Reviewerly/exHarmony huggingface dataset:
Perception of 3D Scene Based on Depth Estimation and Point-Cloud Generation

Depth estimation and object instance recognition capabilities in 3D space are critical for autonomous navigation, localization, mapping, and robotic object manipulation. RGB-D images and LiDAR (Light Detection and Ranging) point clouds are the most descriptive formats for depth information. However, these depth sensors have many shortcomings, such as low effective spatial resolution and capturing a scene from a single perspective. Our work is mainly focused on reproducing a denser and more comprehensive 3D scene structure of given monocular RGB images employing depth and 3D object detection. The first contribution is the proposal of two architectures for estimating depth maps based on an unsupervised learning system, which are utilized to analyze the structure from motion and 3D geometric constraints and deliver better results than existing methodologies. The second contribution involved the generation of a 3D point cloud based on the estimated depth map and a monocular RGB frame. The primary goal of this part was to enhance the visualization of the 3D scene structure. Our proposed solutions achieve better outcomes in terms of ARD, RMSE, RMSE (log), accuracy and other evaluation metrics with lower but equivalent results compared to state-of-the-art methods with a maximum depth of 80 m.
Separable Digitally Signed Video

A very simple scheme of a video stream electronic signature is proposed allowing us to isolate any continuous fragment from the video stream in respect of which the property of an electronic signature is preserved not only with respect to establishing authorship and the absence of modification of each individual frame but also the absence of editing i.e. changing the sequence of frames.
Technology for Assessing Digital Maturity of Educational Organization. Рart I

This article considers the problem of assessing the digital maturity of a higher educational institution (HEO) in order to determine its potential and current areas of digital transformation. The analysis of existing approaches to validate the digital maturity of an organizational object is carried out. The system of digital maturity indicators has been formed. Two existing models are analyzed and the author's model for assessing the digital maturity of HEO is developed. The indicators set of the three models is considered from a systemic standpoint taking into account expert assessments of the information content of indicators in relation to assessing the level of digital maturity. The resulting assessment technology requires additional research in the context of its current application in solving the problem of effective digital transformation of HEO and other type of organizational systems.
A Security and Privacy-Preserving Accessing Data Protocol in Vehicular Crowdsensing Using Blockchain

This paper exploits the advantage of blockchain to fulfill secure data storage and access in vehicular crowdsensing. In vehicular crowdsensing, organizations can have a right to access vehicles' profiles with unlimited privilege, which results in security and privacy disclosure. Thus, based on the proposed blockchain, we design a secure and privacy-preserving vehicle's profile record accessing (BSPPA) protocol. The proposed BSPPA is able to store the vehicle's sensing data and indexes as encrypted data for a secure indexes search. This will result in controlling the privilege of data access and prevent vehicle's data from being revealing private data. Analysis of the security is demonstrated to show the efficiency of the BSPPA scheme. Furthermore, the performance evaluation shows the efficiency of the BSPPA protocol.
Central Bank Digital Currencies (CBDCs) as a New Tool of E-Government: Socio-economic Impacts

A central bank digital currency (CBDC) in comparison with other forms of digital money presents a direct claim on the issuing central bank. There are three architecture types of CBDC, namely indirect CBDCs, hybrid CBDCs, and direct (retail) CBDCs, all based on blockchain technology. This paper briefly discusses these three types and outlines the major socio-economic effects this new e-government tool could have on the economy.
A Survey on Research Directions in Blockchain Applications Usability

This research systematically reviews blockchain usability studies published between 2017 and 2021. It analyzes direction trends aimed at improving the overall blockchain application’s real-world adoption. After determining the inclusion and exclusion criteria, 22 articles were included in the review. This work presents the major existing challenges found in Blockchain applications, such as privacy issues and non-regulation, and the proposed solutions, including increased transparency and simple usable applications. Major findings include the fact that user surveys are the most popular method among these studies compared to expert surveys or observational studies. Most of the current blockchain usability studies are performed for applications related to the financial domain, followed by health care, supply chain applications, energy, and e-voting. The majority of these studies include less than 20 participants for the expert-based and less than 40 for user-based ones. This study further investigates the domain-specific target systems. The healthcare-related surveys concentrated on the usability of users’ data on electronic medical record (EMR). In the financial field, the focus was mostly on the use of crypto wallets or crypto applications. The main concern among participants in healthcare-related applications was the privacy of their medical data while in finance, the concern was the lack of a local regulatory body. This research has important implications that can help researchers address challenges and implement appropriate solutions, which can improve their adoption rate.
DeTi: A Decentralized Ticketing Management Platform

Abstract Event tickets being sold in their electronic instances are subject to counterfeiting, profiteering, and black markets. Therefore, suitable service management mechanisms are required to overcome such deficits. This work designs, develops, and evaluates the approach of a Decentralized Ticketing platform—called DeTi—for managing the distribution of electronic event tickets and “regulating” the aftermarket. DeTi offers a dedicated service management functionality by operating through Smart Contracts of Ethereum, such that users can verify tickets’ validity for a given event. Especially, a new mechanism for users to detect fraudulent events is introduced, too. The evaluation performed indicates that DeTi invalidates or validates tickets efficiently via its decentralized and BC-based service management approach. By securing technically a set of underlying processes, DeTi obviates forging, replication, and scalping of tickets, allowing for a well-managed resale ecosystem of tickets based on and limited to the organizers’ initial pricing.
ES-PPDA: an efficient and secure privacy-protected data aggregation scheme in the IoT with an edge-based XaaS architecture

Abstract In an Internet of Things (IoT) system based on an anything as a service (XaaS) architecture, data are uploaded from heterogeneous nodes in a nonstandardized format and aggregated on the server side. Within this data-intensive architecture, privacy preservation is one of the most important issues. In response to this concern, there are numerous privacy-protection data aggregation (PPDA) solutions available for various IoT applications. Because of the limited resources of intelligent IoT devices, traditional PPDA cannot meet practical privacy and performance needs. To tackle this challenge, we provide a more efficient and secure PPDA solution that guarantees data security and integrity through Paillier homomorphic encryption and online/offline signing technology. Detailed security analysis shows that our system is unpredictable under a chosen message attack, and the data integrity may be guaranteed under the assumption of q-strong Diffie-Hellman (Q-SDH). We choose an M/G/1 priority queue model to maximize system performance. M/G/1 enhances queuing efficiency and accelerates channel access, thus reducing waiting time and increasing reliability. The experimental results show that our data aggregation scheme is reliable with low latency.
A Novel Lightweight Block Encryption Algorithm Based on Combined Chaotic System

• Designing a novel combined chaotic system based on 1D chaotic system. • Designing a novel construction method of S-boxes. • Designing a novel construction method of P-boxes. • Designing a novel lightweight block encryption algorithm based on combined chaotic system. • The encryption algorithm is tested in detail to prove its security and applicability to resource constrained devices Due to the high complexity of traditional block cipher, which is not suitable for resource constrained devices such as wireless sensors. Therefore, this paper proposes a novel lightweight block encryption algorithm based on combined chaotic system. Firstly, a novel combined chaotic system with good pseudo-random characteristics is proposed to construct S-boxes with high security and p-boxes with excellent diffusion performance. Secondly, a novel lightweight encryption and decryption structure are designed based on Feistel encryption structure, which reduces the computational complexity and memory consumption of the encryption and decryption process, and the designed round function can use different lengths of block and key, which means the structure has excellent scalability. The software and hardware implementation test and security test results show that the proposed lightweight block encryption algorithm based on combined chaotic system (LBCCS) provides an efficient and secure encryption way for the resource constrained devices.
Impossible differential cryptanalysis of FBC-128

To promote the theory and application of cryptology, the design and implementation of cryptographic algorithms, in 2018, the Chinese Association for Cryptologic Research held the National Cryptographic Algorithm Design Competition. After the first round evaluation of security and implementation, FBC is selected one of the 10 block ciphers for the second round. FBC adopts 4-branch Extended Generalized Feistel Networks (EGFN) and it is designed with efficient implementation and good resistance against side-channel attacks. In this paper, we focus on the impossible differential attack, which is one of the most basic cryptanalytical methods, against FBC with 128-bit block size and key size (FBC-128). First, an equivalent expression with improved clarity of the round functions was derived. Then a structural property concerning the relationship among branches was explored. Combining those properties of its round function and structure, 9-round truncated impossible differentials were constructed for FBC-128, which is 2 rounds longer than previous works. Using this distinguisher, 13-round key recovery attack was mounted. The data and time complexity is 2126 chosen-plaintexts and 2122.96 encryptions respectively. To our knowledge, this is the best attack so far in terms of attacked rounds. Our attack exploited the properties of both structure and round function of FBC, and those observation and analysis would be beneficial to the understanding of FBC. Moreover, our results demonstrate when constructing impossible differentials, differentials with low hamming weight input and output difference may not always be optimal, which calls for more comprehensive analysis of the differential pattern.
Exploring the use of mobile AR to aid decision-making on-the-go

Extended reality adaptation is gaining traction across a broad spectrum of disciplines. Mobile phones and markerless augmented reality (AR) are fascinating methods for retrieving information because they enable content to be rendered in the user's immediate environment while they are on the move. This method of rendering virtual content within a physical environment will be accessible to a large audience that, for the most part, does not have access to high-end equipment such as head-mounted displays. Along with a large audience, augmented reality on mobile devices can display dynamic content in real time, even in outdoor settings, providing users with information about physical artifacts that is both immediate and contextually relevant. The purpose of this paper is to discuss the development of a mobile application that will assist users in making on-the-spot decisions based on online content displayed nearby. Along with developing an application that assists users in locating nearby restaurants, a strong emphasis is placed on architecture, user interface, and experience design, as the application will be used in environments with limited focus (e.g., traffic noise or occlusion with real objects). The study concludes with a discussion of the application's user experience and future research directions in this field. With the rapid evolution of mobile devices, it is critical to understand some of the challenges and design constraints inherent in developing better user experiences for applications that support on-the-go decision-making.
Quadratic Particle Swarm Optimisation Algorithm for Task Scheduling Based on Cloud Computing Server

The task scheduling is one of the core problems of cloud computing and aims to assign tasks reasonably, realise the optimal scheduling strategy and improve the operating efficiency of overall cloud computing system. For the shortcomings of traditional particle swarm optimisation (PSO) algorithm in total completion time and average completion time, a quadratic particle swarm optimisation (QPSO) algorithm is proposed. Using the proposed algorithm, people can find a scheduling result with the short total completion time of task and also ensuring the short average completion time of task. Finally, the research made a simulation experiment with Cloud Sim. Experiment results show that in the same condition setting, the algorithm proposed is superior to the traditional PSO algorithm. When the number of tasks increases, the comprehensive scheduling performance of QPSO is more than 20% higher than that of PSO.
A remote consultation system for sports injury based on wireless sensor network

INTRODUCTION: Although current research methods can realize the effective collection of human physiological signals in the health monitoring system, they cannot obtain the ideal detection effect due to the influence of the communication performance in the health monitoring system. OBJECTIVES: In order to improve the monitoring performance of remote consultation, a sports injury remote consultation system based on wireless sensor network is designed. METHODS: The wearable sensors is used in the body area network to collect human physiological signals. Through the wireless sensor network of the wireless communication module, the collected human physiological signals are transmitted to the remote consultation module. The wireless communication module selects CC2530 chip as the core chip of the wireless communication module. A fixed partition routing algorithm based on energy balance is used to stably transmit human physiological signals. RESULTS: The consultation personnel of the remote consultation module make a sports injury consultation judgment based on the received physiological signal results of the human body. The system test results show that the designed system can accurately monitor various physiological indicators of the human body. The wireless sensor network energy consumption of the system in this paper is all less than 500J, the energy consumption variance of the cluster head is less than 4×10-3, and the number of surviving nodes can be guaranteed to be higher than 130. It has high communication performance of wireless sensor network. CONCLUSION: The system can accurately judge whether there is a sports injury according to the monitoring results of physiological indicators, and realize the effective consultation of sports injury.
Metric-based rule optimizing system for code smell detection using Salp Swarm and Cockroach Swarm algorithm

Software developers find it difficult to select the specific detection rules for different smell types. A set of metrics, thresholds and labels constitutes a code smells detection rule. The generated rules must be optimized efficiently to ensure successful rule selection. The objective is to identify how rules are generated from the labeled data set and selected using bio-inspired algorithms. The goals are met by employing the C4.5 and RIPPER algorithms to generate rules then, optimized using two bio-inspired algorithms, the Salp Swarm Algorithm (SSA) and Cockroach Swarm Optimization (CSO). The optimized sets of rules are evaluated using the similarity metrics which are computed with the help of expected and the detected code smells. The common rule subsets from SSA and CSO are merged to produce the optimal rule subset which can be used for code smell detection. The proposed work has been experimented on Xerces-J, Log4J, Gantt Project and JFreeChart dataset. The work detected code smells with an accuracy of 91.7% for Xerces-J, 96.7% for JFreeChart, 88.6% for Gantt Project and 98% for Log4J. The findings will be useful for both theory and research since the proposed framework allows focusing on rule selection.
Multi-scale discriminative regions analysis in FDG-PET imaging for early diagnosis of Alzheimer’s disease

Objective. Alzheimer's disease (AD) is a degenerative brain disorder, one of the main causes of death in elderly people, so early diagnosis of AD is vital to prompt access to medication and medical care. Fluorodeoxyglucose positron emission tomography (FDG-PET) proves to be effective to help understand neurological changes via measuring glucose uptake. Our aim is to explore information-rich regions of FDG-PET imaging, which enhance the accuracy and interpretability of AD-related diagnosis.Approach. We develop a novel method for early diagnosis of AD based on multi-scale discriminative regions in FDG-PET imaging, which considers the diagnosis interpretability. Specifically, a multi-scale region localization module is discussed to automatically identify disease-related discriminative regions in full-volume FDG-PET images in an unsupervised manner, upon which a confidence score is designed to evaluate the prioritization of regions according to the density distribution of anomalies. Then, the proposed multi-scale region classification module adaptively fuses multi-scale region representations and makes decision fusion, which not only reduces useless information but also offers complementary information. Most of previous methods concentrate on discriminating AD from cognitively normal (CN), while mild cognitive impairment, a transitional state, facilitates early diagnosis. Therefore, our method is further applied to multiple AD-related diagnosis tasks, not limited to AD vs. CN.Main results. Experimental results on the Alzheimer's Disease Neuroimaging Initiative dataset show that the proposed method achieves superior performance over state-of-the-art FDG-PET-based approaches. Besides, some cerebral cortices highlighted by extracted regions cohere with medical research, further demonstrating the superiority.Significance. This work offers an effective method to achieve AD diagnosis and detect disease-affected regions in FDG-PET imaging. Our results could be beneficial for providing an additional opinion on the clinical diagnosis.
Development and Utilization of English Online Course Resources Based on Fuzzy Neural Network

Since the reform and development, after decades of changes in the times, China’s basic education reform has been promoted several times, and the degree of information technology in the campus will become one of the important factors for parents to choose this school or not. From digital projectors since 2003 to multimedia-assisted English teaching nowadays, it is evident that online English teaching has become an important part of today’s English online resources, but most of the English online resources nowadays are developed by professional research institutions, which leads to a situation where online English curriculum resources are not closely connected to the actual English teaching mode, and more importantly, there is a lack of online English course resources and they are outdated. In the new curriculum reform requirements, the development and utilization of English online resources are clearly pointed out, which shows the importance of developing and utilizing English online course resources and the need to develop in the direction of innovation and modernity, and the need to consider combining the development and utilization of English online course resources with modern technological progress, not only at the theoretical level but also at the practical level. English is a comprehensive and practical language that needs to be used and reinforced by users in order for them to be able to communicate and express their thoughts in English. In this paper, we adopt a fuzzy neural network approach and a gradient descent algorithm to address the shortcomings of current English online course resource development and utilization, and adopt an averaging process for weight updates, and then realize the control of the number of fuzzy rules by adding pooling operations. The experimental results show that this paper can effectively develop English online resources.
On the Influence of Kawakami’s Marxist Historical Materialism under the Background of Intelligent Network Technology

In order to study the influence of the dissemination of Kawakami’s Marxist historical materialism under the background of intelligent network technology, the research on the influence of the dissemination of Kawakami’s Marxist historical materialism under the background of integrated media technology is discussed, through the overall effect and comprehensive performance of the influence of integrated media technology under the background of ordinary network technology and intelligent network technology on the dissemination of Kawakami’s Marxist historical materialism, compared with the coupling degree, and has statistical significance. It is obvious that the analysis and research using intelligent network technology are better than the previous ordinary network technology. At the same time, the propagation speed of intelligent network technology is faster, more convenient, more comprehensive, and more secure, achieving the real era of Internet big data information intelligence. Through the integration of media technology under the background of intelligent network technology, the spread influence of Kawakami’s Marxist historical materialism has increased rapidly. It has broadened the path of the spread of Marxist theory, accelerated the spread of Marxism, and had a certain impact on the spread of Marxist theory, becoming more popular and convenient, so that people all over the world can have a deeper understanding of their ideological theory, thus changing people’s world outlook and values and driving the development of national economy, culture, and humanistic thought.