Reviewerly/exHarmony huggingface dataset:
KGCL: A Knowledge-enhanced Graph Contrastive learning framework for session-based recommendation

Session-based Recommendation (SBR) aims at predicting the next item based on a short-term anonymous user behavior, whose main challenge lies at the sparsity problem of user–item​ interactions. Graph contrastive learning, which discovers ground-truth samples by data augmentation, is a promising paradigm to tackle this problem. However, the following two insights are often overlooked by most of these contrastive learning-based models. First, item knowledge (i.e., item attributes, which can be distilled from open knowledge graphs) provides side information to model the complex high-order relations among items. Second, effective embedding aggregating mechanism is capable of filtering noisy preference signals (i.e., unrelated items) in sessions and retaining higher weight for the related items. These insights motivate us to construct an item attribute hypergraph to summarize associations among items that share common attributes and develop a Knowledge-enhanced Graph Contrastive Learning framework for session-based recommendation (KGCL). Technically, KGCL constructs two independent and complementary views (cross-session graph and item attribute hypergraph) in terms of user–item interactions sequence and item intrinsic attributes respectively, so as to explicitly capture both internal and external factors of items. Then, we encode item and session embeddings with a query-aware graph attention network and a hypergraph convolutional network over the above two views. Finally, we devise two contrastive learning loss — global–global contrastive learning and local–global contrastive learning — that maximize agreement between these two views and generate high-quality recommendation results. Extensive experiments conducted on three real-world datasets show KGCL has a higher expressive power that enables SBR to predict the next item.
Detection of Adversarial Attacks by Observing Deep Features with Structured Data Algorithms

Deep Neural Networks (DNNs) are highly vulnerable to adversarial attacks, which introduce human-imperceptible perturbations on the input to fool a DNN model. Detecting such attacks is fundamental to protect distributed applications that process input data using DNNs. Detection strategies typically rely on complex solutions that include modifications to the input and to the DNN model itself, and/or the deployment of a second DNN that suspects attacks. Despite these efforts, at the present stage of research, the ability to protect against adversarial attacks is unsatisfactory. Alternatively to most approaches, this paper proposes RISOTTO (adveRsarIal attackS detectiOn using strucTured daTa algOrithms), a very simple but effective and fast detection strategy that uses algorithms for structured data. RISOTTO does not modify the DNN model and its inputs and requires only the values of selected deep features at test time. Using the deep features of a single layer, the accuracy in detecting known attacks is 1.0 for the two MNIST models and all the selected attacks. Also, by combining the deep features of multiple layers, we show that our approach is competitive or better than state-of-the-art unsupervised solutions in detecting unknown attacks, especially for MNIST models with few deep features (below 1 million).
Hopf–Hopf bifurcation and chaos in delay-coupled reservoir computing system with two delays

Delay-coupled systems have recently made progress in the field of neuromorphic computing, which can effectively process unprecedented amount of data at high speed. It is owing to the nonlinear relationship between input and output to a certain extent. That is, more complicated dynamic system may bring higher performance. Research shows that the performance of delay-coupled reservoir computing system with two delays can surpass single delayed reservoir computing system. In this article, we mainly study Hopf–Hopf bifurcation and chaos of delay-coupled reservoir computing system. Then we analyze the performance difference caused by the different number of delays from the dynamics perspective. For the analysis of Hopf–Hopf bifurcation, we firstly draw the critical curves by DDE-BIFTOOL and obtain Hopf–Hopf bifurcation points. Subsequently, we unfold and classify aforesaid bifurcation points by amplitude equations of its normal form, which is obtained via method of multiple scales. Finally, we verify our theoretical analysis by numerical simulations. For the analysis of chaos, by plotting the bifurcation diagram, we prove the existence of chaos and determine how the system enters the chaotic state with a specific parameter changing. In conclusion, we find two different types of Hopf–Hopf bifurcation and the route to chaos through Period-Doubling bifurcation. From our analysis, we can determine which parameter has a greater impact on the system and guide us to set the parameters in practical applications.
Building Collaborative Cybersecurity for Critical Infrastructure Protection: Empirical Evidence of Collective Intelligence Information Sharing Dynamics on ThreatFox

Abstract This article describes three collective intelligence dynamics observed on ThreatFox, a free platform operated by abuse.ch that collects and shares indicators of compromise. These three dynamics are empirically analyzed with an exclusive dataset provided by the sharing platform. First, participants’ onboarding dynamics are investigated and the importance of building collaborative cybersecurity on an established network of trust is highlighted. Thus, when a new sharing platform is created by abuse.ch, an existing trusted community with ’power users’ will migrate swiftly to it, in order to enact the first sparks of collective intelligence dynamics. Second, the platform publication dynamics are analyzed and two different superlinear growths are observed. Third, the rewarding dynamics of a credit system is described - a promising incentive mechanism that could improve cooperation and information sharing in open-source intelligence communities through the gamification of the sharing activity. Overall, our study highlights future avenues of research to study the institutional rules enacting collective intelligence dynamics in cybersecurity. Thus, we show how the platform may improve the efficiency of information sharing between critical infrastructures, for example within Information Sharing and Analysis Centers using ThreatFox. Finally, a broad agenda for future empirical research in the field of cybersecurity information sharing is presented - an important activity to reduce information asymmetry between attackers and defenders.
Design and Implementation of a Blockchain-based e-Voting system by using the Algorand platform

In recent years, the increasing accessibility and usage of Internet-based technologies in our daily activities have fostered the possibility of simplifying citizenship and relationships with local and central governments, paving the way to e-governance and e-government. Many government-related services can now be accessed online, and increasing attention has been devoted to implementing e-Voting and overcoming the obstacles that have limited its usage in concrete cases. e-Voting is highly considered to reduce significantly the cost of such large-scale operations and ease the process for elder or disabled voters, but also decrease the stress level and nuisance, which is typically associated with the traditional election. Alongside the benefits, however, many drawbacks have limited its applicability, such as lack of trustability and security/privacy concerns, which lead many governments to avoid its usage during general elections or limit e-Voting to specific niche situations.
A White-box Implementation of SM4 with Self-equivalence Encoding

Abstract White-box implementation can ensure the security of cryptographic algorithm in white-box attack environment without changing the inputs and outputs of the original algorithm. Most existing white-box implementations construct a series of lookup tables to protect the key. However, with the development of white-box attack techniques, many white-box implementations have been proved to be insecure. In this paper, a new white-box implementation of SM4 is proposed, which is based on an equivalent partial SPN structure of the SM4 algorithm. Our implementation includes three types of table lookup operations and XOR operations. The round keys are obfuscated with the self-equivalences of the S-box and random affine encodings. Security analysis shows that our implementation can resist BGE-type attack, the attack based on affine equivalence algorithm, the structure attack, the collision attack and differential computational analysis. Furthermore, our scheme requires 8.125 MB of memory.
Digitale Assistenzsysteme für die effiziente Entwicklung einer neuen Produktgeneration

No AccessProceedings of the 19th Rapid.Tech 3D Conference Erfurt, Germany, 9–11 May 2023Jun 2023Digitale Assistenzsysteme für die effiziente Entwicklung einer neuen Produktgenerationhttps://doi.org/10.3139/9783446479425.016SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapternext chapter FiguresReferencesRelatedDetails 2023Pages: 186-196Print ISBN: 978-3-446-47941-8eISBN: 978-3-446-47942-5 PDF downloadLoading ...
Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images

Explainability is a key requirement for computer-aided diagnosis systems in clinical decision-making. Multiple instance learning with attention pooling provides instance-level explainability, however for many clinical applications a deeper, pixel-level explanation is desirable, but missing so far. In this work, we investigate the use of four attribution methods to explain a multiple instance learning models: GradCAM, Layer-Wise Relevance Propagation (LRP), Information Bottleneck Attribution (IBA), and InputIBA. With this collection of methods, we can derive pixel-level explanations on for the task of diagnosing blood cancer from patients' blood smears. We study two datasets of acute myeloid leukemia with over 100 000 single cell images and observe how each attribution method performs on the multiple instance learning architecture focusing on different properties of the white blood single cells. Additionally, we compare attribution maps with the annotations of a medical expert to see how the model's decision-making differs from the human standard. Our study addresses the challenge of implementing pixel-level explainability in multiple instance learning models and provides insights for clinicians to better understand and trust decisions from computer-aided diagnosis systems.
Source-Free Domain Adaptation for Medical Image Segmentation via Selectively Updated Mean Teacher

Automated medical image segmentation is valuable for disease diagnosis and prognosis, and it has achieved promising performance with deep neural networks. However, a segmentation model trained on a source dataset may not perform well on a different target dataset when the distribution shift or even modality alteration exists between them. To address this problem, domain adaptation techniques can be applied to train the model with the help of the unannotated target dataset. Often when the target data is available, only a segmentation model trained on the source dataset is provided without the source data, and in this case, source-free domain adaptation (SFDA) is needed. In this work, we focus on the development of SFDA techniques for medical image segmentation, where the given source model is updated based on the target data. Since no annotations are available for the target dataset, we propose to leverage the consistency of predictions on the target data when different perturbations are made, and adopt the mean teacher framework that can effectively exploit the consistency. Moreover, we assume that the update of the entire model in vanilla mean teacher is suboptimal because when no annotated data is available the knowledge learned for segmentation in the source model can be easily forgotten. Therefore, we propose selectively updated mean teacher (SUMT), which seeks to adapt the source model parameters that are sensitive to domain variance and retain the parameters that are invariant to domains. In SUMT, we develop a progressive layer update strategy with channel-wise weight restoration that alleviates forgetting. To evaluate the proposed method, experiments were performed on three datasets, where the source and target data used different modalities for segmentation, or their images were acquired at different sites. The results show that our method improves the segmentation accuracy compared with other SFDA approaches.
Popular Tag Recommendation by Neural Network in Social Media

Although “a picture is worth a thousand words,” this may not be enough to get your post seen on social media. This study’s main objective was to determine the best ways to characterize a photo in terms of viral marketing and public appealing. We have to obtain this dataset for this reason from the social media site such as Instagram. A total of 1.4 million hashtags were used in the 570,000 photos that we crawled. Prior to training the text generation module to produce such popular hashtags, we had to determine the components and features of the photo. We trained a multilabel image classification module using a ResNet neural network model for the first section. In order to create hashtags pertaining to their popularity, we trained a cutting-edge GPT-2 language model for the second portion. This work differs from others in that, and it initially offered a cutting-edge GPT-2 model for hashtag generation using a combination of the multilabel image classification module. The popularity issues and ways to make an Instagram post popular are also highlighted in our essay. Social science and marketing research can both be conducted on this subject. Which content can be considered popular from the perspective of consumers can be researched in the social science setting. As a marketing strategy, end users can help by offering such well-liked hashtags for social media accounts. This essay adds to the body of knowledge by demonstrating the two possible uses of popularity. Compared to the base model, our popular hashtag generating algorithm creates 11% more relevant, acceptable, and trending hashtags, according to the evaluation that was carried out.
Are bundles good deals for first-order modal logic?

Bundled products are often offered as good deals to customers. In first-order modal logic (FOML), when we bundle quantifiers and modalities together (as in ∃x□, ◇∀x, etc.), we get new logical operators whose combinations produce interesting fragments of FOML without any restriction on the arity of predicates, the number of variables, or the modal scope. It is well-known that finding decidable fragments of FOML is hard, so we may ask: do bundled fragments that exploit the distinct expressivity of FOML constitute good deals in balancing the expressivity and complexity? There are a few positive earlier results towards identifying the decidable ones. In this paper, we map the terrain of bundled fragments of FOML in (un)decidability, and in the cases without a definite answer yet, we show that they lack the finite model property. Moreover, whether the class of models considered has constant domains (across states/worlds) or increasing domains presents another layer of complexity. We also present the loosely bundled fragment, which generalizes the bundles and yet retains decidability (over increasing domain models).
An overview of digital image analog noise removal based on traditional filtering

Due to interference in the transmission of external equipment, images can suffer from varying noise concentrations. As an immediate and practical step to reduce the impact of noise on an image and to improve its quality and visual presentation, filtering is the best method. The paper examines how traditional filtering methods restore a digital image under salt and pepper and Gaussian noise interference. Also included in this paper is an analysis of the advantages and challenges of traditional noise filtering, as well as suggestions for a filtering method for a variety of noise levels. Lastly, the effects of filtered noise pictures are evaluated through extensive experimental simulations, providing substantial assistance for related research.
Joint Sampling and Reconstruction of Time-Varying Signals Over Directed Graphs

Vertex-domain and temporal-domain smoothness of time-varying graph signals are cardinal properties that can be exploited for effective graph signal reconstruction from limited samples. However, existing approaches are not directly applicable when the signal's frequency occupancy changes with time. Moreover, while e.g., sensor network applications can benefit from directed graph models, the non-orthogonality of the graph eigenvectors can challenge spectral-based signal reconstruction algorithms. In this context, here we consider <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -sparse time-varying signals with unknown frequency supports. By exploiting the smoothness of the varying graph frequency supports and employing shift operations over directed graphs, we study joint sampling of multiple varying signals based on Schur decomposition to reconstruct each signal by orthogonal frequency components. Firstly, joint frequency support of the multiple signals is identified by proposing a two-stage Individual-Joint sampling scheme. Based on the estimated frequency support, the GFT coefficients of each signal can be recovered using data collected in individual sampling stage. Greedy algorithms are proposed for vertex set selection and graph shift order selection, which enable a robust signal reconstruction against additive noise. Considering the signals in applications may be approximately <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -sparse, we further exploit the samples in both individual and joint sampling stages and investigate the optimal signal reconstruction as a convex optimization problem with adaptive frequency support selection. The proposed optimal sampling and reconstruction algorithms outperform several existing schemes in random network and sensor network data gathering.
Unsupervised Cross-Spectrum Depth Estimation by Visible-Light and Thermal Cameras

Cross-spectrum depth estimation aims to provide a reliable depth map under variant-illumination conditions with a pair of dual-spectrum images. It is valuable for autonomous driving applications when vehicles are equipped with two cameras of different modalities. However, images captured by different-modality cameras can be photometrically quite different, which makes cross-spectrum depth estimation a very challenging problem. Moreover, the shortage of large-scale open-source datasets also retards further research in this field. In this paper, we propose an unsupervised visible light(VIS)-image-guided cross-spectrum (i.e., thermal and visible-light, TIR-VIS in short) depth-estimation framework. The input of the framework consists of a cross-spectrum stereo pair (one VIS image and one thermal image). First, we train a depth-estimation base network using VIS-image stereo pairs. To adapt the trained depth-estimation network to the cross-spectrum images, we propose a multi-scale feature-transfer network to transfer features from the TIR domain to the VIS domain at the feature level. Furthermore, we introduce a mechanism of cross-spectrum depth cycle-consistency to improve the depth estimation result of dual-spectrum image pairs. Meanwhile, we release to society a large cross-spectrum dataset with visible-light and thermal stereo images captured in different scenes. The experiment result shows that our method achieves better depth-estimation results than the compared existing methods. Our code and dataset are available on https://github.com/whitecrow1027/CrossSP_Depth.
Device Scheduling in Over-the-Air Federated Learning Via Matching Pursuit

This paper develops a class of low-complexity device scheduling algorithms for over-the-air federated learning via the method of matching pursuit. The proposed scheme tracks closely the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">close-to-optimal</i> performance achieved by difference-of-convex programming, and outperforms significantly the well-known benchmark algorithms based on convex relaxation. Compared to the state-of-the-art, the proposed scheme imposes a drastically lower computational load on the system: for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> devices and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> antennas at the parameter server, the benchmark complexity scales with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(N^{2}+K)^{3} + N^{6}$</tex-math></inline-formula> while the complexity of the proposed scheme scales with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${K^{p} N^{q}}$</tex-math></inline-formula> for some <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0 \lt p,q \leq 2$</tex-math></inline-formula> . The efficiency of the proposed scheme is confirmed through the convergence analysis and numerical experiments on CIFAR-10 dataset.
Usage and Adoption of Chatbot in Education Sector

The Chatbot’s in the education sector are very specific and successful, with the change in the technology the education sector is also emerging with the trends in the adoption of the new and relevant tools. The usage of the Chatbot’s are holding the specific and related conversations based on the artificial intelligence and the machine learning tools a where in the searching of the keywords using the most relevant Chatbot’s in the industry like ALICE and ELIZA are giving responses. It was the first chatbot, created by MIT’s Joseph Weizenbaum) are giving responses. When students need relevant information and want it available 24/7, Chatbots provide answers as needed If the students want a relevant information and also to be available 24*7 the Chatbot is going to give the response as and when required. Chatbot’s also show great importance as it is actually replying and giving answers to the questions being asked by the students. In this study authors have conducted a survey on the basis of the utility, acceptance and user friendly parameters where the 120 students responded the questions. The main objective of the authors is to understand how relevant the Chatbot’s are in the education sector and how it is going to be adopted in future. Authors have conducted a regression analysis and shown the results in the methodology and observations.