Reviewerly/exHarmony huggingface dataset:
Quantum technology and its impact on security in mobile networks

Over the last 50 years, cryptography has evolved from its military and diplomatic origins to become a rich and widely-used tool to create complex cryptographic solutions for a multitude of applications. In the ICT industry, for example, an efficient combination of symmetric and public-key (asymmetric) cryptography is critical to the security of virtually every product, service and interface in use today.
AIoT-based Sustainable Smart Supply Chain Framework

Purpose: Supply chains in today's global environment operate in a market that is increasingly complex and dynamic in nature. In such an environment, a stable supply chain to respond to drastic changes in customer needs becomes inevitable. Based on these studies, it is obvious that organizations operating in the field of supply chain should accelerate their focus on sustainability and use technologies such as "Internet of Things" (IoT) and artificial intelligence to achieve the organization's goal of creating sustainable processes. Methodology: The presence of the Internet of Things, along with artificial intelligence technology, has created Artificial intelligence of things (AIoT) technology, which gives the big data from the Internet of Things great power. In this research, an attempt has been made to study and analyze the key dimensions, components and indicators of the AIoT-based sustainable supply chain. Also, a conceptual framework for Padidar intelligent supply chain based on these evolving technologies is presented, which can help to understand the elements of this intelligent supply chain in order to optimize. Findings: A study of the literature shows that investing in this technology to achieve sustainable benefits is inevitable. In addition, the use of this technology due to networking and the presence of the Internet requires appropriate security solutions for information technology, a workforce with the required set of skills, sharing information in an integrated environment with business partners. Originality/Value: The generalities of AIoT-based sustainable supply chain components in general, and things can be added to different industries and behaviors. Understanding the dimensions of this framework can always help to implement it effectively.
Estimation for Model Parameters and Maximum Power Points of Photovoltaic Modules Using Stochastic Fractal Search Algorithms

The performance of a photovoltaic (PV) power generation system could be improved through the optimal control and operation of a PV module which is one of the fundamental components of this system. Thus, an appropriate PV module model along with precise knowledge of its parameters is necessary. This paper proposes a novel technique to estimate the source current, the saturation current of diodes, the shunt resistance, the series resistance, the ideality coefficient of diodes and the maximum power points (MPPs) of PV modules at the same time. This estimation problem can be described by the minimization of the root mean squared error (RMSE) of the powers obtained from the PV module through estimation and experiment. The improved stochastic fractal search (ISFS) algorithm is proposed to solve this minimization with two modifications. The first replaces the logarithmic function with the exponential function in the standard deviation of the diffusion technique to improve the exploration ability efficiently in the search space. The second utilizes the sine map instead of the uniform distribution in both the diffusion and update techniques for improving the performance of the ISFS algorithm. Numerical results demonstrate the remarkable ability of the ISFS algorithm in obtaining both the model parameters and MPPs of the PV module with high accuracy. The comparison shows that the ISFS algorithm outperforms other meta-heuristic algorithms such as a stochastic fractal search (SFS) algorithm, a particle swarm optimization (PSO) algorithm, and an improved particle swarm optimization (IPSO) algorithm in the proposed parameter estimation application.
SuperPoint Features in Endoscopy

There is often a significant gap between research results and applicability in routine medical practice. This work studies the performance of well-known local features on a medical dataset captured during routine colonoscopy procedures. Local feature extraction and matching is a key step for many computer vision applications, specially regarding 3D modelling. In the medical domain, handcrafted local features such as SIFT, with public pipelines such as COLMAP, are still a predominant tool for this kind of tasks. We explore the potential of the well known self-supervised approach SuperPoint [4], present an adapted variation for the endoscopic domain and propose a challenging evaluation framework. SuperPoint based models achieve significantly higher matching quality than commonly used local features in this domain. Our adapted model avoids features within specularity regions, a frequent and problematic artifact in endoscopic images, with consequent benefits for matching and reconstruction results. Training code and models available https://github.com/LeonBP/SuperPointEndoscopy .
Interclass Prototype Relation for Few-Shot Segmentation

Traditional semantic segmentation requires a large labeled image dataset and can only be predicted within predefined classes. Solving this problem of few-shot segmentation, which requires only a handful of annotations for the new target class, is important. However, with few-shot segmentation, the target class data distribution in the feature space is sparse and has low coverage because of the slight variations in the sample data. Setting the classification boundary that properly separates the target class from other classes is an impossible task. In particular, it is difficult to classify classes that are similar to the target class near the boundary. This study proposes the Interclass Prototype Relation Network (IPRNet), which improves the separation performance by reducing the similarity between other classes. We conducted extensive experiments with Pascal- $$5^i$$ and COCO- $$20^i$$ and showed that IPRNet provides the best segmentation performance compared with previous research.
Social B(eye)as: Human and Machine Descriptions of People Images

Image analysis algorithms have become an indispensable tool in our information ecosystem, facilitating new forms of visual communication and information sharing. At the same time, they enable large-scale socio-technical research which would otherwise be difficult to carry out. However, their outputs may exhibit social bias, especially when analyzing people images. Since most algorithms are proprietary and opaque, we propose a method of auditing their outputs for social biases. To be able to compare how algorithms interpret a controlled set of people images, we collected descriptions across six image tagging algorithms. In order to compare these results to human behavior, we also collected descriptions on the same images from crowdworkers in two anglophone regions. The dataset we present consists of tags from these eight taggers, along with a typology of concepts, and a python script to calculate vector scores for each image and tagger. Using our methodology, researchers can see the behaviors of the image tagging algorithms and compare them to those of crowdworkers. Beyond computer vision auditing, the dataset of humanand machine-produced tags, the typology, and the vectorization method can be used to explore a range of research questions related to both algorithmic and human behaviors.
Toward Achieving Anonymous NFT Trading

With the rapid development of Non-Fungible Token (NFT) market, various types of digital artwork are published via NFT in recent years for sale. In the current NFT system, owner’s address of each NFT is stored in plaintext. This leads to a severe privacy problem: once a person’s blockchain address is known, all his NFT assets are viewable, which may further cause problems related to the privacy of holding sensitive NFT or premeditated scams to high-value NFT owners. However, due to the limitation of Ethereum and smart contract, it is hard to prevent others from tracking the owner of NFT. Meanwhile, current state-of-the-art NFT research usually assumes the relation between blockchain address and owner’s identity is unknown, thus creating the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">so-called anonymity</i> . In this paper, based on the most popular NFT marketplace OpenSea’s system, we propose a new exchange scheme to hides the address of NFT owner during trading. To achieve our goal, a proof of commitment scheme is exploited to bind the owner to an NFT while hiding the identity. Moreover, An anonymous payment method is designed to prevent attackers from tracing the Ether flow in NFT trading. Our scheme is proven to be secure against curious users and malicious active attackers. Implementation on testnet also shows that the increased gas cost is acceptable, meaning it is suitable for application.
Wavelet Interval Type-2 Takagi-Kang-Sugeno Hybrid Controller for Time-Series Prediction and Chaotic Synchronization

This paper presents a new hybrid neural network for time series prediction and chaotic synchronization.The proposed controller consists of a wavelet interval type-2 Takagi Sugeno Kang fuzzy brain emotional learning controller (WIT2TFBELC), a wavelet interval type-2 Takagi Sugeno Kang fuzzy cerebellar model articulation controller (WIT2TFCMAC), and a robust compensator (RB).The main controller combines the WIT2TFBELC and the WIT2TFCMAC.A TSK fuzzy system is used to create a hybrid structure with the WIT2TFBELC and the WIT2TFCMAC.The TSK fuzzy system can effectively adjust the weighting for the main controller to achieve the time series prediction and chaotic prediction with better tracking response.Morever a robust compensator is used to achieve robust ability of the system.A Lyapunov function was used to establish the adaptive laws and effectively adjust the system parameters online.Finally, two examples of the application of the proposed algorithm are presented to point out the performance of proposed method.
ENHANCING APPROACH USING HYBRID PAILLER AND RSA FOR INFORMATION SECURITY IN BIGDATA

The amount of data processed and stored in the cloud is growing dramatically. The traditional storage devices at both hardware and software levels cannot meet the requirement of the cloud. This fact motivates the need for a plat¬form which can handle this problem. Hadoop is a deployed platform proposed to overcome this big data problem which often uses MapReduce architecture to process vast amounts of data of the cloud system. Hadoop has no strategy to assure the safety and confidentiality of the files saved inside the Hadoop distributed File system (HDFS). In the cloud, the protection of sensitive data is a critical issue in which data encryption schemes plays avital rule. This research proposes a hybrid system between two well-known asymmetric key cryptosystems (RSA, and Paillier) to encrypt the files stored in HDFS. Thus before saving data in HDFS, the proposed cryptosystem is utilized for encrypting the data. Each user of the cloud might upload files in two ways, non-safe or secure. The hybrid system shows higher computational complexity and less latency in comparison to the RSA cryptosystem alone.
Real-Time Detection Algorithm of Marine Organisms Based on Improved YOLOv4-Tiny

Marine organisms detection based on machine vision requires high real-time performance and accuracy.Network feature extraction is frequently made more difficult when underwater robots collect information on the seafloor because of the uneven distribution of light on the seafloor, the significant impact of water waves, and the complexity of the seafloor environment.How to detect marine organisms quickly and accurately is a great difficulty and challenge.To address the above problems, we propose a MODA (Marine Organism Detection Algorithm) based on an improved YOLOv4-tiny marine organism detection algorithm.Firstly, the Coordinate Attention module, an ultra-lightweight attention mechanism, is constructed and embedded into the backbone network to retain more information about the target of interest and enhance the feature extraction capability of the network.Secondly, the Hybrid Dilated Convolution (HDC) structure is constructed and added to the improved network to expand the feature map perception field and obtain richer semantic information to improve the network detection accuracy.Finally, a better MODA model is proposed based on the two methods mentioned above.The experimental results show that the improved model MODA improves the mAP metric from 74% to 76.62% on the URPC dataset and only increases the computational effort by 0.06 GM compared with the original YOLOv4-tiny model; the mAP metric improves from 92.37% to 98.41% on the Aquarium dataset.This improvement indicates that the MODA model is more suitable for marine organisms detection tasks.
The effectiveness of immersive technologies for future professional education

The research on the application of immersive technologies in education began relatively recently and the issue remains open. In order to modernize the learning process, educational simulators, simulators, and textbooks are being developed. Immersive technologies help to absolutely immerse in the learning process, interact with objects inaccessible in the usual reality, get used to acting in acute situations, and as a result - to improve the effectiveness of learning. The aim of the work is an empirical analysis of the methodological basis of the experience of using one of the artifacts and mercurial technologies - virtual reality. Despite the fact that virtual reality has recently been referred to ultra-modern technology, today virtual reality headsets are not a novelty of the technology, but mediators of knowledge. The use of virtual reality devices for immersive learning is becoming relevant and accessible. Methods - empirical research using systematic search, and comparative analysis of two pedagogical experiments involving artifacts. Results - it turned out that virtual reality seems to be as effective as previous technological tools used in education, and in certain specialties can contribute to better performance in learning. Practical significance - the results of the work may establish specific learning contexts in which virtual reality is more effective than others and may allow new educational opportunities, especially in shaky geopolitical situations. Virtual reality is a modular technology that adapts to all situations and allows the creation of extended learning materials. Nevertheless, it has proven to be more effective for certain specialties, such as the medical field.
RECOGNITION OF FONT AND TAMIL LETTER IN IMAGES USING DEEP LEARNING

This paper proposes a deep learning approach to recognize Tamil Letter from images which contains text. This is recognition process, the text in the images are divided to letter or characters. Each recognized letters are sending to recognition system and filter the text using deep learning algorithms. Our proposed algorithm is used to separate letter from the text using convolution neural network approach. The filtering system is used for identifying font based on that letters are found. The Tamil letters are test data and loaded in recognition systems. The trained data are input which contains filtered letter from image. For example, Tamil letters such as are available in test dataset. The trained data are applied into deep convolution neural network process. The two dataset are created which contains test data with Tamil letter and second one for recognized input data or trained data. 15 thousands of letters are taken and 512 X 512 X 3 size deep convolution network is created with font and letters. As the result, 85% Tamil letters are recognized and 82% are tested using font. TensorFlow is used for testing the accuracy and success rate.
The Correlation between EFL Students’ Vocabulary Knowledge and Reading Comprehension

This study aimed at investigating the correlation between students ’vocabulary mastery and reading comprehension performance. The population of the study was the whole students at the English Education Department of Universitas Kristen Indonesia. The sample was taken using the cluster sampling technique, i.e., by taking 52 students of Batch 2015, 2016, and Batch 2017 as the participants. Data were collected in May 2019 by administering vocabulary mastery and reading comprehension tests. The instrument’s reliability was tested employing Cronbach Alpha with the results of 0.733 for the vocabulary test and 0.730 for the reading comprehension test, indicating both instruments are reliable. Using Spearman's non-parametric test, the correlation coefficient between the two variables at the sig. (2 tailed) and 0.05 level of significance was 0.014. This indicates there is a positive moderate correlation between vocabulary knowledge and reading comprehension.
Blockchain and federated learning-based security solutions for telesurgery system: a comprehensive review

The advent of telemedicine with its remote surgical procedures has effectively transformed the working of healthcare professionals.The evolution of telemedicine facilitates the remote monitoring of patients that lead to the advent of telesurgery systems, i.e. one of the most critical applications in telemedicine systems.Apart from gaining popularity, the telesurgery system may encounter security and trust issues of patients' data while communicating with the surgeon for their remote treatment.Motivated by this, we have presented a comprehensive survey on secure telesurgery systems comprising healthcare, surgical robots, traditional telesurgery systems, and the role of artificial intelligence to deal with the numerous security attacks associated with the patients' health data.Furthermore, we propose a blockchain and federated learning-based secure telesurgery system to secure the communication between patient and surgeon.The results of the proposed system are better than those of the traditional system in terms of improved latency, low data storage cost, and enhanced data offloading.Finally, we explore the research challenges and issues associated with the telesurgery system.
Secure Optical Image Communication Using Double Random Transformation and Memristive Chaos

The issue of information security in photonics environment has attracted more and more attention, especially when the secure communication of optical digital images has become a research hotspot. In this paper, we propose a hybrid encryption scheme for color images in the frequency and spatial domains based on double random transform and memristor hyperchaotic system. Firstly, we decompose the color image into RGB channels and then perform fast fourier transform (FFT) to transform the digital image from the spatial domain to the frequency domain. Secondly, two-phase masks are generated using the memristor hyperchaotic system, and the Fresnel diffraction optical transformation method is performed twice for encryption. Thirdly, the transformation from the frequency domain to the spatial domain is completed using inverse fast fourier transform (IFFT). Finally, the image is permuted and diffused using the chaotic sequences to obtain the final cipher image. Double random phase encryption expands the key space, while the combination of spatial and frequency domains improves the resistance to attacks. Based on cryptanalysis theory, we introduce a dynamic key associated with plain image, which can effectively resist plain attack. The experimental results show that the optical digital image encryption scheme has a large key space, excellent comprehensive performance, and can resist common attacks. In addition, we verify the hardware feasibility and ease of implementation of the proposed algorithm in an embedded optical communication network experimental platform. Therefore, our proposed scheme in this paper is a preferred optical digital image secure communication technology scheme with good application prospects.
RANKING RESIDENTIAL NEIGHBORHOODS BASED ON THEIR SUSTAINABILITY: A CM-BWM APPROACH

Population growth and rapid urbanization have consequences that are reflected in the economic, environmental, and social stability of city-residential neighborhoods. These impacts directly affect not only residents but also real estate markets and local governments. The professionals working in the latter entities have become increasingly concerned about urban sustainability and its strategic integration into their plans. Strategies have been implemented that focus on both addressing negative aspects of residential neighborhoods and enhancing positive features that can contribute to the continuous improvement of locals’ living conditions. This study applies the multiple-criteria decision analysis approach and a combination of cognitive mapping and the best-worst method (BWM) to identify the most relevant criteria and use these to rank residential neighborhoods according to their sustainability. To apply the selected techniques, two group meetings were held with a panel of decision makers. The results were validated by the panel members and the Funchal City Council councilor for urbanism, who concurred that the proposed ranking system facilitates the identification of the most sustainable residential neighborhoods. The contributions and limitations of the methodological approach are also discussed.
Archimedes Optimizer: Theory, Analysis, Improvements, and Applications

The intricacy of the real-world numerical optimization tribulations has full-fledged and diversely amplified necessitating proficient yet ingenious optimization algorithms. In the domain wherein the classical approaches fall short, the predicament resolving nature-inspired optimization algorithms (NIOA) tend to hit upon an excellent solution to unbendable optimization problems consuming sensible computation time. Nevertheless, in the last few years approaches anchored in nonlinear physics have been anticipated, announced, and flourished. The process based on non-linear physics modeled in the form of optimization algorithms and as a subset of NIOA, in countless cases, has successfully surpassed the existing optimization methods with their effectual exploration knack thus formulating utterly fresh search practices. Archimedes Optimization Algorithm (AOA) is one of the recent and most promising physics optimization algorithms that use meta-heuristics phenomenon to solve real-world problems by either maximizing or minimizing a variety of measurable variables such as performance, profit, and quality. In this paper, Archimedes Optimization Algorithm (AOA) has been discussed in great detail, and also its performance was examined for Multi-Level Thresholding (MLT) based image segmentation domain by considering t-entropy and Tsallis entropy as objective functions. The experimental results showed that among recent Physics Inspired Optimization Algorithms (PIOA), the Archimedes Optimization Algorithm (AOA) produces very promising outcomes with Tsallis entropy rather than with t-entropy in both color standard images and medical pathology images.