Reviewerly/exHarmony huggingface dataset:
Decision on block size in blockchain systems by evolutionary equilibrium analysis

By using the PoW protocol, mining pools compete to successfully mine blocks to pursue rewards. Generally, the reward from a mined block includes the fixed block subsidies and the time-varying transaction fees. The latter are offered by the senders whose transactions are packaged into blocks and are increasing with the block size being larger. However, the larger block size brings the longer latency, resulting in a smaller probability of successfully mining. Therefore, decision on the optimal block size of a block to trade off two factors above mentioned is a complex and crucial problem for the mining pools. In this paper, we model the repeated mining competition dynamics among mining pools as an evolutionary game, in which each mining pool has two strategies: following the upper bound of block size B¯, or selecting a block size smaller than B¯. Because of the bounded rationality, each mining pool pursues its evolutionary stable strategy (ESS) on block size by continuous learning and adjustments during the whole mining process. A framework is built for the general evolutionary game, based on which we then explore the existence and stability of the ESSs for a case of two mining pools. Numerical experiments using the real Bitcoin data are conducted to demonstrate the theoretical results in this paper.
Adiabatic quantum algorithm for artificial graphene

We devise a quantum-circuit algorithm to solve the ground state and ground energy of artificial graphene. The algorithm implements a Trotterized adiabatic evolution from a purely tight-binding Hamiltonian to one including kinetic, spin-orbit, and Coulomb terms. The initial state is obtained efficiently using Gaussian-state preparation, while the readout of the ground energy is organized into seventeen sets of measurements, irrespective of the size of the problem. The total depth of the corresponding quantum circuit scales polynomially with the size of the system. A full simulation of the algorithm is performed and ground energies are obtained for lattices with up to four hexagons. Our results are benchmarked with exact diagonalization for systems with one and two hexagons. For larger systems we use the exact state vector and approximate matrix product state simulation techniques. The latter allows us to systematically trade off precision with memory and therefore to tackle larger systems. We analyze adiabatic and Trotterization errors, providing estimates for optimal periods and time discretizations given a finite accuracy. In the case of large systems we also study approximation errors.
Deep learning-enabled anti-ambient light approach for fringe projection profilometry

Achieving high-quality surface profiles under strong ambient light is challenging in fringe projection profilometry (FPP) since ambient light inhibits functional illumination from exhibiting sinusoidal stripes with high quantization levels. Conventionally, large-step phase shifting approaches are presented to enhance the anti-interference capability of FPP, but the image acquisition process in these approaches is highly time-consuming. Inspired by the promising performance of deep learning in optical metrology, we propose a deep learning-enabled anti-ambient light (DLAL) approach that can help FPP extract phase distributions from a single fringe image exposed to unbalanced lighting. In this work, the interference imposed by ambient light on FPP is creatively modeled as ambient light-induced phase error (ALPE). Guided by the ALPE model, we generate the dataset by precisely adjusting the stripe contrast before performing active projection, overcoming the challenge of collecting a large sample of fringe images with various illumination conditions. Driven by the novel dataset, the generated deep learning model can effectively suppress outliers among surface profiles in the presence of strong ambient light, thereby implementing high-quality 3D surface imaging. Experimentally, we verify the effectiveness and adaptability of the proposed DLAL approach in both indoor and outdoor scenarios with strong irradiation.
Development of a Machine Learning-Based Screening Method for Thyroid Nodules Classification by Solving the Imbalance Challenge in Thyroid Nodules Data

Background: This study aims to show the impact of imbalanced data and the typical evaluation methods in developing and misleading assessments of machine learning-based models for preoperative thyroid nodules screening. Study design: A retrospective study. Methods: The ultrasonography features for 431 thyroid nodules cases were extracted from medical records of 313 patients in Babol, Iran. Since thyroid nodules are commonly benign, the relevant data are usually unbalanced in classes. It can lead to the bias of learning models toward the majority class. To solve it, a hybrid resampling method called the Smote-was used to creating balance data. Following that, the support vector classification (SVC) algorithm was trained by balance and unbalanced datasets as Models 2 and 3, respectively, in Python language programming. Their performance was then compared with the logistic regression model as Model 1 that fitted traditionally. Results: The prevalence of malignant nodules was obtained at 14% (n = 61). In addition, 87% of the patients in this study were women. However, there was no difference in the prevalence of malignancy for gender. Furthermore, the accuracy, area under the curve, and geometric mean values were estimated at 92.1%, 93.2%, and 76.8% for Model 1, 91.3%, 93%, and 77.6% for Model 2, and finally, 91%, 92.6% and 84.2% for Model 3, respectively. Similarly, the results identified Micro calcification, Taller than wide shape, as well as lack of ISO and hyperechogenicity features as the most effective malignant variables. Conclusion: Paying attention to data challenges, such as data imbalances, and using proper criteria measures can improve the performance of machine learning models for preoperative thyroid nodules screening.
An Efficient CRT-Base Power-of-Two Scaling in Minimally Redundant Residue Number System

In this paper, we consider one of the key problems in modular arithmetic. It is known that scaling in the residue number system (RNS) is a rather complicated non-modular procedure, which requires expensive and complex operations at each iteration. Hence, it is time consuming and needs too much hardware for implementation. We propose a novel approach to power-of-two scaling based on the Chinese Remainder Theorem (CRT) and rank form of the number representation in RNS. By using minimal redundancy of residue code, we optimize and speed up the rank calculation and parity determination of divisible integers in each iteration. The proposed enhancements make the power-of-two scaling simpler and faster than the currently known methods. After calculating the rank of the initial number, each iteration of modular scaling by two is performed in one modular clock cycle. The computational complexity of the proposed method of scaling by a constant Sl=2l associated with both required modular addition operations and lookup tables is estimeted as k and 2k+1, respectively, where k equals the number of primary non-redundant RNS moduli. The time complexity is log2k+l modular clock cycles.
Gender and Human-Machine Communication: Where Are We?

In this introduction to the fifth volume of the journal Human-Machine Communication, we present and discuss the five articles focusing on gender and human-machine communication. In this essay, we will analyze the theme of gender, including how this notion has historically and politically been set up, and for what reasons. We will start by considering gender in in-person communication, then we will progress to consider what happens to gender when it is mediated by the most important ICTs that preceded HMC: the telephone, mobile phone, and computer-mediated communication (CMC). We outline the historical framework necessary to analyze the last section of the essay, which focuses on gender in HMC. In the conclusion, we will set up some final sociological and political reflections on the social meaning of these technologies for gender and specifically for women.
Metaverse, Non-Fungible Tokens, Trademarks – Legal Aspects

The metaverse is a concept of cyberspace built on the existing infrastructure of the Internet using emerging technologies of blockchains, cryptocurrency and non-fungible tokens (NFT). In the metaverse, people and various forms of algorithms and artificial intelligence will interact in virtual reality, buy and sell “real world” and “in the metaverse” goods and services and get into legal disputes just like in real life. Trademark law has a decisive role in the development and protection of the brand in virtual reality. The paper first presents the notion of NFT. Then, the protection of trademark in metaverse is analyzed. Special attention is paid to the European Intellectual Property Office Guidelines from July 2022 on its approach to classifying virtual goods and NFTs. Finally, a case from US judicial practice is presented – Hermes Int’l v. Rothschild. This decision provides useful guidance on how courts will treat this issue in the future.
Directed Explorations During Flood Disasters Using Multi-UAV System

The disaster relief operations during floods require time critical information of the flooded area to save lives. Finding critical regions of the disaster struck area in a limited time frame is crucial for effective relief planning. In this paper, we propose a multi-UAV based system with directed explorations of flooded area to gather time-critical ground information using deep reinforcement learning based controls. We learn an exploration policy for the multi-UAV system with limited battery for autonomous coverage of the flooded region. Further, we integrate D8 flow algorithm that approximates the water flow direction based on image pixel information of a sub-region in the UAVs’ exploration strategy. The results show that our proposed method for multi-UAV exploration of flooded area outperforms other methods from the literature. Moreover, the learnt multi-UAV exploration policy is able to generalize to unseen flooded regions without any retraining.
Are Cloud Detection U-Nets Robust Against in-Orbit Image Acquisition Conditions?

Cloud detection is one of the most important image pre-processing steps that can be performed on-board satellites. It may allow us to reduce the amount of data to analyze or downlink by pruning the cloudy areas, or to make the satellites more autonomous through data-driven image acquisition re-scheduling of the areas obscured by clouds. Thus, building the cloud detection algorithms that can be ultimately deployed in orbit became an important research avenue. In this paper, we investigate the robustness of the fully-convolutional neural networks for cloud detection against the atmospheric conditions that resemble real acquisition settings of the Intuition-1 mission. Our experiments, performed over the original and simulated Landsat-8 images, with the latter reflecting target conditions, shed more light on the performance of deep models and showed how can we verify their robustness in Earth observation tasks for which real images do not exist yet.
Swarm Intelligence in Internet of Medical Things

Advancement in information and communication technology leads to different communication channels. One of them is the Internet of Things (IoT) which plays an important role in the new world. The Internet of Medical Things (IoMT) is the sub-branch of IoT which focuses on the communication between medical devices to share their data and information. It helps the healthcare industry to present better services for physicians and patients. It improves the quality of life by increasing the quality and decreasing the time and cost of services. An important challenge in IoMT is optimization in its different sub-problems and Swarm Intelligence (SI) algorithms are the best solution for them. In this paper, we tried to investigate the status of using SI in IoMT. Its applications and their corresponding performance were analyzed and investigated. The reported results show that using SI can efficiently solve the optimization problems in IoMT.
PrivacyAlert: A Dataset for Image Privacy Prediction

Image privacy issues have become an important challenge as millions of images are being shared on social networking sites every day. Often due to users' lack of privacy awareness and social pressure, users' posted images reveal sensitive information and may be easily used to their detriment. To address these issues, several recent studies have proposed machine learning models to automatically identify whether an image contains private information. However, progress on this important task has been hampered by the absence of reliable, publicly available, up-to-date datasets. To this end, we introduce PrivacyAlert, a dataset developed from recent images extracted from Flickr and annotated with privacy labels (private or public). Our data collection process is based on state-of-the-art privacy taxonomy and captures a comprehensive set of image types of various sensitivity. We perform a comprehensive analysis of our dataset and report image privacy prediction results using classic and deep learning models to set the ground for future studies. Our dataset is publicly available at: https://doi.org/10.5281/zenodo.6406870.
Pooling Transformer for Detection of Risk Events in In-The-Wild Video Ego Data

The paper proposes a video transformer architecture for detection of risk events on frail adults with ego video monitoring data. First we introduce an extended taxonomy for risk events, and then we propose a transformer based video recognition model for detection of these risk events. The proposed transformer architecture consists of separable attention for spatial and temporal data. We also introduce a pooling operation on the temporal video data by learning of their importance. The experiments have been conducted on visual data of in-the-wild recorded BIRDS dataset and on Kinetics-400 for benchmarking. The use of the pooling operation in transformers gives an increment of 3% on BIRDS dataset.
Efficient Randomness Extraction in Quantum Random Number Generators

Randomness extraction algorithms play an essential role in Quantum Random Number Generators (QRNGs), where they are used to suppress unwanted classical noise and distill true randomness from their biased output. By employing the SHA-512 hash function and Toeplitz matrix multiplication, we analyse two suitable constructions based on different principles and reach postprocessing rates of 8.69 Mbps and 3.68 Mbps, respectively. Finally, we develop a length-compatible Toeplitz-hashing algorithm able to achieve rates of 143.29 Mbps in a parallelized GPU implementation.
Ships’ Small Target Detection Based on the CBAM-YOLOX Algorithm

In order to solve the problem of low accuracy of small target detection in traditional target detection algorithms, the YOLOX algorithm combined with Convolutional Block Attention Module (CBAM) is proposed. The algorithm first uses CBAM on the shallow feature map to better focus on small target information, and the Focal loss function is used to regress the confidence of the target to overcome the positive and negative sample imbalance problem of the one-stage target detection algorithm. Finally, the Soft Non-Maximum Suppression (SNMS) algorithm is used for post-processing to solve the problem of missed detection in close range ship target detection. The experimental results show that the average accuracy of the proposed CBAM-YOLOX network target detection is improved by 4.01% and the recall rate is improved by 8.81% compared with the traditional YOLOX network, which verifies the effectiveness of the proposed algorithm.
Custody, Provenance, and Reporting of Blockchain and Cryptoassets

Although few fully understand the evolving science of blockchain technology, many agree that such technology promises countless crypto innovative applications. However, institutions using blockchain and cryptoassets face issues. Since more institutions are beginning to explore various private, public, and hybrid blockchains and their related cryptoassets, an increased need exists to understand and anticipate implementation problems. Such problems include contractual issues, privacy concerns, tax implications, jurisdictional issues, financial fraud, and data theft. Others involve intellectual property rights, money laundering, accounting and financial reporting, fork management and governance, and compliance and regulatory obligations. This chapter reviews and analyzes the various problems facing institutions in using blockchain and cryptoassets as financial instruments and mediums of exchange. It focuses on these aspects concerning custody, provenance, and reporting. This chapter also discusses the compliance, disclosure, and regulatory reporting of cryptoassets.
SurvivalPath:A R package for conducting personalized survival path mapping based on time-series survival data

The survival path mapping approach has been proposed for dynamic prognostication of cancer patients using time-series survival data. The SurvivalPath R package was developed to facilitate building personalized survival path models. The package contains functions to convert time-series data into time-slices data by fixed interval based on time information of input medical records. After the pre-processing of data, under a user-defined parameters on covariates, significance level, minimum bifurcation sample size and number of time slices for analysis, survival paths can be computed using the main function, which can be visualized as a tree diagram, with important parameters annotated. The package also includes function for analyzing the connections between exposure/treatment and node transitions, and function for screening patient subgroup with specific features, which can be used for further exploration analysis. In this study, we demonstrate the application of this package in a large dataset of patients with hepatocellular carcinoma, which is embedded in the package. The SurvivalPath R package is freely available from CRAN, with source code and documentation hosted at https://github.com/zhangt369/SurvivalPath.
Adaptive Subgraph Neural Network with Reinforced Critical Structure Mining

While graph representation learning methods have shown success in various graph mining tasks, what knowledge is exploited for predictions is less discussed. This paper proposes a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</i> daptive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i> ubgraph <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> eural <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> etwork named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AdaSNN</i> to find critical structures in graph data, i.e., subgraphs that are dominant to the prediction results. To detect critical subgraphs of arbitrary size and shape in the absence of explicit subgraph-level annotations, AdaSNN designs a Reinforced Subgraph Detection Module to search subgraphs adaptively without heuristic assumptions or predefined rules. To encourage the subgraph to be predictive at the global scale, we design a Bi-Level Mutual Information Enhancement Mechanism including both global-aware and label-aware mutual information maximization to further enhance the subgraph representations in the perspective of information theory. By mining critical subgraphs that reflect the intrinsic property of a graph, AdaSNN can provide sufficient interpretability to the learned results. Comprehensive experimental results on seven typical graph datasets demonstrate that AdaSNN has a significant and consistent performance improvement and provides insightful results.
A data-centric approach to anomaly detection in layer-based additive manufacturing

Abstract Anomaly detection describes methods of finding abnormal states, instances or data points that differ from a normal value space. Industrial processes are a domain where predicitve models are needed for finding anomalous data instances for quality enhancement. A main challenge, however, is absence of labels in this environment. This paper contributes to a data-centric way of approaching artificial intelligence in industrial production. With a use case from additive manufacturing for automotive components we present a deep-learning-based image processing pipeline. We integrate the concept of domain randomisation and synthetic data in the loop that shows promising results for bridging advances in deep learning and its application to real-world, industrial production processes.
Academic literacy among the university students in Mexico and Spain: A holistic perspective

Introduction Currently, young people have access to a large amount of information, so they must have the ability to critically analyze the texts they are exposed to in order to choose those that are useful for their training or research process, as well as to have the necessary skills to interact efficiently with the texts, especially with those specialized documents corresponding to their area of study. In this regard, this article aims to identify how cognitive, emotional, attitudinal, digital and personality aspects influence the development of academic literacy skills in university students. Method Research was conducted with the mixed method, in which quantitative instruments were applied and analyzed: a Likert scale questionnaire to measure the perceived level of mastery of academic literacy skills, which was applied to a sample of 595 students from higher education institutions in Mexico and Spain. In addition, a test was applied to identify personality type. For the qualitative part, the case study was chosen and the qualitative instruments applied were interviews with a teacher and a student and a focus group with five students. Results The findings identified were as follows: (a) the cognitive aspect of academic literacy is the one with the lowest perceived mastery by students, (b) having a positive attitude favors the development of academic literacy, (c) by knowing the aspects of their personality that can favor the development of academic literacy, students can seek strategies to improve that competency, (d) the emotional part has repercussions in the process of developing the competence of academic literacy, (e) students prefer to interact with texts in digital spaces and therefore must learn to interact critically in virtual environments, (f) Mexican students perceive themselves to have a higher level of mastery of the academic literacy competency than Spanish students. Conclusion The literature review and the mixed methods study allowed identifying the relevance of approaching academic literacy in university environments in a holistic manner through the analysis of the influence of cognitive, attitudinal, emotional, digital and personality aspects.