Reviewerly/exHarmony huggingface dataset:
On Learning When to Decompose Graphical Models

Decomposition is a well-known algorithmic technique for Graphical Models. It is commonly believed that such a technique is cost-effective for instances with low width. In this paper, we show on a large data set of real-life inspired instances that this is not the case. To better understand this result, we narrow our study and consider k-tree instances where the width is well controlled and get similar results. Finally, we show that by adding a few simple features and using simple Machine Learning models we can predict the convenience to decompose with an accuracy of more than $$85\%$$ , which produces time reductions in standard benchmarks of nearly $$90\%$$ .
Proffler: Towards Collaborative and Scalable Edge-Assisted Crowdsourced Livecast

In recent years, crowdsourced livecast has seen remarkable progress due to the interactivity and real-time nature, playing an essential role in multimedia applications in the post-epidemic era. Given the delay sensitivity, large viewing volumes, and heterogeneous viewing patterns, the traditional video streaming methods fail to provide the optimized quality of experience (QoE) for viewers using the minimum system cost over an edge-assisted service architecture. The emerging technology of mobile edge computing (MEC) offers a new perspective of reducing user latency and enhancing the quality of dispatched videos in a promising way. In this paper, we propose Proffler, an integrated framework that addresses this problem through effective stream caching at the network edge server. We first examine the underlying correlations in viewing patterns across different regions and propose a novel transformer-based algorithm, Chili-TF, that achieves accurate viewer request prediction, even for regions with insufficient data. We then design a scalable algorithm, U2VR, that achieves near-optimal video stream allocation as well as viewer scheduling. Extensive real-data-driven experiments further confirm that Proffler can achieve improvements of 20%-55% in average QoE compared to state-of-the-art solutions.
Deep Randomized Networks for Fast Learning

Deep learning neural networks show a significant improvement over shallow ones in complex problems. Their main disadvantage is their memory requirements, the vanishing gradient problem, and the time consuming solutions to find the best achievable weights and other parameters. Since many applications (such as continuous learning) would need fast training, one possible solution is the application of sub-networks which can be trained very fast. Randomized single layer networks became very popular due to their fast optimization while their extensions, for more complex structures, could increase their prediction accuracy. In our paper we show a new approach to build deep neural models for classification tasks with an iterative, pseudo-inverse optimization technique. We compare the performance with a state-of-the-art backpropagation method and the best known randomized approach called hierarchical extreme learning machine. Computation time and prediction accuracy are evaluated on 12 benchmark datasets, showing that our approach is competitive in many cases.
Bayesian Decision Trees Inspired from Evolutionary Algorithms

Bayesian Decision Trees (DTs) are generally considered a more advanced and accurate model than a regular Decision Tree (DT) as they can handle complex and uncertain data. Existing work on Bayesian DTs uses Markov Chain Monte Carlo (MCMC) with an accept-reject mechanism and sample using naive proposals to proceed to the next iteration. This method can be slow because of the burn-in time needed. We can reduce the burn-in period by proposing a more sophisticated way of sampling or by designing a different numerical Bayesian approach. In this paper, we propose a replacement of the MCMC with an inherently parallel algorithm, the Sequential Monte Carlo (SMC), and a more effective sampling strategy inspired by the Evolutionary Algorithms (EA). Experiments show that SMC combined with the EA can produce more accurate results compared to MCMC in 100 times fewer iterations.
On Designing Low-Risk Honeypots Using Generative Pre-Trained Transformer Models With Curated Inputs

Honeypots are utilized as defensive tools within a monitored environment to engage attackers and gather artifacts for the development of indicators of compromise. However, once these honeypots are deployed, they are rarely updated, making them obsolete and easier to fingerprint as time passes. Furthermore, using fully functional computing and networking devices as honeypots presents the risk of an attacker breaking out from the controlled environment. Large-scale text-generating models, commonly referred to as Large Language Models (LLMs), have seen wide implementation using generative-pretrained transformer (GPT) models. These models have seen an explosion in popularity and have been tuned for various use cases. This paper investigates the use of these models to simulate honeypots that are adaptive to threat engagement without the risk of unintended breakouts. This investigation finds that the method these models use to generate output has limitations that can reveal the deception to a dedicated attacker in extended sessions. To overcome this challenge, this paper presents a method to manage the inputs and outputs to reduce non-deterministic output and token usage of a model generating text in a way that simulates a terminal. An example honeypot is evaluated against a traditional low-risk honeypot, Cowrie, where greater similarity to an actual machine for single commands is achieved. Furthermore, in several multi-step attack scenarios, the proposed architecture reduced the token usage by up to 77% when compared to a baseline scenario that did not manage the inputs to and outputs from an example model. A discussion on the utilization of LLMs for cyber deception, as well as the limitations hindering their broader adoption indicates that LLMs exhibit promise for cyber deception but necessitate further research before achieving widespread implementation.
Style-exprGAN: Diverse Smile Style Image Generation Via Attention-Guided Adversarial Networks

This paper proposes a data-driven approach for generating personalized smile style images for neutral expressions, which aims to produce diverse smile styles while preserving individual features. Unlike other generator models that require expensive manual facial attribute labeling, we designed an auxiliary expression attention Siamese network (EASN) to extract identity-irrelevant facial expression attention regions and guide the proposed two-stage style-expression generative adversarial network (style-exprGAN). The first generator stage generates the overall facial geometry and virtual smile features, while the second stage refines the image quality. Additionally, we introduced traditional geometry warping methods to include registered neutral expression images for consistent transformation and realistic texture fusion. Results show that the proposed method effectively synthesizes realistic and diverse smile styles while preserving individual features. Furthermore, we demonstrate the potential of our data-driven approach by applying the generated personalized smile style images to image augmentation tasks, improving the stability and robustness of facial recognition models.
Generative Models via Optimal Transport and Gaussian Processes

Generative models have recently gained a renewed interest due to their success in the development of new real-life applications, such as artificial intelligence generated images, texts, audios. The most recent and successful approaches combine neural network learning and Optimal Transport theory, exploiting the so-called transportation map/plan to generate a new element of a domain starting from an element of a different one, while preserving statistical properties of the data generation processes of the two domains. Although effective, the Neural Optimal Transport (NOT) approach is largely computationally expensive – due to the training of two nested deep neural networks – and requires injecting additional noise to improve generative properties. In this paper we present an alternative method, based on Gaussian Process (GP) regression, which overcomes these limitations. Contrary to a neural model, a GP is probabilistic, meaning that, for a given input, it provides both a prediction and the associated uncertainty. Thus, the generative properties are, by design, guaranteed by sampling the generated element around the prediction and depending on the uncertainty. Results on both toy examples and a dataset of images are provided to empirically demonstrate the benefits of the proposed approach.
Breast MRI Segmentation by Deep Learning: Key Gaps and Challenges

Breast MRI segmentation plays a vital role in early diagnosis and treatment planning of breast anomalies. Convolutional neural networks with deep learning have indicated promise in automating this process, but significant gaps and challenges remain to address. This PubMed-based review provides a comprehensive literature overview of the latest deep learning models used for breast segmentation. The article categorizes the literature on deep learning based on input modalities, use of labeled/unlabeled data during training, and different architectures. Additionally, it describes more complex frameworks structured using hierarchical, ensemble, or fused learning. Then, MRI processing approaches, key aspects of convolutional neural networks, and key gaps and challenges are focused. The need for large breast MRI datasets with accurate annotations and the generalization of the proposed structures to diverse and comprehensive datasets are among the gaps.
An Unsupervised Domain Adaption Approach for Cross-Modality RGB-Infrared Person Re-Identification

Dual-camera systems commonly employed in surveillance serve as the foundation for RGB-infrared (IR) cross-modality person re-identification (ReID). However, significant modality differences give rise to inferior performance compared to single-modality scenarios. Furthermore, most existing researches in this area rely on supervised training with meticulously labeled datasets. Labeling RGB-IR image pairs is more complex than labeling conventional image data, and deploying pre-trained models on unlabeled datasets can lead to catastrophic performance degradation. In contrast to previous solutions that focus solely on cross-modality or domain adaptation issues, this paper presents an end-to-end unsupervised domain adaptation (UDA) framework for the cross-modality person ReID, which can simultaneously addresses both of these challenges. This model employs source domain classes, target domain clusters, and unclustered instance samples for the training, maximizing the comprehensive use of the dataset. Moreover, it addresses the problem of mismatched clustering labels between the two modalities in the target domain by incorporating a label matching module that reassigns reliable clusters with labels, ensuring correspondence between different modality labels. We construct the loss function by incorporating distinctiveness loss and multiplicity loss, both of which are determined by the similarity of neighboring features in the predicted feature space and the difference between distant features. This approach enables efficient feature clustering and cluster class assignment to occur concurrently. Eight UDA cross-modality person ReID experiments are conducted on three real datasets and six synthetic datasets. Experimental results unequivocally demonstrate that the proposed model outperforms the existing state-of-the-art algorithms to a significant degree. Notably, in RegDB → RegDB_light, the Rank-1 accuracy exhibits a remarkable improvement of 8.24%.
Landslide-Induced Seismic Signal Clustering With Outlier Removal

Seismic clustering is a vital technique in seismology for identifying patterns in seismic events and providing insights into geological processes. However, its application to ongoing landslide-induced signals and the impact of outliers have not received much research attention. This letter presents a novel consensus clustering strategy with outlier removal for landslide-induced seismic signals. The proposed approach incorporates a parameter setting method to improve clustering accuracy and robustness. Experimental results demonstrate that the proposed approach outperforms state-of-the-art clustering methods.
HTC-DC Net: Monocular Height Estimation From Single Remote Sensing Images

3D geo-information is of great significance for understanding the living environment; however, 3D perception from remote sensing data, especially on a large scale, is restricted, mainly due to the high costs of 3D sensors such as LiDAR. To tackle this problem, we propose a method for monocular height estimation from optical imagery, which is currently one of the richest sources of remote sensing data. As an ill-posed problem, monocular height estimation requires well-designed networks for enhanced representations to improve performance. Moreover, the distribution of height values is long-tailed with the low-height pixels, e.g., the background, as the head, and thus trained networks are usually biased and tend to underestimate building heights. To solve the problems, instead of formalizing the problem as a regression task, we propose HTC-DC Net following the classification-regression paradigm, with the head-tail cut (HTC) and the distribution-based constraints (DCs) as the main contributions. HTC-DC Net is composed of the backbone network as the feature extractor, the HTC-AdaBins module, and the hybrid regression process. The HTC-AdaBins module serves as the classification phase to determine bins adaptive to each input image. It is equipped with a vision transformer encoder to incorporate local context with holistic information and involves an HTC to address the long-tailed problem in monocular height estimation for balancing the performances of foreground and background pixels. The hybrid regression process does the regression via the smoothing of bins from the classification phase, which is trained via DCs. The proposed network is tested on datasets of different resolutions, namely, DFC19 (1.3 m) and GBH (3 m). Experimental results show the superiority of the proposed network over existing methods by large margins. Extensive ablation studies demonstrate the effectiveness of each design component. Codes and trained models are published at https://github.com/zhu-xlab/HTC-DC-Net.
An SDN-Driven Reliable Transmission Architecture for Enhancing Real-Time Video Streaming Quality

This paper introduces a novel framework designed to enhance the reliability and quality of real-time video streaming by implementing a retransmission scheme that integrates the real-time streaming protocol (RTSP) and the software-defined network capabilities (SDN). Conventional data transmission approaches, like those based on TCP, often suffer from high latency and diminished reliability when managing multiple retransmission requests. To address these issues, we implemented the proposed framework in the SDN switches, including a retransmission mechanism that incorporates a buffering agent to mitigate packet loss. Moreover, by utilizing SDN controllers to create a reliable UDP scheme for efficient data transmission, the framework strengthens both practicality and reliability. The framework’s effectiveness is evaluated using 3 quality assessment metrics, and it demonstrates superior performance with a slight compromise in terms of latency compared to standard RTSP-based streaming. These findings suggest that the proposed solution offers a viable and efficient approach to improve real-time video streaming quality in scenarios where packet loss is prevalent.
Class GP: Gaussian Process Modeling for Heterogeneous Functions

Gaussian Processes (GP) are a powerful framework for modeling expensive black-box functions and have thus been adopted for various challenging modeling and optimization problems. In GP-based modeling, we typically default to a stationary covariance kernel to model the underlying function over the input domain, but many real-world applications, such as controls and cyber-physical system safety, often require modeling and optimization of functions that are locally stationary and globally non-stationary across the domain; using standard GPs with a stationary kernel often yields poor modeling performance in such scenarios. In this paper, we propose a novel modeling technique called Class-GP (Class Gaussian Process) to model a class of heterogeneous functions, i.e., non-stationary functions which can be divided into locally stationary functions over the partitions of input space with one active stationary function in each partition. We provide theoretical insights into the modeling power of Class-GP and demonstrate its benefits over standard modeling techniques via extensive empirical evaluations.
Learn, Compare, Search: One Sawmill’s Search for the Best Cutting Patterns Across and/or Trees

A sawmilling process scans a wood log and must establish a series of cutting and rotating operations to perform in order to obtain the set of lumbers having the most value. The search space can be expressed as an and/or tree. Providing an optimal solution, however, may take too much time. The complete search for all possibilities can take several minutes per log and there is no guarantee that a high-value cut for a log will be encountered early in the process. Furthermore, sawmills usually have several hundred logs to process and the available computing time is limited. We propose to learn the best branching decisions from previous wood logs and define a metric to compare two wood logs in order to branch first on the options that worked well for similar logs. This approach (Learn, Compare, Search, or LCS) can be injected into the search process, whether we use a basic Depth-First Search (DFS) or the state-of-the-art Monte Carlo Tree Search (MCTS). Experiments were carried on by modifying an industrial wood cutting simulator. When computation time is limited to five seconds, LCS reduced the lost value by 47.42% when using DFS and by 17.86% when using MCTS.
Anomaly Classification to Enable Self-healing in Cyber Physical Systems Using Process Mining

Industrial Cyber Physical Systems (CPS) are large-scale critical infrastructures that are vulnerable to cyberattacks with wide-ranging consequences. Being a combination of heterogeneous devices and protocols, the large-scale CPS anomaly is also exposed to critical vulnerabilities. These vulnerabilities are treated in terms of anomalies and cyberattacks, and their detection and corresponding self-healing mechanisms on large-scale critical infrastructures can be challenging because of their massive size and interconnections. With the objective of process optimization through anomaly detection and conformance-checking approach, the present work addresses different issues, such as event log generation with tools such as PLG 2.0 for data-driven approach. Self-healing is enabled through machine learning models based on anomaly classification ensemble learning-based machine learning models. The work uses process mining to analyze event log files, and then combinations of conformance-checking methods with ensemble classification models were used to best classify anomalies. Finally, the proposed work establishes that, in comparison to techniques like KNN and C-SVC, the proposed ensemble models perform better, with an accuracy of 84.7% using trace alignment as a conformance technique with gradient boosting to classify anomalies, with the end objectives of process improvement.
Multi-task Predict-then-Optimize

The predict-then-optimize framework arises in a wide variety of applications where the unknown cost coefficients of an optimization problem are first predicted based on contextual features and then used to solve the problem. In this work, we extend the predict-then-optimize framework to a multi-task setting: contextual features must be used to predict cost coefficients of multiple optimization problems, possibly with different feasible regions, simultaneously. For instance, in a vehicle dispatch/routing application, features such as time-of-day, traffic, and weather must be used to predict travel times on the edges of a road network for multiple traveling salesperson problems that span different target locations and multiple $$s-t$$ shortest path problems with different source-target pairs. We propose a set of methods for this setting, with the most sophisticated one drawing on advances in multi-task deep learning that enable information sharing between tasks for improved learning, particularly in the small-data regime. Our experiments demonstrate that multi-task predict-then-optimize methods provide good tradeoffs in performance among different tasks, particularly with less training data and more tasks.
Exploring Adversarial Attacks and Countermeasures in Image Classification and Object Detection: A Survey

Deep learning plays a crucial part in Artificial Intelligence applications like Object Detection, Cyber Security, Natural Language Processing, Robotics, Bioinformatics, Medical Imaging, Image Processing, Automatic Driving, and Computer Vision. Detection of objects, which has a crucial role in the field of video security surveillance is a challenging task in computer vision, and seeking to identify object instances from a vast collection of preset categories of organic images. Deep learning has made incredible strides in the area of generic object detection due to its capability to directly learn representations of features from collections of data. However, recently the security vulnerability of deep learning systems to adversarial patches became a topic of research interest, wherein adversarial patches are nothing but patches with noise or subtle perturbations on images that are provided as input to train the model, leading to misclassification and prediction of incorrect outputs by the model. The goal of this study is to provide a thorough survey of the most recent developments in this field brought on by deep learning techniques. We finish the survey by pointing out intriguing areas for further research.
Explaining the Behavior of Reinforcement Learning Agents Using Association Rules

Deep reinforcement learning algorithms are increasingly used to drive decision-making systems. However, there exists a known tension between the efficiency of a machine learning algorithm and its level of explainability. Generally speaking, increased efficiency comes with the cost of decisions that are harder to explain. This concern is related to explainable artificial intelligence, which is a hot topic in the research community. In this paper, we propose to explain the behaviour of a black-box sequential decision process, built with a deep reinforcement learning algorithm, thanks to standard data mining tools, i.e. association rules. We apply this idea to the design of playing bots, which is ubiquitous in the video game industry. To do so, we designed three agents trained with a deep Q-learning algorithm for the game Street FighterTurbo II. Each agent has a specific playing style and the data mining algorithm aims to find rules maximizing the lift, while ensuring a minimum threshold for the confidence and the support. Experiments show that association rules can provide insights on the behavior of each agent, and reflect their specific playing style. We believe that this work is a next step towards the explanation of complex models in deep reinforcement learning.