Reviewerly/exHarmony huggingface dataset:
Explanation and Analysis of Spatio-Temporal Correlations—Towards a Conceptual Approach of a Semantic Comparison Visualization in a Use Case of Carparks in Mainz, Germany

Geospatial factors, because of their spatio-temporal correlations with demand-driven limited service, offer to improve urban planning decisions and expand the knowledge base in cities. Spatio-temporal analyses require an efficient and comprehensible visualization and explanation in order to analyze and understand geospatial relationships in urban areas. The aim of our research is to support domain experts in these analyses with user-oriented geovisualization. In this article, we propose concepts towards a semantic comparison visualization, which combines a visual analysis view and a visual explanation view. The visual analysis view is a knowledge-oriented view that focuses on analyzing resulting spatio-temporal correlations. The visual explanation view is an understanding-oriented view that focuses on explaining the underlying complex analysis process of geospatial factors and spatio-temporal correlations. We define general requirements for this aim and validate and evaluate our concepts related to these requirements. The results show the benefit of our concepts, but, at the same time, they also point to limitations and potential for optimization in further work.
SSD-Resnet50: Research on Pedestrian Detection Technology

In the era of artificial intelligence, with the rapid development of computer hardware, the defects of neural network algorithm have been further improved, the technology of artificial intelligence is progressing, the algorithm and technology of target detection have been further improved, and pedestrian detection technology is an important research direction of target detection technology. The main research content of this paper consists of three parts: the improvement of the original SSD algorithm, and the fundamental composition and characteristics of the neural network; Changing VGG16, the basis of the original SSD algorithm, to Resnet50 can improve the detection speed and accuracy. A feature fusion module is introduced to fuse the feature map with large receptive field and small receptive field, so as to improve the overall expression ability of the feature map to the image.
Letter2vec+Freq2vec: A Comprehensive Approach to Text Vectorization and Predicting Word Difficulty

This paper introduces the self-developed word embedded algorithm Letter2vec+Freq2vec. It combines with biKmeans and Stacking algorithm to judge word difficulty in Wordle. The algorithm combines Letter2vec, which models letter interactions and context, and Freq2vec, which calculates letter frequencies at different positions of words to produce a more comprehensive text vectorization method. The results show that this algorithm can get better results than BERT algorithm in small sample size when vectorizing a single word. Overall, this paper provides insights into the development and application of new algorithms for text vectorization and the prediction of word difficulty.
An Optimization view on Squash Function of CapsNet

In CapsNet, a bounded measure of the modulus length of the feature is needed, so Squash function is used to compress the feature vector. This paper discusses the definition of Squash function, redefines Squash function based on the idea of information gain rate of decision tree, and constructs CapsNet model on this function. By testing on MNIST, Fashion-MNIST and Cifar-10 datasets, the experimental results show that the Squash function defined in this paper has better classification performance than the traditional Squash function in CapsNet model.
Ethereum Security Based on Blockchain Framework and its Algorithms and Components

With the increasing amount and trading volume, account security has become an issue that people have to consider. The security of Ethereum is discussed in four different aspects. This paper reviews the application of cryptography in Ethereum, including the importance of relevant hash algorithms and digital signature techniques for securing data. The basic structure of the Merkle Tree and the role of its modified data structure, performed in the security mechanism of Ethereum are also analyzed. This paper also analyzes the related Merkle Proof algorithm. Additionally, the definition and working mechanism of Gas in Ethereum are also provided, through which the operating mechanism and creation method of Gas can guarantee the security of Ethereum's processing power. Finally, this paper indicates the underlying vulnerabilities and possible attacks on Bitcoin and Ethereum, including double spending attacks under proof of work and further introduces the related solutions. This paper can provide researchers good references on Ethereum security problem.
An Efficient Dual-Stage Compression Model for Maritime Safety Information Based on BeiDou Short-Message Communication

In the context of utilizing BeiDou short-message communication (SMC) for transmitting maritime safety information, challenges arise regarding information redundancy and limited message length. To address these issues, compressing the data content of SMC becomes essential. This paper proposes a dual-stage compression model based on Beidou SMC for compressing maritime safety information, aiming to achieve efficient compression and reduce information redundancy. In the first stage, a binary encoding method (MBE) specifically designed for maritime safety information is proposed to optimize the byte space of the short messages, ensuring the accuracy, integrity, and reliability of the information. In the second stage, we propose a data compression algorithm called XH based on a hash dictionary, which efficiently compresses maritime safety information and reduces information redundancy. Different maritime data have corresponding structures and compositions, which can have a significant impact on the evaluation of compression algorithms. We create a database considering six categories of objects: waves, sea ice, tsunamis, storms, weather, and navigation warnings. Experimental results demonstrate that the proposed model achieves significant compression efficiency and performance on the maritime safety data set, outperforming other benchmark algorithms.
A study of Global Equity based on AHP &amp; EW Multi-attribute Decision Model

With the shortage of Earth's resources, asteroid mining has received much attention. However, there is no specific policy to address the impact of asteroid mining on global equity, so this paper proposes a targeted mathematical modeling solution-AHP&amp;EM multi-attribute decision model. First, this paper calculates the weights of these different aspects in the model using entropy weights (EW) and hierarchical analysis (AHP), successfully constructs a global stock valuation model, and finds that the global stock index is gradually improving. In addition, this paper designs a scoring model and evaluates the optimal solution using the Pythagorean hesitant fuzzy operator (PFHS) and concludes that the agency model is most beneficial to the growth of the global equity index, while the equity system and tax planning are most suitable for the current model and most beneficial to the global equity.
Convolutional Neural Network Models and Optimization Design for Edge Computation

Convolutional Neural Network (CNN) has been widely implemented to conduct artificial intelligence (AI) projects. Because of its various advantages, CNN plays a significant role in daily applications of AI like automatic driving, semantic segmentation, face identification etc. However, as the application scope expands, many conventional CNN modules met their obstacles, most essential of which is the high memory consumption and abundant computation source expense. For edge computation areas like embedded system and other source or power supply limited devises, many of the CNN modules are unacceptable. From another point of view, not all the time does CNN need to do full precision calculation. To explore more possibility of CNN, low-precision computation and binary neural network (BNN) which occupy less resources than traditional CNN are proposed. The central computation of CNN is multiply-accumulates (MACs), so any advanced architecture designs should base on the acceleration of MAC. Benefiting from customizable data width, reconfigurable logic resources and low power consumption, FPGA is an appealing platform to implement low-precision computation and BNN. The development history and applications of CNN and its limitations was introduced. Comparison was made of different platforms to implement CNN and specific designs of low-precision computation conducted on FPGA. The contemporary accelerating platforms for FPGA which are technically designed to decrease the energy consumption, latency and facilitate performance were reviewed.
Prediction Model of Word EERIE Based on BP Neural Network

This paper uses BP neural network algorithm to determine the difficulty level of the word EERIE by constructing a neural network model, and determine its relationship with the word length, word commonness, letter frequency and other attributes to predict the relevant percentage of the future date, and discusses the uncertainty of the model, the accuracy of the classification model and other characteristics of the data set. In terms of accuracy, the random forest algorithm is the best, 72%, and the k-nearest neighbor algorithm is the worst. In terms of F1 score, logistic regression algorithm performs the best conclusion.
Q‐Learning model for selfish miners with optional stopping theorem for honest miners

Abstract Bitcoin, the most popular cryptocurrency used in the blockchain, has miners join mining pools and get rewarded for the proportion of hash rate they have contributed to the mining pool. This work proposes the prediction of the relativegain of the miners by machine learning and deep learning models, the miners' selection of higher relativegain by the Q‐learning model, and an optional stopping theorem for honest miners in the presence of selfish mining attacks. Relativegain is the ratio of the number of blocks mined by selfish miners in the main canonical chain to the blocks of other miners. A Q‐learning agent with ε‐greedy value iteration, which seeks to increase the relativegain for the selfish miners, that takes into account all the other quintessential parameters, including the hash rate of miners, time warp, the height of the blockchain, the number of times the blockchain was reorganized, and the adjustment of the timestamp of the block, is implemented. Next, the ruin of the honest miners and the optional stopping theorem are analyzed so that the honest miners can quit the mining process before their complete ruin. We obtain a low mean square error of 0.0032 and a mean absolute error of 0.0464 in our deep learning model. Our Q‐learning model exhibits a linearly increasing curve, which denotes the increase in the relativegain caused by the selection of the action of performing the reorganization attack.
Situating and expanding the scope of dispositions towards automation

The four generous commentaries to our article, ‘Dispositions towards automation’, have truly enriched and extended our thinking on ‘dispositions’ as a way of challenging binary readings of automation (i.e. either towards capitulation or adaptation). Two of our interlocutors have encouraged us to think more situationally about the way dispositions develop through user experience and encounter, while the other two welcome a further expansion of our ideas to include infrastructural and other non-human or environmental agencies. In this response, we clarify our original curiosities about capital's affective projects with respect to automation, as well as its attempts at steering dispositions in order to mitigate, precisely, the indeterminacy of labour and user ‘reactions’. At the same time, we discuss how the four provocations have inspired us to re-assemble these tendencies and sentiments more plurally, to incorporate a series of overlapping affects, capitalist plots, shifting design values, detours, exigencies, digital glitches, and material interjections. We are impressed by the holism of perspectives presented in this dialogue, and how the discussion has only reinforced ideas about the fragility of automation's relations.
Securing Embedded System from Code Reuse Attacks: A Lightweight Scheme with Hardware Assistance

The growing prevalence of embedded systems in various applications has raised concerns about their vulnerability to malicious code reuse attacks. Current software-based and hardware-assisted security techniques struggle to detect or block these attacks with minor performance and implementation overhead. To address this issue, this paper presents a lightweight hardware-assisted scheme to enhance the security of embedded systems against code reuse attacks. We develop an on-chip lightweight hardware shadow stack to validate target addresses at runtime for backward-edge control flow integrity, which backs up valid return addresses during function calls and automatically verifies actual return addresses during the return phase. Additionally, we propose a lightweight stream cipher circuit that encrypts and decrypts critical stack data related to control flow manipulation, preventing attackers from analyzing or tampering with them. When designing and implementing the security mechanism for embedded systems, we fully consider the constraints of limited system resources and performance, optimizing both the architecture design and implementation of the proposed hardware. Finally, we integrate both the proposed lightweight hardware shadow stack and the runtime data encryption hardware into the OR1200 processor. We have verified the system security function on the Terasic DE1-SoC FPGA platform and evaluated the system performance as well as implementation overhead. The results show that the proposed lightweight hardware-assisted scheme can provide a dedicated defense capability against code reuse attacks for embedded systems, with an average system performance overhead of 0.39% and an area footprint of 0.316 mm2.
The research nexus and Principles of Open Scholarly Infrastructure (POSI): sharing our goal of an open, connected ecosystem of research objects

As a community, it is impossible to ignore the fact that sharing research and information related to research is a much broader proposition than sharing an article, book, or conference paper. In supporting an evolving scholarly record, making connections between research organizations, contributors, actions, and objects helps give a more complete picture of the scholarly record, which open infrastructure organizations like Crossref call the research nexus. Crossref is working to support this evolution and is thinking about the metadata it collects via its members and that it supplements and curates, to make it broader than the rigid structures traditionally provided by content types. Furthermore, because of Crossref’s commitment to the Principles of Open Scholarly Infrastructure (POSI), this network of information will be global and openly available for anyone in the community to access and reuse. The present article describes this vision in more detail, including why it is increasingly important to support the links between research and elements that contribute or are related to that research; how Crossref, its members, and the wider community can support it; and the work and planning Crossref is doing to make it easier to achieve this.
Differential Flatness-Based Real-Time Trajectory Planning for Multihelicopter Cooperative Transportation in Crowded Environments

Trajectory planning for a multihelicopter transportation system requires optimizing numerous variables while considering obstacle avoidance, formation shape change, multibody dynamics and other feasibility constraints, making it challenging to achieve real-time effectiveness and convergence. To overcome this issue, a real-time trajectory planning method based on differential flatness and a minimum control effort (MINCO) trajectory expression is proposed. This method enables the implicit consideration of dynamic constraints and formation shape change requirements, and it handles obstacle avoidance and feasibility constraints linearly, reducing the complexity of planning. Specifically, the flatness characteristics and related assumptions of the system are given in detail by considering two cases where the load is modeled as a mass point or a rigid body. The obstacle avoidance trajectory planning problem is transformed into an unconstrained optimization problem based on MINCO and its spatial–temporal deformation. This optimization aims to minimize the trajectory energy and total time consumption while considering cost functions related to load obstacle avoidance, load state feasibility, helicopter obstacle avoidance, swarm avoidance between helicopters, cable obstacle avoidance, and cable force feasibility constraints. Finally, simulations are conducted in both cases where the load is modeled as a mass point and a rigid body to verify the feasibility of the proposed method. Overall, the proposed method has the potential to solve the problem of trajectory planning with obstacle avoidance in a multihelicopter transportation system in real time with improved efficiency and effectiveness.
Functional data regression model based on functional mean variance estimation and mutual information weighted ensemble learning

Functional data regression model holds significant application value in various fields such as medicine, economics, finance, and industrial manufacturing. This paper introduces a novel functional regression method that incorporates functional mean variance estimation and mutual information weighted ensemble learning. The proposed method employs functional mean variance estimation to reduce dimension while maintaining the effective information of the predictor related to the response variable. Specifically, this method projects the random function into the function space spanned by finite dimensional basis functions to extract the underlying functional information of the original data. Additionally, considering that the number of sufficient dimension reduction sub-directions is unknown, this paper proposes a model averaging approach based on mutual information weighting to determine the importance of each sub-model. The proposed method can adaptively address the over-fitting and under-fitting problems of the prediction model. The empirical data analysis indicates that the proposed method outperforms other comparison methods in terms of smaller mean square error and absolute error, and it exhibits a certain level of robustness.
Variation in Genuine Handwriting While Writing on an Unusual Surface

Forensic handwriting analysis is a specialized field within forensic science that aims to identify and compare handwriting samples for investigative purposes. This scientific note provides an overview of the fundamental principles, methodologies, and techniques involved in the examination of handwriting evidence. The note explores the importance of handwriting analysis in criminal investigations, discusses the underlying scientific principles, and highlights the challenges faced by forensic experts in this field. Additionally, it explores the advancements in technology that have aided the analysis of handwriting and conclude with the potential future directions of research in forensic handwriting analysis. This study focuses on the natural variation that occurs due to the unusual surfaces of tables and bricks, the variation occurs mostly in the class characteristics of the person like slant, speed, line quality, alignment, etc. Samples are collected on two different surfaces; a table and a wall (smooth and rough surfaces). And on the basis of these two surfaces, the variations in the characteristics of the individual are observed. The significance of the research is to find out the range of natural variation that occurs in the individual handwriting whenever there is a change in the writing surface.
Magnetite Talks: Testing Machine Learning Models to Untangle Ore Deposit Classification—A Case Study in the Ossa-Morena Zone (Portugal, SW Iberia)

A comprehensive investigation into the application of machine learning algorithms for accurately classifying mineral deposit types is presented. The study specifically focuses on iron deposits in the Portuguese Ossa-Morena Zone, employing a limited dataset of trace element geochemistry from magnetites. The research aims to derive meaningful methodological and metallogenic conclusions from the obtained results. The findings demonstrate that the combination of a restricted dataset of trace element geochemistry from magnetites with diverse machine learning models serves as a reliable tool for achieving precise classifications of mineral deposit types. Among the machine learning methods evaluated, random forest, naïve Bayes, and multinomial logistic regression emerge as the most accurate classifiers, whereas the support vector machine, the k-nearest neighbour, and artificial neural networks exhibit lower performance scores. By integrating all literature-proposed classifications, and applying them to selected iron deposits, confident classifications were obtained. Alvito and Azenhas are reliably classified as skarns, whereas Monges, Serrinha, and Vale da Arca are classified as either porphyry or a Banded Iron Formation (BIF). Notably, the classification of Orada proves cryptic, encompassing both BIF and volcanogenic massive sulphide (VMS) deposit types. Moreover, the application of machine learning models to pertinent case studies offers valuable insights not only for classifying mineral deposit types but also for discerning mixed or complex origins. This approach provides meaningful results that can aid in the interpretation of mineral deposit types and may facilitate the identification of new mineral exploration targets. The research highlights the robustness of machine learning algorithms in interpreting magnetite data and underscores their potential significance in exploration projects.
Examining Factors Influencing Cognitive Load of Computer Programmers

In this study, the factors influencing the cognitive load of computer programmers during the perception of different code tasks were investigated. The eye movement features of computer programmers were used to provide a significant relationship between the perceptual processes of the sample codes and cognitive load. Thanks to the relationship, the influence of various personal characteristics of programmers on cognitive load was examined. Various personal parameters such as programming experience, age, native language, and programming frequency were used in the study. The study was performed on the Eye Movements in Programming (EMIP) dataset containing 216 programmers with different characteristics. Eye movement information recorded during two different code comprehension tasks was decomposed into sub-information, such as pupil movement speed and diameter change. Rapid changes in eye movement signals were adaptively detected using the z-score peak detection algorithm. Regarding the cognitive load calculations, canonical correlation analysis was used to build a statistically significant and efficient mathematical model connecting the extracted eye movement features and the different parameters of the programmers, and the results were statistically significant. As a result of the analysis, the factors affecting the cognitive load of computer programmers for the related database were converted into percentages, and it was seen that linguistic distance is an essential factor in the cognitive load of programmers and the effect of gender on cognitive load is quite limited.