Reviewerly/exHarmony huggingface dataset:
Privacy Preserving Blockchain with Energy Aware Clustering Scheme for IoT Healthcare Systems

Due to advancements in information technology, the healthcare sector becomes beneficial and provides distinct methods of managing medical data and enhancing the quality of medical services. The advanced e-healthcare applications are mainly based on the Internet of Things (IoT) and cloud computing platforms. In IoT enabled healthcare sector, the IoT devices usually record the patient data and transfer it to the cloud for further processing. Energy efficiency and security are treated as critical problems in designing IoT networks in the healthcare environment. As IoT devices are limited to energy, designing an effective technique to reduce energy utilization is needed. At the same time, secure transmission of medical data also poses a major challenging design issue. This paper presents a novel artificial intelligence with a blockchain scheme for IoT healthcare systems named AIBS-IoTHS. The AIBS-IoTH model aims to achieve secure and energy-efficient data transmission in IoT networks. The IoT devices are primarily used to collect patients' medical data. The AIBS- IoTH model involves a metaheuristic-based modified sunflower optimization-based clustering (MSFOC) technique to achieve energy efficiency. Then, the blockchain empowered secure medical data transmission process is carried out for both inter-cluster and intra-cluster communication. At last, the Classification Enhancement Generative Adversarial Networks (CEGAN) model performs the diagnostic process on the secured medical data to determine the existence of the diseases. The design of MSFOC and CEGAN techniques shows the novelty of the work. An extensive experimental analysis of the benchmark dataset pointed out the superior performance of the proposed AIBS-IoTH model over the other compared methods.
Financial Development and Income Inequality: Evidence From Advanced, Emerging and Developing Economies

We investigate the effects of financial development on income inequality for a global sample of countries between 1980 and 2019. The study contributes to the current literature by first, making use of a multifaceted index of financial development that captures different aspects of financial developments over time. Second, we compare the effects of financial development on income inequality across economic classifications, namely advanced, emerging and least developed countries. Last, we investigate the non-linear effects of financial development on income inequality across these economic classifications. The findings indicate that in general, financial development reduces inequality across emerging and least developed countries, but is not statistically significant for advanced countries. However, when we disaggregate the financial development index into its sub-components (financial institutions and financial markets), we find different effects on inequality, based on the levels of development. Further investigation on the dimensions under financial institutions and financial markets (depth, access and efficiency) reveals that banking sector development under financial institutions has income inequality-reducing effects in emerging and least developed countries, while stock market development under financial markets widens inequality in least developed countries. We also find heterogeneous non-linear effects between emerging and least developed countries. The findings in our paper firstly highlight the nuances in financial development depending on the level of development in countries, and secondly that policies focused on financial inclusion of the poor can mitigate inequality.
A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design

We develop a fast and scalable computational framework to solve Bayesian optimal experimental design problems governed by partial differential equations (PDEs) with application to optimal sensor placement by maximizing expected information gain (EIG). Such problems are particularly challenging due to the curse of dimensionality for high-dimensional parameters and the expensive solution of large-scale PDEs. To address these challenges, we exploit two fundamental properties: (1) the low-rank structure of the Jacobian of the parameter-to-observable map, to extract the intrinsically low-dimensional data-informed subspace, and (2) a series of approximations of the EIG that reduce the number of PDE solves while retaining high correlation with the true EIG. Based on these properties, we propose an efficient offline-online decomposition for the optimization problem. The offline stage dominates the cost and entails precomputing all components that require PDE solves. The online stage optimizes sensor placement and does not require any PDE solves. For the online stage, we propose a new greedy algorithm that first places an initial set of sensors using leverage scores and then swaps the selected sensors with other candidates until certain convergence criteria are met, which we call a swapping greedy algorithm. We demonstrate the efficiency and scalability of the proposed method by both linear and nonlinear inverse problems. In particular, we show that the number of required PDE solves is small, independent of the parameter dimension, and only weakly dependent on the data dimension for both problems.
Online Learning From Incomplete and Imbalanced Data Streams

Learning with streaming data has attracted extensive research interest in recent years. Existing online learning approaches have specific assumptions regarding data streams, such as requiring fixed or varying feature spaces with explicit patterns and balanced class distributions. While the data streams generated in many real scenarios commonly have arbitrarily incomplete feature spaces and dynamic imbalanced class distributions, making existing approaches be unsuitable for real applications. To address this issue, this paper proposes a novel <u>O</u> nline <u>L</u> earning from <u>I</u> ncomplete and <u>I</u> mbalanced <u>D</u> ata <u>S</u> treams (OLI <inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> DS) algorithm. OLI <inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> DS has a two-fold main idea: 1) it follows the empirical risk minimization principle to identify the most informative features of incomplete feature spaces, and 2) it develops a dynamic cost strategy to handle imbalanced class distributions in real-time by transforming F-measure optimization into a weighted surrogate loss minimization. To evaluate OLI <inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> DS, we compare it with state-of-the-art related algorithms in three kinds of experiments. First, we adopt 14 real datasets to simulate three scenarios of incomplete feature spaces, i.e., trapezoidal, feature evolvable, and capricious data streams. Second, based on a benchmark online analyzer, we generate 13 datasets to simulate incomplete data streams with different imbalance ratios. Third, we analyze concept drift in two simulated scenes, i.e., online learning and data stream mining, and verify the adaption of OLI <inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> DS on repeated concept drifts and variable imbalance ratios. The results demonstrate that OLI <inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> DS achieves a significantly better performance than its rivals. Besides, a real-world case study on movie review classification is conducted to elaborate on our OLI <inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> DS algorithm's effectiveness. Code is released at <uri>https://github.com/youdianlong/OLI2DS</uri> .
UAV Trajectory Planning for Complex Open Storage Environments Based on an Improved RRT Algorithm

Multi-rotor UAVs (Unmanned Aerial Vehicles) have been increasingly used for hazardous inspection tasks in complex open-air warehouse storage environments due to their high maneuverability and aerial perspective. To facilitate rapid response to patrol missions and improve the efficiency of UAV trajectory planning. This paper established a rotary-wing UAV trajectory plan model considering UAV patrol efficiency, trajectory cost, and power consumption cost. Secondly, an improved SSA (salp swarm algorithm) is incorporated for the shortcomings of low algorithmic search efficiency and unsmooth paths when planning paths in the traditional RRT (Rapidly-exploring Random Trees). The predation mechanism of the salps group is incorporated into the random sampling of the RRT algorithm, which reduces the invalid sampling of random points and introduces the adaptive leader structure, and reverses the search strategy to improve the algorithm’s global search for superiority at the later stage of the search. Finally, the designed LASSA-RRT algorithm is subjected to simulation experiments and compared with RRT, RRT*, IRRT, and PF-RRT* in a cross-sectional manner. The results show that the LASSA-RRT algorithm has an average reduction of 55.83% in sampling times, 51.91% in run time, 13.17% in track length, and 0.1491% in flight cost. In summary, this paper’s UAV trajectory planning method can be effectively applied to complex open storage environments. It can provide a helpful reference direction for UAV trajectory planning.
Fundamental and speculative components of the cryptocurrency pricing dynamics

Abstract The driving forces behind cryptoassets’ price dynamics are often perceived as being dominated by speculative factors and inherent bubble-bust episodes. Fundamental components are believed to have a weak, if any, role in the price-formation process. This study examines five cryptoassets with different backgrounds, namely Bitcoin, Ethereum, Litecoin, XRP, and Dogecoin between 2016 and 2022. It utilizes the cusp catastrophe model to connect the fundamental and speculative drivers with possible price bifurcation characteristics of market collapse events. The findings show that the price and return dynamics of all the studied assets, except for Dogecoin, emerge from complex interactions between fundamental and speculative components, including episodes of price bifurcations. Bitcoin shows the strongest fundamentals, with on-chain activity and economic factors driving the fundamental part of the dynamics. Investor attention and off-chain activity drive the speculative component for all studied assets. Among the fundamental drivers, the analyzed cryptoassets present their coin-specific factors, which can be tracked to their protocol specifics and are economically sound.
Remote monitoring system using slow-fast deep convolution neural network model for identifying anti-social activities in surveillance applications

Remote monitoring is the process that monitors and observes information from a distance utilizing sensors or electronic types of equipment. Remote monitoring is used in real-time applications like traffic, forest, military, shops, and hospitals to determine abnormal activities. Earlier research has done video processing methods based on computer vision techniques, but the computational complexity regarding time and memory is high. This paper designs and implements a novel Slow-Fast Convolution Neural Network (SF–CNN) to identify, detect, and classify abnormal behaviours from a surveillance video. The proposed CNN architecture learns the video frames automatically, obtains the most appropriate properties about various objects' behaviour from a large set of videos. The learning process of SF-CNN is carried out in two ways, such as slow learning and fast learning. The slow learning process is enabled when the frame rate is less, and the rapid learning process is enabled when the frame rate is high. Both the learning processes learn spatial and temporal information from the input video. Different objects, such as humans, vehicles, and animals, are detected and recognized according to their actions. All the videos have normal and abnormal activities that vary in various contexts. The proposed SF-CNN architecture provides an end-to-end solution to dealing with multiple constraints abnormal movements. The experiment is carried out on several benchmark datasets, and the performance of the SF-CNN architecture is evaluated. The proposed approach obtained 99.6% of accuracy, which is higher than the other existing techniques.
Decision tree post-pruning without loss of accuracy using the SAT-PP algorithm with an empirical evaluation on clinical data

A decision tree (DT) is one of the most popular and efficient techniques in data mining. Specifically, in the clinical domain, DTs have been widely used thanks to their relatively easy explainable nature, efficient computation time, and relatively accurate predictions. However, some DT constriction algorithms may produce a large tree size structure which is difficult to understand and often leads to misclassification of data in the testing process due to poor generalization. Post pruning (PP) algorithms have been introduced to reduce the size of the tree structure with a minor (or not at all) decrease in the accuracy of classification while trying to improve the model's generalization. In this paper, we propose a new Boolean satisfiability (SAT) based PP algorithm called SAT-PP. Our algorithm reduces the tree size while preserving the accuracy of the unpruned tree. We implemented our algorithm on a medical-related classification data sets since in medical-related tasks we emphatically try to avoid decreasing the model's performance when better training is not an option. Namely, in the case of medical-related tasks, one may prefer an unpruned DT model to a pruned DT model with worse performance. Indeed, we empirically obtained that the SAT-PP algorithm produce the same accuracy and F1 score as the DT model without PP while statistically significantly reducing the model size and as a result computation time (6.8%). In addition, we compared the proposed algorithm with other PP algorithms and found similar generalization capabilities.
Compressed phase coding-based optical image encryption

With the development of artificial intelligence technology, the traditional inflexible optical image encryption system has been difficult to resist security attacks such as chosen-plaintext attack and known-plaintext attack, and its security has been greatly challenged. Therefore, an optical image encryption method based on compressed phase coding is proposed, in which a spatial light modulator (SLM) is used to encode the phase of the optical system randomly, and then combined with the measurement matrix in the single pixel sampling process to build a flexible key system. In the encryption process, a phase-only SLM is firstly implemented to realize phase encoding of the original plaintext image and loading the phase key. Then the modulated reference light is implemented to further hide the complex amplitude distribution of the encrypted image. Finally, the encoded object light and reference light are coherently superimposed in the digital micromirror device (DMD), and the inner product operation is carried out with the measurement matrix to realize further encryption and collected by the single pixel detector. Both simulation and experiment proved that the proposed compressed phase coding-based image encryption system is similar to "one time one pad" cryptographic system, which greatly improves the security of the optical encryption system.
Jupyter Notebooks for the study of advanced topics in Fluid Mechanics

Abstract In recent years, Jupyter Notebooks have become a very useful free and open‐source tool in teaching, as they allow you to combine text, images, mathematical expressions, links and code into a single document. This gives students an interactive document with which they can experiment and learn with the help of high‐level mathematical calculus. In Fluid Mechanics, it is very common for students to deal with complex computations that take away attention from the Mechanic, especially in advanced topics such as Rheology, Turbulence, or Boundary Layer. The subject “Advanced Fluid Mechanics” is an elective one of the last year of the Bachelor's degree in Industrial and Aerospace Technology Engineering at the Terrassa School of Industrial, Aerospace and Audiovisual Engineering at the Universitat Politècnica de Catalunya. This subject has three ECTS credits and has been taught since the academic year 2020–2021 This subject complements the compulsory subject Fluid Mechanics and is developed in 6 weeks with 5 h of class each week. This work presents Fluid Mechanics modules with Jupyter Notebooks that complement the syllabus given in the compulsory subject. An elective subject is presented where subjects of Fluid Mechanics per week are studied independently, using different Python tools: symbolic calculation, modeling of experimental data, statistical analysis, numerical calculation, and so forth. The main goal is for the student to focus on mechanical concepts and actively learn to use the tools available, especially open source, to do the associated mathematical calculations.
Weakly-supervised pre-training for 3D human pose estimation via perspective knowledge

Modern deep learning-based 3D pose estimation approaches require plenty of 3D pose annotations. However, existing 3D datasets lack diversity, which limits the performance of current methods and their generalization ability. Although existing methods utilize 2D pose annotations to help 3D pose estimation, they mainly focus on extracting 2D structural constraints from 2D poses, ignoring the 3D information hidden in the images. In this paper, we propose a novel method to extract weak 3D information directly from 2D images without 3D pose supervision. Firstly, we utilize 2D pose annotations and perspective prior knowledge to generate the relative depth of human joints. Then, we collect a 2D pose dataset (MCPC) and generate relative depth labels. Based on MCPC, we propose a weakly-supervised pre-training (WSP) strategy to distinguish the depth relationship between two points in an image. WSP enables the learning of the relative depth of two keypoints on lots of in-the-wild images, which is more capable of predicting depth and generalization ability for 3D human pose estimation. After fine-tuning the pose model on 3D pose datasets, WSP achieves state-of-the-art results on two widely-used benchmarks.
Quantifying continuity of characteristics of composite quantum systems

General methods of quantitative and qualitative continuity analysis of characteristics of composite quantum systems are described. Several modifications of the Alicki-Fannes-Winter method are considered, which make it applicable to a wide class of characteristics in both finite-dimensional and infinite-dimensional cases. A new approximation method for obtaining local continuity conditions for various characteristics of quantum systems is proposed and described in detail. This method allows us to prove several general results (Simon-type dominated convergence theorem, the theorem about preserving continuity under convex mixtures, etc.). Uniform continuity bounds and local continuity conditions for basic characteristics of composite quantum systems are presented. Along with the results obtained earlier by different authors, a number of new results proved by the proposed methods are described.
Social media bot detection with deep learning methods: a systematic review

Abstract Social bots are automated social media accounts governed by software and controlled by humans at the backend. Some bots have good purposes, such as automatically posting information about news and even to provide help during emergencies. Nevertheless, bots have also been used for malicious purposes, such as for posting fake news or rumour spreading or manipulating political campaigns. There are existing mechanisms that allow for detection and removal of malicious bots automatically. However, the bot landscape changes as the bot creators use more sophisticated methods to avoid being detected. Therefore, new mechanisms for discerning between legitimate and bot accounts are much needed. Over the past few years, a few review studies contributed to the social media bot detection research by presenting a comprehensive survey on various detection methods including cutting-edge solutions like machine learning (ML)/deep learning (DL) techniques. This paper, to the best of our knowledge, is the first one to only highlight the DL techniques and compare the motivation/effectiveness of these techniques among themselves and over other methods, especially the traditional ML ones. We present here a refined taxonomy of the features used in DL studies and details about the associated pre-processing strategies required to make suitable training data for a DL model. We summarize the gaps addressed by the review papers that mentioned about DL/ML studies to provide future directions in this field. Overall, DL techniques turn out to be computation and time efficient techniques for social bot detection with better or compatible performance as traditional ML techniques.
Sign2Pose: A Pose-Based Approach for Gloss Prediction Using a Transformer Model

Word-level sign language recognition (WSLR) is the backbone for continuous sign language recognition (CSLR) that infers glosses from sign videos. Finding the relevant gloss from the sign sequence and detecting explicit boundaries of the glosses from sign videos is a persistent challenge. In this paper, we propose a systematic approach for gloss prediction in WLSR using the Sign2Pose Gloss prediction transformer model. The primary goal of this work is to enhance WLSR's gloss prediction accuracy with reduced time and computational overhead. The proposed approach uses hand-crafted features rather than automated feature extraction, which is computationally expensive and less accurate. A modified key frame extraction technique is proposed that uses histogram difference and Euclidean distance metrics to select and drop redundant frames. To enhance the model's generalization ability, pose vector augmentation using perspective transformation along with joint angle rotation is performed. Further, for normalization, we employed YOLOv3 (You Only Look Once) to detect the signing space and track the hand gestures of the signers in the frames. The proposed model experiments on WLASL datasets achieved the top 1% recognition accuracy of 80.9% in WLASL100 and 64.21% in WLASL300. The performance of the proposed model surpasses state-of-the-art approaches. The integration of key frame extraction, augmentation, and pose estimation improved the performance of the proposed gloss prediction model by increasing the model's precision in locating minor variations in their body posture. We observed that introducing YOLOv3 improved gloss prediction accuracy and helped prevent model overfitting. Overall, the proposed model showed 17% improved performance in the WLASL 100 dataset.
Identity-Based Proxy Signature with Message Recovery over NTRU Lattice

Proxy signature is one of the important primitives of public-key cryptography and plays an essential role in delivering security services in modern communications. However, existing post quantum proxy signature schemes with larger signature sizes might not be fully practical for some resource-constrained devices (e.g., Internet of Things devices). A signature scheme with message recovery has the characteristic that part or all of the message is embedded in the signature, which can reduce the size of the signature. In this paper, we present a new identity-based proxy signature scheme over an NTRU lattice with message recovery (IB-PSSMR), which is more efficient than the other existing identity-based proxy signature schemes in terms of the size of the signature and the cost of energy. We prove that our scheme is secure under a Short Integer Solution (SIS) assumption that is as hard as approximating several worst-case lattice problems in the random oracle model. We also discussed some application scenarios of IB-PSSMR in blockchain and Internet of Things (IOT). This paper provides a new idea for the design of lattice signature schemes in low resource constrained environments.