Reviewerly/exHarmony huggingface dataset:
Selecting High-Quality Proposals for Weakly Supervised Object Detection With Bottom-Up Aggregated Attention and Phase-Aware Loss

Weakly supervised object detection (WSOD) has received widespread attention since it requires only image-category annotations for detector training. Many advanced approaches solve this problem by a two-phase learning framework, that is, instance mining that classifies generated proposals via multiple instance learning, and instance refinement that iteratively refines bounding boxes using the supervision produced by the preceding stage. In this paper, we observe that the detection performance is usually limited by imprecise supervision, including part domination and untight boxes. To mitigate their adverse effects, we focus on selecting high-quality proposals as the supervision for WSOD. To be specific, for the issue of part domination, we propose bottom-up aggregated attention which incorporates low-level features from shallow layers to improve location representation of top-level features. In this manner, the proposals corresponding to entire objects can get high scores. Its advantage is that it can be flexibly plugged into the WSOD framework since there is no need to attach learnable parameters or learning branches. As regards the problem of untight boxes, we propose a phase-aware loss, which is the first work to measure supervision quality by the loss in the instance mining phase, to highlight correct boxes and suppress untight ones. In this work, we unify the proposed two modules into the framework of online instance classifier refinement. Extensive experiments on the PASCAL VOC and the MS COCO demonstrate that our method can significantly improve the performance of WSOD and achieve the state-of-the-art results. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Horatio9702/BUAA_PALoss</uri> .
Multi-granularity graph pooling for video-based person re-identification

The video-based person re-identification (ReID) aims to identify the given pedestrian video sequence across multiple non-overlapping cameras. To aggregate the temporal and spatial features of the video samples, the graph neural networks (GNNs) are introduced. However, existing graph-based models, like STGCN, perform the mean/max pooling on node features to obtain the graph representation, which neglect the graph topology and node importance. In this paper, we propose the graph pooling network (GPNet) to learn the multi-granularity graph representation for the video retrieval, where the graph pooling layer is implemented to downsample the graph. We construct a multi-granular graph by using node features learned from backbone, then implement multiple graph convolutional layers to perform the spatial and temporal aggregation on nodes. To downsample the graph, we propose a multi-head full attention graph pooling (MHFAPool) layer, which integrates the advantages of existing node clustering and node selection pooling methods. Specifically, MHFAPool first learns a full attention matrix for each pooled node, then obtains the principal eigenvector of the attention matrix via the power iteration algorithm, finally takes the softmax of the principal eigenvector as the aggregation coefficients. Extensive experiments demonstrate that our GPNet achieves the competitive results on four widely-used datasets, i.e., MARS, DukeMTMC-VideoReID, iLIDS-VID and PRID-2011.
Generating non-classical correlations in two-level atoms

We address the effective generation of thermal non-classical correlations in two two-level atoms when exposed to a Fock-state cavity incorporated with classical decoherence effects. Using Bell non-locality, quantum coherence, and concurrence, we investigate the generation maps of non-locality, coherence, and entanglement. We show that the two atoms are initially separable, however, they become coherent and entangled within a short period of time when exploited with the Fock-state cavity. Interestingly, the Fock-state cavity shows dominant traits for the complete suppression of the classical decoherence effects. However, the decoherence effects appear initially and delay the generation of non-classical correlations. In addition, the generation delay of non-classical correlations is observed to be highly affected when the atom-cavity configuration is considered in a strong coupling regime. Compared to the entanglement and non-locality, the Fock-state cavity develops quantum coherence faster in the two-level systems. Finally, we also discuss the optimal feature settings of the Fock-state cavity, related significance, and experimental prospects of the study.
Machine learning-based social media bot detection: a comprehensive literature review

Abstract In today’s digitalized era, Online Social Networking platforms are growing to be a vital aspect of each individual’s daily life. The availability of the vast amount of information and their open nature attracts the interest of cybercriminals to create malicious bots. Malicious bots in these platforms are automated or semi-automated entities used in nefarious ways while simulating human behavior. Moreover, such bots pose serious cyber threats and security concerns to society and public opinion. They are used to exploit vulnerabilities for illicit benefits such as spamming, fake profiles, spreading inappropriate/false content, click farming, hashtag hijacking, and much more. Cybercriminals and researchers are always engaged in an arms race as new and updated bots are created to thwart ever-evolving detection technologies. This literature review attempts to compile and compare the most recent advancements in Machine Learning-based techniques for the detection and classification of bots on five primary social media platforms namely Facebook, Instagram, LinkedIn, Twitter, and Weibo. We bring forth a concise overview of all the supervised, semi-supervised, and unsupervised methods, along with the details of the datasets provided by the researchers. Additionally, we provide a thorough breakdown of the extracted feature categories. Furthermore, this study also showcases a brief rundown of the challenges and opportunities encountered in this field, along with prospective research directions and promising angles to explore.
Towards an understanding of metaverse banking: a conceptual paper

Purpose This study aims to synthesize the current literature and present a definition of future smart banking services known as “metaverse banking” as well as to discuss its future potential. Design/methodology/approach This paper proposes a suitable definition for metaverse banking that includes the important elements of metaverse banking and sheds light on its impending potential from distinct aspects. Findings Metaverse banking is a banking channel actualized through the integration of metaverse and online banking services, enabled by a mix of numerous advanced technologies, that provides customers with synchronous banking services accompanied with 3D experiences in a virtual world. There is a high likelihood in the future that metaverse banking is able to be marketed intensively yet effectively, and incur progressive demands, as well as progress significantly in terms of development. Practical implications This study assists bank managers in understanding metaverse banking better and simultaneously makes them realize the metaverse banking’ growth opportunity which can be pursued. Originality/value To the best of the authors’ knowledge, this is the first paper to establish a definition for metaverse banking and expound on the upcoming potential of metaverse banking. There is a lack of related literature because this concept is relatively new. This study assists in enriching the concept and providing future research directions.
An improved density peaks clustering algorithm based on natural neighbor with a merging strategy

Density peaks clustering (DPC) is a novel density-based clustering algorithm that identifies center points quickly through a decision graph and assigns corresponding labels to remaining non-center points. Although DPC can identify clusters with any shape, its clustering performance is still restricted by some aspects. Firstly, DPC works poorly on manifold datasets with different densities. Secondly, DPC is sensitive to the cutoff parameter dc. For the sake of addressing these issues and improving the performance of DPC, an improved density peaks clustering algorithm based on natural neighbor with a merging strategy (IDPC-NNMS) is proposed. IDPC-NNMS identifies a natural neighbor set of each data to obtain its local density adaptively, which can effectively eliminate the impact of the cutoff parameter on final results. Then, sub-clusters are formed after selecting as many center points as possible and allocating labels to remaining non-center points. These sub-clusters are merged by a novel merging strategy until the end conditions are satisfied. The performance of IDPC-NNMS is evaluated on both synthetic and real-world datasets, which fully proves the effectiveness and superiority of the proposed IDPC-NNMS algorithm.
Distributed optimal fusion filtering for singular systems with random transmission delays and packet dropout compensations

This paper is concerned with the fusion filtering problem for time-varying singular systems with random transmission delays (RTDs) and packet dropout (PD) compensations. Here, the phenomena of RTDs and PDs are both characterized by Bernoulli distributed random variables with different probabilities. Generally, the current sensor measurement and one-step delayed sensor measurement can be received by filter. When the sensor measurement is lost, based on the strategy of PD compensations, the one-step predictor of current sensor measurement is used as compensator. Then, the new augmented systems with stochastic parameter matrices and correlated noises are introduced based on the measurement compensation model. Utilizing the innovation analysis approach, the local filters (LFs) dependent on probabilities and corresponding estimation error covariance matrices are derived for augmented systems. Moreover, the matrix-weighted distributed fusion filter (DFF) is designed for original singular systems on the basis of the state transformation. Compared with the LFs, it is not difficult to see that the presented DFF has better precision. In the end, some comparison simulation experiments are carried out to verify the effectiveness of the proposed fusion filtering algorithm.
Teachers’ digital technology use after a period of online teaching

Abstract This article reports on a study that investigated primary-school English-language teachers’ use of digital technologies in their in-person classrooms after a period of online teaching necessitated by the COVID-19 pandemic. It also explored the perceived effects the period of online teaching had on the teachers’ practices. Data were collected through in-depth interviews with sixteen experienced Hong Kong primary-school English-language teachers approximately one year after they returned to in-person teaching. The findings suggest that the period of online teaching has led to a greater utilization of technology for teaching. In addition, they suggest digital technologies are now embedded in teachers’ regular teaching practices, with the digital technologies adopted primarily those that enhance existing practices. Common digital technologies include: (1) learning management systems; (2) digital noticeboards; (3) interactive digital worksheets and quizzes; and (4) interactive presentation software. Examples of how teachers use digital technologies, and considerations for their use, are provided.
Markets in crypto-assets regulation: Does it provide legal certainty and increase adoption of crypto-assets?

This study discusses the European Union's proposal for a Regulation on Markets in Crypto-Assets, now subject to formal approval by the European Parliament. The objective is to explore whether it will positively impact the adoption of crypto-assets in the financial sector. The use of crypto-assets is growing. However, some stakeholders in the financial service sector remain skeptical and hesitant to adopt assets that are yet to be defined and have an unclear legal status. This regulatory uncertainty has been identified as the primary reason for the reluctant adoption. The proposed regulation (part of the EU's Digital Finance Strategy) aims to provide this legal certainty for currently unregulated crypto-assets. This study investigates whether or not the proposed regulation can be expected to have the intended effect by reviewing the proposed regulation itself, the opinions and reactions of the various stakeholders, and secondary literature. Findings reveal that such regulation will most likely not accelerate the adoption of crypto-assets in the EU financial services sector, at least not sufficiently or as intended. Some suggestions are made to improve the proposal.
Feature Enhancement Pyramid and Shallow Feature Reconstruction Network for SAR Ship Detection

Recently, convolutional neural network based methods have been studied for ship detection in optical remote sensing images. However, it is challenging to apply them to microwave synthetic aperture radar (SAR) images. First, most of the regions in the inshore scene include scattered spots and noises, which dramatically interfere with ship detection. Besides, SAR ship images contain ship targets of different sizes, especially small ships with dense distribution. Unfortunately, small ships have fewer distinguishing features making it difficult to be detected. In this article, we propose a novel SAR ship detection network called feature enhanced pyramid and shallow feature reconstruction network (FEPS-Net) to solve the above problems. We design a feature enhancement pyramid, which includes a spatial enhancement module to enhance spatial position information and suppress background noise, and the feature alignment module to solve the problem of feature misalignment during feature fusion. Additionally, to solve the problem of small ship detection in SAR ship images, we design a shallow feature reconstruction module to extract semantic information from small ships. The effectiveness of the proposed network for SAR ship detection is demonstrated by experiments on two publicly available datasets: SAR ship detection dataset and high-resolution SAR images dataset. The experimental results show that the proposed FEPS-Net has advantages in SAR ship detection over the current state-of-the-art methods.
Deep Learning-Based Image 3-D Object Detection for Autonomous Driving: Review

An accurate and robust perception system is key to understanding the driving environment of autonomous driving and robots. Autonomous driving needs 3-D information about objects, including the object’s location and pose, to understand the driving environment clearly. A camera sensor is widely used in autonomous driving because of its richness in color and texture, and low price. The major problem with the camera is the lack of 3-D information, which is necessary to understand the 3-D driving environment. In addition, the object’s scale change and occlusion make 3-D object detection more challenging. Many deep learning-based methods, such as depth estimation, have been developed to solve the lack of 3-D information. This survey presents the image 3-D object detection 3-D bounding box encoding techniques and evaluation metrics. The image-based methods are categorized based on the technique used to estimate an image’s depth information, and insights are added to each method. Then, state-of-the-art (SOTA) monocular and stereo camera-based methods are summarized. We also compare the performance of the selected 3-D object detection models and present challenges and future directions in 3-D object detection.
White Blood Cells Classification Using Entropy-Controlled Deep Features Optimization

White blood cells (WBCs) constitute an essential part of the human immune system. The correct identification of WBC subtypes is critical in the diagnosis of leukemia, a kind of blood cancer defined by the aberrant proliferation of malignant leukocytes in the bone marrow. The traditional approach of classifying WBCs, which involves the visual analysis of blood smear images, is labor-intensive and error-prone. Modern approaches based on deep convolutional neural networks provide significant results for this type of image categorization, but have high processing and implementation costs owing to very large feature sets. This paper presents an improved hybrid approach for efficient WBC subtype classification. First, optimum deep features are extracted from enhanced and segmented WBC images using transfer learning on pre-trained deep neural networks, i.e., DenseNet201 and Darknet53. The serially fused feature vector is then filtered using an entropy-controlled marine predator algorithm (ECMPA). This nature-inspired meta-heuristic optimization algorithm selects the most dominant features while discarding the weak ones. The reduced feature vector is classified with multiple baseline classifiers with various kernel settings. The proposed methodology is validated on a public dataset of 5000 synthetic images that correspond to five different subtypes of WBCs. The system achieves an overall average accuracy of 99.9% with more than 95% reduction in the size of the feature vector. The feature selection algorithm also demonstrates better convergence performance as compared to classical meta-heuristic algorithms. The proposed method also demonstrates a comparable performance with several existing works on WBC classification.
Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals

Humans can learn several tasks in succession with minimal mutual interference but perform more poorly when trained on multiple tasks at once. The opposite is true for standard deep neural networks. Here, we propose novel computational constraints for artificial neural networks, inspired by earlier work on gating in the primate prefrontal cortex, that capture the cost of interleaved training and allow the network to learn two tasks in sequence without forgetting. We augment standard stochastic gradient descent with two algorithmic motifs, so-called "sluggish" task units and a Hebbian training step that strengthens connections between task units and hidden units that encode task-relevant information. We found that the "sluggish" units introduce a switch-cost during training, which biases representations under interleaved training towards a joint representation that ignores the contextual cue, while the Hebbian step promotes the formation of a gating scheme from task units to the hidden layer that produces orthogonal representations which are perfectly guarded against interference. Validating the model on previously published human behavioural data revealed that it matches performance of participants who had been trained on blocked or interleaved curricula, and that these performance differences were driven by misestimation of the true category boundary.
Multi objective trust aware task scheduling algorithm in cloud computing using whale optimization

Task Scheduling is an enormous challenge in cloud computing model as to map diverse tasks arises from various sources there should be an efficient scheduling mechanism which provision resources dynamically to users based on their corresponding requests. Ineffective scheduling leads to increase in makespan, energy consumption and violates SLA made between cloud user and service provider thereby quality of service will be degraded and trust on the cloud service provider will be degraded. Trust typically based on quality-of-service parameters such as Availability of virtual resources, Success rate of tasks, Turnaround efficiency of tasks which are included in SLA. In this paper, we designed a Multi objective trust aware scheduler which takes priority of tasks, VMs and schedule tasks to appropriate virtual resources while minimizing makespan, energy consumption. Whale Optimization algorithm used to model our task scheduler. Entire simulation carried out on Cloudsim. Workload used in this simulation is of both fabricated and real-time worklogs captured from HPC2N and NASA. Our proposed approach compared against existing metaheuristic approaches i.e., ACO, GA, PSO approaches. From Simulation results, we observed that there is a significant improvement in makespan, Energy consumption, total running time and trust parameters i.e., Availability, Success rate, Turnaround efficiency.
GARL-Net: Graph Based Adaptive Regularized Learning Deep Network for Breast Cancer Classification

Across the globe, women suffer from breast cancer fatal disease. It is arising surprisingly due to a lack of awareness among them and the inconvenient reach of diagnostic systems. Many computer-aided diagnostic systems have been developed for the detection of cancer. These systems have quite lower performance, so more accurate diagnosis is the need of the time to save the life of human beings. For large and imbalanced image datasets, efficient learning of the network is very important to detect and classify breast cancer more accurately. In this paper, a graph based adaptive regularized learning of deep network (GARL-Net) is proposed for more accurate breast cancer classification. Transfer learning is used for training the backbone network DenseNet121. furthermore, fine tuning of backbone network is followed by the estimated improved loss function. The improved loss function is actually graph based adaptively regularized complement cross entropy loss. The SoftMax cross entropy in itself is not sufficient to classify image samples accurately, so complement entropy technique is incorporated with the cross-entropy loss to overcome the misclassification issue. Further, by adaptive scaling of regularization term with spatial graph Laplacian basis used to adaptively penalize the complement cross entropy loss for improving the learning of the network. The performance of the proposed method is evaluated using BreakHis and BACH 2018 histopathology image datasets and outperforms the existing state-of-the-art methods and achieved 99.00% of precision, 99.40% of recall, 99.20 % of F-1score, and 99.49% of accuracy for binary classification of breast cancer image samples of BreakHis dataset.
An Efficient Trust-Aware Task Scheduling Algorithm in Cloud Computing Using Firefly Optimization

Task scheduling in the cloud computing paradigm poses a challenge for researchers as the workloads that come onto cloud platforms are dynamic and heterogeneous. Therefore, scheduling these heterogeneous tasks to the appropriate virtual resources is a huge challenge. The inappropriate assignment of tasks to virtual resources leads to the degradation of the quality of services and thereby leads to a violation of the SLA metrics, ultimately leading to the degradation of trust in the cloud provider by the cloud user. Therefore, to preserve trust in the cloud provider and to improve the scheduling process in the cloud paradigm, we propose an efficient task scheduling algorithm that considers the priorities of tasks as well as virtual machines, thereby scheduling tasks accurately to appropriate VMs. This scheduling algorithm is modeled using firefly optimization. The workload for this approach is considered by using fabricated datasets with different distributions and the real-time worklogs of HPC2N and NASA were considered. This algorithm was implemented by using a Cloudsim simulation environment and, finally, our proposed approach is compared over the baseline approaches of ACO, PSO, and the GA. The simulation results revealed that our proposed approach has shown a significant impact over the baseline approaches by minimizing the makespan, availability, success rate, and turnaround efficiency.