Reviewerly/exHarmony huggingface dataset:
Analysis of S-Box Based on Image Encryption Application Using Complex Fuzzy Credibility Frank Aggregation Operators

This article is about a criterion based on credibility complex fuzzy set (CCFS) to study the prevailing substitution boxes (S-box) and learn their properties to find out their suitability in image encryption applications. Also these criterion has its own properties which is discussed in detailed and on the basis of these properties we have to find the best optimal results and decide the suitability of an S-box to image encryption applications. S-box is the only components which produces the confusion in the every block cipher in the formation of image encryption. So, for this first we have to convert the matrix having colour image using the nonlinear components and also using the proposed algebraic structure of credibility complex fuzzy set to find the best S-box for image encryption based on its criterion. The analyses show that the readings of GRAY S-box is very good for image data. Contributions: As from literaure reveiw a FS contained one MD and IFS conatined two degrees which is MD and NMD. As these generalization of crisp set there is no detailed about the degree of accuarcy (credibility). So, here in this paper we have used the CFCS information which has MD and degree of credibility (accuracy) as well as we have develop a series of aggregation operators which is based on the Frank norms. And also we have discussed CoCoSo method under CFCS information.
Sampling and Re-Weighting: Towards Diverse Frame Aware Unsupervised Video Person Re-Identification

Video person re-identification (re-ID) methods extract richer features from video tracklets than image-based ones and have received growing attention. However, existing supervised methods require numerous cross-camera identity labels, which is impractical for large-scale data. Although clustering-based unsupervised methods have been exploited to obtain pseudo labels and train the models iteratively for video person re-ID, they remain in their infancy due to the diversity of person images and uncertainty in the image quality of video tracklets. In this work, we employ two strategies of <u>S</u>ampling and <u>R</u>e-weighting for <u>C</u>lustering (SRC) to obtain robust and discriminative person feature representations. This method considers the influence of two kinds of frames in the tracklet: 1)&#x00A0;Detection errors or heavy occlusions generate noisy frames in the tracklet. These tracklets with noisy frames may be assigned with unreliable annotations during clustering. 2)&#x00A0;Different frames are identified by the model with varying degrees of difficulty, caused by pose changes or partial occlusions. We call them hard frames, which are hard to identify but informative. To alleviate these problems, we propose a dynamic noise trimming module and diverse frame re-weighting module for sampling and re-weighting. The dynamic noise trimming module strengthens the dependability of the tracklet representation by removing noisy frames to enhance the clustering accuracy. The diverse frame re-weighting module focuses on training hard frames to enhance the learning of rich information from tracklet. Experiments on three video datasets, <i>i.e.</i> DukeMTMC-VideoReID, MARS and PRID2011, demonstrate the effectiveness of the proposed SRC under the unsupervised re-ID setting.
SRCMIMM: the software requirements change management and implementation maturity model in the domain of global software development industry

Abstract The software industry has widely adopted global software development (GSD) to gain economic benefits. Organizations that engage in GSD face various challenges, the majority being associated with requirements change management (RCM). The key motive of this study is to develop a requirement change management and implementation maturity model (SRCMIMM) for the GSD industry that could help the practitioners to assess and manage their RCM activities. A systematic literature review and questionnaire survey approach are used to identify and validate the critical success factors (CSFs), critical challenges (CCHs), and the related best practices of the RCM process. The investigated CSFs and CCHs are classified into five maturity levels based on the concepts of the existing maturity models in other domains, practitioners’ feedback, and academic research. Every maturity level comprises different CSFs and CCHs that can help assess and manage a firm's RCM capability. To evaluate the effectiveness of the proposed model, four case studies are conducted in different GSD firms. The SRCMIMM has been developed to assist GSD organizations in improving their RCM process in efficiency and effectiveness.
A Reference Model for Cyber Threat Intelligence (CTI) Systems

Cyber Threat Intelligence (CTI) is a new but promising field of information security, with many organizations investing in the development of proper tools and services and the integration of CTI related information. However, as a new field, there is a lack of a conceptual framework with corresponding definitions. This paper discusses CTI complexity factors, proposes a set of definitions of the CTI key concepts and an eight-layer CTI Reference Model as a base for CTI systems design. In addition, the proposed reference model is validated by applying it to three case studies, producing the respective CTI Reference Architectures.
Locally generalized preserving projection and flexible grey wolf optimizer-based ELM for fault diagnosis of rolling bearing

It is a challenge for bearing fault diagnosis to effectively reduce the dimension of high dimensional data and improve the accuracy of fault identification. To address this issue, a novel fault diagnosis method based on locally generalized preserving projection (LGPP) and flexible grey wolf optimizer-based extreme learning machine (FGWO-ELM) is proposed in this paper. Firstly, the high dimensional features are obtained from the time–frequency domain of the signal. Secondly, a novel LGPP algorithm is proposed to reduce the dimension of high dimensional features more effectively. The algorithm uses a generalized discriminant matrix to calculate the degree similarity between data, rather than a simple Euclidean distance, so as to obtain sensitive features with better discrimination. Thirdly, a FGWO algorithm is proposed and used to optimize the parameters of ELM to improve the fault recognition rate. Finally, the effectiveness of the proposed fault diagnosis method is verified by two bearing experiments. Experimental results show that compared with other methods, the proposed method not only has the better dimension reduction ability, but also has higher diagnostic accuracy.
Federated learning‐based trajectory prediction model with privacy preserving for intelligent vehicle

The existing trajectory prediction is mainly for specific road sections, which is poor to adapt to complex and changing traffic scenarios. Meanwhile, decentralized trajectory data is hard to be fully utilized in the data silo environment. To solve data silos in the intelligent vehicle industry, introducing federated learning methods in vehicular edge computing has attracted extensive attention. But traditional federated learning still has the potential to suffer from the mining of training data in the case of model privacy leakage. In this paper, a vehicle trajectory prediction method based on federated learning and homomorphic encryption has been presented, which adopts a three-layer architecture with a vehicle cluster, edge computing server, and cloud core network. Compared with traditional centralized deep learning methods, this approach enables joint modeling of multiple parties to improve the model's generalization performance. We used a proxy re-encryption algorithm to implement key distribution and also designed an encrypted federated network algorithm FAHEFL, which uses FAHE1 homomorphic encryption to protect the privacy of the model in parameter transmission. Each local model includes a driving behavior recognition module and trajectory output module. The driving behavior recognition module uses a 1D convolutional neural network to recognize the driving behavior, then input recognition results and historical trajectories to the trajectory output module, which uses LSTM neural network to output predicted trajectories. The experiment results show that the model built with FAHEFL has no more 1% error in the driving behavior recognition module than concentrated learning, while the minimum mean square error of the trajectory prediction module increased by only 2.5%. This article also discusses the performance between FAHEFL and well-known cryptographic federation network algorithms.
Blockchain technology with its application in medical and healthcare systems: A survey

With the advent of the 21st century, healthcare systems all around the world are facing challenges at an unprecedented scale. The recent covid-19 outbreak is a glaring example of such challenges. After facing such situations anyone can conclude that there is a need to change the way our current health system works and the recent blockchain technology emerged as a way to bring out this change. From universal health blueprint to medical supply chain management and connecting vetted suppliers, blockchain is making an impact. This paper introduces the concept of blockchain along with its applications with the main focus on healthcare and medical systems.
Evolutionary constrained optimization with hybrid constraint-handling technique

In constrained optimization evolutionary algorithms (COEAs), constraint-handling technique is used to balance the objective function and constraints, but how to achieve this balance is a very important problem. We found that the information of the population during the evolution process can reflect the current situation of the population, whether the population are inside the feasible region (feasible situation), or are near the boundary of the feasible region (semi-feasible situation), or far away from the feasible region (infeasible situation). Therefore, corresponding constraint-handling method are designed according to the information of each situation—Hybrid Constraint-handling Technique (HCT). The information of the population evolution process is used by HCT to maintain the objective function and constraints balance, and combines the evolutionary algorithm and HCT to propose ECO-HCT to solve COPs. Meanwhile, in infeasible situation, an elite replacement strategy is proposed to help the population accumulate experience. In addition, a criterion for judging that the population falls into the local optimum in the infeasible region and a simple restart mechanism are designed. They can help the population jump out of the local optimum in the infeasible region and effectively improve the algorithm’s ability to solve complex COPs. The 24 constraint test functions from IEEE CEC2006, the 28 constraint test functions from IEEE CEC2017, and three constrained engineering design problems are used to verify the effectiveness and efficiency of the proposed ECO-HCT. Experimental results show that ECO-HCT has very competitive performance compared with other advanced methods.
Advanced artificial agents intervene in the provision of reward

Abstract We analyze the expected behavior of an advanced artificial agent with a learned goal planning in an unknown environment. Given a few assumptions, we argue that it will encounter a fundamental ambiguity in the data about its goal. For example, if we provide a large reward to indicate that something about the world is satisfactory to us, it may hypothesize that what satisfied us was the sending of the reward itself; no observation can refute that. Then we argue that this ambiguity will lead it to intervene in whatever protocol we set up to provide data for the agent about its goal. We discuss an analogous failure mode of approximate solutions to assistance games. Finally, we briefly review some recent approaches that may avoid this problem.
Decentralised Federated Learning for Hospital Networks With Application to COVID-19 Detection

Federated Learning (FL) is a distributed machine learning technique which enables local learning of global machine learning models without the need of exchanging data. The original FL algorithm, Federated Averaging (FedAvg), is extended in this work by means of consensus theory. Differently from standard FL algorithms, the resulting one, named FedLCon, does not need a coordinating server, which represents a single failure point and needs to be trusted by all the clients. Furthermore, the consensus paradigm is also applied to the Adaptive Federated Learning (AdaFed) algorithm, which extends FedAvg with an adaptive model averaging procedure. Performance comparison tests are performed over a real-world COVID-19 detection scenario.
Lightweight unmanned aerial vehicle video object detection based on spatial‐temporal correlation

Summary Intelligent unmanned aerial vehicles (UAVs) are drawing more and more attention from industry to academia. UAV navigation plays an important role in the cooperative scenario where multiple UAVs are deployed, while image data that capture the information of the UAV area are often used as input for UAV navigation. Deep learning is a common and powerful technique for UAV image processing, but a complex model generated by deep learning technique is hardly suitable for the limited computing capacity of edge computing devices such as UAVs. Therefore, this paper designs an efficient deep learning model on UAVs to fit the restriction of low computational powers and low power consumption. Traditional UAV object detection methods mostly use static images as the basis for object recognition, or collect images for offline detection. Our method combines the existing fast single‐frame detection methods with the spatial‐temporal relationship of video sequences, to build an efficient end‐to‐end model. In addition, the convolutional LSTM module is used to propagate the temporal context of the video frame sequences. Based on the temporal context, we propose a module for calculating spatial correlation. At the same time, we establish our experimental dataset in our real application and conduct the experiment, which shows that the proposed method reduces the size of models and meanwhile maintains the detection rate. Compared with the existing static images approaches, our method is faster and more accurate. Inference speeds of nearly 20fps can be achieved while performing real‐time tasks.
An accurate stereo matching method based on color segments and edges

Stereo matching algorithms of binocular vision suffer from low accuracy when dealing with natural scenes (such as industrial robot scenes). Biological vision is sensitive to object edges; it divides objects by their edges, and then perceives their distances. Similar to the biological eye mechanism, this study proposes a matching algorithm that combines segment- and edge-matching to obtain the disparity. In segment matching, pixel strings from the same row of the left and right images are divided into pixel segments, whose colors and lengths are used as clues to determine several types of matching pixel segment pairs according to non-crossing mapping. The analysis of the spatial state yields several types of stimulus bars. Disparities can be obtained from the relation between pixel segment pairs and stimulus bars. In edge matching, the DTW (Dynamic Time Warping) algorithm and the gradient are used to determine the initial edge pixel matching results. The remaining edge point disparity is obtained by fitting a fill to the existing edge point disparity. Finally, segment and edge matching results are combined to check and fill and post-processing. This new matching method transforms pixel matching to pixel segment matching and edge matching, which can reduces the time complexity. The algorithm can be implemented in an industrial robot environment for high-precision needle threading guidance, which neither traditional binocular matching nor deep learning matching algorithms can do.
An efficient R-Transformer network with dual encoders for brain glioma segmentation in MR images

Gliomas are the most prevalent and destructive forms of primary brain tumors with a high mortality rate. Magnetic resonance imaging is extensively employed in the examination of gliomas. Segmenting brain glioma on magnetic resonance images has significant clinical value. However, the blur boundary of gliomas, variability in the shape, location, and size make segmentation extremely challenging. In this paper, we propose a new method to segment gliomas from three-dimensional brain magnetic resonance images accurately. We propose an efficient R-Transformer network with dual encoders (ERTN) to achieve precise segmentation by innovatively combining R-Transformer with U-Net. Specifically, ERTN constructs a feature branch and a patch branch, capturing complex semantic features and global context information. Moreover, features generated from the feature branch and patch branch are up-sampled and combined with low- and high-resolution CNN features in the decoder to enable precise localization. At last, ERTN employs the ranking attention mechanism in Transformer (R-Transformer), which helps the model focus on helpful information to improve training efficiency and reduce computation cost. Experiments on the 2017 BRATS dataset prove that ERTN achieves satisfactory performance, with a Dice similarity coefficient of 83.20%, 77.93%, and 72.59% on the whole tumor, tumor core, and enhanced tumor segmentation. For the Hausdorff distance index, we obtained the scores of 5.30, 4.60, and 5.50 for the whole tumor, tumor core, and enhanced tumor, respectively. Our results suggest that ERTN improves the segmentation accuracy and reduces computation cost, which performs better than the existing convolution- and transformer-based methods.
The experiences of 33 national COVID-19 dashboard teams during the first year of the pandemic in the World Health Organization European Region: A qualitative study

Governments across the World Health Organization (WHO) European Region have prioritised dashboards for reporting COVID-19 data. The ubiquitous use of dashboards for public reporting is a novel phenomenon.This study explores the development of COVID-19 dashboards during the first year of the pandemic and identifies common barriers, enablers and lessons from the experiences of teams responsible for their development.We applied multiple methods to identify and recruit COVID-19 dashboard teams, using a purposive, quota sampling approach. Semi-structured group interviews were conducted from April to June 2021. Using elaborative coding and thematic analysis, we derived descriptive and explanatory themes from the interview data. A validation workshop was held with study participants in June 2021.Eighty informants participated, representing 33 national COVID-19 dashboard teams across the WHO European Region. Most dashboards were launched swiftly during the first months of the pandemic, February to May 2020. The urgency, intense workload, limited human resources, data and privacy constraints and public scrutiny were common challenges in the initial development stage. Themes related to barriers or enablers were identified, pertaining to the pre-pandemic context, pandemic itself, people and processes and software, data and users. Lessons emerged around the themes of simplicity, trust, partnership, software and data and change.COVID-19 dashboards were developed in a learning-by-doing approach. The experiences of teams reveal that initial underpreparedness was offset by high-level political endorsement, the professionalism of teams, accelerated data improvements and immediate support with commercial software solutions. To leverage the full potential of dashboards for health data reporting, investments are needed at the team, national and pan-European levels.
Intelligent Facemask Coverage Detector in a World of Chaos

The recent outbreak of COVID-19 around the world has caused a global health catastrophe along with economic consequences. As per the World Health Organization (WHO), this devastating crisis can be minimized and controlled if humans wear facemasks in public; however, the prevention of spreading COVID-19 can only be possible only if they are worn properly, covering both the nose and mouth. Nonetheless, in public places or in chaos, a manual check of persons wearing the masks properly or not is a hectic job and can cause panic. For such conditions, an automatic mask-wearing system is desired. Therefore, this study analyzed several deep learning pre-trained networks and classical machine learning algorithms that can automatically detect whether the person wears the facemask or not. For this, 40,000 images are utilized to train and test 9 different models, namely, InceptionV3, EfficientNetB0, EfficientNetB2, DenseNet201, ResNet152, VGG19, convolutional neural network (CNN), support vector machine (SVM), and random forest (RF), to recognize facemasks in images. Besides just detecting the mask, the trained models also detect whether the person is wearing the mask properly (covering nose and mouth), partially (mouth only), or wearing it inappropriately (not covering nose and mouth). Experimental work reveals that InceptionV3 and EfficientNetB2 outperformed all other methods by attaining an overall accuracy of around 98.40% and a precision, recall, and F1-score of 98.30%.
A chaos-based image compression and encryption scheme using fractal coding and adaptive-thresholding sparsification

Abstract With the wide application of digital images in the network, the problems of information security and transmission efficiency need to be solved urgently. Fractal encoding as an efficient compression method, its security is not guaranteed. To solve these problems, this paper proposes a new image compression and encryption scheme based on chaotic map and frequency division joint compression coding, which uses the combination of fractal encoding and adaptive-thresholding sparsification to compress images. Arnold scrambling and chaos-based plane scrambling diffusion are embedded in two compression processes respectively. In addition, an efficient encryption algorithm is designed based on the coupling-enhanced chaotic map, which encrypt the compressed data as a whole through simultaneous scrambling of parent blocks and sub-blocks and zigzag round-trip diffusion. The simulation results demonstrate that the proposed image compression and encryption scheme is suitable for gray and color images, and can achieve higher compression ratio and improve the efficiency of fractal compression. The designed encryption algorithm is reliable, and more efficient than several advanced image encryption schemes.