OpenRaiser/Intern-Atlas huggingface dataset:
Autonomous Local Catalog Maintenance of Close Proximity Satellite Systems on Closed Natural Motion Trajectories
Venue: arXiv cs.MA 2023
Year: 2023

To enable space mission sets like on-orbit servicing and manufacturing, agents in close proximity maybe operating too close to yield resolved localization solutions to operators from ground sensors. This leads to a requirement on the systems need to maintain a catalog of their local neighborhood, however, this may impose a large burden on each agent by requiring updating and maintenance of this catalog at each node. To alleviate this burden, this paper considers the case of a single satellite agent (a chief) updating a single catalog. More specifically, we consider the case of numerous satellite deputy agents in a local neighborhood of a chief, the goal of the chief satellite is to maintain and update a catalog of all agents within this neighborhood through onboard measurements. We consider the agents having relative translational and attitude motion dynamics between the chief and deputy, with the chief centered at the origin of the frame. We provide an end-to-end solution of the this problem through providing both a supervisory control method coupled with a Bayesian Filter that propagates the belief state and provides the catalog solutions to the supervisor. The goal of the supervisory controller is to determine which agent to look at and at which times while adhering to constraints of the chief satellite. We provide a numerical validation to this problem with three agents.
Adding a Tail in Classes of Perfect Graphs
Venue: arXiv cs.DS 2023
Year: 2023

Consider a graph $G$ which belongs to a graph class ${\cal C}$. We are interested in connecting a node $w \not\in V(G)$ to $G$ by a single edge $u w$ where $u \in V(G)$; we call such an edge a \emph{tail}. As the graph resulting from $G$ after the addition of the tail, denoted $G+uw$, need not belong to the class ${\cal C}$, we want to compute a minimum ${\cal C}$-completion of $G+w$, i.e., the minimum number of non-edges (excluding the tail $u w$) to be added to $G+uw$ so that the resulting graph belongs to ${\cal C}$. In this paper, we study this problem for the classes of split, quasi-threshold, threshold, and $P_4$-sparse graphs and we present linear-time algorithms by exploiting the structure of split graphs and the tree representation of quasi-threshold, threshold, and $P_4$-sparse graphs.
Developing Hands-on Labs for Source Code Vulnerability Detection with AI
Venue: arXiv cs.CR 2023
Year: 2023

As the role of information and communication technologies gradually increases in our lives, source code security becomes a significant issue to protect against malicious attempts Furthermore with the advent of data-driven techniques, there is now a growing interest in leveraging machine learning and natural language processing as a source code assurance method to build trustworthy systems Therefore training our future software developers to write secure source code is in high demand In this thesis we propose a framework including learning modules and hands on labs to guide future IT professionals towards developing secure programming habits and mitigating source code vulnerabilities at the early stages of the software development lifecycle In this thesis our goal is to design learning modules with a set of hands on labs that will introduce students to secure programming practices using source code and log file analysis tools to predict and identify vulnerabilities In a Secure Coding Education framework we will improve students skills and awareness on source code vulnerabilities detection tools and mitigation techniques integrate concepts of source code vulnerabilities from Function API and library level to bad programming habits and practices leverage deep learning NLP and static analysis tools for log file analysis to introduce the root cause of source code vulnerabilities
AmbiCoref: Evaluating Human and Model Sensitivity to Ambiguous Coreference
Venue: arXiv cs.CL 2023
Year: 2023

Given a sentence "Abby told Brittney that she upset Courtney", one would struggle to understand who "she" refers to, and ask for clarification. However, if the word "upset" were replaced with "hugged", "she" unambiguously refers to Abby. We study if modern coreference resolution models are sensitive to such pronominal ambiguity. To this end, we construct AmbiCoref, a diagnostic corpus of minimal sentence pairs with ambiguous and unambiguous referents. Our examples generalize psycholinguistic studies of human perception of ambiguity around particular arrangements of verbs and their arguments. Analysis shows that (1) humans are less sure of referents in ambiguous AmbiCoref examples than unambiguous ones, and (2) most coreference models show little difference in output between ambiguous and unambiguous pairs. We release AmbiCoref as a diagnostic corpus for testing whether models treat ambiguity similarly to humans.
Privacy Risk for anisotropic Langevin dynamics using relative entropy bounds
Venue: arXiv cs.LG 2023
Year: 2023

The privacy preserving properties of Langevin dynamics with additive isotropic noise have been extensively studied. However, the isotropic noise assumption is very restrictive: (a) when adding noise to existing learning algorithms to preserve privacy and maintain the best possible accuracy one should take into account the relative magnitude of the outputs and their correlations; (b) popular algorithms such as stochastic gradient descent (and their continuous time limits) appear to possess anisotropic covariance properties. To study the privacy risks for the anisotropic noise case, one requires general results on the relative entropy between the laws of two Stochastic Differential Equations with different drifts and diffusion coefficients. Our main contribution is to establish such a bound using stability estimates for solutions to the Fokker-Planck equations via functional inequalities. With additional assumptions, the relative entropy bound implies an $(ε,δ)$-differential privacy bound or translates to bounds on the membership inference attack success and we show how anisotropic noise can lead to better privacy-accuracy trade-offs. Finally, the benefits of anisotropic noise are illustrated using numerical results in quadratic loss and neural network setups.
Model Monitoring and Robustness of In-Use Machine Learning Models: Quantifying Data Distribution Shifts Using Population Stability Index
Venue: arXiv cs.LG 2023
Year: 2023

Safety goes first. Meeting and maintaining industry safety standards for robustness of artificial intelligence (AI) and machine learning (ML) models require continuous monitoring for faults and performance drops. Deep learning models are widely used in industrial applications, e.g., computer vision, but the susceptibility of their performance to environment changes (e.g., noise) \emph{after deployment} on the product, are now well-known. A major challenge is detecting data distribution shifts that happen, comparing the following: {\bf (i)} development stage of AI and ML models, i.e., train/validation/test, to {\bf (ii)} deployment stage on the product (i.e., even after `testing') in the environment. We focus on a computer vision example related to autonomous driving and aim at detecting shifts that occur as a result of adding noise to images. We use the population stability index (PSI) as a measure of presence and intensity of shift and present results of our empirical experiments showing a promising potential for the PSI. We further discuss multiple aspects of model monitoring and robustness that need to be analyzed \emph{simultaneously} to achieve robustness for industry safety standards. We propose the need for and the research direction toward \emph{categorizations} of problem classes and examples where monitoring for robustness is required and present challenges and pointers for future work from a \emph{practical} perspective.
User Study for Improving Tools for Bible Translation
Venue: arXiv cs.CL 2023
Year: 2023

Technology has increasingly become an integral part of the Bible translation process. Over time, both the translation process and relevant technology have evolved greatly. More recently, the field of Natural Language Processing (NLP) has made great progress in solving some problems previously thought impenetrable. Through this study we endeavor to better understand and communicate about a segment of the current landscape of the Bible translation process as it relates to technology and identify pertinent issues. We conduct several interviews with individuals working in different levels of the Bible translation process from multiple organizations to identify gaps and bottlenecks where technology (including recent advances in AI) could potentially play a pivotal role in reducing translation time and improving overall quality.
Autonomous Drone Landing: Marked Landing Pads and Solidified Lava Flows
Venue: arXiv cs.RO 2023
Year: 2023

Landing is the most challenging and risky aspect of multirotor drone flight, and only simple landing methods exist for autonomous drones. We explore methods for autonomous drone landing in two scenarios. In the first scenario, we examine methods for landing on known landing pads using fiducial markers and a gimbal-mounted monocular camera. This method has potential in drone applications where a drone must land more accurately than GPS can provide (e.g.~package delivery in an urban canyon). We expand on previous methods by actuating the drone's camera to track the marker over time, and we address the complexities of pose estimation caused by fiducial marker orientation ambiguity. In the second scenario, and in collaboration with the RAVEN project, we explore methods for landing on solidified lava flows in Iceland, which serves as an analog environment for Mars and provides insight into the effectiveness of drone-rover exploration teams. Our drone uses a depth camera to visualize the terrain, and we are developing methods to analyze the terrain data for viable landing sites in real time with minimal sensors and external infrastructure requirements, so that the solution does not heavily influence the drone's behavior, mission structure, or operational environments.
Variational Autoencoder Learns Better Feature Representations for EEG-based Obesity Classification
Venue: arXiv cs.LG 2023
Year: 2023

Obesity is a common issue in modern societies today that can lead to various diseases and significantly reduced quality of life. Currently, research has been conducted to investigate resting state EEG (electroencephalogram) signals with an aim to identify possible neurological characteristics associated with obesity. In this study, we propose a deep learning-based framework to extract the resting state EEG features for obese and lean subject classification. Specifically, a novel variational autoencoder framework is employed to extract subject-invariant features from the raw EEG signals, which are then classified by a 1-D convolutional neural network. Comparing with conventional machine learning and deep learning methods, we demonstrate the superiority of using VAE for feature extraction, as reflected by the significantly improved classification accuracies, better visualizations and reduced impurity measures in the feature representations. Future work can be directed to gaining an in-depth understanding regarding the spatial patterns that have been learned by the proposed model from a neurological view, as well as improving the interpretability of the proposed model by allowing it to uncover any temporal-related information.
How to choose "Good" Samples for Text Data Augmentation
Venue: arXiv cs.CL 2023
Year: 2023

Deep learning-based text classification models need abundant labeled data to obtain competitive performance. Unfortunately, annotating large-size corpus is time-consuming and laborious. To tackle this, multiple researches try to use data augmentation to expand the corpus size. However, data augmentation may potentially produce some noisy augmented samples. There are currently no works exploring sample selection for augmented samples in nature language processing field. In this paper, we propose a novel self-training selection framework with two selectors to select the high-quality samples from data augmentation. Specifically, we firstly use an entropy-based strategy and the model prediction to select augmented samples. Considering some samples with high quality at the above step may be wrongly filtered, we propose to recall them from two perspectives of word overlap and semantic similarity. Experimental results show the effectiveness and simplicity of our framework.
KST-Mixer: Kinematic Spatio-Temporal Data Mixer For Colon Shape Estimation
Venue: arXiv cs.CV 2023
Year: 2023

We propose a spatio-temporal mixing kinematic data estimation method to estimate the shape of the colon with deformations caused by colonoscope insertion. Endoscope tracking or a navigation system that navigates physicians to target positions is needed to reduce such complications as organ perforations. Although many previous methods focused to track bronchoscopes and surgical endoscopes, few number of colonoscope tracking methods were proposed. This is because the colon largely deforms during colonoscope insertion. The deformation causes significant tracking errors. Colon deformation should be taken into account in the tracking process. We propose a colon shape estimation method using a Kinematic Spatio-Temporal data Mixer (KST-Mixer) that can be used during colonoscope insertions to the colon. Kinematic data of a colonoscope and the colon, including positions and directions of their centerlines, are obtained using electromagnetic and depth sensors. The proposed method separates the data into sub-groups along the spatial and temporal axes. The KST-Mixer extracts kinematic features and mix them along the spatial and temporal axes multiple times. We evaluated colon shape estimation accuracies in phantom studies. The proposed method achieved 11.92 mm mean Euclidean distance error, the smallest of the previous methods. Statistical analysis indicated that the proposed method significantly reduced the error compared to the previous methods.
GANalyzer: Analysis and Manipulation of GANs Latent Space for Controllable Face Synthesis
Venue: arXiv cs.CV 2023
Year: 2023

Generative Adversarial Networks (GANs) are capable of synthesizing high-quality facial images. Despite their success, GANs do not provide any information about the relationship between the input vectors and the generated images. Currently, facial GANs are trained on imbalanced datasets, which generate less diverse images. For example, more than 77% of 100K images that we randomly synthesized using the StyleGAN3 are classified as Happy, and only around 3% are Angry. The problem even becomes worse when a mixture of facial attributes is desired: less than 1% of the generated samples are Angry Woman, and only around 2% are Happy Black. To address these problems, this paper proposes a framework, called GANalyzer, for the analysis, and manipulation of the latent space of well-trained GANs. GANalyzer consists of a set of transformation functions designed to manipulate latent vectors for a specific facial attribute such as facial Expression, Age, Gender, and Race. We analyze facial attribute entanglement in the latent space of GANs and apply the proposed transformation for editing the disentangled facial attributes. Our experimental results demonstrate the strength of GANalyzer in editing facial attributes and generating any desired faces. We also create and release a balanced photo-realistic human face dataset. Our code is publicly available on GitHub.
Dynamic Ensemble of Low-fidelity Experts: Mitigating NAS "Cold-Start"
Venue: arXiv cs.LG 2023
Year: 2023

Predictor-based Neural Architecture Search (NAS) employs an architecture performance predictor to improve the sample efficiency. However, predictor-based NAS suffers from the severe ``cold-start'' problem, since a large amount of architecture-performance data is required to get a working predictor. In this paper, we focus on exploiting information in cheaper-to-obtain performance estimations (i.e., low-fidelity information) to mitigate the large data requirements of predictor training. Despite the intuitiveness of this idea, we observe that using inappropriate low-fidelity information even damages the prediction ability and different search spaces have different preferences for low-fidelity information types. To solve the problem and better fuse beneficial information provided by different types of low-fidelity information, we propose a novel dynamic ensemble predictor framework that comprises two steps. In the first step, we train different sub-predictors on different types of available low-fidelity information to extract beneficial knowledge as low-fidelity experts. In the second step, we learn a gating network to dynamically output a set of weighting coefficients conditioned on each input neural architecture, which will be used to combine the predictions of different low-fidelity experts in a weighted sum. The overall predictor is optimized on a small set of actual architecture-performance data to fuse the knowledge from different low-fidelity experts to make the final prediction. We conduct extensive experiments across five search spaces with different architecture encoders under various experimental settings. Our method can easily be incorporated into existing predictor-based NAS frameworks to discover better architectures.
The Fewer Splits are Better: Deconstructing Readability in Sentence Splitting
Venue: arXiv cs.CL 2023
Year: 2023

In this work, we focus on sentence splitting, a subfield of text simplification, motivated largely by an unproven idea that if you divide a sentence in pieces, it should become easier to understand. Our primary goal in this paper is to find out whether this is true. In particular, we ask, does it matter whether we break a sentence into two or three? We report on our findings based on Amazon Mechanical Turk. More specifically, we introduce a Bayesian modeling framework to further investigate to what degree a particular way of splitting the complex sentence affects readability, along with a number of other parameters adopted from diverse perspectives, including clinical linguistics, and cognitive linguistics. The Bayesian modeling experiment provides clear evidence that bisecting the sentence leads to enhanced readability to a degree greater than what we create by trisection.
SPECWANDS: An Efficient Priority-based Scheduler Against Speculation Contention Attacks
Venue: arXiv cs.CR 2023
Year: 2023

Transient Execution Attacks (TEAs) have gradually become a major security threat to modern high-performance processors. They exploit the vulnerability of speculative execution to illegally access private data, and transmit them through timing-based covert channels. While new vulnerabilities are discovered continuously, the covert channels can be categorised to two types: 1) Persistent Type, in which covert channels are based on the layout changes of buffering, e.g. through caches or TLBs; 2) Volatile Type, in which covert channels are based on the contention of sharing resources, e.g. through execution units or issuing ports. The defenses against the persistent-type covert channels have been well addressed, while those for the volatile-type are still rather inadequate. Existing mitigation schemes for the volatile type such as Speculative Compression and Time-Division-Multiplexing will introduce significant overhead due to the need to stall the pipeline or to disallow resource sharing. In this paper, we look into such attacks and defenses with a new perspective, and propose a scheduling-based mitigation scheme, called SPECWANDS. It consists of three priority-based scheduling policies to prevent an attacker from transmitting the secret in different contention situations. SPECWANDS not only can defend against both inter-thread and intra-thread based attacks, but also can keep most of the performance benefit from speculative execution and resource-sharing. We evaluate its runtime overhead on SPEC 2017 benchmarks and realistic programs. The experimental results show that SPECWANDS has a significant performance advantage over the other two representative schemes.
String Compression in FA-Presentable Structures
Venue: arXiv cs.FL 2023
Year: 2023

We construct a FA-presentation $ψ: L \rightarrow \mathbb{N}$ of the structure $(\mathbb{N};\mathrm{S})$ for which a numerical characteristic $r(n)$ defined as the maximum number $ψ(w)$ for all strings $w \in L$ of length less than or equal to $n$ grows faster than any tower of exponents of a fixed height. This result leads us to a more general notion of a compressibility rate defined for FA-presentations of any FA-presentable structure. We show the existence of FA-presentations for the configuration space of a Turing machine and Cayley graphs of some groups for which it grows faster than any tower of exponents of a fixed height. For FA-presentations of the Presburger arithmetic $(\mathbb{N};+)$ we show that it is bounded from above by a linear function.
Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities
Venue: arXiv cs.LG 2023
Year: 2023

Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static) graph-structured data. However, many real-world systems are dynamic in nature, since the graph and node/edge attributes change over time. In recent years, GNN-based models for temporal graphs have emerged as a promising area of research to extend the capabilities of GNNs. In this work, we provide the first comprehensive overview of the current state-of-the-art of temporal GNN, introducing a rigorous formalization of learning settings and tasks and a novel taxonomy categorizing existing approaches in terms of how the temporal aspect is represented and processed. We conclude the survey with a discussion of the most relevant open challenges for the field, from both research and application perspectives.
Generalized Uncertainty Principles for Quantum Cryptography
Venue: arXiv cs.CR 2023
Year: 2023

We know the classical public cryptographic algorithms are based on certain NP-hard problems such as the integer factoring in RSA and the discrete logarithm in Diffie-Hellman. They are going to be vulnerable with fault-tolerant quantum computers. We also know that the uncertainty principle for quantum bits or qubits such as quantum key distribution or QKD based on the quantum uncertainty principle offers the information theoretical security. The interesting implication with the paradigm shifts from classical computing to quantum computing is that the NP-hardness used for classical cryptography may shift to the uncertainty principles for quantum cryptography including quantum symmetric encryption, post-quantum cryptography, as well as quantum encryption in phase space for coherent optical communications. This paper would like to explore those so-called generalized uncertainty principles and explain what their implications are for quantum security. We identified three generalized uncertainty principles offering quantum security: non-commutability between permutation gates, non-commutability between the displacement and phase shift operators for coherent states, and the modular Diophantine Equation Problem in general linear algebra for post-quantum cryptography.