Reviewerly/exHarmony huggingface dataset:
Research on Business Model of Internet of Vehicles Platform Based on Token Economy

With the increasing number of electric vehicles, the scale of the market also increases. In the past, the electric vehicle market had problems such as opaque information, numerous levels and data leakage, which were criticized for the impact of the overall development and policies of the electric vehicle industry. In view of the problems existing in the transparency and security of big data management transactions of the Internet of vehicles, this paper combs the commercial operation framework of the Internet of Vehicles Platform, analyses the feasibility and necessity of establishing the token system of the Internet of Vehicles Platform, and constructs the token economic system architecture of the Internet of Vehicles Platform and its development path.
The Perception of Space in Virtual Reality, Correlation Between Affordances and Spatial Presence

Purpose: To explore the correlation between two concepts in the study of embodied perception in Virtual Reality (VR): affordances and spatial presence.Methodological design: It involved the review of literature on spatial presence and affordances in the fields of phenomenology and cognitive neuroscience, and an experiment with 30 participants using the Spatial Presence Experience Scale (SPES) proposed by Hartmann et al. (2016). Each participant tested 5 VR simulations and answered 5 questionnaires based on the SPES. This study proposes a model for the classification of types and subtypes of affordances in virtual reality.Results: The comparison of means between scores showed a positive correlation between possibilities for action and intensity of spatial presence. Data suggests motor affordances (navigational, goal-oriented and handle grasp types) intensify spatial presence. Frequency of videogame use was found unrelated to the intensity of spatial presence, but it showed an increase in user’s dexterity and the ease of perceiving affordance in virtual environments.Research limitations: The scope of this research is limited to describing the correlation between affordances and intensity of spatial presence in VR. Findings: These confirmed the hypothesis: more possibility types of affordances in VR result in a higher intensity of spatial presence.
Research on Authentication and Key Agreement Protocol of Smart Medical Systems Based on Blockchain Technology

The wireless body area network in the smart medical systems uses wearable devices to remotely monitor the patient’s physiological information and transmits the information to the medical center through an open channel. To prevent security issues such as privacy leakage and malicious attacks in the wireless body area network, anonymous authentication and key negotiation are required between the sensor and the server. The protocol must not only satisfy confidentiality and security but also provide anonymity and untraceability for sensor nodes. In response to this problem, this paper proposes an authentication and key agreement protocol for sensors and servers based on blockchain technology in the wireless body area network, with the help of session keys for subsequent access and data transmission. The safety of our scheme was evaluated through an informal safety analysis. In addition, our scheme was also simulated by using ProVerif. The experimental results showed that the scheme is safe.
Location and identification of suspension insulators based on RRPN

In order to improve the refinement of insulator detection, a suspension insulator location and identification method based on RRPN is proposed in this paper. The RRPN algorithm is used to extract and train the image data of suspension insulator, and the positioning recognition based on inclined box is realized. This paper compares the training curves of the model under different learning rates and selects the optimal learning rate. The average accuracy of the optimal model was 88.47%. The results show that the method proposed in this paper can be combined with the application scenario of suspension insulator UV diagnosis to reduce false detection. The model shows strong robustness in the case of occlusion and truncation and has certain academic innovation and engineering application value.
Lanqas, negative concord and predicate negation in Maltese

This paper examines the interaction of the focus particle lanqas with predicate negation. Using corpus data, we provide a classification of syntactic constructions where such interaction occurs, based on whether lanqas serves as the sole negator, appears alongside the standard negative circumfix ma + -x or co-occurs with the negator la. We analyze and describe the patterns of use for each construction type, also noting the factors influencing the selection and its relationship to other syntactic phenomena, such as dislocation and verbal chains, and especially their effect on negative concord.
Vehicle Re-identification in Smart City Transportation using Hybrid Surveillance Systems

Existing traffic infrastructure management to handle traffic congestion in major cities is aging and is not effective for traffic monitoring. With an increasing demand for developing cities as smart cities, advanced Intelligent transportation systems (ITS) are in need to improve the safety and traffic movements in the cities. As an application of ITS, vehicle re-identification has gained a wide interest in the field of robotics and computer vision. Currently, these tasks are performed from the data acquired by either of the standalone surveillance systems such as CCTV or UAV. Such data acquired for re-identification poses several challenges namely viewpoints, scale, illumination change, occlusion, etc. To address these, a hybrid surveillance system approach is proposed whereby an algorithm is developed for vehicle re-identification. The re-identification algorithm is tested on a dataset containing 33 identical vehicles observed across 20 CCTV cameras and a UAV. Re-identification of vehicles is performed by estimating a transformation that maps a vehicle observed in one modality to another. The performance of vehicle re-identification is compared for a CNN network trained with the vehicle identities with and without the application of homography.
Quantum secure multiparty multiplication based on Lagrange unitary operator

<p indent="0mm">Most of the existing quantum secure multiparty multiplications use entangled states to carry secret information. However, the preparation of these quantum states is difficult. In order to solve this problem, a single-particle secure multiparty product protocol based on a <italic>d</italic>-level Lagrange unitary operator is proposed. In this protocol, a semi-honest third party (Server) and each participant share a private key. The participant first uses the private key and random number to multiply his own secret integer with module <italic>d</italic>, and then he uses Lagrange unitary operator to encode the calculation result into an unbiased basis particle. The server obtains the encoded information by measuring the particle sent back by each participant. Finally, the server uses the module <italic>d</italic> product of all the random numbers sent by the participant’s representative to calculate the product corresponding to the secret integers of multiple participants. Performance analysis shows that, compared with similar protocols, the proposed protocol requires less of the entity’s quantum computing capabilities. In terms of security, this protocol can resist both external and internal attacks. Finally, the proposed protocol uses the <italic>Q</italic># quantum programming language combined with Python language to simulate and verify its correctness.
Survey on Image Steganography Techniques

Steganography is the science that addresses hiding secret data within cover image or audio for the purpose of protection. Steganography acts on a higher layer of protection when compared to encryption because it hides the communication itself, between the sender and receiver, rather than encrypting exchanged messages. Recently, steganography has been received significant amount of attention from research committee due to its valuable applications especially in medical field to preserve integrity of medical data. This work classifies the steganography systems proposed previously into two main categories, which are spatial and frequency domains. Reviewed works are evaluated according to four criteria, which are payload, retrieved bit error rate, complicity, and resistance against geometric and non-geometric attacks. Most common metrics that are used in steganography field are presented along with their mechanism of evaluation. Finally, challenges and new trends in steganography are provided.
Prospects of Remote Sensing for Mineral Deposits Prediction in Kazakhstan

Using remote sensing database (Landsat and Aster materials), promising areas for the detection of endogenous mineralization were identified. On documented cosmo-structural schemes, objects of linear morphology occur: faults, areas of increased fracturing, geological boundaries, bedding elements, dykes and others. Under conditions of weakly dissected relief, multidirectional gradients and various filtering methods are particularly effective for isolating linear structures. Arc and ring structures are recognized based on their boundaries between blocks with different textures of space materials as well as their morphology. Borders of landscape heterogeneity are also taken under consideration. Ring structures are conventionally divided into structures of the second order and smaller. Bodies of intrusive rocks are established using spectral libraries and textural features of satellite images.
Disaster literacy level of teachers and students in secondary school case study in Surakarta City, Indonesia

Abstract This article contains the level of information literacy and disaster literacy skills for teachers and secondary students in Surakarta in reviewing and responding to disaster information and symptoms of disasters. This article is based on Doyle’s theory that the most basic ability in information literacy is the ability to know the accuracy and completeness of information which is the basis for making sound decisions. Quantitative methods were used to test the ability of 20 teachers and 82 secondary students to examine the accuracy and completeness of disaster information and symptoms of disasters and the ability of teachers and secondary students to respond to disaster information and symptoms of disasters. the results presented in this article. First, the ability of information literacy in knowing the accuracy and completeness of information on teachers and secondary students is low because only 5.5% of teachers and 7.8% of students confirm the accuracy of disaster information, and only 5.2% of students who want to find the completeness of the information. Second, most teachers and secondary students generalize their responses to both disaster symptoms and disaster information, they will immediately evacuate if they receive disaster symptoms or information.
Distributed Machine Learning—An Intuitive Approach

The scope for automated systems by enabling artificial intelligence has grown rapidly over the last decade and this growth has been stimulated by advances in machine learning techniques by exploiting hardware acceleration. In order to improve the quality of predictions and utilize machine learning solutions feasible for more complex applications, a tangible amount of training data is necessary. Although relatively tiny machine learning models can be trained with modest amounts of data, the input for training larger models such as neural networks grows exponentially with the number of parameters. Since the demand for processing training data has outpaced the increase in computation power of computing machinery, there is a need for distributing the machine learning workload across different machines. That leads to a switch from the centralized into a distributed system. These distributed systems present new challenges; first and foremost, the efficient parallelism of the training process and the creation of a coherent model. This article provides an extensive overview of the current state-of-the-art in the field by outlining the challenges and opportunities of distributed machine learning over conventional (centralized) machine learning, discussing the techniques used for distributed machine learning, and providing an overview of the systems that are available.
Ecological Education as a Component of Moral Education

Research relevance: ecological problem today is an important problem in the world. One of the ways to solve this global problem is environmental education and upbringing of schoolchildren. Research objectives: this article reveals the relevance, goals, objectives and principles of ecological culture in schoolchildren, indicates methods and techniques of means and ways in organizing classes to establish moral and ecological thinking in schoolchildren. Research materials and methods: analysis of psychological, pedagogical, philosophical literature, documents of governments and ministries, as well as the study of school documentation on the environmental education of students. Research results: ecological culture in modern conditions is one of the leading components of personality and can be considered as the main system-forming factor that contributes to the formation of true intelligence and civilization in a person. Conclusion: the theoretical basis of ecological culture is based on solving problems in their unity: training, education and development.
On the $ k $-error linear complexity of binary sequences of periods $ p^n $ from new cyclotomy

&lt;abstract&gt;&lt;p&gt;In this paper, we study the $ k $-error linear complexity of binary sequences with periods $ p^n $, which are derived from new generalized cyclotomic classes modulo a power of an odd prime $ p $. We establish a recursive relation and then estimate the $ k $-error linear complexity of the binary sequences with periods $ p^n $, the results extend the case $ p^2 $ that has been studied in an earlier work of Wu et al. at 2019. Our results show that the $ k $-error linear complexity of these sequences does not decrease dramatically for $ k &amp;lt; (p^{n}-p^{n-1})/2 $.&lt;/p&gt;&lt;/abstract&gt;
Music-Based/Inspired Scientific Research and Liberal Arts Education

Music has been a common culture in human society since ancient times, and is also one of the basic subjects in the liberal arts. The skills of playing music can be improved through prolonged practice. In this chapter, we will introduce scientific research based on/inspired by music, and also the practice of music education as a liberal arts education based on the tacit knowledge of musician and explicit knowledge from scientific findings. For scientific research inspired by music, we will introduce neuroscientific and dynamical approach to elucidate human sensorimotor skills to achieve advanced musical performance. Regarding music education practice, we will introduce the contents of “human body as a musical instrument: practice and science of vocal music,” which was offered as a specialized seminar at the College of Arts and Sciences, the University of Tokyo.
SparseDet: Towards End-to-End 3D Object Detection

In this paper, we propose SparseDet for end-to-end 3D object detection from point cloud. Existing works on 3D object detection rely on dense object candidates over all locations in a 3D or 2D grid following the mainstream methods for object detection in 2D images. However, this dense paradigm requires expertise in data to fulfill the gap between label and detection. As a new detection paradigm, SparseDet maintains a fixed set of learnable proposals to represent latent candidates and directly perform classification and localization for 3D objects through stacked transformers. It demonstrates that effective 3D object detection can be achieved with none of post-processing such as redundant removal and non-maximum suppression. With a properly designed network, SparseDet achieves highly competitive detection accuracy while running with a more efficient speed of 34.5 FPS. We believe this end-to-end paradigm of SparseDet will inspire new thinking on the sparsity of 3D object detection.
Vulnerability Analysis and Testing of Wireless Networks through Warstorming

With the world rapidly shifting towards digital and wireless technology, visibility and vulnerability are to be taken into consideration in order to avoid conflict and protect one’s data since security is a major concern to be addressed. The proposed system tests wireless networks through a programmed Unmanned Aircraft System (UAS) which detects and helps analyze datasets obtained such as locations of Access Points, Media Access Control (MAC) addresses, authentication, power, privacy, and cipher settings, which are then filtered and sorted to be represented visually in the form of heatmap and bar charts. The latter working of the model involves various attacks like Deauthentication attack, Beacon attack, Probe attack and Rogue Access Points which contribute to communication jamming, Denial of Service (DoS), replication of Access Points and logging data of the same; These showcase strength of security and can be developed to contribute in defense purposes and threat analysis.
Predicting gray fault based on context graph in container-based cloud

Distributed Container-based cloud system has the advantages of rapid deployment, efficient virtualization, simplified configuration, and well-scalability. However, good scalability may slow down container-based cloud because it is more vulnerable to gray faults. As a new fault model similar with fail-slow and limping, gray fault has so many root causes that current studies focus only on a certain type of fault are not sufficient. And unlike traditional cloud, container is a black box provided by service providers, making it difficult for traditional API intrusion-based diagnosis methods to implement. A better approach should shield low-level causes from high-level processing. A Gray Fault Prediction Strategy based on Context Graph is proposed according to the correlation between gray faults and application scenarios. From historical data, the performance metrics related to how above context evolve to fault scenarios are established, and scenarios represented by corresponding data are stored in a graph. A scenario will be predicted as a fault scenario, if its isomorphic scenario is found in the graph. The experimental results show that the success rate of prediction is stable at more than 90%, and it is verified the overhead is optimized well.
Video action re-localization using spatio-temporal correlation

Video re-localization plays an important role in locating the moments of interest in a long videos, and is critical for a variety of applications such as surveillance video monitoring and retrieving similar archived videos for further comparison and analysis. Current re-localization approaches compute a feature vector using a video query for each video frame, and explore various feature matching techniques. These features do not capture information from varying temporal windows, and the dimension reduction to a vector leads to loss of spatio-temporal context. For efficient feature comparison and matching among thousands of videos, we design a Siamese Spatio-Temporal network comprising Convolution Neural Network and Long Short-term Memory blocks (CNN-LSTM) for feature extraction, followed by a correlation layer for spatio-temporal feature matching. We extract video features at varying temporal scales, and localize one or more segments in the reference video that semantically match the query clip. Our approach is evaluated on two benchmark datasets: AVAv2.1- Search and ActivityNet-Search. We show an improvement of over 12% in the mean average precision compared to existing approaches. We perform ablation experiments and show that the modular architecture and the holistic feature extraction expands the scope of this work to multiple video search applications.
ANTICIPATION AS A MEANS OF ENHANCING EMPLOYMENT PROSPECTS AND PROFESSIONAL EFFECTIVENESS FOR FUTURE SPECIALISTS

This paper studies the anticipation not as a governmental educational strategy for collaboration with employers, but as an opportunity for individuals (in this case, teachers and students) that can be measured by an algorithm. The paper provides: an individual strategy for successful professional development; a cognitive basis for anticipation; an emotional basis for anticipation; and an anticipation-potential index.
A High-Availability K-modes Clustering Method Based on Differential Privacy

In categorical data mining, the K-modes algorithm is a classic algorithm that has been widely used. However, the data analyzed by the K-modes algorithm usually contains sensitive user information. If these data are leaked, it will seriously threaten the privacy of users. In response to this problem, the existing method that combines differential privacy with the K-modes algorithm can effectively prevent privacy leakage. Nevertheless, differential privacy adds noise to the data while protecting data privacy, which will reduce the availability of clustering results. In this paper, we propose a high-availability K-modes clustering mechanism based on differential privacy(HAKC). In this mechanism, based on the use of differential privacy to protect data privacy, we select the initial centroid of the clustering by calculation, and improve the calculation method of the distance between the data point and the centroid in the iterative process.
Digital Solution to Combat Bribery and Justice Restoration System

Bribery has rooted all over our system and it has started making our system hollow over a period of time. It has hindered the cycle of workflow and as well the justice system. With a rise in the number of instances where people ask for bribes even for basic and fundamental works there is a dire need to find a solution, thus an efficient way to report a bribe has become a prevalent topic. With the increase in popularity of smartphones, application-based solutions would be an appropriate method to make the reporting process more efficient. Our solution is a mobile application that helps in reporting the bribe and decentralized storage that would mitigate the risks involved in maintaining the security of data, ease the reporting process, reduce the chances of false reports, and provide direct insight regarding the progress of the report. The mobile application will collect data from users regarding bribery and will also allow uploading of evidence on a portal which will store the evidence on a decentralized storage. All this data can be accessed by an admin panel system which is basically a website dedicated for admins to monitor all the complaints and help them to restore the justice in the system by having access to tamper-proof data and insights about the complaints. Application consist of certain additional feature which can easy and fasten the decision process, this feature includes video calling, Image recognition on media files and filling for government documents like NOC and NCR.