Reviewerly/exHarmony huggingface dataset:
Non-Fungible Tokens Based on ERC-4519 for the Rental of Smart Homes

The rental of houses is a common economic activity. However, there are many inconveniences that arise when renting a property. The lack of trust between the landlord and the tenant due to fraud or squatters makes it necessary to involve third parties to minimize risk. A blockchain (such as Ethereum) provides an ideal solution to act as a low-cost intermediary. This paper proposes the use of non-fungible tokens (NFTs) based on ERC-4519 for smart home tokenization. The ERC-4519 is an Ethereum standard for describing NFTs tied to physical assets, allowing smart homes (assets) to be linked to NFTs so that the smart homes can interact with the blockchain and perform transactions, know their landlord (owner) and assigned tenant (user), whether they are authenticated or not, and know their operating mode (NFT state). The payments associated with the rental process are made using the NFT, eliminating the need for additional fungible tokens and simplifying the process. The entire rental process is described and illustrated with a proof of concept using a Pycom Wipy 3.0 as a smart home gateway and a smart contract programmed in Solidity, which is deployed on the Goerli Testnet for Ethereum. Experimental results show that the smart home gateway takes a few tens of milliseconds to complete a transaction, and the transaction costs of the relevant functions of the smart contract are quite affordable.
Multi-Agent Chronological Planning with Model-Agnostic Meta Reinforcement Learning

In this study, we propose an innovative approach to address a chronological planning problem involving the multiple agents required to complete tasks under precedence constraints. We model this problem as a stochastic game and solve it with multi-agent reinforcement learning algorithms. However, these algorithms necessitate relearning from scratch when confronted with changes in the chronological order of tasks, resulting in distinct stochastic games and consuming a substantial amount of time. To overcome this challenge, we present a novel framework that incorporates meta-learning into a multi-agent reinforcement learning algorithm. This approach enables the extraction of meta-parameters from past experiences, facilitating rapid adaptation to new tasks with altered chronological orders and circumventing the time-intensive nature of reinforcement learning. Then, the proposed framework is demonstrated through the implementation of a method named Reptile-MADDPG. The performance of the pre-trained model is evaluated using average rewards before and after fine-tuning. Our method, in two testing tasks, improves the average rewards from −44 to −37 through 10,000 steps of fine-tuning in two testing tasks, significantly surpassing the two baseline methods that only attained −51 and −44, respectively. The experimental results demonstrate the superior generalization capabilities of our method across various tasks, thus constituting a significant contribution towards the design of intelligent unmanned systems.
Medical Image Encryption: A Comprehensive Review

In medical information systems, image data can be considered crucial information. As imaging technology and methods for analyzing medical images advance, there will be a greater wealth of data available for study. Hence, protecting those images is essential. Image encryption methods are crucial in multimedia applications for ensuring the security and authenticity of digital images. Recently, the encryption of medical images has garnered significant attention from academics due to concerns about the safety of medical communication. Advanced approaches, such as e-health, smart health, and telemedicine applications, are employed in the medical profession. This has highlighted the issue that medical images are often produced and shared online, necessitating protection against unauthorized use.
A new analysis of small S-boxes based on a new notion of algebraic immunity

We analyze five newly emerged cryptographic characteristics and a new notion of algebraic immunity proposed by us of 16 best affine equivalence classes of 4 × 4 S-boxes, as well as the only best affine equivalence class of 3-bit ones. The best class among the 16 classes of 4-bit ones is determined based on our analysis. A strategy to search for good candidates in the best class is also proposed, which can generate many 4 × 4 S-boxes with better cryptographic characteristics than the ones previously proposed. We also obtain many candidates with better cryptographic characteristics than the previously proposed 3-bit S-boxes by a similar method. The implementations of our candidates and the previously proposed ones are also discussed. The discussion shows that our candidates can achieve a good balance between implementation cost and security.
Bayesian Joint Modeling Analysis of Longitudinal Proportional and Survival Data

This paper focuses on a joint model to analyze longitudinal proportional and survival data. We utilize a logit transformation on the longitudinal proportional data and employ a partially linear mixed-effect model. With this model, we estimate the unknown function of time using the B-splines technique. Additionally, we introduce a centered Dirichlet process mixture model (CDPMM) to capture the random effects, allowing for a flexible distribution. The survival data are assumed using a Cox proportional hazard model, and the sharing random effects joint model is developed for the two types of data. We develop a Bayesian Lasso (BLasso) approach that combines the Gibbs sampler and the Metropolis–Hastings algorithm. The proposed method allows for the estimation of unknown parameters and the selection of significant covariates simultaneously. We evaluate the performance of our proposed methods through simulation studies and also provide an illustration of our methodologies using an example from the MA.5 research experiment.
A Blockchain-Based Continuous Micropayment Scheme Using Lockable Signature

A blockchain-oriented continuous micropayment system forms an integral element of the digital economy, enabling seamless transactions and encouraging minor purchases. However, due to the inherent throughput constraints of blockchain, payment channels (PCs) are customarily deployed for managing high-volume transactions in an off-chain mode. Despite this, the integration of a PC necessitates a trusted intermediary to safeguard transactional security, thereby imposing an extra security assumption as only the initial and concluding transactions are visible to other system participants. To circumvent this limitation, we introduce an innovative micropayment structure utilizing lockable signatures. Each transaction within the PC coalesces into a continuous microtransaction hash-chain (CMHC), precluding unscrupulous users from accessing transactional data during the process. Additionally, all locks within the CMHC remain concealed throughout the transaction, with all information relayed in a tamper-proof manner. We provide a comprehensive security analysis and perform a gamut of evaluations. Empirical evidence indicates that our proposed system outperforms existing state-of-the-art solutions in transaction time and verification expenses. This lockable signature-dependent micropayment system not only ensures robust security but also enhances transactional speed and efficiency, thereby catalyzing the growth of the digital economy.
HPG-GAN: High-Quality Prior-Guided Blind Face Restoration Generative Adversarial Network

To address the problems of low resolution, compression artifacts, complex noise, and color loss in image restoration, we propose a High-Quality Prior-Guided Blind Face Restoration Generative Adversarial Network (HPG-GAN). This mainly consists of Coarse Restoration Sub-Network (CR-Net) and Fine Restoration Sub-Network (FR-Net). HPG-GAN extracts high-quality structural and textural priors and facial feature priors from coarse restoration images to reconstruct clear and high-quality facial images. FR-Net includes the Facial Feature Enhancement Module (FFEM) and the Asymmetric Feature Fusion Module (AFFM). FFEM enhances facial feature information using high-definition facial feature priors obtained from ArcFace. AFFM fuses and selects asymmetric high-quality structural and textural information from ResNet34 to recover overall structural and textural information. The comparative evaluations on synthetic and real-world datasets demonstrate superior performance and visual restoration effects compared to state-of-the-art methods. The ablation experiments validate the importance of each module. HPG-GAN is an effective and robust blind face deblurring and restoration network. The experimental results demonstrate the effectiveness of the proposed network, which achieves better visual quality against state-of-the-art methods.
Demonstration of a Quantum Switch in a Sagnac Configuration

The quantum switch is an example of a process with an indefinite causal structure, and has attracted attention for its ability to outperform causally ordered computations within the quantum circuit model. To date, realizations of the quantum switch have made a trade-off between relying on optical interferometers susceptible to minute path length fluctuations and limitations on the range and fidelity of the implementable channels, thereby complicating their design, limiting their performance, and posing an obstacle to extending the quantum switch to multiple parties. In this Letter, we overcome these limitations by demonstrating an intrinsically stable quantum switch utilizing a common-path geometry facilitated by a novel reciprocal and universal SU(2) polarization gadget. We certify our design by successfully performing a channel discrimination task with near unity success probability.
AraMAMS: Arabic Multi-Aspect, Multi-Sentiment Restaurants Reviews Corpus for Aspect-Based Sentiment Analysis

The abundance of data on the internet makes analysis a must. Aspect-based sentiment analysis helps extract valuable information from textual data. Because of limited Arabic resources, this paper enriches the Arabic dataset landscape by creating AraMA, the first and largest Arabic multi-aspect corpus. AraMA comprises 10,750 Google Maps reviews for restaurants in Riyadh, Saudi Arabia. It covers four aspect categories—food, environment, service, and price—along with four sentiment polarities: positive, negative, neutral, and conflict. All AraMA reviews are labeled with at least two aspect categories. A second version, named AraMAMS, includes reviews labeled with at least two different sentiments, making it the first Arabic multi-aspect, multi-sentiment dataset. AraMAMS has 5312 reviews covering the same four aspect categories and sentiment polarities. Both corpora were evaluated using naïve biased (NB), support vector classification (SVC), linear SVC, and stochastic gradient descent (SGD) models. In the AraMA corpus, the aspect categories task achieved a 91.41% F1 measure result using the SVC model, while in the AraMAMS corpus, the best F1 measure result for aspect categories task reached 91.70% using the linear SVC model.
Learning to Rank Hypernyms of Financial Terms Using Semantic Textual Similarity

Over the years, there has been a paradigm shift in how users access financial services. With the advancement of digitalization more users have been preferring the online mode of performing financial activities. This has led to the generation of a huge volume of financial content. Most investors prefer to go through these contents before making decisions. Every industry has terms that are specific to the domain it operates in. Banking and Financial Services are not an exception to this. In order to fully comprehend these contents, one needs to have a thorough understanding of the financial terms. Getting a basic idea about a term becomes easy when it is explained with the help of the broad category to which it belongs. This broad category is referred to as hypernym. For example, "bond" is a hypernym of the financial term "alternative debenture". In this paper, we propose a system capable of extracting and ranking hypernyms for a given financial term. The system has been trained with financial text corpora obtained from various sources like DBpedia [4], Investopedia, Financial Industry Business Ontology (FIBO), prospectus and so on. Embeddings of these terms have been extracted using FinBERT [3], FinISH [1] and fine-tuned using SentenceBERT [54]. A novel approach has been used to augment the training set with negative samples. It uses the hierarchy present in FIBO. Finally, we benchmark the system performance with that of the existing ones. We establish that it performs better than the existing ones and is also scalable.
Estimation of the Deformation Gradient Tensor by Particle Tracking Near a Free Boundary with Quantified Error

Obtaining accurate displacement measurements for large material deformation and/or rotation presents a distinct challenge to digital image correlation (DIC) due to cumulative and decorrelation errors, particularly near material boundaries.We aim to accurately measure the deformation gradient tensor near boundary discontinuities in situations of large deformation and large deformation gradients.To achieve this goal, the locations of randomly distributed particles are tracked using an open-source particle-tracking software, Trackpy. A least-squares estimate of the deformation gradient tensor field uses nearest-neighbor material vectors and a first-order Finite Difference (FD) approximation, circumventing common errors in other methods. The error caused by FD approximation and that incurred by measurement are derived and tested with exhaustive numerical simulations. Furthermore, a uniaxial tensile test and mode-I fracture experiment are conducted with particle-embedded hydrogels to validate the method.Numerical results corroborate a theoretical expression of measurement error. They show that the FD error increases while the measurement error decreases for a growing estimating radius. Moreover, measurement error is linearly correlated to displacement noise. A benchmark uniaxial tensile test validates the accuracy of the proposed estimator, and the near-crack-tip measurements in a tensile fracture experiment demonstrate the estimator's capabilities near a free surface, when a material undergoes large deformation and rotation. The results of the displacement and strain data are benchmarked against kinematic data obtained using an open-source DIC software, Ncorr. Computation time for both methods is compared.A deformation gradient tensor estimator is developed based on a particle tracking technique and a least squares routine. Theoretical error bounds on the estimator are verified by numerical simulations, and the method's capability is confirmed by physical experiments in evaluating large deformation and rotation near a free boundary. The proposed estimator is expected to open a door towards future material tests and experimental mechanics studies, especially in large deformation and large rotation scenarios.
Guided Local Feature Matching with Transformer

GLFNet is proposed to be utilized for the detection and matching of local features among remote-sensing images, with existing sparse feature points being leveraged as guided points. Local feature matching is a crucial step in remote-sensing applications and 3D reconstruction. However, existing methods that detect feature points in image pairs and match them separately may fail to establish correct matches among images with significant differences in lighting or perspectives. To address this issue, the problem is reformulated as the extraction of corresponding features in the target image, given guided points from the source image as explicit guidance. The approach is designed to encourage the sharing of landmarks by searching for regions in the target image with features similar to the guided points in the source image. For this purpose, GLFNet is developed as a feature extraction and search network. The main challenge lies in efficiently searching for accurate matches, considering the massive number of guided points. To tackle this problem, the search network is divided into a coarse-level match network-based guided point transformer that narrows the search space and a fine-level regression network that produces accurate matches. The experimental results on challenging datasets demonstrate that the proposed method provides robust matching and benefits various applications, including remote-sensing image registration, optical flow estimation, visual localization, and reconstruction registration. Overall, a promising solution is offered by this approach to the problem of local feature matching in remote-sensing applications.
A World Information Strategy for the Future

The conception of ‘world information strategy for the future’ that is proposed in this paper assumes the ascendancy of the non-Western world in the context of an information infrastructure that has so far been Western oriented. The strategy advanced here is neither to imitate nor to criticize that infrastructure; rather, it is to repurpose it for non-Western ends. This paper divides the strategy into three parts: (1) the globalization of English (i.e., the dominance of non-native over native speakers); (2) undiscovered public knowledge (i.e., the vast number of academic publications that are underutilized); and (3) precipitatory governance (i.e., innovation for a world where current default settings of information flows are bound to change).
Are Large Language Models Intelligent? Are Humans?

Claims that large language models lack intelligence are abundant in current AI discourse. To the extent that the claims are supported by arguments, these usually amount to claims that the models (a) lack common sense, (b) know only facts they have been trained with, (c) are merely matrix multiplications, (d) only predict the next word in a chain, (e) lack a world model, (f) have no grounding of symbols, (g) lack creativity, or (h) lack consciousness. Here, each of these arguments is applied, with minor modifications, to demonstrate that humans also lack intelligence. This should make us suspicious of the validity of these arguments.
Precise Email Classification using Deep Learning

Email spam has rapidly expanded in recent years along with the enormous surge in internet users. People are making illegal and immoral use of them. scams conduct, and phishing. It is against the law to send unsolicited emails with dangerous links that could damage our system or try to access your system. The people who are unaware of these scams are the main targets of spammers, who easily construct fake identities and email addresses. In their spam emails, they employ a legitimate name. Therefore, it's crucial to recognize phony spam emails. To accomplish this, this research will make use of machine learning techniques.
Psychological Study of Cyber-Bullying Against Adolescent Girls in India Using Twitter

Due to the rise in digital activity of students as well as increased social media presence, the lack of regulation of platforms has given rise to another form of bullying, popularly known as cyberbullying. Cyberbullying is one of the most adverse issues prevalent in schools nationwide. Cyberbullying refers to bullying that happens over any web-interfaced or electronic platform. It is an activity that significantly affects the mental and physical health of its victims. With increased secrecy, the frequency and propagation of cyberbullying remain high due to the information technology infrastructure available today. Understanding cyberbullying trends and preventing them, using suitable machine learning algorithms, could help numerous school students lead better lives, as well as make better decisions, which help them grow and flourish into capable future leaders. Hence, the authors' aim for this research paper is to focus on adolescent girls using various tools and techniques like text analytics and image analytics. For this paper, the authors study a sample of netizens. The location where the analysis is conducted is New Delhi, and the real-world data is extracted from Twitter in English. The real-world data is extracted using appropriate data mining algorithms to find hidden patterns and then conduct the analyses required to understand the psychology of girls and boys and the tonality and voice of the tweets/posts. This is done from the open-source information available on the platform (Twitter) from tweets by the users. There is little to no bias as the entire process can be automated; hence, tweets will be filtered or flagged based on data. Such a method allows one to get access to unbiased data. Bias, in this case, can be defined as prejudice in action and response received from a participant. The results are then analysed using polarity and subjectivity. Understanding psychology and personality traits helps in drawing insights from the expressions collected. The authors will be studying the sample bios, likes, and comments of the sample using a lexical and syntactical approach. Six thousand top tweets are extracted, and the 15 tweets which score the highest on polarity and subjectivity values are taken for further analysis. The tweets are filtered based on 16 responses from a focus group filtering the 20 most popular profane words. Since the data is extracted using Twitter (i.e., a secondary data source), the authors address the gap in current psychological analyses. In such studies, one usually circulates questionnaires to understand the participant, but, for this research though, the authors will be studying the data without bringing the concerned individual into play, thereby eliminating the human bias, which is a significant limitation of gathering responses through a questionnaire. There is increased scope for further streamlining the model. The inferences include understanding the regulation of a social media platform, the degree of aggression on the platform, and an effort to distinguish those who cause such aggression.
MONTE-CARLO AND SENSITIVITY TRANSPORT MODEL: APPLICATION IN SOLAR ENERGY

The classical transport model of radiative transfer uses the specific intensity as the descriptor. By differentiating the transport model of intensity with respect to a geometric parameter, a model of geometric sensitivity can be built. In this work, a classical model of intensity and models of geometric sensitivity are built with specular flat mirrors as the boundary conditions and are applied in a concentrated solar power plant. The sensitivities of the impacting power on the receiver with respect to the geometric parameters of each mirror are estimated by the Monte Carlo Method (MCM). Furthermore, by analyzing the sources in the models of geometric sensitivity, the contributions of the typical optical effects: spillage, blocking, and shadowing, to the sensitivity can also be estimated and analyzed, which is essential information to optimize the system.