Reviewerly/exHarmony huggingface dataset:
METAVERSE: TECHNOLOGY OF THE FUTURE

Since the birth of the internet, its fast development and the introduction of a new technological era with Metaverse recently, transferred many things done by the individuals, to digital world from the real one. All necessities of humans, in this digital world, as shopping to education are done in a 3D virtual setting where payments are done with crypto money instead of real money. Even the artwork is now available to buyers in the digital environment. With Metaverse, new professions have emerged and various money-making means shifted the economy to different dimensions. Metaverse brings conveniences to human lives but there are also some difficulties appearing on the way presented as disadvantages and uncertainties. As a new thing for human life, its positive sides catch more attention. Through the augmented reality and virtual reality technologies, real-life objects can be carried into virtual environments where its users experience a real life feeling. In this study, the historical development of the internet and its current version are discussed. The history of the Internet, which started with Web 1.0 with a non-interactive and static structure, continued with Web 2.0 with an interactive and dynamic structure, has developed to the present day as Web 3.0 which can be interpreted by computers and machines. Moreover, in this study, the positive and negative effects of Metaverse that may have on human lives and games in the future is discussed and specified. In addition, financially important Blockchain technology that is widely used nowadays is also analyzed.
The Use of Ontology in the Process of Designing Adaptive Software Systems

The analysis of approaches and methods for creating adaptive and self-adaptive software systems has been carried out. It has been determined that most approaches require reconfiguration to change the functionality of the software. An ontology of the adaptive software system has been formed, which allows to present the main components of the software without reference to the structure of the subject area. The architecture of an adaptive software system was designed, which combines the principles of component-oriented and client-server architecture. Such solution will allow the software to be dynamically adapted depending on the needs of the user. An analysis of the characteristics of the ontological model based on quality assessment metrics has been carried out.
PLRec: An Efficient Approach Towards E-Learning Recommendation Using LSTM-CNN Technique

With the rapid increase in Social Web applications, a significant amount of research has been dedicated to the analysis and development of a personalized recommendation using social tags. The social tags will allow the users to select their preferences and object of interest. Different machine learning and neural networks are applied widely to address the problem of tag ambiguity. However, conventional machine learning algorithms require additional training models to train recommendation models for processing large scale data. Hence they are considered to be inappropriate for generating personalized recommendations in learning systems. This research presents an LSTM-CNN-based approach for generating appropriate learning recommendations. The paper presents an ontology-based similarity metric to overcome the issue of tag ambiguity without requiring any additional training model. The proposed approach semantically examines and quantifies the similarities between user and item profiles by matching the concepts of tags. The effectiveness of this approach was validated using different performance metrics and results validate the performance of the proposed approach.
G-DriverAUT: A Growable Driver Authentication Scheme Based on Incremental Learning

As an important part of intelligent and connected vehicles (ICVs) safety, driver authentication is a hot research direction in recent years. The existing driver authentication schemes in the industry are based on scret key, biometric characteristics or image characteristics, and these schemes always have efficiency and security problems in real-time driver authentication. In order to make up for these shortcomings, researchers propose driver authentication schemes based on driver behaviors and conduct in-depth explorations. However, the existing behavior-based schemes rely on a fixed number of drivers, when a new driver is added as an authorized user, existing schemes need to retrain the whole model to authenticate all drivers, cost many computing resources and can not meet the low latency requirements of ICVs. To solve the above deficiencies, we propose a growable driver authentication scheme based on incremental learning for the first time to improve authentication efficiency in dynamic scenarios where the number of drivers is constantly increasing. We extract driver behavior characteristic data from two real vehicles and design a growable model based on incremental learning for driver authentication. Besides these, we also use the support vector machine (SVM), convolutional neural network (CNN) and some other algorithms to make comparative experiments. The experimental results indicate that our scheme not only has high authentication accuracy, but also greatly reduces the time of model retraining.
Autoencoder for end-to-end learning communication system based on NOMA

For the future wireless communication system, non-orthogonal multiple access (NOMA) has attracted much attention as a key candidate in recent years. The advent of Deep Learning (DL) into the realm of wireless communication has created a new paradigm in the design of the physical layer. Deep learning based Autoencoder (AE) is a promising tool to create a full-fledged communication system. In the field of neural network-based communication systems, there has been a lot of research done, to find near optimal solutions in the past few years. In this paper, the combination of both AE and NOMA for end-to-end transmission is proposed. The proposed AE-NOMA signal detection method shows feasible improvement of 6 dB performance gain for user1 and user2 in terms of signal-to-noise ratio (SNR) as compared to previous Deep neural network-based NOMA systems.
Characterizing gas–liquid two-phase flow behavior using complex network and deep learning

Gas–liquid two-phase flow is polymorphic and unstable, and characterizing its flow behavior is a major challenge in the study of multiphase flow. We first conduct dynamic experiments on gas–liquid two-phase flow in a vertical tube and obtain multi-channel signals using a self-designed four-sector distributed conductivity sensor. In order to characterize the evolution of gas–liquid two-phase flow, we transform the obtained signals using the adaptive optimal kernel time-frequency representation and build a complex network based on the time-frequency energy distribution. As quantitative indicators, global clustering coefficients of the complex network at various sparsity levels are computed to analyze the dynamic behavior of various flow structures. The results demonstrate that the proposed approach enables effective analysis of multi-channel measurement information for revealing the evolutionary mechanisms of gas–liquid two-phase flow. Furthermore, for the purpose of flow structure recognition, we propose a temporal-spatio convolutional neural network and achieve a classification accuracy of 95.83%.
Purpose tendency-aware diversified strategy for effective session-based recommendation

Session-Based Recommender Systems (SBRSs) process time-aware user–item interactions to capture users’ dynamic preferences. Most of the existing SBRSs mainly strive to improve recommendation accuracy by exploiting different deep learning techniques to represent each user on the basis of the most recent session. However, they generally ignore to capture the diversity preference at the session level. In this view, we consider both recommendation diversity and accuracy when generating recommendations. Particularly, we treat that users’ preferences towards diversity might be varied across users (sessions). Thus, we propose an end-to-end neural network model, namely Purpose Tendency-aware Diversified Strategy for Session-based Recommendation (PTDS-SR), where we design a Purpose Tendency Probability (PTP) module matching with a two-channel decoder to guide whether to recommend similar or diversified items given a session. We thus obtain a relatively personalized strategy for each session to exploit the diversity to facilitate recommendation accuracy regarding short-term user preferences (within a session). We compare our approach with representative, state-of-the-art baselines on three real-world datasets, in terms of recommendation accuracy, diversity and a comprehensive metric (considering both accuracy and diversity). Experimental results demonstrate the effectiveness of our approach over the state-of-the-art approaches. Meanwhile, with PTDS-SR, more purposeless sessions (e.g., searching products of various categories) will obtain more diverse recommendation list, and vice versa.
Interoperable Industry 4.0 Plant Blockchain and Data Homogenization via Decentralized Oracles

The use of Distributed Ledger Technologies (DLTs) in the field of Industry 4.0 has been constantly increasing over the last years. In a complete Industry 4.0 ecosystem there are multiple smart factory clusters that belong to one or more companies and openly interact with their business partners, clients and suppliers. In such a complex ecosystem, multiple interoperable DLTs are typically required in order to ensure the integrity of the data throughout the whole process from when the data is generated up until it is processed at higher levels. Furthermore, industrial data is commonly heterogeneous and needs to be homogenized, without neglecting security. Therefore, in this work we address the aforementioned issues by (1) providing a production line data homogenization approach via secure blockchain oracles and (2) designing an interoperable plant blockchain for trustworthy homogenized data management across several industrial plants. Finally, we also present a prototype implementation of our proposal and discuss our results.
An Industrial Meter Detection Method Based on Lightweight YOLOX-CAlite

With the application of inspection robots, the demand for automatic detection and identification of pointers in pumping stations, substations, and laboratories has increased. This paper proposes the YOLOX-CAlite meter detection algorithm to address the lack of performance in detecting targets involved in the specific meter detection process. The core of the YOLOX-CAlite is improving the drawbacks of the original algorithm with its large backbone network, a large number of parameters, and large calculation volume. It mainly consists of using data augmentation on the input side. We removed the Focus structure from the original YOLOX and replace it with a convolutional pooling form. The backbone was replaced with Shufflenet v2 and the Ghost module was introduced into the neck to improve real-time performance. Meanwhile, the neck was changed to BiFPN structure to enhance feature fusion. coordinate attention was introduced in front of the detection head to enhance feature extraction. Finally, the binary cross-entropy loss in YOLOX was replaced by focal loss, and the multi-task joint loss of the algorithm was optimized using the complete-IoU loss function. The experimental results on our dataset showed the improved YOLOX-CAlite achieved an AP of 90.4%. Compared to the YOLOX-s and YOLOv5, YOLOX-CAlite improved the AP by 1.4% and 2.2%. And the gflops have been reduced massively, from 26.6 gflops to 8.8 gflops, a reduction of about 68%. The detection speed increased by 25% while the number of parameter sizes is reduced. The model size has been reduced from 17.2 M in YOLOX-s to 4.89 M, a reduction of about 71%. The inspection robots can identify the meter better in the following works.
Robot Formation Performing a Collaborative Load Transport and Delivery Task by Using Lifting Electromagnets

This paper presents a practical validation of a heterogeneous formation of mobile robots in performing a load lifting, transportation, and delivery task. Assuming that an unmanned ground vehicle (UGV) is unable to perform a mission by itself due to the presence of an obstacle in the navigation route, an unmanned aerial vehicle (UAV) is then assigned to lift the cargo over this UGV, transport the obstacle, and deliver over another UGV. The UAV uses an electromagnetic actuator supported by a cable to pick up the load, the mass of which is 32% of that of the UAV. Experimental results demonstrate that the developed system is capable of performing cargo transport missions and can be scalable for applications such as package delivery in urban or remote areas and supply delivery in conflict or disaster zones.
A Perception Study for Unit Charts in the Context of Large-Magnitude Data Representation

Unit charts are a common type of chart for visualizing scientific data. A unit chart is a chart used to communicate quantities of things by making the number of symbols on the chart proportional to the number of items represented. An accurate perception of the order of magnitude is essential to evaluating whether a unit chart can effectively convey information. Previous studies have primarily focused on perceptual properties at small order-of-magnitude scales or the efficacy of pictographs in unit charts. However, few researchers have explored the perceptual effectiveness of unit charts when representing large orders of magnitude. In this study, we performed a series of sampling measurements to investigate the visual–perceptual characteristics of unit charts when representing asymmetric interactions such as large-scale numbers. The results showed that under the restriction of the current conventional display medium, unit charts still offer a significant advantage over bar charts in a single-scale visual overview. However, this comes at the cost of a longer response time. Although this study constitutes basic research, accumulating evidence about how people reason about magnitudes beyond human perception is critical to the field of information science. This study may contribute to understanding how viewers perceive unit charts and the factors that influence graphical perception. This article provides some specific guidelines for designing unit charts that may be useful to visualization designers.
Chromaticity characteristics of soil profiles in the coastal areas of Fujian and Guangdong, southern China and their climatic significance

In recent years, there has been an increasing interest in the use of soil color as a climatic proxy. We investigated the variation of soil chromaticity parameters in subtropical and tropical coastal areas in China and their relationships with climatic factors (mean annual precipitation and temperature). Latitudinal zonal soil profiles in the coastal areas of Fujian and Guangdong were selected for study, and chromaticity analysis of soil samples was performed using the CIELAB color system. The principal results are as follows: (1) Soil color in the coastal areas of Fujian and Guangdong is mainly controlled by hematite and goethite. Redness (a∗) varies significantly with depth within the soil profiles, and two types of profile can be defined: type I in which a∗ decreases with increasing depth within the profile, and type II in which a∗ increases with increasing depth within the profile. (2) Overall, a∗, b∗ (yellowness) and the b∗/a∗ ratio do not show a significant relationship with latitude. The a∗ of some of the Fujian profiles and the Guangdong soil profiles are significantly negatively correlated with the MAP, the b∗/a∗ of some of the Fujian is highly positively correlated with MAP. A precipitation threshold may exist for both sets of profiles. There is a high correlation coefficient between b∗/a∗ and MAT, and a temperature threshold of ∼23 °C is evident. b∗/a∗ is correlated with MAP and MAT, and it can potentially be used as a climate proxy.
IBA: A secure and efficient device-to-device interaction-based authentication scheme for Internet of Things

The Internet of Things (IoT) enables a variety of services through frequent communication and interaction between devices, bringing a great deal of benefits to human life. The complexity and variability of network environments, together with the diversity of IoT devices introduce a large range of security and efficiency concerns to device authentication. In this paper, a novel interaction-based authentication (IBA) scheme is proposed to respond to the above problems. Device characteristics that can be used as identity credentials are extracted from previous interactions, and secure authentication is realized through elements matching. IoT devices can independently authenticate the identity of other devices without persistent connection to trusted third parties. From the informal analysis and simulation results, IBA supports dynamic adaptability, and high scalability, and resists several well-known attacks, such as reply attacks, impersonation attacks, and man-in-the-middle attacks. The comparison analysis indicates that IBA has better security and performance compared with related authentication schemes, thereby demonstrating it is more suitable for the actual IoT environment.
Implementation of Feature Selection Based on Particle Swarm Optimization and Genetic Algorithm on Support Vector Regression Algorithm to Predict Student Performance

Identifying student learning outcomes data in a subject to predict student performance is interesting research. This study was conducted to identify the factors that affect student performance by applying feature selection techniques based on Particle Swarm Optimization (FSPSO) and feature selection techniques based on Genetic Algorithm (FSGA) on the Machine Learning algorithm, namely Support Vector Regression (SVR). The data used are students' personal data and data on the academic value of Mathematics in Portugal. Experiments were carried out by applying the PSO feature selection (FSPSO) and GA (FSGA) feature selection techniques to obtain the selected features. Then the prediction modeling of student performance was carried out using SVR. The experiment was carried out using the pycharm 2021.1 application with the python programming language. This study uses a 10-fold cross-validation method. Evaluation of student performance prediction modeling is done by comparing the error values of regression performance metrics and processing time with feature selection and without feature selection. The experimental results show that FSPSO-SVR modeling compared to SVR modeling, the error reduction of the regression performance metric on RMSE is 34.36%, while for MAE it is 45.09% in 500 iterations. In FSGA-SVR modeling compared to SVR modeling, the error reduction of the regression performance metric in RMSE is 1.59% %, while the MAE is 3.04% at iteration 50. The improvement of the time process value in the FSPSO-SVR modeling compared to the SVR modeling, resulted in a decrease in process time of 44.68%. while the FSGA-SVR modeling compared to SVR modeling, resulted in a decrease in process time of 33.33%. From the comparison of experimental results, it can be concluded that the FSPSO-SVR modeling is the best predictive modeling of student performance with the lowest error value and the fastest time process in 500 iterations, so it can be used as a reference in making decisions to improve student performance in the future.
Mind the TWEAKEY Schedule: Cryptanalysis on SKINNYe-64-256

Designing symmetric ciphers for particular applications becomes a hot topic. At EUROCRYPT 2020, Naito, Sasaki and Sugawara invented the threshold implementation friendly cipher SKINNYe-64-256 to meet the requirement of the authenticated encryption PFB $$\_$$ Plus. Soon, Thomas Peyrin pointed out that SKINNYe-64-256 may lose the security expectation due the new tweakey schedule. Although the security issue of SKINNYe-64-256 is still unclear, Naito et al. decided to introduce SKINNYe-64-256 v2 as a response. In this paper, we give a formal cryptanalysis on the new tweakey schedule of SKINNYe-64-256 and discover unexpected differential cancellations in the tweakey schedule. For example, we find the number of cancellations can be up to 8 within 30 consecutive rounds, which is significantly larger than the expected 3 cancellations. Moreover, we take our new discoveries into rectangle, MITM and impossible differential attacks, and adapt the corresponding automatic tools with new constraints from our discoveries. Finally, we find a 41-round related-tweakey rectangle attack on SKINNYe-64-256 and leave a security margin of 3 rounds only. As STK accepts arbitrary tweakey size, but SKINNY and SKINNYe-64-256 v2 only support up to 4n tweakey size. We introduce a new design of tweakey schedule for SKINNY-64 to further extend the supported tweakey size. We give a formal proof that our new tweakey schedule inherits the security requirement of STK and SKINNY. We also discuss possible ways to extend the tweakey size for SKINNY-128.
Military Doctors' and Nurses' Perceptions of Telemedicine and the Factors Affecting Use Intention

Purpose:This study investigated military doctors' and nurses' perceptions of telemedicine and the factors influencing their intention to use it based on the unified theory of acceptance and use of technology. Method:This study adopted a questionnaire-based, cross-sectional descriptive approach. It used a web questionnaire for data collection over a 5-week period, starting in June 2021. Results:A total of 72.6% of participants indicated that telemedicine is required in the military. The intention to use telemedicine was significantly higher among women, younger individuals (<30 years), and military nurses. In addition, factors such as voluntariness of use, performance expectancy, social influence, and facilitating conditions positively affected the intention to use telemedicine. Conclusions:To improve military doctors' and nurses' use and understanding of telemedicine, consensus must be reached regarding its use in military contexts. Discussions that incorporate opposing views should be encouraged as well. Moreover, the voluntariness of use significantly affected respondents' intention to use telemedicine. There is an urgent need, therefore, for in-depth analyses of the various factors associated with voluntariness of use of telemedicine; the resulting insights could be used to encourage military doctors and nurses to adopt telemedicine. Finally, along with promoting the use of smartphones for medical consultation among military personnel, military nurses' role should be extended to include health consultation using smartphones. This could promote the active use of telemedicine in military nursing, which could contribute to health promotion among military personnel.