Reviewerly/exHarmony huggingface dataset:
Extensive Knowledge Distillation Model: An End-to-End Effective Anomaly Detection Model for Real-Time Industrial Applications

Detecting anomalies is an essential task in many industries.Current state-of-the-art methods rely on a large number of parameters for high accuracy, which may not be suitable for implementing cost-effective real-time applications.Additionally, developing robust detection models is difficult due to limited anomaly samples.To address these issues, we propose an end-to-end anomaly detection method that utilizes effective data generation and comprehensive knowledge distillation.In particular, the proposed approach first employs a highly effective generative model to generate realistic anomaly images.It then transfers the pretrained master network's essential intermediate layers and final layer knowledge to a novice network by using the knowledge distillation technique.In the conducted experiments with 4 real-life datasets, the proposed model outperforms its counterparts, including state-of-the-art models, by 0.6% on MNIST and CIFAR-10 datasets, 0.2% on the custom dataset, and stays competitive on the MVTec AD dataset.Additionally, the proposed model outperforms all of its peers in trainable parameter numbers by having only 0.17 M parameters.This is at least twice as few parameters as the baseline model.Overall, the proposed approach offers an efficient solution to anomaly detection that achieves high accuracy despite limited anomaly samples and fewer parameters.
Trustiness-based hierarchical decentralized federated learning

Federated Learning (FL) breaks the “data island” and lets clients cooperate in training a shared model with private data locally. And hierarchical framework is used in FL to alleviate the problem of excessive communication overhead. FL provides a certain degree of privacy, but privacy inference and Byzantine attacks still affect the existing FL methods. Moreover, the parameter server brings new security troubles, and the group size limits the model’s robustness. Thus, this paper presents a new decentralized FL framework called Trustiness-based Hierarchical Decentralized Federated Learning (TH-DFL) via a Security Robust Aggregation (SRA) rule, which introduces a trust mechanism. This study provides privacy protection and robustness even if there are malicious nodes. Besides, it somewhat reduces communication overhead. A series of comparative studies are also implemented to evaluate the performance of the model. In this process, the Gaussian, the bit-flipping, and other attacks are launched in the simulated federated learning. The results show that on the basis of ensuring privacy and robustness, TH-DFL seeks a better balance between privacy and robustness when the size of the group changes.
Path planning of mobile robots in dynamic environment based on analytic geometry and cubic Bézier curve with three shape parameters

With the improvement of technology, autonomous mobile robots have become an indispensable part of human life. Since path planning is the crucial part of mobile robots, these paths should be feasible, smooth and collision-free in a certain environment. However, forming desirable paths in a dynamic environment with static and moving obstacles is still a problem in the literature. In this paper, we present a new approach based on the analytic geometry for collision avoidance and the cubic Bézier curve with three shape parameters to obtain global and local paths in a dynamic environment. In this study, the environment for the mobile robot is considered to be a square plane without a grid consisting of several static obstacles in different geometric shapes, a mobile robot, and several dynamic obstacles in the shape of a circle with the same size. Moreover, we expressed the influence of the shape parameters on the optimal path using the objective function defined by the path length. Also, the paths generated by the cubic Bézier curve with three shape parameters and the classical cubic Bézier curve are compared in terms of their path length.
Gesture image recognition method based on DC-Res2Net and a feature fusion attention module

To extract decisive features from gesture images and solve the problem of information redundancy in the existing gesture recognition methods, we propose a new multi-scale feature extraction module named densely connected Res2Net (DC-Res2Net) and design a feature fusion attention module (FFA). Firstly, based on the new dimension residual network (Res2Net), the DC-Res2Net uses channel grouping to extract fine-grained multi-scale features, and dense connection has been adopted to extract stronger features of different scales. Then, we apply a selective kernel network (SK-Net) to enhance the representation of effective features. Afterwards, the FFA has been designed to remove redundant information in features by fusing low-level location features with high-level semantic features. Finally, experiments have been conducted to validate our method on the OUHANDS, ASL, and NUS-II datasets. The results demonstrate the superiority of DC-Res2Net and FFA, which can extract more decisive features and remove redundant information while ensuring high recognition accuracy and low computational complexity.
Complete Hierarchy for High-Dimensional Steering Certification

High-dimensional quantum steering can be seen as a test for the dimensionality of entanglement, where the devices at one side are not characterized. As such, it is an important component in quantum informational protocols that make use of high-dimensional entanglement. Although it has been recently observed experimentally, the phenomenon of high-dimensional steering is lacking a general certification procedure. We provide necessary and sufficient conditions to certify the entanglement dimension in a steering scenario. These conditions are stated in terms of a hierarchy of semidefinite programs, which can also be used to quantify the phenomenon using the steering dimension robustness. To demonstrate the practical viability of our method, we characterize the dimensionality of entanglement in steering scenarios prepared with maximally entangled states measured in mutually unbiased bases. Our methods give significantly stronger bounds on the noise robustness necessary to experimentally certify high-dimensional entanglement.
The Impact of Deviant Social Media Influencers and Consumer Characteristics on Purchasing Counterfeit Goods

Product counterfeiting is a large and global economic crime that causes significant economic, social and personal harms. Facilitated by the efficiency and convenience of e-commerce, finding and buying counterfeits have never been easier. Going beyond product listings on websites, counterfeiters are now using sophisticated marketing techniques to promote their illicit wares. This includes employing deviant social media influencers who violate social norms and the law to peddle counterfeit goods via channels such as YouTube and Instagram. However, very little is known about their market or their impact. Based on two UK surveys, the empirical research described in this article estimates the prevalence of consumers who purchase counterfeits because of SMI endorsements. We believe this is the first estimate of its kind anywhere. Further, the research finds that influencer marketing exploits characteristics of consumers that make them susceptible to the charms of the deviant SMIs: high susceptibility to the influence of trusted digital others, low risk awareness, high risk appetite, and prone to rationalizations that morally justify the purchases. The higher prevalence of these characteristics in young adults and males helps to explain why these demographic groups are the most likely to purchase endorsed counterfeits.
Entanglement is better teleported than transmitted

We show that, for the purpose of quantum communication via a quantum field, it is essential to view the field not only as a medium for transmission but also as a source of entanglement that can aid in the communication task. To this end, we consider the quantum communication scenario where Alice is initially entangled with an ancilla and intends to communicate with Bob through a quantum field, so as to make Bob entangled with the ancilla. We find that if Alice and Bob communicate by directly coupling to the quantum field, then they can generate negativity between Bob and the ancilla only at orders that are higher than second perturbative order. We then present a protocol based on quantum teleportation in which Alice and Bob consume entanglement that they obtained from the field via interaction or harvesting. We show that this protocol can transfer negativity already to second perturbative order.
Reinforcement learning with modified exploration strategy for mobile robot path planning

Abstract Driven by the remarkable developments we have observed in recent years, path planning for mobile robots is a difficult part of robot navigation. Artificial intelligence applied to mobile robotics is also a distinct challenge; reinforcement learning (RL) is one of the most used algorithms in robotics. The exploration-exploitation dilemma is a motivating challenge for the performance of RL algorithms. The problem is balancing exploitation and exploration, as too much exploration leads to a decrease in cumulative reward, while too much exploitation locks the agent in a local optimum. This paper proposes a new path planning method for mobile robot based on Q-learning with an improved exploration strategy. In addition, a comparative study of Boltzmann distribution and $\epsilon$ -greedy politics is presented. Through simulations, the better performance of the proposed method in terms of execution time, path length, and cost function is confirmed.
Characterization of Hydrothermal Alteration in Palagonitized Deposits Using Short-Wave Infrared Spectroscopy and X-Ray Diffraction Methods

Abstract In this contribution, we evaluate the applicability of short-wave infrared spectroscopy to characterizing mineral assemblages in palagonitized glaciovolcanic edifices at Cracked Mountain, a basaltic volcano within the Garibaldi Volcanic Belt, and Kima Kho, a basaltic tuya within the Northern Cordilleran Volcanic Province. Second-order evaluation of the approach was established through comparison with data obtained by semi-empirical X-ray diffraction methods. Reflectance spectra show an increase in the depth of water and hydroxyl bands in samples with increasing amounts of hydrated minerals and decreasing amounts of amorphous materials (as determined through X-ray diffraction), indicating that the relative strengths of H2O- and OH-related absorption features may be used as a proxy for the degree of palagonite alteration (hydrated minerals crystallized from basaltic glass). In addition, the full width at half maximum of the OH- and H2O-related absorption bands decreased with the formation of zeolites, indicating that the full width at half maximum of OH- and H2O-related features may be used to estimate the degree of crystallinity across the progressive palagonitization process. Finally, short-wave infrared spectroscopy revealed a decrease in band depth of water-related absorption features with no change in the full width at half maximum along the devitrification or alteration process that converts analcime to chabazite to wairakite, indicating that spectroscopy may be used to identify the final dehydration and cementation stages of palagonitization. Results show that the short-wave infrared spectroscopy method is more robust in identifying poorly crystalline hydrated samples, while X-ray diffraction methods are better suited to understanding the crystalline components of palagonite. Short-wave infrared spectroscopy is a remote sensing technique that has proven to successfully characterize the state of H2O in hydrated clay-rich material and thus may serve as an invaluable tool in identifying stages of palagonitization not only on subglacial edifices on Earth but also on off-planet environs, including the Martian surface.
Integrating Reinforcement Learning and Optimization Task: Evaluating an Agent to Dynamically Select PSO Communication Topology

A recent study catalogued hundreds of meta-heuristics proposed over the past three decades in Swarm Intelligence (SI) literature. This scenario makes it difficult for the practitioner to choose the most suitable meta-heuristic (RL) for a specific problem. This paper shows that Reinforcement Learning could be a powerful tool for SI. First, we describe a Reinforcement Learning environment to solve an optimization problem. Then, we investigate the usage of Proximal Policy Optimization to dynamically set the Particle Swarm Optimization topology accordingly to the simulation states. Our RL proposal reached competitive fitness values, even when evaluated in non-trained scenarios. In addition, we show the actions' distribution by simulation in the Rastrigin. The paper demonstrates how RL could be integrated to improve meta-heuristics capabilities, opening new research paths where RL will be used to improve meta-heuristics or select them accordingly to their strengths.
Analysis of Jamrao canal for potential of hybrid photovoltaic/hydrokinetic turbine system

The rapid depletion of fossil fuels, increased electricity consumption, and global environmental concerns have led to shift electricity generation from non-renewable to renewable energy sources. Solar photovoltaic (PV) technology is increasing with significant land requirement. To conserve this valuable land and water evaporation, PV panels are being installed on water bodies such as irrigation canals, lakes and dams. Researchers have found that hybrid power generation systems are more efficient than single power generating system, thus by covering canals with solar panels and harnessing the kinetic energy of flowing water using hydrokinetic turbines (HKT), a massive amount of electricity can be generated. The Hybrid Optimization of Multiple Energy Resources (HOMER) software is used in this study to stimulate and acquire the best results and configuration for a hybrid PV/hydrokinetic system in district Sanghar of Sindh province in Pakistan. Model is designed by covering the 1 km canal surface with PV and the hydrokinetic turbines along with it. The overall potential for 1 km length of canal for both PV/hydrokinetic turbine system is found around 2.8 MW with payback period in 5.7 years and by saving 324.58 million cubic meters of water in terms of evaporation with annual saving in GHG emissions of 416,451.74 kg/yr.
Challenges and Emerging Trends in Cyber Security

Cyber Security plays a very important role in the field of information technology. Securing the information has become one of the biggest challenges in the contemporary society. Whenever we think about the cyber security, the first thing that comes to our mind is ‘cyber crimes’ which are increasing tremendously day by day. Various government authorities and private organizations are taking many measures to prevent these cybercrimes. Besides various measures cyber security is still a very big concern to all of us. This paper mainly focuses on challenges faced by cyber security on the latest technologies. It also focuses on latest cyber security techniques, ethics and the trends changing the face of cyber security.
Fourier Transform Layer: A proof of work in different training scenarios

In this work two established methods were merged: Fourier Transform and Convolutional Neural Network to classify images in several datasets. Fourier Transform is commonly used in signal processing as well as in image processing. Convolutional Neural Networks are well-suited to image analysis tasks, yet require a lot of processing power and/or time during training process. Fourier Transform Layer is introduced to increase the processing speed without sacrificing accuracy. The motivation is to present and compare an alternative approach to Convolutional Neural Networks, which could reduce the need for GPU training. Models containing only the novel layer were trained to classify images from widely accepted datasets and compared to classification results of simple models containing one convolutional layer. The comparison was performed in terms of test accuracy, Area-Under-Curve and training times. The results showed that, for images of size 128 × 128 and larger, models with the proposed layer reached test accuracy comparable to that reached by convolutional models (accuracy: 96% and 98% respectively), with at least 27% decrease in training time per one epoch on Central Processing Unit.
PAGCL: An unsupervised graph poisoned attack for graph contrastive learning model

Graph-contrastive learning has aided the development of unsupervised graph representation learning, comparable to supervised models in terms of performance. However, the robustness of the graph contrastive learning model still has a bottleneck problem, most of the current adversarial attacks are supervised, and the acquisition of labels cannot be guaranteed when attacking unsupervised graph contrastive learning models. We propose an unsupervised attack method for graph contrastive learning because the traditional supervised graph adversarial attack method is unsuitable for the attack graph contrastive learning model. It combines the graph inject attack with the poison feature matrix and uses gradients in different contrast views of the poison adjacency matrix. Extensive experiments are conducted on various datasets and our method shows notable superiority among relevant methods, even compared to supervised ones. The code is publicly available at https://github.com/lizehaodashuaibi/paper.
Development and validation of a symptom illustration scale from the patient-reported outcome common terminology criteria for adverse events for patients with breast cancer

Emojis are commonly used for daily communication and may be useful in assessing patient-reported outcomes (PROs) in breast cancer. The purpose of this study is to develop and validate a Symptom Illustration Scale (SIS) as a new PRO measurement.Eighteen original SIS items were developed from the PRO-CTCAE. In cohort one, the SIS validity and reliability were examined in patients with breast cancer, using a semi-structured five-question survey to investigate content validity. PROs with PRO-CTCAE and SIS were examined twice to determine criteria validity and test-retest reliability. In cohort two, the responsiveness of the scales were examined in patients treated with anthracycline, docetaxel, paclitaxel, and endocrine therapy. PROs with PRO-CTCAE and SIS were investigated two or three times, depending on the therapy.Patients were enrolled from August 2019 to October 2020. In cohort one (n = 70), most patients had no difficulties with the SIS, but 16 patients indicated that it was difficult to understand severities in the SIS. For criterion validity, Spearman rank correlation coefficients (rs) between PRO-CTCAE and SIS items were ≥ 0.41, except for "Decreased appetite." For test-retest reliability, κ coefficients of the SIS were ≥ 0.41 for 16/18 items (88.9%). Response time was significantly shorter for the SIS than for PRO-CTCAE (p < 0.001). In cohort two (n = 106), score changes between PRO-CTCAE and SIS for relevant symptoms all had correlations with rs ≥ 0.41.An original SIS from the PRO-CTCAE for patients with breast cancer were verified the validity, reliability, and responsiveness. Further studies to improve and validate the SIS are needed.
Visualizing high dimensional structures in geochemical datasets using a combined compositional data analysis and Databionic swarm approach

Classical tools for exploratory analysis of large geochemical datasets (e.g., cluster analysis, principal component analysis, etc.) have been successfully utilized for decades to understand complex structures and identify natural, multi-dimensional data clusters. More recent development of machine learning algorithms, both supervised and unsupervised, have increasingly been shown to derive deeper insights from complex datasets than older methods. Moreover, geochemists have long recognized that concentration data are compositional and require special mathematical treatment, such as application of Compositional Data Analysis (CoDA) techniques. Proper approaches for linking CoDA and machine learning is an area of active research. Here, we investigate the behavior of trace elements in coal and coal combustion products from a power plant burning Appalachian Basin coals using a combination of CoDa methods and a relatively new tool for visualizing complex multivariate structures, Databionic swarm (DBS). Databionic swarm is an unsupervised method which combines concepts from emergence, self-organization, game theory, and swarm intelligence, and allows for mapping of higher-order structures onto a lower-dimensional output space (2-dimensions in this case). A suggested approach for converting the raw geochemical data to isometric log-ratios is developed for the system and results from DBS and robust PCA are compared and contrasted. Both PCA and DBS results show similar clustering of feed coal (FC) and pulverized coal (PC) samples and bottom ash (BA) with economizer fly ash (EFA) samples. However, within group variation of the samples was relatively difficult to identify in the PCA analysis. Results from DBS show that the BA and EFA samples are geochemically distinguishable based on the relative abundances of As, Cd, and Pb vs. Cr and Ni and As and Cd vs. Pb. Similarly, the PC and FC samples exhibit statistically different Cr vs. Ni ratios, which was not obvious from the PCA scores. Abnormally low Hg to Cl ratios in the fly ash (FA) samples relative to the rest of the samples, provided insight into the preferential removal of Hg vs. Cl, possibly in response to the generation of more gaseous oxidized mercury in the flue gas, owing to elevated Cl concentrations in the coal. Such findings demonstrate that DBS and other machine learning techniques allow for quick visualization of complex geochemical data structures in a way that is both consistent with the principals of CoDA and an improvement over classical methods.
A novel neural network model with distributed evolutionary approach for big data classification

The considerable improvement of technology produced for various applications has resulted in a growth in data sizes, such as healthcare data, which is renowned for having a large number of variables and data samples. Artificial neural networks (ANN) have demonstrated adaptability and effectiveness in classification, regression, and function approximation tasks. ANN is used extensively in function approximation, prediction, and classification. Irrespective of the task, ANN learns from the data by adjusting the edge weights to minimize the error between the actual and predicted values. Back Propagation is the most frequent learning technique that is used to learn the weights of ANN. However, this approach is prone to the problem of sluggish convergence, which is especially problematic in the case of Big Data. In this paper, we propose a Distributed Genetic Algorithm based ANN Learning Algorithm for addressing challenges associated with ANN learning for Big data. Genetic Algorithm is one of the well-utilized bio-inspired combinatorial optimization methods. Also, it is possible to parallelize it at multiple stages, and this may be done in an extremely effective manner for the distributed learning process. The proposed model is tested with various datasets to evaluate its realizability and efficiency. The results obtained from the experiments show that after a specific volume of data, the proposed learning method outperformed the traditional methods in terms of convergence time and accuracy. The proposed model outperformed the traditional model by almost 80% improvement in computational time.