Reviewerly/exHarmony huggingface dataset:
The Application of Smart Supply Chain Technologies in The Moroccan Logistics

The new technology has recently experienced remarkable progress which enlarged its domain of application. In supply chain domain, this increase appeals to new models that can collect, analyze and control big data. Emerging technologies form the 4th industrial revolution which consider the smart supply chain as essential component of it. This composition articulates on many concepts such as Internet of Things and Blockchain. These smart concepts help supervising the supply chain by supervising its equipment. Internet of Things consists of creating an interconnected field of all the objects composing the logistic chain and makes them active and accessible in real time. While Blockchain stills a very trending and complex concept that can be exploited advantageously in the transmission and storage of data in a secure and decentralized manner. The purpose of this study is to present, initially, a global view of the Moroccan industry classification according to its Gross Domestic Product, then, studying the percentage of major contributions of the industrial branches. In addition to that, to design a survey about the application of smart supply chain technologies in the Moroccan industry.
Estimating the Secret Key of Spread Spectrum Watermarking Based on Equivalent Keys

The security of spread spectrum (SS) watermarking largely depends on the difficulty of estimating its secret key. Some estimators have been proposed to estimate the secret key in the known-message attack (KMA) scenario. However, the estimation accuracies of existing estimators are not satisfactory when the number of observations is not large enough. Currently, it is still a challenging and open problem to design more effective estimators. In this paper, we propose an equivalent keys (EK)-based estimator to estimate the secret key for both the traditional and more secure SS watermarking methods. Equivalent keys form an equivalent region, which is the intersection of a unit hypersphere and a hypercone. According to the Monte Carlo simulation, we find that the secret key can be estimated by adding up the equivalent keys uniformly sampled from the equivalent region. Thus, the proposed estimator selects equivalent keys from randomly-generated vectors by exploiting the pairs of watermarked signals and their embedded messages. A theoretical analysis is performed for the proposed estimator to evaluate the estimation accuracy. Experimental results verify the theoretical analysis and show the superiority of the proposed estimator over existing estimation methods. Furthermore, this paper also shows the insecurity of the more secure SS watermarking methods in the KMA scenario from a practical perspective for the first time.
A Rapid Evaluation Technology for SEU in Convolutional Neural Network Circuits

This paper proposes a rapid Single Event Upset (SEU) evaluation platform that can perform fault injection at the algorithm level in Convolutional Neural Network (CNN), which is designed by software and hardware co-design. This proposed platform analyzes the layer structure and input parameters of CNN, generates the corresponding fault list and then performs fault injection at the algorithm-level in software side and mapped to the registers of hardware acceleration. Finally, the operation results of CNN after fault injection is analyzed to evaluate the robustness of CNN against SEU, and identify the sensitive areas of SEU. In this paper, SEU fault injection experiments are carried out on YOLOv2 neural network. Experimental results show that fault injection and sensitivity evaluation based on this method can analyze the overall robustness of CNN against SEU quickly and effectively. From the analysis of experimental results, the algorithmic nodes which are sensitive to SEU are located, and the efficiency of this rapid evaluation technology is also verified.
Toward the Creation and Obstruction of DeepFakes

Abstract AI-synthesized face-swapping videos, commonly known as DeepFakes , is an emerging problem threatening the trustworthiness of online information. The need to develop and evaluate DeepFake detection algorithms calls for large-scale datasets. However, current DeepFake datasets suffer from low visual quality and do not resemble DeepFake videos circulated on the Internet. We present a new large-scale challenging DeepFake video dataset, Celeb-DF , which contains 5, 639 high-quality DeepFake videos of celebrities generated using an improved synthesis process. We conduct a comprehensive evaluation of DeepFake detection methods and datasets to demonstrate the escalated level of challenges posed by Celeb-DF. Then we introduce Landmark Breaker , the first dedicated method to disrupt facial landmark extraction, and apply it to the obstruction of the generation of DeepFake videos. The experiments are conducted on three state-of-the-art facial landmark extractors using our Celeb-DF dataset.
Translating quality-driven code change selection to an instance of multiple-criteria decision making

The definition and assessment of software quality have not converged to a single specification. Each team may formulate its own notion of quality and tools and methodologies for measuring it. Software quality can be improved via code changes, most often as part of a software maintenance loop. This manuscript contributes towards providing decision support for code change selection given a) a set of preferences on a software product’s qualities and b) a pool of heterogeneous code changes to select from. We formulate the problem as an instance of Multiple-Criteria Decision Making, for which we provide both an abstract flavor and a prototype implementation. Our prototype targets energy efficiency, technical debt and dependability. This prototype achieved inconsistent results, in the sense of not always recommending changes reflecting the decision maker’s preferences. Encouraged from some positive cases and cognizant of our prototype’s shortcomings, we propose directions for future research. This paper should thus be viewed as an imperfect first step towards quality-driven, code change-centered decision support and, simultaneously, as a curious yet pragmatic enough gaze on the road ahead.
Distance Education Technologies and the Present Situation Influenced by the Pandemic

This paper deals with the issue of digitalisation in education. Due to the COVID-19 global pandemic, the issue of distance education has become extremely important and is of great benefit for pedagogical practices. The transition from in-person forms of study to distance education with the use of online teaching is resulting in a number of new trends that are contributing to the development of digital literacy among both teachers and students. However, online teaching also has some negative consequences, especially in the social and interpersonal fields. In the paper, empirical research is presented with the aim of identifying the quality of online teaching among the students at selected secondary vocational schools.
An Efficient Minimum Spanning Tree-Based Color Image Segmentation Approach

In the contemporary scenario, image segmentation possesses a major role in a wide spectrum of applications as a pre-processing step such as object detection, face recognition, medical imaging, aerial imaging, etc. The efficient segmentation step in the practical application of the discussed tasks can significantly enhance their accuracy. However, a fast and efficacious segmentation approach is the need of the hour. Therefore, the authors proposed an efficient graph-based approach that adapts the clustering based on the Minimum Spanning Tree for the segmentation of color images. It divides the image into various sub-regions based on the spatial and color distance in an image. The key feature of the proposed approach is that it can process the megapixel-sized image in seconds. In the present paper, Berkeley Segmentation Dataset is utilized to conduct all the experiments. The evaluation of the proposed approach is done by considering the various evaluation parameters such as precision, recall, F-measure, specificity, Negative Rate Matrix (NRM), accuracy, error rate, and segmentation speed. The proposed approach outperforms the existing state-of-the-art approach (i.e., Simple Linear Iterative Clustering (SLIC) based segmentation).
Solar Radiation Components on a Horizontal Surface in a Tropical Coastal City of Salvador

Renewable energy must be prioritized by humankind, mainly if there is an expected increase of 50% in energy consumption by 2030 and climate change scenarios are also confirmed. Urban areas consume 70% of the available energy on the planet. Brazil, the largest country in South America, concentrates more than 85% of its population in urban areas, facing a challenge to increase the renewable power plants in its energy matrix. This work presents the solar radiation components behavior for the city of Salvador to contribute with initiatives for the use of solar energy resource. Firstly, a radiometric platform was implemented to obtain direct measurements of global (EG) and diffuse (EDF) components of incoming solar radiation at the surface. The knowledge of EDF is an important requirement to support photovoltaic system projects, and there is no information on direct measurements of this component in the State of Bahia. The diffuse radiation measurement device (DRMD) was designed and built for this purpose. The measurements of solar radiation components performed in this research were submitted to a specific quality control, statistically analyzed and used to evaluate the performance of different empirical models to represent the behavior of EDF in the tropical coastal city. The results demonstrate the potential to explore solar energy in the city of Salvador, with annual values of sunshine higher than 2200 h year−1 and average daily intensities of EG and EDF equal to 18.7 MJm−2day−1 and 7.35 MJm−2day−1, respectively. The analysis of the diurnal cycle shows that EG in summer reaches a maximum of 4.2 MJm−2h−1 and in the rainy season it reaches a minimum of 3.7 MJm−2h−1, both at noon, and in summer the EDFh is 35% of the EGh and it is 46% in the rainy season.
LightCvT: Audio Forgery Detection via Fusion of Light CNN and Transformer

The development of machine learning has greatly improved the quality and authenticity of artificial intelligence-generated audio, image, and video. However, the existence of such fake content poses a risk to people's daily life. It is important to explore and develop forensic technology to identify real and synthesized audio. We observe that these deep neural networks have a certain uniformity in cross-modal tasks, that is, the deep neural networks used for image forgery detection can also be applied to audio forgery detection. We propose a light transformer-based network LightCvT for audio forgery detection. LightCvT attempts to capture both local feature and non-local long-range dependencies to improve the detection accuracy of audio forgery. We combine the enhanced Light CNN structure and Vision Transformer (ViT) to take full advantage of them. Specifically, Light CNN's ability to extract local feature and ViT's ability to capture long-distance feature dependencies are adaptively complementary, leading to significant improvement of the global perception ability and the discriminability of the system. In addition, we introduce the self-attention mechanism into the convolutional token embedding to further model the relations between local regions. Comprehensive experiments are conducted and the results show that the proposed method achieves better performance than the state-of-the-art methods.
Crowd Anomaly Detection in Public Surveillance via Spatio-temporal Descriptors and Zero-Shot Classifier

Anomaly detection has become an imperative defense line in public surveillance systems to avert disasters and to monitor crowd behavior. In this paper, we proposed a robust anomaly detection system using Zero-Shot Learning (ZSL) classifier with hybrid Spatio-temporal descriptors, firstly, we pre-processed extracted video frames then we introduced semantic segmentation for foreground extraction depicting human pixels only after that we extract new robust Spatio-temporal descriptors including Geometric, SIFT, time domain and crowd contour variation descriptors. Next, the Particle Swarm optimization algorithm was used for optimal features selection, and finally, we introduced ZSL for decision-making based on extracted optimal features. We evaluate the performance of our proposed system on two publicly available UMN and MED benchmark datasets and achieved an accuracy rate of 93.5&#x0025; and 91&#x0025; respectively. Experimental results reveal that our system achieves superior accuracy as compared to existing well-known state-of-the-art models. The proposed system can be utilized at numerous public places, like train stations, shopping malls, airports, city centers, and university campuses to automatically detect, control, and supervise the crowd.
Gray Image and Colour Image Security using AES Algorithm

Abstract For picture security and confirmation we utilize cryptographic. In cryptographic we scramble the information. There are various symmetric and deviated encryption procedures, which are used for encryption and unscrambling reason. Symmetric encryption approach work on ordinary key (same key utilized for encryption and unscrambling reason), but unbalanced work on with one public key and private key. Public key utilized for encryption and private key for unscrambling. The security of the stowing away is measure utilizing the MSE, PSNR, Cross-Correlation and Entropy limits. For better stowing away get the low MSE and high PSNR respects. So to make the rapid reaction we need calculation which requires some speculation and give reaction quick. From this point forward additionally take apart the time reaction of purposed assessments to additionally foster security and expedient response.
Detection of Anomalies in Time Series Data

Time Series are the observations that are noted in regular manner and are correlated with time. They help to extract meaning and summary of dataset and are arranged in chronological order. Time series data help us to analyze from past behavior and predict future behavior as well. Some of Time series data which are available are data from satellite, ola cab rides, stock market, online transactions etc. Now-a-days determining anomalies in time series is a major area of interests among researchers. Data is growing rapidly in every area. For example every hour stock market price will get updated and handling such a huge data become difficult for humans. If the anomaly is not detected, people will invest in wrong business lose their money. In this study deep learning models like ARIMA, Isolation forest, and LSTM based autoencoders are used to detect the anomalies in the dataset. Here stock market dataset is used to check whether the closing price at the end of the day is correct value or not. The dataset is preprocessed and then send to any one of the above mentioned model and an analysis in the performance is done between the models.
Dilated Residual Shrinkage Network for SAR Image Despeckling

This paper proposes a dilated residual shrinkage network for SAR image despeckling (SAR-DRSN). The proposed SAR-DRSN consists of one main network and one shrinkage subnetwork. The main network is based on the dilated convolution and residual learning, and combined with the soft thresholding. The soft thresholding and shrinkage subnetwork are embedded in our network. In our SAR-DRSN, different thresholds are set for each feature adaptively by the shrinkage network. The feature map is calculated by the dilated convolution and shrinkage network, which can enhance the information of useful feature channels and make the target information more important. Experimental results show that, compare with the existing deep learning network for SAR image despeckling, the proposed SAR-DRSN has better despeckling performance according to both subjective visual assessment of image quality and objective evaluation.
EMG-Based Gesture Recognition System and Its Hardware Implementation

The starting point of this method is to help disabled people control their bodies better. This paper introduces a study of gesture recognition based on electromyography (EMG). To identify the muscle control signals in gestures, we used the six lead muscle electrical sensors. Through the use of the mix of six lead mscle electrical sensor and MATLAB, the data can be read and the available dataset can be generated. We use the statistical characteristics of mean and standard deviation to determine the threshold value, so as to reduce noise and ensure acceptable data accuracy. SVM and AdaBoost algorithm are fused. In a word, the accuracy of recognition is greatly increased by using the appropriate combination classifier. In order to present better experimental results and ensure the reliability and practicability of this study, we choose a robot car as the carrier to control the movement of the robot by five gestures of four subjects.
A blockchain based approach for the authorization policies delegation in emergency situations

Abstract There are many facets to implementing security within an organization and one such measure is the authorization or the access control process. The recent rise of decentralized file systems such as interplanetary file system (IPFS) allow for decentralized storage of resource using content addressing. Naturally, the authorization process for handling these resources needs to be decentralized as well and decentralized ledger technologies, such as the blockchain with the support of smart contracts can be used to manage the authorization process. In this article, we propose an approach which allows for decoupling authorization logic from the core capabilities of a smart contract and providing advance authorization capabilities, such as contextual delegation, to be readily available. The proposed approach is expressive and can handle many aspects related to authorization such as role‐based access control, conflicts, and redundancy reduction. We have used an efficient and modular approach for designing smart contracts. This makes them easier to manage and redeploy, if needed. The data structures used in the smart contracts are carefully chosen to minimize the gas cost, as evident from performance evaluation results.
Automated Classification of Potentially Insulting Speech Acts on Social Network Sites

Insulting speech acts have become the subject of public discussion in the media, social media, the basis for speculation in political communication, and a working concept in the legal environment. The present research article explores insulting speech acts on the social network site “VKontakte” aiming to develop an algorithm for automatic classification of text data. We conducted semantic analysis of the text of “Article 5.61” of the Code of Administrative Offenses of the Russian Federation, which made it possible to formulate inclusion criteria for formal classification. We used three common word embeddings models (BERT, ELMo, and fastText) on the original Russian language dataset consisting of 4596 annotated messages perceived as insulting speech acts. General findings argue that even in a specialized dataset the share of messages that meet criteria of inclusion is negligible. This indicates a low probability of going to court on the fact of an administrative offense under Article 5.61 based on speech communication on social network sites, even though such communication is public in nature and is automatically recorded in writing. Machine learning text classifier based on BERT model showed best performance.
Arabic Text Classification: A Literature Review

Automatic text classification or categorization consists to assign predefined classes or categories to a given set of text documents aiming to organize the document collection based on conceptual views. Although there are many text classifiers in the literature, most of them are assessed using English or other non-Arabic languages text collections. The lack of availability of a large collection in the Arabic language is one of the most important challenges facing the few numbers of existing Arabic text classifiers (ATC). We present in this paper a literature review in the domain of Arabic text classification. We firstly overview the ATC based on machine learning algorithms. Then, we investigate ATC based on deep learning techniques as well as a set of other classifiers based on non-ML algorithms. The assessment of these ATC is also discussed. Finally, we focus on some open problems and we suggest some future directions.
Use of Marker AR by Implementation on Production Equipment for Education of Students of Technical Fields: A Study

This chapter focuses on pointing out the possibility of implementing markers in augmented reality. Today, it has become a factor that is more and more popular. In this chapter, we gradually describe the basic concepts of augmented reality. Subsequently, we will show a practical example of the implementation of IRQ codes as markers, to which three basic types of information were assigned. As information, we will present a 3D model, a static image, and a text description. The advantage of our design is the variability and the ability to implement markers in a real model but also in a technical drawing. This chapter can serve as a manual for creating similar applications on the same principle. In the future, it is possible to expand the database of acquired knowledge and implement the presented principles for production practice.
User Scheduling in Federated Learning over Energy Harvesting Wireless Networks

In this paper, the deployment of federated learning (FL) is investigated in an energy harvesting wireless network in which the base station (BS) is equipped with a massive multiple-input multiple-output (MIMO) system and a set of users powered by independent energy harvesting sources to cooperatively perform FL. Since a certain number of users may not be served due to interference and energy constraints, a joint energy management and user scheduling problem is considered. This problem is formulated as an optimization problem whose goal is to minimize the FL training loss via optimizing user scheduling. To determine the effect of various wireless factors (transmit power and number of scheduled users) on training loss, the convergence rate of the FL algorithm is analyzed. Given this analytical result, the original user scheduling and energy management optimization problem can be decomposed, simplified and solved. Simulation results show that the proposed algorithm can reduce training loss compared to a standard FL algorithm.
The Application of Computer Technology in Mathematical Modeling

Abstract Based on the analysis of mathematical modeling, this paper further expounds the application of computer technology in mathematical modeling. The specific role of computer technology mainly includes establishing mathematical modeling by using computer programming and using computer software. At present, the development of mathematical modeling cannot be separated from the support of computer technology. Through the specific analysis of computer technology model formula, the value of computer technology to mathematical modeling is clarified.