OpenRaiser/Intern-Atlas huggingface dataset:
Mitigating Bias in Machine Learning Models for Phishing Webpage Detection
Venue: arXiv cs.CR 2024
Year: 2024

The widespread accessibility of the Internet has led to a surge in online fraudulent activities, underscoring the necessity of shielding users' sensitive information from cybercriminals. Phishing, a well-known cyberattack, revolves around the creation of phishing webpages and the dissemination of corresponding URLs, aiming to deceive users into sharing their sensitive information, often for identity theft or financial gain. Various techniques are available for preemptively categorizing zero-day phishing URLs by distilling unique attributes and constructing predictive models. However, these existing techniques encounter unresolved issues. This proposal delves into persistent challenges within phishing detection solutions, particularly concentrated on the preliminary phase of assembling comprehensive datasets, and proposes a potential solution in the form of a tool engineered to alleviate bias in ML models. Such a tool can generate phishing webpages for any given set of legitimate URLs, infusing randomly selected content and visual-based phishing features. Furthermore, we contend that the tool holds the potential to assess the efficacy of existing phishing detection solutions, especially those trained on confined datasets.
Pareto Curves for Compositionally Model Checking String Diagrams of MDPs
Venue: arXiv cs.LO 2024
Year: 2024

Computing schedulers that optimize reachability probabilities in MDPs is a standard verification task. To address scalability concerns, we focus on MDPs that are compositionally described in a high-level description formalism. In particular, this paper considers string diagrams, which specify an algebraic, sequential composition of subMDPs. Towards their compositional verification, the key challenge is to locally optimize schedulers on subMDPs without considering their context in the string diagram. This paper proposes to consider the schedulers in a subMDP which form a Pareto curve on a combination of local objectives. While considering all such schedulers is intractable, it gives rise to a highly efficient sound approximation algorithm. The prototype on top of the model checker Storm demonstrates the scalability of this approach.
An Efficient VCGen-based Modular Verification of Relational Properties
Venue: arXiv cs.SE 2024
Year: 2024

Deductive verification typically relies on function contracts that specify the behavior of each function for a single function call. Relational properties link several function calls together within a single specification. They can express more advanced properties of a given function, such as non-interference, continuity, or monotonicity, or relate calls to different functions, possibly run in parallel, for instance, to show the equivalence of two implementations. However, relational properties cannot be expressed and verified directly in the traditional setting of modular deductive verification. Recent work proposed a new technique for relational property verification that relies on a verification condition generator to produce logical formulas that must be verified to ensure a given relational property. This paper presents an overview of this approach and proposes important enhancements. We integrate an optimized verification condition generator and extend the underlying theory to show how relational properties can be proved in a modular way, where one relational property can be used to prove another one, like in modular verification of function contracts. Our results have been fully formalized and proved sound in the Coq proof assistant.
A Micro Architectural Events Aware Real-Time Embedded System Fault Injector
Venue: arXiv cs.AR 2024
Year: 2024

In contemporary times, the increasing complexity of the system poses significant challenges to the reliability, trustworthiness, and security of the SACRES. Key issues include the susceptibility to phenomena such as instantaneous voltage spikes, electromagnetic interference, neutron strikes, and out-of-range temperatures. These factors can induce switch state changes in transistors, resulting in bit-flipping, soft errors, and transient corruption of stored data in memory. The occurrence of soft errors, in turn, may lead to system faults that can propel the system into a hazardous state. Particularly in critical sectors like automotive, avionics, or aerospace, such malfunctions can have real-world implications, potentially causing harm to individuals. This paper introduces a novel fault injector designed to facilitate the monitoring, aggregation, and examination of micro-architectural events. This is achieved by harnessing the microprocessor's PMU and the debugging interface, specifically focusing on ensuring the repeatability of fault injections. The fault injection methodology targets bit-flipping within the memory system, affecting CPU registers and RAM. The outcomes of these fault injections enable a thorough analysis of the impact of soft errors and establish a robust correlation between the identified faults and the essential timing predictability demanded by SACRES.
U-DIADS-Bib: a full and few-shot pixel-precise dataset for document layout analysis of ancient manuscripts
Venue: arXiv cs.CV 2024
Year: 2024

Document Layout Analysis, which is the task of identifying different semantic regions inside of a document page, is a subject of great interest for both computer scientists and humanities scholars as it represents a fundamental step towards further analysis tasks for the former and a powerful tool to improve and facilitate the study of the documents for the latter. However, many of the works currently present in the literature, especially when it comes to the available datasets, fail to meet the needs of both worlds and, in particular, tend to lean towards the needs and common practices of the computer science side, leading to resources that are not representative of the humanities real needs. For this reason, the present paper introduces U-DIADS-Bib, a novel, pixel-precise, non-overlapping and noiseless document layout analysis dataset developed in close collaboration between specialists in the fields of computer vision and humanities. Furthermore, we propose a novel, computer-aided, segmentation pipeline in order to alleviate the burden represented by the time-consuming process of manual annotation, necessary for the generation of the ground truth segmentation maps. Finally, we present a standardized few-shot version of the dataset (U-DIADS-BibFS), with the aim of encouraging the development of models and solutions able to address this task with as few samples as possible, which would allow for more effective use in a real-world scenario, where collecting a large number of segmentations is not always feasible.
Machine Translation with Large Language Models: Prompt Engineering for Persian, English, and Russian Directions
Venue: arXiv cs.CL 2024
Year: 2024

Generative large language models (LLMs) have demonstrated exceptional proficiency in various natural language processing (NLP) tasks, including machine translation, question answering, text summarization, and natural language understanding. To further enhance the performance of LLMs in machine translation, we conducted an investigation into two popular prompting methods and their combination, focusing on cross-language combinations of Persian, English, and Russian. We employed n-shot feeding and tailored prompting frameworks. Our findings indicate that multilingual LLMs like PaLM exhibit human-like machine translation outputs, enabling superior fine-tuning of desired translation nuances in accordance with style guidelines and linguistic considerations. These models also excel in processing and applying prompts. However, the choice of language model, machine translation task, and the specific source and target languages necessitate certain considerations when adopting prompting frameworks and utilizing n-shot in-context learning. Furthermore, we identified errors and limitations inherent in popular LLMs as machine translation tools and categorized them based on various linguistic metrics. This typology of errors provides valuable insights for utilizing LLMs effectively and offers methods for designing prompts for in-context learning. Our report aims to contribute to the advancement of machine translation with LLMs by improving both the accuracy and reliability of evaluation metrics.
A Mobility Analysis of UE-Side Beamforming for Multi-Panel User Equipment with Hand Blockage
Venue: arXiv cs.NI 2024
Year: 2024

The hand blockage effect of the human hand around the user equipment (UE) is too considerable to be ignored in frequency range 2 (FR2). This adds another layer of complexity to the link budget design in FR2 for 5G networks, which already suffer from high path and diffraction loss. More recently, multipanel UEs (MPUEs) have been proposed as a way to address this problem, whereby multiple distinct antenna panels are integrated into the UE body as a way to leverage gains from antenna directivity. MPUEs also enhance the Rx-beamforming gain because it is now subject to each individual antenna panel. In this paper, the mobility performance of hand blockage induced by three practical hand grips is analyzed in a system-level simulation, where in each grip both the UE orientation and the hand positioning around the UE is different. It is seen that each hand grip has a significant impact on mobility performance of the network, where in the worst case mobility failures increase by 43% compared to the non-hand blockage case. Moreover, a detailed analysis of the tradeoff between the mobility key performance indicators and the panel and Rx beam switching frequency is also studied. Results have shown that both the panel and Rx beam switches can be reduced considerably without compromising on the mobility performance. This is beneficial because it helps in reducing UE power consumption.
Video Quality Assessment Based on Swin TransformerV2 and Coarse to Fine Strategy
Venue: arXiv cs.CV 2024
Year: 2024

The objective of non-reference video quality assessment is to evaluate the quality of distorted video without access to reference high-definition references. In this study, we introduce an enhanced spatial perception module, pre-trained on multiple image quality assessment datasets, and a lightweight temporal fusion module to address the no-reference visual quality assessment (NR-VQA) task. This model implements Swin Transformer V2 as a local-level spatial feature extractor and fuses these multi-stage representations through a series of transformer layers. Furthermore, a temporal transformer is utilized for spatiotemporal feature fusion across the video. To accommodate compressed videos of varying bitrates, we incorporate a coarse-to-fine contrastive strategy to enrich the model's capability to discriminate features from videos of different bitrates. This is an expanded version of the one-page abstract.
DiConStruct: Causal Concept-based Explanations through Black-Box Distillation
Venue: arXiv cs.LG 2024
Year: 2024

Model interpretability plays a central role in human-AI decision-making systems. Ideally, explanations should be expressed using human-interpretable semantic concepts. Moreover, the causal relations between these concepts should be captured by the explainer to allow for reasoning about the explanations. Lastly, explanation methods should be efficient and not compromise the performance of the predictive task. Despite the rapid advances in AI explainability in recent years, as far as we know to date, no method fulfills these three properties. Indeed, mainstream methods for local concept explainability do not produce causal explanations and incur a trade-off between explainability and prediction performance. We present DiConStruct, an explanation method that is both concept-based and causal, with the goal of creating more interpretable local explanations in the form of structural causal models and concept attributions. Our explainer works as a distillation model to any black-box machine learning model by approximating its predictions while producing the respective explanations. Because of this, DiConStruct generates explanations efficiently while not impacting the black-box prediction task. We validate our method on an image dataset and a tabular dataset, showing that DiConStruct approximates the black-box models with higher fidelity than other concept explainability baselines, while providing explanations that include the causal relations between the concepts.
Mapping low-resolution edges to high-resolution paths: the case of traffic measurements in cities
Venue: arXiv cs.SI 2024
Year: 2024

We consider the following problem : we have a high-resolution street network of a given city, and low-resolution measurements of traffic within this city. We want to associate to each measurement the set of streets corresponding to the observed traffic. To do so, we take benefit of specific properties of these data to match measured links to links in the street network. We propose several success criteria for the obtained matching. They show that the matching algorithm generally performs very well, and they give complementary ways to detect data discrepancies that makes any matching highly dubious.
Tuning Language Models by Proxy
Venue: arXiv cs.CL 2024
Year: 2024

Despite the general capabilities of large pretrained language models, they consistently benefit from further adaptation to better achieve desired behaviors. However, tuning these models has become increasingly resource-intensive, or impossible when model weights are private. We introduce proxy-tuning, a lightweight decoding-time algorithm that operates on top of black-box LMs to achieve the same end as direct tuning, but by accessing only its predictions over the output vocabulary, not its parameters. Our method tunes a smaller LM, then applies the difference between the predictions of the small tuned and untuned LMs to shift the original predictions of the larger untuned model in the direction of tuning, while retaining the benefits of larger-scale pretraining. In experiments, when we apply proxy-tuning to Llama2-70B using proxies of only 7B size, we can close 88% of the gap between Llama2-70B and its truly-tuned chat version, when evaluated across knowledge, reasoning, and safety benchmarks. We then demonstrate the generality of proxy-tuning by applying it to domain adaptation on code, and task-specific finetuning on question-answering and math problems. Finally, we show how to proxy-tune a truly black-box LM, GPT-3.5, for temporal adaptation, increasing its knowledge about recent events. Our work demonstrates the promise of using small tuned LMs to efficiently customize large, potentially proprietary LMs through decoding-time guidance.
Deductive Closure Training of Language Models for Coherence, Accuracy, and Updatability
Venue: arXiv cs.CL 2024
Year: 2024

While language models (LMs) can sometimes generate factually correct text and estimate truth values of individual claims, these generally do not reflect a globally coherent, manipulable model of the world. As a consequence, current LMs also generate incorrect or nonsensical content, and are difficult to edit and bring up to date. We present a method called Deductive Closure Training (DCT) that uses LMs themselves to identify implications of (and contradictions within) the text that they generate, yielding an efficient self-supervised procedure for improving LM factuality. Given a collection of seed documents, DCT prompts LMs to generate additional text implied by these documents, reason globally about the correctness of this generated text, and finally fine-tune on text inferred to be correct. Given seed documents from a trusted source, DCT provides a tool for supervised model updating; if seed documents are sampled from the LM itself, DCT enables fully unsupervised fine-tuning for improved coherence and accuracy. Across the CREAK, MQUaKE, and Reversal Curse datasets, supervised DCT improves LM fact verification and text generation accuracy by 3-26%; on CREAK fully unsupervised DCT improves verification accuracy by 12%. These results show that LMs' reasoning capabilities during inference can be leveraged during training to improve their reliability.
Thermodynamic Perspectives on Computational Complexity: Exploring the P vs. NP Problem
Venue: arXiv cs.IT 2024
Year: 2024

The resolution of the P vs. NP problem, a cornerstone in computational theory, remains elusive despite extensive exploration through mathematical logic and algorithmic theory. This paper takes a novel approach by integrating information theory, thermodynamics, and computational complexity, offering a comprehensive landscape of interdisciplinary study. We focus on entropy, a concept traditionally linked with uncertainty and disorder, and reinterpret it to assess the complexity of computational problems. Our research presents a structured framework for establishing entropy profiles within computational tasks, enabling a clear distinction between P and NP-classified problems. This framework quantifies the 'information cost' associated with these problem categories, highlighting their intrinsic computational complexity. We introduce Entropy-Driven Annealing (EDA) as a new method to decipher the energy landscapes of computational problems, focusing on the unique characteristics of NP problems. This method proposes a differential thermodynamic profile for NP problems in contrast to P problems and explores potential thermodynamic routes for finding polynomial-time solutions to NP challenges. Our introduction of EDA and its application to complex computational problems like the Boolean satisfiability problem (SAT) and protein-DNA complexes suggests a potential pathway toward unraveling the intricacies of the P vs. NP problem.
Standard energy data competition procedure: A comprehensive review with a case study of the ADRENALIN load disaggregation competition
Venue: arXiv cs.HC 2024
Year: 2024

Crowdsourcing data science competitions has become popular as a cost-effective alternative to solving complex energy-related challenges. How-ever, comprehensive reviews on hosting processes remain scarce. Therefore, this paper undertakes a detailed review of 33 existing data competitions and 12 hosting platforms, complemented by an in-depth case study of the ADRENALIN load disaggregation competition. The review identifies essential elements of competition procedure, including platform selection, timeline, datasets, and submission and evaluation mechanisms. Based on proposed 16 evaluation criteria, the similarities and differences between data competition hosting platforms can be categorized into platform scoring and popularity, platform features, community engagement, open-source platforms, region-specific platforms, platform-specific purposes, and multi-purpose platforms. The case study underscores strategic planning's critical role, particularly platform selection. The case study also shows the importance of defining competition scope which influences the whole com-petition content and procedure, especially the datasets.
NODI: Out-Of-Distribution Detection with Noise from Diffusion
Venue: arXiv cs.CV 2024
Year: 2024

Out-of-distribution (OOD) detection is a crucial part of deploying machine learning models safely. It has been extensively studied with a plethora of methods developed in the literature. This problem is tackled with an OOD score computation, however, previous methods compute the OOD scores with limited usage of the in-distribution dataset. For instance, the OOD scores are computed with information from a small portion of the in-distribution data. Furthermore, these methods encode images with a neural image encoder. The robustness of these methods is rarely checked with respect to image encoders of different training methods and architectures. In this work, we introduce the diffusion process into the OOD task. The diffusion model integrates information on the whole training set into the predicted noise vectors. What's more, we deduce a closed-form solution for the noise vector (stable point). Then the noise vector is converted into our OOD score, we test both the deep model predicted noise vector and the closed-form noise vector on the OOD benchmarks \cite{openood}. Our method outperforms previous OOD methods across all types of image encoders (Table. \ref{main}). A $3.5\%$ performance gain is achieved with the MAE-based image encoder. Moreover, we studied the robustness of OOD methods by applying different types of image encoders. Some OOD methods failed to generalize well when switching image encoders from ResNet to Vision Transformers, our method performs exhibits good robustness with all the image encoders.
Decoupled Prototype Learning for Reliable Test-Time Adaptation
Venue: arXiv cs.LG 2024
Year: 2024

Test-time adaptation (TTA) is a task that continually adapts a pre-trained source model to the target domain during inference. One popular approach involves fine-tuning model with cross-entropy loss according to estimated pseudo-labels. However, its performance is significantly affected by noisy pseudo-labels. This study reveals that minimizing the classification error of each sample causes the cross-entropy loss's vulnerability to label noise. To address this issue, we propose a novel Decoupled Prototype Learning (DPL) method that features prototype-centric loss computation. First, we decouple the optimization of class prototypes. For each class prototype, we reduce its distance with positive samples and enlarge its distance with negative samples in a contrastive manner. This strategy prevents the model from overfitting to noisy pseudo-labels. Second, we propose a memory-based strategy to enhance DPL's robustness for the small batch sizes often encountered in TTA. We update each class's pseudo-feature from a memory in a momentum manner and insert an additional DPL loss. Finally, we introduce a consistency regularization-based approach to leverage samples with unconfident pseudo-labels. This approach transfers feature styles of samples with unconfident pseudo-labels to those with confident pseudo-labels. Thus, more reliable samples for TTA are created. The experimental results demonstrate that our methods achieve state-of-the-art performance on domain generalization benchmarks, and reliably improve the performance of self-training-based methods on image corruption benchmarks. The code will be released.
Dynamic Voltage and Frequency Scaling for Intermittent Computing
Venue: arXiv cs.AR 2024
Year: 2024

We present hardware/software techniques to intelligently regulate supply voltage and clock frequency of intermittently-computing devices. These devices rely on ambient energy harvesting to power their operation and small capacitors as energy buffers. Statically setting their clock frequency fails to capture the unique relations these devices expose between capacitor voltage, energy efficiency at a given operating frequency, and the corresponding operating range. Existing dynamic voltage and frequency scaling techniques are also largely inapplicable due to extreme energy scarcity and peculiar hardware features. We introduce two hardware/software co-designs that accommodate the distinct hardware features and function within a constrained energy envelope, offering varied trade-offs and functionalities. Our experimental evaluation combines tests on custom-manufactured hardware and detailed emulation experiments. The data gathered indicate that our approaches result in up to 3.75x reduced energy consumption and 12x swifter execution times compared to the considered baselines, all while utilizing smaller capacitors to accomplish identical workloads.