Reviewerly/exHarmony huggingface dataset:
Improved Whale Optimization with Local-Search Method for燜eature燬election

Various feature selection algorithms are usually employed to improve classification models' overall performance.Optimization algorithms typically accompany such algorithms to select the optimal set of features.Among the most currently attractive trends within optimization algorithms are hybrid metaheuristics.The present paper presents two Stages of Local Search models for feature selection based on WOA (Whale Optimization Algorithm) and Great Deluge (GD).GD Algorithm is integrated with the WOA algorithm to improve exploitation by identifying the most promising regions during the search.Another version is employed using the best solution found by the WOA algorithm and exploited by the GD algorithm.In addition, disruptive selection (DS) is employed to select the solutions from the population for local search.DS is chosen to maintain the diversity of the population via enhancing low and high-quality solutions.Fifteen (15) standard benchmark datasets provided by the University of California Irvine (UCI) repository were used in evaluating the proposed approaches' performance.Next, a comparison was made with four population-based algorithms as wrapper feature selection methods from the literature.The 1372 CMC, 2023, vol.75, no.1 proposed techniques have proved their efficiency in enhancing classification accuracy compared to other wrapper methods.Hence, the WOA can search effectively in the feature space and choose the most relevant attributes for classification tasks.
An Early Warning Model of Telecommunication Network Fraud Based on User Portrait

With the frequent occurrence of telecommunications and network fraud crimes in recent years, new frauds have emerged one after another which has caused huge losses to the people.However, due to the lack of an effective preventive mechanism, the police are often in a passive position.Using technologies such as web crawlers, feature engineering, deep learning, and artificial intelligence, this paper proposes a user portrait fraud warning scheme based on Weibo public data.First, we perform preliminary screening and cleaning based on the keyword "defrauded" to obtain valid fraudulent user Identity Documents (IDs).The basic information and account information of these users is user-labeled to achieve the purpose of distinguishing the types of fraud.Secondly, through feature engineering technologies such as avatar recognition, Artificial Intelligence (AI) sentiment analysis, data screening, and follower blogger type analysis, these pictures and texts will be abstracted into user preferences and personality characteristics which integrate multidimensional information to build user portraits.Third, deep neural network training is performed on the cube.80% percent of the data is predicted based on the N-way K-shot problem and used to train the model, and the remaining 20% is used for model accuracy evaluation.Experiments have shown that Few-short learning has higher accuracy compared with Long Short Term Memory (LSTM), Recurrent Neural Networks (RNN) and Convolutional Neural Network (CNN).On this basis, this paper develops a WeChat small program for early warning of telecommunications network fraud based on user portraits.When the user enters some personal information on the front end, the back-end database can perform correlation analysis by itself, so as to match the most likely fraud types and give relevant early warning information.The fraud warning model is highly scaleable.The data of other Applications (APPs) can be extended to further improve the efficiency of anti-fraud which has extremely high public welfare value.
ACO-Inspired Load Balancing Strategy for Cloud-Based Data Centre with燩redictive Machine Learning Approach

Virtual Machines are the core of cloud computing and are utilized to get the benefits of cloud computing.Other essential features include portability, recovery after failure, and, most importantly, creating the core mechanism for load balancing.Several study results have been reported in enhancing loadbalancing systems employing stochastic or biogenetic optimization methods.It examines the underlying issues with load balancing and the limitations of present load balance genetic optimization approaches.They are criticized for using higher-order probability distributions, more complicated solution search spaces, and adding factors to improve decision-making skills.Thus, this paper explores the possibility of summarizing load characteristics.Second, this study offers an improved prediction technique for pheromone level prediction over other typical genetic optimization methods during load balancing.It also uses web-based third-party cloud service providers to test and validate the principles provided in this study.It also reduces VM migrations, time complexity, and service level agreements compared to other parallel standard approaches.
Modeling Stroke Mask for End-to-End Text Erasing

Scene text erasing aims to wipe text regions in scene images with reasonable background. Most previous approaches employ scene text detectors to assist localization of the text regions. However, detected text boxes contain both text strokes and background clutters, and directly in-painting on the whole boxes may remain text artifacts and make regions unnatural. In this paper, we present an end-to-end network that focuses on modeling text stroke masks that provide more accurate locations to compute erased images. The network consists of two stages, i.e., a basic network with stroke generation and a refinement network with stroke awareness. The basic network predicts the text stroke masks and initial erasing results simultaneously. The refinement network receives the masks as supervision to generate natural erased results. Experiments on both synthetic and real-world scene images demonstrate the effectiveness of our framework in producing high quality erasing results.
Federated Feature Concatenate Method for Heterogeneous Computing in Federated Learning

Federated learning is an emerging machine learning technique that enables clients to collaboratively train a deep learning model without uploading raw data to the aggregation server.Each client may be equipped with different computing resources for model training.The client equipped with a lower computing capability requires more time for model training, resulting in a prolonged training time in federated learning.Moreover, it may fail to train the entire model because of the out-of-memory issue.This study aims to tackle these problems and propose the federated feature concatenate (FedFC) method for federated learning considering heterogeneous clients.FedFC leverages the model splitting and feature concatenate for offloading a portion of the training loads from clients to the aggregation server.Each client in FedFC can collaboratively train a model with different cutting layers.Therefore, the specific features learned in the deeper layer of the serverside model are more identical for the data class classification.Accordingly, FedFC can reduce the computation loading for the resource-constrained client and accelerate the convergence time.The performance effectiveness is verified by considering different dataset scenarios, such as data and class imbalance for the participant clients in the experiments.The performance impacts of different cutting layers are evaluated during the model training.The experimental results show that the co-adapted features have a critical impact on the adequate classification of the deep learning model.Overall, FedFC not only shortens the convergence time, but also improves the best accuracy by up to 5.9% and 14.5% when compared to conventional federated learning and splitfed, respectively.In conclusion, the proposed approach is feasible and effective for heterogeneous clients in federated learning.
A Protocol for Evaluating Model Interpretation Methods from Visual Explanations

With the continuous development of Convolutional Neural Networks (CNNs), there is an increasing requirement towards the understanding of the representations they internally encode. The task of studying such encoded representations is referred to as model interpretation. Efforts along this direction, despite being proved efficient, stand with two weaknesses. First, there is low semanticity on the feedback they provide which leads toward subjective visualizations. Second, there is no unified protocol for the quantitative evaluation of interpretation methods which makes the comparison between current and future methods complex.To address these issues, we propose a unified evaluation protocol for the quantitative evaluation of interpretation methods. This is achieved by enhancing existing interpretation methods to be capable of generating visual explanations and then linking these explanations with a semantic label. To achieve this, we introduce the Weighted Average Intersection-over-Union (WAIoU) metric to estimate the coverage rate between explanation heatmaps and semantic annotations. This is complemented with an analysis of several binarization techniques for heatmaps, necessary when measuring coverage. Experiments considering several interpretation methods covering different CNN architectures pre-trained on multiple datasets show the effectiveness of the proposed protocol.
DE-CROP: Data-efficient Certified Robustness for Pretrained Classifiers

Certified defense using randomized smoothing is a popular technique to provide robustness guarantees for deep neural networks against l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> adversarial attacks. Existing works use this technique to provably secure a pretrained non-robust model by training a custom denoiser network on entire training data. However, access to the training set may be restricted to a handful of data samples due to constraints such as high transmission cost and the proprietary nature of the data. Thus, we formulate a novel problem of "how to certify the robustness of pretrained models using only a few training samples". We observe that training the custom denoiser directly using the existing techniques on limited samples yields poor certification. To overcome this, our proposed approach (DE-CROP) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> generates class-boundary and interpolated samples corresponding to each training sample, ensuring high diversity in the feature space of the pretrained classifier. We train the denoiser by maximizing the similarity between the denoised output of the generated sample and the original training sample in the classifier’s logit space. We also perform distribution level matching using domain discriminator and maximum mean discrepancy that yields further benefit. In white box setup, we obtain significant improvements over the baseline on multiple benchmark datasets and also report similar performance under the challenging black box setup.
A Novel Contour Tracing Algorithm for Object Shape Reconstruction Using Parametric Curves

Parametric curves such as Bézier and B-splines, originally developed for the design of automobile bodies, are now also used in image processing and computer vision.For example, reconstructing an object shape in an image, including different translations, scales, and orientations, can be performed using these parametric curves.For this, Bézier and B-spline curves can be generated using a point set that belongs to the outer boundary of the object.The resulting object shape can be used in computer vision fields, such as searching and segmentation methods and training machine learning algorithms.The prerequisite for reconstructing the shape with parametric curves is to obtain sequentially the points in the point set.In this study, a novel algorithm has been developed that sequentially obtains the pixel locations constituting the outer boundary of the object.The proposed algorithm, unlike the methods in the literature, is implemented using a filter containing weights and an outer circle surrounding the object.In a binary format image, the starting point of the tracing is determined using the outer circle, and the next tracing movement and the pixel to be labeled as the boundary point is found by the filter weights.Then, control points that define the curve shape are selected by reducing the number of sequential points.Thus, the Bézier and B-spline curve equations describing the shape are obtained using these points.In addition, different translations, scales, and rotations of the object shape are easily provided by changing the positions of the control points.It has also been shown that the missing part of the object can be completed thanks to the parametric curves.
Optimal Location to Use Solar Energy in an Urban Situation

This study conducted in Lima, Peru, a combination of spatial decision making system and machine learning was utilized to identify potential solar power plant construction sites within the city.Sundial measurements of solar radiation, precipitation, temperature, and altitude were collected for the study.Gene Expression Programming (GEP), which is based on the evolution of intelligent models, and Artificial Neural Networks (ANN) were both utilized in this investigation, and the results obtained from each were compared.Eighty percent of the data was utilized during the training phase, while the remaining twenty percent was utilized during the testing phase.On the basis of the findings, it was determined that the GEP is the most suitable network for predicting the location.The test state's Nash-Sutcliffe efficiency (NSE) was 0.90, and its root-mean-square error (RMSE) was 0.04.Following the generation of the final map based on the results of the GEP model, it was determined that 9.2% of the province's study area is suitable for the construction of photovoltaic solar power plants, while 53.5% is acceptable and 37.3% is unsuitable.The ANN model reveals that only 1.7% of the study area is suitable for the construction of photovoltaic solar power plants, while 66.8% is acceptable and 31.5% is unsuitable.
Towards Robust Rain Removal with Unet++

Image deraining has become a hot topic in the field of computer vision.It is the process of removing rain streaks from an image to reconstruct a high-quality background.This study aims at improving the performance of image rain streak removal and reducing the disruptive effects caused by rain.To better fit the rain removal task, an innovative image deraining method is proposed, where a kernel prediction network with Unet++ is designed and used to filter rainy images, and rainy-day images are used to estimate the pixel-level kernel for rain removal.To minimize the gap between synthetic and real data and improve the performance in real rainy image handling, a loss function and an effective data optimization method are suggested.In contrast with other methods, the loss function consists of Structural Similarity Index loss, edge loss, and L1 loss, and it is adopted to improve performance.The proposed algorithm can improve the Peak Signal-to-Noise ratio by 1.3% when compared to conventional approaches.Experimental results indicate that the proposed method can achieve a better efficiency and preserve more image structure than several classical methods.
TRS Scheduling for Improved QoS Performance in Cloud System

Numerous methods are analysed in detail to improve task scheduling and data security performance in the cloud environment.The methods involve scheduling according to the factors like makespan, waiting time, cost, deadline, and popularity.However, the methods are inappropriate for achieving higher scheduling performance.Regarding data security, existing methods use various encryption schemes but introduce significant service interruption.This article sketches a practical Real-time Application Centric TRS (Throughput-Resource utilization-Success) Scheduling with Data Security (RATRSDS) model by considering all these issues in task scheduling and data security.The method identifies the required resource and their claim time by receiving the service requests.Further, for the list of resources as services, the method computes throughput support (Thrs) according to the number of statements executed and the complete statements of the service.Similarly, the method computes Resource utilization support (Ruts) according to the idle time on any duty cycle and total servicing time.Also, the method computes the value of Success support (Sus) according to the number of completions for the number of allocations.The method estimates the TRS score (Throughput Resource utilization Success) for different resources using all these support measures.According to the value of the TRS score, the services are ranked and scheduled.On the other side, based on the requirement of service requests, the method computes Requirement Support (RS).The selection of service is performed and allocated.Similarly, choosing the route according to the Route Support Measure (RSM) enforced route security.Finally, data security has gets implemented with a service-based encryption technique.The RATRSDS scheme has claimed higher performance in data security and scheduling.
RANCER: Non-Axis Aligned Anisotropic Certification with Randomized Smoothing

As modern networks have been proven to be unprotected from adversarial attacks and are applied in safety-critical applications, defense against them is very crucial. Many works were dedicated to this topic, but randomized smoothing has been recently proven to be an effective approach for the certified defense of deep neural networks and getting robust classifiers. Some prior results were obtained utilizing the techniques of adding extra parameters to extend the limits of the certification regions. In this way, sample-wise optimization was proposed to maximize the certification radius per input. The idea was further extended with the generalized anisotropic counterparts of ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> and ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> certificates which allow achieving larger certified region volume avoiding worst-case certification near potentially larger safe regions. However, anisotropic certification is limited by the aligned axis lacking the freedom to extend in any direction. To mitigate this constraint, in this work, we (i) revisit the anisotropic certification, provide an analysis of its non-axis aligned counterpart and propose its rotation-free extension, (ii) conduct experiments on the CIFAR-10 dataset to report the improved performance.
Fine Gaze Redirection Learning with Gaze Hardness-aware Transformation

The gaze redirection is a task to adjust the gaze of a given face or eye image toward the desired direction and aims to learn the gaze direction of a face image through a neural network-based generator. Considering that the prior arts have learned coarse gaze directions, learning fine gaze directions is very challenging. In addition, explicit discriminative learning of high-dimensional gaze features has not been reported yet. This paper presents solutions to overcome the above limitations. First, we propose the feature-level transformation which provides gaze features corresponding to various gaze directions in the latent feature space. Second, we propose a novel loss function for discriminative learning of gaze features. Specifically, features with insignificant or irrelevant effects on gaze (e.g., head pose and appearance) are set as negative pairs, and important gaze features are set as positive pairs, and then pair-wise similarity learning is performed. As a result, the proposed method showed a redirection error of only 2° for the Gaze-Capture dataset. This is a 10% better performance than a state-of-the-art method, i.e., STED. Additionally, the rationale for why latent features of various attributes should be discriminated is presented through activation visualization. Code is available at https://github.com/san9569/Gaze-Redir-Learning
HOOT: Heavy Occlusions in Object Tracking Benchmark

In this paper, we present HOOT, the Heavy Occlusions in Object Tracking Benchmark, a new visual object tracking dataset aimed towards handling high occlusion scenarios for single-object tracking tasks. The dataset consists of 581 high-quality videos, which have 436K frames densely annotated with rotated bounding boxes for targets spanning 74 object classes. The dataset is geared for development, evaluation and analysis of visual tracking algorithms that are robust to occlusions. It is comprised of videos with high occlusion levels, where the median percentage of occluded frames per-video is 68%. It also provides critical attributes on occlusions, which include defining a taxonomy for occluders, providing occlusion masks for every bounding box, per-frame partial/full occlusion labels and more. HOOT has been compiled to encourage development of new methods targeting occlusion handling in visual tracking, by providing training and test splits with high occlusion levels. This makes HOOT the first densely-annotated, large dataset designed for single-object tracking under severe occlusion. We evaluate 15 state-of-the-art trackers on this new dataset to act as a baseline for future work focusing on occlusions.
Exploiting Instance-based Mixed Sampling via Auxiliary Source Domain Supervision for Domain-adaptive Action Detection

We propose a novel domain adaptive action detection approach and a new adaptation protocol that leverages the recent advancements in image-level unsupervised domain adaptation (UDA) techniques and handle vagaries of instance-level video data. Self-training combined with cross-domain mixed sampling has shown remarkable performance gain in semantic segmentation in UDA (unsupervised domain adaptation) context. Motivated by this fact, we propose an approach for human action detection in videos that transfers knowledge from the source domain (annotated dataset) to the target domain (unannotated dataset) using mixed sampling and pseudo-label-based self-training. The existing UDA techniques follow a Class-Mix algorithm for semantic segmentation. However, simply adopting ClassMix for action detection does not work, mainly because these are two entirely different problems, i.e., pixel-label classification vs. instance-label detection. To tackle this, we propose a novel action instance mixed sampling technique that combines information across domains based on action instances instead of action classes. Moreover, we propose a new UDA training protocol that addresses the long-tail sample distribution and domain shift problem by using supervision from an auxiliary source domain (ASD). For the ASD, we propose a new action detection dataset with dense frame-level annotations. We name our proposed framework as domain-adaptive action instance mixing (DA-AIM). We demonstrate that DA-AIM consistently outperforms prior works on challenging domain adaptation benchmarks. The source code is available at https://github.com/wwwfan628/DA-AIM.