Reviewerly/exHarmony huggingface dataset:
Ensemble feature selection with adaptive weights

Most of the existing ensemble feature selection algorithms directly assign different feature selection algorithms the same weights or simply treat accuracy as weight and then vote for each feature. However, these research methods either ignored or did not fully consider the fitness of the feature selecttion algorithm to the data set. To deal with this challenge, we propose a new ensemble feature selection approach with adaptive weights. In detail, we use the softmax function to dynamically adjust the weight of the base feature selector according to its fitness to dataset in the k-round training process. We name it ensemble feature selection based on softmax function (EFS-BSF) algorithm. We demonstrate the superiority of the EFS-BSF approach over previous methods through mathematical analysis and experiments on multiple data sets.
A text-driven image style transfer model based on CLIP and SCBAM

To address the issues of low image quality and inadequate detail features encountered in current zero-shot style transfer algorithms, we propose a new text-driven image style transfer model. The model first uses CLIP (Contrastive LanguageImage Pre-Training) model to convey the semantic information of text conditions. In addition, We designed a lightweight network, which can quickly express texture information according to text conditions, minimize the similarity cosine distance between the transferred image and the text conditions by CLIP model, and finally obtain the style transfer image. Furthermore, we introduce dual attention mechanism, identity consistency loss, content and style feature loss to make the translated image more vivid and realistic. Extensive experimental results demonstrate that our approach enables the transfer of multiple styles based on text conditions, achieving a broader, more realistic, and faster style transfer compared to existing methods.
RiskControl: A Bayesian Network-based Tool to Support Risk Management in Software Projects

Researchers have investigated several software project risk management approaches and developed promising techniques and tools for identifying, measuring, and monitoring risks. Among these techniques, Bayesian Networks (BNs) provide quick responses when variables change. However, using these tools requires solid knowledge of BNs because they rely on different platforms, such as AgenaRisk and NETICA, to create probabilistic models. This paper presents RiskControl, a BN-based tool that helps practitioners manage risks. RiskControl simplifies Bayesian theory concepts for the end user, improving the tool’s usability. It also provides mechanisms for identifying risks based on a project’s technical constraints, measuring them, recommending contingency plans, and monitoring these events. Preliminary tests of RiskControl in the industry have produced satisfactory results. We provide examples of its use and evidence that our proposed tool can support practitioners’ decision-making in risk management.
Advancing ultrasound image segmentation with a hybrid framework: integrating convolutional neural networks and active contours

Ultrasound image segmentation is a critical task in computer vision and medical image analysis. Accurate segmentation of key objects in ultrasound images is essential for clinical diagnosis. In this paper, we propose a novel hybrid method that combines convolutional neural networks and active contours for ultrasound image segmentation. The proposed method aims to enhance the accuracy and robustness of ultrasound image segmentation. The CNN model extracts features from the ultrasound images, while the active contour model refines segmentation boundaries. We evaluate the proposed method on a publicly available dataset of thyroid ultrasound images and demonstrate its superior performance compared to stateof-the-art methods. Our results show that the proposed method achieves an average Dice Similarity Coefficient of 0.91, which is significantly better than the 0.87 DSC achieved by the U-Net method. The proposed method can provide more accurate and robust segmentation of key objects in ultrasound images, which can facilitate clinical diagnosis and treatment planning.
Secure Communications of Visual Data Over CF Relays

The security of visual data transmission over unsecured wireless cooperative networks is a current issue of paramount importance. This paper proposes a scheme for the secure communication of images over compress- and-forward relays. The scheme employs keys generated from cellular automata and OpenSSL, as well as an S-box constructed from extended cellular automata. The encrypted images are then compressed using JPEG2000 at the relays before re-transmission. Experimental results demonstrate that the proposed scheme successfully encrypts the images, while compression results in only minor degradation in their quality. The scheme also achieves significant data reduction before transmission. The use of compress-and-forward relays improves the transmission efficiency, while the cellular automata, their extended version, and OpenSSL key ensure security against unauthorized access to the visual content.
Breast Cancer Classification Using Machine and Deep Learning

One of the diseases that causes the highest figure of deaths among the female population is breast cancer. Using Malignant and Benign tumors as the basis of classification we can detect the disease. The diagnosing process is required to differentiate; therefore, the process can be automated to recognize tumors. Various research has been conducted on breast cancer classification by applying different machine and deep learning algorithms to yield maximum efficiency and accuracy. In this paper, the algorithms SVM, Logistic Regression, KNN, Random Forest, Naive Bayes classifier and ANN have been used to classify Breast Cancer using Wisconsin Breast Cancer (Diagnostic) dataset. The performance of these classification algorithms has been compared to analyze which algorithm gives maximum accuracy. It was found that an ANN provided the most accurate results, with an accuracy of 98.7 %. The loss function used was binary cross entropy and the metric for backpropagation is accuracy. Standard batch size of 32 was used. Experimentation revealed that epochs less than or more than 100 gave diminishing results, hence the model was run for 100 epochs.
Anomaly Detection for Multivariate Telemetry Series of Satellite with Improved HVAE

Spacecrafts, as import devices for exploration of outer space, whose performance and health status is mainly reflected by telemetry data. Effective anomaly detection for telemetry data can improve the operational safety and reliability of spacecraft in orbit. While the complex temporal characteristics and spatial correlations of multivariate telemetry sequences bring great challenges for typical methods. In this work, an anomaly detection method based on imporved Hierarchical Variational Auto-Encoder (HAVE) is proposed. Where the HVAE with two random hidden variables is employed to model the normal patterns of multivariate telemetry sequences, each of which learns temporal correlation and variables correlation respectively. Moreover, the Self-Attention mechanism based on the gated recurrent unit neural network is integrated to augment the model's ability to handle long-term series dependencies between telemetry data and control commands. The experiments are performed on public telemetry datasets of SMAP and MSL from NASA spacecrafts. These experimental results verify its effectiveness and applicability compared with other related methods.
Design and implementation of algorithm execution time predictor for remote sensing product workflow

Remote sensing product production is an important task in the field of remote sensing engineering. Compared with the production process of remote sensing products, the implementation process of remote sensing product production is a multi-task serial and highly concurrent process that integrates a large number of remote sensing digital image processing algorithms and various processes. Remote sensing product workflows transform complex computing tasks into processes that can be described, managed, and automated. In the workflow, algorithms are the smallest execution unit of tasks, and their execution time has a significant impact on the performance and efficiency of the entire workflow. Therefore, accurate prediction of algorithm execution time is crucial for optimizing workflow performance. This paper proposes and designs an algorithm execution time predictor for remote sensing product workflows. Firstly, feature extraction algorithms are used to extract features from algorithm execution logs, and the prediction model suitable for this scenario is determined by analyzing the characteristics of operators in remote sensing product workflows. Then, the model is trained using historical data sets, and the test set results are compared with the actual execution time to verify the accuracy and practicality of the model. Experimental results show that the proposed algorithm execution time predictor outperforms the baseline model in terms of prediction accuracy and time efficiency
Hybrid Codebook Optimization Technique for Vector Quantization to Preserve the Quality of the Decompressed Image

Most commonly, an ideal codebook for vector quantization (VQ) based image compression is generated using the Linde Buzo Gray (LBG) method. However, the PSNR values are found to be lower when using the LBG technique. To obtain a near-global optimal codebook many naturally inspired metaheuristic algorithms were used recently, and the search procedure used by them follows the Gaussian probability distribution. Cuckoo search (CS), a metaheuristic optimization method, can be used to optimize the codebook developed by the LBG algorithm. The CS algorithm uses a levy flight distribution function, realized by Mantegna's algorithm, rather than a Gaussian distribution. But the cuckoo search, on the other hand, takes more convergence time. So, to reduce the convergence time, the CS is hybridized with the Bat algorithm in this work. The proposed hybrid optimization technique is called the Hybrid Cuckoo- Bat algorithm (HCUBAT). In this approach, the cuckoo and bat algorithms form the cascaded structure, and the convergence time is balanced by dividing the total available iteration equally among both algorithms for optimization. We have evaluated the efficiency of the proposed methodology for 100 iterations on the 2018 BraTS dataset image and compared its performance to that of the Cuckoo Search algorithm (CSA) alone over 100 iterations. A 5-11% and 0.5-4.55 % improvement in PSNR was achieved using the hybrid technique in comparison with LBG and LBG-CS.
Tor traffic identification based on federated learning using data temporal series

Tor, as an anonymous communication network tool, presents challenges in terms of identification due to its anonymous and multi-encrypted characteristics. The advent of deep learning has provided new solutions for identifying this type of traffic. However, in practical scenarios, sharing large amounts of traffic data is often not feasible. To address this issue, this paper proposes a Tor traffic identification scheme based on federated learning. By processing distributed data on federated learning clients, we can extract bidirectional temporal information from the traffic and perform model training. Utilizing federated learning, we can aggregate model parameters and achieve distributed identification and classification of Tor traffic. Experimental evaluation was conducted using a publicly available dataset to validate the effectiveness of our proposed approach.
Feature distillation with mixup for unsupervised continual learning

We learn feature representations on unlabeled task sequences. In order to make the disparity between unsupervised representational learning and continuous learning narrow, interpolate examples of current and previous tasks to build new examples. On this basis, we suggest a innovative knowledge extraction method which takes into account feature location and distance function of the extraction. We use the position before ReLU as the distillation point and design a new margin ReLU function. This allows for the centralization of useful information in the middle of the network and further performance improvement, minimizing the forgetting of past activities while increasing learning of new ones.
A pixel affinity adaptive expansion weakly supervised skin image segmentation method

Image annotation of medical scenes is expensive, then weak supervision methods have emerged. Therefore, this paper proposes a pixel affinity adaptive expansion model for medical image label generation in skin scenes. The method is divided into two stages. In the first stage, initial pseudo label is generated based on the characteristics of the skin image using the proposed self-adaptation expansion module . In the second stage, the generated initial pseudo label uses the characteristics of the pixel pair to distinguish the foreground background and class boundaries to generate the final label. Finally, the generated tags are used for fully supervised segmentation. Experiments were conducted on the ISIC 2016 and ISIC 2017 datasets, respectively. The MIoU of the proposed method was 63.24% and 50.43% on the two datasets, while the Dice-score was 76.13% and 67.79%, the accuracy rate was 81.28% and 64.79%, and the recall rate was 73.54% and 71.03%, which is higher than most existing weakly supervised semantic segmentation algorithms, and achieved Sota (State of the arts) on the two skin datasets.
A new feature extraction method for short wave signal

Shortwave signal type recognition plays a very important role in the field of non-cooperative communication, but because of the low signal-to-noise ratio of the shortwave channel, it is often very difficult to identify the shortwave signal type. In this paper, the difference between signal and noise in the wavelet transform is utilized, and the method of correlation denoising is adopted to process the wavelet transform sequence. The correlation denoising wavelet and is taken as the signal characteristics so as to effectively reduce the influence of noise on the signal. Through the experimental performance analysis of 6 kinds of 8PSK signals, the correlation denoising wavelet and feature extraction method proposed in this paper can effectively reduce the impact of noise, and still be effective under the condition of low SNR. Finally, by using the features proposed in this paper as the input of the deep learning network, the recognition accuracy of 98.5% can be achieved when the SNR is 6dB.
Attribute-based Encryption with Flexible Revocation for IoV

Attribute-based encryption (ABE) has been used to provide data confidentiality and fine-grained access control in the Internet of Vehicles (IoV). However, the attributes of vehicles in IoV might change frequently due to the movements of vehicles. Thus, the invalid attributes need to be revoked in time and efficiently to ensure the security of the system. In this paper, we propose a data sharing scheme based on ABE for IoV. By using a binary tree and attribute version keys, flexible revocation can be achieved for IoV. Moreover, the ciphertext can be stored on clouds, and the distribution and revocation of attribute keys can be realized by distributed attribute authorities. We performed the security analysis and proved the security of the proposed scheme. The results showed that the proposed scheme has lower average computing overhead in terms of attribute revocations compared with other schemes based on ABE, and can satisfy the performance requirement of data sharing for IoV.
Ensemble Methods for Spatial Data Stream Classification

In this paper, we study the problem of classification of data in a spatial data stream. The classification of spatial objects in a static data set has been well studied. However, classifying objects in a spatial data stream has received very little attention. We propose an iterative ensemble approach for deep learning of a spatial data stream. Using several deep neural networks, our strategy iteratively performs training and testing of the classifier, with the goal of reaching a desired accuracy, and the same accuracy that would be achieved as a classifier that is trained and tested with the entire dataset. An experimental evaluation and comparison of the ensemble approach shows improvement over a previously proposed iterative deep learning strategy.
Residuality and Representation: Toward a Coherent Philosophy of Software Architecture.

Software architecture involves making decisions about the structure of applications in respect to the environment in which software must execute. In the process of making these decisions architects produce representations of both the software and the environment. These representations take the form of diagrams, models, textual descriptions, and sometimes loose conversations. Modern approaches to software architecture, such as agile, or Enterprise Architecture approaches, do not attempt to challenge traditional modes of representation. Residuality Theory states that an architecture can be expressed as a stack of residues, or leftovers, that are the results of stressors, unpredictable events that may arise in the environment. This creates a dynamic representation that expresses temporality, flux, and uncertainty as opposed to the traditional static representations of component structure, patterns, and process diagrams that constitute most architectural approaches. Residuality thus entails a challenging conceptual shift and it is necessary to highlight this in order to avoid confusion in practitioners and to make clear just how stark the differences between residuality and traditional approaches are. The aim of this conceptual article is to make it easier for architects to understand the differences and avoid falling back toward default approaches. This article will describe the three important underlying concepts in residuality that inform this shift– processuality, criticality, and difference.
An Efficient VGG19 Framework for Malaria Detection in Blood Cell Images

Malaria diagnosis by microscopy is a method for identifying malaria using cell pictures. In order to do this, a blood sample must be examined under a microscope to determine whether red blood cells contain the malaria parasite. Computer vision method is used to examine the photos and determine if the malaria parasite species are present in order to automate this procedure utilising cell images. Large datasets of cell pictures are used to train machine learning algorithms to spot patterns and traits that indicate a malaria infection. In this work, we aim to develop a sustainable picture classification model based on exchange learning that can identify malaria using cell pictures. Our VGG19 model was shown to have good classification performance for identifying malaria, with an accuracy rate of more than 90% which shall improve ecosystem management.
Liver and Liver Tumor Segmentation Using Modified Encoder Decoder Network and Find the Tumor Geometry

Segmentation of images is necessary for the diagnosis of diseases to know about the detailed content of an image. Liver and liver tumor segmentation is a difficult process due to the complexity of the organ position. In this paper, liver and liver tumor segmentation is performed using architectures like Pyramid Scene Parsing Network (PSP Net) and Modified Encoder Decoder Network (MED Net) using 3D Image Reconstructed for Comparison of Algorithm Database (3D-IRCADb) which includes Computed Tomography (CT) images of the liver. After segmenting the images, preprocessing techniques like median blur, dilation, and erosion is performed on the segmented images. Finally, the area of contours is calculated for finding the tumor geometry of the liver. Therefore, the results obtain for this work are 93.62% and 94.92% accuracy for PSP Net and MED Net respectively in the case of liver segmentation and 95.57% and 96.42% accuracy respectively in the case of liver tumor segmentation.
Explaining the Unseen: Leveraging XAI to Enhance the Trustworthiness of Black-Box Models in Performance Testing

This paper presents a novel approach to enhance the trustworthiness of black-box and interpretable models in performance testing through the application of Explainable Artificial Intelligence (XAI). The proposed approach utilizes the Shapley Additive Explanation (SHAP) algorithm as a surrogate model, enabling performance analysts to gain insights into the decision-making process of black-box machine learning models. By incorporating SHAP around black-box models, analysts can obtain explanations regarding the pass or fail status of a test, as well as identify the relative significance of performance data to the machine learning models. To evaluate the effectiveness of the proposed approach, load testing experiments were conducted on a real testbed, employing industry-standard benchmarks and manually injected performance bugs. The results demonstrate that the proposed approach significantly enhances the trustworthiness of machine learning models by providing interpretable explanations for their decision-making. Furthermore, the approach showcases its versatility across domains and requires minimal effort to operate, making it highly applicable in various contexts.
Artificial Intelligence in Control of BLDC Motor

This article presents control methods for a three-phase BLDC motor using artificial intelligence. BLDC motor model and the control scheme used in simulation are presented in introductory part. Three control methods were compared in next part of the work, where we used a classic PI regulator compared with Fuzzy controller and neural network controller. Individual types of presented control methods were compared based on established parameters and results analysis of control methods, conclusions were drawn about the quality of individual methods of control.
Criminal Reidentification Using Multiple CCTV Footage

Closed Circuit Televisions also known as CCTV cameras are widely installed in most public places for security purposes. These cameras provide a wealth of video footage that can aid in crime investigation and identifying suspects by law enforcement agencies. The identification of a criminal may be hindered by the limitations of a single CCTV camera depending on its visibility and location. A deep learning-based computer vision system is utilized to perform person reidentification. It takes an image of a suspected lawbreaker and all CCTV videos from nearby areas as input. The process of extracting the features that are unique to each individual,changes in camera viewpoint, illumination, background, size of the target population and the variability of the images and videos is done. The system analyzes the extracted features and compares them to the features of the query image. It identifies the frames in which there is a match between the features, indicating the presence of the criminal.
Design of Real-Time Vision-Based Face Verification and Tracking Via Pan Tilt Camera

Computer vision and autonomous systems are sub-fields of artificial intelligence. Vision-based applications are significantly applied in surveillance, healthcare, and assistance fields. Pan Tilt Camera is assembled and implemented in the proposed system. The face verification stage in Pan Tilt-based face tracking system is processed using One-Shot Encoding models. The study indicates the implementation of two encoding models, Histogram of Oriented Gradient (HOG) and Convolutional Neural Network (CNN) encoding models. The locomotion algorithm is based on a sequence of arithmetical equations. The CNN encoding model performance shows more accurate verification values, whereas HOG encoding model performance shows less accurate verification values. The HOG encoding model-based tracking performance shows highly accurate, streamlined, and rapid locomotion- behavior, whereas the CNN encoding model-based tracking performance shows less accurate, inefficient, and slow locomotion-behavior.