Reviewerly/exHarmony huggingface dataset:
A cyber‐physical management system for medium‐scale solar‐powered data centers

Summary The effort to reduce the environmental impact and carbon footprint of data‐center operations has led to the emergence of “green” data centers, which are designed to reduce energy consumption and to increase their use of Renewable Energy Sources (RES). Despite the advances demonstrated by hyper‐scale facilities in energy efficiency and the use of green energy, small and medium‐scale data centers, which contribute to over 50% of the total electricity consumption and carbon emissions of the sector, face significant challenges in the adoption and exploitation of RES. In this article, we present the steps taken to transform a medium‐scale, academic data center into a “green” one that uses solar power. In particular, we describe the design and implementation of: (i) a collocated photovoltaic facility and (ii) a cyber‐physical system comprising IoT sensor devices, a microservices platform, and a visualization and analytics dashboard that supports the configuration and monitoring of the infrastructure. Using data collected from the platform and dashboard, we show the environmental and financial advantages derived from this transformation, and the potential that arises from the availability of integrated operational data.
Offline handwritten Tai Le character recognition using wavelet deep convolution features and ensemble deep variationally sparse Gaussian processes

Handwriting recognition is an important application in pattern recognition. Because handwritten Tai Le has similar characters and the proportion of characters with similar shapes is relatively high, this paper proposes a wavelet deep convolution (WDC) feature combined with an ensemble deep variational sparse Gaussian process (EDVSGP) offline handwritten Tai Le recognition method. A Tai Le recognition sample database is constructed, 2 and 3 levels of wavelet decomposition prior features are extracted, and then, the wavelet a priori feature is transformed into a WDC feature. To avoid the dimensional disaster caused by feature fusion, a dimensionality reduction method combining LDA and PCA is used to reducing the dimensionality of the fused features without reducing the accuracy rate. Moreover, to better recognize handwritten Tai Le characters, 6 deep variationally sparse Gaussian process (DVSGP) models are integrated as an EDVSGP classification model. Using the handwritten Tai Le character database, the proposed method is superior to the existing methods, with an accuracy, recall and F1-score of 94.15%, 94.15%, and 94.15%, respectively. The universality of the method is verified on a dataset of handwritten Chinese characters with similar shapes (HCFC2021.4) and the Devanagari dataset.
An Optimized Convolutional Neural Network for the 3D Point-Cloud Compression

Due to the tremendous volume taken by the 3D point-cloud models, knowing how to achieve the balance between a high compression ratio, a low distortion rate, and computing cost in point-cloud compression is a significant issue in the field of virtual reality (VR). Convolutional neural networks have been used in numerous point-cloud compression research approaches during the past few years in an effort to progress the research state. In this work, we have evaluated the effects of different network parameters, including neural network depth, stride, and activation function on point-cloud compression, resulting in an optimized convolutional neural network for compression. We first have analyzed earlier research on point-cloud compression based on convolutional neural networks before designing our own convolutional neural network. Then, we have modified our model parameters using the experimental data to further enhance the effect of point-cloud compression. Based on the experimental results, we have found that the neural network with the 4 layers and 2 strides parameter configuration using the Sigmoid activation function outperforms the default configuration by 208% in terms of the compression-distortion rate. The experimental results show that our findings are effective and universal and make a great contribution to the research of point-cloud compression using convolutional neural networks.
The irresponsibility of not using AI in the military

Abstract The ongoing debate on the ethics of using artificial intelligence (AI) in military contexts has been negatively impacted by the predominant focus on the use of lethal autonomous weapon systems (LAWS) in war. However, AI technologies have a considerably broader scope and present opportunities for decision support optimization across the entire spectrum of the military decision-making process (MDMP). These opportunities cannot be ignored. Instead of mainly focusing on the risks of the use of AI in target engagement, the debate about responsible AI should (i) concern each step in the MDMP, and (ii) take ethical considerations and enhanced performance in military operations into account. A characterization of the debate on responsible AI in the military, considering both machine and human weaknesses and strengths, is provided in this paper. We present inroads into the improvement of the MDMP, and thus military operations, through the use of AI for decision support, taking each quadrant of this characterization into account.
A Federated Learning and Deep Reinforcement Learning-Based Method with Two Types of Agents for Computation Offload

With the rise of latency-sensitive and computationally intensive applications in mobile edge computing (MEC) environments, the computation offloading strategy has been widely studied to meet the low-latency demands of these applications. However, the uncertainty of various tasks and the time-varying conditions of wireless networks make it difficult for mobile devices to make efficient decisions. The existing methods also face the problems of long-delay decisions and user data privacy disclosures. In this paper, we present the FDRT, a federated learning and deep reinforcement learning-based method with two types of agents for computation offload, to minimize the system latency. FDRT uses a multi-agent collaborative computation offloading strategy, namely, DRT. DRT divides the offloading decision into whether to compute tasks locally and whether to offload tasks to MEC servers. The designed DDQN agent considers the task information, its own resources, and the network status conditions of mobile devices, and the designed D3QN agent considers these conditions of all MEC servers in the collaborative cloud-side end MEC system; both jointly learn the optimal decision. FDRT also applies federated learning to reduce communication overhead and optimize the model training of DRT by designing a new parameter aggregation method, while protecting user data privacy. The simulation results showed that DRT effectively reduced the average task execution delay by up to 50% compared with several baselines and state-of-the-art offloading strategies. FRDT also accelerates the convergence rate of multi-agent training and reduces the training time of DRT by 61.7%.
Deeply‐Recursive Attention Network for video steganography

Abstract Video steganography plays an important role in secret communication that conceals a secret video in a cover video by perturbing the value of pixels in the cover frames. Imperceptibility is the first and foremost requirement of any steganographic approach. Inspired by the fact that human eyes perceive pixel perturbation differently in different video areas, a novel effective and efficient Deeply‐Recursive Attention Network (DRANet) for video steganography to find suitable areas for information hiding via modelling spatio‐temporal attention is proposed. The DRANet mainly contains two important components, a Non‐Local Self‐Attention (NLSA) block and a Non‐Local Co‐Attention (NLCA) block. Specifically, the NLSA block can select the cover frame areas which are suitable for hiding by computing the correlations among inter‐ and intra‐cover frames. The NLCA block aims to effectively produce the enhanced representations of the secret frames to enhance the robustness of the model and alleviate the influence of different areas in the secret video. Furthermore, the DRANet reduces the model parameters by performing similar operations on the different frames within an input video recursively. Experimental results show the proposed DRANet achieves better performance with fewer parameters than the state‐of‐the‐art competitors.
A Hybrid STA Based on Nelder–Mead Simplex Search and Quadratic Interpolation

State transition algorithm (STA) is a metaheuristic method for global optimization. However, due to the insufficient utilization of historical information, it still suffers from slow convergence speed and low solution accuracy on specific problems in the later stages. This paper proposes a hybrid STA based on Nelder–Mead (NM) simplex search and quadratic interpolation (QI). In the exploration stage, NM simplex search utilizes the historical information of STA to generate promising solutions. In the exploitation stage, QI utilizes the historical information to enhance the local search capacity. The proposed method uses an eagle strategy to maximize the efficiency and stability. The proposed method successfully combines the merits of the three distinct approaches: the powerful exploration capacity of STA, the fast convergence speed of NM simplex search and the strong exploitation capacity of QI. The hybrid STA is evaluated using 15 benchmark functions with dimensions of 20, 30, 50 and 100. Moreover, the results are statistically analyzed using the Wilcoxon signed-rank sum test. In addition, the applicability of the hybrid STA to solve real-world problems is assessed using the wireless sensor network localization problem. Compared with six state-of-the-art metaheuristic methods, the experimental results demonstrate the superiority and effectiveness of the proposed method.
The Recognition Algorithm of Two-Phase Flow Patterns Based on GoogLeNet+5 Coord Attention

The two-phase flow in a microchannel consists of liquid-liquid and gas-liquid material components. The automatic recognition of flow patterns using deep learning approaches has been emerging. This study aimed to improve the recognition accuracy of flow patterns in the two-phase flow images. The different convolutional kernels in the GoogLeNet algorithm extracted the image features with different scales. In order to strengthen the important channel and spatial features, this paper proposes the combined five-layer Coord attention and GoogLeNet algorithm to enhance the accuracy of the new algorithm. The optimized algorithm model was derived from image datasets with different liquid-liquid two-phase flows (NaAlg-Oil, GaInSn-Water), and its accuracy was 95.09% in training and 98.12% in testing. This new model was also applied to predict the flow patterns, with a recognition accuracy of more than 97% in both the liquid-liquid and gas-liquid two-phase flows (water-soybean oil, water-lubricating oil, and argon-water).
The AGP Model for Risk Management in Agile I.T. Projects

The vast majority of articles on risk in agile-managed projects fail to adequately address the interplay between the agile methodology, the risk management process, and the elements that ultimately determine the success or failure of the project. Too frequently, processes and models are given undue priority over the human element. The aim of this article is to create a risk management model for agile I.T. projects (AGP model). The study sample consists of 1868 valid survey responses from European and Asian countries received between February 2022 and January 2023. We subjected the data to Exploratory Factor Analysis (EFA) and Cronbach’s alpha to identify four key factors for dealing with risks in I.T. projects and create the AGP model. The proposed AGP model outlines up to 76% variability in the potential risks that could arise during an I.T. project’s deployment. The findings of this study are critical for project managers, I.T. professionals, developers, and system architects involved in I.T. projects. Other stakeholders may be interested in understanding the risks associated with the project and developing strategies to mitigate these risks.
Crossing Levels: Meta-induction and the Problem of Induction

Abstract Gerhard Schurz claims to have a solution to Hume’s problem of induction based on results from machine learning concerning meta-induction. His argument has two steps. The first is to establish a justification for following a certain meta-inductive strategy based on its predictive optimality. The second step is to show how this justification can be transferred to object-induction. I unpack the second step and fail to find a convincing argument supporting the transfer of justification from meta-induction to object-induction. My conclusion is that the problem of induction has not yet been solved by appeal to meta-induction.
On the Use of Knowledge Transfer Techniques for Biomedical Named Entity Recognition

Biomedical named entity recognition (BioNER) is a preliminary task for many other tasks, e.g., relation extraction and semantic search. Extracting the text of interest from biomedical documents becomes more demanding as the availability of online data is increasing. Deep learning models have been adopted for biomedical named entity recognition (BioNER) as deep learning has been found very successful in many other tasks. Nevertheless, the complex structure of biomedical text data is still a challenging aspect for deep learning models. Limited annotated biomedical text data make it more difficult to train deep learning models with millions of trainable parameters. The single-task model, which focuses on learning a specific task, has issues in learning complex feature representations from a limited quantity of annotated data. Moreover, manually constructing annotated data is a time-consuming job. It is, therefore, vital to exploit other efficient ways to train deep learning models on the available annotated data. This work enhances the performance of the BioNER task by taking advantage of various knowledge transfer techniques: multitask learning and transfer learning. This work presents two multitask models (MTMs), which learn shared features and task-specific features by implementing the shared and task-specific layers. In addition, the presented trained MTM is also fine-tuned for each specific dataset to tailor it from a general features representation to a specialized features representation. The presented empirical results and statistical analysis from this work illustrate that the proposed techniques enhance significantly the performance of the corresponding single-task model (STM).
Darknet traffic detection and characterization with models based on decision trees and neural networks

The Darknet is a set of networks and technologies, having as fundamental principles anonymity and security. In many cases, they are associated with illicit activities, opening space for malware traffic and attacks to legitimate services. To prevent Darknet misuse is necessary to classify and characterize its existing traffic. In this paper, we characterize and classify the real Darknet traffic available from the CIC-Darknet2020 dataset. In that sense, we performed the feature extraction and grouped the possible subnets with an n-gram approach. Furthermore, we evaluated the relevance of the best features selected by the Recursive Feature Elimination method for the problem. Our results indicate that simple models, like Decision Trees and Random Forests, reach an accuracy above 99% on traffic classification. Our methodology represents a gain of up to 13% in comparison with the state-of-the-art.
Impact of Demographics on Automated Criminal Tendency Detection from Facial Images

An individual’s face can provide important insight about his personal traits like age, psychology, health, ethnicity, emotions, kinship, and much more. The biometric potentials of facial images make them an ideal tool for various forensic inferences. One such interesting area of research is the detection of criminal tendencies in people from their facial images. Several studies have proposed machine and deep learning-based solutions for this purpose. However, to the best of our knowledge, none have explicitly analyzed the impact of demographic attributes on the performance of such systems. In this paper, we provide an in-depth analysis to measure the impact of three important demographic properties i.e. age, gender, and ethnicity on facial image-based criminality detection systems. For this purpose, a balanced dataset is prepared as there was no such dataset available with age, race, and gender splits. The performance of various convolutional neural network architectures (VGG-16, VGG-19, and FaceNet) is evaluated to assess their potential in perceiving criminal tendencies. Based on the outstanding performance of FaceNet, it is selected to measure the impact of different demographic groups in detecting criminal tendencies from facial images. The analysis presented in this study can prove vital for the development of robust and unbiased systems that can provide reliable proactive solutions for the security of all communities.
Tri-model ensemble with Grid Search optimization for Bipolar Disorder Diagnosis

Bipolar disorder is one of the common mood disorders and diagnosis is the most important part of mood disorders. This research involves sequence classification of bipolar 1, bipolar 2, and cyclothymia using psychiatric cliff notes, there were 200 samples of bipolar 1, 200 samples of bipolar 2, and 200 samples of cyclothymia. This work uses a novel tri-model based ensemble for the diagnosis of bipolar disorder with grid search based optimization. The paper involved several textual preprocessing techniques like lower casing, punctuation removal, and lemmatization and it involved the tfidf approach for feature extraction of important attention words from paragraph based textual data. After preprocessing the ensemble was created using three models Decision tree, Random Forest, and Adaboost. The ensemble was optimized with grid search optimization with an early stopping mechanism to prevent overfitting. The ensemble’s classification prediction was determined by the highest vote from the 3 individual models. The tri-model ensemble produced excellent results with an accuracy of 99% and precision, recall, and f1-score of 98%, outperforming other studies on text based mental health disorders. This work is the first work to include cyclothymia variants of bipolar disorder and involved complete coverage of all the 3 types of bipolar disorder. This work can help to facilitate bipolar patients and provide an extremely accurate diagnosis of all types of bipolar disorder in a real world scenario.
Application of Deep Learning in Microseismic Waveform Classification: A Case Study of The Yebatan Hydropower Station Project

Deep learning plays an important role in automatic classification of microseismic (MS) waveforms, but there is still a problem of low recognition accuracy based on small training samples in engineering applications. We propose an enhanced convolutional natural network (ECNN) based on ACGAN structure for MS waveforms classification, where the generator is used to synthesize samples of specified type and the discriminator is used to identify class and authenticity. The generator and the discriminator are confronted with each other through alternate training, so as to ensure the accurate classification of MS waveforms with small training samples. Then, relying on the Yebatan hydropower project, 15503 microseismic waveforms are processed to study the impact of training sample changes on ECNN and traditional CNN model. The results show that the classification accuracy of the two models tends to be stable with more than 1024 samples, and decreases rapidly with less than 512 samples; ECNN method has higher classification accuracy than traditional CNN method under the same training samples, and the accuracy increases more significantly with the reduction in sample size. The research results can reduce the difficulty of modeling waveform classification and help the application of deep learning in MS monitoring.
IMPLEMENTATION OF DIGITAL FILTERS USING NEURAL NETWORKS WHEN THE OBJECT MOVEMENT DYNAMICS ARE NOT KNOWN

Considered: Neural network-based algorithms to obtain an optimal digital filter.Recurrent neural networks can be used to implement an adaptive digital filter considering the characteristics of both interference and useful signal.The developed program trains recurrent neural networks and as a result obtains the coefficients of recursive digital filters.The efficiency of the proposed algorithm is confirmed with numerical experiments results.
Pictonaut: movie cartoonization using 3D human pose estimation and GANs

Abstract This article describes Pictonaut, a novel method to automatically synthetise animated shots from motion picture footage. Its results are editable (backgrounds, characters, lighting, etc.) with conventional 3D software, and they have the finish of professional 2D animation. Rather than addressing the challenge solely as an image translation problem, a hybrid approach combining multi-person 3D human pose estimation and GANs is taken. Sub-sampled video frames are processed with OpenPose and SMPLify-X to obtain the 3D parameters of the pose (body, hands and face expression) of all depicted characters. The captured parameters are retargeted into manually selected 3D models, cel shaded to mimic the style of a 2D cartoon. The results of sub-sampled frames are interpolated to generate a complete and smooth motion for all the characters. The background is cartoonized with a GAN. Qualitative evaluation shows that the approach is feasible, and a small dataset of synthetised shots obtained from real movie scenes is provided.
Federated learning privacy incentives: Reverse auctions and negotiations

Abstract The incentive mechanism of federated learning has been a hot topic, but little research has been done on the compensation of privacy loss. To this end, this study uses the Local SGD federal learning framework and gives a theoretical analysis under the use of differential privacy protection. Based on the analysis, a multi‐attribute reverse auction model is proposed to be used for user selection as well as payment calculation for participation in federal learning. The model uses a mixture of economic and non‐economic attributes in making choices for users and is transformed into an optimisation equation to solve the user choice problem. In addition, a post‐auction negotiation model that uses the Rubinstein bargaining model as well as optimisation equations to describe the negotiation process and theoretically demonstrate the improvement of social welfare is proposed. In the experimental part, the authors find that their algorithm improves both the model accuracy and the F1‐score values relative to the comparison algorithms to varying degrees.