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

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

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.
A multispectral transmission image cluster analysis method based on “Terrace compression Method” and window function

Multispectral transmission imaging has a great potential for the early screening of breast cancer due to the low cost, safety and ease in operation. Accurate detection of heterogeneity is important for the diagnosis of breast disease. The low contrast and unclear heterogeneity boundaries of transmission images can lead to difficulties in recognition and segmentation. Therefore, we propose a clustering segmentation method of multispectral transmission images based on "terrace compression method" and window transformation. The images are preprocessed by the frame accumulation to improve the signal-to-noise ratio. After that, the "terrace compression method" is used to compress the images nonlinearly, to reduce data redundancy and then to improve the edge information of heterogeneities. Afterwards, the window function is used to eliminate the redundant information of the background and to reduce the influence of background noise on clustering. Finally, the processed images at each wavelength are transformed into multidimensional data for cluster analysis. The multispectral transmission images of breast phantom are acquired for experimental validation. Then, compared the method with common clustering segmentation methods (including K-means, K-means++, Mean-shift, Gaussian Mixture). The results showed that this processing method can effectively segment and classify the three heterogeneities in the breast phantom. Among these methods, the method proposed in the paper has the best segmentation and classification results for the three types of heterogeneities in the breast phantom. The Dice coefficients of all the heterogeneities segmentation reached more than 0.84 and increased by a maximum of 1.08 times as compared to the common clustering methods. The applications of the terrace compression method and the grayscale window transformation improved the effect of image clustering segmentation.
Machine Translation for Historical Research: A Case Study of Aramaic-Ancient Hebrew Translations

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

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

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

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

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

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

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

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

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.
The (+)-extended twisted generalized Reed-Solomon code

In this paper, we give a parity check matrix of the (+)-extended twisted generalized Reed Solomon code, and then not only prove that it is MDS or NMDS, but also determine the weight distribution. Especially, based on Schur method, we show that the (+)-extended twisted generalized Reed Solomon code is neither the generalized Reed Solomon code nor extended generalized Reed Solomon code. Furthermore, we present necessary and sufficient conditions for the (+)-twisted generalized Reed Solomon code and the (+)-extended twisted generalized Reed Solomon code to be self-orthogonal, respectively. Finally, several classes of self-dual (+)-twisted generalized Reed Solomon code codes and almost self-dual (+)-extended twisted generalized Reed Solomon codes are constructed.
Boundary fusion multi-scale enhanced network for gland segmentation in colon histology images

Morphological features of glands serve as essential diagnostic criteria for colon cancer, and physicians must accurately segment glands in histopathological sections to grade the cancer. However, owing to the diverse gland morphologies and blurred edges of malignant glands, the contextual representations of edges have received limited attention in current segmentation methods, leading to less-than-ideal segmentation results, particularly in malignant cases. We present an edge-based multi-scale enhanced network (BFMSE-Net) for gland segmentation to address these issues. This network is based on U-net and fuses an edge fusion module (BFM) and a multi-scale enhancement module (MSEM). The BFM extracts detailed edge features, fuses edge information, and enhances edge pixel values, which effectively facilitates the segmentation of glands with blurred edges and adhesions. MSEM can handle complex scenarios where gland size and shape significantly change because it adaptively gathers information from various receptive fields in the region of interest. The method applied to evaluate this proposed network was to compare it to current methods through experiments, applying the gland segmentation (GlaS) and colorectal adenocarcinoma gland (CRAG) datasets. The experimental results demonstrate that our proposed method achieves clear gland segmentation, with shape similarity indices of 72.617 and 69.792 on the GlaS and CRAG datasets, respectively. These results show an improvement of 20.627 and 76.978, respectively, over current state-of-the-art methods. In conclusion, BFMSE-Net, which clearly focuses on edge representation, can accurately segment various types of glands.