Reviewerly/exHarmony huggingface dataset:
Is Bitcoin an Alternative Investment Asset? An Econometric Investigation of China and the USA

This paper evaluated Bitcoin ﬁnancial and economic behaviour by using the econometric model on Bitcoin rate of returns compared to the alternatives assets like precious metals, stock market, and exchange rate risk. The study employed the various Quantile Regression models to observe the hedging ability of Bitcoin under bearish and bullish scenarios. The daily data of China and the USA have been collected, from July 18, 2010, to August 31, 2021. The result indicates that under diﬀerent market phenomena, Bitcoin holds hedge and safe-haven asset properties against precious metals such as gold, silver, and platinum. Bitcoin can be used as an alternative to money during the currency devaluation against the US Dollar since it holds a hedge and safe- haven properties against S&amp;P 500 Index and SSEC Index. The study elaborates the several implications for investors portfolios. Finally, the study draws the attention of policymakers towards the legalisation of Bitcoin as a currency alternative considering its eﬃcient performance under diﬀerent economic conditions, supported by detailed theoretical and empirical analyses.
Efficient Continuous Subgraph Matching Scheme Based on Trie Indexing for Graph Stream Processing

With the expansion of the application range of big data and artificial intelligence technologies, graph data have been increasingly used to analyze the relationships among objects. With the advancement of network technology and the spread of social network services, there has been an increasing need for a continuous query processing algorithm that can manage large-volume graph streams generated in real time. In this paper, a sliding-window-based continuous subgraph matching algorithm that can efficiently control graph streams is proposed. The proposed scheme uses a query processing technique based on trie indexing. It establishes an index based on a materialized view of similar queries and conducts continuous query processing based on the materialized view to perform continuous query processing efficiently. It also provides wildcard operations on vertices and edges to consider various query types. Moreover, in this study, a two-level cache technique that can manage frequently used subgraphs and subgraphs that may be used in the future is developed, to handle intermediate query results in the form of a materialized view. Cache replacement techniques based on statistical data are also presented to improve the performance of the developed cache technique. The excellent performance of the proposed algorithm is verified by a conducting independent performance evaluation and comparative performance evaluation.
Verifiable Privacy-Preserving Outsourced Frequent Itemset Mining on Vertically Partitioned Databases

In the data era, to simultaneously relieve the heavy computational burden of mining data information from data owners and protecting data privacy, privacy-preserving frequent itemset mining (PPFIM) is presented and has attracted much attention. In PPFIM, data owners and miners outsource the complex task of data mining to the cloud server, which supports strong storage and computing power, and the cloud server cannot extract additional data privacy other than that which is shown by data owners or miners. However, most existing solutions assume that cloud servers will honestly perform the mining process and return the correct results, whereas cloud services are usually provided by a charging third party that may in practice return incorrect results due to computation errors, malicious or criminal activities, etc. To solve this problem, in this paper, we present a verifiable PPFIM protocol on vertically partitioned databases to support the verifiability of the integrity of the mining results, where data owners can authorize the cloud server to perform federated mining on vertically partitioned databases without leaking data information and detect dishonest behaviors in the cloud server from the returned results. We adopt a dual cloud setting to enable data owners to be offline after uploading their encrypted databases to the cloud server, which further relieves the burden on data owners. We implement our protocol and give a detailed analysis in terms of verification accuracy, which shows that the dishonest behaviors of the cloud server can be detected with a probability close to 1 and a sacrifice of only a 1% increase in database size.
Learn to Train Like You Fight

This qualitative study includes nine semi-structured interviews with cybersecurity experts from different security-related organizations who are familiar with cybersecurity exercises. Its contribution to cybersecurity workforce development focuses on organizational learning rather than individual skills development and relevant competencies. It was found that the utilization of thematic analysis methods develop individual readiness and expertise. It also enhances the maturity of an organization's processes, roles, communication, and exercise capabilities. Moreover, the exercises increase social trust between individuals and organizations through business-to-business cooperation. However, there were several barriers and challenges in utilizing learned skills and competencies within an organization, including a need to increase the capacity of staff members who participate in the exercise.
A Phase Difference Measurement Method for Integrated Optical Interferometric Imagers

Interferometric imagers based on integrated optics have the advantages of miniaturization and low cost compared with traditional telescope imaging systems and are expected to be applied in the field of space target detection. Phase measurement of the complex coherence factor is crucial for the image reconstruction of interferometric imaging technology. This study discovers the effect of the phase of the complex coherence factor on the extrema of the interference fringes in the interferometric imager and proposes a method for calculating the phase difference of the complex coherence factor of two interference signals by comparing the extrema of the interferometric fringes in the area of approximate linear change in the envelope shape to obtain the phase information required for imaging. Experiments using two interferometric signals with a phase difference of π were conducted to verify the validity and feasibility of the phase difference measurement method. Compared with the existing phase measurement methods, this method does not need to calibrate the position of the zero optical path difference and can be applied to the integrated optical interferometric imager using a single-mode fiber, which also allows the imager to work in a more flexible way. The theoretical phase measurement accuracy of this method is higher than 0.05 π, which meets the image reconstruction requirements.
Exploration of Teaching Mode Reform in Higher Vocational Mechanism Major under the Background of Diversified Student Source

With the reform of college entrance examination policies, various vocational colleges have formed a diverse student source structure, with uneven student sources, bringing new challenges to teaching. As one of the industries that drive the rapid development of Wenzhou's local economy, the export of skilled personnel in the machinery industry has also been highly valued. This paper takes Wenzhou Vocational and Technical College as an example to explore some ideas for the reform of the teaching mode of the mechanism major in higher vocational colleges under the background of diverse student sources by analyzing the situation of student sources and the problems existing in the teaching mode of the mechanism major in Rui'an College in the past five years.
Research on English Autonomous Learning Model of College Students in Baoding

The rise of "Internet +" in today's era has provided a great port for the English learning mode of contemporary college students. Bilibili bullet-screen website (hereinafter referred to as "B Website"), as a new media platform that has been booming in recent years, has attracted a large number of contemporary college students to study on this platform, featuring rich learning resources, high participation sense of bullet-screen and strong course selection. High-quality videos and precise push algorithm make Station B more professional and create a better education and teaching area. Literature research shows that there are few studies on new English learning models for college students on new media platforms. In order to better explore college students' independent English learning mode under the reform of education mode in the new era, this study is based on the APP Bilibili, focusing on college students themselves, to explore the beneficial new mode of knowledge sharing platform such as Station B for college students' English learning.
Federated FAIR principles: Ownership, localisation and regulatory compliance (OLR)

FAIR-Guidelines can be enriched to include ownership of data, localisation of storing data and stewardship that respects regulatory frameworks relevant to the jurisdiction. It is proposed that this is referred as FAIR-OLR; OLR referring to Ownership, Localisation and Regulation of data production in jurisdiction.
Research on computer vision technology based on BP-LSTM hybrid network

Abstract The computer vision direction in the field of artificial intelligence analyses the latest progress of computer vision technology from visual perception and visual generation, including but not limited to image recognition, target detection and image segmentation. First of all, for computer vision technology, this paper introduces the detailed application of image recognition technology, object detection technology and image segmentation technology. Then, we build a BP neural network combined with a deep LSTM neural network, use the BP network algorithm to select the input variables to reduce the dimension and complexity of the model, and use the selected variables as the input of the deep LSTM network. At the same time, deep LSTM is used to perform high-dimensional deep memory learning features on the selected variables. Finally, the model is separately experimented in computer vision. The experimental results show that the present model and other single models can be selected by BP neural network variables in computer vision applications, which can effectively reduce the complexity of the model and improve the generalisation ability of the model, so that it can be used in computer vision research.
Developing augmented reality applications to promote digital storytelling: The cases of Choirokoitia and Sandby Borg

Drawing on Kucher’s (2021) framework on digital game-based learning (DGBL), this paper introduces the development of game-based scenarios in two culturally distinctive and salient heritage sites in Cyprus and Sweden, the Neolithic village of Choirokoitia and the Iron Age Ringfort of Sandby Borg. The aim was to implement Kucher’s (2021) five proposed principles and digital storytelling as a framework for the development of game-based Augmented Reality (AR) scenarios to promote exploration, understanding, collaboration, and learning on culturally important sites and guide users in becoming acquainted with cultural artifacts that reflect on cultural values, practices, and norms of that era. Using a bottom-up approach in the design and development process, the two case studies were designed to promote interactive and immersive learning experiences entrenched in problem-solving activities, constructive evaluative feedback, and enact opportunities for flexibility in exploration. To achieve this goal, an introductory scene and three subsequent AR game-based scenarios were developed for Choirokoitia, and an introductory scene and three subsequent AR game-based scenarios for Sandby Borg.
Graphical User Interface for the Development of Probabilistic Convolutional Neural Networks

Through the development of artificial intelligence, some capabilities of human beings have been replicated in computers. Among the developed models, convolutional neural networks stand out considerably because they make it possible for systems to have the inherent capabilities of humans, such as pattern recognition in images and signals. However, conventional methods are based on deterministic models, which cannot express the epistemic uncertainty of their predictions. The alternative consists of probabilistic models, although these are considerably more difficult to develop. To address the problems related to the development of probabilistic networks and the choice of network architecture, this article proposes the development of an application that allows the user to choose the desired architecture with the trained model for the given data. This application, named “Graphical User Interface for Probabilistic Neural Networks”, allows the user to develop or to use a standard convolutional neural network for the provided data, with networks already adapted to implement a probabilistic model. Contrary to the existing models for generic use, which are deterministic and already pre-trained on databases to be used in transfer learning, the approach followed in this work creates the network layer by layer, with training performed on the provided data, originating a specific model for the data in question.
Research on the Teaching Reform of "Fund Investment Practice" under the Integration of "Post Course and Competition Certificate

In April 2021, the National Vocational Education Conference, Sun Chunlan clearly said that it must "strictly follow the establishment of moral education, improve the type of positioning, and accelerate the creation of a modern vocational education system. Adhere to the comprehensive education model of 'post-course competition certificate', improve the efficiency and quality of education, enhance the smoothness and flexibility of career development channels, deal with problems efficiently, and strengthen the attractiveness and recognition of vocational education for the audience". As the core course of wealth management in Wenzhou Vocational and Technical College, as one of the three major directions of future employment for wealth management students, and as one of the means for mass investors to unlock their wealth freedom, how to conduct the course of Securities Investment Funds well, enhance students' interest in learning, improve the practical skills of students in higher education institutions, and meet enterprises' demand for talents in fund management is an urgent issue to be dealt with at present. The article focuses on the overall design of the Securities Investment Fund course as a carrier, deeply analyzes the problems of the teaching of Securities Investment Fund, and proposes the integration of the "post-course, race and certificate" education mechanism, so as to break the traditional teaching mode, reduce the negative impact caused by it, and reconstruct the curriculum system from multiple perspectives, such as the integration of the course and certificate and job requirements. The curriculum system is reconstructed from various perspectives, such as the integration of courses and certificates and job requirements, in order to improve the vocational ability of wealth students in a comprehensive manner.
Three-Dimensional Underwater Path Planning Based on Modified Potential Field Algorithm in Time-Varying Current

Abstract The article addresses the three-dimensional (3D) underwater path planning problem of an autonomous underwater vehicle (AUV) in a time-varying current. A modified artificial potential field algorithm combining the velocity vector synthesis method is proposed to search for the optimal path. The modified potential field (MPF) algorithm is designed to dynamically plan the non-collision path. Meanwhile, this modified method is also proved to be an effective solution to the goals not reachable with obstacles nearby (GNRON), U-shaped trap, and rotation unreachable problems. To offset the influence of time-varying current, the velocity synthesis approach is designed to adjust the AUV movement direction. Besides, considering path planning in the complex underwater environment, the multi-beam forward-looking sonar (FLS) model is used. Finally, simulation studies substantiate that the designed algorithm can implement the AUV path planning effectively and successfully in a 3D underwater environment.
Multi-Target Tracking in Multi-Static Networks with Autonomous Underwater Vehicles Using a Robust Multi-Sensor Labeled Multi-Bernoulli Filter

This paper proposes a centralized MTT method based on a state-of-the-art multi-sensor labeled multi-Bernoulli (LMB) filter in underwater multi-static networks with autonomous underwater vehicles (AUVs). The LMB filter can accurately extract the number of targets and trajectories from measurements affected by noise, missed detections, false alarms and port–starboard ambiguity. However, its complexity increases as the number of sensors increases. In addition, due to the time-varying underwater environment, AUV detection probabilities are time-varying, and their mismatches often lead to poor MTT performance. Consequently, we detail a robust multi-sensor LMB filter that estimates detection probabilities and multi-target states simultaneously in real time. Moreover, we derive an effective approximate form of the multi-sensor LMB filter using Kullback–Leibler divergence and develop an efficient belief propagation (BP) implementation of the multi-sensor LMB filter. Our method scales linearly with the number of AUVs, providing good scalability and low computational complexity. The proposed method demonstrates superior performance in underwater multi-AUV network MTT simulations.
Research on Business English Translation Strategies under the Background of Cultural Differences

With the development of globalization and closer exchanges between countries, business English translation has increasingly shown the importance of cultural and language communication functions. To do a good job in business English translation, we must understand the differences between Chinese and Western cultures, fully study business translation theories, better translate business content into English, and make corresponding changes and adaptations according to different cultures. Based on a cross-cultural perspective, this paper analyzes the cultural factors affecting business English translation by expounding the cultural differences between China and the West, and proposes business English translation strategies under the background of cultural differences.
Heterogeneity in kimberlite structure and composition as a reflection of its petrogenesis peculiarities

New data on the geological structure and petrochemical characteristics of the first kimberlite pipe in the Syuldyukar kimberlite field and its position in the Ygyatta diamond-bearing area are presented. The petrographic identification of the petrochemical varieties of kimberlites in this area was performed for the first time. The compositions of the rockforming and accessory minerals of kimberlites were studied using a modern hardware complex. The results showed that the directed change in the structural and material characteristics of kimberlites from peripheral to central parts in extended vertical bodies of the Syuldyukar tube type is expressed in the replacement of calcium kimberlites by magnesium kimberlites. Mg-kimberlites are characterized by the largest crystals of diamond, garnets, and ilmenites, high concentrations of magnesium, and a number of other components. All of these changes in kimberlites are caused by flow differentiation. This information allowed us to apply the obtained petrological results to the kimberlites of the Syuldyukar tube as search criteria for forecasting and identifying new kimberlite bodies in the Ygyatta diamond-bearing area.
Exploring Neural Dynamics in Source Code Processing Domain

Deep neural networks have proven to be able to learn rich internal representations, including for features that can also be used for different purposes than those the networks are originally developed for. In this paper, we are interested in exploring such ability and, to this aim, we propose a novel approach for investigating the internal behavior of networks trained for source code processing tasks. Using a simple autoencoder trained in the reconstruction of vectors representing programs (i.e., program embeddings), we first analyze the performance of the internal neurons in classifying programs according to different labeling policies inspired by real programming issues, showing that some neurons can actually detect different program properties. We then study the dynamics of the network from an information-theoretic standpoint, namely by considering the neurons as signaling systems and by computing the corresponding entropy. Further, we define a way to distinguish neurons according to their behavior, to consider them as formally associated with different abstract concepts, and through the application of nonparametric statistical tests to pairs of neurons, we look for neurons with unique (or almost unique) associated concepts, showing that the entropy value of a neuron is related to the rareness of its concept. Finally, we discuss how the proposed approaches for ranking the neurons can be generalized to different domains and applied to more sophisticated and specialized networks so as to help the research in the growing field of explainable artificial intelligence.
Formation and Development of Teachers Professionalism

With the development of the world economy, the international cooperation and exchanges have gradually increased, while the international competition has become increasingly fierce. More and more countries have realized that in order to be invincible in international competition, they need to cultivate more talents with international competitiveness. The development and formation of teacher quality is the part that we continue to explore and improve.
Signals Intelligence System with Software-Defined Radio

In this paper, we present the implementation of a system that identifies the modulation of complex radio signals. This is realized using an artificial intelligence model developed, trained, and integrated with Microsoft Azure cloud. We consider that cloud-based platforms offer enough flexibility and processing power to use them instead of conventional computers for signal processing based on artificial intelligence. We tested the implementation using a software-defined radio platform developed in GNU Radio that generates and receives real modulated signals. This process ensures that the solution proposed is viable to be used in real signal processing systems. The results obtained show that for certain modulation types, the identification is performed with a high degree of success. The use of a cloud-based platform allows quick access to the system. The user is able to identify the signal modulation using only a laptop that has access to the internet.
Research of Online Education in the 5G Era in China

This research takes Guangzhou University as an example, mainly through literature analysis, questionnaire survey, expert interview and other methods, the core issue of this research is to conduct in-depth research and analysis on online education from the three perspectives of "teaching, learning and management" under the 5G enabling, and explore the path to promote the development of online education in the 5G era. The research results show that the integration and upgrading of 5G communication technology and intelligent information technology can truly enable the innovative development of intelligent education, promote the transformation of online education application scenarios and learning and teaching models, gradually form a new pattern of online education, and further realize the great goal of building an educational power.