the generated features, the selected test samples with pseudo labels, and the training samples. Extensive experimental results on both the standard-dataset and the cross-dataset settings demonstrate that the derived transductive methods, by introducing two typical inductive OSR methods into the proposed IT-OSR framework, achieve better performances than 19 state-of-the-art methods in most cases.
Weighted subspace anomaly detection in high-dimensional space

Authors:
raw_author_name: Jiankai Tu
raw_affiliation_string: College of Information Science and Electronic Engineering & the Zhejiang Provincial Key Laboratory of Information Processing, Communication and Networking, Zhejiang University, Hangzhou 310027, People's Republic of China
raw_author_name: Huan Liu
raw_affiliation_string: College of Information Science and Electronic Engineering & the Zhejiang Provincial Key Laboratory of Information Processing, Communication and Networking, Zhejiang University, Hangzhou 310027, People's Republic of China
raw_author_name: Chunguang Li
raw_affiliation_string: College of Information Science and Electronic Engineering & the Zhejiang Provincial Key Laboratory of Information Processing, Communication and Networking, Zhejiang University, Hangzhou 310027, People's Republic of China

Abstract: Anomaly detection aims at finding anomalies deviating from the normal data patterns. Virtually all anomaly detection methods create a model of the normal patterns before finding anomalies. In high-dimensional scenarios, due to the curse of dimensionality, it is difficult to construct the model of normal patterns in the full dimensional space. Subspace methods assuming that data can be characterized by low-dimensional manifolds have attracted a great deal of research. However, in unsupervised setting, unlabeled data is composed of both the normal and the abnormal data. The existence of anomalies might affect the establishment of the underlying normal subspaces. The undetermined number of the underlying subspaces also brings difficulties in subspace selection. To tackle the aforementioned problems, we come up with a weighted subspace anomaly detection (WSAD) method. We utilize correntropy to construct an objective function to mitigate the influence of the anomalies, which can be regarded as a weighting method for different data. Besides, we introduce an auxiliary variable with block sparsity regularization to achieve adaptive subspace selection, which can be regarded as a weighting method for different subspaces. After the normal underlying subspaces being established, we define the outlier scores by considering the deviation from the underlying subspaces, the local outlier score within subspaces, and the subspace scale. We use the half-quadratic theory to transform the optimization problem defined in WSAD, and apply alternating optimization to solve the transformed problem. Theoretically, we prove the convergence of the optimization algorithm. Experimentally, we demonstrate the effectiveness of the proposed method on both synthetic data and real datasets.
Deep image clustering with contrastive learning and multi-scale graph convolutional networks

Authors:
raw_author_name: Dong Huang
raw_affiliation_string: College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China; Key Laboratory of Smart Agricultural Technology in Tropical South China, Ministry of Agriculture and Rural Affairs, China
raw_author_name: Chang-Dong Wang
raw_affiliation_string: Guangdong Provincial Key Laboratory of Intellectual Property and Big Data, Guangzhou, China; School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
raw_author_name: Jian-Huang Lai
raw_affiliation_string: School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China

Abstract: Deep clustering has shown its promising capability in joint representation learning and clustering via deep neural networks. Despite the significant progress, the existing deep clustering works mostly utilize some distribution-based clustering loss, lacking the ability to unify representation learning and multi-scale structure learning. To address this, this paper presents a new deep clustering approach termed Image clustering with contrastive learning and multi-scale Graph Convolutional Networks (IcicleGCN), which bridges the gap between convolutional neural network (CNN) and graph convolutional network (GCN) as well as the gap between contrastive learning and multi-scale structure learning for the deep clustering task. Our framework consists of four main modules, namely, the CNN-based backbone, the Instance Similarity Module (ISM), the Joint Cluster Structure Learning and Instance reconstruction Module (JC-SLIM), and the Multi-scale GCN module (M-GCN). Specifically, the backbone network with two weight-sharing views is utilized to learn the representations for the two augmented samples (from each image). The learned representations are then fed to ISM and JC-SLIM for joint instance-level and cluster-level contrastive learning, respectively, during which an auto-encoder in JC-SLIM is also pretrained to serve as a bridge to the M-GCN module. Further, to enforce multi-scale neighborhood structure learning, two streams of GCNs and the auto-encoder are simultaneously trained via (i) the layer-wise interaction with representation fusion and (ii) the joint self-adaptive learning. Experiments on multiple image datasets demonstrate the superior clustering performance of IcicleGCN over the state-of-the-art. The code is available at https://github.com/xuyuankun631/IcicleGCN.
Open set transfer learning through distribution driven active learning

Authors:
raw_author_name: Min Wang
raw_affiliation_string: School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu 610500, China
raw_author_name: Xiao-Yu Jiang
raw_affiliation_string: School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu 610500, China
raw_author_name: An-An Zhang
raw_affiliation_string: School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu 610500, China

Abstract: Domain adaptation enables effective transfer between source and target domains with different distributions. The latest research focuses on open set domain adaptation; that is, the target domain contains unknown categories that do not exist in the source domain. The existing open set domain adaptation cannot realize the fine-grained recognition of unknown categories. In this paper, we propose an uncertainty analysis evidence model and design a distribution driven active transfer learning (DATL) algorithm. DATL realizes fine-grained recognition of unknown categories with no requirements on the source domain to contain the unknown categories. To explore unknown distributions, the uncertainty analysis evidence model was adopted to divide the high uncertainty space. To select critical instances, a cluster-diversity query strategy was proposed to identify new categories. To enrich the label categories of the source domain, a global dynamic alignment strategy was designed to avoid negative transfers. Comparative experiments with state-of-the-art methods on the standard Office-31/Office-Home/Office-Caltech10 benchmarks showed that the DATL algorithm: (1) outperformed its competitors; (2) realized accurate identification of unknown subcategories from a fine-grained perspective; and (3) achieved outstanding performance even with a very high degree of openness.
A Novel Attention-Driven Framework for Unsupervised Pedestrian Re-identification with Clustering Optimization

Authors:
raw_author_name: Xuan Wang
raw_affiliation_string: School of Computer and Control Engineering, Yantai University, Qingquan Road 30, Yantai, 264005, China
raw_author_name: Zhaojie Sun
raw_affiliation_string: School of Computer and Control Engineering, Yantai University, Qingquan Road 30, Yantai, 264005, China
raw_author_name: Abdellah Chehri
raw_affiliation_string: Department of Mathematics and Computer Science, Royal Military College of Canada, Kingston, ON K7K 7B4, Canada
raw_author_name: Gwanggil Jeon
raw_affiliation_string: Department of Embedded Systems Engineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon, 22012, Republic of Korea
raw_author_name: Yongchao Song
raw_affiliation_string: School of Computer and Control Engineering, Yantai University, Qingquan Road 30, Yantai, 264005, China

Abstract: Unsupervised pedestrian re-identification (re-ID) is not merely a visual recognition task; it represents a significant sub-field within the domain of pattern recognition. Despite the remarkable success of Convolutional Neural Networks (CNN) in re-ID, they still face challenges in handling variations in pose, occlusion, and lighting conditions. To effectively tackle these challenges, it is imperative to prioritize implementing efficient sampling strategies. We propose a Novel Attention-Driven Framework for Unsupervised Pedestrian re-ID with Clustering Optimization (AFC) to address the above issues. First, we introduce a new attention mechanism that enhances multi-scale spatial attention and reduces the number of trainable parameters. Then, we employed a straightforward and effective method of group sampling. In addition, we apply a clustering consensus approach to estimate pseudo-label similarity in continuous training and use temporal propagation and ensembles to improve pseudo-labels. Extensive experiments on Market-1501, duketmc-reID and MSMT17 datasets show that our method achieves significant performance improvement in unsupervised pedestrian re-ID, which provides important theoretical and practical value for the research on deep fusion of pattern recognition field with pedestrian re-ID and promotes the further development of the related fields.
So Many Heads, So Many Wits: Multimodal Graph Reasoning for Text-Based Visual Question Answering

Authors:
raw_author_name: Wenbo Zheng
raw_affiliation_string: School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China
raw_author_name: Lan Yan
raw_affiliation_string: College of Computer Science and Engineering, Hunan University, Changsha, China
raw_author_name: Fei-Yue Wang
raw_affiliation_string: State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

Abstract: While texts related to images convey fundamental messages for scene understanding and reasoning, text-based visual question answering tasks concentrate on visual questions that require reading texts from images. However, most current methods add multimodal features that are independently extracted from a given image into a reasoning model without considering their inter-and intra-relationships according to three modalities (i.e., scene texts, questions, and images). To this end, we propose a novel text-based visual question answering model, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multimodal graph reasoning</i> . Our model first extracts intramodality relationships by taking the representations from identical modalities as semantic graphs. Then, we present graph multihead self-attention, which boosts each graph representation through graph-by-graph aggregation to capture the intermodality relationship. It is a case of “so many heads, so many wits” in the sense that as more semantic graphs are involved in this process, each graph representation becomes more effective. Finally, these representations are reprojected, and we perform answer prediction with their outputs. The experimental results demonstrate that our approach realizes substantially better performance compared with other state-of-the-art models.