Reviewerly/exHarmony huggingface dataset:
Robust data hiding for JPEG images with invertible neural network

JPEG compression will cause severe distortion to the shared compressed image, which brings great challenges to extracting messages correctly from the stego image. To address such challenges, we propose a novel end-to-end robust data hiding scheme for JPEG images. The embedding and extracting secret messages on the quantized discrete cosine transform (DCT) coefficients are implemented by the bi-directional process of the invertible neural network (INN), which can provide intrinsic robustness against lossy JPEG compression. We design a JPEG compression attack module to simulate the JPEG compression process, which helps the network automatically learn how to recover the secret message from JPEG compressed image. Experimental results have demonstrated that our method achieves strong robustness against lossy JPEG compression, and also significantly improves the security compared with the existing data hiding methods on the premise of ensuring image quality and high capacity. For example, the detection error of our method against XuNet has been increased by 3.45% over the existing data hiding methods.
Differential evolution based dual adversarial camouflage: Fooling human eyes and object detectors

Deep neural network-based object detectors are vulnerable to adversarial examples. Among existing works to fool object detectors, the camouflage-based method is more often adopted due to its adaptation to multi-view scenarios and non-planar objects. However, most of them can still be easily observed by human eyes, which limits their application in the real world. To fool human eyes and object detectors simultaneously, we propose a differential evolution based dual adversarial camouflage method. Specifically, we try to obtain the camouflage texture by the two-stage training, which can be wrapped over the surface of the object. In the first stage, we optimize the global texture to minimize the discrepancy between the rendered object and the scene background, making human eyes difficult to distinguish. In the second stage, we design three loss functions to optimize the local texture, which is selected from the global texture, making object detectors ineffective. In addition, we introduce the differential evolution algorithm to search for the near-optimal areas of the object to attack, improving the adversarial performance under certain attack area limitations. Experimental results show that our proposed method can obtain a good trade-off between fooling human eyes and object detectors under multiple specific scenes and objects.
Affective-Knowledge-Enhanced Graph Convolutional Networks for Aspect-Based Sentiment Analysis with Multi-Head Attention

Aspect-based sentiment analysis (ABSA) is a task in natural language processing (NLP) that involves predicting the sentiment polarity towards a specific aspect in text. Graph neural networks (GNNs) have been shown to be effective tools for sentiment analysis tasks, but current research often overlooks affective information in the text, leading to irrelevant information being learned for specific aspects. To address this issue, we propose a novel GNN model, MHAKE-GCN, which is based on the graph convolutional neural network (GCN) and multi-head attention (MHA). Our model incorporates external sentiment knowledge into the GCN and fully extracts semantic and syntactic information from a sentence using MHA. By adding weights to sentiment words associated with aspect words, our model can better learn sentiment expressions related to specific aspects. Our model was evaluated on four publicly benchmark datasets and compared against twelve other methods. The results of the experiments demonstrate the effectiveness of the proposed model for the task of aspect-based sentiment analysis.
StableFees: A Predictable Fee Market for Cryptocurrencies

Blockchain-based cryptocurrencies must solve the problem of assigning priorities to competing transactions. The most widely used mechanism involves each transaction offering a fee to be paid once the transaction is processed, but this discriminatory price mechanism fails to yield stable equilibria with predictable prices. We propose an alternate fee setting mechanism, StableFees, that is based on uniform price auctions. We prove that our proposed protocol is free from manipulation by users and miners as the number of users and miners increases and show empirically that gains from manipulation are small in practice. We show that StableFees reduces the fees paid by users and reduces the variance of fee income to miners. Data from December 2017 show that, if implemented, StableFees could have saved Bitcoin users $272,528,000 USD in transaction fees while reducing the variance of miner’s fee income, on average, by a factor of 7.4. We argue that our fee protocol also has important social welfare and environmental benefits. This paper was accepted by Agostino Capponi, Special Section of Management Science: Blockchains and Crypto Economics. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4735 .
Harvesting spatiotemporal correlation from sky image sequence to improve ultra-short-term solar irradiance forecasting

With photovoltaic power being increasingly integrated into power grid, accurately forecasting solar irradiance is of critical for ensuring stable and economical operation of power systems. The forecast accuracy in ultra-short-term horizons can be greatly improved by employing ground-based sky images. Although wide range of computer vision methods have been used for modelling, effectively extracting spatiotemporal features from sky image sequence is still a tough task. In this study, a sparse spatiotemporal feature descriptor is introduced to enhance the process of dynamic spatiotemporal information extraction from continuous grayscale sky images, while spatial pyramid pooling is used for feature refinement. Parallelly, dense convolutional network is used to extract static features from the nearest single-frame RGB sky images. Both dynamic and static spatiotemporal features were adequately extracted and subsequently fused for the multi-step prediction of global horizontal irradiance. In addition, various competitive models in object detection are adopted as benchmarks for comparison. The experimental results revealed that the proposed method outperformed baseline models, with up to 5.51% reduction on normalized root mean square error (NRMSE) and 9.38% improvement on ramp event forecast. The proposed method can be widely applied to photovoltaic stations equipped with all-sky-imagers.
VR2Gather: A Collaborative, Social Virtual Reality System for Adaptive, Multiparty Real-Time Communication

Virtual Reality telecommunication systems promise to overcome the limitations of current real-time teleconferencing solutions, by enabling a better sense of immersion and fostering more natural interpersonal interactions.Many solutions that currently enable immersive teleconferencing employ synthetic avatars to represent their users.However, photorealistic reconstructions have been shown to increase the sense of presence with respect to synthetic avatars in tele-immersive scenarios.In this paper, we present VR2Gather, a costumizable end-to-end system to transmit volumetric contents in multi-party real-time communication.We present the architecture and evaluate the costs and benefits of using different modules and transport mechanisms in terms of CPU usage, latency, and bandwidth.Moreover, we report the user experience based on applications the system has been used for, and how it was customised to meet the requirements using different acquisition and rendering modules.
Decentralized Society: Student’s Soul Using Soulbound Tokens

While the web3 is used to form a decentralized society and focuses particularly on financial transactions in the form of transferable tokens rather than ensuring trust among its entities. With this paper we discuss how non-transferable tokens referred as soulbound tokens (SBTs) can be used to make individuals more credible by having their affiliations encoded in the form of souls to ensure trust within the network. More specifically we discuss how the credentials of students regards to their academic achievements and their credibility can be stored and verified in an decentralized society which shall provide more privacy and security than a centralized system which shall eventually lead to have a higher credibility of data.
Improved Security for Cloud Storage Using Elgamal Algorithms Authentication Key Validation

Cloud computing is a newly developing concept in the area of computer technology. In one data centre, the data has traditionally been kept and managed. However, cloud computing nowadays offers computation as a service. It has the benefits of optimization, dependability, superior efficiency, and being a comparatively low-cost feasible solution compared to dedicated infrastructures. It expected to grow in the future. In this case, the system administrator will first encrypt the usable index using the ElGamal cryptosystem, which will then use to protect the data. Therefore, depending on the service, extensive data transferred over the network and this enormous amount of data retained in cloud service centers. The result introduces a new method of encrypting data known as batch key generation, which is a unique idea since it enables certain operations to done on the encrypted files, it is used to analysis of security and in which two parties exchange a large number of keys to enhance security. ElGamal variants divided into two kinds based on the method of creating signatures used in producing them. Afterwards, we build a general process that converts a collection of ElGamal variations into their threshold versions, and we demonstrate that the schemes we construct are unforgeable. In this article, we present a key encryption method for enhancing the privacy and security of information kept in the cloud while also reducing storage costs.
Envy Prediction from Users’ Photos using Convolutional Neural Networks

Envy is often considered a negative trait in human behavior. However, envy also has a positive insight that can motivate a person to accomplish her desired goals. In this paper, we propose a novel method to identify a user’s state of envy (i.e., benign or malicious) based on features from her photos. Specifically, we build a fine-tuned Convolutional Neural Network (CNN) model that takes the user’s photo as input and predicts whether the user has benign or malicious envy characteristics in the given photo. For this study, we create a new dataset containing photos of 255 users of different gender and age group. We conduct ablation studies to build an optimal CNN model to obtain a commendable test accuracy of 97.9%.
Blockchain Integration with Machine Learning for Securing Fog Computing Vulnerability in Smart City Sustainability

The advent of a smart city-based industrial Internet of Things (IIoT) is confidently built on the combined protocols of a virtual IPv6 addressing scheme and the fifth generation (5G) mobile network. For better network service and to achieve Quality of Experience (QoE) in the architecture. But this intelligent city architecture is vulnerable to several cyber-attack and malicious actors at the different layers which make it exposed to the same attacks as in the conventional IPv4 wireless sensor networks. However, this work aims to develop a blockchain-based machine learning (BML) security framework that secures the fog computing layer vulnerability in the smart city’s sustainability. The machine learning approach is firstly implemented between the edge layer and fog server nodes of the city architecture for the variants of intrusion detection using different ML algorithms for the attack’s discovery and classification. While the augmented blockchain technology is implemented between the fog layer and cloud computing to enhance the privacy and confidentiality of packet traffic broadcast to the public. The results obtained from ML-IDS show high-performance detection accuracy and low processing time. While the blockchain framework is also evaluated based on the certmcate generation, and retrieval size in bytes and time in milliseconds.
How Many Annotations Do We Need for Generalizing New-Coming Shadow Images?

Unlabeled data is often used to improve the generalization ability of one segmentation model. However, it tends to neglect the inherent difficulty of unlabeled samples, and then produces inaccurate pseudo masks in some unseen scenes, resulting in severe confirmation bias and potential performance degradation. These motivate two unexplored questions for new-coming data: (1) How many images do we need to annotate; and (2) how to annotate them? In this paper, two kinds of shadow detectors (i.e., SDTR and SDTR+) based on the Transformer and self-training scheme are successively proposed. The main difference between them is whether weak annotations are required for partial unlabeled data. Specifically, in SDTR, we first introduce an image-level sample selection scheme to separate the unlabeled data into reliable and unreliable samples from the holistic prediction-level stability. Then, we perform selective retraining to exploit the unlabeled images progressively in a curriculum learning manner. While in SDTR+, we further provide various weak labels (i.e., point, box and scribble) for the rest unreliable samples and design corresponding loss functions. By doing this, it can achieve a better trade-off between performance improvement and annotation cost. Experimental results on public benchmarks (i.e., SBU, UCF and ISTD) show that both SDTR and SDTR+ can be favorable against state-of-the-art methods.
Weak-Key Analysis for BIKE Post-Quantum Key Encapsulation Mechanism

The evolution of quantum computers poses a serious threat to contemporary public-key encryption (PKE) schemes. To address this impending issue, the National Institute of Standards and Technology (NIST) is currently undertaking the Post-Quantum Cryptography (PQC) standardization project intending to evaluate and subsequently standardize the suitable PQC scheme(s). One such attractive approach, called Bit Flipping Key Encapsulation (BIKE), has entered the final round of the competition. Despite having some attractive features, the IND-CCA security of BIKE depends on the average decoder failure rate (DFR), a higher value of which can facilitate a particular type of side-channel attack. Although BIKE adopts the Black-Grey-Flip (BGF) decoder that offers a negligible DFR, the effect of weak-keys on the average DFR has not been fully investigated. In this paper, we implement the BIKE scheme, and then through extensive experiments show that the weak-keys can be a potential threat to IND-CCA security of the BIKE scheme and thus need attention from the relevant research community. We also propose a key-check algorithm that can potentially supplement the BIKE mechanism and prevent users from adopting weak-keys.
Artificial Intelligence Based Virtual Machine Allocation and Migration Policy using Improved MBFD

With rising demand and an increase in the number of servers in data centres, cloud computing is a new era of technology that is based on pay-per-use. As the number of servers rises, so do the virtual machines (VMs), which must be distributed among physical hosts to satisfy client demands. These VMs must be appropriately assigned; otherwise, their erroneous allocation increases energy consumption. This can be avoided by reallocating these VMs to a suitable physical host that can accommodate their needs. The mechanism for allocating and migrating virtual machines is presented in this work. With the aid of our suggested optimization technique, the uniqueness in the two primary virtualization processes- VM allocation and VM migration-was explained in this study. In terms of VM allocation and migration, the proposed study reduced energy consumption relative to earlier research. The proposed work is broken down into two main sections: 1. VM allocation using Improved MBFD. b. SVM-based allocation verification to cut down on false migrations. This article is divided into five sections: Section 1 introduces virtualization, VM allocation, and migration; A brief summary of the literature is presented in Section 2, while Section 3 provides a suggested allocation and migration policy using an improved MBFD; and Section 4 offers results and discussion, followed by a conclusion and discussion of the article's future directions.
Towards fast ECC signing algorithms for Blockchain

Blockchain (BC) is a revolutionary technology that has dramatically impacted modern society thanks to its decentralization, transparency, and security properties. The BC is a distributed database that stores a register of assets and digitally signed transactions. Beginning in the financial field, BC was quickly applied to several fields, including the Internet of Things (IoT), supply chain, education, healthcare, and energy. In most BC-based applications, transactions are signed using ECDSA or EdDSA algorithms. However, scalar multiplication (SM) is a complex and central operation in these signing algorithms. This paper presents the most efficient techniques for computing SM to provide fast signature algorithms for BC-enabled applications.
VDoTR: Vulnerability detection based on tensor representation of comprehensive code graphs

Code vulnerability detection has long been a critical issue due to its potential threat to computer systems. It is imperative to detect source code vulnerabilities in software and remediate them to avoid cyber attacks. To automate detection and reduce labor costs, many deep learning-based methods have been proposed. However, these approaches have been found to be either ineffective in detecting multiple classes of vulnerabilities or limited by treating original source code as a natural language sequence without exploiting the structural information of code. In this paper, we propose VDoTR, a model that leverages a new tensor representation of comprehensive code graphs, including AST, CFG, DFG, and NCS, to detect multiple types of vulnerabilities. Firstly, a tensor structure is introduced to represent the structured information of code, which deeply captures code features. Secondly, a new Circle Gated Graph Neural Network (CircleGGNN) is designed based on tensor for hidden state embedding of nodes. CircleGGNN can perform heterogeneous graph information fusion more directly and effectively. Lastly, a 1-D convolution-based output layer is applied to hidden embedding features for classification. The experimental results demonstrate that the detection performance of VDoTR is superior to other approaches with higher accuracy, precision, recall, and F1-measure on multiple datasets for vulnerability detection. Moreover, we illustrate which code graph contributes the most to the performance of VDoTR and which code graph is more sensitive to represent vulnerability features for different types of vulnerabilities through ablation experiments.
An Investigation of Challenges Encountered When Specifying Training Data and Runtime Monitors for Safety Critical ML Applications

[Context and motivation] The development and operation of critical software that contains machine learning (ML) models requires diligence and established processes. Especially the training data used during the development of ML models have major influences on the later behaviour of the system. Runtime monitors are used to provide guarantees for that behaviour. [Question/problem] We see major uncertainty in how to specify training data and runtime monitoring for critical ML models and by this specifying the final functionality of the system. In this interview-based study we investigate the underlying challenges for these difficulties. [Principal ideas/results] Based on ten interviews with practitioners who develop ML models for critical applications in the automotive and telecommunication sector, we identified 17 underlying challenges in 6 challenge groups that relate to the challenge of specifying training data and runtime monitoring. [Contribution] The article provides a list of the identified underlying challenges related to the difficulties practitioners experience when specifying training data and runtime monitoring for ML models. Furthermore, interconnection between the challenges were found and based on these connections recommendation proposed to overcome the root causes for the challenges.
MQTT-A: A broker-bridging P2P architecture to achieve anonymity in MQTT

The demand for privacy in the current digital era is continuously growing. This is particularly true in the context of IoT, in which huge amounts of data are handled. Communication anonymity is a fundamental requirement when high privacy levels should be guaranteed. On the other hand, very little attention has been devoted to this problem in the past scientific literature, when referring to MQTT, which is the de-facto standard for IoT communication. In this paper, we try to cover this gap. Specifically, we propose a new protocol, called MQTT-A, which extends the MQTT bridging mechanism to support the anonymity of both publishers and subscribers. This task is accomplished through the P2P collaboration of intermediate bridge brokers, which forward the requests of clients so that the final broker cannot understand the actual source/destination. Moreover, an anonymity-preserving topic discovery mechanism is provided, which allows clients to discover available topics and associated brokers, preventing client identification. Importantly, all the MQTT-A messages are exchanged by leveraging standard MQTT primitives and the bridging mechanism natively offered by MQTT. This allows us not to require changes in the standard MQTT infrastructure. To validate the performance of our solution, we performed a deep experimental campaign by deploying the bridge brokers on cloud platforms in various countries of the world. The experimental validation shows that, the price of latency we have to pay because of the trade-off with anonymity is quite reasonable. Moreover, no significant impact on goodput occurs in the case of good network conditions.