Reviewerly/exHarmony huggingface dataset:
Eliminating Adversarial Perturbations Using Image-to-Image Translation Method

Convolutional neural networks are widely used for image recognition tasks, but they are vulnerable to adversarial attacks that can cause the model to misclassify an image. Such attacks pose a significant security risk in safety-critical applications like facial recognition and autonomous driving. Researchers have made progress in defending against adversarial attacks through two approaches: enhancing the neural networks themselves to be more robust and removing the perturbation added to the image through pre-processing. This paper is based upon a recent defense model that belongs to the latter approach, which utilizes image-to-image translation to regenerate images perturbed by adversarial attacks. We optimized the training process of their model and tested the model performance against more recent and strong attacks. The results show that the model is able to regenerate images attacked by the state-of-the-art attack, the AutoAttack, and restores the classification accuracy to a level over 83% to that of the original images.
Blockchain-Enabled Data Sharing in Connected Autonomous Vehicles for Heterogeneous Networks

In this paper, we consider the use of blockchain for a challenging Connected Autonomous Vehicles (CAV) application scenario of data sharing that has stringent security requirements. We discuss enhanced delegated Proof-of-Stake (dPoS) consensus and combine it with three different reputation schemes, which are Multi-Weight-Subjective-Logic (MWSL), beta, and sigmoid-based reputation schemes. To determine malicious miners in the system, we first compute the overall latency of block generation as the sum of communication, computational, information propagation and queuing latency. We then evaluate the performance of this scheme under simple and orchestrated adversary attack models. We do the same analysis for a heterogeneous network, where we validate our results with a 5G/6G hybrid network. Our results indicate that in large scale networks, MWSL reputation based enhanced dPoS scheme can detect orchestrated attacks in few seconds for 6G networks. Heterogeneous deployment schemes also perform relatively well. In comparison, 4G and 5G perform poorly, and might not be suitable for blockchain implementations.
Hybrid Post-quantum Signatures in Hardware Security Keys

Recent advances in quantum computing are increasingly jeopardizing the security of cryptosystems currently in widespread use, such as RSA or elliptic-curve signatures. To address this threat, researchers and standardization institutes have accelerated the transition to quantum-resistant cryptosystems, collectively known as Post-Quantum Cryptography (PQC). These PQC schemes present new challenges due to their larger memory and computational footprints and their higher chance of latent vulnerabilities. In this work, we address these challenges by introducing a scheme to upgrade the digital signatures used by security keys to PQC. We introduce a hybrid digital signature scheme based on two building blocks: a classically-secure scheme, ECDSA, and a post-quantum secure one, Dilithium. Our hybrid scheme maintains the guarantees of each underlying building block even if the other one is broken, thus being resistant to classical and quantum attacks. We experimentally show that our hybrid signature scheme can successfully execute on current security keys, even though secure PQC schemes are known to require substantial resources. We publish an open-source implementation of our scheme at https://github.com/google/OpenSK/releases/tag/hybrid-pqc so that other researchers can reproduce our results on a nRF52840 development kit.
A Comparison of Multi-task Learning and Single-Task Learning Approaches

In this paper, we provide experimental evidence for the benefits of multi-task learning in the context of masked AES implementations (via the ASCADv1-r and ASCADv2 databases). We develop an approach for comparing single-task and multi-task approaches rather than comparing specific resulting models: we do this by training many models with random hyperparameters (instead of comparing a few highly tuned models). We find that multi-task learning has significant practical advantages that make it an attractive option in the context of device evaluations: the multi-task approach leads to performant networks quickly in particular in situations where knowledge of internal randomness is not available during training.
SoK: Assisted Fault Simulation

Fault injection attacks have caused implementations to behave unexpectedly, resulting in a spectacular bypass of security features and even the extraction of cryptographic keys. Clearly, developers want to ensure the robustness of the software against faults and eliminate production weaknesses that could lead to exploitation. Several fault simulators have been released that promise cost-effective evaluations against fault attacks. In this paper, we set out to discover how suitable such tools are, for a developer who wishes to create robust software against fault attacks. We found four open-source fault simulators that employ different techniques to navigate faults, which we objectively compare and discuss their benefits and drawbacks. Unfortunately, none of the four open-source fault simulators employ artificial intelligence (AI) techniques. However, AI was successfully applied to improve the fault simulation of cryptographic algorithms, though none of these tools is open source. We suggest improvements to open-source fault simulators inspired by the AI techniques used by cryptographic fault simulators.
A Cloud-Scale Characterization of Remote Procedure Calls

The global scale and challenging requirements of modern cloud applications have led to the development of complex, widely distributed, service-oriented applications. One enabler of such applications is the remote procedure call (RPC), which provides location-independent communication and hides the myriad of cloud communication complexities and requirements within the RPC stack. Understanding RPCs is thus one key to understanding the behavior of cloud applications. While there have been numerous studies of RPCs in distributed systems, as well as attempts to optimize RPC overheads with both software and hardware, there is still a lack of knowledge about the characteristics of RPCs "in the wild" in the modern cloud environment.
Federated Learning with Swift: An Extension of Flower and Performance Evaluation

<h2>Abstract</h2> Federated learning (FL) enables the optimization of machine learning models on distributed clients without sharing local data. The integration of FL into a mobile environment is becoming more feasible due to increasing on-device processing capabilities. However, there is limited open-source support for the iOS platform. The article introduces a Swift-based client implementation of the user-friendly FL framework Flower. The objective is facilitating FL client processes based on a modular and easy-to-integrate software development kit. A benchmark test demonstrates consistent stability and performance using the software, further motivating its use for research.
How Much Unlabeled Data is Really Needed for Effective Self-Supervised Human Activity Recognition?

The prospect of learning effective representations from unlabeled data alone has led to a boost in developing self-supervised learning (SSL) methods for sensor-based Human Activity Recognition (HAR). Typically, (large-scale) unlabeled data are used for pre-training, with the learned weights being used as feature extractors for recognizing activities. While prior works have focused on the impact of increased data scale on performance, instead, we aim to discover the pre-training data efficiency of self-supervised methods. We empirically determine the minimal quantities of unlabeled data required for obtaining comparable performance to using all available data. We investigate three established SSL methods for HAR on three target datasets. Out of these three methods, we discover that Contrastive Predictive Coding (CPC) is the most efficient in terms of pre-training data requirements: just 15 minutes of sensor data across participants is sufficient to obtain competitive activity recognition performance. Further, around 5 minutes of source data is enough when there are sufficient amounts of target application data available. These findings can serve as starting point for more efficient data collection practices.
Detecting Thumb-Posture for One-handed Interactions with Smartphone using Acoustic Sensing

This paper presents a novel approach for expanding one-handed interactions using the thumb positioned above the smartphone screen. Our approach is based on acoustic sensing, a technique for leveraging the built-in speaker and microphone of the smartphone without requiring additional sensors or attachments. We explored the feasibility of our approach on smartphones with the conventional speaker and microphone arrangement and investigated the enhancement of recognition accuracy by using smartphones equipped with Acoustic Surface, which is a technology enabling the entire screen to vibrate and emit sound over a wider area and installed in several commercial smartphones such as LG G8 ThinQ and Huawei P30 Pro. We focused on classifying 12 different thumb postures and developed models that achieve prediction accuracies of 78.6% (conventional smartphone) and 87.0% (Acoustic Surface).
Acoustic+Pose: Adding Input Modality to Smartphones with Near-Surface Hand-Pose Recognition using Acoustic Surface

To achieve mid-air interactions for smartphones, acoustic-sensing, a technique using the built-in speaker and microphone of smartphones, is promising. However, detecting hand poses on the near-surface of touchscreens remains challenging due to the arrangement of the built-in speaker and microphone. To address this, we present Acoustic+Pose, a novel approach for combining conventional touch interactions with near-surface hand-pose estimation to enable a wide range of interactions. We focused on smartphones incorporating Acoustic Surface, a technology that vibrates the entire smartphone screen to emit sound over a wide area. We used this technology to extend the input space to the near surface of touchscreens. We trained machine-learning models to recognize hand poses in the near-surface area and demonstrated interaction techniques to use the recognized poses for a new modality of smartphone input. Through an evaluation, we confirmed that the trained models recognized 10 hand poses with 90.2% accuracy.
Private Web Search with Tiptoe

Tiptoe is a private web search engine that allows clients to search over hundreds of millions of documents, while revealing no information about their search query to the search engine's servers. Tiptoe's privacy guarantee is based on cryptography alone; it does not require hardware enclaves or non-colluding servers. Tiptoe uses semantic embeddings to reduce the problem of private full-text search to private nearest-neighbor search. Then, Tiptoe implements private nearest-neighbor search with a new, high-throughput protocol based on linearly homomorphic encryption. Running on a 45-server cluster, Tiptoe can privately search over 360 million web pages with 145 core-seconds of server compute, 56.9 MiB of client-server communication (74% of which occurs before the client enters its search query), and 2.7 seconds of end-to-end latency. Tiptoe's search works best on conceptual queries ("knee pain") and less well on exact string matches ("123 Main Street, New York"). On the MS MARCO search-quality benchmark, Tiptoe ranks the best-matching result in position 7.7 on average. This is worse than a state-of-the-art, non-private neural search algorithm (average rank: 2.3), but is close to the classical tf-idf algorithm (average rank: 6.7). Finally, Tiptoe is extensible: it also supports private text-to-image search and, with minor modifications, it can search over audio, code, and more.
Honey, I Cached our Security Tokens Re-usage of Security Tokens in the Wild

In order to mitigate the effect of Web attacks, modern browsers support a plethora of different security mechanisms. Mechanisms such as anti-Cross-Site Request Forgery (CSRF) tokens or nonces in a Content Security Policy rely on a random number that must only be used once. Notably, those Web security mechanisms are shipped through HTML tags or HTTP response headers from the server to the client side. To decrease the server load and the traffic burdened on the server infrastructure, many Web applications are served via a Content Delivery Network (CDN), which caches certain responses from the server to deliver them to multiple clients. This, however, affects not only the content but also the settings of the security mechanisms deployed via HTML meta tags or HTTP headers. If those are also cached, their content is fixed, and the security tokens are no longer random for each request. Even if the responses are not cached, operators may re-use tokens, as generating random numbers that are unique for each request introduces additional complexity for preserving the state on the server side. This work sheds light on the re-usage of security tokens in the wild, investigates what caused the static tokens, and elaborates on the security impact of the non-random security tokens.
Effects of non-Markovian squeezed bath on the dynamics of open systems

Control of the dynamics of an open quantum system is crucial in quantum information processing. Basically there are two ways: one is the control on the system and the other is tuning the bath parameters. In this paper, we use the latter to analyze the non-Markovian dynamics of the open system. The model is that the system is immersed in non-Markovian squeezed baths. For the dynamics, a non-Markovian master equation is obtained using the quantum state diffusion (QSD) equation technique for the weak system–bath couplings. We take the adiabatic evolution or quantum state transmission as examples to analyze the effects of the bath parameters: non-Markovianity γ, the squeezed direction θ and squeezed strength r. For the adiabatic or state transmission fidelity, the calculation results show that they both can be enhanced by a smaller γ or bigger p-quadrature. Interestingly, when 0<θ<π/2, the squeezed quadrature is determined by the combination of r and θ, and by numerical simulation we find that the fidelity peak occurs at r=1−2θ/π. The fidelities increase with increasing r when r∈(0,1−2θ/π]. When θ≥π/2, lower fidelities are obtained due to the squeezed baths. Our results show that the dynamics of the open systems can be effectively controlled by reservoir engineering.
Research and application of Big data encryption technology based on quantum lightweight image encryption

Given the limitations of current classic image encryption algorithms and the unconditional security characteristics of quantum key distribution protocols, the author proposes an image encryption method based on quantum key distribution and pixel scrambling. The author first analyzed the security issues of quantum image encryption algorithms based on image scrambling methods, namely that image scrambling methods have periodicity, which can ensure that the image can be restored after scrambling. If an image cannot be guaranteed to return to its original state after performing scrambling operations, legitimate users cannot obtain the secret information in the original image, and image scrambling loses its original meaning; However, the periodicity of image scrambling methods not only ensures that legitimate users can decrypt images, but also provides convenience for attackers to steal original image information. Secondly, it is pointed out that when evaluating and analyzing quantum image encryption algorithms, the evaluation indicators should include two parts: One is performance analysis, which is the analysis of the network complexity of quantum circuits. In quantum computing, the network complexity of quantum circuits is usually represented by the number of quantum basic logic gates. The second is security analysis, which mainly includes key analysis (including key space size and key sensitivity analysis), statistical analysis (including pixel correlation, histogram analysis, information fusion, and scatter plot distribution density analysis). Finally, experimental simulations have shown that the NPCR values of the plaintext and ciphertext images of the three images tend to 100%. The encrypted image histogram is evenly distributed, with very weak pixel correlation and high pixel change rate and information entropy.
"I Want to Reveal, but I Also Want to Hide" Understanding the Conflict of Revealing and Hiding Needs in Virtual Study Rooms

Since the COVID-19 pandemic, video conferencing platforms have given rise to new virtual activities, such as virtual study rooms where users utilize video to share ambient presence for study motivation. In virtual study rooms, it can be challenging for the users to determine what to reveal and what to hide on camera, as the video needs to strongly convey their presence without revealing more than necessary. In this paper, we investigate the conflicting needs of virtual study room users to reveal and hide on camera, as well as the methods they employ to cope with these needs using videos. To this end, we conducted a three-step qualitative study. The first study involved interviews to discover the key user needs that entail the conflict to reveal and hide. The second study utilized virtual study room screen analysis to identify the video features that characterize virtual study room videos. In the last study, we employed interviews to associate the video features with the key user needs. Based on these findings, we discussed the effects of studying together that could be applied to a non-physical and non-interactive co-studying environment and the need for further development of video conferencing tools to effectively share ambient presence.
Enhancing Supply Chain Traceability with Blockchain Technology: A Study on Dairy, Agriculture, and Seafood Supply Chains

The aim of this research study is to investigate how blockchain technology can enhance transparency and responsibility within the food supply chain. with a focus on three supply chains: dairy, agriculture, and seafood. The challenges associated with these supply chains, including issues related to food safety, sustainability, and labor practices, are discussed. This research study proposes three different solutions based on blockchain technology to address these challenges. Firstly, a framework model for traceability in the dairy supply chain is presented. Secondly, a proposal for a theoretical framework of an agriculture food supply chain system that utilizes blockchain technology. Finally, a blockchain-based solution is presented to enhance traceability and accountability in the seafood supply chain. In the agriculture supply chain, the current centralized system has communication gaps and lacks product origin information. This research study proposes a fully decentralized and secure system using smart contracts for transaction coordination, which ensures transparency and integrity in the supply chain. Food traceability is a critical issue for dairy products globally, but reliable solutions are lacking. This research study discusses the need for advancement in traceability solutions for domestic dairy brands to enhance food safety. The dairy industry's concerns regarding traceability and food safety can potentially be addressed by exploring the use of blockchain and its related technologies. A review of the use of blockchain in the fish supply chain, suggests that while it is a promising concept that creates a transparent, supervisable, safe, and convenient supply chain, it is still immature and challenging to implement due to its complexity. The review highlights the need for further research and development in this area to fully realize the potential of blockchain technology in the fish supply chain.
A Blockchain-Based E-Voting System for India: Addressing Security Challenges with Aadhaar Card Authentication

The integration of technology in various spheres of social life has brought about positive transformations, and one significant game-changer is blockchain technology. With its decentralized architecture and immutability property, blockchain has revolutionized numerous services, offering a level playing field for both consumers and large entities like corporations and governments. The immutability feature ensures the integrity of the voting process by eliminating any possibility of tampering, while the decentralized nature of the blockchain allows for transparency, enabling independent verification of election results. Although blockchain-based e-voting systems provide advantages such as transparency, immutability, and decentralization, there are also challenges that need to be addressed. Among these challenges, scalability is a major concern. Additionally, ensuring the security of the election process is crucial, covering aspects like voter impersonation, hacking, and tampering of votes. However, by incorporating robust measures such as Aadhaar card authentication for voter identification, along with the implementation of advanced security protocols like multi-factor authentication and encryption.
Ludic and Narrative Immersion in Virtual Reality Exposure Therapy to Animal Phobias: A Systematic Literature Review

In the context of therapeutic exposure to phobias, virtual reality (VR) offers innovative ways to motivate patients to confront their fears, an opportunity not feasible in traditional non-digital settings. This systematic literature review explores the utilization of narratives and digital games in this context, focusing on identifying the most common ludic and narrative immersion features employed in studies dedicated to animal phobias. Via a search on the Scopus and Web of Science scientific databases, twenty-nine studies were selected for in-depth analysis. The primary objective was to evaluate the presence of ludic and narrative elements in each study to understand their immersive potential across both dimensions. Findings suggest that ludic elements are more commonly used than narrative elements, which are notably scarce, and the exploration of the emotional dimension of narrative immersion is limited. An essential takeaway is that features fostering narrative immersion are invariably linked to the ludic dimension, often functioning as secondary components. This study provides a guiding framework for developing therapeutic interventions in VR, emphasizing the incorporation of ludic and narrative aspects. Additionally, it identifies untapped research opportunities, particularly the integration of autonomous narratives that are less reliant on ludic elements.