Reviewerly/exHarmony huggingface dataset:
Pose Estimation of Yoga Poses using ML Techniques

POSE ESTIMATION is a technique to identify joints in a human body from an image or video given as input to a computer. Pose estimation can be performed using Machine Learning (ML) techniques and Deep Learning techniques. Lately, it has been receiving lots of attention in the fields of Human Sensing and Artificial Intelligence. The main aim of pose estimation is to predict the poses of humans by locating key points like elbows, knees, wrists etc.In this work, we have proposed a model which uses Mediapipe, an ML framework, to obtain key point coordinates and ML algorithms like SVM, Gaussian Naive Bayes, Random Forest, Gradient Boost and K Neighbours classifier, which are compared and used to predict Yoga poses. Yoga is practised by people of all ages alike these days to fight issues caused both physically and mentally, thus improving the overall quality of life. Especially since the rise of the COVID-19 pandemic, the number of people practising yoga has only been increasing. In the model, human joint coordinates obtained are used as features. The model with the best accuracy and f score (MediaPipe+ SVM) is chosen for the final work.The yoga poses we used are Plank, Warrior 2, Downdog, Goddess, Tree and Cobra. On implementing the work, a real-time video feed from the webcam of the user’s system is obtained, and pose estimation and classification of the yoga pose are done. Unlike in most current systems, suggestive measures to correct the yoga posture are also displayed in real-time alongside the webcam display of the person performing yoga along with some other basic pose information.
Image Sterilization through Adaptive Noise Blending in Integer Wavelet Transformation

Detecting steganography can be more resource-consuming than corrupting the stego-data concealed inside a cover media. Based on this fact, the current work proposes an Integer Wavelet Transformation (IWT) based image sterilization technique that corrupts text based secret data hidden inside image cover. The technique infuses random noise into the IWT sub-bands of a given cover image in such way that the image quality and visual perceptibility is maintained beyond the thresholds of Human Visual System (HVS). According to the principle of wavelet decomposition, the noise susceptibility of the IWT sub-bands differ to an wide extent. Inducing same amount of noise in all sub-bands can lead to visible degradation of the sterilization output. To mitigate this issue, the proposed work employs adaptive noise injection depending on the sub-bands being attacked. Partial bit-flipping noise is used for the most noise sensitive approximation sub-band. Moderate impact noise is infused into vertical and horizontal detail bands. The most noise susceptible diagonal detail band is corrupted with high impact noise blending. The proposed method, when applied on stegograms generated by seven popular steganography methods, is able to achieve up to 88.61% stego-clean images with average PSNR of 46.08 dB and average SSIM of 0.99.
Spatial location constraint prototype loss for open set recognition

One of the challenges in pattern recognition is open set recognition. Compared with closed set recognition, open set recognition needs to reduce not only the empirical risk, but also the open space risk, and how to reduce the open space risk is the key of open set recognition. Compared with the previous work, this paper analyzes the distribution rules of the known and unknown features, which is highly related to the open space risk. Then, this paper proposes the matching theory to explain the origin of the open space risk. On this basis, the spatial location constraint prototype loss function is proposed to reduce both risks simultaneously. Extensive experiments on multiple benchmark datasets and many visualization results verify the validity of the proposed matching theory and the effectiveness of the proposed method.
Fast orthogonal locality-preserving projections for unsupervised feature selection

Graph-based sparsity learning is one of the most successful unsupervised feature selection methods that has been widely adopted in many real-world applications. However, traditional graph-based unsupervised feature selection methods have several drawbacks: (1) being time-consuming and unable to deal with large-scale problems; (2) having difficulty tuning the regularization parameter with the sparsity regularization term; and (3) being unable to find explicit solutions owing to the limitation of sparsity, that is, feature selection with the ℓ2,1-norm constrained problem. Thus, this paper proposes OLPPFS, a method to preserve the local geometric structure within the feature subspace by imposing the ℓ2,0-norm constraint. First, the linear mapping capability of the proposed model is enhanced using locality-preserving projections (LPPs), whichpreserve the local and global geometric manifold structure of the data while enhancing the ability to reconstruct data. Second, the graph-embedding learning method can accelerate the construction of a sparsity affinity graph and describe the intrinsic structure of the dataset well. More importantly, we propose a method for solving a projection matrix with the ℓ2,0-norm constrained, which can accurately select a explicit group of discriminative feature subsets. This method can yield a more accurate sparse projection matrix than the ℓ2,1-norm. We also adopt FOLPPFS, an effective anchor-based strategy to further accelerate our model with two flexible options. Extensive experiments on eight datasets demonstrate that the proposed method is superior to the other methods and can preserve a better local geometric structure of the dataset with less time consumption.
Scalable and Optimal QoS-Aware Manufacturing Service Composition via Business Process Decomposition

With the adoption of service-oriented manufacturing modes, more and more manufacturing services are released over manufacturing service platforms. As it is known, the problem of the QoS (Quality of Service)-aware manufacturing service composition is NP-hard. Thus, the optimization remains a challenging research issue, especially in the situation of large-scale manufacturing service data which arouse a scalability problem as well. To improve both the optimization performance and scalability of the QoS-aware manufacturing service composition, this paper proposes a scalable and optimal QoS-aware manufacturing service composition approach via business process decomposition. Specifically, the service composition process is decomposed by using a refined process structure tree (RPST). Moreover, an optimized service composition is achieved layer by layer based on the refined process structure tree in a bottom-up manner. For the atomic tasks or the compound tasks in the same layer of RPST, the corresponding QoS-aware service selection is optimized by calculating Skyline services, which can be carried out in parallel if necessary. When the optimization arrives at the root node, the complete service composition plans are derived. In our approach, the optimal manufacturing service candidates are picked out stage by stage. In this way, both the optimality and scalability of the whole approach can be guaranteed. Extensive experiments are conducted to verify the optimality and scalability of our approach.
Blockchain Barriers in Supply Chain: A Literature Review

Most private permissioned blockchains in business enterprises are still in the sandbox stage, with troubles of interoperability and resource limits looming large. This structured literature review reports a current inventory of barriers to blockchain use, categorized by the largest sources of friction. In a review of 1,166 recent articles, filtering for those specifically reporting on the challenges of blockchain use in supply chains left 76 for detailed analysis. The challenges reported are clustered around four themes: (1) technical, (2) financial, (3) business ecosystem, and (4) social/cultural/regulatory, with the last two themes as prevalent as technical and financial ones. We discuss why not all industries, organizations, and locales may be ripe for blockchain use. Under specific circumstances, the value of chained data helps nudge participant, industry, and regulator coordination, critical for successful blockchain use.
Deep feature selection using adaptive <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si82.svg" display="inline" id="d1e1282"><mml:mi>β</mml:mi></mml:math>-Hill Climbing aided whale optimization algorithm for lung and colon cancer detection

One of the most frightening and talked-about diseases in the modern world is cancer. Huge amounts of research are conducted worldwide to make this ailment less fearsome, be it by finding its cure, discovering ways to detect it in much earlier stages to reduce the mortality rate, or identifying precautions for humans to avoid it. The availability of large collections of biomedical and clinical data has ushered in the use of computer vision for cancer detection, especially for two of its most common types, lung and colon carcinomas. In this work, we present a framework wherein both deep learning and meta-heuristic approaches have been used for the prediction of colon or lung cancer, or both, from histopathological images with near-perfect precision. Initially, deep learning models, namely ResNet-18 for 2-class classification and EfficientNet-b4-widese for 3-class and 5-class classification, have been trained on the LC25000 dataset, followed by the extraction of deep features. The feature vector obtained from a deep learning model may have some redundancy. Hence, the selection of the most useful features has been done with the application of our proposed hybrid meta-heuristic optimization algorithm, AdBet-WOA (Whale optimization algorithm with integrated Adaptive β-Hill Climbing local search), utilizing which the Support Vector Machine (SVM) classifier classifies the colon cancer test data, lung cancer test data, and both combined with an accuracy of 99.99%, 99.97%, and 99.96%, respectively, matching the benchmark results comprehensively. For comparison, we have used a few independent as well as hybrid optimization algorithms. Our proposed approach succeeds greatly in reducing the number of features and also leads to better classification performance, as indicated by the obtained results. The relevant codes for our proposed approach are publicly available at: https://github.com/raj-1411/AdBet-WOA
Dynamic context management in context-aware recommender systems

Context-aware recommendation is an essential part of advanced advertising systems. Most of the existing context-aware recommender systems (CARS) build recommendation models considering context as a static entity. In practice, user preferences are dynamic and change over time. In this study, we argue that CARS must be able to dynamically opt for the evolving behavior of contextual attributes. We propose a solution with a runtime identification of contextual information and jointly integrate content analysis to boost recommendation performance. Initially, the proposed model constructs a list of contextually similar candidates. The users’ context information extracted in the initial step is modeled together with the content information to estimate a context score used to find contextually reliable neighbors. Instead of relying on a fixed rating matrix, we dynamically exploit contextual information for effective rating prediction. Experiments on three real-world datasets demonstrate that our method outperforms classical and state-of-the-art recommendation algorithms.
Performance assessment of clear-sky solar irradiance predictions using state-of-the-art radiation models and input atmospheric data from reanalysis or ground measurements

In this work, the performance of clear-sky direct normal irradiance (DNI) and global horizontal irradiance (GHI) predictions generated with three state-of-the-art solar radiation models with different degrees of complexity is assessed by comparison with high-quality measured irradiance data at Évora, Portugal. The libRadtran, SMARTS, and REST2 radiation models are alternatively operated using input data from three different data sources: co-located AERONET ground-based measurements, and CAMS and MERRA-2 gridded reanalysis data. For these nine combinations (three models and three data sources), the results are assessed using five statistical indicators, namely mean bias error (MBE), root mean square error (RMSE), fractional bias (FB), fractional gross error (FGE), and coefficient of determination (R2). Overall, it is found that AERONET is the data source that provides the best DNI estimates. In general, libRadtran and SMARTS produced closer estimates to the ground-based DNI observations. For GHI, however, no firm conclusion can be drawn regarding the best data source. MERRA-2 produces better estimates in combination with libRadtran and SMARTS according to all statistical indicators except R2, whereas AERONET is to be preferred according to FB, FGE, and R2 when using REST2. Curiously, the latter generates better GHI estimates despite being the simplest model. Overall, it is concluded that the best combinations of model/data source to estimate DNI are either libRadtran/MERRA-2 (according to MBE and FB) and SMARTS/AERONET (according to RMSE, FGE, and R2). In the case of GHI, the best combinations are REST2/AERONET (according to FB, FGE, and R2) and REST2/MERRA-2 (according to MBE and RMSE).
Preserving Resource Handiness and Exigency-Based Migration Algorithm (PRH-EM) for Energy Efficient Federated Cloud Management Systems

On-demand computing ability and efficient service delivery are the major benefits of cloud systems. The limitation in resource availability in single data centers causes the extraction of additional resources from the cloud providers group. The federation scheme dynamically increases resource availability in response to service requests. The dynamic increase in resource count leads to excessive energy consumption, maximum cost, and carbon footprints emission. Hence, the reduction of resources is the major requirement to construct the optimized cloud source models for profit maximization without considering energy mix and CO2. This paper proposes the novel migration method to reduce carbon emissions and energy consumption. The initial stage in the proposed work is the categorization of data centers based on the MIPS and cost prior to job allocation offers scalable and efficient services and resources to the cloud user. Then, the job with the maximum size is allotted to the VM only if its capacity is less than the cumulative capacity of data centers. A novel migration based on overutilized and underutilized levels provides the services to the user even if the particular VM fails. The proposed work offers efficient maintenance of resource availability and maximizes the profit of the cloud providers associated with the federated cloud environment. The comparative analysis of the proposed algorithm with the existing methods regarding the response time, accuracy, profit, carbon emission, and energy consumption assures the effectiveness in a confederated cloud environment.
Re-sampling of multi-class imbalanced data using belief function theory and ensemble learning

Imbalanced classification refers to problems in which there are significantly more instances available for some classes than for others. Such scenarios require special attention because traditional classifiers tend to be biased towards the majority class which has a large number of examples. Different strategies, such as re-sampling, have been suggested to improve imbalanced learning. Ensemble methods have also been proven to yield promising results in the presence of class-imbalance. However, most of them only deal with binary imbalanced datasets. In this paper, we propose a re-sampling approach based on belief function theory and ensemble learning for dealing with class imbalance in the multi-class setting. This technique assigns soft evidential labels to each instance. This evidential modeling provides more information about each object's region, which improves the selection of objects in both undersampling and oversampling. Our approach firstly selects ambiguous majority instances for undersampling, then oversamples minority objects through the generation of synthetic examples in borderline regions to better improve minority class borders. Finally, to improve the induced results, the proposed re-sampling approach is incorporated into an evidential classifier-independent fusion-based ensemble. The comparative study against well-known ensemble methods reveals that our method is efficient according to the G-Mean and F1-score measures, independently from the chosen classifier.
Vision-Based Basic Safety Message (BSM) Broadcasting System

With the growing demands from smart city services, development of intelligent transportation systems has become a popular research field. In this work, we proposed a vision-based basic safety message (BSM) broadcasting system based on SAE J2735 through 3GPP 4G-LTE eMBMS broadcasting technique. Publicly available open government CCTV video data were firstly adopted as the detection source. Through a designed workflow, detection and tracking of the vehicles in the videos can be obtained by YOLOv5x and DeepSORT, respectively. Hence, the speed, location, and ID of each vehicle can be calculated by a BSM generation algorithm to obtain the content elements in SAE J2735 basic safety message. According to the experimental result, the speed and location information can be well transmitted and displayed on the mobile edge device. We expect that the proposed work can be widely applied in ITS applications, benefiting future smart city implementations.
Impact of the Hash Rate on the Theoretical Fork Rate of Blockchain

Forks in a blockchain make it less secure. So the fork rate can be used as a measure of security. Theoretical fork rates have been used in a number of studies, not all of which consider the distribution of hash rates. In this paper, we propose a theoretical fork rate that considers the distribution of hash rates. We compare this theoretical fork rate that considers the distribution of hash rates, a theoretical fork rate that does not consider the distribution of hash rates, and the real fork rate obtained by simulation experiments. Using a simulator, we show that the proposed theoretical fork rate is closer to the real value.
An Integrated Framework for Enhancing Security and Privacy in IoT-Based Business Intelligence Applications

Business intelligence (BI) is the procedure of strategically planning and using a variety of tools and techniques to get important data insights and make wise business decisions. The Internet of Things (IoT) has emerged as the main source of big data with the most real-time data used across all business sectors in recent years. However, the expansion of business results is impacted by the integration of IoT as data sources with conventional BI systems. This is because there are now more security and privacy concerns in IoT ecosystems as a result of adversaries undertaking data inference and poisoning attacks on networked IoT devices via the open communication medium Internet. This study proposes an integrated architecture for strengthening security and privacy in IoT-based BI applications, which is inspired by the discussion above. The suggested structure contains two engines: an intrusion detection engine and a two-level privacy engine. Due to adversaries conducting data inference and poisoning attacks on networked IoT devices over the open communication medium Internet, there are now additional security and privacy risks in IoT ecosystems. Based on the discussion above, this study suggests an integrated architecture for enhancing security and privacy in IoT-based BI applications. The two different engines are designed namely two-level privacy and intrusion detection engine. The experimental outcomes utilising the real IoT-based dataset ToN-IoT show that the suggested strategy outperforms previous peer privacy-preserving machine learning algorithms in terms of detection rate, accuracy, F1 score, and precision.
Multi-scales feature extraction model for water segmentation in satellite image

This paper proposed multi-scales feature extraction architecture and loss function focusing on water segmentation. In this paper, Res2Net is used feature extraction on the encoder, which incorporates receptive field of different sizes to facilitate the extraction of water areas and surrounding information such as land, cloud. The skip connection is used as the input of the attention gate, which can increase the weight of the water to highlight the water area. In this study, IoU of water areas are 0.76, which is a significant improvement compared to other architectures.
Political Security Threat Prediction Framework Using Hybrid Lexicon-Based Approach and Machine Learning Technique

The internet offers a powerful medium for expressing opinions, emotions and ideas, using online platforms supported by smartphone usage and high internet penetration. Most internet posts are textual based and can include people’s emotional feelings for a particular moment or sentiment. Monitoring online sentiments or opinions is important for detecting any excessive emotions triggered by citizens which can lead to unintended consequences and threats to national security. Riots and civil war, for instance, must be addressed due to the risk of jeopardizing social stability and political security, which are crucial elements of national security. Mining opinions according to the national security domain is a relevant research topic that must be enhanced. Mechanisms and techniques that can mine opinions in the aspect of political security require significant improvements to obtain optimum results. Researchers have noted that there is a strong relationship between emotion, sentiment and political security threats. This study proposes a new theoretical framework for predicting political security threats using a hybrid technique: the combination of lexicon-based approach and machine learning in cyberspace. In the proposed framework, Decision Tree, Naive Bayes, and Support Vector Machine have been deployed as threat classifiers. To validate our proposed framework, an experimental analysis is accomplished. The performance of each technique used in the experiments is reported. In this study, our proposed framework reveals that the hybrid Lexicon-based approach with the Decision Tree classifier recorded the highest performance score for predicting political security threats. These findings offer valuable insight to ongoing research on opinion mining in predicting threats based on the political security domain.
Full-Reference Image Quality Expression via Genetic Programming

Full-reference image quality measures are a fundamental tool to approximate the human visual system in various applications for digital data management: from retrieval to compression to detection of unauthorized uses. Inspired by both the effectiveness and the simplicity of hand-crafted Structural Similarity Index Measure (SSIM), in this work, we present a framework for the formulation of SSIM-like image quality measures through genetic programming. We explore different terminal sets, defined from the building blocks of structural similarity at different levels of abstraction, and we propose a two-stage genetic optimization that exploits hoist mutation to constrain the complexity of the solutions. Our optimized measures are selected through a cross-dataset validation procedure, which results in superior performance against different versions of structural similarity, measured as correlation with human mean opinion scores. We also demonstrate how, by tuning on specific datasets, it is possible to obtain solutions that are competitive with (or even outperform) more complex image quality measures.