Reviewerly/exHarmony huggingface dataset:
Network topology facilitates internet traffic control in autocracies

Authors:
raw_author_name: Nils B Weidmann
raw_affiliation_string: Department of Politics and Public Administration, University of Konstanz , 78457 Konstanz , Germany
raw_author_name: Alberto Dainotti
raw_affiliation_string: School of Computer Science, Georgia Institute of Technology , Atlanta, GA 30332 , USA
raw_author_name: Alex C Snoeren
raw_affiliation_string: Department of Computer Science and Engineering, University of California San Diego , La Jolla, CA 92093 , USA

Abstract: Recent years have seen an increase in governmental interference in digital communication. Most research on this topic has focused on the application level, studying how content is manipulated or removed on websites, blogs, or social media. However, in order for governments to obtain and maintain control of digital data flows, they need to secure access to the network infrastructure at the level of Internet service providers. In this paper, we study how the network topology of the Internet varies across different political environments, distinguishing between control at the level of individual Internet users (access) and a higher level in the hierarchy of network carriers (transit). Using a novel method to estimate the structure of the Internet from network measurements, we show that in autocratic countries, state-owned (rather than privately owned) providers have a markedly higher degree of control over transit networks. We also show that state-owned Internet providers often provide Internet access abroad, with a clear focus on other autocratic countries. Together, these results suggest that in autocracies, the network infrastructure is organized in a way that is more susceptible to the monitoring and manipulation of Internet data flows by state-owned providers both domestically and abroad.
Particle Swarm Optimization in Biomedical Technologies

Authors:
raw_author_name: Kannadhasan Suriyan
raw_affiliation_string: Study World College of Engineering, India
raw_author_name: R. Nagarajan
raw_affiliation_string: Gnanamani College of Technology, India

Abstract: This chapter explores particle swarm optimization (PSO) in the rapidly evolving landscape of biomedical technologies. The study begins by introducing the fundamental principles of PSO, emphasizing its advantages in addressing complex optimization problems common in biomedical applications. The authors delve into innovative uses of PSO in various biomedical fields, including image enhancement, data clustering, and drug development, highlighting how PSO contributes to more accurate diagnoses, efficient treatment plans, and streamlined research methodologies. Significantly, this chapter identifies emerging opportunities where PSO can be further leveraged, particularly in personalized medicine and predictive health analytics, suggesting a roadmap for future research and development. By combining theoretical insights with practical examples, this work aims to provide a comprehensive overview of PSO's role in advancing biomedical technologies, offering valuable perspectives for researchers, practitioners, and policymakers in the field.
Fractional Approximation of Broad Learning System

Authors:
raw_author_name: Shujun Wu
raw_affiliation_string: College of Science, China University of Petroleum (East China), Qingdao 266580, China.
raw_author_name: Jian Wang
raw_affiliation_string: College of Science, China University of Petroleum (East China), Qingdao 266580, China (e-mail: wangjiannl@upc.edu.cn)
raw_author_name: Huaying Sun
raw_affiliation_string: College of Science, China University of Petroleum (East China), Qingdao 266580, China.
raw_author_name: Kai Zhang
raw_affiliation_string: College of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China.
raw_author_name: Nikhil R. Pal
raw_affiliation_string: Electronics and Communication Sciences Unit and the Centre for Artificial Intelligence and Machine Learning, Indian Statistical Institute, Kolkata 700108, India.

Abstract: Approximation ability is of much importance for neural networks. The broad learning system (BLS) (Chen and Liu, 2018), widely used in the industry with good performance, has been proved to be a universal approximator from the aspect of density. This kind of approximation property is very important, which proves the existence of the desired network but does not provide a means of construction that is commonly implemented through complexity aspect. Thus, such an approach lacks the advantage of determining constructively the network architecture and its weights. To the best of our knowledge, for a BLS, there is a few theory providing a constructive approach to obtain the network structure along with weights ensuring the approximation properties. By virtue of the long-term memory and nonlocality properties, fractional calculus has observed many distinctive applications. The purpose of this article is to study the BLS approximation ability constructively, which is valid for fractional case as well. Specifically, first we introduce two simplified BLSs by means of extending functions. For each of the simplified BLSs, an upper bound of error is derived through the modulus of continuity of Caputo fractional derivatives. As a result, two types of fractional convergent behaviors of BLS, that is: 1) pointwise and 2) uniform convergence, have been rigorously proved as well. Finally, some numerical experiments are conducted to demonstrate the approximation capabilities of BLSs.
Application of augmented reality in automotive industry

Authors:
raw_author_name: Denis Gonzalez-Argote
raw_affiliation_string: Universidad Argentina de la Empresa
raw_author_name: Adrián Alejandro Vitón-Castillo
raw_affiliation_string: Universidad de Ciencias Médicas de Pinar del Río
raw_author_name: Javier Gonzalez-Argote
raw_affiliation_string: Fundación Salud, Ciencia y Tecnologia

Abstract: Introduction: Augmented reality is defined as a direct or indirect vision of a physically real environment, parts of which they are enriched with additional digital information relevant to the object that is being looked at. In the field of engineering design, there is a wide range of industries that use this technology, such as automotive, aircraft manufacturing, electronics, engineering; so that it has gained popularity in assembly, maintenance and inspection tasks. The objective was to characterize the use of augmented reality in the automotive industry.Methods: a total of 20 articles in Spanish and English were reviewed, from Scopus, Science and Dialnet; Using as keywords: augmented reality, automotive industry, manufacturing, being more than 50 % of the last five years.Result: its main advantage is considered its potential as an interactive and intuitive interface. It promises to provide the correct information to the human operator at the right time and place. If it is considered an ideal environment in which the RA is applied safely, in adequate balance between automated processes and human control over them; The level of production and its quality will be positively affected.Conclusions: Augmented reality is applied in areas of the automotive industry such as logistics design, assembly, maintenance, evaluation, diagnosis, repair, inspection, quality control, instruction and marketing; in order to guarantee better work performance, productivity and efficiency, mainly mediated by portable devices. Its degree of acceptance, although growing, is not yet clear.
Toward Efficient Deep Learning for Graph Drawing (DL4GD)

Authors:
raw_author_name: Loann Giovannangeli
raw_affiliation_string: CNRS, Bordeaux INP, INRIA, LaBRI, UMR 5800, University Bordeaux, Talence, France
raw_author_name: Frederic Lalanne
raw_affiliation_string: CNRS, Bordeaux INP, INRIA, LaBRI, UMR 5800, University Bordeaux, Talence, France
raw_author_name: Romain Giot
raw_affiliation_string: CNRS, Bordeaux INP, INRIA, LaBRI, UMR 5800, University Bordeaux, Talence, France
raw_author_name: Romain Bourqui
raw_affiliation_string: CNRS, Bordeaux INP, INRIA, LaBRI, UMR 5800, University Bordeaux, Talence, France

Abstract: Due to their great performance in many challenges, Deep Learning (DL) techniques keep gaining popularity in many fields. They have been adapted to process graph data structures to solve various complicated tasks such as graph classification and edge prediction. Eventually, they reached the Graph Drawing (GD) task. This paper is an extended version of the previously published <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(DNN)^{2}$</tex-math></inline-formula> and presents a framework to leverage DL techniques for graph drawing (DL4GD). We demonstrate how it is possible to train a Deep Learning model to extract features from a graph and project them into a graph layout. The method proposes to leverage efficient Convolutional Neural Networks, adapting them to graphs using Graph Convolutions. The graph layout projection is learned by optimizing a cost function that does not require any ground truth layout, as opposed to prior work. This paper also proposes an implementation and benchmark of the framework to study its sensitivity to certain deep learning-related conditions. As the field is novel, and many questions remain to be answered, we do not focus on finding the most optimal implementation of the method, but rather contribute toward a better understanding of the approach potential. More precisely, we study different learning strategies relative to the models training datasets. Finally, we discuss the main advantages and limitations of DL4GD.
Enabling Data Sharing Through Data Trusts in LEO Satellite Internet

Authors:
raw_author_name: Ruyan Wang
raw_affiliation_string: School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications,China
raw_author_name: Shiqi Zhang
raw_affiliation_string: School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications,China
raw_author_name: Boran Yang
raw_affiliation_string: School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications,China
raw_author_name: Zhigang Yang
raw_affiliation_string: School of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications,China
raw_author_name: Puning Zhang
raw_affiliation_string: School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications,China
raw_author_name: Dapeng Wu
raw_affiliation_string: School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications,China

Abstract: Due to the advantages of low latency, small propagation loss, and global coverage, LEO satellite network becomes a cost-effective long-distance communication solution for IoT devices in remote areas. Nevertheless, the LEO satellite IoT devices deployed for specific tasks are idle most of the time, raising the obvious question for future research: how to effectively share these idle sensing resources. Fortunately, data trusts emerge as a promising strategy to leverage the idle sensing resources. However, the data trust model faces privacy and security challenges in LEO satellite IoT scenarios to achieve trusted data trading. Therefore, this paper proposes a blockchainenabled data trust model and a trusted data trading scheme for device providers, data trustees, and general users. Furthermore, exploiting the homomorphic encryption technology, we realize the fine-grained data query and device match in the premise of privacy protection for device providers and general users. Lastly, considering the openness of satellite-terrestrial communication links and the limited computing power of LEO satellite IoT devices, we design a non-interactive lightweight authentication protocol based on zero-knowledge proof. The simulation results verify the feasibility, effectiveness, and security performance of the proposed data sharing for LEO satellite IoT.
FADS: Fourier-Augmentation Based Data-Shunting for Few-Shot Classification

Authors:
raw_author_name: Shuai Shao
raw_affiliation_string: Zhejiang Laboratory, Hangzhou, China
raw_author_name: Yan Wang
raw_affiliation_string: School of Instrumentation and Optoelectronic Engineering, and the State Key Laboratory of Software Development Environment, Beihang University, Beijing, China
raw_author_name: Bin Liu
raw_affiliation_string: Zhejiang Laboratory, Hangzhou, China
raw_author_name: Weifeng Liu
raw_affiliation_string: College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China
raw_author_name: Yanjiang Wang
raw_affiliation_string: College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China
raw_author_name: Baodi Liu
raw_affiliation_string: Zhejiang Laboratory, Hangzhou, China

Abstract: Collecting a substantial number of labeled samples is infeasible in many real-world scenarios, thereby bringing out challenges for supervised classification. The research on Few-Shot Classification (FSC) aims to address this issue. Current FSC methods mainly leverage ideas such as meta-learning, self-supervised learning, and data augmentation. Among them, data augmentation appears to be an extremely efficient approach to alleviate the aforementioned data-deficiency problem. Here, we propose a novel data augmentation based FSC method termed Fourier-Augmentation based Data-Shunting (FADS). FADS mainly contains two operations, namely Fourier-based data augmentation (FDA) and data shunting. (i) Fourier transform has a desirable property for classification tasks: the image’s phase and amplitude components in the frequency domain correspond to its high-level structure ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., semantic) and low-level style ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., statistic) information, which do not interfere with each other. Inspired by this observation, we design the FDA operation, which changes the amplitude spectrum of the to-be-augmented images to obtain new images of the same category. (ii) Then we design the data shunting operation to cooperate with the FDA to accomplish FSC. Specifically, it splits the augmented data into different groups to get independent, weak decisions and then fuses them to obtain a unified, strong decision. We conduct experiments on four benchmark datasets. Results show that utilizing our method brings a performance gain of 0.3%-2% in terms of classification accuracy, compared with the classical methods.
Transfer-transfer model with MSNet: An automated accurate multiple sclerosis and myelitis detection system

Authors:
raw_author_name: Burak Tasci
raw_affiliation_string: Vocational School of Technical Sciences, Firat University, Elazig 23119, Turkey
raw_author_name: Prabal Datta Barua
raw_affiliation_string: School of Business (Information System), University of Southern Queensland, Australia
raw_author_name: Turker Tuncer
raw_affiliation_string: Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
raw_author_name: Sengul Dogan
raw_affiliation_string: Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
raw_author_name: Edward J. Ciaccio
raw_affiliation_string: Department of Medicine, Columbia University Irving Medical Center, USA
raw_author_name: U Rajendra Acharya
raw_affiliation_string: School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia

Abstract: Multiple sclerosis (MS) is a commonly seen neurodegenerative disorder, and early diagnosis of MS is a crucial issue to promote patient health. Since MS diagnosis is a computer vision problem, machine learning can be utilized for this purpose. Important research has used transfer learning (TL) to rapidly apply the advantages of deep learning models. Therefore, TL has developed a wide usage in computer vision applications. Herein, we describe a new algorithm in this regard, termed transfer-transfer (TT). To implement the algorithm, a multi-result machine learning model is required. In order to determine efficacy, we use transfer learning-based and hybrid feature engineering. The goal is to demonstrate the classifiability of the TT model. A new magnetic resonance image dataset containing three classes were collected to obtain TT model results, i.e.: (1) multiple sclerosis (MS), (2) myelitis, and (3) control patients. We have designed this model for MS detection. Thus, we named it MSNet. For deep feature engineering, MSNet with two layers of pretrained DenseNet201 and two layers of ResNet50 was incorporated into the system since these networks are highly accurate. By deploying these four layers, four feature vectors were calculated. ReliefF (RF), Chi2, and Neighborhood Component Analysis (NCA) were utilized in the feature selection phase, and the number of the feature vectors is increased from 4 to 12 (=4 × 3). By using k-nearest neighbor (kNN) and support vector machine (SVM) classifiers, 24 (=12 × 2) outputs were calculated, with the best result created by applying information fusion. TT incorporates the information fusion findings to construct a new feature vector. The most salient features were selected by deploying an iterative feature selector, and the features chosen were then classified. The TT-based MSNet was applied to the magnetic resonance (MR) image dataset, yielding a 97.63% classification accuracy. The findings and computed results demonstrate that the TT model with MSNet outperforms existing systems with increased transfer learning classifiability.
POS-GIFT: A geometric and intensity-invariant feature transformation for multimodal images

Authors:
raw_author_name: Zhuolu Hou
raw_affiliation_string: Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping (CASM), Beijing 100036, China
raw_author_name: Yuxuan Liu
raw_affiliation_string: Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping (CASM), Beijing 100036, China
raw_author_name: Li Zhang
raw_affiliation_string: Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping (CASM), Beijing 100036, China

Abstract: Multimodal image matching suffers from severe geometric and nonlinear intensity distortion (NID). Towards this problem, we propose a multimodal image matching algorithm based on multi-orientation filtering results, called position-orientation-scale guided geometric and intensity-invariant feature transformation (POS-GIFT). First, we design a multi-layer circular point sampling pattern to effectively capture the local image structure. Then, we propose a novel feature descriptor that can work robustly across rotational differences in [0°, 360°) in the presence of NID. Specifically, we (1) integrate the multi-orientation filtering response in the local neighborhood with a Gaussian weight to form the feature of each sampled point (GFP), (2) build feature vectors for each orientation by concatenating the features of points grouped by orientation, (3) estimate the primary orientation by finding the feature vector with the largest norm which is constructed in the previous step, (4) modify the order of elements of GFP, and (5) finally concatenate the features of all sampled points in a certain order to form the complete feature descriptor. At last, we propose a position-orientation-scale guided inlier recovery strategy (POS) by integrating the global position, orientation, and scale information to further improve the matching performance, especially the number and distribution of correct matches in texture-less and complex areas. Experimental results on various multimodal datasets from remote sensing, medical, and computer vision imaging domains show that POS-GIFT outperforms eight state-of-the-art multimodal image feature matching algorithms which are five handcrafted-based methods, OS-SIFT, PSO-SIFT, LGHD, RIFT, and LNIFT, and three learning-based methods RedFeat, MatchFormer, and SemLA by several times in terms of correct matches while improving the root-mean-square error to around 1 pixel. Our implementation is available at https://github.com/Zhuolu-Hou/POS-GIFT.
Large Language Models are Edge-Case Generators: Crafting Unusual Programs for Fuzzing Deep Learning Libraries

Authors:
raw_author_name: Yinlin Deng
raw_affiliation_string: University of Illinois Urbana-Champaign, Champaign, Illinois, USA
raw_author_name: Chunqiu Steven Xia
raw_affiliation_string: University of Illinois Urbana-Champaign, Champaign, Illinois, USA
raw_author_name: Chenyuan Yang
raw_affiliation_string: University of Illinois Urbana-Champaign, Champaign, Illinois, USA
raw_author_name: Lingming Zhang
raw_affiliation_string: University of Illinois Urbana-Champaign, Champaign, Illinois, USA

Abstract: Bugs in Deep Learning (DL) libraries may affect almost all downstream DL applications, and it is crucial to ensure the quality of such systems. It is challenging to generate valid input programs for fuzzing DL libraries, since the input programs need to satisfy both the syntax/semantics of the supported languages (e.g., Python) and the tensor/operator constraints for constructing valid computational graphs. Recently, the TitanFuzz work demonstrates that modern Large Language Models (LLMs) can be directly leveraged to implicitly learn all the language and DL computation constraints to generate valid programs for fuzzing DL libraries (and beyond). However, LLMs tend to generate ordinary programs following similar patterns/tokens with typical programs seen in their massive pre-training corpora (e.g., GitHub), while fuzzing favors unusual inputs that cover edge cases or are unlikely to be manually produced.