Reviewerly/exHarmony huggingface dataset:
Quantum walks-based simple authenticated quantum cryptography protocols for secure wireless sensor networks

Abstract Wireless sensor networks (WSNs) play a crucial role in various applications, ranging from environmental monitoring to industrial automation that require high levels of security. With the development of quantum technologies, many security mechanisms may be hacked due to the promising capabilities of quantum computation. To address this challenge, quantum protocols have emerged as a promising solution for enhancing the security of wireless sensor communications. One of the common types of quantum protocols is quantum key distribution (QKD) protocols, which are investigated to allow two participants with fully quantum capabilities to share a random secret key, while semi-quantum key distribution (SQKD) protocols are designed to perform the same task using fewer quantum resources to make quantum communications more realizable and practical. Quantum walk (QW) plays an essential role in quantum computing, which is a universal quantum computational paradigm. In this work, we utilize the advantages of QW to design three authenticated quantum cryptographic protocols to establish secure channels for data transmission between sensor nodes: the first one is authenticated quantum key distribution (AQKD), the second one is authenticated semi-quantum key distribution (ASQKD) with one of the two participants having limited quantum capabilities, and the last one is ASQKD but both legitimate users possess limited quantum resources. The advantages of the proposed protocols are that the partners can exchange several different keys with the same exchanged qubits, and the presented protocols depend on a one-way quantum communication channel. In contrast, all previously designed SQKD protocols rely on two-way quantum communication. Security analyses prove that the presented protocols are secure against various well-known attacks and highly efficient. The utilization of the presented protocols in wireless sensor communications opens up new avenues for secure and trustworthy data transmission, enabling the deployment of resilient WSNs in critical applications. This work also paves the way for future exploration of quantum-based security protocols and their integration into WSNs for enhanced data protection.
Comprehension of the inner form of a musical work or the constitution of a new melos by means of a musical movement?

The paper discusses an alternative: comprehension of the inner form of a musical work or the constitution of a new melos by means of musical movement. We are talking about a special genre of art, free dance, called "musical movement", and in its two important contexts - staging performances and teaching. The concept of internal form and the appeal to it by the teacher and the director of performances Aida Aylamazyan is analyzed. The author offers a different understanding of the inner form than Aylamazyan. The musical movement formally consists of two different beginnings of the perception of serious music and organized movement in a broad sense (poses, tensions, emotions, coordination of movements with other dancers); the first beginning was formed in recording and listening to music, the second is mastered in the classroom. Entry into a musical movement occurs if these two beginnings merge into one. The movements of the dancers should not contradict the music, not destroy its perception. In addition, they should set a new organization of music, namely one that meets the idea of the organizer of the dance. As a result, a new objectivity is formed - a musical intonation organized by the musical movement. The article offers a new look at the concept of the inner form of a musical work, as well as an understanding of the musical movement itself. The formulated alternative is allowed if we distinguish between two different types of work: teaching musical movement and staging new performances. As part of the training, an adequate understanding of music is important. An important role here is played by the interpretation of a piece of music, which is created on the basis of semiotic schemes. Equally important is the training of dancers of the musical movement, during which they form a new hearing and understanding of music. As a teacher, Aylamazyan relies on the concept of inner form, as a director, she must discover a new content of a musical work that can be revealed in a concert with the help of musical movement.
PYS: A classification and extraction model of photovoltaics for providing more detailed data to support photovoltaic sustainable development

Photovoltaic power stations have grown unprecedentedly over the last few years. Accurate information regarding the photovoltaic (PV) types and spatial information is crucial for estimating power generation, modeling the environmental impact, and policymaking. However, most previous studies only focused on how to further improve the accuracy of PV spatial information extraction ignoring the importance of distinguishing PV types. Here we propose an instance segmentation model, named PYS, which can directly obtain PV type and spatial information. The PYS is an improved model that incorporates LSPPODC module, BiFormer module, and Wise-IoUv3 loss function into the YOLOv8l-seg model, thereby enhancing the PV extraction and classification performance. Experiments reveal that the PYS model effectively extracts and classifies photovoltaics into four types: land, roof, floating water, and stationary water photovoltaics. The Mask-mIoU, Mask-mAP and Box-mAP of the PYS model achieved 83.2 %, 92.5 % and 92.6 % respectively, outperforming other advanced instance segmentation models in terms of PV extraction and classification. The PYS model can help improve the accuracy and pertinence of extracting and classifying photovoltaics from remote sensing images, as well as provide critical information for the sustainable development of solar energy.
Coping with Bullying Incidents by the Narrative and Multi-modal Interaction in Virtual Reality

Bullying poses a significant threat to students’ well-being, causing enduring psychological and physical harm. Traditional teaching falls short in preparing students to confront and counteract bullying. This study introduces a VR-based approach, immersing users in bullying scenarios, allowing them to select responses leading to diverse outcomes. Employing multi-modal interaction through gestures and voice commands enhances immersion and user engagement. This method facilitates multidimensional engagement, fostering a tangible sense of presence. The system was assessed with twelve university students who underwent pre-interview evaluations of their bullying awareness. Results highlight the potential of virtual reality simulations as an educational tool, enabling effective bullying coping strategies. Participants praised the natural and fluid multi-modal interaction, along with the captivating interactive narrative, leading to repeated system engagement. Combining multi-modal interactions and VR-based bullying scenarios not only deepened participants’ comprehension of bullying but also equipped them with vital coping skills. In conclusion, this study showcases the promise of VR simulations and multi-modal interactions in educating students about bullying, empowering them to navigate such challenges adeptly.
Securing Digital Data: A New Edge Detection and XOR Coding Approach for Imperceptible Image Steganography

The rapid progress of digital devices and technol-ogy, coupled with the emergence of the internet has amplified the risks and perils associated with malicious attacks. Consequently, it becomes crucial to protect valuable information transmitted through the internet. Steganography is a tried-and-true technique for hiding information beneath digital content, such as pictures, texts, audio, and video. Various methodologies of image steganog-raphy have been developed recently. In image recognition, edge detection secures an image into well-defined areas. This paper introduces a novel image steganography algorithm with edge detection and XOR coding techniques. The proposed approach aims to conceal a confidential message within the spatial domain of the original image. In contrast to uniform regions, the Human Visual System (HVS) is less responsive to variations in the sharp areas; an edge detection algorithm is applied to identify edge pixels. Furthermore, to enhance the efficiency and reduce the embedding impact, XOR operation has been utilized to embed the secret message in the Least Significant Bit (LSB). According to the results of the experiments, the proposed method embeds confidential data without causing noticeable modifications to the stego image. The proposed method system produced imperceptible stego images with minimal embedding distortions compared to existing methods. Based on the results, the proposed approach outperforms the conventional methods regarding image distortion techniques. The PSNR values achieved by the proposed method are higher than the acceptable level.
Unpacking the Impact of Bitcoin Halving on the Crypto Market: Benefits and Limitations

Bitcoin halving events have become pivotal moments in the cryptocurrency market, generating widespread anticipation and speculation among investors, miners, and enthusiasts. Understanding the impact of these halving events requires a nuanced examination of their effects on supply dynamics, market sentiment, and the broader crypto ecosystem. Bitcoin halving events, while not directly affecting NFTs (Non-Fungible Tokens) and the metaverse, can have a notable impact on these ecosystems through interconnected market dynamics and investor sentiment. The relationship between Bitcoin, as the flagship cryptocurrency, and the broader crypto market often influences trends in specific sectors, including NFTs and the metaverse. As Bitcoin undergoes halving, the reduction in block rewards tends to generate heightened interest and speculation in the cryptocurrency space. This positive sentiment can create a favorable environment for various crypto assets, including those within the NFT and metaverse realms. Present research has focused on all these aspects.
Anonymization of face images with Contrastive Learning

Abstract Photos or videos taken by individuals often carry sensitive details such as facial identities, which has led to an escalating societal interest in privacy protection measures. We suggest an improved face identity transformer that offers password-protected anonymization and de-anonymization of photo-realistic facial images in visual data. Our face identity transformer is designed to (1) erase facial identity information after anonymization, (2) restore the original face when a correct password is provided and (3) generate an incorrect but realistic face when given an incorrect password. The processes of image anonymization and de-anonymization are facilitated through a password scheme, a multi-task learning objective and generative adversarial networks comprising InfoGAN and contrastive learning. In-depth experiments indicate that our methodology can execute anonymization and de-anonymization based on password conditions whilst reducing training time and enhancing image quality compared to existing anonymization procedures. Additionally, it maintains a recognition rate as low as 4.8% for anonymized images without sacrificing the face detection rate of the original method.
Privacy-Preserving Confidential Reporting System With Designated Reporters

Abstract A confidential reporting system (CRS) allows reporters to report concerns or problems in confidence without the fear of blame or reprisals. Nevertheless, privacy has been the primary concern of reporters. In this paper, we propose a privacy-preserving confidential reporting system with designated reporters (PPCRS-DR) to protect the privacy of reporters and the confidentiality of reports. Our PPCRS-DR provides the following interesting features: (1) for an event, an auditor can designate a reporter to report; (2) an auditor can neither see the report nor know the reporter’s identity from an encrypted report if the reporter is not the designated one; (3) when an auditor is unavailable, he/she can temporarily designate a delegatee to collect and review reports on behalf of him/her. We formalize both the definition and security model of our PPCRS-DR, and propose a concrete construction. Furthermore, the security of the proposed PPCRS-DR is formally proven. The implementation shows that it is efficient. The novelty is to implement flexible decryption delegation of CRSs and protect reporters’ privacy.
Can Large Language Models Provide Security &amp; Privacy Advice? Measuring the Ability of LLMs to Refute Misconceptions

Users seek security & privacy (S&P) advice from online resources, including trusted websites and content-sharing platforms. These resources help users understand S&P technologies and tools and suggest actionable strategies. Large Language Models (LLMs) have recently emerged as trusted information sources. However, their accuracy and correctness have been called into question. Prior research has outlined the shortcomings of LLMs in answering multiple-choice questions and user ability to inadvertently circumvent model restrictions (e.g., to produce toxic content). Yet, the ability of LLMs to provide reliable S&P advice is not well-explored.
Continuous Authentication Using Human-Induced Electric Potential

Most terminal devices authenticate users only once at the time of initial login, leaving the terminal unprotected during an active session when the original user leaves it unattended. To address this issue, continuous authentication has been proposed by automatically locking the terminal after a period of inactivity. However, it does not fully eliminate the risk of unauthorized access before the session expires. Recent research has also investigated the feasibility of using physiological and behavioral patterns as biometrics. This study presents a novel two-factor continuous authentication that explores a new form of signal called human-induced electric potential captured by wearables in contact with the user’s body. By analyzing this signal, we can determine the time of user-terminal interactions and compare it with information recorded by the terminal’s OS. If the original user remains on the same terminal, the two-source readings would match. Additionally, the proposed scheme includes an extra layer of protection by extracting terminal’s physical fingerprints from the human-induced electric potential to defend against advanced mimicry attacks. To test the effectiveness of our design, a low-cost wearable prototype is developed. Through extensive experiments, it is found that the proposed scheme has a low error rate of 2.3%, with minimal computational and energy requirements.
Artemis: Defanging Software Supply Chain Attacks in Multi-repository Update Systems

Modern software installation tools often use packages from more than one repository, presenting a unique set of security challenges. Such a configuration increases the risk of repository compromise and introduces attacks like dependency confusion and repository fallback. In this paper, we offer the first exploration of attacks that specifically target multiple repository update systems, and propose a unique defensive strategy we call articulated trust. Articulated trust is a principle that allows software installation tools to specify trusted developers and repositories for each package. To implement articulated trust, we built Artemis, a framework that introduces several new security techniques, such as per-package prioritization of repositories, multi-role delegations, multiple-repository consensus, and key pinning. These techniques allow for a greater diversity of trust relationships while eliminating the security risk of single points of failure.
SealClub: Computer-aided Paper Document Authentication

Paper documents, where digital signatures are not directly applicable, are still widely utilized due to usability and legal reasons. We propose a novel approach to authenticating paper documents by taking short videos of them with smartphones. Our solution combines cryptographic and image comparison techniques to detect and highlight semantic-changing attacks on rich documents, containing text and graphics. We provide geometrical arguments for the security of our novel comparison algorithm, and prove that its combination with a cryptographic protocol is secure against strong adversaries capable of compromising different system components. We also measure its accuracy on a set of 128 videos of paper documents and a set of 960 synthetically generated warped documents, half containing subtle forgeries. Our algorithm finds all forgeries accurately with no false positives. The highlighted regions are large enough to be visible to users, but small enough to precisely locate forgeries.
Poisoning Network Flow Classifiers

As machine learning (ML) classifiers increasingly oversee the automated monitoring of network traffic, studying their resilience against adversarial attacks becomes critical. This paper focuses on poisoning attacks, specifically backdoor attacks, against network traffic flow classifiers. We investigate the challenging scenario of clean-label poisoning where the adversary’s capabilities are constrained to tampering only with the training data — without the ability to arbitrarily modify the training labels or any other component of the training process. We describe a trigger crafting strategy that leverages model interpretability techniques to generate trigger patterns that are effective even at very low poisoning rates. Finally, we design novel strategies to generate stealthy triggers, including an approach based on generative Bayesian network models, with the goal of minimizing the conspicuousness of the trigger, and thus making detection of an ongoing poisoning campaign more challenging. Our findings provide significant insights into the feasibility of poisoning attacks on network traffic classifiers used in multiple scenarios, including detecting malicious communication and application classification.
DeepContract: Controllable Authorization of Deep Learning Models

Well-trained deep learning (DL) models are widely used in various fields and recognized as valuable intellectual property. However, most existing efforts to fully exploit their value either require users to upload input data to provide machine learning services, which raises serious privacy concerns, or deploy DL models on the user side, resulting in a loss of control over the models. While a few active model authorization methods protect the model from unauthorized users, they cannot prevent the model from being redistributed or abused by authorized users. To address the urgent need to efficiently protect both model confidentiality and input data privacy, and achieve uninterrupted model controllability, we propose a contract-based model authorization framework called DeepContract. This framework enables model owners to deploy their models on the user side for local inference without revealing original model weights. Moreover, it allows them to grant and revoke the right to use their models at any time. Specifically, we propose a generic model encryption method that significantly outperforms the state-of-the-art method in both efficiency and security. Leveraging the integrity verification in the Trusted Execution Environment, contract-based and verifiable enclave codes generated by DeepContract can perform controlled inference using the encrypted model distributed on the user side. Our extensive evaluations show that DeepContract can achieve efficient and secure controllable model authorization for the pre-signed contract.
PSP-Mal: Evading Malware Detection via Prioritized Experience-based Reinforcement Learning with Shapley Prior

With the widespread application of machine learning techniques in malware detection, researchers have proposed various adversarial attack methods to generate adversarial examples (AEs) of malware, thereby evading detection. Previous studies have shown that the reinforcement learning (RL) framework can enable black-box attacks by performing a sequence of function-preserving operations, which produces functional evasive malware samples. However, it is difficult to obtain the useful guidance and feedbacks from the environment for agent training in the black-box scenario, which results in the RL framework being unable to learn the effective evasion policy. In this paper, we propose the Shapley prior and establish a prior-guidance-based RL framework, namely PSP-Mal, to generate AEs against Portable Executable (PE) malware detectors. Our framework improves on existing methods in three aspects: 1) We explore feature effects of the black-box model by computing Shapley values and further propose the Shapley prior to represent the expected impact of operations. 2) A novel prioritized experience utilization mechanism is established regarding the Shapley prior guidance in the RL framework. 3) The actions are expanded into item-content pairs and we use the Thompson sampling to choose effective content, which helps to reduce randomness and ensure repeatability. We compare the attack performance of our framework with other methods, and experimental results demonstrate that our algorithm is more effective. The evasion rates of PSP-Mal against the LightGBM models trained on EMBER and SOREL-20M reach 76.88% and 72.03%, respectively.
ABFL: A Blockchain-enabled Robust Framework for Secure and Trustworthy Federated Learning

The demand for effective, safe, and privacy-preserving machine learning methods has increased due to the rapid growth of large pre-trained models in recent years. In large-scale AI applications, federated learning (FL) has emerged as a cutting-edge method for addressing privacy and data silos issues. However, FL systems are vulnerable to poisoning attacks, and centralized master-slave architectures have reliability, fairness, and security limitations. We propose a secure and efficient decentralized FL framework called ABFL to address these challenges. The framework tightly integrates FL with blockchain technology to strengthen data ownership guarantees and significantly lessen the negative impact of malicious nodes on the global model. Using historical data stored on the blockchain, ABFL enables model update prediction and identifies malicious nodes by verifying consistency. In addition, we present a novel agent consensus mechanism to lower the expense of model cross-validation and increase consensus efficiency. The ABFL framework’s robustness to various sophisticated poisoning attacks while maintaining high model performance and increasing consensus efficiency is demonstrated in a comprehensive analysis of three benchmark datasets.
PAVUDI: Patch-based Vulnerability Discovery using Machine Learning

Machine learning has been increasingly adopted for automatic security vulnerability discovery in research and industry. The ability to automatically identify and prioritize bugs in patches is crucial to organizations seeking to defend against potential threats. Previous works, however only consider bug discovery on statement, function or file level. How one would apply them to patches in realistic scenarios remains unclear. This paper presents a novel deep learning-based approach leveraging an interprocedural patch graph representation and graph neural networks to analyze software patches for identifying and locating potential security vulnerabilities. We modify current state-of-the-art learning-based static analyzers to be applicable to patches and show that our patch-based vulnerability discovery method, a context and flow-sensitive learning-based model, has a more than increased detection performance, is twice as robust against concept drift after model deployment and is particularly better suited for analyzing large patches. In comparison, other methods already lose their efficiency when a patch touches more than five methods.