OpenRaiser/Intern-Atlas huggingface dataset:
Enhancing Fine-Grained Visual Recognition in the Low-Data Regime Through Feature Magnitude Regularization
Venue: arXiv cs.CV 2024
Year: 2024

Training a fine-grained image recognition model with limited data presents a significant challenge, as the subtle differences between categories may not be easily discernible amidst distracting noise patterns. One commonly employed strategy is to leverage pretrained neural networks, which can generate effective feature representations for constructing an image classification model with a restricted dataset. However, these pretrained neural networks are typically trained for different tasks than the fine-grained visual recognition (FGVR) task at hand, which can lead to the extraction of less relevant features. Moreover, in the context of building FGVR models with limited data, these irrelevant features can dominate the training process, overshadowing more useful, generalizable discriminative features. Our research has identified a surprisingly simple solution to this challenge: we introduce a regularization technique to ensure that the magnitudes of the extracted features are evenly distributed. This regularization is achieved by maximizing the uniformity of feature magnitude distribution, measured through the entropy of the normalized features. The motivation behind this regularization is to remove bias in feature magnitudes from pretrained models, where some features may be more prominent and, consequently, more likely to be used for classification. Additionally, we have developed a dynamic weighting mechanism to adjust the strength of this regularization throughout the learning process. Despite its apparent simplicity, our approach has demonstrated significant performance improvements across various fine-grained visual recognition datasets.
Low Layer Functional Split Management in 5G and Beyond: Architecture and Self-adaptation
Venue: arXiv cs.NI 2024
Year: 2024

Radio Access Network (RAN) disaggregation is emerging as a key trend in beyond 5G, as it offers new opportunities for more flexible deployments and intelligent network management. A relevant problem in disaggregated RAN is the functional split selection, which dynamically decides which baseband (BB) functions of a base station are kept close to the radio units and which ones are centralized. In this context, this paper firstly presents an architectural framework for supporting this concept relying on the O-RAN architecture. Then, the paper analyzes how the functional split can be optimized to adapt to the different load conditions while minimizing energy costs.
Query complexity lower bounds for local list-decoding and hard-core predicates (even for small rate and huge lists)
Venue: arXiv cs.CC 2024
Year: 2024

A binary code Enc$:\{0,1\}^k \to \{0,1\}^n$ is $(0.5-ε,L)$-list decodable if for all $w \in \{0,1\}^n$, the set List$(w)$ of all messages $m \in \{0,1\}^k$ such that the relative Hamming distance between Enc$(m)$ and $w$ is at most $0.5 -ε$, has size at most $L$. Informally, a $q$-query local list-decoder for Enc is a randomized procedure Dec$:[k]\times [L] \to \{0,1\}$ that when given oracle access to a string $w$, makes at most $q$ oracle calls, and for every message $m \in \text{List}(w)$, with high probability, there exists $j \in [L]$ such that for every $i \in [k]$, with high probability, Dec$^w(i,j)=m_i$. We prove lower bounds on $q$, that apply even if $L$ is huge (say $L=2^{k^{0.9}}$) and the rate of Enc is small (meaning that $n \ge 2^{k}$): 1. For $ε\geq 1/k^ν$ for some universal constant $0< ν< 1$, we prove a lower bound of $q=Ω(\frac{\log(1/δ)}{ε^2})$, where $δ$ is the error probability of the local list-decoder. This bound is tight as there is a matching upper bound by Goldreich and Levin (STOC 1989) of $q=O(\frac{\log(1/δ)}{ε^2})$ for the Hadamard code (which has $n=2^k$). This bound extends an earlier work of Grinberg, Shaltiel and Viola (FOCS 2018) which only works if $n \le 2^{k^γ}$ for some universal constant $0<γ<1$, and the number of coins tossed by Dec is small (and therefore does not apply to the Hadamard code, or other codes with low rate). 2. For smaller $ε$, we prove a lower bound of roughly $q = Ω(\frac{1}{\sqrtε})$. To the best of our knowledge, this is the first lower bound on the number of queries of local list-decoders that gives $q \ge k$ for small $ε$. We also prove black-box limitations for improving some of the parameters of the Goldreich-Levin hard-core predicate construction.
QPOPSS: Query and Parallelism Optimized Space-Saving for Finding Frequent Stream Elements
Venue: arXiv cs.DC 2024
Year: 2024

The frequent elements problem, a key component in demanding stream-data analytics, involves selecting elements whose occurrence exceeds a user-specified threshold. Fast, memory-efficient $ε$-approximate synopsis algorithms select all frequent elements but may overestimate them depending on $ε$ (user-defined parameter). Evolving applications demand performance only achievable by parallelization. However, algorithmic guarantees concerning concurrent updates and queries have been overlooked. We propose Query and Parallelism Optimized Space-Saving (QPOPSS), providing concurrency guarantees. The design includes an implementation of the \emph{Space-Saving} algorithm supporting fast queries, implying minimal overlap with concurrent updates. QPOPSS integrates this with the distribution of work and fine-grained synchronization among threads, swiftly balancing high throughput, high accuracy, and low memory consumption. Our analysis, under various concurrency and data distribution conditions, shows space and approximation bounds. Our empirical evaluation relative to representative state-of-the-art methods reveals that QPOPSS's multi-threaded throughput scales linearly while maintaining the highest accuracy, with orders of magnitude smaller memory footprint.
Strengthening Solidity Invariant Generation: From Post- to Pre-Deployment
Venue: arXiv cs.SE 2024
Year: 2024

Invariants are essential for ensuring the security and correctness of Solidity smart contracts, particularly in the context of blockchain's immutability and decentralized execution. This paper introduces InvSol, a novel framework for pre-deployment invariant generation tailored specifically for Solidity smart contracts. Unlike existing solutions, namely InvCon, InvCon+, and Trace2Inv, that rely on post-deployment transaction histories on Ethereum mainnet, InvSol identifies invariants before deployment and offers comprehensive coverage of Solidity language constructs, including loops. Additionally, InvSol incorporates custom templates to effectively prevent critical issues such as reentrancy, out-of-gas errors, and exceptions during invariant generation. We rigorously evaluate InvSol using a benchmark set of smart contracts and compare its performance with state-of-the-art solutions. Our findings reveal that InvSol significantly outperforms these tools, demonstrating its effectiveness in handling new contracts with limited transaction histories. Notably, InvSol achieves a 15% improvement in identifying common vulnerabilities compared to InvCon+ and is able to address certain crucial vulnerabilities using specific invariant templates, better than Trace2Inv.
GeoBEV: Learning Geometric BEV Representation for Multi-view 3D Object Detection
Venue: arXiv cs.CV 2024
Year: 2024

Bird's-Eye-View (BEV) representation has emerged as a mainstream paradigm for multi-view 3D object detection, demonstrating impressive perceptual capabilities. However, existing methods overlook the geometric quality of BEV representation, leaving it in a low-resolution state and failing to restore the authentic geometric information of the scene. In this paper, we identify the drawbacks of previous approaches that limit the geometric quality of BEV representation and propose Radial-Cartesian BEV Sampling (RC-Sampling), which outperforms other feature transformation methods in efficiently generating high-resolution dense BEV representation to restore fine-grained geometric information. Additionally, we design a novel In-Box Label to substitute the traditional depth label generated from the LiDAR points. This label reflects the actual geometric structure of objects rather than just their surfaces, injecting real-world geometric information into the BEV representation. In conjunction with the In-Box Label, Centroid-Aware Inner Loss (CAI Loss) is developed to capture the inner geometric structure of objects. Finally, we integrate the aforementioned modules into a novel multi-view 3D object detector, dubbed GeoBEV, which achieves a state-of-the-art result of 66.2\% NDS on the nuScenes test set. The code is available at https://github.com/mengtan00/GeoBEV.git.
BinSub: The Simple Essence of Polymorphic Type Inference for Machine Code
Venue: arXiv cs.PL 2024
Year: 2024

Recovering high-level type information in binaries is a key task in reverse engineering and binary analysis. Binaries contain very little explicit type information. The structure of binary code is incredibly flexible allowing for ad-hoc subtyping and polymorphism. Prior work has shown that precise type inference on binary code requires expressive subtyping and polymorphism. Implementations of these type system features in a binary type inference algorithm have thus-far been too inefficient to achieve widespread adoption. Recent advances in traditional type inference have achieved simple and efficient principal type inference in an ML like language with subtyping and polymorphism through the framework of algebraic subtyping. BinSub, a new binary type inference algorithm, recognizes the connection between algebraic subtyping and the type system features required to analyze binaries effectively. Using this connection, BinSub achieves simple, precise, and efficient binary type inference. We show that BinSub maintains a similar precision to prior work, while achieving a 63x improvement in average runtime for 1568 functions. We also present a formalization of BinSub and show that BinSub's type system maintains the expressiveness of prior work.
Graph-based Modeling and Simulation of Emergency Services Communication Systems
Venue: arXiv cs.CR 2024
Year: 2024

Emergency Services Communication Systems (ESCS) are evolving into Internet Protocol based communication networks, promising enhancements to their function, availability, and resilience. This increase in complexity and cyber-attack surface demands better understanding of these systems' breakdown dynamics under extreme circumstances. Existing ESCS research largely overlooks simulation and the little work that exists focuses primarily on cybersecurity threats and neglects critical factors such as non-stationarity of call arrivals. This paper introduces a robust, adaptable graph-based simulation framework and essential mathematical models for ESCS simulation. The framework uses a representation of ESCSes where each vertex is a communicating finite-state machine that exchanges messages along edges and whose behavior is governed by a discrete event queuing model. Call arrival burstiness and its connection to emergency incidents is modeled through a cluster point process. Model applicability is demonstrated through simulations of the Seattle Police Department ESCS. Ongoing work is developing GPU implementation and exploring use in cybersecurity tabletop exercises.
Robust Second-order LiDAR Bundle Adjustment Algorithm Using Mean Squared Group Metric
Venue: arXiv cs.CV 2024
Year: 2024

The bundle adjustment (BA) algorithm is a widely used nonlinear optimization technique in the backend of Simultaneous Localization and Mapping (SLAM) systems. By leveraging the co-view relationships of landmarks from multiple perspectives, the BA method constructs a joint estimation model for both poses and landmarks, enabling the system to generate refined maps and reduce front-end localization errors. However, there are unique challenges when applying the BA for LiDAR data, due to the large volume of 3D points. Exploring a robust LiDAR BA estimator and achieving accurate solutions is a very important issue. In this work, firstly we propose a novel mean square group metric (MSGM) to build the optimization objective in the LiDAR BA algorithm. This metric applies mean square transformation to uniformly process the measurement of plane landmarks from one sampling period. The transformed metric ensures scale interpretability, and does not requie a time-consuming point-by-point calculation. Secondly, by integrating a robust kernel function, the metrics involved in the BA algorithm are reweighted, and thus enhancing the robustness of the solution process. Thirdly, based on the proposed robust LiDAR BA model, we derived an explicit second-order estimator (RSO-BA). This estimator employs analytical formulas for Hessian and gradient calculations, ensuring the precision of the BA solution. Finally, we verify the merits of the proposed RSO-BA estimator against existing implicit second-order and explicit approximate second-order estimators using the publicly available datasets. The experimental results demonstrate that the RSO-BA estimator outperforms its counterparts regarding registration accuracy and robustness, particularly in large-scale or complex unstructured environments.
The Impact of Run-Time Variability on Side-Channel Attacks Targeting FPGAs
Venue: arXiv cs.CR 2024
Year: 2024

To defeat side-channel attacks, many recent countermeasures work by enforcing random run-time variability to the target computing platform in terms of clock jitters, frequency and voltage scaling, and phase shift, also combining the contributions from different actuators to maximize the side-channel resistance of the target. However, the robustness of such solutions seems strongly influenced by several hyper-parameters for which an in-depth analysis is still missing. This work proposes a fine-grained dynamic voltage and frequency scaling actuator to investigate the effectiveness of recent desynchronization countermeasures with the goal of highlighting the link between the enforced run-time variability and the vulnerability to side-channel attacks of cryptographic implementations targeting FPGAs. The analysis of the results collected from real hardware allowed for a comprehensive understanding of the protection offered by run-time variability countermeasures against side-channel attacks.
Investigating Expert-in-the-Loop LLM Discourse Patterns for Ancient Intertextual Analysis
Venue: arXiv cs.CL 2024
Year: 2024

This study explores the potential of large language models (LLMs) for identifying and examining intertextual relationships within biblical, Koine Greek texts. By evaluating the performance of LLMs on various intertextuality scenarios the study demonstrates that these models can detect direct quotations, allusions, and echoes between texts. The LLM's ability to generate novel intertextual observations and connections highlights its potential to uncover new insights. However, the model also struggles with long query passages and the inclusion of false intertextual dependences, emphasizing the importance of expert evaluation. The expert-in-the-loop methodology presented offers a scalable approach for intertextual research into the complex web of intertextuality within and beyond the biblical corpus.
A randomized simulation trial evaluating ABiMed, a clinical decision support system for medication reviews and polypharmacy management
Venue: arXiv cs.AI 2024
Year: 2024

Background: Medication review is a structured interview of the patient, performed by the pharmacist and aimed at optimizing drug treatments. In practice, medication review is a long and cognitively-demanding task that requires specific knowledge. Clinical practice guidelines have been proposed, but their application is tedious. Methods: We designed ABiMed, a clinical decision support system for medication reviews, based on the implementation of the STOPP/START v2 guidelines and on the visual presentation of aggregated drug knowledge using tables, graphs and flower glyphs. We evaluated ABiMed with 39 community pharmacists during a randomized simulation trial, each pharmacist performing a medication review for two fictitious patients without ABiMed, and two others with ABiMed. We recorded the problems identified by the pharmacists, the interventions proposed, the response time, the perceived usability and the comments. Pharmacists' medication reviews were compared to an expert-designed gold standard. Results: With ABiMed, pharmacists found 1.6 times more relevant drug-related problems during the medication review (p=1.1e-12) and proposed better interventions (p=9.8e-9), without needing more time (p=0.56). The System Usability Scale score is 82.7, which is ranked "excellent". In their comments, pharmacists appreciated the visual aspect of ABiMed and its ability to compare the current treatment with the proposed one. A multifactor analysis showed no difference in the support offered by ABiMed according to the pharmacist's age or sex, in terms of percentage of problems identified or quality of the proposed interventions. Conclusions: The use of an intelligent and visual clinical decision support system can help pharmacists when they perform medication reviews. Our main perspective is the validation of the system in clinical conditions.
Optimizing CLIP Models for Image Retrieval with Maintained Joint-Embedding Alignment
Venue: arXiv cs.CV 2024
Year: 2024

Contrastive Language and Image Pairing (CLIP), a transformative method in multimedia retrieval, typically trains two neural networks concurrently to generate joint embeddings for text and image pairs. However, when applied directly, these models often struggle to differentiate between visually distinct images that have similar captions, resulting in suboptimal performance for image-based similarity searches. This paper addresses the challenge of optimizing CLIP models for various image-based similarity search scenarios, while maintaining their effectiveness in text-based search tasks such as text-to-image retrieval and zero-shot classification. We propose and evaluate two novel methods aimed at refining the retrieval capabilities of CLIP without compromising the alignment between text and image embeddings. The first method involves a sequential fine-tuning process: initially optimizing the image encoder for more precise image retrieval and subsequently realigning the text encoder to these optimized image embeddings. The second approach integrates pseudo-captions during the retrieval-optimization phase to foster direct alignment within the embedding space. Through comprehensive experiments, we demonstrate that these methods enhance CLIP's performance on various benchmarks, including image retrieval, k-NN classification, and zero-shot text-based classification, while maintaining robustness in text-to-image retrieval. Our optimized models permit maintaining a single embedding per image, significantly simplifying the infrastructure needed for large-scale multi-modal similarity search systems.
Observing Context Improves Disparity Estimation when Race is Unobserved
Venue: arXiv cs.CY 2024
Year: 2024

In many domains, it is difficult to obtain the race data that is required to estimate racial disparity. To address this problem, practitioners have adopted the use of proxy methods which predict race using non-protected covariates. However, these proxies often yield biased estimates, especially for minority groups, limiting their real-world utility. In this paper, we introduce two new contextual proxy models that advance existing methods by incorporating contextual features in order to improve race estimates. We show that these algorithms demonstrate significant performance improvements in estimating disparities on real-world home loan and voter data. We establish that achieving unbiased disparity estimates with contextual proxies relies on mean-consistency, a calibration-like condition.
QueryCheetah: Fast Automated Discovery of Attribute Inference Attacks Against Query-Based Systems
Venue: arXiv cs.CR 2024
Year: 2024

Query-based systems (QBSs) are one of the key approaches for sharing data. QBSs allow analysts to request aggregate information from a private protected dataset. Attacks are a crucial part of ensuring QBSs are truly privacy-preserving. The development and testing of attacks is however very labor-intensive and unable to cope with the increasing complexity of systems. Automated approaches have been shown to be promising but are currently extremely computationally intensive, limiting their applicability in practice. We here propose QueryCheetah, a fast and effective method for automated discovery of privacy attacks against QBSs. We instantiate QueryCheetah on attribute inference attacks and show it to discover stronger attacks than previous methods while being 18 times faster than the state-of-the-art automated approach. We then show how QueryCheetah allows system developers to thoroughly evaluate the privacy risk, including for various attacker strengths and target individuals. We finally show how QueryCheetah can be used out-of-the-box to find attacks in larger syntaxes and workarounds around ad-hoc defenses.
BinPRE: Enhancing Field Inference in Binary Analysis Based Protocol Reverse Engineering
Venue: arXiv cs.SE 2024
Year: 2024

Protocol reverse engineering (PRE) aims to infer the specification of network protocols when the source code is not available. Specifically, field inference is one crucial step in PRE to infer the field formats and semantics. To perform field inference, binary analysis based PRE techniques are one major approach category. However, such techniques face two key challenges - (1) the format inference is fragile when the logics of processing input messages may vary among different protocol implementations, and (2) the semantic inference is limited by inadequate and inaccurate inference rules. To tackle these challenges, we present BinPRE, a binary analysis based PRE tool. BinPRE incorporates (1) an instruction-based semantic similarity analysis strategy for format extraction; (2) a novel library composed of atomic semantic detectors for improving semantic inference adequacy; and (3) a cluster-and-refine paradigm to further improve semantic inference accuracy. We have evaluated BinPRE against five existing PRE tools, including Polyglot, AutoFormat, Tupni, BinaryInferno and DynPRE. The evaluation results on eight widely-used protocols show that BinPRE outperforms the prior PRE tools in both format and semantic inference. BinPRE achieves the perfection of 0.73 on format extraction and the F1-score of 0.74 (0.81) on semantic inference of types (functions), respectively. The field inference results of BinPRE have helped improve the effectiveness of protocol fuzzing by achieving 5-29% higher branch coverage, compared to those of the best prior PRE tool. BinPRE has also helped discover one new zero-day vulnerability, which otherwise cannot be found.