OpenRaiser/Intern-Atlas huggingface dataset:
Understanding crypter-as-a-service in a popular underground marketplace
Venue: arXiv cs.CR 2024
Year: 2024

Crypters are pieces of software whose main goal is to transform a target binary so it can avoid detection from Anti Viruses (AVs from now on) applications. They work similar to packers, by taking a malware binary and applying a series of modifications, obfuscations and encryptions to output a binary that evades one or more AVs. The goal is to remain fully undetected, or FUD in the hacking jargon, while maintaining its (often malicious) functionality. In line to the growth of commoditization in cybercrime, the crypter-as-a-service model has gained popularity, in response to the increased sophistication of detection mechanisms. In this business model, customers receive an initial crypter which is soon updated once becomes detected by anti-viruses. This paper provides the first study on an online underground market dedicated to crypter-as-a-service. We compare the most relevant products in sale, analyzing the existent social network on the platform and comparing the different features that they provide. We also conduct an experiment as a case study, to validate the usage of one of the most popular crypters sold in the market, and compare the results before and after crypting binaries (both benign and malware), to show its effectiveness when evading antivirus engines.
Robust Deep Reinforcement Learning with Adaptive Adversarial Perturbations in Action Space
Venue: arXiv cs.LG 2024
Year: 2024

Deep reinforcement learning (DRL) algorithms can suffer from modeling errors between the simulation and the real world. Many studies use adversarial learning to generate perturbation during training process to model the discrepancy and improve the robustness of DRL. However, most of these approaches use a fixed parameter to control the intensity of the adversarial perturbation, which can lead to a trade-off between average performance and robustness. In fact, finding the optimal parameter of the perturbation is challenging, as excessive perturbations may destabilize training and compromise agent performance, while insufficient perturbations may not impart enough information to enhance robustness. To keep the training stable while improving robustness, we propose a simple but effective method, namely, Adaptive Adversarial Perturbation (A2P), which can dynamically select appropriate adversarial perturbations for each sample. Specifically, we propose an adaptive adversarial coefficient framework to adjust the effect of the adversarial perturbation during training. By designing a metric for the current intensity of the perturbation, our method can calculate the suitable perturbation levels based on the current relative performance. The appealing feature of our method is that it is simple to deploy in real-world applications and does not require accessing the simulator in advance. The experiments in MuJoCo show that our method can improve the training stability and learn a robust policy when migrated to different test environments. The code is available at https://github.com/Lqm00/A2P-SAC.
Can AI Relate: Testing Large Language Model Response for Mental Health Support
Venue: arXiv cs.CL 2024
Year: 2024

Large language models (LLMs) are already being piloted for clinical use in hospital systems like NYU Langone, Dana-Farber and the NHS. A proposed deployment use case is psychotherapy, where a LLM-powered chatbot can treat a patient undergoing a mental health crisis. Deployment of LLMs for mental health response could hypothetically broaden access to psychotherapy and provide new possibilities for personalizing care. However, recent high-profile failures, like damaging dieting advice offered by the Tessa chatbot to patients with eating disorders, have led to doubt about their reliability in high-stakes and safety-critical settings. In this work, we develop an evaluation framework for determining whether LLM response is a viable and ethical path forward for the automation of mental health treatment. Our framework measures equity in empathy and adherence of LLM responses to motivational interviewing theory. Using human evaluation with trained clinicians and automatic quality-of-care metrics grounded in psychology research, we compare the responses provided by peer-to-peer responders to those provided by a state-of-the-art LLM. We show that LLMs like GPT-4 use implicit and explicit cues to infer patient demographics like race. We then show that there are statistically significant discrepancies between patient subgroups: Responses to Black posters consistently have lower empathy than for any other demographic group (2%-13% lower than the control group). Promisingly, we do find that the manner in which responses are generated significantly impacts the quality of the response. We conclude by proposing safety guidelines for the potential deployment of LLMs for mental health response.
Enzymatic cycle-based receivers for approximate maximum a posteriori demodulation of concentration modulated signals
Venue: arXiv cs.IT 2024
Year: 2024

Molecular communication is a bio-inspired communication paradigm where molecules are used as the information carrier. This paper considers a molecular communication network where the transmitter uses concentration modulated signals for communication. Our focus is to design receivers that can demodulate these signals. We want the receivers to use enzymatic cycles as their building blocks and can work approximately as a maximum a posteriori (MAP) demodulator. No receivers with all these features exist in the current molecular communication literature. We consider enzymatic cycles because they are a very common class of chemical reactions that are found in living cells. In addition, a MAP receiver has good statistical performance. In this paper, we study the operating regime of an enzymatic cycle and how the parameters of the enzymatic cycles can be chosen so that the receiver can approximately implement a MAP demodulator. We use simulation to study the performance of this receiver. We show that we can reduce the bit-error ratio of the demodulator if the enzymatic cycle operates in specific parameter regimes.
Neighborhood Attention Transformer with Progressive Channel Fusion for Speaker Verification
Venue: arXiv cs.SD 2024
Year: 2024

Transformer-based architectures for speaker verification typically require more training data than ECAPA-TDNN. Therefore, recent work has generally been trained on VoxCeleb1&2. We propose a backbone network based on self-attention, which can achieve competitive results when trained on VoxCeleb2 alone. The network alternates between neighborhood attention and global attention to capture local and global features, then aggregates features of different hierarchical levels, and finally performs attentive statistics pooling. Additionally, we employ a progressive channel fusion strategy to expand the receptive field in the channel dimension as the network deepens. We trained the proposed PCF-NAT model on VoxCeleb2 and evaluated it on VoxCeleb1 and the validation sets of VoxSRC. The EER and minDCF of the shallow PCF-NAT are on average more than 20% lower than those of similarly sized ECAPA-TDNN. Deep PCF-NAT achieves an EER lower than 0.5% on VoxCeleb1-O. The code and models are publicly available at https://github.com/ChenNan1996/PCF-NAT.
The Incoherency Risk in the EU's New Cyber Security Policies
Venue: arXiv cs.CR 2024
Year: 2024

The European Union (EU) has been pursuing new cyber security policies in recent years. This paper presents a short reflection of four such policies. The focus is on potential incoherency, meaning a lack of integration, divergence between the member states, institutional dysfunction, and other related problems that should be at least partially avoidable by sound policy-making. According to the results, the four policies have substantially increased the complexity of the EU's cyber security framework. In addition, there are potential problems with trust, divergence between industry sectors and different technologies, bureaucratic conflicts, and technical issues, among other things. With these insights, the paper not only contributes to the study of EU policies but also advances the understanding of cyber security policies in general.
Parallelization of the K-Means Algorithm with Applications to Big Data Clustering
Venue: arXiv cs.DC 2024
Year: 2024

The K-Means clustering using LLoyd's algorithm is an iterative approach to partition the given dataset into K different clusters. The algorithm assigns each point to the cluster based on the following objective function \[\ \min Σ_{i=1}^{n}||x_i-μ_{x_i}||^2\] The serial algorithm involves iterative steps where we compute the distance of each datapoint from the centroids and assign the datapoint to the nearest centroid. This approach is essentially known as the expectation-maximization step. Clustering involves extensive computations to calculate distances at each iteration, which increases as the number of data points increases. This provides scope for parallelism. However, we must ensure that in a parallel process, each thread has access to the updated centroid value and no racing condition exists on any centroid values. We will compare two different approaches in this project. The first approach is an OpenMP flat synchronous method where all processes are run in parallel, and we use synchronization to ensure safe updates of clusters. The second approach we adopt is a GPU based parallelization approach using OpenACC wherein we will try to make use of GPU architecture to parallelize chunks of the algorithm to observe decreased computation time. We will analyze metrics such as speed up, efficiency,time taken with varying data points, and number of processes to compare the two approaches and understand the relative performance improvement we can get.
Clap: a Semantic-Preserving Optimizing eDSL for Plonkish Proof Systems
Venue: arXiv cs.CR 2024
Year: 2024

Plonkish is a popular circuit format for developing zero-knowledge proof systems that powers a number of major projects in the blockchain space, responsible for holding billions of dollars and processing millions of transactions per day. These projects, including zero-knowledge rollups, rely on highly hand-optimized circuits whose correctness comes at the cost of time-consuming testing and auditing. In this paper, we present Clap, the first Rust eDSL with a proof system agnostic circuit format, facilitating extensibility, automatic optimizations, and formal assurances for the resultant constraint system. Clap casts the problem of producing Plonkish constraint systems and their witness generators as a semantic-preserving compilation problem. Soundness and completeness of the transformation guarantees the absence of subtle bugs caused by under- or over-constraining. Our experimental evaluation shows that its automatic optimizations achieve better performance compared to manual circuit optimization. The optimizer can also be used to automatically derive custom gates from circuit descriptions.
Strongly-Consistent Distributed Discrete-event Systems
Venue: arXiv cs.DC 2024
Year: 2024

Discrete-event (DE) systems are concurrent programs where components communicate via tagged events, where tags are drawn from a totally ordered set. Reactors are an emerging model of computation based on DE and realized in the open-source coordination language Lingua Franca. Distributed DE (DDE) systems are DE systems where the components (reactors) communicate over networks. The prior art has required that for DDE systems with cycles, each cycle must contain at least one logical delay, where the tag of events is incremented. Such delays, however, are not required by the elegant fixed-point semantics of DE. The only requirement is that the program be constructive, meaning it is free of causality cycles. This paper gives a way to coordinate the execution of DDE systems that can execute any constructive program, even one with zero-delay cycles. It provides a formal model that exposes exactly the information that must be shared across networks for such execution to be possible. Furthermore, it describes a concrete implementation that is an extension of the coordination mechanisms in Lingua Franca.
An Active Learning Framework with a Class Balancing Strategy for Time Series Classification
Venue: arXiv cs.LG 2024
Year: 2024

Training machine learning models for classification tasks often requires labeling numerous samples, which is costly and time-consuming, especially in time series analysis. This research investigates Active Learning (AL) strategies to reduce the amount of labeled data needed for effective time series classification. Traditional AL techniques cannot control the selection of instances per class for labeling, leading to potential bias in classification performance and instance selection, particularly in imbalanced time series datasets. To address this, we propose a novel class-balancing instance selection algorithm integrated with standard AL strategies. Our approach aims to select more instances from classes with fewer labeled examples, thereby addressing imbalance in time series datasets. We demonstrate the effectiveness of our AL framework in selecting informative data samples for two distinct domains of tactile texture recognition and industrial fault detection. In robotics, our method achieves high-performance texture categorization while significantly reducing labeled training data requirements to 70%. We also evaluate the impact of different sliding window time intervals on robotic texture classification using AL strategies. In synthetic fiber manufacturing, we adapt AL techniques to address the challenge of fault classification, aiming to minimize data annotation cost and time for industries. We also address real-life class imbalances in the multiclass industrial anomalous dataset using our class-balancing instance algorithm integrated with AL strategies. Overall, this thesis highlights the potential of our AL framework across these two distinct domains.
Fennec: Fine-grained Language Model Evaluation and Correction Extended through Branching and Bridging
Venue: arXiv cs.CL 2024
Year: 2024

The rapid advancement of large language models has given rise to a plethora of applications across a myriad of real-world tasks, mainly centered on aligning with human intent. However, the complexities inherent in human intent necessitate a dependence on labor-intensive and time-consuming human evaluation. To alleviate this constraint, we delve into the paradigm of employing open-source large language models as evaluators, aligning with the prevailing trend of utilizing GPT-4. Particularly, we present a step-by-step evaluation framework: \textbf{Fennec}, capable of \textbf{F}ine-grained \textbf{E}valuatio\textbf{N} and correctio\textbf{N} \textbf{E}xtended through bran\textbf{C}hing and bridging. Specifically, the branching operation dissects the evaluation task into various dimensions and granularities, thereby alleviating the challenges associated with evaluation. Concurrently, the bridging operation amalgamates diverse training datasets, augmenting the variety of evaluation tasks. In experimental trials, our 7B model consistently outperforms open-source larger-scale evaluation models across various widely adopted benchmarks in terms of both \textit{Agreement} and \textit{Consistency}, closely approaching the capabilities of GPT-4. We employ the fine-grained correction capabilities induced by the evaluation model to refine multiple model responses, and the results show that the refinement elevates the quality of responses, leading to an improvement of 1-2 points on the MT-Bench. Our code is available at Github\footnote{\url{https://github.com/dropreg/Fennec}}.
WiDRa -- Enabling Millimeter-Level Differential Ranging Accuracy in Wi-Fi Using Carrier Phase
Venue: arXiv cs.IT 2024
Year: 2024

Although Wi-Fi is an ideal technology for many ranging applications, the performance of current methods is limited by the system bandwidth, leading to low accuracy of $\sim 1$ m. For many applications, measuring differential range, viz., the change in the range between adjacent measurements, is sufficient. Correspondingly, this work proposes WiDRa - a Wi-Fi based Differential Ranging solution that provides differential range estimates by using the sum-carrier-phase information. The proposed method is not limited by system bandwidth and can track range changes even smaller than the carrier wavelength. The proposed method is first theoretically justified, while taking into consideration the various hardware impairments affecting Wi-Fi chips. In the process, methods to isolate the sum-carrier phase from the hardware impairments are proposed. Extensive simulation results show that WiDRa can achieve a differential range estimation root-mean-square-error (RMSE) of $\approx 1$ mm in channels with a Rician-factor $\geq 7$ (a $100 \times$ improvement to existing methods). The proposed methods are also validated on off-the-shelf Wi-Fi hardware to demonstrate feasibility, where they achieve an RMSE of $< 1$ mm in the differential range. Finally, limitations of current investigation and future directions of exploration are suggested, to further tap into the potential of WiDRa.
State of the Practice for Medical Imaging Software
Venue: arXiv cs.SE 2024
Year: 2024

We selected 29 medical imaging projects from 48 candidates, assessed 10 software qualities by answering 108 questions for each software project, and interviewed 8 of the 29 development teams. Based on the quantitative data, we ranked the MI software with the Analytic Hierarchy Process (AHP). The four top-ranked software products are 3D Slicer, ImageJ, Fiji, and OHIF Viewer. Generally, MI software is in a healthy state as shown by the following: we observed 88% of the documentation artifacts recommended by research software development guidelines, 100% of MI projects use version control tools, and developers appear to use the common quasi-agile research software development process. However, the current state of the practice deviates from the existing guidelines because of the rarity of some recommended artifacts, low usage of continuous integration (17% of the projects), low use of unit testing (about 50% of projects), and room for improvement with documentation (six of nine developers felt their documentation was not clear enough). From interviewing the developers, we identified five pain points and two qualities of potential concern: lack of development time, lack of funding, technology hurdles, ensuring correctness, usability, maintainability, and reproducibility. The interviewees proposed strategies to improve the state of the practice, to address the identified pain points, and to improve software quality. Combining their ideas with ours, we have the following list of recommendations: increase documentation, increase testing by enriching datasets, increase continuous integration usage, move to web applications, employ linters, use peer reviews, design for change, add assurance cases, and incorporate a "Generate All Things" approach.
Brewer-Nash Scrutinised: Mechanised Checking of Policies featuring Write Revocation
Venue: arXiv cs.CR 2024
Year: 2024

This paper revisits the Brewer-Nash security policy model inspired by ethical Chinese Wall policies. We draw attention to the fact that write access can be revoked in the Brewer-Nash model. The semantics of write access were underspecified originally, leading to multiple interpretations for which we provide a modern operational semantics. We go on to modernise the analysis of information flow in the Brewer-Nash model, by adopting a more precise definition adapted from Kessler. For our modernised reformulation, we provide full mechanised coverage for all theorems proposed by Brewer & Nash. Most theorems are established automatically using the tool {log} with the exception of a theorem regarding information flow, which combines a lemma in {log} with a theorem mechanised in Coq. Having covered all theorems originally posed by Brewer-Nash, achieving modern precision and mechanisation, we propose this work as a step towards a methodology for automated checking of more complex security policy models.
Automated Hardware Logic Obfuscation Framework Using GPT
Venue: arXiv cs.CR 2024
Year: 2024

Obfuscation stands as a promising solution for safeguarding hardware intellectual property (IP) against a spectrum of threats including reverse engineering, IP piracy, and tampering. In this paper, we introduce Obfus-chat, a novel framework leveraging Generative Pre-trained Transformer (GPT) models to automate the obfuscation process. The proposed framework accepts hardware design netlists and key sizes as inputs, and autonomously generates obfuscated code tailored to enhance security. To evaluate the effectiveness of our approach, we employ the Trust-Hub Obfuscation Benchmark for comparative analysis. We employed SAT attacks to assess the security of the design, along with functional verification procedures to ensure that the obfuscated design remains consistent with the original. Our results demonstrate the efficacy and efficiency of the proposed framework in fortifying hardware IP against potential threats, thus providing a valuable contribution to the field of hardware security.
Modeling citation worthiness by using attention-based bidirectional long short-term memory networks and interpretable models
Venue: arXiv cs.CL 2024
Year: 2024

Scientist learn early on how to cite scientific sources to support their claims. Sometimes, however, scientists have challenges determining where a citation should be situated -- or, even worse, fail to cite a source altogether. Automatically detecting sentences that need a citation (i.e., citation worthiness) could solve both of these issues, leading to more robust and well-constructed scientific arguments. Previous researchers have applied machine learning to this task but have used small datasets and models that do not take advantage of recent algorithmic developments such as attention mechanisms in deep learning. We hypothesize that we can develop significantly accurate deep learning architectures that learn from large supervised datasets constructed from open access publications. In this work, we propose a Bidirectional Long Short-Term Memory (BiLSTM) network with attention mechanism and contextual information to detect sentences that need citations. We also produce a new, large dataset (PMOA-CITE) based on PubMed Open Access Subset, which is orders of magnitude larger than previous datasets. Our experiments show that our architecture achieves state of the art performance on the standard ACL-ARC dataset ($F_{1}=0.507$) and exhibits high performance ($F_{1}=0.856$) on the new PMOA-CITE. Moreover, we show that it can transfer learning across these datasets. We further use interpretable models to illuminate how specific language is used to promote and inhibit citations. We discover that sections and surrounding sentences are crucial for our improved predictions. We further examined purported mispredictions of the model, and uncovered systematic human mistakes in citation behavior and source data. This opens the door for our model to check documents during pre-submission and pre-archival procedures. We make this new dataset, the code, and a web-based tool available to the community.
Low Complexity Successive Cancellation Decoding of Polar Codes based on Pruning Strategy in Deletion Error Channels
Venue: arXiv cs.IT 2024
Year: 2024

A novel SC decoding method of polar codes is proposed in $d$-deletion channels, where a new pruning strategy is designed to reduce decoding complexity. Considering the difference of the scenario weight distributions, pruning thresholds for each node are designed separately according to a uniform constraint on the pruning error probability, which further reduce the number of scenarios that need to be calculated during the decoding procedure. In addition, by exploiting the properties of the joint weight distribution, a simplified calculation method of thresholds is proposed. Using this simplified calculation method, the number of scenarios that required to be calculated is reduced from $(d+1)(d+2)/2$ to $d+1$.