Reviewerly/exHarmony huggingface dataset:
Intelligent computing scheme of blockchain based on trusted execution environment

To solve the issues that the existing blockchain technology suffers from insufficient computing power and unreliable computing when using the computing-intensive machine learning model for decision-making, a blockchain intelligent computing scheme that is based on a trusted execution environment is proposed. By placing heavy computing tasks in the trusted execution environment off-chain, the computing burden of processing on-chain data is reduced and trusted computing capability is provided. At the same time, in order to apply the trusted execution environment to the intelligent calculation of data on the blockchain chain, this scheme proposes a data trust management module to ensure the trusted supply and consumption of data and a data computing process and input and output trusted intelligent computing module. Finally, the security of the scheme is analyzed theoretically, and verify the feasibility of the scheme which is based on the scheme prototype implemented on the private Ethereum blockchain.
Superconducting Single-photon detector design optimization

A new SNSPD topology is proposed, in which, according to the simulation results, the least current crowding effect is observed, which makes it the most preferable for the creation of an SNSPD with a high quantum efficiency and a low dark count rate.
Where Is My Tag? Unveiling Alternative Uses of the Apple FindMy Service

Bluetooth trackers, or tags, have quickly become ubiquitous and widely supported by multiple vendors. Beyond their original design of finding lost objects, these devices have the ability to extend the capabilities of current wireless smart devices. Since its launch in 2019, Apple’s FindMy enables any devices from their brand to be easily tracked by more than 1 billion active iPhones and iPads on the market. While convenient, these systems may even serve further uses, including as a result of this work, crowd sensing and a side channel for mobile communication. But they also raise privacy concerns for their users. In this paper, we demonstrate how Apple FindMy can be used as a privacy-friendly tool for crowd monitoring, and how it may inadvertently leak information on a person’s location in case of deliberate tracking. Additionally, we design and evaluate a proof of concept protocol, using the Apple FindMy and a crafted tag using a simple microcontroller. We show how such system could be used to transmit information at very low bit rates, while the devices transporting the information remain unaware of this covert channel, yielding an out of band communication channel.
The Effects of Augmented Reality Learning Experiences on Students' Academic Performance: A Meta-Analysis Review

Augmented Reality (AR) has been widely concerned around the world in recent years for its immersive, vivid, and visual learning experience, but it is also accompanied by people&#x0027;s doubts and reflections on its learning effectiveness. Many systematic reviews have analyzed the usefulness of AR and the impact of AR on students learning. They also measured the effect of moderators, such as level of education, fields, control treatment, learning environment, display devices, the sampling size, intervention duration and pedagogical approaches. However, most of the previous studies failed to analyze the difference of AR on different types of academic performance, nor did they make relevant studies on the different effects of different types of AR applications on students&#x0027; academic performance. This meta-analysis review studies the specific impact of augmented reality learning experiences (ARLEs) on students&#x0027; academic performance. Moreover, the effect of ARLEs on different types of academic performance was also measured. Along with taking into account moderating factors such as education level and fields, we also examined the impact of different types of ARLEs on academic performance. The results show that: (a) the combined effect size of the included researches is 0.997, which is a large effect, indicating that ARLEs has a positive effect on the academic performance of students; (b) The impact of AR on the four types of academic performance in &#x201C;the 21st Century&#x201D; is significantly different, of which the learning and innovative skills has the greatest impact; (c) The impact of ARLEs on students&#x0027; academic performance is positively correlated with student age; (d)Fields and AR types do not significantly regulate the main effect value. The findings of this study will contribute to the acceptance of ARLEs by teachers and students, and will provide a guiding principle for the rational development and application of ARLEs.
Resource Management in Cloud Based on Deep Reinforcement Learning

In order to provide users timely and reliable services, resources and facilities in cloud data centers are often more than actual needs, which leads to low resource utilization and high operating costs. However, controling the quantity of resources and facilities may cause low throughput. Therefore, many researches are devoted to increasing resource utilization, improving jobs throughput and reducing operating costs through effective cloud resource management. The cloud resource management in cloud data center can be regarded as a multi-resource demand job allocation problem. This paper proposed an allocation algorithm of jobs based on deep reinforcement learning. The algorithm allocates waiting jobs to clusters respectively, so as to optimize throughput and improve resource utilization. The algorithm is implemented in cloud data center simulation model CloudSim. The experimental results show that the method based on Deep Reinforcement Learning is better than traditional method in multi-resource job allocation problem.
The University in the Metaverse. Proposal of application scenarios and roadmap model

The Metaverse is gaining strength as a scenario where the real and the virtual converge. The motivation with this work arises to know and project its application, from specific scenarios such as laboratories to the University in the Metaverse. Therefore, research and discussion on the subject is necessary. A model is proposed as a roadmap that allows the incursion into these immersive experiences and a proposal of characteristics towards which a Metaverse solution should aim.
The Implementation of Tiered Loyalty Membership Program in Mobile Application via Behavioural Science for Customer Retention in Businesses

In today's world that emphasizes on efficiency and speed, Internet technology has also brought a great change to food and beverage industry. The aim of this study is to implement tiered loyalty membership program in mobile application via behavioural science to retain customer. Clients are assigned to different tiers based on their past purchases in a tier-based loyalty membership program, which distinguishes them in the hierarchical categories like member, silver, gold, and platinum. The goal of the tiered loyalty membership program is to reward the customer who has made a purchase by triggering the behavioural science impact to induce positive emotions such as feeling exceptional, highly valued and makes individuals stand out. Tiered loyalty membership programs can have a huge impact on customer retention as it involves customer satisfaction and long-term engagement. In this study, preliminary survey has been conducted prior to the actual survey distribution to ensure that the mobile application is functioning well and able to achieve the objectives. User satisfaction survey is carried out to capture the user experience in terms of Usability, Usefulness and Satisfaction upon exploiting the tiered loyalty membership program mobile application.
A Survey on Patients Privacy Protection with Steganography and Visual Encryption

In this survey, thirty models for steganography and visual encryption methods have been discussed to provide patients privacy protection. Image steganography techniques are classified into two domains: Spatial Domain and Frequency Domain. Spatial hiding models are divided into three groups: Least Significant Bit (LSB), Most Significant Bit (MSB) and other spatial hiding while frequency models are divided into two groups: Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). Moreover, some models have been used to encrypt medical images. Technically, PSNR (Peak-Signal-to-Noise-Ratio) have been computed and compared to investigate and evaluate the model’s performance. As a result of this survey analysis, Frequency Hiding models for DCT (Discrete Cosine Transform) have shown the best average PSNR with 70.66 dB, compared to other approaches.
HrreNet: semantic segmentation network for moderate and high-resolution satellite images

Semantic segmentation is a commonly used intelligent interpretation method. However, semantic segmentation via deep learning usually faces several problems: resolution loss due to downsampling, difficulty in obtaining context information, and imprecise object boundaries. These problems lead to a poor performance when using satellite images instead of conventional images and limit the value of intelligent interpretation of satellite images in practical application. In this study, we construct a Qilian Ecological Resource Extraction Dataset (QERED) with multiple scene satellite images of Qilian after preprocessing via correction, fusion, mosaic, and annotation. We then propose a segmentation network, namely, High-resolution Resource Extracting Network (HrreNet), by using high-resolution feature representation, multi-scale context fusion, boundary refinement with relearning, and structural similarity loss. Experiments on QERED show that HrreNet greatly improves performance on small-size objects and slightly improves performance on larger-size objects. HrreNet achieves the best result of 73.36% mIoU on a moderate-resolution satellite dataset CCFD and 80.87% mIoU on a high-resolution satellite dataset Vaihingen.
A two-stage integer programming model considering transaction equivalence for privacy preservation

Preserving privacy is one of the fundamental requirements of firms that share data with their business partners for building advanced data mining models. Firms often aim to protect the disclosure of sensitive knowledge or information discovered during the data mining process. In this study, we investigate the problem of Frequent Itemset Hiding (FIH) which aims to hide sensitive itemset relationships present in a transactional database. We propose a two-stage integer programming model that maximizes the proportion of unaltered transactions in the sanitized database and protects sensitive itemset relationships. The model exploits the concept of transactional equivalence and significantly reduces the size of the FIH problem. In addition, our model enables the identification of solutions with minimal side effects. We conduct an experimental evaluation on both real and synthetic databases to show that our approach is scalable and produces a sanitized database with maximum accuracy. The generated solution is also found to have lower side effects (itemset information loss) compared to other state-of-the-art methods. Our experiments on very large problem instances show problem size reductions of one to three orders of magnitude. The proposed approach is quite attractive and practically useful for solving large-scale FIH problem instances and preserving privacy in increasingly shared and big data-driven organizational environments. • Presents a two-stage integer programming model for the frequent itemset hiding problem. • Utilizes transaction equivalence to transform a database to a compact and lossless form. • Proposes primary and secondary transaction folding mechanisms. • Impact of the use of a frequency based sanitization heuristic on itemset information loss is assessed. • Solves large problem instances considering upto one billion transactions and thousand sensitive itemsets.
Worldwide validation of an Earth Polychromatic Imaging Camera (EPIC) derived radiation product and comparison with recent reanalyses

A very recent gridded product for the hourly global horizontal irradiance (GHI), derived from the measurements of the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) launched by a NOAA/NASA/USAF consortium, is validated at 31 locations worldwide, from January, 2017 to June, 2019. In contrast to those traditional methods that leverage (simplified) radiative transfer, this EPIC-derived product uses machine learning – a random forest model – to map out the connection between satellite-observed variables of various kinds and GHI. Nonetheless, the detailed validation conducted here shows that the quality of this EPIC-derived GHI dataset not only does not outperform those traditional gridded solar radiation datasets, but also contains undesirable artifacts that can be possibly attributed to inadequacies in the machine-learning procedure. For these reasons, it is not recommended to use this EPIC-derived dataset in its current form for solar resource assessment purposes.
A pseudo-random bit generator based on chaotic maps enhanced with a bit-XOR operation

Owing to their applications in cryptography, pseudo-random number generators and Pseudo-Random Bit Generators (PRBG) have played an important role in the computer age and their importance is expected to grow exponentially in the coming decades. The chaos phenomenon is commonly used as an entropy source due to its peculiar properties like sensitivity to initial conditions and ergodicity. With emphasis on simplicity of implementation, the present work proposes a simple and novel enhancement to the simple chaotic maps which are otherwise found inadequate to serve the high end requirements of cryptography. The enhancement is based on introducing further randomization of the chaotic map based sequence by implementing a simple XOR operation among the bits of the mantissa of a number constituting the sequence. With this simple XOR operation, simple chaotic maps like the 1-dimensional logistic map and the 2-dimensional Henon map are found to exhibit highly chaotic behaviour over a wide range of their system parameters. A wide range of experiments are conducted to demonstrate and characterize the resulting chaotic maps. The enhanced 2D Henon map is, then, used to implement a PRBG. The PRBG is verified against a wide range statistical parameters and tests. The simplicity of the proposed approach makes it applicable on a wide range of software and hardware platforms. • XOR operation based enhancement for simple chaotic maps is proposed. • The XOR operation is applied on the mantissa bits of the state variables. • As examples, logistic and Henon maps are enhanced. • A new PRBG based on the enhanced maps is proposed. • Statistical and security tests are conducted to establish the new PRBG.
Important Features Associated with Depression Prediction and Explainable AI

Depression is a debilitating disease that leaves individuals persistently feeling sad or hopeless for more than two weeks affecting more than 300 million people globally. We applied several machine learning models with model explainability to a publicly available depression dataset. Several experiments were performed to assess the use of feature selection methods and technique to address dataset imbalance on diagnostic accuracy. The top performing model was obtained by logistic regression with excellent performance metrics (91% accuracy, 93% sensitivity, 85% specificity, 93% precision, 93% F1-score and 0.78 Matthews correlation coefficient). Feature importance was also generated for the best model. Explainable artificial intelligence method using LIME was applied to help understand the reasoning behind the model's classification of depression leading to better understanding of physicians, thus demonstrating its use in clinical practice.
Can the same rule representation change its matching area?

This paper focuses on the rule representation in Learning Classifier System (LCS) and proposes a flexible representation mechanism that can generate a variety of shapes of its matching area with one rule condition of a classifier. Concretely, the proposed representation mechanism changes the shape of the matching area according to the logical product or multiplication of the values of the probability distribution in the multiple dimension. As one of its implementation, this paper introduces the beta distribution in XCS for continuous space. Through intensive experiments of different types of continuous space problems, the following implications have been revealed: XCS based on the beta distribution (1) can match line and curved shapes using the same classifier; (2) can obtain higher reward values with the same or fewer classifiers than the conventional representations (i.e., the hyperrectangular and hyperellipsoidal representations); and (3) is robust to variations in the shape of the class boundary, contributing to stable classification performance.
The Evaluation Model of College English Diagnostic Exercises Based on Machine Learning

Online learning is an important way for college students to learn English independently. The evaluation information provided by the previous online teaching platform is more summative evaluation, which cannot make students have a more intuitive and comprehensive understanding of their English learning status and lack of personalized guiding suggestions. Therefore, this paper combines data mining technology with machine learning to build an English diagnostic exercise model that can analyze students’ learning status, the correlation between knowledge points and question types, and predict English achievement, so as to provide students with more comprehensive analysis data information. The experimental results show that the evaluation model of college English diagnostic practice based on machine learning has the classification results of learning state with finer granularity, effectively analyzes the association rules of knowledge points and question types, and has high prediction performance. It can help students fully understand their English learning status, provide students with personalized analysis data and effective guiding suggestions, and enhance students’ English application ability, improving CET-4.
Voice and hand gesture controlled robotic vehicle

Adaptable human vehicle interface is the interaction between human and robot. Nowadays the use of robots is gaining popularity in commercial and residential use. The primary objective of the work is to make a robotic vehicle which can be controlled by voice and hand gestures. Usually we interact with computers or any electronic gadgets with our hand. So, we need to visit the place where computers or gadgets are placed and we have to use our hands to control and operate the computer or any gadgets which obviously consumes more time as well as energy. So if we use our voice and hand gesture to operate and control the computers or electronic gadgets then we can easily operate them from a far distance efficiently, faster which will finally reduce time and energy loss. Also, the classical method of controlling robot requires long and bulky wires which are very much complicated and cumbersome process. By using voice and hand gesture recognition technology, we can reduce the use of long wires up to some extent which can provide comfort to the users to interact with the computers and robots easily and effectively. In this project we are using voice and hand gesture to control the robotic vehicle using the Arduino interface.
Design and implementation of smart electric wheelchair

The Smart Electric Wheelchair is a mobility-assist device for people with physical limitations, such as leg or spinal cord injuries, who must be mentally stable to ensure the wheelchair’s movement. The vision of this study is to provide a method for connecting a smart device to a wheelchair, which will suit the demands of a large number of persons with disabilities. Through the use of an accelerometer sensor, the wheelchair’s motion is controlled by head movement. The thumb stick control mechanism is another form of operation that secures wheelchair movement. A Bluetooth control system is also included, allowing the wheelchair’s movements to be controlled via a smartphone application. There is a panic button to alert the person to an urgency. In this paper, an effort is made to make the lives of disabled people easier so that they can be self-sufficient and live a high-quality life, restoring their trust and satisfaction.
The Dutch COVID-19 Notification App: Lessons Learned From a Mixed Methods Evaluation Among End Users and Contact-Tracing Employees

The Dutch CoronaMelder (CM) app is the official Dutch contact-tracing app (CTA). It has been used to contain the spread of the SARS-CoV-2 in the Netherlands. It allows its users and those of connected apps to anonymously exchange warnings about potentially high-risk contacts with individuals infected with the SARS-CoV-2.The goal of this mixed methods study is to understand the use of CTA in the pandemic and its integration into the Municipal Health Services (MHS) efforts of containment through contact tracing. Moreover, the study aims to investigate both the motivations and user experience-related factors concerning adherence to quarantine and isolation measures.A topic analysis of 56 emails and a web-based survey of 1937 adults from the Netherlands, combined with a series of 48 in-depth interviews with end users of the app and 14 employees of the Dutch MHS involved in contact tracing, were conducted. Mirroring sessions were held (n=2) with representatives from the development (n=2) and communication teams (n=2) responsible for the creation and implementation of the CM app.Topic analysis and interviews identified procedural and technical issues in the use of the CTA. Procedural issues included the lack of training of MHS employees in the use of CTAs. Technical issues identified for the end users included the inability to send notifications without phone contact with the MHS, unwarranted notifications, and nightly notifications. Together, these issues undermined confidence in and satisfaction with the app's use. The interviews offered a deeper understanding of the various factors at play and their effects on users; for example, the mixed experiences of the app's users, the end user's own fears, and uncertainties concerning the SARS-CoV-2; problematic infrastructure at the time of the app's implementation on the side of the health services; the effects of the society-wide efforts in containment of the SARS-CoV-2 on the CM app's perception, resulting in further doubts concerning the app's effectiveness among MHS workers and citizens; and problems with adherence to behavioral measures propagated by the app because of the lack of confidence in the app and uncertainty concerning the execution of the behavioral measures. All findings were evaluated with the app's creators and have since contributed to improvements.Although most participants perceived the app positively, procedural and technical issues identified in this study limited satisfaction and confidence in the CM app and affected its adoption and long-term use. Moreover, these same issues negatively affected the CM app's effectiveness in improving compliance with behavioral measures aimed at reducing the spread of the SARS-CoV-2. This study offers lessons learned for future eHealth interventions in pandemics. Lessons that can aid in more effective design, implementation, and communication for more effective and readily adoptable eHealth applications.