Reviewerly/exHarmony huggingface dataset:
Machine Learning-Based Jamming Detection for Safety Applications in Vehicular Networks: Individual Detection?

Intelligent transportation system (ITS) refers to advanced applications to make transportation safer and more intelligent. The dynamic and diverse natures of the system have been creating many challenges in ITS deployment and security. The progression in recent years of machine learning provides potentially strong methods to exploit data sources from transportation networks. Machine learning-based approaches promise to deal with various challenges in networks thanks to their ability to adapt to the changing network topology and network scale. This paper investigates the application of machine-learning models in jamming detection by analyzing how the approach works on individual observations of vehicles in different scenarios. We propose a machine learning-based approach that explores hidden rules of how the observation changes under a reactive jamming attack. Our proposed machine learning-based approach improves detection accuracy compared to existing methods. The performance of the proposed method depends closely on dataset selection. Though we evaluate our approach with synthetic data, the data are generated with justifiable simulations calibrated to match a real-referenced dataset. Our analysis provides a connection between the machine-learning domain and vehicular networks by utilizing specific domain techniques for jamming detection.
Deep Learning-Based Semantic Segmentation of Urban Areas Using Heterogeneous Unmanned Aerial Vehicle Datasets

Deep learning techniques have recently shown remarkable efficacy in the semantic segmentation of natural and remote sensing (RS) images. However, these techniques heavily rely on the size of the training data, and obtaining large RS imagery datasets is difficult (compared to RGB images), primarily due to environmental factors such as atmospheric conditions and relief displacement. Unmanned aerial vehicle (UAV) imagery presents unique challenges, such as variations in object appearance due to UAV flight altitude and shadows in urban areas. This study analyzed the combined segmentation network (CSN) designed to train heterogeneous UAV datasets effectively for their segmentation performance across different data types. Results confirmed that CSN yielded high segmentation accuracy on specific classes and can be used on diverse data sources for UAV image segmentation. The main contributions of this study include analyzing the impact of CSN on segmentation accuracy, experimenting with structures with shared encoding layers to enhance segmentation accuracy, and investigating the influence of data types on segmentation accuracy.
Classification of integers based on residue classes via modern deep learning algorithms

Judging whether an integer can be divided by prime numbers such as 2 or 3 may appear trivial to human beings, but it can be less straightforward for computers. Here, we tested multiple deep learning architectures and feature engineering approaches to classifying integers based on their residues when divided by small prime numbers. We found that the ability of classification critically depends on the feature space. We also evaluated automated machine learning (AutoML) platforms from Amazon, Google, and Microsoft and found that, without appropriately engineered features, they failed on this task. Furthermore, we introduced a method that utilizes linear regression on Fourier series basis vectors and demonstrated its effectiveness. Finally, we evaluated large language models (LLMs) such as GPT-4, GPT-J, LLaMA, and Falcon, and we demonstrated their failures. In conclusion, feature engineering remains an important task to improve performance and increase interpretability of machine learning models, even in the era of AutoML and LLMs.
Least-squares method constrained by phase smoothness for correcting illumination fluctuation errors in phase-shifting profilometry

Phase-shifting fringe projection profilometry usually suffers from the errors induced by illumination fluctuations. As a result, ripple-like artifacts that have the same periods as fringes appear on the phase map. Because the illumination fluctuations cannot be simply modeled using fringe harmonics, their induced errors are difficult to remove by use of a phase-shifting algorithm. To solve this problem, this paper suggests a least-squares method constrained by phase smoothness. This method calculates fringe phases using the generalized phase-shifting algorithm and estimates coefficients related to illumination fluctuation by use of smoothed phase map. Alternately implementing these two steps enables one to eliminate effects of illumination fluctuations on the measurement results. Experimental results demonstrate that this proposed algorithm is helpful for improving measurement accuracy.
Knowledge Base Question Answering via Semantic Analysis

Knowledge Question Answering is one of the important research directions in the field of robot intelligence. It is mainly based on background knowledge to analyze users’ questions and generate answers. It is one of the important application methods of knowledge graph technology. Compared with the traditional expert system of question and answer, it has the advantage of a large-scale background knowledge base and the traceability and interpretability of the question-answering process. Compared with the current ChatGPT (Chat Generative Pre-trained Transformer) technology, it has advantages in the proprietary segmentation field. Aiming at the problem of the accuracy of existing knowledge question-answering methods being low, this paper studies the method of semantic analysis for knowledge question-answering under the support of a knowledge database, proposes a knowledge question-answering method based on the superposition of multiple neural network models, and conducts experimental verification on the publicly available NLPCC2016KBQA(Knowledge Q&amp;A Tasks in the 2016 Natural Language Processing and Chinese Computing Conference) data set. The experimental results show that the F1 value of this method is higher than that of the baseline model.
SAFP-YOLO: Enhanced Object Detection Speed Using Spatial Attention-Based Filter Pruning

Because object detection accuracy has significantly improved advancements in deep learning techniques, many real-time applications have applied one-stage detectors, such as You Only Look Once (YOLO), owing to their fast execution speed and accuracy. However, for a practical deployment, the deployment cost should be considered. In this paper, a method for pruning the unimportant filters of YOLO is proposed to satisfy the real-time requirements of a low-cost embedded board. Attention mechanisms have been widely used to improve the accuracy of deep learning models. However, the proposed method uses spatial attention to improve the execution speed of YOLO by evaluating the importance of each YOLO filter. The feature maps before and after spatial attention are compared, and then the unimportant filters of YOLO can be pruned based on this comparison. To the best of our knowledge, this is the first report considering both accuracy and speed with Spatial Attention-based Filter Pruning (SAFP) for lightweight object detectors. To demonstrate the effectiveness of the proposed method, it was applied to the YOLOv4 and YOLOv7 baseline models. With the pig (baseline YOLOv4 84.4%@3.9FPS vs. proposed SAFP-YOLO 78.6%@20.9FPS) and vehicle (baseline YOLOv7 81.8%@3.8FPS vs. proposed SAFP-YOLO 75.7%@20.0FPS) datasets, the proposed method significantly improved the execution speed of YOLOv4 and YOLOv7 (i.e., by a factor of five) on a low-cost embedded board, TX-2, with acceptable accuracy.
U-Net based vessel segmentation for murine brains with small micro-magnetic resonance imaging reference datasets

Identification and quantitative segmentation of individual blood vessels in mice visualized with preclinical imaging techniques is a tedious, manual or semiautomated task that can require weeks of reviewing hundreds of levels of individual data sets. Preclinical imaging, such as micro-magnetic resonance imaging (μMRI) can produce tomographic datasets of murine vasculature across length scales and organs, which is of outmost importance to study tumor progression, angiogenesis, or vascular risk factors for diseases such as Alzheimer's. Training a neural network capable of accurate segmentation results requires a sufficiently large amount of labelled data, which takes a long time to compile. Recently, several reasonably automated approaches have emerged in the preclinical context but still require significant manual input and are less accurate than the deep learning approach presented in this paper-quantified by the Dice score. In this work, the implementation of a shallow, three-dimensional U-Net architecture for the segmentation of vessels in murine brains is presented, which is (1) open-source, (2) can be achieved with a small dataset (in this work only 8 μMRI imaging stacks of mouse brains were available), and (3) requires only a small subset of labelled training data. The presented model is evaluated together with two post-processing methodologies using a cross-validation, which results in an average Dice score of 61.34% in its best setup. The results show, that the methodology is able to detect blood vessels faster and more reliably compared to state-of-the-art vesselness filters with an average Dice score of 43.88% for the used dataset.
Learn to Few-Shot Segment Remote Sensing Images from Irrelevant Data

Few-shot semantic segmentation (FSS) is committed to segmenting new classes with only a few labels. Generally, FSS assumes that base classes and novel classes belong to the same domain, which limits FSS’s application in a wide range of areas. In particular, since annotation is time-consuming, it is not cost-effective to process remote sensing images using FSS. To address this issue, we designed a feature transformation network (FTNet) for learning to few-shot segment remote sensing images from irrelevant data (FSS-RSI). The main idea is to train networks on irrelevant, already labeled data but inference on remote sensing images. In other words, the training and testing data neither belong to the same domain nor category. The FTNet contains two main modules: a feature transformation module (FTM) and a hierarchical transformer module (HTM). Among them, the FTM transforms features into a domain-agnostic high-level anchor, and the HTM hierarchically enhances matching between support and query features. Moreover, to promote the development of FSS-RSI, we established a new benchmark, which other researchers may use. Our experiments demonstrate that our model outperforms the cutting-edge few-shot semantic segmentation method by 25.39% and 21.31% in the one-shot and five-shot settings, respectively.
Improved Dual-Population Genetic Algorithm: A Straightforward Optimizer Applied to Engineering Optimization

Aiming at the current limitations of the dual-population genetic algorithm, an improved dual-population genetic algorithm (IDPGA) for solving multi-constrained optimization problems is proposed by introducing a series of strategies, such as remaining elite individuals, a dynamic immigration operator, separating the objective and constraints, normalized constraints, etc. We selected 14 standard mathematical benchmarks to check the performance of IDPGA, and the results were compared with the theoretical value of CEC 2006. The results show that IDPGA with the current parameters obtains good solutions for most problems. Then 6 well-known engineering optimization problems were solved and compared with other algorithms. The results show that all of the solutions are feasible, the solution precision of IDPGA is better than other algorithms, and IDPGA performs with good efficiency and robustness. Meanwhile, no parameters need to be ignored when IDPGA is applied to solving engineering problems, which is enough to prove that IDPGA is suitable for solving engineering optimization. A Friedman test showed no significant difference between IDPGA and six algorithms, but significant differences between IDPGA and seven other algorithms; thus, a larger number of evaluators will be needed in the future. In addition, further research is still needed about the performance of IDPGA for solving practical large-scale engineering problems.
Minimal spanning tree-based isolation forest with anomaly score function built on the basis of fuzzy rules

Modern data analysis allows to infer from the data to a very large extent. Particularly noteworthy are techniques based on the detection of anomalies in datasets. Unsupervised approaches with no labeled records are gaining ground. One of the most frequently used and efficient unsupervised methods is Isolation Forest (IF) with its numerous specialized modifications. The considerations of this paper are devoted to the enhancement of Minimal Spanning Tree-Based Isolation Forest (MSTBIF). MSTBIF exhibits high effectiveness. Its evaluation function consists of the sum of two parameters. Hence, the main motivation of the work is a better composition of the MSTBIF evaluation function parameters. Crucial improvements are made in two directions. Firstly, the output parameters related to the tree height and the distance to the nearest tree leaf are normalized. Normalizations take place in a new manner. The properties of created minimal spanning trees are used. Secondly, a simple aggregation of output parameters has been modified. The work introduces an innovative anomaly evaluation scheme. Namely, fuzzy rules prepared in the Takagi-Sugeno inference model are applied. Novel proposal allows for a more precise inference about the occurrence of anomalies in the considered elements. In order to compare the results, the AUC values are measured. A series of experiments with IF, MSTBIF, and new approach are carried out. The research results on the new proposal prove a significant refinement of the anomaly assessment. The improvement over MSTBIF is clearly noticeable. Compared to the basic IF method, the results are better by about 6%.
Real time two-wheeler recognition for Surveillance

Abstract In today’s intelligent transportation systems, vision-based traffic tracking systems and automatic vehicle make and model recognition is of significant importance for surveillance. From the literature, vehicle make and model recognition has been implemented for four wheelers easily due to the presence of logo in the frontal and rear parts. But it is not feasible for two wheelers due to the insignificant differential features. Hence in this work, a robust two-wheeler recognition system has been developed with frontal, rear and side images of two wheelers which is implementable in edge devices. The contributions of the paper are (i) Development of a two-wheeler dataset with the frontal, rear and side images of two classes in Indian Scenario. (ii) Development of a two-wheeler recognition framework implementable in Raspberry pi-based devices. (iii) Evaluation of the framework in real time outdoor environment. From the experimental results it is observed that the proposed network classifies two wheelers in real time at 4s with 94% accuracy.
Design of Digital Music Copyright Protection System Based on Blockchain Technology

The traditional copyright protection method has some problems, such as difficulty in confirming the right, complicated steps and long time for confirming the right, which can not effectively protect the legitimate rights and interests of the original author. Under the blockchain technology, the process of reshaping the trust mechanism through decentralization, and the trust mechanism determines the right subject and determines that the right subject is beneficial to copyright ownership can clarify the copyright ownership of digital music. Based on the in-depth analysis of existing technologies, standards and systems, this paper designs and implements a complete digital music copyright protection system. When reading resources, users are required to provide relevant authentication information, and users can browse resources only after providing correct authentication information and meeting the validity period of resources. A digital audio watermarking algorithm based on chaotic encryption is designed, which introduces chaos theory. Firstly, the watermark information is encrypted by low-dimensional chaotic mapping, and then the watermark is encrypted by three-dimensional Rossler chaotic attractor, which further improves the security of the watermark and enhances the robustness of the watermark. The research results show that the embedding time of this algorithm is kept within 2 s, which meets the real-time embedding requirements of watermark. The algorithm adopts the way of embedding multiple identical watermarks at the same time, which improves the robustness of watermarks.
Flexible constraint compilation in the parity architecture

We present tools and methods to generalize parity compilation to digital quantum computing devices with arbitrary connectivity graphs and construct circuit implementations for the constraint Hamiltonian of higher-order constrained binary optimization problems. In particular, we show how even nonlocal constraints can be efficiently implemented without expensive SWAP gates. We show how the presented tools can be used to optimize the total circuit depth and CNOT count of the quantum approximate optimization algorithm in the parity architecture and highlight the advantages of the flexible compilation using various examples. We derive the relation between the developed gate sequences and the traditional approach that uses SWAP gates. The result can be applied to improve the implementation of many other nonlocal operators.
Discovering Rules for Rule-Based Machine Learning with the Help of Novelty Search

Abstract Automated prediction systems based on machine learning (ML) are employed in practical applications with increasing frequency and stakeholders demand explanations of their decisions. ML algorithms that learn accurate sets of rules, such as learning classifier systems (LCSs), produce transparent and human-readable models by design. However, whether such models can be effectively used, both for predictions and analyses, strongly relies on the optimal placement and selection of rules (in ML this task is known as model selection). In this article, we broaden a previous analysis on a variety of techniques to efficiently place good rules within the search space based on their local prediction errors as well as their generality. This investigation is done within a specific pre-existing LCS, named SupRB, where the placement of rules and the selection of good subsets of rules are strictly separated—in contrast to other LCSs where these tasks sometimes blend. We compare two baselines, random search and $$(1, \lambda )$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mi>λ</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> -evolution strategy (ES), with six novelty search variants: three novelty-/fitness weighing variants and for each of those two differing approaches on the usage of the archiving mechanism. We find that random search is not sufficient and sensible criteria, i.e., error and generality, are indeed needed. However, we cannot confirm that the more complicated-to-explain novelty search variants would provide better results than $$(1, \lambda )$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mi>λ</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> -ES which allows a good balance between low error and low complexity in the resulting models.
To Close is Impossible to Leave it: Where Should be Put a Comma?

The article is a response to the article by V. K. Stepanov “A great reduction in quantity of libraries: particular management errors or a general perspective for the field (analysis of the experience of the Moscow region)”, published in the previous issue of Bibliosphere. Three main theoretical and methodological contradictions that reduce the reliability of the results obtained by the author and the validity of the recommendations proposed by him for regional librarianship are identified and analyzed. The first of them concerns the departure from the principle of historicism, as well as the complexity of the consideration of the phenomenon under study, and as a result the social nature of librarianship is presented incorrectly by the author of the article. It is noted that the basic social function determining the vector of domestic library construction was the function of educating a Soviet person, his ideological, cultural, scientific and technical enlightenment through the guidance of book reading in a specially selected library fund. Intermediary functions were associated with the shortcomings of book publishing in the field of leisure literature and played a secondary role. Thus, automation and computerization of book publishing and bookselling affect librarianship only indirectly, since the material nature of book publishing does not affect the cultural and educational functions of libraries. The second contradiction is related to the correctness of the analysis and interpretation of statistical data reflecting the process of library development in the Moscow region. The analysis of statistics shows that the negative dynamics in regional library construction is caused by the process of reduction of settlements, its natural decline and aging of the population. The increase in the number of users of municipal libraries indicates that the reduction in the number of libraries does not lead to a decrease in the level of library services of the population. The third contradiction concerns following the principle of scientific objectivity of research. The author noted only negative trends, leaving regional library construction programs aimed at the development of model libraries outside the scope of his analysis.
Implementation of Chaotic Reverse Slime Mould Algorithm Based on the Dandelion Optimizer

This paper presents a hybrid algorithm based on the slime mould algorithm (SMA) and the mixed dandelion optimizer. The hybrid algorithm improves the convergence speed and prevents the algorithm from falling into the local optimal. (1) The Bernoulli chaotic mapping is added in the initialization phase to enrich the population diversity. (2) The Brownian motion and Lévy flight strategy are added to further enhance the global search ability and local exploitation performance of the slime mould. (3) The specular reflection learning is added in the late iteration to improve the population search ability and avoid falling into local optimality. The experimental results show that the convergence speed and precision of the improved algorithm are improved in the standard test functions. At last, this paper optimizes the parameters of the Extreme Learning Machine (ELM) model with the improved method and applies it to the power load forecasting problem. The effectiveness of the improved method in solving practical engineering problems is further verified.