OpenRaiser/Intern-Atlas huggingface dataset:
Decentralizing Custodial Wallets with MFKDF
Venue: arXiv cs.CR 2023
Year: 2023

The average cryptocurrency user today faces a difficult choice between centralized custodial wallets, which are notoriously prone to spontaneous collapse, or cumbersome self-custody solutions, which if not managed properly can cause a total loss of funds. In this paper, we present a "best of both worlds" cryptocurrency wallet design that looks like, and inherits the user experience of, a centralized custodial solution, while in fact being entirely decentralized in design and implementation. In our design, private keys are not stored on any device, but are instead derived directly from a user's authentication factors, such as passwords, soft tokens (e.g., Google Authenticator), hard tokens (e.g., YubiKey), or out-of-band authentication (e.g., SMS). Public parameters (salts, one-time pads, etc.) needed to access the wallet can be safely stored in public view, such as on a public blockchain, thereby providing strong availability guarantees. Users can then simply "log in" to their decentralized wallet on any device using standard credentials and even recover from lost credentials, thereby providing the usability of a custodial wallet with the trust and security of a decentralized approach.
Inductive Linear Probing for Few-shot Node Classification
Venue: arXiv cs.LG 2023
Year: 2023

Meta-learning has emerged as a powerful training strategy for few-shot node classification, demonstrating its effectiveness in the transductive setting. However, the existing literature predominantly focuses on transductive few-shot node classification, neglecting the widely studied inductive setting in the broader few-shot learning community. This oversight limits our comprehensive understanding of the performance of meta-learning based methods on graph data. In this work, we conduct an empirical study to highlight the limitations of current frameworks in the inductive few-shot node classification setting. Additionally, we propose a simple yet competitive baseline approach specifically tailored for inductive few-shot node classification tasks. We hope our work can provide a new path forward to better understand how the meta-learning paradigm works in the graph domain.
Agile Catching with Whole-Body MPC and Blackbox Policy Learning
Venue: arXiv cs.RO 2023
Year: 2023

We address a benchmark task in agile robotics: catching objects thrown at high-speed. This is a challenging task that involves tracking, intercepting, and cradling a thrown object with access only to visual observations of the object and the proprioceptive state of the robot, all within a fraction of a second. We present the relative merits of two fundamentally different solution strategies: (i) Model Predictive Control using accelerated constrained trajectory optimization, and (ii) Reinforcement Learning using zeroth-order optimization. We provide insights into various performance trade-offs including sample efficiency, sim-to-real transfer, robustness to distribution shifts, and whole-body multimodality via extensive on-hardware experiments. We conclude with proposals on fusing "classical" and "learning-based" techniques for agile robot control. Videos of our experiments may be found at https://sites.google.com/view/agile-catching
CLIPXPlore: Coupled CLIP and Shape Spaces for 3D Shape Exploration
Venue: arXiv cs.CV 2023
Year: 2023

This paper presents CLIPXPlore, a new framework that leverages a vision-language model to guide the exploration of the 3D shape space. Many recent methods have been developed to encode 3D shapes into a learned latent shape space to enable generative design and modeling. Yet, existing methods lack effective exploration mechanisms, despite the rich information. To this end, we propose to leverage CLIP, a powerful pre-trained vision-language model, to aid the shape-space exploration. Our idea is threefold. First, we couple the CLIP and shape spaces by generating paired CLIP and shape codes through sketch images and training a mapper network to connect the two spaces. Second, to explore the space around a given shape, we formulate a co-optimization strategy to search for the CLIP code that better matches the geometry of the shape. Third, we design three exploration modes, binary-attribute-guided, text-guided, and sketch-guided, to locate suitable exploration trajectories in shape space and induce meaningful changes to the shape. We perform a series of experiments to quantitatively and visually compare CLIPXPlore with different baselines in each of the three exploration modes, showing that CLIPXPlore can produce many meaningful exploration results that cannot be achieved by the existing solutions.
Measures and Optimization for Robustness and Vulnerability in Disconnected Networks
Venue: arXiv cs.SI 2023
Year: 2023

The function or performance of a network is strongly dependent on its robustness, quantifying the ability of the network to continue functioning under perturbations. While a wide variety of robustness metrics have been proposed, they have their respective limitations. In this paper, we propose to use the forest index as a measure of network robustness, which overcomes the deficiencies of existing metrics. Using such a measure as an optimization criterion, we propose and study the problem of breaking down a network by attacking some key edges. We show that the objective function of the problem is monotonic but not submodular, which impose more challenging on the problem. We thus resort to greedy algorithms extended for non-submodular functions by iteratively deleting the most promising edges. We first propose a simple greedy algorithm with a proved bound for the approximation ratio and cubic-time complexity. To confront the computation challenge for large networks, we further propose an improved nearly-linear time greedy algorithm, which significantly speeds up the process for edge selection but sacrifices little accuracy. Extensive experimental results for a large set of real-world networks verify the effectiveness and efficiency of our algorithms, demonstrating that our algorithms outperform several baseline schemes.
Quantum Computing Enhanced Distance-Minimizing Data-Driven Computational Mechanics
Venue: arXiv cs.CE 2023
Year: 2023

The distance-minimizing data-driven computational mechanics has great potential in engineering applications by eliminating material modeling error and uncertainty. In this computational framework, the solution-seeking procedure relies on minimizing the distance between the constitutive database and the conservation law. However, the distance calculation is time-consuming and often takes up most of the computational time in the case of a huge database. In this paper, we show how to use quantum computing to enhance data-driven computational mechanics by exponentially reducing the computational complexity of distance calculation. The proposed method is not only validated on the quantum computer simulator Qiskit, but also on the real quantum computer from OriginQ. We believe that this work represents a promising step towards integrating quantum computing into data-driven computational mechanics.
Cryptography approach for Secure Outsourced Data Storage in Cloud Environment
Venue: arXiv cs.CR 2023
Year: 2023

A large amount of data and applications are migrated by researchers, stakeholders, academia, and business organizations to the cloud environment due to its large variety of services, which involve the least maintenance cost, maximum flexibility, and on-demand service for storage, computation, and data distribution intentions. Despite the various characteristics the cloud environment supports, it also faces many challenges. However, data users may not completely trust a cloud environment that is engaged by a third party. Every cloud user always has a prime concern, i.e., security. Numerous methods have been designed to solve the issue of data security during data storage, calculation, and sharing across stakeholders and users. Nevertheless, there is a lack of existing methods that tackle the issue of the security of data when it is stored in a cloud environment. This article presents a precise security method that has handled the security of data while it is being shared and stored in the cloud. These methods have been utilized to lessen security assaults and prevent unauthorized parties from accessing the actual data. The article is concluded with some limitations and recommendations for the future in terms of secure data retention and distribution.
Global-Local Processing in Convolutional Neural Networks
Venue: arXiv cs.CV 2023
Year: 2023

Convolutional Neural Networks (CNNs) have achieved outstanding performance on image processing challenges. Actually, CNNs imitate the typically developed human brain structures at the micro-level (Artificial neurons). At the same time, they distance themselves from imitating natural visual perception in humans at the macro architectures (high-level cognition). Recently it has been investigated that CNNs are highly biased toward local features and fail to detect the global aspects of their input. Nevertheless, the literature offers limited clues on this problem. To this end, we propose a simple yet effective solution inspired by the unconscious behavior of the human pupil. We devise a simple module called Global Advantage Stream (GAS) to learn and capture the holistic features of input samples (i.e., the global features). Then, the GAS features were combined with a CNN network as a plug-and-play component called the Global/Local Processing (GLP) model. The experimental results confirm that this stream improves the accuracy with an insignificant additional computational/temporal load and makes the network more robust to adversarial attacks. Furthermore, investigating the interpretation of the model shows that it learns a more holistic representation similar to the perceptual system of healthy humans
Understanding Privacy Over-collection in WeChat Sub-app Ecosystem
Venue: arXiv cs.CR 2023
Year: 2023

Nowadays the app-in-app paradigm is becoming increasingly popular, and sub-apps have become an important form of mobile applications. WeChat, the leading app-in-app platform, provides millions of sub-apps that can be used for online shopping, financing, social networking, etc. However, privacy issues in this new ecosystem have not been well understood. This paper performs the first systematic study of privacy over-collection in sub-apps (denoted as SPO), where sub-apps actually collect more privacy data than they claim in their privacy policies. We propose a taxonomy of privacy for this ecosystem and a framework named SPOChecker to automatically detect SPO in real-world sub-apps. Based on SPOChecker, we collect 5,521 popular and representative WeChat sub-apps and conduct a measurement study to understand SPO from three aspects: its landscape, accountability, and defense methods. The result is worrisome, that more than half of all studied sub-apps do not provide users with privacy policies. Among 2,511 sub-apps that provide privacy policies, 489 (19.47%) of them contain SPO. We look into the detailed characteristics of SPO, figure out possible reasons and the responsibilities of stakeholders in the ecosystem, and rethink current defense methods. The measurement leads to several insightful findings that can help the community to better understand SPO and protect privacy in sub-apps.
Compatibility of Fairness Metrics with EU Non-Discrimination Laws: Demographic Parity & Conditional Demographic Disparity
Venue: arXiv cs.CY 2023
Year: 2023

Empirical evidence suggests that algorithmic decisions driven by Machine Learning (ML) techniques threaten to discriminate against legally protected groups or create new sources of unfairness. This work supports the contextual approach to fairness in EU non-discrimination legal framework and aims at assessing up to what point we can assure legal fairness through fairness metrics and under fairness constraints. For that, we analyze the legal notion of non-discrimination and differential treatment with the fairness definition Demographic Parity (DP) through Conditional Demographic Disparity (CDD). We train and compare different classifiers with fairness constraints to assess whether it is possible to reduce bias in the prediction while enabling the contextual approach to judicial interpretation practiced under EU non-discrimination laws. Our experimental results on three scenarios show that the in-processing bias mitigation algorithm leads to different performances in each of them. Our experiments and analysis suggest that AI-assisted decision-making can be fair from a legal perspective depending on the case at hand and the legal justification. These preliminary results encourage future work which will involve further case studies, metrics, and fairness notions.
PoetryDiffusion: Towards Joint Semantic and Metrical Manipulation in Poetry Generation
Venue: arXiv cs.CL 2023
Year: 2023

Controllable text generation is a challenging and meaningful field in natural language generation (NLG). Especially, poetry generation is a typical one with well-defined and strict conditions for text generation which is an ideal playground for the assessment of current methodologies. While prior works succeeded in controlling either semantic or metrical aspects of poetry generation, simultaneously addressing both remains a challenge. In this paper, we pioneer the use of the Diffusion model for generating sonnets and Chinese SongCi poetry to tackle such challenges. In terms of semantics, our PoetryDiffusion model, built upon the Diffusion model, generates entire sentences or poetry by comprehensively considering the entirety of sentence information. This approach enhances semantic expression, distinguishing it from autoregressive and large language models (LLMs). For metrical control, the separation feature of diffusion generation and its constraint control module enable us to flexibly incorporate a novel metrical controller to manipulate and evaluate metrics (format and rhythm). The denoising process in PoetryDiffusion allows for gradual enhancement of semantics and flexible integration of the metrical controller which can calculate and impose penalties on states that stray significantly from the target control distribution. Experimental results on two datasets demonstrate that our model outperforms existing models in automatic evaluation of semantic, metrical, and overall performance as well as human evaluation.
A note on the complexity of addition
Venue: arXiv cs.CC 2023
Year: 2023

We show that the sum of a sequence of integers can be computed in linear time on a Turing machine. In particular, the most obvious algorithm for this problem, which appears to require quadratic time due to carry propagation, actually runs in linear time by amortized analysis.
An Exploratory Study of Masked Face Recognition with Machine Learning Algorithms
Venue: arXiv cs.CV 2023
Year: 2023

Automated face recognition is a widely adopted machine learning technology for contactless identification of people in various processes such as automated border control, secure login to electronic devices, community surveillance, tracking school attendance, workplace clock in and clock out. Using face masks have become crucial in our daily life with the recent world-wide COVID-19 pandemic. The use of face masks causes the performance of conventional face recognition technologies to degrade considerably. The effect of mask-wearing in face recognition is yet an understudied issue. In this paper, we address this issue by evaluating the performance of a number of face recognition models which are tested by identifying masked and unmasked face images. We use six conventional machine learning algorithms, which are SVC, KNN, LDA, DT, LR and NB, to find out the ones which perform best, besides the ones which poorly perform, in the presence of masked face images. Local Binary Pattern (LBP) is utilized as the feature extraction operator. We generated and used synthesized masked face images. We prepared unmasked, masked, and half-masked training datasets and evaluated the face recognition performance against both masked and unmasked images to present a broad view of this crucial problem. We believe that our study is unique in elaborating the mask-aware facial recognition with almost all possible scenarios including half_masked-to-masked and half_masked-to-unmasked besides evaluating a larger number of conventional machine learning algorithms compared the other studies in the literature.
Federated Learning-based Vehicle Trajectory Prediction against Cyberattacks
Venue: arXiv cs.CR 2023
Year: 2023

With the development of the Internet of Vehicles (IoV), vehicle wireless communication poses serious cybersecurity challenges. Faulty information, such as fake vehicle positions and speeds sent by surrounding vehicles, could cause vehicle collisions, traffic jams, and even casualties. Additionally, private vehicle data leakages, such as vehicle trajectory and user account information, may damage user property and security. Therefore, achieving a cyberattack-defense scheme in the IoV system with faulty data saturation is necessary. This paper proposes a Federated Learning-based Vehicle Trajectory Prediction Algorithm against Cyberattacks (FL-TP) to address the above problems. The FL-TP is intensively trained and tested using a publicly available Vehicular Reference Misbehavior (VeReMi) dataset with five types of cyberattacks: constant, constant offset, random, random offset, and eventual stop. The results show that the proposed FL-TP algorithm can improve cyberattack detection and trajectory prediction by up to 6.99% and 54.86%, respectively, under the maximum cyberattack permeability scenarios compared with benchmark methods.
Improving Code-Switching and Named Entity Recognition in ASR with Speech Editing based Data Augmentation
Venue: arXiv cs.CL 2023
Year: 2023

Recently, end-to-end (E2E) automatic speech recognition (ASR) models have made great strides and exhibit excellent performance in general speech recognition. However, there remain several challenging scenarios that E2E models are not competent in, such as code-switching and named entity recognition (NER). Data augmentation is a common and effective practice for these two scenarios. However, the current data augmentation methods mainly rely on audio splicing and text-to-speech (TTS) models, which might result in discontinuous, unrealistic, and less diversified speech. To mitigate these potential issues, we propose a novel data augmentation method by applying the text-based speech editing model. The augmented speech from speech editing systems is more coherent and diversified, also more akin to real speech. The experimental results on code-switching and NER tasks show that our proposed method can significantly outperform the audio splicing and neural TTS based data augmentation systems.
TomoSAM: a 3D Slicer extension using SAM for tomography segmentation
Venue: arXiv cs.CV 2023
Year: 2023

TomoSAM has been developed to integrate the cutting-edge Segment Anything Model (SAM) into 3D Slicer, a highly capable software platform used for 3D image processing and visualization. SAM is a promptable deep learning model that is able to identify objects and create image masks in a zero-shot manner, based only on a few user clicks. The synergy between these tools aids in the segmentation of complex 3D datasets from tomography or other imaging techniques, which would otherwise require a laborious manual segmentation process. The source code associated with this article can be found at https://github.com/fsemerar/SlicerTomoSAM
Vulnerability Assessment of Industrial Control System with an Improved CVSS
Venue: arXiv cs.CR 2023
Year: 2023

Cyberattacks on industrial control systems (ICS) have been drawing attention in academia. However, this has not raised adequate concerns among some industrial practitioners. Therefore, it is necessary to identify the vulnerable locations and components in the ICS and investigate the attack scenarios and techniques. This study proposes a method to assess the risk of cyberattacks on ICS with an improved Common Vulnerability Scoring System (CVSS) and applies it to a continuous stirred tank reactor (CSTR) model. The results show the physical system levels of ICS have the highest severity once cyberattacked, and controllers, workstations, and human-machine interface are the crucial components in the cyberattack and defense.
Using Wikipedia Editor Information to Build High-performance Recommender Systems
Venue: arXiv cs.IR 2023
Year: 2023

Wikipedia has high-quality articles on a variety of topics and has been used in diverse research areas. In this study, a method is presented for using Wikipedia's editor information to build recommender systems in various domains that outperform content-based systems.
Your Email Address Holds the Key: Understanding the Connection Between Email and Password Security with Deep Learning
Venue: arXiv cs.CR 2023
Year: 2023

In this work, we investigate the effectiveness of deep-learning-based password guessing models for targeted attacks on human-chosen passwords. In recent years, service providers have increased the level of security of users'passwords. This is done by requiring more complex password generation patterns and by using computationally expensive hash functions. For the attackers this means a reduced number of available guessing attempts, which introduces the necessity to target their guess by exploiting a victim's publicly available information. In this work, we introduce a context-aware password guessing model that better capture attackers'behavior. We demonstrate that knowing a victim's email address is already critical in compromising the associated password and provide an in-depth analysis of the relationship between them. We also show the potential of such models to identify clusters of users based on their password generation behaviour, which can spot fake profiles and populations more vulnerable to context-aware guesses. The code is publicly available at https://github.com/spring-epfl/DCM_sp
Transpiling RTL Pseudo-code of the POWER Instruction Set Architecture to C for Real-time Performance Analysis on Cavatools Simulator
Venue: arXiv cs.SE 2023
Year: 2023

This paper presents a transpiler framework for converting RTL pseudo code of the POWER Instruction Set Architecture (ISA) to C code, enabling its execution on the Cavatools simulator. The transpiler consists of a lexer and parser, which parse the RTL pseudo code and generate corresponding C code representations. The lexer tokenizes the input code, while the parser applies grammar rules to build an abstract syntax tree (AST). The transpiler ensures compatibility with the Cavatools simulator by generating C code that adheres to its requirements. The resulting C code can be executed on the Cavatools simulator, allowing developers to analyze the instruction-level performance of the Power ISA in real time. The proposed framework facilitates the seamless integration of RTL pseudo code into the Cavatools ecosystem, enabling comprehensive performance analysis and optimization of Power ISA-based code.