Reviewerly/exHarmony huggingface dataset:
ElGamal algorithm with hyperchaotic sequence to enhance security of cloud data

Purpose In the cloud-computing environment, privacy preservation and enabling security to the cloud data is a crucial and demanding task. In both the commercial and academic world, the privacy of important and sensitive data needs to be safeguarded from unauthorized users to improve its security. Therefore, several key generations, encryption and decryption algorithms are developed for data privacy preservation in the cloud environment. Still, the outsourced data remains with the problems like minimum data security, time consumption and increased computational complexity. The purpose of this research study is to develop an effective cryptosystem algorithm to secure the outsourced data with minimum computational complexity. Design/methodology/approach A new cryptosystem algorithm is proposed in this paper to address the above-mentioned concerns. The introduced cryptosystem algorithm has combined the ElGamal algorithm and hyperchaotic sequence, which effectively encrypts the outsourced data and diminishes the computational complexity of the system. Findings In the resulting section, the proposed improved ElGamal cryptosystem (IEC) algorithm performance is validated using the performance metrics like encryption time, execution time, decryption time and key generation comparison time. The IEC algorithm approximately reduced 0.08–1.786 ms of encryption and decryption time compared to the existing model: secure data deletion and verification. Originality/value The IEC algorithm significantly enhances the data security in cloud environments by increasing the power of key pairs. In this manuscript, the conventional ElGamal algorithm is integrated with the pseudorandom sequences for a pseudorandom key generation for improving the outsourced cloud data security.
The Impact of ERP Systems on the Organization’s Sustainable Development in the Era of Digitalization

The International Conferences on Economics and Social Sciences (ICESS) organized by Bucharest University of Economic Studies provide an opportunity for all those interested in Economics and Social Sciences to discuss and exchange research ideas. The conference takes place in Bucharest University of Economic Studies, Piata Romana 6, Bucharest, Romania.Contact: conference.icess[@]ase.ro The papers presented at the Conference are available online in the Conference Proceedings series (ISSN 2704-6524): Volume 2019 Collaborative Research for Excellence in Economics and Social Sciences, ISBN 9788366675322 Volume 2020 Innovative Models to Revive the Global Economy, ISBN 9788395815072 Volume 2021 Resilience and Economic Intelligence Through Digitalization and Big Data Analytics, ISBN 9788366675704 The past years’ uncertainties and pressures resulted from the pandemic, alongside new developments in social technology, 5G, cloud computing, augmented and virtual reality, generated the perfect setting for the metaverse to gain traction. Several companies found there’s a critical need to redefine the work environment and started exploring the metaverse concept beyond the entertainment sector. Thus, new tools for collaboration and data visualization within a digital shared space are being created, aiming for a fully immersive interaction between the virtual and the physical worlds within the next few
New Communication Trends in Education: digital technologies and artificial intelligence

The article discusses new communication trends in education and reveals major trends in the media sphere, in particular, new data transfer rates, the use of artificial intelligence, the digital revolution in educational formats. The authors note that innovative technologies play an important role in the development of a culture of learning and open up new opportunities for professional development. These should increase the efficiency of the educational process, offer flexibility in the methods of obtaining knowledge, reveal in a new way the potential and social role of the teacher, acting in a new educational paradigm. The digitalization of the educational process, the inclusion of virtual technologies and artificial intelligence in the development of knowledge, the development of software are turning into a new educational industry, becoming a factor that effectively influences the depth of knowledge acquisition and determines the development dynamics of modern advanced universities and ed-tech companies. Based on the analysis of the recent publications and the following universal socio-cultural study with the participation of scientists from Russia, Kazakhstan and the international research company Ipsos – Сomcon, the authors outline key communication trends and scenarios for the development of education using digital technologies and artificial intelligence in the foreseeable future.
Multi-Stage Transformer 3D Object Detection Method

With the development of autonomous driving, 3D object detection has experience great expectations. As the light detection and ranging (LiDAR) sensor can precisely measure the distance between environments and themselves, it has become the key component of current 3D object detection methods. However, the varing density and unstructure storage of LiDAR points cloud make it hard for feature learning. To tackle this problem, this paper proposes a multi-task transformer 3D object detection method.This method include a fast transformer based 3D encoder and a multi-stage transformer decoder. Extensive experiments demonstrate that our method can supress current other 3D object detection methods with a clear margin.
A Sentiment Analysis Method Based on Bidirectional Long Short-Term Memory Networks

Abstract Although the traditional recurrent neural network (RNN) model can cover the time information of the whole sentence theoretically, the gradient is dominated by the short-term gradient, and the long-term gradient is very small, which makes it difficult for the model to learn the long-distance information, and thus the effect of RNN on long text sentence recognition is poor. The long short-term memory network (LSTM) introduces the gate mechanism, especially the forgetting gate, which improves the disappearance of the gradient of RNN. Therefore, LSTM can store long text information and remove or increase the ability of information interaction by adding gate structure, which has natural advantages for long text processing. Based on the word vector matrix of GloVe model, on the open-source comment sentiment140 data set, we use the TensorFlow framework to construct the LSTM neural network and divide the data into the training set and test set based on the ratio of 4:1, design and implement the sentiment analysis published by Twitter users based on LSTM model, and then propose the bidirectional LSTM (Bi-LSTM) sentiment analysis method. The experimental results show that the accuracy of bidirectional LSTM is higher than that of unidirectional LSTM in sentiment analysis.
Efficient anomaly recognition using surveillance videos

Smart surveillance is a difficult task that is gaining popularity due to its direct link to human safety. Today, many indoor and outdoor surveillance systems are in use at public places and smart cities. Because these systems are expensive to deploy, these are out of reach for the vast majority of the public and private sectors. Due to the lack of a precise definition of an anomaly, automated surveillance is a challenging task, especially when large amounts of data, such as 24/7 CCTV footage, must be processed. When implementing such systems in real-time environments, the high computational resource requirements for automated surveillance becomes a major bottleneck. Another challenge is to recognize anomalies accurately as achieving high accuracy while reducing computational cost is more challenging. To address these challenge, this research is based on the developing a system that is both efficient and cost effective. Although 3D convolutional neural networks have proven to be accurate, they are prohibitively expensive for practical use, particularly in real-time surveillance. In this article, we present two contributions: a resource-efficient framework for anomaly recognition problems and two-class and multi-class anomaly recognition on spatially augmented surveillance videos. This research aims to address the problem of computation overhead while maintaining recognition accuracy. The proposed Temporal based Anomaly Recognizer (TAR) framework combines a partial shift strategy with a 2D convolutional architecture-based model, namely MobileNetV2. Extensive experiments were carried out to evaluate the model’s performance on the UCF Crime dataset, with MobileNetV2 as the baseline architecture; it achieved an accuracy of 88% which is 2.47% increased performance than available state-of-the-art. The proposed framework achieves 52.7% accuracy for multiclass anomaly recognition on the UCF Crime2Local dataset. The proposed model has been tested in real-time camera stream settings and can handle six streams simultaneously without the need for additional resources.
Quantum information processing, quantum computing, and quantum error correction: an engineering approach, 2nd edition

"Quantum information processing, quantum computing, and quantum error correction: an engineering approach, 2nd edition." Contemporary Physics, ahead-of-print(ahead-of-print), p. 1
Deep Reinforcement Learning with Omnidirectional Images: application to UAV Navigation in Forests

Deep Reinforcement Learning (DRL) is highly efficient for solving complex tasks such as drone obstacle avoidance using cameras. However, these methods are often limited by the camera perception capabilities. In this paper, we demonstrate that point-goal navigation performances can be improved by using cameras with a wider Field-Of-View (FOV). To this end, we present a DRL solution based on equirectangular images and demonstrates its relevance, especially compared to its perspective version. Several visual modalities are compared: ground truth depth, RGB, and depth directly estimated from these <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$360^{\circ}$</tex> RGB images using Deep Learning methods. Next, we propose a spherical adaptation to take into account the spherical distortions of omnidirectional images in the convolutional neural networks (CNNs) used in the actor-critic network and show a significant improvement in navigation performance. Finally, we modify the perspective depth estimation network using this spherical adaptation and demonstrate a further performance improvement.
Paraconsistent inference relations induced from inconsistency measures

This article presents a family of paraconsistent inference relations which aim at reasoning with inconsistent knowledge bases. These inference relations are defined from inconsistency measures. We check under which conditions these inference relations satisfy a series of properties for nonmonotonic reasoning. We also show that they are paraconsistent when the corresponding inconsistency measures satisfy some rationality postulates, and even strictly paraconsistent in most cases. In addition, some dependencies and incompatibilities among some rationality postulates are shown.
Detection of Fake Job Advertisements using Machine Learning algorithms

Most companies nowadays use digital platforms to host conferences, job interviews, and other business events. The unexpected increase in the need for internet platforms has resulted in a rapid rise of fraud advertising. The agencies as well as fraudsters recruit the job seekers using a variety of techniques, including sources from online job-providing websites. By applying Machine Learning algorithms, researchers aim to decrease the number of such fraudulent and fake attempts. In this article, classifiers such as K-Nearest Neighbour, Support Vector Machine, and Extreme Gradient Boosting algorithms are implemented for fake advertisement prediction. The performances of the machine learning algorithms are evaluated using metrics such as accuracy, F1 measures, precision and recall.
Geochemical Baseline of Heavy Metals in Topsoil on Local Scale and Its Application

Bengbu is the central city of Northern Anhui Province in the Huai River Basin, China.No relevant research has yet discussed the geochemical baseline of heavy metals in topsoil of Bengbu.In this study, 139 topsoil samples were collected and analyzed for heavy metal content from different districts in Bengbu main urban area, and the methods of normalization and cumulative frequency distribution (CFD) were used to determine the geochemical baseline.The results showed that the mean baseline values of Cr, Cu, Ni, Pb, Zn, Mn, and As were 40.10, 30.77, 31.41,30.30, 54.23, 422.92, and 1.21 mg/kg, respectively.Based on these values, baseline factor pollution index (BFPI) and geoaccumulation index (I geo ) were applied to assess the enrichment level of the heavy metals.The mean indices (I geo and K i ) for Cr, Cu, Ni, Pb, Zn, Mn, and As were all I geo < 0 and K i ≤ 1.2, indicating there was no heavy metal accumulation in study area as a whole.I geo showed that 1.44% and 0.72% of the samples for Zn and Pb were moderately accumulated, and BFPI showed that the high accumulation rates of Zn, As, Pb and Cu in the samples accounted for 6.47%, 4.32%, 1.44% and 0.72%, respectively, the enrichment of heavy metals at individual sampling points needs attention.
Problems of Humanitarian Knowledge in the Era of Total Digitalization

The paper describes the problems that are posed to humanitarian education in the era of global digitalization. The study’s aim is to confirm the substantiation of the significance of humanitarian knowledge in the formation of conceptual thinking as a basic skill of human intellectual activity in the modern world. The study uses a comparative analytical method which examines the features of the conceptual thinking formation in a modern school and university, as well as substantiates the importance of humanitarian knowledge as the basis for the of conceptual thinking formation as an axiological basis for the personal and civic education of students; the formation of their social intelligence, patriotism, civil and personal responsibility. The author analyzed features of the use of visual aids as a deforming method in the conceptual thinking formation of modern students and schoolchildren in the era of total digitalization, as well as the experience of distance learning in the humanities in a pandemic situation. It is concluded that humanitarian knowledge is the most important resource in the formation of conceptual thinking as a basic component of intellectual development, socialization and education of civil and personal self-awareness of students in higher education.
Robot Dialog System in the Context of Hospital Receptionist and its Demonstration

Abstract Task-oriented dialogue systems play an important role in human-robot interaction. Task-oriented dialogue systems help users achieve specific goals, and robots with systems are very effective in terms of reducing time and effort when replacing human workers. The conventional pipeline method, which has disadvantages such as a large amount of cost and time for development, has been applied to reception robots. For example, developers manually define responses that correspond to user input. Also, they use predefined robotic actions such as gestures and facial expressions. Recently, end-to-end learning of Recurrent Neural Networks is an attractive solution for the dialogue system. Based on the strengths of RNNs, we propose a social robot system in the context of hospital receptionists. We utilize Hybrid Code Network as an end-to-end dialogue system and extend it to select both response and gesture using RNN-based gesture selector. A user study is conducted comparing our proposed system with one of the existing methods, a rule-based approach. The empirical results show that the proposed method has an advantage in terms of dialog efficiency, which indicates how efficiently the user performed a given task with the help of a robot. In addition, there is no significant difference in experimental results between the proposed RNN based gesture selection and the rule-based gesture selection.
Development of a Multi-User Remote Video Monitoring System Using a Single Mirror-Drive Pan-Tilt Mechanism

In this paper, we developed a concept of video monitoring system using a single mirror-drive pan-tilt mechanism. The system provides multiple zoomed videos with controllable viewing angle for each zoomed video and a wide-angle video. The system can be accessed by several users by connecting their personal computer (PC) to the server PC through the network. Every user is granted to change of their respected viewing angle of zoomed videos. The system is suitable for the remote observation deck for sight-seeing purpose. The system is composed of two high-speed cameras with wide-angle and zoomed lens, and a high-speed mirror-drive pan-tilt mechanism. The system implements a convoluted neural network-based (CNN-based) object detection to assist every user client identifying objects appearing on wide-angle and zoomed videos. We demonstrated that our proposed system is capable to provide wide-angle and zoomed videos with CNN-based object detection to four clients, where each client receives a 30 frames per second zoomed video.
An improved practical Byzantine fault-tolerant consensus algorithm combined with aggregating signature

The consensus algorithm is a key technology of blockchain and determines the performance of distributed systems. The PBFT algorithm with high communication complexity and poor scalability is not suitable for large-scale networks with a large number of nodes. Aiming at these problems, an Improved Practical Byzantine Fault-Tolerant consensus algorithm (IPBFT) was proposed. Combined with the aggregating signature, IPBFT improved the consensus process and reduce the communication complexity. In addition, the introduction of Security Node Set (SNS) could quickly locate honest nodes, and the honest nodes were used as the candidates for the primary nodes of the next round to effectively reduce the time delay caused by view switching. At the same time, nodes were allowed to dynamically join or withdraw from the consensus network. New added nodes participate in a new round of consensus process when one consensus was completed. Simulation results show that the IPBFT contributes to reducing communication overhead and improving scalability. Meanwhile, the activity and security of the system can be maintained by the algorithm.
Embedding cognitive framework with self-attention for interpretable knowledge tracing

Recently, deep neural network-based cognitive models such as deep knowledge tracing have been introduced into the field of learning analytics and educational data mining. Despite an accurate predictive performance of such models, it is challenging to interpret their behaviors and obtain an intuitive insight into latent student learning status. To address these challenges, this paper proposes a new learner modeling framework named the EAKT, which embeds a structured cognitive model into a transformer. In this way, the EAKT not only can achieve an excellent prediction result of learning outcome but also can depict students' knowledge state on a multi-dimensional knowledge component(KC) level. By performing the fine-grained analysis of the student learning process, the proposed framework provides better explanatory learner models for designing and implementing intelligent tutoring systems. The proposed EAKT is verified by experiments. The performance experiments show that the EAKT can better predict the future performance of student learning(more than 2.6% higher than the baseline method on two of three real-world datasets). The interpretability experiments demonstrate that the student knowledge state obtained by EAKT is closer to ground truth than other models, which means EAKT can more accurately trace changes in the students' knowledge state.
New Insights into the Evolution and Footprints of the Paraíba Au-Cu-Mo Deposit, Alta Floresta Mineral Province (Brazil), through Integration of Spectral and Conventional Methods

The Paraíba is an Au-Cu-Mo deposit located in the southern part of the Amazon Craton, in the Alta Floresta Mineral Province, Mato Grosso State, Brazil. It is composed of Au-Cu-bearing quartz veins and Cu-Mo-rich hydrothermal breccias and stockworks, both associated with several hydrothermal alteration zones. The integration of spectral (reflectance and imaging spectroscopy) and conventional techniques (core logging, petrography, mineral chemistry, and scanning electron microscopy) was applied to map alteration zones, identify mineral parageneses, and determine the evolution of the deposit to generate a solid proxy for mineralized zones. This study shows an overlapping of two different mineralization systems in the AFMP, referenced hereafter as alteration Groups 1 and 2. Group 1 is an Orogenic Gold system related to Au-Cu-rich quartz veins in phyllonites and mylonites. Group 2 is a Cu-Mo Porphyry system related to a syenogranite intrusion and the subsequent generation of several hydrothermal alteration zones, Cu and Mo hydrothermal breccias, and stockwork mineralization. This study reveals several alteration footprints, guides, and vectors for the mineral exploration of these deposits in the Alta Floresta Mineral Province.
Challenges of Cyber Security and a Fundamental Way to Address Cyber Security

There is almost never a day that the topic of cyber security is not on the front page of a leading newspaper or a prominent website. It seems to be a constant game of cat and mouse between the hacker community and the security community in terms of providing the protection for information, user data (corporate, financial, etc.) and user privacy to the point that the security community cannot provide hot patches and fixes fast enough. Adding to this is the undisputed evidential linking of many hacking incidents and capabilities to sponsorship from powerful and capable states that can provide almost unlimited attack avenues. In the past, security controls and protocols for the Internet, communication, and systems are all practically built as an after thought and the need to have the system working and demonstrated trumps the need to make sure the system works in a secure manner. Functions over security, as many have seen and employed in the past simply does not work and it is the number one cause for many of the cyber security attacks that are on the front page today. Obvious, this model has not worked very well in the industry because we continue to see cyber security threat as the number one threat in many literatures and surveys. While recognizing this is a fundamental problem that we are facing for many years, why is it that this continues to be the number one issue, year after year? What is preventing the problems to be dealt with head-on? 190There are countless of security professionals, organizations, and governmental agencies that are committed to deal with this and yet the problem persists. What is working and what is not?