Reviewerly/exHarmony huggingface dataset:
Predictive spatial analysis for a critical assessment of the preservation potential of Palaeolithic record in the Leiria region (central Portugal)

Predictive spatial analysis in archaeology refers to the use of geostatistical and computational techniques to assess the relationships between the distribution of archaeological sites and the environmental context. It is increasingly becoming an essential tool in scientific-academic research and rescue archaeology. In the interconnection between these two working contexts, this study aims to explore the advantages of applying an inductive approach, combining Geographical Information Systems (GIS) and geostatistical methods, to identify areas with different degrees of probability for the preservation of the Palaeolithic record in the lower valley of the Lis River (Leiria region, central Portugal). Topographic, hydrographic, geological, and geomorphological data were collected, processed, and integrated into a spatial database, using a GIS, to define the environmental conditioning factors. After applying a spatial multicollinearity procedure, slope, topographical position index, cost-weighted distance to access the watercourses, heat load index and lithology were chosen as input variables for the predictive modelling. Archaeological data, corresponding to the location of known Palaeolithic sites, were also retrieved from the official records. Three models were implemented using regression and multi-criteria methods; for Model 1 a Generalised Additive Model (GAM) was applied, while for Model 2 and Model 3 two distinct combinations of Probabilistic Frequency Ratio (PFR) and Analytic Hierarchy Process (AHP) were used. The spatial cross-tabulation between the obtained predictive maps and the location of the known Palaeolithic sites shows that all the models are acceptable. However, the combinations of PFR + AHP seem to evidence greater efficacy compared to the GAM. Model 3 offers the best performance (90%), followed by Model 2 (87%) and Model 1 (68%). Possible sources of inaccuracies in the input data and the spatial modelling results were critically discussed. Model 3 generated the most robust and reliable predictive prototype map. The maps produced can serve local and regional agencies as a complement and aid decision-making in public policies for spatial planning, and in the conservation/management of cultural heritage. These cartographic representations can provide quicker identification of areas of interest, for example, in the context of rescue archaeology, along with a substantial reduction in time and costs for project promoters, as well as helping in the environmental impact assessments.
Wigner coefficients of U(4) ⊃ SU (2) ⊗ SU (2)

The Wigner coefficients of U(4) ⊃ SUS(2) ⊗ SUT(2) for the coupling [n14,n24,n34,n44]⊗[n14′,n24′,n34′,n44′]↓[n14″,n24″,n34″,n44″] with arbitrary irreducible representations [n14,n24,n34,n44] and [n14′,n24′,n34′,n44′] of U(4) are derived with an expression involving the Wigner coefficients of U(4) in the canonical U(4) ⊃ U(3)⊃U(2) ⊃U(1) basis and the state expansion coefficients of U(4)⊃SUS(2)⊗SUT(2) in terms of those of the U(4) canonical basis determined previously. Algebraic expressions of the elementary Wigner coefficients of U(4)⊃SUS(2)⊗SUT(2) in terms of the state expansion coefficients are explicitly provided.
Trojan Triggers for Poisoning Unmanned Aerial Vehicles Navigation: A Deep Learning Approach

Cybersecurity for unmanned aerial vehicles (UAVs) has recently gained much attention due to an increase in cyberattacks against drone systems. Many significant cyber security attacks on UAVs have occurred in recent years due to a lack of vulnerability assessments and inadequate security countermeasures. A Trojan attack is a type of cyberattack where Deep Neural Networks (DNN) models are poisoned by injecting malicious modifications into the original design, which leads the DNN to misclassify certain inputs after being triggered. In this paper, we investigate Trojan attacks against neural networks. For a Trojan attack, we consider the DroNet architecture. DroNet is a convolutional neural network capable of safely driving a UAV across city streets. DroNet navigates UAVs by predicting steering angles and collision probabilities from camera images. For the attacking purpose, we have generated poisonous collision and steering angle datasets for DroNet. The TrojAI software framework is used to generate poisonous datasets and Trojan models. First, the effectiveness of the Trojan attack is examined on the DroNet model using poisonous and steering angle datasets. Then, we regulate the intensity of the designed trigger and review the performance of the DroNet architecture. Finally, we proposed a trojan detection technique using label visualization for clean and poisonous datasets.
IoT Forensics: Investigating the Mobile App of Dahua Technology

Modern smart homes and their habitants are being protected by both CCTV surveillance systems and diverse security devices like motion sensors. Due to their ever-increasing adoption, such appliances may witness incidents occurring to the loT security systems they form. These complex systems can be configured and monitored by simply using a mobile application. Dahua Technology is a manufacturer of such loT devices that offers a variety of applications to utilize with its products. Research related to the forensic analysis of these applications is scarce yet this unexplored piece of evidence may hide the answer to a number of investigative questions. In this paper the mobile application of Dahua Technology is thoroughly examined in both Android and iOS operating systems so as to uncover evidentiary data that may reside within. Making use of the findings of this study authors contributed to FOSS in an effort to assist investigators with their examinations.
Exposed: Critical Vulnerabilities in USSD Banking Authentication Protocols

Unstructured Supplementary Service Data (USSD) authentication has been widely adopted as a popular method for verifying user identity and securing transactions in mobile financial banking, particularly in Sub-Saharan African countries. This is due to the convenience, speed, and accessibility since they do not require high-powered computing phones, large storage, or internet connectivity. However, like every technological advancement, this has been widely exploited by malicious actors due to weak authentication requirements. This study critically examines all 19 commercial banks in Nigeria, which has the largest USSD banking usage in Sub-Saharan Africa. We analyse 30 scenarios to conduct an anatomy and build a timeline of USSD banking attacks. Furthermore, we critically but anonymously examine each USSD banking platform against several security factors selected from government guidelines, the National Institute of Standards (NIST) SP800-63B framework and the National Cyber Security Centre (NCSC) recommendations. This led to the revelation that certain services only require a single authentication, such as PIN, while others require no authentication at all. Also, most of the banks failed to comply with governmental and industrial authentication standards. Furthermore, we present a 5-phase timeline of USSD attacks and address present recommendations for different stakeholders at the various stages.
The Jeopardy of Learning from Over-Sampled Class-Imbalanced Medical Datasets

The usefulness of the oversampling approach to class-imbalanced structured medical datasets is discussed in this paper. In this regard, we basically look into the oversampling approach's prevailing assumption that synthesized instances do belong to the minority class. We used an off-the-shelf oversampling validation system to test this assumption. According to the experimental results from the validation system, at least one of the three medical datasets used had newly generated samples that were not belonging to the minority class as a result of the oversampling methods validated. Additionally, the error rate varied based on the dataset and oversampling method tested. Therefore, we claim that synthesizing new instances without first confirming that they are aligned with the minority class is a risky approach, especially in medical fields where misdiagnosis can have serious repercussions. As alternatives to oversampling, ensemble, data partitioning, and method-level approaches are advised since they do not make false assumptions.
Empowering Caregivers of Alzheimer's Disease and Related Dementias (ADRD) with a GPT-Powered Voice Assistant: Leveraging Peer Insights from Social Media

Caring for individuals with Alzheimer's Disease and Related Dementias (ADRD) is a complex and challenging task, especially for unprofessional caregivers who often lack the necessary training and resources. While online peer support groups have been shown to be useful in providing caregivers with information and emotional support, many caregivers are unable to benefit from them due to time constraints and limited knowledge of social media platforms. To address this issue, we propose the development of a voice assistant app that can collect relevant information and discussions from online peer support groups on social media. This app will use the collected information as a knowledge base and fine-tune a Generative Pre-trained Transformers (GPT) model to facilitate caregivers in accessing shared experiences and practical tips from peers. Initial evaluation of the app has shown promising results in terms of feasibility and potential impact on caregivers.
Balancing Robustness and Covertness in NLP Model Watermarking: A Multi-Task Learning Approach

The popularity of ChatGPT demonstrates the immense commercial value of natural language processing (NLP) technology. However, NLP models are vulnerable to piracy and redistribution, which harms the economic interests of model owners. Existing NLP model watermarking schemes struggle to balance robustness and covertness. Robust watermarking require embedding more information, which compromises their covertness; conversely, covert watermarking are challenging to embed more information, which affects their robustness. This paper proposes an NLP model watermarking framework that uses multi-task learning to address the conflict between robustness and covertness in existing schemes. Specifically, a covert trigger set is established to implement remote verification of the watermark model, and a covert auxiliary network is designed to enhance the watermark model's robustness. The proposed watermarking framework is evaluated on two benchmark datasets and three mainstream NLP models. The experiments validate the frame-work's excellent covertness, robustness, and low false positive rate.
Aggregating Multiple Embeddings: A Novel Approach to Enhance Reliability and Reduce Complexity in Facial Recognition

Facial recognition is widely used, but the reliability of the embeddings extracted by most computer vision-based approaches is a challenge due to the high similarity among human faces and the effect of facial expressions and lighting. Our proposed approach aggregates multiple embeddings to generate a more robust reference for facial embedding comparison and explores the distances metrics to use in order to optimize the comparison efficiency while preserving complexity. We also apply our method to the state-of-the-art algorithm that extracts embeddings from faces in an image. The proposed approach was compared with several approaches. It optimizes the Resnet accuracy to 99.77%, Facenet to 99.79%, and Inception-ResnetV1 to 99.16%. Our approach preserves the inference time of the model while increasing its reliability since the number of comparisons is kept at a minimum. Our results demonstrate that our proposed approach offers an effective solution for addressing facial recognition in real-world environments.
CuckChain: A Cuckoo Rule Based Secure, High Incentive and Low Latency Blockchain System via Sharding

Sharding is one of the dominant techniques for designing an efficient blockchain system. However, previous sharding designs have two major limitations. First, a sharding system based on reconstructing all committees can result in significant time overhead during reconfiguration. Second, random-based sharding systems are plagued by poor security. In this paper, we propose CuckChain, a cuckoo rule based secure, high incentive and low latency blockchain system via sharding. CuckChain is reconstructed through the optimization rule of genetic algorithm and cuckoo rule, which enables new nodes to be parallelized join, solving the problem of long reconstruction latency. CuckChain utilizes a reputation scheme based on a reward and punishment mechanism to detect and mark malicious nodes early so that the nodes are evenly distributed. We implemented CuckChain and evaluated its performance. The results show our solution can reduce the reconstruction latency, improve the efficiency of sharding and the security of sharding.
Artificial Intelligence Narration in Virtual Reality

Virtual reality technology provides users with an immersive experience that is different from reality, creating more vivid, realistic, and interesting narrative forms. Artificial intelligence can play multiple roles in VR storytelling, helping to create more attractive, immersive, and emotionally connected narratives. With the development and integration of virtual reality and artificial intelligence, more and more people have begun to realize the potential of their combination in storytelling. Based on case studies, this article explores in detail how artificial intelligence technology is applied to virtual reality storytelling, including interactive movies, narrative-based games, and virtual art. The article summarizes the unique advantages and challenges of virtual reality technology in storytelling and emphasizes the safety and social responsibility issues of AI storytelling in virtual reality, as well as the social morals and ethical values we should uphold.
Rate-Controllable and Target-Dependent JPEG-Based Image Compression Using Feature Modulation

While conventional image compression techniques are optimized for human visual perception, the rise of machine learning techniques has led to the emergence of image compression methods tailored for machine vision tasks. Although a few recent studies explored target-dependent reconfiguration of lightweight codecs such as JPEG, these approaches are limited to specific trained bitrates only. Moreover, existing deep learning-based compression frameworks entail a high computational cost, making them impractical for real-time compression on devices with limited resources. In this paper, we present a novel JPEG compression framework that can adaptively generate an optimal quantization table (QT) depending on both the target bitrate and the target metric (quality or accuracy). To provide fine controllability over a wide range of bitrates, we employ a feature modulation technique to a QT generator and bitrate predictor, which are trained by a novel training method called bitrate range partitioning. Our simulation results show that the proposed framework enhances the performance of standard JPEG by up to 2dB in PSNR and 10% in accuracy at the same bitrate, while incurring minimal computational overhead compared to JPEG.
Statement-Oblivious Threshold Witness Encryption

The notion of witness encryption introduced by Garg et al. (STOC'13) allows to encrypt a message under a statement <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$x$</tex> from some NP-language <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{L}$</tex> with associated relation <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(x, w)\in \mathcal{R}$</tex> , where decryption can be carried out with the corresponding witness <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$w$</tex> . Unfortunately, known constructions for general-purpose witness encryption rely on strong assumptions, and are mostly of theoretical interest. To address these short-comings, Goyal et al. (PKC'22) recently introduced a blockchain-based alternative, where a committee decrypts ciphertexts when provided with a valid witness <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$w$</tex> . Blockchain-based committee solutions have recently gained broad interest to offer security against more powerful adversaries and construct new cryptographic primitives. We follow this line of work, and propose a new notion of statement-oblivious threshold witness encryption. Our new notion offers the functionality of committee-based witness encryption while additionally hiding the statement used for encryption. We present two ways to build statement-oblivious threshold witness encryption, one generic transformation based on anonymous threshold identity-based encryption (A-TIBE) and one direct construction based on bilinear maps. Due to the lack of efficient A-TIBE schemes, the former mainly constitutes a feasibility result, while the latter yields a concretely efficient scheme.
Securing Optimized Code Against Power Side Channels

Side-channel attacks impose a serious threat to cryptographic algorithms, including widely employed ones, such as AES and RSA. These attacks take advantage of the algorithm implementation in hardware or software to extract secret information via side channels. Software masking is a mitigation approach against power side-channel attacks aiming at hiding the secret-revealing dependencies from the power footprint of a vulnerable implementation. However, this type of software mitigation often depends on general-purpose compilers, which do not preserve non-functional properties. Moreover, microarchitectural features, such as the memory bus and register reuse, may also leak secret information. These abstractions are not visible at the high-level implementation of the program. Instead, they are decided at compile time. To remedy these problems, security engineers often sacrifice code efficiency by turning off compiler optimization and/or performing local, post-compilation transformations. This paper proposes Secure by Construction Code Generation (SecCG), a constraint-based compiler approach that generates optimized yet protected against power side channels code. SecCG controls the quality of the mitigated program by efficiently searching the best possible low-level implementation according to a processor cost model. In our experiments with twelve masked cryptographic functions up to 100 lines of code on Mips32 and ARM Thumb, SecCG speeds up the generated code from 77% to 6.6 times compared to non-optimized secure code with an overhead of up to 13% compared to non-secure optimized code at the expense of a high compilation cost. For security and compiler researchers, this paper proposes a formal model to generate power side channel free low-level code. For software engineers, SecCG provides a practical approach to optimize performance critical and vulnerable cryptographic implementations that preserve security properties against power side channels.
HoRStify: Sound Security Analysis of Smart Contracts

The cryptocurrency Ethereum is the most widely used execution platform for smart contracts. Smart contracts are distributed applications, which govern financial assets and, hence, can implement advanced financial instruments, such as decentralized exchanges or autonomous organizations (DAOs). Their financial nature makes smart contracts an attractive attack target, as demonstrated by numerous exploits on popular contracts resulting in financial damage of millions of dollars. This omnipresent attack hazard motivates the need for sound static analysis tools, which assist smart contract developers in eliminating contract vulnerabilities a priori to deployment. Vulnerability assessment that is sound and insightful for EVM contracts is a formidable challenge because contracts execute low-level bytecode in a largely unknown and potentially hostile execution environment. So far, there exists no provably sound automated analyzer that allows for the verification of security properties based on program dependencies, even though prevalent attack classes fall into this category. In this work, we present HoRStify, the first automated analyzer for dependency properties of Ethereum smart contracts based on sound static analysis. HoRStify grounds its soundness proof on a formal proof framework for static program slicing that we instantiate to the semantics of EVM bytecode. We demonstrate that HoRStify is flexible enough to soundly verify the absence of famous attack classes such as timestamp dependency and, at the same time, performant enough to analyze real-world smart contracts.
SoK: Delay-Based Cryptography

In this work, we provide a systematisation of knowledge of delay-based cryptography, in which we discuss and compare the existing primitives within cryptography that utilise a time-delay. We start by considering the role of time within cryptography, explaining broadly what a delay aimed to achieve at its inception and now, in the modern age. We then move on to describing the underlying assumptions used to achieve these goals, and analyse topics including trust, decentralisation and concrete methods to implement a delay. We then survey the existing primitives, discussing their security properties, instantiations and applications. We make explicit the relationships between these primitives, identifying a hierarchy and the theoretical gaps that exist. We end this systematisation of knowledge by highlighting relevant future research directions within the field of delay-based cryptography, from which this area would greatly benefit.
Zero-Knowledge in EasyCrypt

We formalize security properties of zero-knowledge protocols and their proofs in EasyCrypt. Specifically, we focus on sigma protocols (three-round protocols). Most importantly, we also cover properties whose security proofs require the use of rewinding; prior work has focused on properties that do not need this more advanced technique. On our way we give generic definitions of the main properties associated with sigma protocols, both in the computational and information-theoretical setting. We give generic derivations of soundness, (malicious-verifier) zero-knowledge, and proof of knowledge from simpler assumptions with proofs which rely on rewinding. Also, we address sequential composition of sigma protocols. Finally, we illustrate the applicability of our results on three zero-knowledge protocols: Fiat-Shamir (for quadratic residues), Schnorr (for discrete logarithms), and Blum (for Hamiltonian cycles, NP-complete).