Reviewerly/exHarmony huggingface dataset:
Performance Evaluation of Load-Balancing Algorithms with Different Service Broker Policies for Cloud Computing

Cloud computing has seen a major boom during the past few years. Many people have switched to cloud computing because traditional systems require complex resource distribution and cloud solutions are less expensive. Load balancing (LB) is one of the essential challenges in cloud computing used to balance the workload of cloud services. This research paper presents a performance evaluation of the existing load-balancing algorithms which are particle swarm optimization (PSO), round robin (RR), equally spread current execution (ESCE), and throttled load balancing. This study offers a detailed performance evaluation of various load-balancing algorithms by employing a cloud analyst platform. Efficiency concerning various service broker policy configurations for load-balancing algorithms’ virtual machine load balance was also calculated using metrics such as optimized response time (ORT), data center processing time (DCPT), virtual machine costs, data transfer costs, and total cost for different workloads and user bases. Many of the past papers that were mentioned in the literature worked on round robin and equally spread current execution, and throttled load-balancing algorithms were based on efficiency and response time in virtual machines without recognizing the relation between the task and the virtual machines, and the practical significance of the application. A comparison of specific load-balancing algorithms has been investigated. Different service broker policy (SBP) tests have been conducted to illustrate the load-balancing algorithm capabilities.
Reference-State Error Mitigation: A Strategy for High Accuracy Quantum Computation of Chemistry

Decoherence and gate errors severely limit the capabilities of state-of-the-art quantum computers. This work introduces a strategy for reference-state error mitigation (REM) of quantum chemistry that can be straightforwardly implemented on current and near-term devices. REM can be applied alongside existing mitigation procedures, while requiring minimal postprocessing and only one or no additional measurements. The approach is agnostic to the underlying quantum mechanical ansatz and is designed for the variational quantum eigensolver. Up to two orders-of-magnitude improvement in the computational accuracy of ground state energies of small molecules (H2, HeH+, and LiH) is demonstrated on superconducting quantum hardware. Simulations of noisy circuits with a depth exceeding 1000 two-qubit gates are used to demonstrate the scalability of the method.
Pseudo-labeling Integrating Centers and Samples with Consistent Selection Mechanism for Unsupervised Domain Adaptation

Pseudo-labeling is widely applied to generate pseudo labels of target samples in most Unsupervised Domain Adaptation (UDA) methods. Existing UDA methods designed the pseudo-labeling strategy using the label information from a single source (sample or center information), which ignored the joint effect of the center and sample information on improving the robustness of pseudo-labeling. To address this issue, we propose Pseudo-labeling Integrating Centers and Samples with Consistent Selection mechanism (PICSCS) for UDA. First, PICSCS assigns the label vector with (1 + K) pseudo labels to the target sample. Specifically, the 1 pseudo label represents the center information, which is determined by its nearest class center. The K pseudo labels symbolize adequate sample information, and they are determined by its K nearest source samples. Second, to use the label information from different sources in pseudo-labeling, PICSCS defines the consistent selection mechanism by judging whether (1 + K) pseudo labels in the label vector are the same. Then, label vectors can be identified as consistent or inconsistent, and only target samples with consistent label vectors are adopted in the iteration. Finally, extensive experiments on four benchmark datasets (ImageCLEF-DA, Office-31, Office-Caltech, and Office-Home) show that PICSCS makes the iteration stable, and PICSCS outperforms the state-of-the-art UDA methods.
Fusion Algorithm of the Improved A* Algorithm and Segmented Bézier Curves for the Path Planning of Mobile Robots

In terms of mobile robot path planning, the traditional A* algorithm has the following problems: a long searching time, an excessive number of redundant nodes, and too many path-turning points. As a result, the shortest path obtained from planning may not be the optimal movement route of actual robots, and it will accelerate the hardware loss of robots. To address the aforementioned problems, a fusion algorithm for path planning, combining the improved A* algorithm with segmented second-order Bézier curves, is proposed in this paper. On the one hand, the improved A* algorithm is presented to reduce unnecessary expansion nodes and shorten the search time, which was achieved from three aspects: (1) the traditional 8-neighborhood search strategy was adjusted to 5-neighborhood according to the orientation of the target point relative to the current node; (2) the dynamic weighting factor of the heuristic function was introduced into the evaluation function of the traditional A* algorithm; and (3) the key node extraction strategy was designed to reduce the redundant nodes of the optimal path. On the other hand, the optimal path planned by the improved A* algorithm was smoothed using segmented second-order Bézier curves. The simulation results show that the improved A* algorithm can effectively reduce the search time and redundant nodes and the fusion algorithm can reduce the path curvature and path length to a certain extent, improving path safety.
Risk-Aware Trajectory Sampling for Quadrotor Obstacle Avoidance in Dynamic Environments

Obstacle avoidance of quadrotors in dynamic environments is still a very open problem. Current works commonly leverage traditional static maps to represent static obstacles, and the detection and tracking of moving objects method to model dynamic obstacles separately. The detection module requires pretraining, and the dynamic obstacles can only be modeled with certain shapes, such as cylinders or ellipsoids. This work utilizes the dual-structure particle-based (DSP) dynamic occupancy map to represent the arbitrarily-shaped static obstacles and dynamic obstacles simultaneously, and proposes an efficient risk-aware sampling-based local trajectory planner to realize safe flights. The trajectory is planned by sampling motion primitives generated in the state space. Each motion primitive is divided into two phases: a short-term phase with a strict risk limitation and a relatively long-term phase designed to avoid high-risk regions. The risk is evaluated with the predicted particle-form future occupancy status, considering the time dimension. With an approach of splitting from and merging to an arbitrary global trajectory, the planner can also be used in tasks with preplanned global trajectories. Comparison experiments show that the obstacle avoidance system composed of the DSP map and our planner performs the best in dynamic environments. In real-world tests, our quadrotor reaches a speed of 6 m/s with the motion capture system and 2.5 m/s with everything running on a low-price single-board computer.
CDA-Net: A contrastive deep adversarial model for prostate cancer segmentation in MRI images

Prostate cancer is becoming one of the deadliest cancer in men, and the early diagnosis and detection of cancer can be effective in improving patient survival. Computer-aided diagnosis (CAD) has evolved rapidly in recent years, and more and more computer technologies are now being used for prostate cancer detection. Apparently, automatic and accurate segmentation is the most important step in it. However, the existing prostate cancer segmentation methods still have problems with low accuracy and efficiency, and these deficiencies prevent them from providing adequate results at the pixel level. In this paper, a prostate cancer segmentation network (CDA-Net) is proposed from the perspectives of the global cancer region localization and the local cancer edge recognition. Specifically, we propose a parallel dilated U-Net (dila-UNet) to extract deep features for more accurate localization, as well as design a connectivity mechanism of a generative adversarial network (GAN) and a contrastive learning module for finer edge recognition. Compared with some classic and state-of-the-art (SOTA) segmentation methods, the results show the segmentation performance of the proposed network is superiorly increased by ∼ 1.7%, ∼ 3.8% and ∼ 1.7% on IoU, PA and Dice, respectively, and the 95%hausdorff distance is decreased by ∼ 1.82, in MRI images.
Global and local structure preserving nonnegative subspace clustering

Most subspace clustering methods construct the similarity matrix based on self-expressive property and apply the spectral relaxation on the similarity matrix to get the final clusters. Despite the advantages of this framework, it has two limitations that are easily ignored. Firstly, the original self-expressive model only considers the global structure of data, and the ubiquitous local structure among data is not paid enough attention. Secondly, spectral relaxation is naturally suitable for 2-way clustering tasks, but when dealing with multi-way clustering tasks, the assignment of cluster members becomes indirect and requires additional steps. To overcome these problems, this paper proposes a global and local structure preserving nonnegative subspace clustering method, which learns data similarities and cluster indicators in a mutually enhanced way within a unified framework. Besides, the model is extended to kernel space to strengthen its capability of dealing with nonlinear data structures. For optimizing the objective function of the method, multiplicative updating rules based on nonnegative Lagrangian relaxation are developed, and the convergence is guaranteed in theory. Abundant experiments have shown that the proposed model is better than many advanced clustering methods in most cases.
Improving Classification Performance in Credit Card Fraud Detection by Using New Data Augmentation

In many industrialized and developing nations, credit cards are one of the most widely used methods of payment for online transactions. Credit card invention has streamlined, facilitated, and enhanced internet transactions. It has, however, also given criminals more opportunities to commit fraud, which has raised the rate of fraud. Credit card fraud has a concerning global impact; many businesses and ordinary users have lost millions of US dollars as a result. Since there is a large number of transactions, many businesses and organizations rely heavily on applying machine learning techniques to automatically classify or identify fraudulent transactions. As the performance of machine learning techniques greatly depends on the quality of the training data, the imbalance in the data is not a trivial issue. In general, only a small percentage of fraudulent transactions are presented in the data. This greatly affects the performance of machine learning classifiers. In order to deal with the rarity of fraudulent occurrences, this paper investigates a variety of data augmentation techniques to address the imbalanced data problem and introduces a new data augmentation model, K-CGAN, for credit card fraud detection. A number of the main classification techniques are then used to evaluate the performance of the augmentation techniques. These results show that B-SMOTE, K-CGAN, and SMOTE have the highest Precision and Recall compared with other augmentation methods. Among those, K-CGAN has the highest F1 Score and Accuracy.
K-Anonymity Privacy Protection Algorithm for Multi-Dimensional Data against Skewness and Similarity Attacks

Currently, a significant focus has been established on the privacy protection of multi-dimensional data publishing in various application scenarios, such as scientific research and policy-making. The K-anonymity mechanism based on clustering is the main method of shared-data desensitization, but it will cause problems of inconsistent clustering results and low clustering accuracy. It also cannot defend against several common attacks, such as skewness and similarity attacks at the same time. To defend against these attacks, we propose a K-anonymity privacy protection algorithm for multi-dimensional data against skewness and similarity attacks (KAPP) combined with t-closeness. Firstly, we propose a multi-dimensional sensitive data clustering algorithm based on improved African vultures optimization. More specifically, we improve the initialization, fitness calculation, and solution update strategy of the clustering center. The improved African vultures optimization can provide the optimal solution with various dimensions and achieve highly accurate clustering of the multi-dimensional dataset based on multiple sensitive attributes. It ensures that multi-dimensional data of different clusters are different in sensitive data. After the dataset anonymization, similar sensitive data of the same equivalence class will become less, and it eventually does not satisfy the premise of being theft by skewness and similarity attacks. We also propose an equivalence class partition method based on the sensitive data distribution difference value measurement and t-closeness. Namely, we calculate the sensitive data distribution’s difference value of each equivalence class and then combine the equivalence classes with larger difference values. Each equivalence class satisfies t-closeness. This method can ensure that multi-dimensional data of the same equivalence class are different in multiple sensitive attributes, and thus can effectively defend against skewness and similarity attacks. Moreover, we generalize sensitive attributes with significant weight and all quasi-identifier attributes to achieve anonymous protection of the dataset. The experimental results show that KAPP improves clustering accuracy, diversity, and anonymity compared to other similar methods under skewness and similarity attacks.
U-DARTS: Uniform-space differentiable architecture search

Differentiable architecture search (DARTS) is an effective neural architecture search algorithm based on gradient descent. However, there are two limitations in DARTS. First, a small proxy search space is exploited due to memory and computational resource constraints. Second, too many simple operations are preferred, which leads to the network deterioration. In this paper, we propose a uniform-space differentiable architecture search, named U-DARTS, to address the above problems. In one hand, the search space is redesigned to enable the search and evaluation of the architectures in the same space, and the new search space couples with a sampling and parameter sharing strategy to reduce resource overheads. This means that various cell structures are explored directly rather than cells with same structure are stacked to compose the network. In another hand, a regularization method, which takes the depth and the complexity of the operations into account, is proposed to prevent network deterioration. Our experiments show that U-DARTS is able to find excellent architectures. Specifically, we achieve an error rate of 2.59% with 3.3M parameters on CIFAR-10. The code is released in https://github.com/Sun-Shiqi/U-DARTS.
Impact of University Teachers’ Technological Training on Educational Inclusion and Quality of Life of Students with Disabilities: A Systematic Review

Higher education institutions moving towards the inclusive education model have to offer quality education for all students. In this sense, the use of technologies favours not only the education of people with disabilities, but also their quality of life. However, these people may encounter real problems of access to technologies due, among other factors, to the lack of teacher training, causing a public health problem. In this line, our study includes a systematic review of the literature that aims to know the impact of the technological training of university teachers in relation to educational inclusion and the improvement of the quality of life of students with disabilities. To this end, a search of the literature published in the last decade was carried out in six databases (Dialnet, Google Scholar, Scopus, Web of Science, ERIC, and SciELO), selecting 14 articles out of 1204 initial ones. The studies were analysed following the PRISMA methodology. The main findings are the scarcity of research literature on the digital competence of university teachers and its impact on the educational inclusion and quality of life of students with disabilities. Likewise, there is a clear lack of knowledge about the use of digital tools and a lack of knowledge about the different disabilities that teachers may encounter in higher education classrooms.
Knowledge-Preserving continual person re-identification using Graph Attention Network

Person re-identification (ReID), considered as a sub-problem of image retrieval, is critical for intelligent security. The general practice is to train a deep model on images from a particular scenario (also known as a domain) and perform retrieval tests on images from the same domain. Thus, the model has to be retrained to ensure good performance on unseen domains. Unfortunately, retraining will introduce the so called catastrophic forgetting problem existing in deep learning models. To address this problem, we propose a Continual person re-identification model via a Knowledge-Preserving (CKP) mechanism. The proposed model is able to accumulate knowledge from continuously changing scenarios. The knowledge is updated via a graph attention network from the human cognitive-inspired perspective as the scenario changes. The accumulated knowledge is used to guide the learning process of the proposed model on image samples from new-coming domains. We finally evaluate and compare CKP with fine-tuning, continual learning in image classification and person re-identification, and joint training. Experiments on representative benchmark datasets (Market1501, DukeMTMC, CUHK03, CUHK-SYSU, and MSMT17, which arrive in different orders) demonstrate the advantages of the proposed model in preventing forgetting, and experiments on other benchmark datasets (GRID, SenseReID, CUHK01, CUHK02, VIPER, iLIDS, and PRID, which are not available during training) demonstrate the generalization ability of the proposed model. The CKP outperforms the best comparative model by 0.58% and 0.65% on seen domains (datasets available during training), and by 0.95% and 1.02% on never seen domains (datasets not available during training) in terms of mAP and Rank1, respectively. Arrival order of the training datasets, guidance of accumulated knowledge for learning new knowledge and parameter settings are also discussed.
Diversity feature constraint based on heterogeneous data for unsupervised person re-identification

Person re-identification (ReID) based on heterogeneous data aims to search for the same pedestrian from different modalities. The existing unsupervised heterogeneous ReID method overly relies on pseudo labels and ignores the inter-image feature relationship. In the paper, we propose a novel Diversity Feature Constraint (DFC) method to simultaneously consider the clustering-level and instance-level feature relationship for unsupervised heterogeneous ReID. On the one hand, we employ the clustering algorithm to produce pseudo labels for heterogeneous images. Then, the clustering-level constraint is designed to optimize the model. On the other hand, considering that the clustering algorithm may generate some noise, we propose the complementary intra-modality instance-level constraint to correlate any two intra-modality images. Meanwhile, for eliminating the inter-modality discrepancy, the inter-modality instance-level constraint is developed to decrease the large inter-modality gap. We construct the potential feature relationship between heterogeneous images to constrain the feature distribution. By experiments, we prove that over-reliance on pseudo labels generates limited performance. Exploring inter-image potential relationships is an important way to solve the unsupervised problem. Extensive results demonstrate that DFC achieves superior performance that outperforms other methods by a large margin, improving 15.23% and 9.37% at rank-1 and mAP indexes compared with the clustering method on SYSU-MM01.
MOE/RF: A Novel Phishing Detection Model Based on Revised Multiobjective Evolution Optimization Algorithm and Random Forest

To effectively boost computer usage, machine learning models are used in several phishing detection systems to classify enormous phishing datasets. Based on phishing patterns, researchers prefer to extract a considerable number of features to improve phishing detection performance. However, redundant and useless features in the feature set degrade the performance of the underlying classification models. In addition, several existing phishing detection models mainly focus on detection accuracy and overlook recall rates. However, in phishing detection, it is more harmful to falsely detect a phishing website as a legitimate website than it is to detect a legitimate website as a phishing website. This study proposes a novel phishing detection model, multi-objective evolution/random forest (MOE/RF), which is based on the revised multi-objective evolution optimization algorithm (MOE) and random forest (RF). The MOE/RF model uses accuracy as the detection target and minimizes the probability of false detection of phishing sites. In addition, two new strategies, the symmetric uncertainty-based population initialization and the population state-based adaptive environmental selection, are proposed to improve the performance of the MOE. Experimental results on testing five different phishing datasets demonstrated that the MOE/RF performs superior to several existing methods.
Image Denoising Algorithm Based on Gradient Domain Guided Filtering and NSST

Traditional image denoising methods, which do not depend on data training, have good interpretability. However, traditional image denoising methods hardly achieve the denoising effect of deep learning methods. Based on traditional image processing techniques, this paper proposes a new hybrid image denoising model. The block-batching and 3-D filtering (BM3D) algorithm is used to obtain the first denoised image. The weighted kernel norm minimization (WNNM) and non-subsampled shearlet transform (NSST) algorithms are successively adopted to get the second denoised image. By the gradient domain guided filtering, the texture information of the first denoised image is extracted to enhance the details of the second denoised image. Specially, we propose the adaptive iterative NSST algorithm based on the improved soft thresholding, in order to solve the problems about the discontinuity of the hard thresholding and the constant deviation of the soft thresholding. Our approach can not only attenuate excessive smoothing, but also restore the natural appearance of the image. Experiments are conducted to demonstrate that our proposed method enjoys PSNR and SSIM performance gains over several deep learning denoising methods.