Reviewerly/exHarmony huggingface dataset:
Improved Artificial Bee Colony Algorithm-based Path Planning of Unmanned Aerial Vehicle Using Late Acceptance Hill Climbing

The unmanned aerial vehicle's primary goal (UAV) in route planning is to create a flight path from starting point to the ending point in space to avoid obstacles.The UAV path planning needs a real-time adaption and rapid reaction to the dynamic nature of the operational area since the path computing time, and average path length is very important factors over some cost function that reflects its effectiveness, such as power consumption or average trip time.Creating an ideal path must embrace a particular standard to reduce the distance the drone must travel.The artificial bee colony (ABC) represents one of the most important global search algorithms.The main problem with ABC is that it neglects the local search factor and uses the total search to find the optimal path to the target point since it searches the path to the target in remote areas rather than dealing with the nearby or neighboring areas, and this requires more time to find the path to reach the goal.On the other hand, late acceptance hill climbing (LAHC) algorithm uses the local search operator, which searches for the target point in the areas close to the starting point, thus providing a shorter path to reach a goal.This paper proposes and evaluates a new drone path planning algorithm, hybrid artificial bee colony (HABC).HABC algorithm design is based on modifying the ABC algorithm by cross-layer design between ABC and LAHC algorithms.The HABC is different from the original ABC algorithm in that it modifies the original one to reduce the total search by 3% and pushes ABC's search agent to use local search by 3% to start the process of rediscovering a new path to the target.The evaluation and analysis were performed for several performance metrics under different static evaluation scenarios.It has been observed from the results that the HABC outperforms the original ABC concerning average path length and standard deviation by a reduction reach to 25%, which leads to improved path planning.
Super-Resolution of Remote Sensing Images for ×4 Resolution without Reference Images

Sentinel-2 satellites can provide free optical remote-sensing images with a spatial resolution of up to 10 M, but the spatial details provided are not enough for many applications, so it is worth considering improving the spatial resolution of Sentinel-2 satellites images through super-resolution (SR). Currently, the most effective SR models are mainly based on deep learning, especially the generative adversarial network (GAN). Models based on GAN need to be trained on LR–HR image pairs. In this paper, a two-step super-resolution generative adversarial network (TS-SRGAN) model is proposed. The first step is having the GAN train the degraded models. Without supervised HR images, only the 10 m resolution images provided by Sentinel-2 satellites are used to generate the degraded images, which are in the same domain as the real LR images, and then to construct the near-natural LR–HR image pairs. The second step is to design a super-resolution generative adversarial network with strengthened perceptual features, to enhance the perceptual effects of the generated images. Through experiments, the proposed method obtained an average NIQE as low as 2.54, and outperformed state-of-the-art models according to other two NR-IQA metrics, such as BRISQUE and PIQE. At the same time, the comparison of the intuitive visual effects of the generated images also proved the effectiveness of TS-SRGAN.
Remote sensing image super-resolution using multi-scale convolutional sparse coding network

With the development of convolutional neural networks, impressive success has been achieved in remote sensing image super-resolution. However, the performance of super-resolution reconstruction is unsatisfactory due to the lack of details in remote sensing images when compared to natural images. Therefore, this paper presents a novel multiscale convolutional sparse coding network (MCSCN) to carry out the remote sensing images SR reconstruction with rich details. The MCSCN, which consists of a multiscale convolutional sparse coding module (MCSCM) with dictionary convolution units, can improve the extraction of high frequency features. We can obtain more plentiful feature information by combining multiple sizes of sparse features. Finally, a layer based on sub-pixel convolution that combines global and local features takes as the reconstruction block. The experimental results show that the MCSCN gains an advantage over several existing state-of-the-art methods in terms of peak signal-to-noise ratio and structural similarity.
The Metaverse in mathematics education: The opinions of secondary school mathematics teachers

With the Covid 19 pandemic, technology-supported applications have been quickly integrated into education. At this point, Metaverse will inevitably be involved in the education sector. The need to determine the knowledge, attitudes, and awareness of our teachers, the practitioners of education, about the concept of metaverse has arisen. The convergent parallel mixed method, which allows simultaneous collection of quantitative and qualitative data, was used. 70 mathematics teachers who teach in different regions of Türkiye and secondary schools, selected by the easily accessible case sampling method, participated in the study. A semi-structured form prepared by the researchers was used to collect the data. The first part of the interview form is the "Metaverse Scale" and the second part is one closed-ended and three open-ended questions. Quantitative data were analyzed by using descriptive statistics included in and t-test for independent samples, while qualitative data were analyzed by content analysis. Teachers' opinions about the availability of the Metaverse in mathematics education are generally positive, and they indicate that it can be used mainly for concretization, geometry and they find it useful. The reasons for the lack of research on the concept of the Metaverse are themed as personal reasons, negative attitudes, Metaverse-related causes, and lack of resources, and explanations are made mostly for personal reasons. Knowledge, attitudes, and awareness scores regarding the concept of metaverse do not differ significantly by gender; female and male mathematics teachers have similar views, The results of the research also shed light on the future of students studying in different conditions in different regions of the country following the opinions of mathematics teachers who perform their duties in different regions of Türkiye.
Improving Malaria Detection Using L1 Regularization Neural Network

Malaria is a huge public health concern around the world. The conventional method of diagnosing malaria is for qualified technicians to visually examine blood smears for parasite-infected red blood cells under a microscope. This procedure is ineffective. It takes time and requires the expertise of a skilled specialist. The diagnosis is dependent on the individual performing the examination&amp;rsquo;s experience and understanding. This article offers a new and robust deep learning model for automatically classifying malaria cells as infected or uninfected. This approach is based on a convolutional neural network (CNN). It improved by the regularization method on a publicly available dataset which contains 27, 558 cell images with equal instances of parasitized and uninfected cells from the National Institute of health. The performance of our proposed model is 99.70% of accuracy and 0.0476 loss value.
Liability for Artificial Intelligence

In this chapter, the law scholar Christine Wendehorst analyses the different potential risks posed by AI as part of two main categories, safety risks and fundamental rights risks. Based on this, the author considers why AI challenges existing liability regimes. She spells out the main solutions put forward so far and evaluates them. This chapter highlights the fact that liability for fundamental rights risks is largely unchartered while being AI-specific. Such risks are now being addressed at the level of AI safety law, by way of prohibiting certain AI practices and by imposing strict legal requirements concerning data governance, transparency, and human oversight. Wendehorst nevertheless argues that a number of changes have to be made for the emerging AI safety regime to be used as a ‘backbone’ for the future AI liability regime if this is going to help address liability for fundamental rights risks. As a result, she suggests that further negotiations about the AI Act proposed by the European Commission should be closely aligned with the preparatory work on a future AI liability regime.
Comparison of the anxiety levels of teacher candidates during actual and 360° video virtual reality presentations

This study considers if the 360° video Virtual Reality (VR) environment is a less stressful alternative to giving actual presentations, and thus better able to support the development of the presentation skills of teacher candidates. Mixed method research was conducted in which 12 teacher candidates gave an average of 5 minutes of presentations on different days, both in a virtual and in a face-to-face school environment, before a group of listeners whom they hadn’t seen before. The candidates’ pulses were compared during presentations conducted in both environments, and the audio recordings were analysed with the PRAAT program. Further evaluation was conducted at the end of the process using an opinion form and interviews. No significant differences were found between the two groups, except in 2 of the 9 different voice analysis variables related to anxiety and the pulse of the candidates. The candidates generally felt that the 360° video VR experience was quite realistic, and that it could be an effective method of addressing concerns about giving presentations. It was therefore concluded that 360° video VR environments could be effectively used in teacher education as an alternative to the actual presentation experience.
MapReduce-iterative support vector machine classifier: novel fraud detection systems in healthcare insurance industry

&lt;span&gt;Fraud in healthcare insurance claims is one of the significant research challenges that affect the growth of the healthcare services. The healthcare frauds are happening through subscribers, companies and the providers. The development of a decision support is to automate the claim data from service provider and to offset the patient’s challenges. In this paper, a novel hybridized big data and statistical machine learning technique, named MapReduce based iterative support vector machine (MR-ISVM) that provide a set of sophisticated steps for the automatic detection of fraudulent claims in the health insurance databases. The experimental results have proven that the MR-ISVM classifier outperforms better in classification and detection than other support vector machine (SVM) kernel classifiers. From the results, a positive impact seen in declining the computational time on processing the healthcare insurance claims without compromising the classification accuracy is achieved. The proposed MR-ISVM classifier achieves 87.73% accuracy than the linear (75.3%) and radial basis function (79.98%).&lt;/span&gt;
Working with cryptographic key information

It is important to create a cryptographic system such that the encryption system does not depend on the secret storage of the algorithm that is part of it, but only on the private key that is kept secret. In practice, key management is a separate area of cryptography, which is considered a problematic area. This paper describes the main characteristics of working with cryptographic key information. In that, the formation of keys and working with cryptographic key information are stored on external media. The random-number generator for generating random numbers used for cryptographic key generation is elucidated. To initialize the sensor, a source of external entropy, mechanism “Electronic Roulette” (biological random number), is used. The generated random bits were checked on the basis of National Institute of Standards and Technology (NIST) statistical tests. As a result of the survey, the sequence of random bits was obtained from the tests at a value of P≥0.01. The value of P is between 0 and 1, and the closer the value of P is to 1, the more random the sequence of bits is generated. This means that random bits that are generated based on the proposed algorithm can be used in cryptography to generate crypto-resistant keys.
Blockchain-Based Access Control and Behavior Regulation System for IoT

With the development of 5G and the Internet of things (IoT), the multi-domain access of massive devices brings serious data security and privacy issues. At the same time, most access systems lack the ability to identify network attacks and cannot adopt dynamic and timely defenses against various security threats. To this end, we propose a blockchain-based access control and behavior regulation system for IoT. Relying on the attribute-based access control model, this system deploys smart contracts on the blockchain to achieve distributed and fine-grained access control and ensures that the identity and authority of access users can be trusted. At the same time, an inter-domain communication mechanism is designed based on the locator/identifier separation protocol and ensures the traffic of access users are authorized. A feedback module that combines traffic detection and credit evaluation is proposed, ensuring real-time detection and fast, proactive responses against malicious behavior. Ultimately, all modules are linked together through workflows to form an integrated security model. Experiments and analysis show that the system can effectively provide comprehensive security protection in IoT scenarios.
A Patch-Based Approach for Artistic Style Transfer Via Constrained Multi-Scale Image Matching

Since a few years and the advent of convolutional neural networks, algorithms for artistic style transfer between images have developed considerably. However, these methods require a relatively long training phase in order to succeed. This is why non-learning image processing approaches recently strove to propose patch-based algorithms able to aesthetically compete with neural methods. This paper goes one step further in this direction by introducing a new patch-based method for style transfer, using a constrained multi-scale version of the fast approximate nearest-neighbor algorithm PatchMatch, enforcing uniform sampling of style featurepatch. Our method also aims to mix the patch-based and neural paradigms by enabling the embedding of image patches in the feature space of the VGG-16 network.
Defining Key Performance Indicators for the California COVID-19 Exposure Notification System (CA Notify)

Toward common methods for system monitoring and evaluation, we proposed a key performance indicator framework and discussed lessons learned while implementing a statewide exposure notification (EN) system in California during the COVID-19 epidemic.California deployed the Google Apple Exposure Notification framework, branded CA Notify, on December 10, 2020, to supplement traditional COVID-19 contact tracing programs. For system evaluation, we defined 6 key performance indicators: adoption, retention, sharing of unique codes, identification of potential contacts, behavior change, and impact. We aggregated and analyzed data from December 10, 2020, to July 1, 2021, in compliance with the CA Notify privacy policy.We estimated CA Notify adoption at nearly 11 million smartphone activations during the study period. Among 1 654 201 CA Notify users who received a positive test result for SARS-CoV-2, 446 634 (27%) shared their unique code, leading to ENs for other CA Notify users who were in close proximity to the SARS-CoV-2-positive individual. We identified at least 122 970 CA Notify users as contacts through this process. Contact identification occurred a median of 4 days after symptom onset or specimen collection date of the user who received a positive test result for SARS-CoV-2.Smartphone-based EN systems are promising new tools to supplement traditional contact tracing and public health interventions, particularly when efficient scaling is not feasible for other approaches. Methods to collect and interpret appropriate measures of system performance must be refined while maintaining trust and privacy.
Design and implementation of virtual fitting system based on gesture recognition and clothing transfer algorithm

Abstract Virtual fitting can bring fast and convenient fitting experience for people. The two core problems of virtual fitting system are human–computer interaction and clothing simulation. Interaction is an important factor in determining the fitting experience. The previous virtual fitting products usually use the mouse and keyboard interaction, and users rarely have a good sense of substitution and interaction. While, the method of using multiple cameras to take user images from different angles and then carry out posture recognition has the defect of low recognition accuracy. In view of clothing simulation and human–computer interaction of virtual fitting system, in order to achieve better customer immersion experience, this paper implemented a real-time interactive virtual fitting system based on Microsoft Kinect motion sensing device, and proposed a gesture determination algorithm based on finger recognition and an image transfer algorithm based on skeleton information matching. Using OpenNI development library and multi-threading technology, we have developed a motion-sensing capture module and a complete real-time virtual fitting system, and the system test results show that it has a good user experience.
A Novel Separable Scheme for Encryption and Reversible Data Hiding

With the increasing emphasis on security and privacy, video in the cloud sometimes needs to be stored and processed in an encrypted format. To facilitate the indexing and tampering detection of encrypted videos, data hiding is performed in encrypted videos. This paper proposes a novel separable scheme for encryption and reversible data hiding. In terms of encryption method, intra-prediction mode and motion vector difference are encrypted by XOR encryption, and quantized discrete cosine transform block is permutated based on logistic chaotic mapping. In terms of the reversible data hiding algorithm, difference expansion is applied in encrypted video for the first time in this paper. The encryption method and the data hiding algorithm are separable, and the embedded information can be accurately extracted in both encrypted video bitstream and decrypted video bitstream. The experimental results show that the proposed encryption method can resist sketch attack and has higher security than other schemes, keeping the bit rate unchanged. The embedding algorithm used in the proposed scheme can provide higher capacity in the video with lower quantization parameter and good visual quality of the labeled decrypted video, maintaining low bit rate variation. The video encryption and the reversible data hiding are separable and the scheme can be applied in more scenarios.
A new approach to achieve the users’ habitual opportunities on social media

&lt;span lang="EN-US"&gt;The data generated from social media is very large, while the use of data from social media has not been fully utilized to become new knowledge. One of the things that can become new knowledge is user habits on social media. Searching for user habits on Twitter by using user tweets can be done by using modeling, the use of modeling lies when the data has been preprocessed, and the ranking will then be checked in the dictionary, this is where the role of the model is carried out to get a chance that the words that have been ranked will perform check the word in the dictionary. The benefit of the model in general is to get an understanding of the mechanism in the problem so that it can predict events that will arise from a phenomenon which in this case is user habits. So that with the availability of this model, it can be a model in getting opportunities for user habits on Twitter social media.&lt;/span&gt;
Hybrid gated recurrent unit bidirectional-long short-term memory model to improve cryptocurrency prediction accuracy

&lt;span lang="EN-US"&gt;Cryptocurrency is a virtual or digital currency used in financial systems that utilizes blockchain technology and cryptographic functions to gain transparency, decentralization, and conservation. Cryptocurrency prices have a high level of fluctuation; thus, tools are needed to monitor and predict them. RNN is a deep learning model that is capable of strongly predicting data time series. Some types of Recurrent Nureal Network layers, such as Long Short Term Memory, have been used in previous studies to prediction common used currency. In this study, we used the Gate Recurrent Unit and Bidirectional&lt;/span&gt;&lt;span lang="EN-US"&gt;–&lt;/span&gt;&lt;span lang="EN-US"&gt;LSTM hybrid model to predict cryptocurrency prices to improve the accuracy of previously proposed prediction LSTM Model to predict the Bitcoin, Using four cryptocurrencies (Bitcoin, Ehtereum, Ripple, and Binance), we obtained very good results with RMSE after normalization the results get closer to 0 and with MAPE values all below &amp;lt;10%.&lt;/span&gt;
How do Users Experience Traceability of AI Systems? Examining Subjective Information Processing Awareness in Automated Insulin Delivery (AID) Systems

When interacting with artificial intelligence (AI) in the medical domain, users frequently face automated information processing, which can remain opaque to them. For example, users with diabetes may interact daily with automated insulin delivery (AID). However, effective AID therapy requires traceability of automated decisions for diverse users. Grounded in research on human-automation interaction, we study Subjective Information Processing Awareness (SIPA) as a key construct to research users' experience of explainable AI. The objective of the present research was to examine how users experience differing levels of traceability of an AI algorithm. We developed a basic AID simulation to create realistic scenarios for an experiment with N = 80, where we examined the effect of three levels of information disclosure on SIPA and performance. Attributes serving as the basis for insulin needs calculation were shown to users, who predicted the AID system's calculation after over 60 observations. Results showed a difference in SIPA after repeated observations, associated with a general decline of SIPA ratings over time. Supporting scale validity, SIPA was strongly correlated with trust and satisfaction with explanations. The present research indicates that the effect of different levels of information disclosure may need several repetitions before it manifests. Additionally, high levels of information disclosure may lead to a miscalibration between SIPA and performance in predicting the system's results. The results indicate that for a responsible design of XAI, system designers could utilize prediction tasks in order to calibrate experienced traceability.