Reviewerly/exHarmony huggingface dataset:
Reconstruction of Sparse Graph Signals from Reduced Sets of Samples

The reconstruction of sparse graph signals based on a reduced set of samples is a relevant topic, since graphs, acting as irregular signal domains, may comprise a large number of vertices, millions, or even billions. The related storage and computational issues motivate the involvement of subsampling and compressive sensing concepts applied to graphs. We investigate the fundamental differences in the conditions for the reconstruction based on a reduced set of measurements, in the general case of graph signal processing, versus the case of traditional signal processing. There are two frameworks for such reconstruction – one assuming that the positions of nonzero sparsity domain coefficients are a priori known, and the other – when those positions are unknown and must be estimated based on the available measurements. In the first framework, on which we focus in this article, the conditions are in general far less conservative than in the second framework, which involves the compressive sensing (CS) paradigm. The particular structure of the graph may significantly influence the results, therefore bringing to the fore the concept of measurements formed as a linear combination of samples.
Augmented Reality Supported Learning for Cultural Heritage of Taiwan in On-Site and Off-Site Environments: The Case of a Daxi Old Street

This study aims to explore whether the integration of Augmented Reality (AR) technology used in community-based art education can improve the participants’ learning effectiveness and to compare its effects in two different learning environments (on-site and off-site). A quasi-experimental research design has been conducted to check the effectiveness of integrating AR activities in terms of learners’ effectiveness. The sample for this study includes 40 and 64 participants in on-site and off-site groups, respectively. The on-site group visited the real environment and viewed AR-integrated materials such as posters and rhymes; the off-site group visited the school exhibition and viewed the same materials as the on-site group. The data was obtained through motivation, learning satisfaction, technology acceptance, and learning effectiveness scales designed by the researchers. Descriptive statistics and an independent t -test were utilized for the data analysis. There was a significant difference in learning motivation, learning effectiveness, and learning satisfaction between on-site and off-site groups; however, there was no significant difference in the mean of technology acceptance. This study finds that the use of AR digital materials facilitated the visitors’ experience of cultural heritage and improved participants learning effectiveness, and particularly learning satisfaction and learning motivation, more so in the on-site experience than in the off-site experience. Results also revealed that there was a significant difference in gender and age toward learning motivation and learning effectiveness. With respect to participants with different artistic and educational backgrounds, the results showed no significant differences in learning motivation, learning effectiveness, learning satisfaction, and technology acceptance. Finally, recommendations for future practices and research are discussed.
Privacy-Preserving federated learning: An application for big data load forecast in buildings

Internet of Everything (IoE) is playing key role to enable smart energy management in buildings. A huge amount of load data are accumulated continuously to control the energy systems inside buildings. Big data analysis and prediction play increasingly vital roles in making the building energy systems smarter. Meanwhile, the big data forecasting technique represents the key element for an efficient energy control mechanism in buildings. However, the load forecast models require a huge amount of consumption data with enough variety to achieve high accuracy. Moreover, the collected IoE data are privacy-sensitive. That is, the fine-grained load data collected at the level of buildings may reveal information about the occupant’s behaviors. In this paper, by using Federating Learning (FL) as a decentralized machine learning technique, buildings occupants’ information can efficiently participate in the decision-making without revealing privacy through a cloud/Edge computing framework. In the FL approach, the big data are widely distributed between buildings, where a shared learning model is trained locally on each of the buildings. Nonetheless, the FL approach does not provide provable privacy guarantees. Moreover, FL requires participating buildings to regularly exchange huge updates resulting in huge bandwidth consumption. The paper presents a new privacy-preserving bandwidth-efficient FL that relies on Differential Privacy to theoretically provide privacy. Furthermore, to ensure the fast convergence of the FL model, a POWER-SELECTION protocol is introduced to select between buildings to participate in the FL round. By numerical analysis, the proposed FL approach is demonstrated to improve the accuracy of load forecast while compromising provable privacy.
DeFi Common Sense: Crypto‐backed Lending in <i>Janesh s/o Rajkumar v Unknown Person (‘CHEFPIERRE’)</i>

One of the selling points of cryptoassets has been the ability to subject them to so‐called ‘smart contracts’ embedded upon blockchains; yet, despite numerous common law decisions accepting cryptoassets as property, until Janesh s/o Rajkumar v Unknown Person (‘CHEFPIERRE’ ) no courts have had the occasion to consider how such property (in this case, an NFT) interact with these ‘smart contracts’. The case considers ‘smart contracts’ in the context of decentralised finance (DeFi), thus also raising questions concerning the legal effectiveness and prudence of using cryptoassets as objects of security. Although the non‐participation of the defendant meant that the court was deprived of full arguments, the judgment remains worthy of consideration, both for what the court does consider – specifically, criticisms of the Ainsworth test of property – and what it does not.
Anonymous Multi-Hop Payment for Payment Channel Networks

Payment Channel Networks (PCNs) have flourished as one of the most promising solutions to the blockchain scalability problem. Unfortunately, the existing PCN solutions either fail to provide path privacy guarantees or require the not-always-true All-Anonymous-Connected assumption (i.e., an anonymous communication channel always exists for any two participants). To alleviate these problems, we first present a new cryptographic primitive named anonymous multi-hop payment (AMHP), which is an improvement of anonymous multi-hop lock (AMHL). Using AMHP and payment channels, we can have a new PCN solution with path privacy but removing the All-Anonymous-Connected assumption. After that, we present the first AMHP scheme, called AMHL+, by adapting the generic construction of AMHL, but at the cost of high communication overhead. To reduce the communication cost, we further present a new AMHP scheme (named EAMHL+) using bilinear pairing. The communication cost of the EAMHL+ is reduced by 92.3% compared to the AMHL+. The rigorous security analysis demonstrates that the EAMHL+ holds consistency, balance security, and path privacy. Finally, we implement the proposed AMHP schemes using Java. The extensive experimental results show that, though the EAMHL+ requires more computational cost than the AMHL+, it is more efficient than the latter in terms of communication overhead.
Blockchain-Driven Revocable Ciphertext-Policy Attribute-Based Encryption for Public Cloud Data Sharing

Sharing data through the public cloud has turned into a popular way to exploit the data value today, and ciphertext-policy attribute-based encryption (CP-ABE) can protect data confidentiality while improving sharing efficiency. In CP-ABE, attribute change raises the issue of revocation, which is solved by a third party in the existing schemes. However, it cannot achieve timely and certain revocation. Thus, this paper proposes a blockchain-driven revocable CP-ABE scheme. We design a re-writable blockchain to realize attribute revocation, and the ciphertext no longer needs to be updated. Attribute authority only needs to update an update factor bound to the attribute and the private keys for non-revoked users, while the revoked users cannot recover the outdated ciphertext. Moreover, our scheme is selectively secure and collusion resistant. Compared with other schemes, the performance of our scheme is satisfactory.
Phenotype Search Trajectory Networks for Linear Genetic Programming

In this study, we visualise the search trajectories of a genetic programming system as graph-based models, where nodes are genotypes/phenotypes and edges represent their mutational transitions. We also quantitatively measure the characteristics of phenotypes including their genotypic abundance (the requirement for neutrality) and Kolmogorov complexity. We connect these quantified metrics with search trajectory visualisations, and find that more complex phenotypes are under-represented by fewer genotypes and are harder for evolution to discover. Less complex phenotypes, on the other hand, are over-represented by genotypes, are easier to find, and frequently serve as stepping-stones for evolution.
A High-Precision Intelligent Retrieval Algorithm for Bill of Quantities

To improve the retrieval efficiency and accuracy of bill of quantities, a high-precision intelligent retrieval algorithm based on Elasticsearch (ES) engine, improved TF-IDF algorithm, and Jaccard similarity dynamic weighting algorithm is proposed. According to the features of bill of quantities, a kind of characteristic word extracting method based on TF-IDF and RE regular expression is proposed. The extracted characteristic words are divided into text features, specification features, and numeric features. The text features and specification features are stored in the NoSQL database of MongoDB to generate a historical feature database. The retrieval process of the to-be-retrieved bill is divided into two steps. The first step is named approximately retrieving, which extracts the ES searching words of the bill based on the historical feature database, and uses the ES engine to obtain the approximate matching database. The second step is designed to accurately retrieve the bill by dynamically computing the final similarity based on the three types of features and their weighted factors.
Global Path Planning for Multi-objective UAV-Assisted Sensor Data Collection: A DRL Approach

With the rapid development of the Internet of Things (IOT), the amount of business data carried by wireless sensor networks (WSNs) has exploded. Unmanned aerial vehicle (UAV)-assisted sensor data collection is effective and environmentally friendly. A UAV should determine which sensor nodes to visit, and the order in which to visit them according to the target requirements. Due to UAV energy and data-collection demands, a mission can have more than one goal. This study investigates the multi-objective global path planning of UAV-assisted sensor data collection (SDC). It is modeled as a multi-objective optimization problem (MOP) to maximize the amount of data collected and minimize UAV flight time. Based on a deep reinforcement learning (DRL) framework, we decompose the MOP into a series of subproblems, model them as neural networks and use an actor-critic algorithm and modified pointer network to solve each subproblem. Then the Pareto front of the path planning solution under the above constraints can be obtained through the forward propagation of the neural network. Experimental results show that the proposed method is superior to a traditional multi-objective evolutionary algorithms in terms of convergence, diversity of solutions, and time complexity. In addtion, the proposed method can be directly applied to the situation in which the number of sensors changes without retraining, and it has better robustness.
Blockchain Development

In recent years, blockchain research has set off an upsurge in academia, and it is called the next generation of value Internet. Because of its decentralization, anonymity, security, immutability, traceability and other characteristics, blockchain is gradually accepted and developed by people. With the deepening of research and the integration of technologies such as deep learning, blockchain has gradually been applied to various fields such as credit reporting, government, medical care, and industrial Internet of Things, not just the initial virtual currency field. This article mainly discusses the three important stages of blockchain public chain development, namely Bitcoin, Ethereum, and meta-verse, and introduces some basic supporting technologies of blockchain, as well as the research status and future trends of blockchain. Simple Analysis. By vertically introducing the development history of the blockchain, researchers can have a more concrete understanding of the status quo of the blockchain, and provide ideas for blockchain-related research.
Indistinguishable Obfuscated Encryption and Decryption Based on Transformer Model

To solve the problem in secure encryption in cryptography, indistinguishability Obfuscation (iO) was born. It is a crypto-complete idea, based on which we can build many cryptographic construction. The implementation of it can hide both the dataset and the program itself. In this paper, we use the idea of translation in the (Natural Language Processing) NLP-like language model to realize the conversion between plaintexts and ciphertexts with the help of hints. We trained a self-attention transformer model, successfully hiding the dataset as well as the encryption and decryption programs. The input of the encryption model is a plaintext prefixed with a hint and the output is the result of encryption using one of the specified algorithms. The input and output of the decryption model are the opposite of the encryption one.
Effects of Using Immersive Virtual Reality for Science Education on Learning Outcomes: A Randomized Controlled Pilot Study

Immersive virtual reality (IVR) technology influences students cognitive process and affective experience according to previous research findings. Yet empirical studies that investigate the impact of immersive virtual reality (IVR) on science education in secondary schools are limited. This article reports a pilot study that has two objectives: 1) To investigate whether learning science with IVR leads to more positive learning outcomes than that with a traditional video-viewing activity; 2) To investigate whether corrective feedback produces more positive learning outcomes than explanatory feedback in IVR-mediated contexts. A randomized controlled pilot trial was designed during a school year at a secondary school in western China. Forty-seven participatory students were randomly assigned to a control group (learning with instructional videos, n = 17), an intervention group 1 (learning with IVR with corrective feedback, n = 15), and an intervention group 2 (learning with IVR with explanatory feedback, n = 15). We evaluated an array of student learning-related outcomes, including extraneous and germane cognitive load, intrinsic learning motivation, academic self-efficacy, learner satisfaction, and academic achievement. The results revealed significant difference in intrinsic learning motivation, academic self-efficacy, learner satisfaction, and academic achievement and insignificant difference in extraneous and germane cognitive load when students learned with video and IVR. In terms of the type of feedback provided in IVR-mediated environments, students who were prompted with corrective feedback and explanatory feedback showed little difference in their learning outcomes. Reasons related to the findings and limitations are discussed.
Grain and pore shape analysis and generation of digital twins from digital images

The properties of granular or sintered manufactured materials or natural materials depend on their microstructure. Obtaining meaningful insights on the microstructure of these materials often relies on the correct identification of individual grains and pores. Their direct identification on 3D digital images is challenging but opens the possibility to detailed simulations and accurate predition of material behavior, such as mechanical simulations that depend on the grain orientation. Such simulations give vital information to figure out the most promising designs of manufactured materials such as electrodes or to reach decisions based on reservoir rock properties and determined by Digital Rock Physics. With this motivation, several new algorithms have been integrated into the GeoDict® [1] software. GeoDict’s capabilities to segment 3D scans, e.g. CT or FIB-SEM scans have been expanded to identify also individual grains and pores through the new modules GrainFind and PoreFind that are presented here, together with the option to use the results of GrainFind for the creation of digital twins of granular materials.
The effect of flipped digital classroom method using moodle and student engagement on reading comprehension

English is a very urgent subject in academic life. In order to improve reading comprehension skills, teachers need to use online learning during the COVID-19 pandemic. This method is to provide space for students to study independently online. This study aimed to analyze the effect of the flipped digital classroom method using Moodle e-learning material and students’ engagement on students’ reading comprehension skills. This research used a quantitative approach with a quasi-experimental design. The instruments used in this research were observation sheets, questionnaires, and reading comprehension tests. The data analysis used Two Way ANOVA. The results showed that the flipped digital classroom method using Moodle and student engagement affect reading comprehension skills. English lecturers should apply this method by building a pleasant online learning atmosphere so that student engagement and reading comprehension skills increase.
Coal rank data analytic for ASTM and PSDBMP classification

The large amount of coal production in Indonesia generates a significant amount of data that can be used to understand the rank of the coal. To effectively process and interpret this data, our study employs the use of big data techniques, including big data management and big data analysis. Big data management allows us to organize and understand the data patterns, while big data analysis is used to gain insights and knowledge about the data, such as coal rank analysis and identifying the type of coal. Our study uses a python-based approach to define variables and automatically classify the coal rank based on the threshold values obtained from the two basic analyses described earlier. Our results show that this method is able to accurately classify the coal according to the given threshold. We found that according to the Indonesian Coal Standardization based on Pusat Sumber Daya Mineral Batubara dan Panas Bumi (PSDBMP) standard, the calorific value (in adb) is dominated in low to medium calories for 14 boreholes. The coal rank in American Standard Testing and Material (ASTM) analysis is dominated by Lignite A and B for 14 boreholes. The last analysis, according to the atomic ratio, shows that the coal can be classified as Lignite and Subbituminous Coal. Thus, by implementing the big data concept, we can easily analyze the coal classification with comprehensive and large amount of data.
Clase Invertida en el Logro de Aprendizaje de la Investigación en Comunicación en Estudiantes Universitarios

The purpose of the article is to analyze what is the contribution of the invested classroom strategy in the achievement of learning in communication research in undergraduate university students. The study design was quasi-experimental with pre-and post-test to the experimental and control group. The sample was made up of 50 students (section 1 and 2) of Communication Sciences of the Santiago Antúnez de Mayolo National University (UNASAM) enrolled in the communication research subject in the 2021-I semester. The rubric and the checklist were applied to measure the variables and results that were analyzed using descriptive statistics and the T-Studnt. It was concluded that despite all the obstacles faced by the application of the flipped classroom strategy for the achievement of competency-based learning, the data resulting from the present research reliably demonstrate the effectiveness of this 4 strategy in educational processes because significant differences were identified in the achievement of research learning in communication between the control and experimental group, the latter being the one that reached high levels of achievement.
A hand gestures recognition system for industrial robot control

In order to improve the interactivity of industrial robot, this paper has designed a hand gestures recognition system for industrial robot control. The system contains two MPU6050 modules, Arduino Uno, computer and a ABB IRB 120 industry robot. Two MPU6050 modules collects the human hands movement signals which will be transmitted to Arduino Uno to process. The upper computer can receive signals processed and send commands through serial port. Also, the upper computer can send signals which include the robot movement mode parameters , speed parameters , direction parameters and so on to ABB IRB 120.Thus, the movements of the robot can be controlled by the movement of both hands where the prototypes were worn . The experimental results show that the correct rate of gesture recognition of the system higher than is 99% and the system works well, which achieves the expected purpose.
A Longitudinal and Comprehensive Measurement of DNS Strict Privacy

The DNS privacy protection mechanisms, DNS over TLS (DoT) and DNS over HTTPS (DoH), only work correctly if both the server and client support the Strict Privacy profile and no vulnerability exists in the implemented TLS/HTTPS. A natural question then arises: what is the landscape of DNS Strict Privacy? To this end, we provide the first longitudinal and comprehensive measurement of DoT/DoH deployments in recursive resolvers, authoritative servers, and browsers. With the collected data, we find the number of DoT/DoH servers increased substantially during our ten-month-long scan. However, around 60% of DoT and 44% of DoH recursive resolver certificates are invalid. Worryingly, our measurements confirm the centralization problem of DoT/DoH. Furthermore, we classify DNS Strict Privacy servers into four levels according to daily scanning results on TLS/HTTPS-related security features. Unfortunately, around 25% of DoH Strict Privacy recursive resolvers fail to meet the minimum level requirements. To help the Internet community better perceive the landscape of DNS Strict Privacy, we implement a DoT/DoH server search engine and recommender system. Additionally, we investigate five popular browsers across four operating systems and find some inconsistent behavior with their DNS privacy implementations. For example, Firefox in Windows, Linux, and Android allows DoH communication with the server without the SAN certificate. At last, we advocate that all participants head together for a bright DNS Strict Privacy landscape by discussing current hindrances and controversies in DNS privacy.