Reviewerly/exHarmony huggingface dataset:
A Case Study: Cat-Dog Face Detector Based on YOLOv5

Object detection is a common research topic for many fields. In particular, objects that are close together are difficult to detect. The breed of cats and dogs includes many species. These species are similar to each other and to some species in the other class. Therefore, it is difficult to distinguish the faces of cats and dogs, especially for some species. The study uses the YOLO algorithms, which has very high sensitivity and speed in numerous object detection challenges. The Oxford pets dataset, consisting of approximately 3600 images, containing images from 37 different types of cat/dog classes, is utilized for training and testing. We propose a method based on YOLOv5 to find cats and dogs. We utilized the YOLOv5 algorithm with different parameters. Four different models are compared and evaluated. Experiments demonstrate that YOLOv5 models achieve successful results for the respective task. The mAP of YOLOv5l is 94.1, demonstrating the efficacy of YOLOv5-based cat/dog detection.
Stabilization of Complementarity Systems via Contact-Aware Controllers

We propose a control framework, which can utilize tactile information by exploiting the complementarity structure of contact dynamics. Since many robotic tasks, like manipulation and locomotion, are fundamentally based in making and breaking contact with the environment, state-of-the-art control policies struggle to deal with the hybrid nature of multicontact motion. Such controllers often rely heavily upon heuristics or, due to the combinatorial structure in the dynamics, are unsuitable for real-time control. Principled deployment of tactile sensors offers a promising mechanism for stable and robust control, but modern approaches often use this data in an ad hoc manner, for instance to guide guarded moves. This framework can close the loop on tactile sensors and it is noncombinatorial, enabling optimization algorithms to automatically synthesize provably stable control policies. We demonstrate this approach on multiple numerical examples, including quasi-static friction problems and a high dimensional problem with 10 contacts. We also validate our results on an experimental setup and show the effectiveness of the proposed method on an underactuated multicontact system.
Multi-Class Image Classification Based on MobileNetV2 for Detecting the Proper Use of Face Mask

Wearing face mask in public has become a health protocol standard during this pandemic to prevent further spread of COVID-19. Even though the detection of inappropriate use of face mask is important considering that people sometimes ignore the health protocols by lowering their face mask so it does not cover their nose, studies regarding automatic detection of proper use of face mask are still few. Therefore, in this research we propose a multi-class image classification for detecting the proper use of face mask based on MobileNetV2 architecture as the base model. We also propose a trainable head model for the network, consisting of a depthwise convolution layer and two fully-connected layers, that gives high classification performance. The experimental results show that the proposed system gives a high multi-class classification performance with an accuracy of 97%, precision of 97%, recall of 97%, and F1-score of 97%. The running time of the proposed method is 265.94 seconds which is considered efficient compared with other models. Because of its light-weight network architecture, the proposed method is suitable for further implementation towards a real-time application of surveillance systems. Therefore, in this research we present the results of an initial experiment of the proposed model on a real-time detection system by using a web camera.
Enhancing Scalability of Trusted Blockchains through Optimal Sharding

Sharding technology has been rapidly developed to improve blockchain scalability in recent years. However, in practical applications, a blockchain sharding system may face such issues as malicious node attacks and large cross-shard communications. An optimization method is thus demanded to improve their sharding performance. Based on the status and behavioral data of shard nodes stored in a trusted blockchain, can node trust be calculated and used to improve a sharding-based blockchain’s security? To answer this question, this work presents a sharding model to optimize fault tolerance ratio, the number of cross-shard transactions, and shard trust. Its application to a blockchain system can help it improve the credibility of its shard transaction processing and reduce its transaction delay, thereby enhancing the scalability of over the state of the art. Some open issues are discussed.
Bounding Box Dataset Augmentation for Long-range Object Distance Estimation

Autonomous long-range obstacle detection and distance estimation plays an important role in numerous applications such as railway applications when it comes to locomotive drivers support or developments towards driverless trains. To overcome the problem of small training datasets, this paper presents two data augmentation methods for training the ANN DisNet to perform reliable long-range distance estimation.
Pose Estimation of Hurdles Athletes using OpenPose

Automatic athlete pose estimation from images has recently received considerable attention from the computer vision community to understand the correct pose of athletes during training or competitions. However, human pose estimation from images or videos is still a challenging task because of inadequate training data, depth obscurity, and occlusion. In this paper, we presented a real-time 2D athlete pose estimation system based on OpenPose [1]. The system captured 2D positions of a hurdles athlete's body parts such as wrists, ankles, hips and knees from an image. For experiments, we recorded videos from a top-tier athlete in hurdling to infer motion timing and kinematic parameters. We also explore the performance of different feature extractorsin OpenPose in terms of accuracy and run-time. Experimental results show that the system with the extended version of VGG19 [2] as feature extractor outperforms the other algorithms.
A Comparative Study on Breast Cancer Prediction using Optimized Algorithms

Breast cancer is a type of cancer that develops in women as a result of abnormal cell growth in their breast and it is the second largest cause of mortality in women. Breast cancer detection and treatment have improved as a result of breakthroughs in the field; around 89 percentages of women who are diagnosed with breast cancer survive even after their diagnosis. Early detection of breast cancer is essential for the survival; therefore, a technology that can detect breast cancer in its early stages must be developed. Various machine learning algorithms from data mining have been used to identify breast cancer. The objective of this research is to assess the prediction accuracy of Divide and Conquer Kernal Support Vector Machine (DCKSVM) and Hybrid Radial Basis Function Neural Network machine learning algorithms for breast cancer disease using the Wisconsin Diagnostic Breast Cancer (W DBC) dataset. The assessment of the algorithms is made using three different parameters termed as Accuracy, Precision and Recall. In this research work the pre-processing of the dataset to fill the missing attribute values is done using the K-Nearest Neighbor algorithm. The result proves that the HRBFNN algorithm gives better accuracy when compared with the DCKSVM algorithm.
EvolveCluster: an evolutionary clustering algorithm for streaming data

Abstract Data has become an integral part of our society in the past years, arriving faster and in larger quantities than before. Traditional clustering algorithms rely on the availability of entire datasets to model them correctly and efficiently. Such requirements are not possible in the data stream clustering scenario, where data arrives and needs to be analyzed continuously. This paper proposes a novel evolutionary clustering algorithm, entitled EvolveCluster, capable of modeling evolving data streams. We compare EvolveCluster against two other evolutionary clustering algorithms, PivotBiCluster and Split-Merge Evolutionary Clustering, by conducting experiments on three different datasets. Furthermore, we perform additional experiments on EvolveCluster to further evaluate its capabilities on clustering evolving data streams. Our results show that EvolveCluster manages to capture evolving data stream behaviors and adapts accordingly.
Using meta-learning for multi-target regression

Choosing the most suitable algorithm to perform a machine learning task for a new problem is a recurrent and complex task. In multi-target regression tasks, when problem transformation methods are applied, this choice is even harder. The reason is the need to simultaneously choose the problem transformation method and the base learning algorithm. This work investigates how to bridge the gap of method/base learner recommendation for problems with multiple outputs. In meta-learning experiments, we use a large number of multi-target regression datasets to investigate whether using meta-learning can provide good recommendations. To do this, we compared the meta-models induced by 3 different ML algorithms, including three variations for each of them, and selected 58 meta-features that we believe are relevant for extracting good dataset descriptions for the meta-learning process. In the experimental results, the meta-models outperformed the baselines (Majority and Random) by recommending the most suitable solution for multi-target regression (for the transformation method and base-learner) with high predictive performance, including real-world applications. The meta-features and the relation between the transformation method and base-learner provided important insights regarding the optimal problem transformation method. Furthermore, when comparing the application of algorithm adaptation and problem transformation methods, our meta-learning proposal was capable of statistically overcoming all competitors, which resulted in a predictive performance using the best choice per problem.
Beyond IQ: The Importance of Metacognition for the Promotion of Global Wellbeing

Global policy makers increasingly adopt subjective wellbeing as a framework within which to measure and address human development challenges, including policies to mitigate consequential societal problems. In this review, we take a systems-level perspective to assemble evidence from studies of wellbeing, of collective intelligence, and of metacognition and argue for a virtuous cycle for health promotion in which the increased collective intelligence of groups: (1) enhances the ability of such groups to address consequential societal problems; (2) promotes the wellbeing of societies and the individual wellbeing of people within groups; and, finally, (3) enables prosocial actions that further promote collective problem-solving and global wellbeing. Notably, evidence demonstrates that effective collaboration and teamwork largely depend on social skills for metacognitive awareness-the capacity to evaluate and control our own mental processes in the service of social problem-solving. Yet, despite their importance, metacognitive skills may not be well-captured by measures of general intelligence. These skills have instead been the focus of decades of research in the psychology of human judgment and decision-making. This literature provides well-validated tests of metacognitive awareness and demonstrates that the capacity to use analysis and deliberation to evaluate intuitive responses is an important source of individual differences in decision-making. Research in network neuroscience further elucidates the topology and dynamics of brain networks that enable metacognitive awareness, providing key targets for intervention. As such, we further discuss emerging scientific interventions to enhance metacognitive skills (e.g., based on mindfulness meditation, and physical activity and aerobic fitness), and how such interventions may catalyze the virtuous cycle to improve collective intelligence, societal problem-solving, and global wellbeing.
Multivariate cluster weighted models using skewed distributions

Much work has been done in the area of the cluster weighted model (CWM), which extends the finite mixture of regression model to include modelling of the covariates. Although many types of distributions have been considered for both the response(s) and covariates, to our knowledge skewed distributions have not yet been considered in this paradigm. Herein, a family of 24 novel CWMs is considered which allows both the responses and covariates to be modelled using one of four skewed distributions (the generalized hyberbolic and three of its skewed special cases, i.e., the skew-t, the variance-gamma and the normal-inverse Gaussian distributions) or the normal distribution. Parameter estimation is performed using the expectation-maximization algorithm and both simulated and real data are used for illustration.
Quantum Algorithm for Full Configuration Interaction Calculations without Controlled Time Evolutions

A quantum phase estimation algorithm allows us to perform full configuration interaction (full-CI) calculations on quantum computers with polynomial costs against the system size under study, but it requires quantum simulation of the time evolution of the wave function conditional on an ancillary qubit, which makes the algorithm implementation on real quantum devices difficult. Here, we discuss an application of the Bayesian phase difference estimation algorithm that is free from controlled time evolution operations to the full-CI calculations.
SRNMSM: A Deep Light-Weight Image Super Resolution Network Using Multi-Scale Spatial and Morphological Feature Generating Residual Blocks

Generating features representing the textures and structures of an image is very important characteristic of a super resolution network. Morphological operations are the nonlinear mathematical operations that can process signals focusing on their structures and textures. In this paper, we propose a novel residual block to generate and process morphological features and fuse them with the conventional spatial features, in order to produce a very rich and highly representational set of residual feature maps. The proposed residual block is then used in a deep convolutional neural network for the task of image super resolution. It is shown that the capability of the proposed block in generating and using the morphological features can significantly improve the super resolution performance of a deep network. The super resolution network employing the proposed residual block is shown to outperform the state-of-the-art low-complexity image super resolution networks on various benchmark datasets.
Secure Data Exchange Scheme for Smart Grids

the transmission of smart grid data over public networks exposes it to numerous privacy and security violations. To curb these threats, a number of authentication protocols have been developed. However, majority of them are sophisticated and still susceptible to privacy as well as security attacks. In addition, some of them have performance issues due to computationally expensive operations such as bilinear pairings. In this paper, a secure data exchange scheme for smart grids is presented based on lightweight elliptic curve cryptography (ECC) scalar multiplication and point additions. The security analysis shows that it is robust against common smart grid attack vectors such as packet replays, privileged insider and impersonation attacks. In addition, it offers perfect backward and forward key secrecy and secure mutual authentication among the smart meter, utility control center and the registration authority. In terms of performance, it experiences average computation costs and the best communication overheads among other related protocols.
Multi-input bio-inspired weights and structure determination neuronet with applications in European Central Bank publications

This paper introduces a 3-layer feed-forward neuronet model, trained by novel beetle antennae search weights-and-structure-determination (BASWASD) algorithm. On the one hand, the beetle antennae search (BAS) algorithm is a memetic meta-heuristic optimization algorithm capable of solving combinatorial optimization problems. On the other hand, neuronets trained by a weights-and-structure-determination (WASD) algorithm are known to resolve the shortcomings of traditional back-propagation neuronets, including slow speed of training and local minimum. Combining the BAS and WASD algorithms, a novel BASWASD algorithm is created for training neuronets, and a multi-input BASWASD neuronet (MI-BASWASDN) model is introduced. Using a power sigmoid activation function and while managing the model fitting and validation, the BASWASD algorithm finds the optimal weights and structure of the MI-BASWASDN. Four financial datasets, taken from the European Central Bank publications, validate and demonstrate the MI-BASWASDN model’s outstanding learning and predicting performance. Also included is a comparison of the MI-BASWASDN model to three other well-performing neural network models, as well as a MATLAB kit that is publicly available on GitHub to promote and support this research.
HADFL: Heterogeneity-aware Decentralized Federated Learning Framework

Federated learning (FL) supports training models on geographically distributed devices. However, traditional FL systems adopt a centralized synchronous strategy, putting high communication pressure and model generalization challenge. Existing optimizations on FL either fail to speedup training on heterogeneous devices or suffer from poor communication efficiency. In this paper, we propose HADFL, a framework that supports decentralized asynchronous training on heterogeneous devices. The devices train model locally with heterogeneity-aware local steps using local data. In each aggregation cycle, they are selected based on probability to perform model synchronization and aggregation. Compared with the traditional FL system, HADFL can relieve the central server’s communication pressure, efficiently utilize heterogeneous computing power, and can achieve a maximum speedup of 3.15x than decentralized-FedAvg and 4.68x than Pytorch distributed training scheme, respectively, with almost no loss of convergence accuracy.
Evaluation of a low-cost approach to 2-D digital image correlation vs. a commercial stereo-DIC system in Brazilian testing of soil specimens

Abstract Due to their cost, high-end commercial 3D-DIC (digital image correlation) systems are still inaccessible for many laboratories or small factories interested in lab testing materials. These professional systems can provide reliable and rapid full-field measurements that are essential in some laboratory tests with high-strain rate events or high dynamic loading. However, in many stress-controlled experiments, such as the Brazilian tensile strength (BTS) test of compacted soils, samples are usually large and fail within a timeframe of several minutes. In those cases, alternative low-cost methods could be successfully used instead of commercial systems. This paper proposes a methodology to apply 2D-DIC techniques using consumer-grade cameras and the open-source image processing software DICe (Sandia National Lab) for monitoring the standardized BTS test. Unlike most previous studies that theoretically estimate systematic errors or use local measures from strain gauges for accuracy assessment, we propose a contrast methodology with independent full-field measures. The displacement fields obtained with the low-cost system are benchmarked with the professional stereo-DIC system Aramis-3D (GOM GmbH) in four BTS experiments using compacted soil specimens. Both approaches proved to be valid tools for obtaining full-field measurements and showing the sequence of crack initiation, propagation and termination in the BTS, constituting reliable alternatives to traditional strain gauges. Mean deviations obtained between the low-cost 2D-DIC approach and Aramis-3D in measuring in-plane components were 0.08 mm in the perpendicular direction of loading (ΔX) and 0.06 mm in the loading direction (ΔY). The proposed low-cost approach implies considerable savings compared to commercial systems.
An investigation of security risk &amp; taxonomy of Cloud Computing environment

Since the inception of this new technology, inclusion of cloud computing in IT sectors has grown in popularity. This is a new movement in industrial technology that will continue to expand and develop their competency in the coming years. Because of numerous advantages of cloud computing over traditional computing, most businesses are fast switching to it. As per one of the report published by IBM in 2020 the average cost for a data breach place 3.8 million dollar. 1001 data breaches are came in to the figure in United States alone in 2020. More and more security issues and challenges are swelling with the growth in the complexity of this technology. So, to enable the proper security wheels with the services hosted by the CSPs is the leading concern. The motive here is to investigate security vulnerabilities & challenges of the cloud-computing environment which further provide an ease to applications deployment. It also present a taxonomy of various building blocks of Cloud Computing platform.
Weakly Supervised Video Anomaly Detection Based on 3D Convolution and LSTM

Weakly supervised video anomaly detection is a recent focus of computer vision research thanks to the availability of large-scale weakly supervised video datasets. However, most existing research works are limited to the frame-level classification with emphasis on finding the presence of specific objects or activities. In this article, a new neural network architecture is proposed to efficiently extract the prominent features for detecting whether a video contains anomalies. A video is treated as an integral input and the detection follows the procedure of video-label assignment. The extraction of spatial and temporal features is carried out by three-dimensional convolutions, and then their relationship is further modeled using an LSTM network. The concise structure of the proposed method enables high computational efficiency, and extensive experiments demonstrate its effectiveness.
National-scale distribution and its influence factors of calcium concentrations in Chinese soils from the China Global Baselines project

The China Geochemical Baselines (CGB) project provides geochemical baselines for nearly all chemical elements, including Ca, by sampling transported soils at a density of 1 sample per approximately 3000 km2 across nearly all of China. A total of 3382 top samples (TS) were collected at depths of 0–25 cm and 3380 deep samples (DS) at depths >100 cm. The median (background) values of Ca in the TS and DS were 1.96% and 1.84%, respectively. On the spatial distribution maps, Ca concentrations generally decreased from northwestern to southeastern China. The natural climate, especially the aridity/humidity status, primarily influences this national-scale distribution. The median Ca values were ranked as follows: arid (4.61% in the TS and 4.15% in the DS) > semi-arid (2.73% in the TS and 2.55% in the DS) > sub-humid (1.37% in the TS and DS) > humid (0.56% in the TS and 0.52% in the DS) areas. In the arid areas of northwestern China, evaporite minerals can be deposited in soils, resulting in a regional high-Ca background. In the humid regions of southeastern China, soil water readily leaches Ca, leading to a regional low-Ca background. The highest Ca concentrations sparsely distributed in northwestern China (>90% quantile, Q90) and the higher Ca contents in the semi-arid and sub-humid areas (Q75–Q90) are linked to the parent Ca-bearing rocks. While no high Ca concentrations exist in both the TS and DS in southeastern China, although carbonate rocks are widely distributed. The lowest Ca concentrations distributed in southeastern China (<Q10) are predominately a result of continuous exposure to human-induced acid rainfall. Both natural and anthropogenic factors control the Ca concentrations and distribution in Chinese soils.