Reviewerly/exHarmony huggingface dataset:
An eXplainable Artificial Intelligence Method for Deep Learning-Based Face Detection in Paintings

Recently, despite the impressive success of deep learning, eXplainable Artificial Intelligence (XAI) is becoming increasingly important research area for ensuring transparency and trust in deep models, especially in the field of artwork analysis. In this paper, we conduct an analysis of major research contribution milestones in perturbation-based XAI methods and propose a novel iterative method based guided perturbations to explain face detection in Tenebrism painting images. Our method is independent of the model's architecture, outperforms the state-of-the-art method and requires very little computational resources (no need for GPUs). Quantitative and qualitative evaluation shows effectiveness of the proposed method.
Conditional and Residual Methods in Scalable Coding for Humans and Machines

We present methods for conditional and residual coding in the context of scalable coding for humans and machines. Our focus is on optimizing the rate-distortion performance of the reconstruction task using the information available in the computer vision task. We include an information analysis of both approaches to provide baselines and also propose an entropy model suitable for conditional coding with increased modelling capacity and similar tractability as previous work. We apply these methods to image reconstruction, using, in one instance, representations created for semantic segmentation on the Cityscapes dataset, and in another instance, representations created for object detection on the COCO dataset. In both experiments, we obtain similar performance between the conditional and residual methods, with the resulting ratedistortion curves contained within our baselines.
Docflow: Supervised Multi-Method Document Anonymization Engine

Nowadays the process of anonymization of documents has been the subject of several studies and debates. By anonymization of documents, we mean the process of replacing sensitive data in order to preserve the confidentiality of documents without altering their content. In this work, we introduce Docflow, an open-source document anonymization engine capable of anonymizing documents based on specific filters chosen by the user. We applied Docflow to anonymize a set of legal documents and performed a processing performance analysis. By providing a Markdown input file to be anonymized, Docflow is able to redact all information according to users' choices, preserving the document content. Docflow will be integrated with NLP algorithms for the generation of the Markdown source file starting from documents already processed in different formats, but always with human supervision in the loop.
An E-Commerce Conversational Virtual Assistant in Service of Mild Cognitive Impairment Patients

Conversational Agent-based Virtual Assistants have seen an increase on functionality in the past recent years in E-commerce. Basing their functionality on advanced technology (NLP, ML, DL), they are able to satisfy the majority of the customer service needs of an e-shop, thus increasing user satisfaction while reducing operational costs. In our work, we propose and implement a Conversational Virtual Assistant addressing consumers diagnosed with Mild Cognitive Impairment. We design its features through the prism of their specific needs while navigating e-shop websites, in order to ameliorate their commercial transactions. The results drawn from the psychiatric trial conducted indicate a significant improvement in key indicators and metrics, along with stated enhancement of the users' experience. To the best of out knowledge, this is the first research work that addresses the subject within the aforementioned context.
Improved Dynamic Spatial-Temporal Attention Network for Early Anticipation of Traffic Accidents

The proliferation of dashcams has significantly increased the volume of recorded data on road traffic, offering a unique opportunity to develop sophisticated algorithms that can analyze this data and predict potential accidents. In this paper, we introduces a new approach for predicting car accidents in videos by leveraging the Dynamic Spatial-temporal Attention Network (DSTA). We incorporate a frame-level loss and a bag-level loss into our method to aid in the model's learning process. Moreover, given that car crashes often involve continuous processes, we introduce soft labels to smoothen the label transitions in each video frame, thereby helping the model to more accurately identify the accident frame number and minimizing the impact of labeling noise. Additionally, we employ the output of the temporal self-attention aggregation (TSAA) module to enhance the prediction's robustness, which extracts information from all frames of the current video and to avoid interference from individual difficult frames. Our experimental results indicate that our Improved-DSTA (IDSTA) method outperforms the original DSTA method and performs exceptionally well in the AVA dataset. Overall, our proposed approach demonstrates significant potential in predicting car accidents from dashcam videos.
CCSv6: A Detection Model for DNS-over-HTTPS Tunnel Using Attention Mechanism over IPv6

In this paper, we first show DNS-over-HTTPS (DoH) tunneling detection methods verified to be effective over IPv4 can be applied to IPv6, and then propose a new model called CCSv6, using attention-based convolution neural network to build classifiers with flow-based features to detect DoH tunneling over IPv6, achieve 99.99% accuracy on the IPv6 dataset. In addition, we discuss the influence of various factors such as locations or DoH resolvers on the detection results in detail over IPv6. All the more important, our model shows better transfer learning ability, which can achieve the F1-score of 96% when trained on the IPv6 dataset and tested on the IPv4 dataset.
White Blood Cell Segmentation Using DeepLabv3+ for Improved Hematological Disease Detection

The morphological analysis of the White Blood Cell (WBC) plays a crucial role in disease diagnosis using medical image analysis, particularly in treating haematological disorders like leukemia, lymphoma, anemia, and sickle cell disease. Clinical medical image analysis is manual and susceptible to subjectivity and human error. Therefore, deep learning-based methods are used to segment WBCs from microscopic blood smear images. This paper introduces a new DeepLabv3+-based WBC segmentation approach. The performance of the proposed approach is evaluated using the science bowl challenge dataset. The dataset is augmented using traditional techniques such as centre cropping, rotation, grid distortion, horizontal flipping, and vertical flipping. The performance of the proposed approach is measured using the parameters such as dice coefficient, Intersection of Union (IoU), precision, and recall. The results are compared with the results of traditional U-Net-based WBC segmentation. The experimental results show that training loss, training dice coefficient, validation dice coefficient, training IoU, validation IoU, precision and recall of the proposed approach are increased by 23.78%, 2.32%, 0.43%, 4.29%, 0.78%, 0.96%, 1.01%, respectively compared to U-Net-based WBC segmentation. Thus, DeepLabv3+-based WBC segmentation outperforms U-Net-based WBC segmentation.
Contrastive Learning with Attention Mechanism and Multi-Scale Sample Network for Unpaired Image-to-Image Translation

The aim of unpaired image translation is to learn how to transform images from a source to a target domain, while preserving as many domain-invariant features as possible. Previous methods have not been able to separate foreground and background well, resulting in texture being added to the background. Moreover, these methods often fail to distinguish different objects or different parts of the same object. In this paper, we propose an attention-based generator (AG) that can redistribute the weights of visual features, significantly enhancing the network's performance in separating foreground and background. We also embed a multi-scale multilayer perceptron (MSMLP) into the framework to capture features across a broader range of scales, which improves the discrimination of various parts of objects. Our method outperforms existing methods on various datasets in terms of Fréchet inception distance. We further analyze the impact of different modules in our approach through subsequent ablation experiments.
A Comprehensive Evaluation of the Impact on Tor Network Anonymity Caused by ShadowRelay

As a distributed anonymous network run by volunteers, Tor relays are often manipulated by operators to achieve their goals. Our work reveals that some relays, named ShadowRelay, are bound to hidden nodes and actively forward user traffic to the next-hop relay or target without the user's knowledge. To detect ShadowRelays, we developed HiddenSniffer based on client and Tor relay collusion, and found 162 hidden nodes distributed across 22 countries, along with 85 Shadow Relays which account for 2.08% of the total relay bandwidth. Additionally, there exists a family relationship among the Shadow Relays, with the largest family containing 24 members. The experimental results indicate that ShadowRelays have increased the number of ASes capable of sniffing user traffic by 27.6%, and improved the ability of 14.7% of attackers to launch traffic confirmation attacks. Furthermore, ShadowRelays adversely impact the Tor network's availability by introducing increased transmission delay within the circuits.
Image Coding Based on the Automatic Generation of Structural Shapes Using Evolutionary Methods

This study examines the efficient representation of natural objects with structural shapes, such as ferns and snow crystals. Based on the L-System, a formal grammar suitable for representing such structural shapes, evolutionary methods generate rules and parameters to produce images similar to a given image. MSE and SSIM have been the standard measures for quantifying similarity, but they were ineffective for this purpose because they were sensitive to differences in shape. Therefore, in this study, we used the LPIPS rating scale based on deep learning to quantify similarity robust to shape differences. Experimental results confirmed that the codes of the L-System rule that produce images similar to the input image could be obtained.
The Effect of Non-Reference Point Cloud Quality Assessment (NR-PCQA) Loss on 3D Scene Reconstruction from a Single Image

This paper proposes a two-stage approach for 3D scene reconstruction from a single image. The first stage involves a monocular depth estimation model, and the second stage involves a point cloud model that recovers depth shift and the focal length from the generated depth map. The paper investigates the use of various pre-trained state-of-the-art transformer models and compares them to existing work without transformers. The loss function is improved by adding a No-Reference point cloud quality assessment (NR-PCQA) to account for the quality of the generated point cloud structure. The paper reports results on four datasets using Locally Scale Invariant RMSE (LSIV) as the metric of evaluation. The paper shows that transformer models outperform previous methods, and transformer models that took into account NR-PCQA outperformed those that did not.
IBAM: IPFS and Blockchain based Authentication for MQTT protocol in IoT

Decentralized systems have proven themselves as a dominant authentication and storage paradigm for IoT systems where smartwatches are integrated with MQTT messaging framework for transmitting medical data to doctors. However, the security concerns with centralized frameworks presented vital security challenges regarding data privacy and network security for healthcare systems. This paper will present an integrated framework involving a reliable and lightweight e-health data-sharing framework that combines the decentralized interplanetary file system (IPFS) and blockchain on a smartwatch platform. Particularly, this framework helps in trustworthy control mechanisms which use smart contracts to achieve authentication and storage for both subscribers and publishers. We presented a simulation using Ethereum blockchain and IPFS in a real data-sharing scenario with the MQTT protocol. Our analysis proved that our approach satisfies lightweight access control requirements since it demonstrates the low latency of the framework and optimized energy consumption with high security and data privacy levels.
Face Restoration-Based Scalable Quality Coding for Video Conferencing

We investigate a new scalable framework for human face coding, where the reconstruction at the decoder synthesizes the output of two coding layers. The base layer embeds the input into a discrete code space described by a learned codebook and transmits the codebook indices to the decoder, from which the output of the base layer is generated. In addition, the input is optionally rescaled and coded using a neural network (NN)-based coder for the enhancement layer to add supplemental information from the original input to the generative output from the base layer. The final reconstruction then synthesizes the outputs from the two layers using a learned NN module with a weighting parameter. Furthermore, we apply an online learning scheme to the enhancement layer along with the weighting parameter to adaptively optimize the rate and distortion based on input. Experimental results show superior coding gains in various metrics as well as noticeable visual improvements with our method at the extremely lower bits per pixel compared to the latest H.266/VVC standard.
BVSNO: Binary Code Vulnerability Detection Based on Slice Semantic and Node Order

The proliferation of code reuse and the prevalence of CPU architecture and compilation environment diversity inevitably lead to many similar cross-platform binary vulnerability codes. This paper designs a deep learning model based on slice semantic and node order to detect similarity vulnerabilities. Firstly, it traverses the program dependence graph (PDG) forward and backward from the library/API function node to generate the binary slice and then uses the bidirectional long short-term memory (BLSTM) network and attention mechanism to form the semantic feature vector of the binary slice. Secondly, it extracts the order information of the slice nodes in the PDG and forms the adjacency matrix, which is then fed into the convolutional neural network (CNN) to form the order feature vector. Finally, the semantic and order feature vector are fused and inputted into the siamese network for similarity vulnerability detection. The detection results show that our method can effectively detect vulnerability.
The Importance of Robust Features in Mitigating Catastrophic Forgetting

Continual learning (CL) is an approach to address catastrophic forgetting, which refers to forgetting previously learned knowledge by neural networks when trained on new tasks or data distributions. The adversarial robustness has decomposed features into robust and non-robust types and demonstrated that models trained on robust features significantly enhance adversarial robustness. However, no study has been conducted on the efficacy of robust features from the lens of the CL model in mitigating catastrophic forgetting in CL. In this paper, we introduce the CL robust dataset and train four baseline models on both the standard and CL robust datasets. Our results demonstrate that the CL models trained on the CL robust dataset experienced less catastrophic forgetting of the previously learned tasks than when trained on the standard dataset. Our observations highlight the significance of the features provided to the underlying CL models, showing that CL robust features can alleviate catastrophic forgetting.
Towards One-Shot Learning for Text Classification using Inductive Logic Programming

With the ever-increasing potential of AI to perform personalised tasks, it is becoming essential to develop new machine learning techniques which are data-efficient and do not require hundreds or thousands of training data.In this paper, we explore an Inductive Logic Programming approach for one-shot text classification.In particular, we explore the framework of Meta-Interpretive Learning (MIL), along with using common-sense background knowledge extracted from ConceptNet.Results indicate that MIL can learn text classification rules from a small number of training examples, even one example.Moreover, the higher complexity of chosen example for one-shot learning, the higher accuracy of the outcome.Finally, we utilise two approaches, Background Knowledge Splitting and Average One-Shot Learning, to evaluate our model on a public News Category dataset.The outcomes validate MIL's superior performance to the Siamese net for one-shot learning from text.
The growing importance of digital risk&amp;governance

The aim of the paper is to explain what is meant by Digital Risk&amp;Governance. For this purpose, it is important to retrace the technological evolution that has affected the last few decades: from branches to Mobile Banking, from the digitalization of transactions to the creation of Fintech, from the first process automations to Artificial Intelligence. This evolutionary journey has not only involved and still involves the birth of new technologies, but also the possibility of seizing new business opportunities and therefore necessarily of facing new types of risk, which are not always intuitive and easy to fully understand and manage. In this context, the role of the Regulator is fundamental not only to make available to companies elements for a correct and complete understanding of Digital/ICT Risk, but also to provide guidelines that allow for the construction of an organizational and governance model suitable for gaining awareness risk and to assess, manage and monitor it. A fundamental role is played by the Digital Operational Resilience Act (DORA), which certainly better defines some aspects that until recently did not find a clear place, but - even more important - which allows these aspects to be included in an organic and holistic framework. Governance and organization are essential in this panorama, the only functions capable of spreading the risk culture necessary to overcome the silo mentality and to establish the cultural paradigm change essential for managing ICT Risk. Given the extension of the perimeter that is generally included under this risk, the paper goes on to underline the most relevant aspects and suggests in a practical way the components on which companies should concentrate in order to implement and make usable an all-round management framework: from the identification of critical functions to the importance of having tools capable of certifying the correctness, completeness and quality of the data. Another high-sounding and closely related theme, which therefore could not fail to be addressed in the paper, is represented by the cyberattack and its impacts on the market. The paper then closes with a theme which, in our opinion, plays an even more stately role than the creation of an overall framework can play: the Digital Strategy, consciously accessible only through a Digital Risk&amp;Governance framework, but which represents the ultimate goal to which companies should aspire.
Progressive Learning Vision Transformer for Open Set Recognition of Fine-Grained Objects in Remote Sensing Images

Open set recognition (OSR) aims to classify known classes and recognize unknown classes simultaneously. Existing OSR methods have primarily focused on learning decision boundaries based on overall feature representations, and have achieved good performance on various coarse-grained image datasets. However, the overall feature representations of objects in fine-grained image datasets are highly similar, making it difficult to distinguish between known and unknown classes by overall feature-based decision boundaries. To address this problem, we propose a progressive learning vision transformer (PLViT) with a coarse-to-fine optimization strategy. In PLViT, the overall feature representations are first optimized in the distance space to learn the initial decision boundaries. Then, a context-aware patch selection module is designed to locate the discriminative part regions. Afterwards, the multi-layer representations of each selected patch are aggregated according to the self-attention weights, and input into the last transformer layer to extract local feature representations. Finally, overall and local feature representations are adaptively fused and optimized in the angular space to further refine the decision boundaries. Experimental results on four fine-grained remote sensing object recognition datasets show that PLViT outperforms state-of-the-art methods.
Super-Resolution Reconstruction for Stereoscopic Omnidirectional Display Systems via Dynamic Convolutions and Cross-View Transformer

Stereoscopic OmniDirectional Images (SODIs) usually require recording very High-Resolution (HR) information whereby it is beneficial to exploit a Super-Resolution (SR) scheme to super-resolve Low-Resolution (LR) SODIs. Compared with traditional 2D SR approaches, the algorithms of SODI SR (SODI-SR) need to deal with two extra aspects: binocular information and panoramic characteristics. In this paper, we first build a synthetic-specific SODI-SR dataset with LR-HR image pairs. Then we propose a Dynamic convolutions and Transformer Network (Dconv-Trans-Net) for SODI-SR. Specifically, due to the non-uniform sampling of EquiRectangular Projection (ERP), we deploy dynamic convolutions with the structure of Atrous Spatial Pyramid Pooling (ASPP) to adaptively select content-aware and weight-aware kernels for patch-wise feature extraction. To capture diverse feature embeddings from left and right views, we utilize a symmetric bi-directional Parallax Attention Module (biPAM) to extract local features along epipolar lines and propose Cross-view Transformer (CvTrans) to mine global contextual features apart from the epipolar line. Finally, quantitative and qualitative experiments demonstrate that our proposed approach outperforms the state-of-the-art panoramic or stereoscopic SR methods on the constructed SODI-SR dataset with two upscaling factors.
ADPTriage: Approximate Dynamic Programming for Bug Triage

Bug triaging is a critical task in any software development project. It entails triagers going over a list of open bugs, deciding whether each is required to be addressed, and, if so, which developer should fix it. However, the manual bug assignment in Issue Tracking Systems (ITS) offers only a limited solution and might easily fail when triagers are required to handle a large number of bug reports. During the automated assignment, there are multiple sources of uncertainties in the ITS, which should be addressed meticulously. In this study, we develop a Markov decision process (MDP) model for an online bug triage problem. In addition to an optimization-based myopic technique, we provide an ADP-based bug triage solution, called ADPTriage, which has the ability to reflect the downstream uncertainty in the bug arrivals and developers’ timetables. Specifically, without placing any limits on the underlying stochastic process, this technique enables real-time decision-making on bug assignments while taking into consideration developers’ expertise, bug type, and bug fixing time. Our result shows a significant improvement over the myopic approach in terms of assignment accuracy and fixing time. We also demonstrate the empirical convergence of the model and conduct sensitivity analysis with various model parameters. Accordingly, this work constitutes a significant step forward in addressing the uncertainty in bug triage.