Reviewerly/exHarmony huggingface dataset:
Lung cancer diagnosis based on weighted convolutional neural network using gene data expression

Authors:
raw_author_name: Thangamani M
raw_affiliation_string: Department of Computer Science and Engineering, Hindusthan Institute of Technology, Valley Campus, Pollachi Highway, Othakkalmandapam (Post), Coimbatore, Tamil Nadu, 641032, India
raw_author_name: Manjula Sanjay Koti
raw_affiliation_string: Department of Master of Computer Applications, Dayananda Sagar Academy of Technology and Management, Bangalore, Karnataka, 560082, India
raw_author_name: Geetha V
raw_affiliation_string: Department of Computer Science,  School of Applied Sciences, REVA University, Bangalore, 560064, India
raw_author_name: Sandeep Kumar Mathivanan
raw_affiliation_string: School of Computing Science and Engineering, Galgotias University, Greater Noida, 203201, India
raw_author_name: Gemmachis Teshite Dalu
raw_affiliation_string: Department of Software Engineering, College of Computing and Informatics, Haramaya University, POB 138, Dire Dawa, Ethiopia

Abstract: Abstract Lung cancer is thought to be a genetic disease with a variety of unknown origins. Globocan2020 report tells in 2020 new cancer cases identified was 19.3 million and nearly 10.0 million died owed to cancer. GLOBOCAN envisages that the cancer cases will raised to 28.4 million in 2040. This charge is superior to the combined rates of the former generally prevalent malignancies, like breast, colorectal, and prostate cancers. For attribute selection in previous work, the information gain model was applied. Then, for lung cancer prediction, multilayer perceptron, random subspace, and sequential minimal optimization (SMO) are used. However, the total number of parameters in a multilayer perceptron can become extremely large. This is inefficient because of the duplication in such high dimensions, and SMO can become ineffective due to its calculating method and maintaining a single threshold value for prediction. To avoid these difficulties, our research presented a novel technique including Z-score normalization, levy flight cuckoo search optimization, and a weighted convolutional neural network for predicting lung cancer. This result findings show that the proposed technique is effective in precision, recall, and accuracy for the Kent Ridge Bio-Medical Dataset Repository.
The Evaluation of Course Teaching Effect Based on Improved RBF Neural Network

Authors:
raw_author_name: Hanmei Wu
raw_affiliation_string: School of Construction Management, Chongqing Metropolitan College of Science and Technology, Chongqing, 400065, China
raw_author_name: Man Feng
raw_affiliation_string: School of Construction Management, Chongqing Metropolitan College of Science and Technology, Chongqing, 400065, China

Abstract: As basic education is increasingly digitized, the need for better teaching and learning quality also rises. Teaching reform is crucial to achieve this, and incorporating the Levenberg-Marquardt (L-M) into the Radial Basis Function (RBF) can help establish a fair online teaching evaluation system. The experimental results showed that the convergence ability of the model was significantly improved compared with the traditional RBF neural network. The overall mean square error of the improved model was 100. The actual value prediction accuracy of the improved model is higher than that of the Backpropagation (BP). When the actual value was at its peak, the accuracy reached 98%, the overall fluctuation range of absolute error was low, the highest absolute error value reached 0.78, and the average absolute error was below 0.5. With targeted improvements, teachers and students could better understand and change their own learning situations, as reflected in empirical evaluations.
Assessment of catastrophic forgetting in continual credit card fraud detection

Authors:
raw_author_name: B. Lebichot
raw_affiliation_string: Machine Learning Group, Computer Science Departement, Faculty of Sciences, Université Libre de Bruxelles, Belgium
raw_author_name: W. Siblini
raw_affiliation_string: Research, Development and Innovation, Worldline, Lyon, France
raw_author_name: G.M. Paldino
raw_affiliation_string: Machine Learning Group, Computer Science Departement, Faculty of Sciences, Université Libre de Bruxelles, Belgium
raw_author_name: F. Oblé
raw_affiliation_string: Research, Development and Innovation, Worldline, Lyon, France
raw_author_name: G. Bontempi
raw_affiliation_string: Machine Learning Group, Computer Science Departement, Faculty of Sciences, Université Libre de Bruxelles, Belgium

Abstract: The volume of e-commerce continues to increase year after year. Buying goods on the internet is easy and practical, and took a huge boost during the lockdowns of the Covid crisis. However, this is also an open window for fraudsters and the corresponding financial loss costs billions of dollars. In this paper, we study e-commerce credit card fraud detection, in collaboration with our industrial partner, Worldline. Transactional companies are more and more dependent on machine learning models such as deep learning anomaly detection models, as part of real-world fraud detection systems (FDS). We focus on continual learning to find the best model with respect to two objectives: to maximize the accuracy and to minimize the catastrophic forgetting phenomenon. For the latter, we proposed an evaluation procedure to quantify the forgetting in data streams with delayed feedback: the plasticity/stability visualization matrix. We also investigated six strategies and 13 methods on a real-size case study including five months of e-commerce credit card transactions. Finally, we discuss how the trade-off between plasticity and stability is set, in practice, in the case of FDS.
A novel Elman neural network based on Gaussian kernel and Improved SOA and Its Applications

Authors:
raw_author_name: Zhilei Liu
raw_affiliation_string: Naval Architecture and Ocean Engineering College, Dalian Maritime University, Dalian 116026, China
raw_author_name: Jiaoyi Hou
raw_affiliation_string: Naval Architecture and Ocean Engineering College, Dalian Maritime University, Dalian 116026, China

Abstract: To address challenges encountered in traditional Elman neural networks (ENNs), such as low convergence accuracy, difficulties in hyperparameter selection, and issues with gradient disappearance, a new ENN based on the Gaussian kernel and an improved seagull optimization algorithm (SOA) named GSENN is proposed. First, the principle of the ENN is introduced, and the effects of the network structure, parameters, and gradient descent algorithm on its output are analyzed. Building upon the analysis results, an input layer for the ENN using the Gaussian kernel is designed. The outstanding local feature extraction capability of the Gaussian kernel is used to enhance convergence accuracy, and the predicted trends are more in line with real data. In addition, we propose an SOA incorporating a nonlinear factor to optimize the weights and thresholds of the ENN. This approach aims to resolve the challenges in hyperparameter selection and problems of gradient vanishing in the ENN. Seven commonly used neural network structures and the latest improved techniques were chosen for comparative experiments across different datasets. The experimental results show that GSENN achieves a mean relative error rate of 4.286%. The MSE results indicate a 45.600% improvement over the original ENN and a 37.142% improvement over BP neural networks. The experimental results demonstrate that GSENN exhibits efficient forecasting capability and offers reliable and effective forecasting for the relevant departments.
Robust image steganography against JPEG compression based on DCT residual modulation

Authors:
raw_author_name: Yingkai Huang
raw_affiliation_string: Center for Agroforestry Mega Data Science, School of Future Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China
raw_author_name: Zhuxian Liu
raw_affiliation_string: Center for Agroforestry Mega Data Science, School of Future Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China
raw_author_name: Qiwen Wu
raw_affiliation_string: Center for Agroforestry Mega Data Science, School of Future Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China
raw_author_name: Xiaolong Liu
raw_affiliation_string: Center for Agroforestry Mega Data Science, School of Future Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China

Abstract: Robust steganography is the technology of hiding secret message in cover image so that the message can be recovered after additional image processing across social network. Traditional robust steganography schemes mainly relied on error correction code and embedding domain selection, which could not effectively address the additional errors introduced during JPEG compression. In this paper, we analyze the lossy operations of JPEG compression and present the DCT Residual Modulation (DRM) algorithm to specifically address spatial pixel overflow, which is a predominant factor leading to alterations in quantized DCT coefficients following recompression. To enhance robustness, unit-based shuffling is applied to the encoded message, and the DRM algorithm is employed for processing the cover image and the stego image. To enhance resistance against steganalysis, the embedding domain is shifted to lower-frequency regions and the redundancy of error correction coding is reduced. Experimental results show that the proposed scheme outperforms existing schemes in terms of robustness and maintains a high level of anti-steganalysis capabilities.
RASNet: Renal Automatic Segmentation Using an Improved U-Net with Multi-scale Perception and Attention Unit

Authors:
raw_author_name: Gaoyu Cao
raw_affiliation_string: School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
raw_author_name: Zhanquan Sun
raw_affiliation_string: School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
raw_author_name: Hongquan Geng
raw_affiliation_string: Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, 200092, China
raw_author_name: Zhong Yin
raw_affiliation_string: School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
raw_author_name: Minlan Pan
raw_affiliation_string: School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

Abstract: For the treatment of renal disease, the application of radioactive equipment has become one of the important methods. Accurate segmentation of renal contour plays an important role in clinical diagnosis. However, manual renal contour drawing is not only inefficient but also prone to inaccurate outlining results due to different manual proficiency and fatigue caused by long-term work. There is little research on automatic renal segmentation with renal dynamic imaging. To address this issue, an improved model based on a deep neural network called Renal Automatic Segmentation Network (RASNet) is proposed, to aid in the automatic segmentation of renal contours. Besides, a multi-scale spatial perception module and a decoding module with attention connection are introduced to enrich the semantic information and further improve the accuracy of network segmentation. Extensive experiments were conducted on a renal dynamic medical image database established in this paper. Analysis results show the superiority of the proposed RASNet to several existing segmentation frameworks.
A heuristic method for discovering multi-class classification rules from multi-source data in cloud–edge system

Authors:
raw_author_name: Jing Shang
raw_affiliation_string: China Mobile Information Technology Center, Beijing, 100033, China
raw_author_name: Zhiwen Xiao
raw_affiliation_string: China Mobile Information Technology Center, Beijing, 100033, China
raw_author_name: Tao Tao
raw_affiliation_string: China Mobile Information Technology Center, Beijing, 100033, China
raw_author_name: Jibin Wang
raw_affiliation_string: China Mobile Information Technology Center, Beijing, 100033, China
raw_author_name: Zhihui Wu
raw_affiliation_string: China Mobile Information Technology Center, Beijing, 100033, China

Abstract: The integration of diverse devices has made the establishment of multi-class classification models for multi-source data a primary concern for data mining in cloud–edge system. Developing rule-based classifiers is essential because they present results that express the reasons for classification. However, existing rule learning methods are not compatible with multi-source tabular data containing mixed features, imbalanced labels and missing values, making it difficult to build cross-data source and cross-device data mining models. We developed a heuristic multi-class rule learning method that can handle complex tabular datasets without relying too much on cumbersome preprocessing techniques. We abstract the training process of the classifier into a multi-objective optimization problem and design a novel hybrid evolutionary algorithm to obtain Pareto-optimal solutions as rule-based classifier. Compared with the existing explainable classification methods, this method has obvious advantages in the classification performance of tabular data.
MVDet: Encrypted malware traffic detection via multi-view analysis

Authors:
raw_author_name: Susu Cui
raw_affiliation_string: Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Xueying Han
raw_affiliation_string: Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Cong Dong
raw_affiliation_string: Zhongguancun Laboratory, Beijing, China
raw_author_name: Yun Li
raw_affiliation_string: Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Song Liu
raw_affiliation_string: Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Zhigang Lu
raw_affiliation_string: Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Yuling Liu
raw_affiliation_string: Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

Abstract: Detecting encrypted malware traffic promptly to halt the further propagation of an attack is critical. Currently, machine learning becomes a key technique for extracting encrypted malware traffic patterns. However, due to the dynamic nature of network environments and the frequent updates of malware, current methods face the challenges of detecting unknown malware traffic in open-world environment. To address the issue, we introduce MVDet, a novel method that employs machine learning to mine the behavioral features of malware traffic based on multi-view analysis. Unlike traditional methods, MVDet innovatively characterizes the behavioral features of malware traffic at 4-tuple flows from four views: statistical view, DNS view, TLS view, and business view, which is a more stable feature representation capable of handling complex network environments and malware updates. Additionally, we achieve a short-time behavioral features construction, significantly reducing the time cost for feature extraction and malware detection. As a result, we can detect malware behavior at an early stage promptly. Our evaluation demonstrates that MVDet can detect a wide variety of known malware traffic and exhibits efficient and robust detection in both open-world and unknown malware scenarios. MVDet outperforms state-of-the-art methods in closed-world known malware detection, open-world known malware detection, and open-world unknown malware detection.
d2kg: An integrated ontology for knowledge graph-based representation of government decisions and acts

Authors:
raw_author_name: Ioannis Konstantinidis
raw_affiliation_string: International Hellenic University, Greece
raw_author_name: Georgios Meditskos
raw_affiliation_string: School of Informatics, Aristotle University of Thessaloniki, Greece
raw_author_name: Vassilios Peristeras
raw_affiliation_string: International Hellenic University, Greece
raw_author_name: Nick Bassiliades
raw_affiliation_string: School of Informatics, Aristotle University of Thessaloniki, Greece

Abstract: To implement Open Governance a crucial element is the efficient use of the big amounts of open data produced in the public domain. Public administration is a rich source of data and potentially new knowledge. It is a data intensive sector producing vast amounts of information encoded in government decisions and acts, published nowadays on the World Wide Web. The knowledge shared on the Web is mostly made available via semi-structured documents written in natural language. To exploit this knowledge, technologies such as Natural Language Processing, Information Extraction, Data mining and the Semantic Web could be used, embedding into documents explicit semantics based on formal knowledge representations such as ontologies. Knowledge representation can be made possible by the deployment of Knowledge Graphs, collections of interlinked representations of entities, events or concepts, based on underlying ontologies. This can assist data analysts to achieve a higher level of situational awareness, facilitating automated reasoning towards different objectives, such as for knowledge management, data maintenance, transparency and cybersecurity. This paper presents a new ontology d2kg [d(iavgeia) 2(to) k(nowledge) g(raph)] integrating in a unique way standard EU ontologies, core and controlled vocabularies to enable exploitation of publicly available data from government decisions and acts published on the Greek platform Diavgeia with the aim to facilitate data sharing, re-usability and interoperability. It demonstrates a characteristic example of a Knowledge Graph based representation of government decisions and acts, highlighting its added value to respond to real practical use cases for the promotion of transparency, accountability and public awareness. The developed d2kg ontology in owl is accessible at: http://w3id.org/d2kg, as well as documented at: http://w3id.org/d2kg/documentation.
A Survey on Implementing Augmented Reality in Learning Data Structures

Authors:
raw_author_name: None Anshu Dubey
raw_affiliation_string: Global Academy of Technology, Bengaluru, India
raw_author_name: None B Sahana Nayak
raw_affiliation_string: Global Academy of Technology, Bengaluru, India

Abstract: Data structures are fundamental building blocks of computer science, but their abstract nature can pose challenges for students. This research explores the potential of Augmented Reality (AR) to enhance the learning of data structures. The paper describes the development and analysis of an AR-based learning system for specific data structures. The system utilizes data visualization and interactive manipulation through 3D models and animations to explain how AR features aid in learning. The results were compelling, demonstrating significant improvement in both understanding and performance among students utilizing the AR tool. In general, this study indicates that AR can function as a valuable tool for studying data structures. This study not only highlights the effectiveness of AR in demonstrating data structures but also paves the way for future advancements in computer science education, ultimately leading to a more easily accessible and engaging learning environment for all.
Dura-Europos Stories: Developing interactive storytelling applications using knowledge graphs for cultural heritage exploration

Authors:
raw_author_name: Katherine Thornton
raw_affiliation_string: Stories Services Collaborative, USA

Abstract: We introduce Dura-Europos Stories, a multimedia application for viewing artifacts and places related to the Dura-Europos archaeological excavation. We describe the process of mapping data to the Wikidata data model as well as the process of contributing data to Wikidata. We provide an overview of the functionality of an interactive application for viewing images of the artifacts in the context of their metadata. We contextualize this project as an example of using knowledge graphs in research projects in order to leverage technologies of the Semantic Web in such a way that data related to the project can be easily combined with other data on the web. Presenting artifacts in this story-based application allows users to explore these objects visually, and provides pathways for further exploration of related information.
Exponential Capacity of Dense Associative Memories

Authors:
raw_author_name: Carlo Lucibello
raw_affiliation_string: Department of Computing Sciences, Bocconi University, Milano 20136, Italy and Bocconi Institute for Data Science and Analytics (BIDSA), Milano 20136, Italy
raw_author_name: Marc Mézard
raw_affiliation_string: Department of Computing Sciences, Bocconi University, Milano 20136, Italy and Bocconi Institute for Data Science and Analytics (BIDSA), Milano 20136, Italy

Abstract: Recent generalizations of the Hopfield model of associative memories are able to store a number P of random patterns that grows exponentially with the number N of neurons, P=exp(αN). Besides the huge storage capacity, another interesting feature of these networks is their connection to the attention mechanism which is part of the Transformer architecture widely applied in deep learning. In this work, we study a generic family of pattern ensembles using a statistical mechanics analysis which gives exact asymptotic thresholds for the retrieval of a typical pattern, α1, and lower bounds for the maximum of the load α for which all patterns can be retrieved, αc, as well as sizes of attraction basins. We discuss in detail the cases of Gaussian and spherical patterns, and show that they display rich and qualitatively different phase diagrams.Received 26 July 2023Revised 29 November 2023Accepted 11 January 2024DOI:https://doi.org/10.1103/PhysRevLett.132.077301© 2024 American Physical SocietyPhysics Subject Headings (PhySH)Physical SystemsDisordered systemsSpin glassesTechniquesDeep learningMachine learningReplica theoryStatistical Physics & ThermodynamicsInterdisciplinary Physics
Simulating photon counting from dynamic quantum emitters by exploiting zero-photon measurements

Authors:
raw_author_name: Stephen C. Wein
raw_affiliation_string: Quandela, 7 Rue Léonard de Vinci, 91300 Massy, France

Abstract: Many applications of quantum optics demand delicate quantum properties of light carefully tailored to accomplish a specific task. To this end, numerical simulations of quantum light sources are vital for designing, characterizing, and optimizing quantum photonic technology. Here, I show that exploiting information hidden in zero-photon measurement outcomes provides an exponential speedup for time-integrated photon-counting simulations, realizing eight orders of magnitude reduction in the time to compute six-photon detection probabilities while achieving 10 orders of magnitude higher precision compared to the state of the art. This enables simulations of large photonic experiments with an unprecedented level of physical detail. It can accelerate the design of sources to generate photonic resource states for quantum sensing and measurement-based quantum computing while capturing realistic imperfections. It also establishes a general theoretical framework to study dynamic interactions between stationary qubits mediated by measurements of flying qubits, which can be used to model distributed quantum computing and quantum communication.