OpenRaiser/Intern-Atlas huggingface dataset:
RNN Based Channel Estimation in Doubly Selective Environments
Venue: arXiv cs.IT 2023
Year: 2023

Doubly-selective channel estimation represents a key element in ensuring communication reliability in wireless systems. Due to the impact of multi-path propagation and Doppler interference in dynamic environments, doubly-selective channel estimation becomes challenging. Conventional symbol-by-symbol (SBS) and frame-by-frame (FBF) channel estimation schemes encounter performance degradation in high mobility scenarios due to the usage of limited training pilots. Recently, deep learning (DL) has been utilized for doubly-selective channel estimation, where long short-term memory (LSTM) and convolutional neural network (CNN) networks are employed in the SBS and FBF, respectively. However, their usage is not optimal, since LSTM suffers from long-term memory problem, whereas, CNN-based estimators require high complexity. For this purpose, we overcome these issues by proposing an optimized recurrent neural network (RNN)-based channel estimation schemes, where gated recurrent unit (GRU) and Bi-GRU units are used in SBS and FBF channel estimation, respectively. The proposed estimators are based on the average correlation of the channel in different mobility scenarios, where several performance-complexity trade-offs are provided. Moreover, the performance of several RNN networks is analyzed. The performance superiority of the proposed estimators against the recently proposed DL-based SBS and FBF estimators is demonstrated for different scenarios while recording a significant reduction in complexity.
Freezing of Gait Prediction From Accelerometer Data Using a Simple 1D-Convolutional Neural Network -- 8th Place Solution for Kaggle's Parkinson's Freezing of Gait Prediction Competition
Venue: arXiv cs.CV 2023
Year: 2023

Freezing of Gait (FOG) is a common motor symptom in patients with Parkinson's disease (PD). During episodes of FOG, patients suddenly lose their ability to stride as intended. Patient-worn accelerometers can capture information on the patient's movement during these episodes and machine learning algorithms can potentially classify this data. The combination therefore holds the potential to detect FOG in real-time. In this work I present a simple 1-D convolutional neural network that was trained to detect FOG events in accelerometer data. Model performance was assessed by measuring the success of the model to discriminate normal movement from FOG episodes and resulted in a mean average precision of 0.356 on the private leaderboard on Kaggle. Ultimately, the model ranked 8th out of 1379 teams in the Parkinson's Freezing of Gait Prediction competition. The results underscore the potential of Deep Learning-based solutions in advancing the field of FOG detection, contributing to improved interventions and management strategies for PD patients.
RCDN -- Robust X-Corner Detection Algorithm based on Advanced CNN Model
Venue: arXiv cs.CV 2023
Year: 2023

Accurate detection and localization of X-corner on both planar and non-planar patterns is a core step in robotics and machine vision. However, previous works could not make a good balance between accuracy and robustness, which are both crucial criteria to evaluate the detectors performance. To address this problem, in this paper we present a novel detection algorithm which can maintain high sub-pixel precision on inputs under multiple interference, such as lens distortion, extreme poses and noise. The whole algorithm, adopting a coarse-to-fine strategy, contains a X-corner detection network and three post-processing techniques to distinguish the correct corner candidates, as well as a mixed sub-pixel refinement technique and an improved region growth strategy to recover the checkerboard pattern partially visible or occluded automatically. Evaluations on real and synthetic images indicate that the presented algorithm has the higher detection rate, sub-pixel accuracy and robustness than other commonly used methods. Finally, experiments of camera calibration and pose estimation verify it can also get smaller re-projection error in quantitative comparisons to the state-of-the-art.
The CHiME-7 UDASE task: Unsupervised domain adaptation for conversational speech enhancement
Venue: arXiv cs.SD 2023
Year: 2023

Supervised speech enhancement models are trained using artificially generated mixtures of clean speech and noise signals, which may not match real-world recording conditions at test time. This mismatch can lead to poor performance if the test domain significantly differs from the synthetic training domain. This paper introduces the unsupervised domain adaptation for conversational speech enhancement (UDASE) task of the 7th CHiME challenge. This task aims to leverage real-world noisy speech recordings from the target domain for unsupervised domain adaptation of speech enhancement models. The target domain corresponds to the multi-speaker reverberant conversational speech recordings of the CHiME-5 dataset, for which the ground-truth clean speech reference is unavailable. Given a CHiME-5 recording, the task is to estimate the clean, potentially multi-speaker, reverberant speech, removing the additive background noise. We discuss the motivation for the CHiME-7 UDASE task and describe the data, the task, and the baseline system.
Random Number Generators and Seeding for Differential Privacy
Venue: arXiv cs.CR 2023
Year: 2023

Differential Privacy (DP) relies on random numbers to preserve privacy, typically utilising Pseudorandom Number Generators (PRNGs) as a source of randomness. In order to allow for consistent reproducibility, testing and bug-fixing in DP algorithms and results, it is important to allow for the seeding of the PRNGs used therein. In this work, we examine the landscape of Random Number Generators (RNGs), and the considerations software engineers should make when choosing and seeding a PRNG for DP. We hope it serves as a suitable guide for DP practitioners, and includes many lessons learned when implementing seeding for diffprivlib.
DWReCO at CheckThat! 2023: Enhancing Subjectivity Detection through Style-based Data Sampling
Venue: arXiv cs.CL 2023
Year: 2023

This paper describes our submission for the subjectivity detection task at the CheckThat! Lab. To tackle class imbalances in the task, we have generated additional training materials with GPT-3 models using prompts of different styles from a subjectivity checklist based on journalistic perspective. We used the extended training set to fine-tune language-specific transformer models. Our experiments in English, German and Turkish demonstrate that different subjective styles are effective across all languages. In addition, we observe that the style-based oversampling is better than paraphrasing in Turkish and English. Lastly, the GPT-3 models sometimes produce lacklustre results when generating style-based texts in non-English languages.
VariGrad: A Novel Feature Vector Architecture for Geometric Deep Learning on Unregistered Data
Venue: arXiv cs.CV 2023
Year: 2023

We present a novel geometric deep learning layer that leverages the varifold gradient (VariGrad) to compute feature vector representations of 3D geometric data. These feature vectors can be used in a variety of downstream learning tasks such as classification, registration, and shape reconstruction. Our model's use of parameterization independent varifold representations of geometric data allows our model to be both trained and tested on data independent of the given sampling or parameterization. We demonstrate the efficiency, generalizability, and robustness to resampling demonstrated by the proposed VariGrad layer.
Complexity of Membership and Non-Emptiness Problems in Unbounded Memory Automata
Venue: arXiv cs.FL 2023
Year: 2023

We study the complexity relationship between three models of unbounded memory automata: nu-automata ($ν$-A), Layered Memory Automata (LaMA)and History-Register Automata (HRA). These are all extensions of finite state automata with unbounded memory over infinite alphabets. We prove that the membership problem is NP-complete for all of them, while they fall into different classes for what concerns non-emptiness. The problem of non-emptiness is known to be Ackermann-complete for HRA, we prove that it is PSPACE-complete for $ν$-A.
GEANN: Scalable Graph Augmentations for Multi-Horizon Time Series Forecasting
Venue: arXiv cs.LG 2023
Year: 2023

Encoder-decoder deep neural networks have been increasingly studied for multi-horizon time series forecasting, especially in real-world applications. However, to forecast accurately, these sophisticated models typically rely on a large number of time series examples with substantial history. A rapidly growing topic of interest is forecasting time series which lack sufficient historical data -- often referred to as the ``cold start'' problem. In this paper, we introduce a novel yet simple method to address this problem by leveraging graph neural networks (GNNs) as a data augmentation for enhancing the encoder used by such forecasters. These GNN-based features can capture complex inter-series relationships, and their generation process can be optimized end-to-end with the forecasting task. We show that our architecture can use either data-driven or domain knowledge-defined graphs, scaling to incorporate information from multiple very large graphs with millions of nodes. In our target application of demand forecasting for a large e-commerce retailer, we demonstrate on both a small dataset of 100K products and a large dataset with over 2 million products that our method improves overall performance over competitive baseline models. More importantly, we show that it brings substantially more gains to ``cold start'' products such as those newly launched or recently out-of-stock.
The impact of body and head dynamics on motion comfort assessment
Venue: arXiv cs.RO 2023
Year: 2023

Head motion is a key determinant of motion comfort and differs substantially from seat motion due to seat and body compliance and dynamic postural stabilization. This paper compares different human body model fidelities to transmit seat accelerations to the head for the assessment of motion comfort through simulations. Six-degree of freedom dynamics were analyzed using frequency response functions derived from an advanced human model (AHM), a computationally efficient human model (EHM) and experimental studies. Simulations of dynamic driving show that human models strongly affected the predicted ride comfort (increased up to a factor 3). Furthermore, they modestly affected sickness using the available filters from the literature and ISO-2631 (increased up to 30%), but more strongly affected sickness predicted by the subjective vertical conflict (SVC) model (increased up to 70%).
PAC bounds of continuous Linear Parameter-Varying systems related to neural ODEs
Venue: arXiv cs.LG 2023
Year: 2023

We consider the problem of learning Neural Ordinary Differential Equations (neural ODEs) within the context of Linear Parameter-Varying (LPV) systems in continuous-time. LPV systems contain bilinear systems which are known to be universal approximators for non-linear systems. Moreover, a large class of neural ODEs can be embedded into LPV systems. As our main contribution we provide Probably Approximately Correct (PAC) bounds under stability for LPV systems related to neural ODEs. The resulting bounds have the advantage that they do not depend on the integration interval.
Comparing Traditional and LLM-based Search for Consumer Choice: A Randomized Experiment
Venue: arXiv cs.HC 2023
Year: 2023

Recent advances in the development of large language models are rapidly changing how online applications function. LLM-based search tools, for instance, offer a natural language interface that can accommodate complex queries and provide detailed, direct responses. At the same time, there have been concerns about the veracity of the information provided by LLM-based tools due to potential mistakes or fabrications that can arise in algorithmically generated text. In a set of online experiments we investigate how LLM-based search changes people's behavior relative to traditional search, and what can be done to mitigate overreliance on LLM-based output. Participants in our experiments were asked to solve a series of decision tasks that involved researching and comparing different products, and were randomly assigned to do so with either an LLM-based search tool or a traditional search engine. In our first experiment, we find that participants using the LLM-based tool were able to complete their tasks more quickly, using fewer but more complex queries than those who used traditional search. Moreover, these participants reported a more satisfying experience with the LLM-based search tool. When the information presented by the LLM was reliable, participants using the tool made decisions with a comparable level of accuracy to those using traditional search, however we observed overreliance on incorrect information when the LLM erred. Our second experiment further investigated this issue by randomly assigning some users to see a simple color-coded highlighting scheme to alert them to potentially incorrect or misleading information in the LLM responses. Overall we find that this confidence-based highlighting substantially increases the rate at which users spot incorrect information, improving the accuracy of their overall decisions while leaving most other measures unaffected.
Blocks2World: Controlling Realistic Scenes with Editable Primitives
Venue: arXiv cs.CV 2023
Year: 2023

We present Blocks2World, a novel method for 3D scene rendering and editing that leverages a two-step process: convex decomposition of images and conditioned synthesis. Our technique begins by extracting 3D parallelepipeds from various objects in a given scene using convex decomposition, thus obtaining a primitive representation of the scene. These primitives are then utilized to generate paired data through simple ray-traced depth maps. The next stage involves training a conditioned model that learns to generate images from the 2D-rendered convex primitives. This step establishes a direct mapping between the 3D model and its 2D representation, effectively learning the transition from a 3D model to an image. Once the model is fully trained, it offers remarkable control over the synthesis of novel and edited scenes. This is achieved by manipulating the primitives at test time, including translating or adding them, thereby enabling a highly customizable scene rendering process. Our method provides a fresh perspective on 3D scene rendering and editing, offering control and flexibility. It opens up new avenues for research and applications in the field, including authoring and data augmentation.
Towards Automated Classification of Code Review Feedback to Support Analytics
Venue: arXiv cs.SE 2023
Year: 2023

Background: As improving code review (CR) effectiveness is a priority for many software development organizations, projects have deployed CR analytics platforms to identify potential improvement areas. The number of issues identified, which is a crucial metric to measure CR effectiveness, can be misleading if all issues are placed in the same bin. Therefore, a finer-grained classification of issues identified during CRs can provide actionable insights to improve CR effectiveness. Although a recent work by Fregnan et al. proposed automated models to classify CR-induced changes, we have noticed two potential improvement areas -- i) classifying comments that do not induce changes and ii) using deep neural networks (DNN) in conjunction with code context to improve performances. Aims: This study aims to develop an automated CR comment classifier that leverages DNN models to achieve a more reliable performance than Fregnan et al. Method: Using a manually labeled dataset of 1,828 CR comments, we trained and evaluated supervised learning-based DNN models leveraging code context, comment text, and a set of code metrics to classify CR comments into one of the five high-level categories proposed by Turzo and Bosu. Results: Based on our 10-fold cross-validation-based evaluations of multiple combinations of tokenization approaches, we found a model using CodeBERT achieving the best accuracy of 59.3%. Our approach outperforms Fregnan et al.'s approach by achieving 18.7% higher accuracy. Conclusion: Besides facilitating improved CR analytics, our proposed model can be useful for developers in prioritizing code review feedback and selecting reviewers.
Personalized Resource Allocation in Wireless Networks: An AI-Enabled and Big Data-Driven Multi-Objective Optimization
Venue: arXiv cs.IT 2023
Year: 2023

The design and optimization of wireless networks have mostly been based on strong mathematical and theoretical modeling. Nonetheless, as novel applications emerge in the era of 5G and beyond, unprecedented levels of complexity will be encountered in the design and optimization of the network. As a result, the use of Artificial Intelligence (AI) is envisioned for wireless network design and optimization due to the flexibility and adaptability it offers in solving extremely complex problems in real-time. One of the main future applications of AI is enabling user-level personalization for numerous use cases. AI will revolutionize the way we interact with computers in which computers will be able to sense commands and emotions from humans in a non-intrusive manner, making the entire process transparent to users. By leveraging this capability, and accelerated by the advances in computing technologies, wireless networks can be redesigned to enable the personalization of network services to the user level in real-time. While current wireless networks are being optimized to achieve a predefined set of quality requirements, the personalization technology advocated in this article is supported by an intelligent big data-driven layer designed to micro-manage the scarce network resources. This layer provides the intelligence required to decide the necessary service quality that achieves the target satisfaction level for each user. Due to its dynamic and flexible design, personalized networks are expected to achieve unprecedented improvements in optimizing two contradicting objectives in wireless networks: saving resources and improving user satisfaction levels.
Designing Mixed-Initiative Video Games
Venue: arXiv cs.HC 2023
Year: 2023

The development of Artificial Intelligence (AI) enables humans to co-create content with machines. The unexpectedness of AI-generated content can bring inspiration and entertainment to users. However, the co-creation interactions are always designed for content creators and have poor accessibility. To explore gamification of mixed-initiative co-creation and make human-AI interactions accessible and fun for players, I prototyped Snake Story, a mixed-initiative game where players can select AI-generated texts to write a story of a snake by playing a "Snake" like game. A controlled experiment was conducted to investigate the dynamics of player-AI interactions with and without the game component in the designed interface. As a result of a study with 11 players (n=11), I found that players utilized different strategies when playing with the two versions, game mechanics significantly affected the output stories, players' creative process, as well as role perceptions, and players with different backgrounds showed different preferences for the two versions. Based on these results, I further discussed considerations for mixed-initiative game design. This work aims to inspire the design of engaging co-creation experiences.
A Novel Pseudo-Random Number Generator Based on Multi-Objective Optimization for Image-Cryptographic Applications
Venue: arXiv cs.CR 2023
Year: 2023

Pseudo-random number generators (PRNGs) play an important role to ensure the security and confidentiality of image cryptographic algorithms. Their primary function is to generate a sequence of numbers that possesses unpredictability and randomness, which is crucial for the algorithms to work effectively and provide the desired level of security. However, traditional PRNGs frequently encounter limitations like insufficient randomness, predictability, and vulnerability to cryptanalysis attacks. To overcome these limitations, we propose a novel method namely an elliptic curve genetic algorithm (ECGA) for the construction of an image-dependent pseudo-random number generator (IDPRNG) that merges elliptic curves (ECs) and a multi-objective genetic algorithm (MOGA). The ECGA consists of two primary stages. First, we generate an EC-based initial sequence of random numbers using pixels of a plain-image and parameters of an EC, that depart from traditional methods of population initialization. In our proposed approach, the image itself serves as the seed for the initial population in the genetic algorithm optimization, taking into account the image-dependent nature of cryptographic applications. This allows the PRNG to adapt its behavior to the unique characteristics of the input image, leading to enhanced security and improved resistance against differential attacks. Furthermore, the use of a good initial population reduces the number of generations required by a genetic algorithm, which results in decreased computational cost. In the second stage, we use well-known operations of a genetic algorithm to optimize the generated sequence by maximizing a multi-objective fitness function that is based on both the information entropy and the period of the PRNG. By combining elliptic curves and genetic algorithms, we enhance the randomness and security of the ECGA.
Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy
Venue: arXiv cs.CR 2023
Year: 2023

We explore Reconstruction Robustness (ReRo), which was recently proposed as an upper bound on the success of data reconstruction attacks against machine learning models. Previous research has demonstrated that differential privacy (DP) mechanisms also provide ReRo, but so far, only asymptotic Monte Carlo estimates of a tight ReRo bound have been shown. Directly computable ReRo bounds for general DP mechanisms are thus desirable. In this work, we establish a connection between hypothesis testing DP and ReRo and derive closed-form, analytic or numerical ReRo bounds for the Laplace and Gaussian mechanisms and their subsampled variants.
Secrets Revealed in Container Images: An Internet-wide Study on Occurrence and Impact
Venue: arXiv cs.CR 2023
Year: 2023

Containerization allows bundling applications and their dependencies into a single image. The containerization framework Docker eases the use of this concept and enables sharing images publicly, gaining high momentum. However, it can lead to users creating and sharing images that include private keys or API secrets-either by mistake or out of negligence. This leakage impairs the creator's security and that of everyone using the image. Yet, the extent of this practice and how to counteract it remains unclear. In this paper, we analyze 337,171 images from Docker Hub and 8,076 other private registries unveiling that 8.5% of images indeed include secrets. Specifically, we find 52,107 private keys and 3,158 leaked API secrets, both opening a large attack surface, i.e., putting authentication and confidentiality of privacy-sensitive data at stake and even allow active attacks. We further document that those leaked keys are used in the wild: While we discovered 1,060 certificates relying on compromised keys being issued by public certificate authorities, based on further active Internet measurements, we find 275,269 TLS and SSH hosts using leaked private keys for authentication. To counteract this issue, we discuss how our methodology can be used to prevent secret leakage and reuse.