Reviewerly/exHarmony huggingface dataset:
Semi-CT: Certificates Transparent to Identity Owners but Opaque to Snoopers

Certificate Transparency (CT) enables timely detection of problematic certification authorities (CAs) by publicly recording all CA-issued certificates. This transparency inevitably leaks the privacy of identity owners (IdOs) through the identity information bound in certificates. In response to the privacy leakage, several privacy-preserving schemes have been proposed that transform/hash/encrypt the privacy-carrying part in certificates. However, these certificates conceal identity while also making it opaque to the IdO, which defeats the purpose of CT. To address the contradiction between transparency and privacy, we propose Semi-CT, a semi-transparency mechanism that makes the certificates transparent to IdOs but opaque to snoopers. Inspired by public-key encryption with keyword search (PEKS), Semi-CT based on bilinear pairing enables trapdoor-holding IdOs to retrieve certificates associated with their identity. Semi-CT also addresses protocol deviation detection and trapdoor protection in the malicious model. Finally, through theoretical and experimental analysis, we prove the security and feasibility of Semi-CT for practical applications.
Deep learning approach for Tunisian hate Speech detection on Facebook

We have witnessed a sharp increase in violence in Tunisia over the past few years. Violence affecting households, minorities, political parties, and public figures has increased more widely on social media. As a result, it has become easier for extremist, racist, misogynistic, and offensive articles, posts, and comments to be shared. Today, various international and governmental groups vowed to fight internet hate speech. This paper proposes a deep-learning solution to find hateful and offensive speech on Arabic social media sites like Facebook. We introduce two models: a Bi-LSTM based on an attention mechanism with integrating the BERT for Facebook comment classification toward hate speech detection. For this task, we collected 2k Tunisian dialect comments from Facebook. The proposed approach has been evaluated on three datasets, and the obtained results demonstrate that the proposed models can improve Arabic hate detection with an accuracy of 98.89%.
An Automatic Vision Transformer Pruning Method Based on Binary Particle Swarm Optimization

This paper presents an automatic vision transformer pruning method that aims to alleviate the difficulty of deploying vision transformer models on resource-constrained devices. The proposed method aims to automatically search for the optimal pruned model by removing irrelevant units while maintaining the original accuracy. Specifically, the model pruning is formulated as an optimization problem using binary particle swarm optimization. To demonstrate its effectiveness, our method was tested on the DeiT Transformer model with CIFAR-10 and CIFAR-100 datasets. Experimental results demonstrate that our method achieves a significant reduction in computational cost with slight performance degradation.
Driving into Danger: Adversarial Patch Attack on End-to-End Autonomous Driving Systems Using Deep Learning

Deep learning-based autonomous driving systems have been extensively researched due to their superior performance compared to traditional methods. Specifically, end-to-end deep learning systems have been developed, which directly output control signals for vehicles using various sensor inputs. However, deep learning techniques are vulnerable to security issues, generating adversarial examples that can attack the output of the relevant model. This paper proposes an adversarial example generation method that applies a patch to pedestrians' clothing, which can generate dangerous behaviors when the pedestrian appears within the camera lens, thereby attacking the end-to-end autonomous driving system. The proposed method is validated using the CARLA simulator, and the results demonstrate successful attacks in various weather and lighting conditions, exposing the security vulnerabilities of this type of system. This study highlights the need for further research to address these vulnerabilities and ensure the safety of autonomous driving systems.
A Fact-Searching and Matching System Based on News Content About COVID-19

With the global outbreak of the novel coronavirus, China has also been seriously affected, and news reports about the epidemic have flooded the internet. For some vulnerable groups and the general public, identifying the authenticity of the epidemic news content remains to be studied. This paper mainly addresses how to obtain effective knowledge of COVID-19 in the epidemic news content, and determines the optimal semantic model of retrieval and matching by means of multi-model fusion. Finally, the retrieval and matching system is constructed to obtain the best-matched news content in the form of fact questions. Based on the above scenarios, the main research contents of this paper include the construction of the COVID-19 fact knowledge map and research on multi-model fusion of fact question and answer retrieval and matching. In order to obtain the best fact matching more accurately, the basic Word2vec, BERT model, and unsupervised SimCSE two-tower model are selected in this paper. And the optimal semantic model is determined through the weighted fusion of the three models, as well as the construction of the fact retrieval and matching system of COVID-19 news content. Combined with the above research content, the main fact retrieval model of the system is constructed to obtain the sorted epidemic fact question-and-answer data as the main knowledge source. For the general COVID-19 fact issues, such as pneumonia symptoms, and treatment opinions, a fact retrieval and matching system based on the COVID-19 news content has been preliminarily realized.
A Novel Approach of ESN Reservoir Structure Learning for Improved Predictive Performance

This paper presents a novel method to enhance the predictive performance of the Echo State Network (ESN) model by adopting reservoir topology learning. ESNs are a type of Recurrent Neural Network (RNN) that have demonstrated considerable potential in various applications, but they can be challenging to train and optimize due to their random initialization. To improve the learning capabilities of ESNs and enhance their effectiveness in a broad range of predictive tasks, we utilize a structure learning algorithm. The proposed approach modifies the ESN reservoir's connectivity by applying techniques such as reversing, deleting, and adding new connections. We evaluate our proposal performance using both synthetic and real datasets, and our results indicate that it can substantially improve predictive accuracy compared to traditional ESNs.
When Robotics Meets Distributed Learning: the Federated Learning Robotic Network Framework

Federated Learning (FL) is a cutting-edge technology for distributed solving of large-scale problems using local data exclusively. The potential of Federated Learning is nowadays clear in different context from automatic analysis of healthcare data to object recognition in video sources coming from public video streams, from distributed search for data breach and finance frauds to collaborative learning of hand typing on mobile phone. Multi-robot systems can also largely benefit from FL concerning resolution of problems like trajectory prediction, non colliding trajectory generation, distributed localization and mapping or distributed reinforcement learning. In this paper we propose a multi-robot framework that includes distributed learning capabilities by using Decentralized Stochastic Gradient Descent on graphs. First of all we motivate the position of the paper discussing the privacy preserving problem for multi robot systems and the need of decentralized learning. Then we build our methodology starting from a set of prior definitions. Finally we discuss in details the possible applications in robotics field.
A Robust Prototype-Free Retrieval Method for Automatic Check-Out

In recent years, automatic check-out (ACO) gains increasing interest and has been widely used in daily life. However, current works mainly rely on both counter and product prototype images in the training phase, and it is hard to maintain the performance in an incremental setting. To deal with this problem, we propose a robust prototype-free retrieval method (ROPREM) for ACO, which is a cascaded framework composed of a product detector module and a product retrieval module. We use the product detector module without product class information to locate products. Additionally, we first attempt the check-out process as a retrieval process rather than a classification process. The retrieval result is considered as the product class by comparing the feature similarity between a query image and gallery templates. As a result, our method require much fewer training samples and achieves state-of-the-art performance on the public Retail Product Checkout (RPC) dataset.
Which2learn: A Vulnerability Dataset Complexity Measurement Method for Data-Driven Detectors

The increasing number of software vulnerabilities on complex programs has posed potential threats to cyberspace security. Recently, many data-driven methods have been proposed to detect such a large number of vulnerabilities. However, most of these data-driven detectors mainly focus on developing different models to improve the classification performance, ignoring the important research question that prior knowledge is learned from vulnerability datasets when training models. In this work, we propose a novel method to determine which dataset is relatively high-complexity for a data-driven detector to learn prior knowledge. Our method is called Which21earn for short. Our dataset complexity measure method is based on the sample's Program Dependence Graph. Experiments show that our dataset measurement method can improve the state-of-the-art GNN-based model's F1-score by about 9.5% in popular memory-related vulnerability detection. Moreover, our dataset measurement method can be easily extended to select training samples in most graph embedding machine learning tasks.
On the Efficacy of Differential Cryptanalysis Attacks on K-Cipher

K-Cipher is a hardware-efficient bit-length parameterizable cipher, which has been designed to be a flexible component of computing and communication systems. K-Cipher is latency- and area-efficient and can operate on all block lengths from 24 up to 1024 bits. In the paper, we show that the recently published Mahzoun-Kraleva-Posteuca-Ashur attack on K-Cipher [M. Mahzoun, L. Kraleva, R. Posteuca and T. Ashur, Differential Cryptanalysis of K-Cipher, IEEE ISCC 2022] is characterized by complexity significantly higher than <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2^{n}$</tex> , with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> being the block length. This holds for all block lengths specified by the cipher. Whereas the developers of the attack suggest that the key of the 24-bit version of K-Cipher can be successfully recovered at complexity 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">29.7</sup> , we show that, in reality, the complexity of this attack is at least 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">46</sup> . Similarly, the complexity of attacking the 32-bit version of K-Cipher is at least 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">54</sup> and the complexity of attacking the 64-bit version of K-Cipher is at least 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">86</sup> . Our conclusion is that unless the attack is redesigned, K-Cipher cannot be considered broken at this time, and its security needs to be further investigated by the community.
Streaming Session Recommendation Based on User's Global Attributes

Session-based recommendations are designed to predict the next item a user will click on. It is able to maintain the session structure of the session data. In practical scenarios, session data is dynamic and rapidly generated, which suggests the streaming nature of session data. Recent studies have shown that graph neural network (GNN) based approaches mainly focus on the current session and cannot process the latest session data. In this paper, we propose a streaming session-based recommendation system (UGNN). We use the Wasserstein model for data sampling. Then, the user configuration is embedded as a global attribute in the session graph and incorporated into the embedding vector of the recommendation system. Meanwhile, since the residual network of the Transformer model can effectively alleviate the pressure during model updates, we innovatively added Transformers to the model. Experiments on two real-world datasets show that our model outperforms state-of-the-art models.
Design and Implementation of Distributed Video AI Super-Resolution Reconstruction System

This article proposes a distributed video AI super-resolution reconstruction solution based on MapReduce to address the issue of slow video super-resolution reconstruction in a single-machine environment. The solution utilizes scene detection algorithms and precise video segmentation algorithms to divide long videos into multiple video segments based on scenes. These segments are then assigned to different processing nodes for parallel processing, and finally merged to reduce the time required for video AI super-resolution reconstruction. Experimental results demonstrate that this solution effectively improves the speed of video AI super-resolution reconstruction. In addition, this article addresses the issues of low efficiency and low storage space utilization in the distributed storage system HDFS (Hadoop Distributed File System) when storing small files, specifically video segments. It proposes an optimized storage solution based on HDFS and Redis. This solution merges small files and constructs a small file merging and storage model on Redis, effectively alleviating the memory pressure on the NameNode in HDFS when storing a massive number of small files. Experimental results demonstrate that this solution sacrifices a certain degree of read/write performance but mitigates the rapid growth of NameNode memory. This solution holds significant theoretical and practical significance.
A Blockchain-Based Personal Health Knowledge Graph for Secure Integrated Health Data Management

The increasing use of electronic health records (EHRs) and wearable devices has led to the creation of massive amounts of personal health data (PHD) that can be utilized for research and patient care. However, managing and integrating various types of PHD from different sources poses significant challenges, including data interoperability, data privacy, and data security. To address these challenges, this paper proposes a blockchain-based personal health knowledge graph for integrated health data management. The proposed approach utilizes knowledge graphs to structure and integrate various types of PHD, such as EHR, sensing, and insurance data, to provide a comprehensive view of an individual's health. The proposed approach utilizes blockchain to ensure data privacy and security. By storing PHD on a decentralized blockchain platform, patients have full control over their data and can grant access to specific entities as needed providing enhanced privacy and security.
Performance Evaluation of YOLOv5 in Adverse Weather Conditions

The technology of object detection is widely utilized in Intelligent Transportation Systems (ITSs) to ensure safe and dependable road experiences. However, adverse weather conditions can pose significant challenges for object detection applications in ITSs. This challenge arises from the traditional machine learning assumption that the training and testing sets have similar data distributions, which may not hold in some real-world situations. In this paper, the YOLOv5 model is trained and tested on the DAWN dataset, which is a diverse object detection dataset concerning weather conditions, to provide an analysis on this domain shift basis. The experiments are also conducted with a transfer learning-based YOLOv5 model to provide comparative analysis.
Robust Zero-watermarking Algorithm for Medical Images Based on GFTT-KAZE and DCT

As medical technology advances, medical images are becoming increasingly important in diagnosis and therapy. However, due to the potential repercussions of leaking or tampering, which could jeopardize patient privacy and treatment, the protection and management of medical images has become a critical concern. As a result, ensuring the encryption and confidentiality of medical images has become critical. Based on GFTT-KAZE-DCT, this research provides a strong digital watermarking system for medical photos. To begin, the GFTT (Good Features to Track) method is used to extract feature points from medical pictures. To describe the feature points, the KAZE method is used, resulting in a feature descriptor matrix. Perceptual hashing is used to further process the initial image. The feature matrix created by the KAZE descriptor is then subjected to DCT (Discrete Cosine Transform). The coefficient matrix generated by DCT is used to acquire the desired eigenvector. To better resist geometric and conventional attacks, the watermark is flexibly integrated with chaos encryption technology, hash function, and the “third-party concept.” In conclusion, our algorithm provides an effective way for protecting the privacy and integrity of medical images. It incorporates advanced techniques such as GFTT-KAZE-DCT and perceptual hash, and it can withstand a variety of attacks.
Personal Health Trajectory Traceability using Blockchain Technology

Healthcare delivery transformations and the use of connected health devices are paving the way to a paradigm shift from current healthcare systems towards patient-centered systems. Many proposals successfully reorient health information systems so that data are still distributed among the institutions and services that generate them, while being accessed jointly from a single point of view per patient. However, this means that control over the operations involving this data is lost. Mechanisms to maintain the traceability of health data are needed. This will enable the verification of the integrity of the records and will provide assurances that they have not been compromised. This problem has already been addressed in other domains such as food supply chains, where traceability allows to know all interactions with a food supply from the time it is produced until it is consumed. This paper proposes a blockchain solution to achieve the traceability of health data in patient-centered distributed environments. To validate this proposal, a case study involving 50 sociosanitary institutions in Portugal have been chosen. Different performance tests have been conducted to demonstrate the suitability, feasibility and scalability of our proposal.
Federated Learning Model for Contextual Sensitive Data Quality Applications: Healthcare Use Case

This paper proposes the novel approach of federated learning model with applying contextual data quality indexes to fit business goal. And this presents a federated learning model that applies a data quality management index that adapts to the purpose of business application of healthcare application with contextual data characteristics. The WoO based ontology data model was used to analyze the quality of contextual healthcare data.
A Network Vulnerability Assessment Method Based on Asset Weight

According to the characteristics of frequent node interaction and obvious differences in applications of marine network, this paper takes the degree of information interaction between assets as the basis for measurement. With the concept of logical edge number introduced, serial static and dynamic elements, such as upstream traffic, downstream traffic, number of logical edges, and number of applications, are taken as the evaluation indexes. Assigned values based on expert experience, the entropy weight method is applied to calculate and verify the comprehensive weight of network assets. According to the preset classification rules and CVSS vulnerability-scoring model, the vulnerability assessment of the virtual marine network is carried out. The results show that the selected indexes can calculate the weight of assets scientifically, and the method can provide a basis for the security optimization of the measured network.
Scattering-based Despeckling of Multi-frequency SAR Data

Speckle noise is a major factor impairing synthetic aperture radar (SAR) imagery interpretation and processing. In this paper, we propose a simple approach to deal with the despeckling of multi-frequency SAR data acquired over bare soil surfaces. The basic idea is to perform a proper normalization step in order to compensate the frequency dependence of the SAR measurements and make histograms of the multi-frequency images comparable. Once this pre-processing step is performed, conventional multi-temporal filtering can be applied. We tested this simple approach on both simulated and real-world multi-frequency datasets. Obtained results show that the proposed idea leads to increased speckle suppression capabilities with respect to both single-channel filtering and pure multi-temporal despeckling.
Variations and Extensions of Information Leakage Metrics with Applications to Privacy Problems with Imperfect Statistical Information

The conventional information leakage metrics assume that an adversary has complete knowledge of the distribution of the mechanism used to disclose information correlated with the sensitive attributes of a system. The only uncertainty arises from the specific realizations that are drawn from this distribution. This assumption does not hold in various practical scenarios where an adversary usually lacks complete information about the joint statistics of the private, utility, and the disclosed data. As a result, the typical information leakage metrics fail to measure the leakage appropriately. In this paper, we introduce multiple new versions of the traditional information-theoretic leakage metrics, that aptly represent information leakage for an adversary who lacks complete knowledge of the joint data statistics, and we provide insights into the potential uses of each. We experiment on a real-world dataset to further demonstrate how the introduced leakage metrics compare with the conventional notions of leakage. Finally, we show how privacy-utility optimization problems can be formulated in this context, such that their solutions result in the optimal information disclosure mechanisms, for various applications.
Aesthetic Visual Question Answering of Photographs

Aesthetic assessment of images can be categorized into two main forms: numerical assessment and language assessment. In this paper, we propose a new task of aesthetic language assessment: aesthetic visual question and answering (AVQA) of images. We use images from www.flickr.com. The objective QA pairs are generated by the proposed aesthetic attributes analysis algorithms. Moreover, we introduce subjective QA pairs that are converted from aesthetic numerical labels and sentiment analysis from large-scale pre-train models. We build the first aesthetic visual question answering dataset, AesVQA, that contains 72,168 high-quality images and 324,756 pairs of aesthetic questions. This is the first work that both addresses the task of aesthetic VQA and introduces subjectiveness into VQA tasks. The experimental results reveal that our methods outperform other VQA models on this new task.