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This paper proposes a novel encryption-based access control mechanism for Named Data Networking (NDN). The scheme allows data producers to share their content in encrypted form before transmitting it to consumers. The encryption mechanism incorporates time-based subscription access policies directly into the encrypted content, enabling only consumers with valid subscriptions to decrypt it. This makes the scheme well-suited for real-world, subscription-based applications like Netflix. Additionally, the scheme introduces an anonymous and unlinkable signature-based authentication mechanism that empowers edge routers to block bogus content requests at the network's entry point, thereby mitigating Denial of Service (DoS) attacks. A formal security proof demonstrates the scheme's resistance to Chosen Plaintext Attacks (CPA). Performance analysis, using Mini-NDN-based emulation and a Charm library implementation, further confirms the practicality of the scheme. Moreover, it outperforms closely related works in terms of functionality, security, and communication overhead., Comment: 25 pages, 8 figures

The exploration of backdoor vulnerabilities in object detectors, particularly in real-world scenarios, remains limited. A significant challenge lies in the absence of a natural physical backdoor dataset, and constructing such a dataset is both time- and labor-intensive. In this work, we address this gap by creating a large-scale dataset comprising approximately 11,800 images/frames with annotations featuring natural objects (e.g., T-shirts and hats) as triggers to incur cloaking adversarial effects in diverse real-world scenarios. This dataset is tailored for the study of physical backdoors in object detectors. Leveraging this dataset, we conduct a comprehensive evaluation of an insidious cloaking backdoor effect against object detectors, wherein the bounding box around a person vanishes when the individual is near a natural object (e.g., a commonly available T-shirt) in front of the detector. Our evaluations encompass three prevalent attack surfaces: data outsourcing, model outsourcing, and the use of pretrained models. The cloaking effect is successfully implanted in object detectors across all three attack surfaces. We extensively evaluate four popular object detection algorithms (anchor-based Yolo-V3, Yolo-V4, Faster R-CNN, and anchor-free CenterNet) using 19 videos (totaling approximately 11,800 frames) in real-world scenarios. Our results demonstrate that the backdoor attack exhibits remarkable robustness against various factors, including movement, distance, angle, non-rigid deformation, and lighting. In data and model outsourcing scenarios, the attack success rate (ASR) in most videos reaches 100% or near it, while the clean data accuracy of the backdoored model remains indistinguishable from that of the clean model, making it impossible to detect backdoor behavior through a validation set., Comment: arXiv admin note: text overlap with arXiv:2201.08619

Metallic amorphous ferrimagnets derived from alloying 3d transition metals with 4f electron rare earths host fascinating effects of compensation between the 3d and 4f magnetic sublattices. Here, a detailed study of anisotropic magnetoresistance (AMR), planar Hall effect (PHE) and anomalous Hall effect (AHE) are reported on a series of CoHo thin films over a wide field temperature phase space. Close to magnetic compensation temperature, the AHE loops show a double sign reversal and signatures of spin flop transition at higher fields. The AMR and PHE also display strong deviations from the classical angular dependence seen in soft ferromagnets like permalloy as the angle between in-plane current and magnetic field is scanned from 0 to 360 degrees. It is argued that the non zero orbital angular momentum of Ho ions in the lattice and stabilization of bubble domains below magnetic saturation may be responsible for such features. Direct imaging of magnetic textures with X ray photoelectron microscopy shows formation of stripe domain patterns in the regime of sublattice compensation. Such stripes are likely to transform into magnetic bubbles before full saturation is reached in a large magnetic field.

Content providers increasingly utilise Content Delivery Networks (CDNs) to enhance users' content download experience. However, this deployment scenario raises significant security concerns regarding content confidentiality and user privacy due to the involvement of third-party providers. Prior proposals using private information retrieval (PIR) and oblivious RAM (ORAM) have proven impractical due to high computation and communication costs, as well as integration challenges within distributed CDN architectures. In response, we present \textsf{OblivCDN}, a practical privacy-preserving system meticulously designed for seamless integration with the existing real-world Internet-CDN infrastructure. Our design strategically adapts Range ORAM primitives to optimise memory and disk seeks when accessing contiguous blocks of CDN content, both at the origin and edge servers, while preserving both content confidentiality and user access pattern hiding features. Also, we carefully customise several oblivious building blocks that integrate the distributed trust model into the ORAM client, thereby eliminating the computational bottleneck in the origin server and reducing communication costs between the origin server and edge servers. Moreover, the newly-designed ORAM client also eliminates the need for trusted hardware on edge servers, and thus significantly ameliorates the compatibility towards networks with massive legacy devices.In real-world streaming evaluations, OblivCDN} demonstrates remarkable performance, downloading a $256$ MB video in just $5.6$ seconds. This achievement represents a speedup of $90\times$ compared to a strawman approach (direct ORAM adoption) and a $366\times$ improvement over the prior art, OblivP2P., Comment: The 20th ACM ASIA Conference on Computer and Communications Security (ACM ASIACCS 2025)

We present new atomistic models of amorphous silicon (a-Si) and hydrogenated amorphous silicon (a-Si:H) surfaces. The a-Si model included 4096 atoms and was obtained using local orbital density functional theory. By analyzing a slab model (periodic in two dimensions with a slab about 44 \AA{} thick), we observed a strong correlation between surface structure and surface charge density, which might be compared to STM experiments. Hydrogen atoms added near the under-coordinated surface atoms passivate dangling bonds and induce structural rearrangements. We analyze the electronic structure, including the localization of the states, and note resonant mixing between bulk and surface defect structures. We also compute the classical normal modes of the hydrogenated a-Si and compare them to experiments where possible. Our work is a step toward understanding the meaning of ``surface reconstruction" for a noncrystalline material.

Multimodal foundation models (MFMs) represent a significant advancement in artificial intelligence, combining diverse data modalities to enhance learning and understanding across a wide range of applications. However, this integration also brings unique safety and security challenges. In this paper, we conceptualize cybersafety and cybersecurity in the context of multimodal learning and present a comprehensive Systematization of Knowledge (SoK) to unify these concepts in MFMs, identifying key threats to these models. We propose a taxonomy framework grounded in information theory, evaluating and categorizing threats through the concepts of channel capacity, signal, noise, and bandwidth. This approach provides a novel framework that unifies model safety and system security in MFMs, offering a more comprehensive and actionable understanding of the risks involved. We used this to explore existing defense mechanisms, and identified gaps in current research - particularly, a lack of protection for alignment between modalities and a need for more systematic defense methods. Our work contributes to a deeper understanding of the security and safety landscape in MFMs, providing researchers and practitioners with valuable insights for improving the robustness and reliability of these models.

Searches for continuous-wave gravitational radiation in data collected by modern long-baseline interferometers, such as the Laser Interferometer Gravitational-wave Observatory (LIGO), the Virgo interferometer and the Kamioka Gravitational Wave Detector (KAGRA), can be memory intensive. A digitisation scheme is described that reduces the 64-bit interferometer output to a one- or two-bit data stream while minimising distortion and achieving considerable reduction in storage and input/output cost. For the representative example of the coherent, maximum-likelihood matched filter known as the F-statistic, it is found using Monte-Carlo simulations that the injected signal only needs to be ~24 per cent stronger (for one-bit data) and ~6.4 per cent stronger (for two bit data with optimal thresholds) than a 64-bit signal in order to be detected with 90 per cent probability in Gaussian noise. The foregoing percentages do not change significantly when the signal frequency decreases secularly, or when the noise statistics are not Gaussian, as verified with LIGO Science Run 6 data., Comment: 17 pages, 6 figures; accepted for publication in Astrophysical Journal

This systematic review examines the synergistic and individual influences of resistance exercise, dietary protein supplementation, and sleep/recovery on muscle protein synthesis (MPS). Electronic databases such as Scopus, Google Scholar, and Web of Science were extensively used. Studies were selected based on relevance to the criteria and were ensured to be directly applicable to the objectives. Research indicates that a protein dose of 20 to 25 grams maximally stimulates MPS post-resistance training. It is observed that physically frail individuals aged 76 to 92 and middle-aged adults aged 62 to 74 have lower mixed muscle protein synthetic rates than individuals aged 20 to 32. High-whey protein and leucine-enriched supplements enhance MPS more efficiently than standard dairy products in older adults engaged in resistance programs. Similarly, protein intake before sleep boosts overnight MPS rates, which helps prevent muscle loss associated with sleep debt, exercise-induced damage, and muscle-wasting conditions like sarcopenia and cachexia. Resistance exercise is a functional intervention to achieve muscular adaptation and improve function. Future research should focus on variables such as fluctuating fitness levels, age groups, genetics, and lifestyle factors to generate more accurate and beneficial results.

Large Language Models (LLMs) have shown remarkable potential in code generation, making them increasingly important in the field. However, the security issues of generated code have not been fully addressed, and the usability of LLMs in code generation still requires further exploration. This work introduces SecCode, a framework that leverages an innovative interactive encouragement prompting (EP) technique for secure code generation with \textit{only NL} prompts. This approach ensures that the prompts can be easily shared and understood by general users. SecCode functions through three stages: 1) Code Generation using NL Prompts; 2) Code Vulnerability Detection and Fixing, utilising our proposed encouragement prompting; 3) Vulnerability Cross-Checking and Code Security Refinement. These stages are executed in multiple interactive iterations to progressively enhance security. By using both proprietary LLMs (i.e., GPT-3.5 Turbo, GPT-4 and GPT-4o) and open-source LLMs (i.e., Llama 3.1 8B Instruct, DeepSeek Coder V2 Lite Instruct) evaluated on three benchmark datasets, extensive experimental results show that our proposed SecCode greatly outperforms compared baselines, generating secure code with a high vulnerability correction rate. For example, SecCode exhibits a high fix success rate of over 76\% after running 5 automated EP interactive iterations and over 89\% after running 10 automated EP interactive iterations. To the best of our knowledge, this work is the first to formulate secure code generation with NL prompts only. We have open-sourced our code and encourage the community to focus on secure code generation.

This work presents global random walk approximations of solutions to one-dimensional Stefan-type moving-boundary problems. We are particularly interested in the case when the moving boundary is driven by an explicit representation of its speed. This situation is usually referred to in the literature as moving-boundary problem with kinetic condition. As a direct application, we propose a numerical scheme to forecast the penetration of small diffusants into a rubber-based material. To check the quality of our results, we compare the numerical results obtained by global random walks either using the analytical solution to selected benchmark cases or relying on finite element approximations with a priori known convergence rates. It turns out that the global random walk concept can be used to produce good quality approximations of the weak solutions to the target class of problems., Comment: 13 pages, 2 Figures, 5 Tables

Recent works have shown that Federated Learning (FL) is vulnerable to backdoor attacks. Existing defenses cluster submitted updates from clients and select the best cluster for aggregation. However, they often rely on unrealistic assumptions regarding client submissions and sampled clients population while choosing the best cluster. We show that in realistic FL settings, state-of-the-art (SOTA) defenses struggle to perform well against backdoor attacks in FL. To address this, we highlight that backdoored submissions are adversarially biased and overconfident compared to clean submissions. We, therefore, propose an Adversarially Guided Stateful Defense (AGSD) against backdoor attacks on Deep Neural Networks (DNNs) in FL scenarios. AGSD employs adversarial perturbations to a small held-out dataset to compute a novel metric, called the trust index, that guides the cluster selection without relying on any unrealistic assumptions regarding client submissions. Moreover, AGSD maintains a trust state history of each client that adaptively penalizes backdoored clients and rewards clean clients. In realistic FL settings, where SOTA defenses mostly fail to resist attacks, AGSD mostly outperforms all SOTA defenses with minimal drop in clean accuracy (5% in the worst-case compared to best accuracy) even when (a) given a very small held-out dataset -- typically AGSD assumes 50 samples (<= 0.1% of the training data) and (b) no heldout dataset is available, and out-of-distribution data is used instead. For reproducibility, our code will be openly available at: https://github.com/hassanalikhatim/AGSD., Comment: 16 pages, Accepted at ACSAC 2024

With the rapid advance of high-performance computing and electronic technologies, understanding thermal conductivity in materials has become increasingly important. This study presents a novel method: the Site-projected Thermal Conductivity (SPTC) that quantitatively estimates the local (atomic) contribution to heat transport, leveraging the Green-Kubo thermal transport equations. We demonstrate the effectiveness of this approach on disordered and amorphous graphene, amorphous silicon, and grain boundaries in silicon-germanium alloys. Amorphous graphene reveals a percolation behavior for thermal transport. The results highlight the potential of our method to provide new insights into the thermal behavior of materials, offering a promising avenue for materials design and performance optimization., Comment: 7 pages, 8 figures, Accepted for PSS (RRL)

We present a workflow that iteratively combines \textit{ab-initio} calculations with a machine-learning (ML) guided search for superconducting compounds with both dynamical stability and instability from imaginary phonon modes, the latter of which have been largely overlooked in previous studies. Electron-phonon coupling (EPC) properties and critical temperature (T$_c$) of 417 boron, carbon, and borocarbide compounds have been calculated with density functional perturbation theory (DFPT) and isotropic Eliashberg approximation. Our study addresses T$_c$ convergence of Brillouin zone sampling with an ansatz test, stabilizing imaginary phonon modes for significant EPC contributions and comparing performance of two ML models especially when including compounds of dynamical instability. We predict a few promising superconducting compounds with formation energy just above the ground state convex hull, such as Ca$_5$B$_3$N$_6$ (35 K), TaNbC$_2$ (28.4 K), Nb$_3$B$_3$C (16.4 K), Y$_2$B$_3$C$_2$ (4.0 K), Pd$_3$CaB (7.0 K), MoRuB$_2$ (15.6 K), RuVB$_2$ (15.0 K), RuSc$_3$C$_4$ (6.6 K) among others.

Mental health concerns are prevalent among college students, highlighting the need for effective interventions that promote self-awareness and holistic well-being. MindScape pioneers a novel approach to AI-powered journaling by integrating passively collected behavioral patterns such as conversational engagement, sleep, and location with Large Language Models (LLMs). This integration creates a highly personalized and context-aware journaling experience, enhancing self-awareness and well-being by embedding behavioral intelligence into AI. We present an 8-week exploratory study with 20 college students, demonstrating the MindScape app's efficacy in enhancing positive affect (7%), reducing negative affect (11%), loneliness (6%), and anxiety and depression, with a significant week-over-week decrease in PHQ-4 scores (-0.25 coefficient), alongside improvements in mindfulness (7%) and self-reflection (6%). The study highlights the advantages of contextual AI journaling, with participants particularly appreciating the tailored prompts and insights provided by the MindScape app. Our analysis also includes a comparison of responses to AI-driven contextual versus generic prompts, participant feedback insights, and proposed strategies for leveraging contextual AI journaling to improve well-being on college campuses. By showcasing the potential of contextual AI journaling to support mental health, we provide a foundation for further investigation into the effects of contextual AI journaling on mental health and well-being., Comment: arXiv admin note: text overlap with arXiv:2404.00487

Software vulnerabilities (SVs) have emerged as a prevalent and critical concern for safety-critical security systems. This has spurred significant advancements in utilizing AI-based methods, including machine learning and deep learning, for software vulnerability detection (SVD). While AI-based methods have shown promising performance in SVD, their effectiveness on real-world, complex, and diverse source code datasets remains limited in practice. To tackle this challenge, in this paper, we propose a novel framework that enhances the capability of large language models to learn and utilize semantic and syntactic relationships from source code data for SVD. As a result, our approach can enable the acquisition of fundamental knowledge from source code data while adeptly utilizing crucial relationships, i.e., semantic and syntactic associations, to effectively address the software vulnerability detection (SVD) problem. The rigorous and extensive experimental results on three real-world challenging datasets (i.e., ReVeal, D2A, and Devign) demonstrate the superiority of our approach over the effective and state-of-the-art baselines. In summary, on average, our SAFE approach achieves higher performances from 4.79% to 9.15% for F1-measure and from 16.93% to 21.70% for Recall compared to the baselines across all datasets used.

Quantum materials with stacked van der Waals (vdW) layers hosting non-trivial band structure topology and magnetism have shown many interesting properties. Using high-throughput density functional theory calculations, we design and predict tetragonal vdW-layered quantum materials in the MPd$_5$I$_2$ structure (M=Ga, In and 3d transition metals). We show that besides the known AlPd$_5$I$_2$, the -MPd$_5$- structural motif of three-atomic-layer slabs separated by two I layers can accommodate a variety of metal atoms giving arise to topologically non-trivial features and highly tunable magnetic properties in both bulk and single layer 2D structures. Among them, TiPd$_5$I$_2$ and InPd$_5$I$_2$ host a pair of Dirac points and likely an additional strong topological insulator state for the band manifolds just above and below the top valence band, respectively, with their single layers hosting or near quantum spin Hall states. CrPd$_5$I$_2$ is a ferromagnet with a large out-of-plane magneto-anisotropy energy, desirable for rare-earth-free permanent magnets., Comment: 25 pages, 6 figures

In this paper, we explore the feasibility of using machine learning regression as a method of extracting basic stellar parameters and line-of-sight extinctions from spectro-photometric data. We built a stable gradient-boosted random-forest regressor (xgboost), trained on spectroscopic data, capable of producing output parameters with reliable uncertainties from Gaia DR3 data (most notably the low-resolution XP spectra), without ground-based spectroscopic observations. Using Shapley additive explanations, we interpret how the predictions for each star are influenced by each data feature. For the training and testing of the network, we used high-quality parameters obtained from the StarHorse code for a sample of around eight million stars observed by major spectroscopic stellar surveys, complemented by curated samples of hot stars, very metal-poor stars, white dwarfs, and hot sub-dwarfs. The training data cover the whole sky, all Galactic components, and almost the full magnitude range of the Gaia DR3 XP sample of more than 217 million objects that also have reported parallaxes. We have achieved median uncertainties of 0.20 mag in V-band extinction, 0.01 dex in logarithmic effective temperature, 0.20 dex in surface gravity, 0.18 dex in metallicity, and $12\%$ in mass (over the full Gaia DR3 XP sample, with considerable variations in precision as a function of magnitude and stellar type). We succeeded in predicting competitive results based on Gaia DR3 XP spectra compared to classical isochrone or spectral-energy distribution fitting methods we employed in earlier works, especially for parameters $A_V$ and $T_{\rm eff}$, along with the metallicity values. Finally, we showcase some potential applications of this new catalogue, including extinction maps, metallicity trends in the Milky Way, and extended maps of young massive stars, metal-poor stars, and metal-rich stars). [abridged], Comment: A&A, accepted. 13 pages, 13 figures + references & appendices. Data available at https://data.aip.de/projects/shboost2024.html

Energy-efficient spintronic devices require a large spin-orbit torque (SOT) and low damping to excite magnetic precession. In conventional devices with heavy-metal/ferromagnet bilayers, reducing the ferromagnet thickness to $\sim$1 nm enhances the SOT but dramatically increases damping. Here, we investigate an alternative approach based on a 10 nm thick single-layer ferromagnet to attain both low damping and a sizable SOT. Instead of relying on a single interface, we continuously break the bulk inversion symmetry with a vertical compositional gradient of two ferromagnetic elements: Fe with low intrinsic damping and Ni with sizable spin-orbit coupling. We find low effective damping parameters of $\alpha_\mathrm{eff} < 5\times10^{-3}$ in the FeNi alloy films, despite the steep compositional gradients. Moreover, we reveal a sizable anti-damping SOT efficiency of $|\theta_\mathrm{DL}| \approx 0.05$, even without an intentional compositional gradient. Through depth-resolved x-ray diffraction, we identify a lattice strain gradient as crucial symmetry breaking that underpins the SOT. Our findings provide fresh insights into damping and SOTs in single-layer ferromagnets for power-efficient spintronic devices.

Artificial Intelligence (AI) has advanced significantly in various domains like healthcare, finance, and cybersecurity, with successes such as DeepMind's medical imaging and Tesla's autonomous vehicles. As telecommunications transition from 5G to 6G, integrating AI is crucial for complex demands like data processing, network optimization, and security. Despite ongoing research, there's a gap in consolidating AI-enabled Spectrum Management (AISM) advancements. Traditional spectrum management methods are inadequate for 6G due to its dynamic and complex demands, making AI essential for spectrum optimization, security, and network efficiency. This study aims to address this gap by: (i) Conducting a systematic review of AISM methodologies, focusing on learning models, data handling techniques, and performance metrics. (ii) Examining security and privacy concerns related to AI and traditional network threats within AISM contexts. Using the Systematic Literature Review (SLR) methodology, we meticulously analyzed 110 primary studies to: (a) Identify AI's utility in spectrum management. (b) Develop a taxonomy of AI approaches. (c) Classify datasets and performance metrics used. (d) Detail security and privacy threats and countermeasures. Our findings reveal challenges such as under-explored AI usage in critical AISM systems, computational resource demands, transparency issues, the need for real-world datasets, imbalances in security and privacy research, and the absence of testbeds, benchmarks, and security analysis tools. Addressing these challenges is vital for maximizing AI's potential in advancing 6G technology., Comment: 35 pages

Given the rapid emergence and applications of Large Language This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLO11 (or YOLOv11). Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv11 and progressing through YOLOv10, YOLOv9, YOLOv8, and subsequent versions to explore each version's contributions to enhancing speed, detection accuracy, and computational efficiency in real-time object detection. By detailing the incremental technological advancements in subsequent YOLO versions, this review chronicles the evolution of YOLO, and discusses the challenges and limitations in each earlier versions. The evolution signifies a path towards integrating YOLO with multimodal, context-aware, and Artificial General Intelligence (AGI) systems for the next YOLO decade, promising significant implications for future developments in AI-driven applications. YOLOV11 to YOLOv1, Comment: 11 Figures, 7 Tables

Single crystals of XPt$_{5}$P (X = Al, Ga, and In) were grown from a Pt-P solution at high temperatures, and ambient-pressure measurements of temperature-dependent magnetization, resistivity, and X-ray diffraction were made. Also, the ambient-pressure Hall resistivity and temperature-dependent resistance under pressure were measured on GaPt$_{5}$P. All three compounds have tetragonal $P4/mmm$ crystal structure at room-temperature with metallic transport and weak diamagnetism over the $2-300$~K temperature range. Surprisingly, at ambient pressure, both the transport and magnetization measurements on GaPt$_{5}$P show a step-like feature in $70-90$~K region suggesting a possible structural phase transition, and no such features were observed in (Al/In)Pt$_{5}$P. Both the hysteretic nature and sharpness of the feature suggest the first-order transition, and single-crystal X-ray diffraction measurements provided further details of the structural transition with a crystal symmetry likely different than $P4/mmm$ below transition. The transition is characterized by anisotropic changes in the lattice parameters, a volume collapse, and satellite peaks at two distinct wave-vectors. Density functional theory calculations present phonon softening as a possible driving mechanism. Additionally, the structural transition temperature increases rapidly with increasing pressure, reaching room temperature by $\sim 2.2$~GPa, highlighting the high degree of pressure sensitivity and fragile nature of GaPt$_{5}$P room-temperature structure. Although the volume collapse and extreme pressure sensitivity suggest chemical pressure should drive a similar structural change in AlPt$_{5}$P, with smaller unit cell dimensions and volume, its structure is found to be $P4/mmm$ as well. Overall, GaPt$_{5}$P stands out as a sole member of the 1-5-1 family of compounds with a temperature-driven structural change., Comment: 18 pages, 19 figures

Due to the unavailability of solar irradiance data for many potential sites of Nepal, the paper proposes predicting solar irradiance based on alternative meteorological parameters. The study focuses on five distinct regions in Nepal and utilizes a dataset spanning almost ten years, obtained from CERES SYN1deg and MERRA-2. Machine learning models such as Random Forest, XGBoost, K-Nearest Neighbors, and deep learning models like LSTM and ANN-MLP are employed and evaluated for their performance. The results indicate high accuracy in predicting solar irradiance, with R-squared(R2) scores close to unity for both train and test datasets. The impact of parameter integration on model performance is analyzed, revealing the significance of various parameters in enhancing predictive accuracy. Each model demonstrates strong performance across all parameters, consistently achieving MAE values below 6, RMSE values under 10, MBE within |2|, and nearly unity R2 values. Upon removal of various solar parameters such as "Solar_Irradiance_Clear_Sky", "UVA", etc. from the datasets, the model's performance is significantly affected. This exclusion leads to considerable increases in MAE, reaching up to 82, RMSE up to 135, and MBE up to |7|. Among the models, KNN displays the weakest performance, with an R2 of 0.7582546. Conversely, ANN exhibits the strongest performance, boasting an R2 value of 0.9245877. Hence, the study concludes that Artificial Neural Network (ANN) performs exceptionally well, showcasing its versatility even under sparse data parameter conditions., Comment: 20 pages, 12 figures, 5 tables

This paper presents a simulation approach to enhance the power quality of a PV-micro hydro grid supplying both linear consumer load and non-linear industrial load by integrating Shunt Active Power Filter (SAPF), utilizing instantaneous PQ theory and hysteresis current control band logic. The non-linear load draws reactive power and harmonic current from the source thereby affecting the power quality. The integration of the SAPF at the point of common coupling (PCC) offers reactive power and harmonic current compensation, ensuring that the current supply to the grid remains nearly sinusoidal and proportional to the active power. By injecting equal and opposite harmonic components, the SAPF effectively reduces Total Harmonic Distortion (THD) from 7% to 2.96%, thereby enhancing the overall power quality of the PV-micro hydro grid system., Comment: 5 pages, 21 figures, submitted on IEEE powercon 2024 conference

High-throughput $ab$ $initio$ calculations are the indispensable parts of data-driven discovery of new materials with desirable properties, as reflected in the establishment of several online material databases. The accumulation of extensive theoretical data through computations enables data-driven discovery by constructing machine learning and artificial intelligence models to predict novel compounds and forecast their properties. Efficient usage and extraction of data from these existing online material databases can accelerate the next stage materials discovery that targets different and more advanced properties, such as electron-phonon coupling for phonon-mediated superconductivity. However, extracting data from these databases, generating tailored input files for different $ab$ $initio$ calculations, performing such calculations, and analyzing new results can be demanding tasks. Here, we introduce a software package named "HTESP" (High-Throughput Electronic Structure Package) written in Python and Bash languages, which automates the entire workflow including data extraction, input file generation, calculation submission, result collection and plotting. Our HTESP will help speed up future computational materials discovery processes.

AI-enabled critical infrastructures (ACIs) integrate artificial intelligence (AI) technologies into various essential systems and services that are vital to the functioning of society, offering significant implications for efficiency, security and resilience. While adopting decentralized AI approaches (such as federated learning technology) in ACIs is plausible, private and sensitive data are still susceptible to data reconstruction attacks through gradient optimization. In this work, we propose Compressed Differentially Private Aggregation (CDPA), a leakage-resilient, communication-efficient, and carbon-neutral approach for ACI networks. Specifically, CDPA has introduced a novel random bit-flipping mechanism as its primary innovation. This mechanism first converts gradients into a specific binary representation and then selectively flips masked bits with a certain probability. The proposed bit-flipping introduces a larger variance to the noise while providing differentially private protection and commendable efforts in energy savings while applying vector quantization techniques within the context of federated learning. The experimental evaluation indicates that CDPA can reduce communication cost by half while preserving model utility. Moreover, we demonstrate that CDPA can effectively defend against state-of-the-art data reconstruction attacks in both computer vision and natural language processing tasks. We highlight existing benchmarks that generate 2.6x to over 100x more carbon emissions than CDPA. We hope that the CDPA developed in this paper can inform the federated AI-enabled critical infrastructure of a more balanced trade-off between utility and privacy, resilience protection, as well as a better carbon offset with less communication overhead.

Honeyfiles are a particularly useful type of honeypot: fake files deployed to detect and infer information from malicious behaviour. This paper considers the challenge of naming honeyfiles so they are camouflaged when placed amongst real files in a file system. Based on cosine distances in semantic vector spaces, we develop two metrics for filename camouflage: one based on simple averaging and one on clustering with mixture fitting. We evaluate and compare the metrics, showing that both perform well on a publicly available GitHub software repository dataset., Comment: 3rd Workshop on the security implications of Deepfakes and Cheapfakes (WDC) co-located at ACM ASIACCS 2024

Software vulnerabilities (SVs) have become a common, serious, and crucial concern to safety-critical security systems. That leads to significant progress in the use of AI-based methods for software vulnerability detection (SVD). In practice, although AI-based methods have been achieving promising performances in SVD and other domain applications (e.g., computer vision), they are well-known to fail in detecting the ground-truth label of input data (referred to as out-of-distribution, OOD, data) lying far away from the training data distribution (i.e., in-distribution, ID). This drawback leads to serious issues where the models fail to indicate when they are likely mistaken. To address this problem, OOD detectors (i.e., determining whether an input is ID or OOD) have been applied before feeding the input data to the downstream AI-based modules. While OOD detection has been widely designed for computer vision and medical diagnosis applications, automated AI-based techniques for OOD source code data detection have not yet been well-studied and explored. To this end, in this paper, we propose an innovative deep learning-based approach addressing the OOD source code data identification problem. Our method is derived from an information-theoretic perspective with the use of innovative cluster-contrastive learning to effectively learn and leverage source code characteristics, enhancing data representation learning for solving the problem. The rigorous and comprehensive experiments on real-world source code datasets show the effectiveness and advancement of our approach compared to state-of-the-art baselines by a wide margin. In short, on average, our method achieves a significantly higher performance from around 15.27%, 7.39%, and 4.93% on the FPR, AUROC, and AUPR measures, respectively, in comparison with the baselines.

Honeyfiles are security assets designed to attract and detect intruders on compromised systems. Honeyfiles are a type of honeypot that mimic real, sensitive documents, creating the illusion of the presence of valuable data. Interaction with a honeyfile reveals the presence of an intruder, and can provide insights into their goals and intentions. Their practical use, however, is limited by the time, cost and effort associated with manually creating realistic content. The introduction of large language models has made high-quality text generation accessible, but honeyfiles contain a variety of content including charts, tables and images. This content needs to be plausible and realistic, as well as semantically consistent both within honeyfiles and with the real documents they mimic, to successfully deceive an intruder. In this paper, we focus on an important component of the honeyfile content generation problem: document charts. Charts are ubiquitous in corporate documents and are commonly used to communicate quantitative and scientific data. Existing image generation models, such as DALL-E, are rather prone to generating charts with incomprehensible text and unconvincing data. We take a multi-modal approach to this problem by combining two purpose-built generative models: a multitask Transformer and a specialized multi-head autoencoder. The Transformer generates realistic captions and plot text, while the autoencoder generates the underlying tabular data for the plot. To advance the field of automated honeyplot generation, we also release a new document-chart dataset and propose a novel metric Keyword Semantic Matching (KSM). This metric measures the semantic consistency between keywords of a corpus and a smaller bag of words. Extensive experiments demonstrate excellent performance against multiple large language models, including ChatGPT and GPT4., Comment: 13 pages including references

MindScape aims to study the benefits of integrating time series behavioral patterns (e.g., conversational engagement, sleep, location) with Large Language Models (LLMs) to create a new form of contextual AI journaling, promoting self-reflection and well-being. We argue that integrating behavioral sensing in LLMs will likely lead to a new frontier in AI. In this Late-Breaking Work paper, we discuss the MindScape contextual journal App design that uses LLMs and behavioral sensing to generate contextual and personalized journaling prompts crafted to encourage self-reflection and emotional development. We also discuss the MindScape study of college students based on a preliminary user study and our upcoming study to assess the effectiveness of contextual AI journaling in promoting better well-being on college campuses. MindScape represents a new application class that embeds behavioral intelligence in AI.

While location trajectories represent a valuable data source for analyses and location-based services, they can reveal sensitive information, such as political and religious preferences. Differentially private publication mechanisms have been proposed to allow for analyses under rigorous privacy guarantees. However, the traditional protection schemes suffer from a limiting privacy-utility trade-off and are vulnerable to correlation and reconstruction attacks. Synthetic trajectory data generation and release represent a promising alternative to protection algorithms. While initial proposals achieve remarkable utility, they fail to provide rigorous privacy guarantees. This paper proposes a framework for designing a privacy-preserving trajectory publication approach by defining five design goals, particularly stressing the importance of choosing an appropriate Unit of Privacy. Based on this framework, we briefly discuss the existing trajectory protection approaches, emphasising their shortcomings. This work focuses on the systematisation of the state-of-the-art generative models for trajectories in the context of the proposed framework. We find that no existing solution satisfies all requirements. Thus, we perform an experimental study evaluating the applicability of six sequential generative models to the trajectory domain. Finally, we conclude that a generative trajectory model providing semantic guarantees remains an open research question and propose concrete next steps for future research., Comment: Added DOI: 10.56553/popets-2024-0068