OpenRaiser/Intern-Atlas huggingface dataset:
Intrusion Response Systems: Past, Present and Future
Venue: arXiv cs.CR 2023
Year: 2023

The rapid expansion of the Internet of Things and the emergence of edge computing-based applications has led to a new wave of cyber-attacks, with intensity and complexity that has never been seen before. Historically most research has focused on Intrusion Detection Systems (IDS), however due to the volume and speed of this new generation of cyber-attacks it is no longer sufficient to solely detect attacks and leave the response to security analysts. Consequently, research into Intrusion Response Systems (IRS) is accelerating rapidly. As such, new intrusion response approaches, methods and systems have been investigated, prototyped, and deployed. This paper is intended to provide a comprehensive review of the state of the art of IRSs. Specifically, a taxonomy to characterize the lifecycle of IRSs ranging from response selection to response deployment and response implementation is presented. A 10-phase structure to organize the core technical constituents of IRSs is also presented. Following this, an extensive review and analysis of the literature on IRSs published during the past decade is provided, and further classifies them into corresponding phases based on the proposed taxonomy and phase structure. This study provides a new way of classifying IRS research, thus offering in-depth insights into the latest discoveries and findings. In addition, through critical analysis and comparison, expert views, guidance and best practices on intrusion response approaches, system development and standardization are presented, upon which future research challenges and directions are postulated.
Cost Sharing under Private Valuation and Connection Control
Venue: arXiv cs.GT 2023
Year: 2023

We consider a cost sharing problem on a weighted undirected graph, where all the nodes want to connect to a special node called source, and they need to share the total cost (weights) of the used edges. Each node except for the source has a private valuation of the connection, and it may block others' connections by strategically cutting its adjacent edges to reduce its cost share, which may increase the total cost. We aim to design mechanisms to prevent the nodes from misreporting their valuations and cutting their adjacent edges. We first show that it is impossible for such a mechanism to further satisfy budget balance (cover the total cost) and efficiency (maximize social welfare). Then, we design two feasible cost sharing mechanisms that incentivize each node to offer all its adjacent edges and truthfully report its valuation, and also satisfy either budget balance or efficiency.
IFAN: An Explainability-Focused Interaction Framework for Humans and NLP Models
Venue: arXiv cs.CL 2023
Year: 2023

Interpretability and human oversight are fundamental pillars of deploying complex NLP models into real-world applications. However, applying explainability and human-in-the-loop methods requires technical proficiency. Despite existing toolkits for model understanding and analysis, options to integrate human feedback are still limited. We propose IFAN, a framework for real-time explanation-based interaction with NLP models. Through IFAN's interface, users can provide feedback to selected model explanations, which is then integrated through adapter layers to align the model with human rationale. We show the system to be effective in debiasing a hate speech classifier with minimal impact on performance. IFAN also offers a visual admin system and API to manage models (and datasets) as well as control access rights. A demo is live at https://ifan.ml.
Asynchronous Modal FRP
Venue: arXiv cs.PL 2023
Year: 2023

Over the past decade, a number of languages for functional reactive programming (FRP) have been suggested, which use modal types to ensure properties like causality, productivity and lack of space leaks. So far, almost all of these languages have included a modal operator for delay on a global clock. For some applications, however, the notion of global clock is unnatural and leads to leaky abstractions as well as inefficient implementations. While modal languages without a global clock have been proposed, no operational properties have been proved about them, yet. This paper proposes Async RaTT, a new modal language for asynchronous FRP, equipped with an operational semantics mapping complete programs to machines that take asynchronous input signals and produce output signals. The main novelty of Async RaTT is a new modality for asynchronous delay, allowing each output channel to be associated at runtime with the set of input channels it depends on, thus causing the machine to only compute new output when necessary. We prove a series of operational properties including causality, productivity and lack of space leaks. We also show that, although the set of input channels associated with an output channel can change during execution, upper bounds on these can be determined statically by the type system.
A System for Generalized 3D Multi-Object Search
Venue: arXiv cs.RO 2023
Year: 2023

Searching for objects is a fundamental skill for robots. As such, we expect object search to eventually become an off-the-shelf capability for robots, similar to e.g., object detection and SLAM. In contrast, however, no system for 3D object search exists that generalizes across real robots and environments. In this paper, building upon a recent theoretical framework that exploited the octree structure for representing belief in 3D, we present GenMOS (Generalized Multi-Object Search), the first general-purpose system for multi-object search (MOS) in a 3D region that is robot-independent and environment-agnostic. GenMOS takes as input point cloud observations of the local region, object detection results, and localization of the robot's view pose, and outputs a 6D viewpoint to move to through online planning. In particular, GenMOS uses point cloud observations in three ways: (1) to simulate occlusion; (2) to inform occupancy and initialize octree belief; and (3) to sample a belief-dependent graph of view positions that avoid obstacles. We evaluate our system both in simulation and on two real robot platforms. Our system enables, for example, a Boston Dynamics Spot robot to find a toy cat hidden underneath a couch in under one minute. We further integrate 3D local search with 2D global search to handle larger areas, demonstrating the resulting system in a 25m$^2$ lobby area.
Odd Chromatic Number of Graph Classes
Venue: arXiv cs.DM 2023
Year: 2023

A graph is called odd (respectively, even) if every vertex has odd (respectively, even) degree. Gallai proved that every graph can be partitioned into two even induced subgraphs, or into an odd and an even induced subgraph. We refer to a partition into odd subgraphs as an odd colouring of G. Scott [Graphs and Combinatorics, 2001] proved that a graph admits an odd colouring if and only if it has an even number of vertices. We say that a graph G is k-odd colourable if it can be partitioned into at most k odd induced subgraphs. We initiate the systematic study of odd colouring and odd chromatic number of graph classes. In particular, we consider for a number of classes whether they have bounded odd chromatic number. Our main results are that interval graphs, graphs of bounded modular-width and graphs of bounded maximum degree all have bounded odd chromatic number.
Visual Place Recognition: A Tutorial
Venue: arXiv cs.CV 2023
Year: 2023

Localization is an essential capability for mobile robots. A rapidly growing field of research in this area is Visual Place Recognition (VPR), which is the ability to recognize previously seen places in the world based solely on images. This present work is the first tutorial paper on visual place recognition. It unifies the terminology of VPR and complements prior research in two important directions: 1) It provides a systematic introduction for newcomers to the field, covering topics such as the formulation of the VPR problem, a general-purpose algorithmic pipeline, an evaluation methodology for VPR approaches, and the major challenges for VPR and how they may be addressed. 2) As a contribution for researchers acquainted with the VPR problem, it examines the intricacies of different VPR problem types regarding input, data processing, and output. The tutorial also discusses the subtleties behind the evaluation of VPR algorithms, e.g., the evaluation of a VPR system that has to find all matching database images per query, as opposed to just a single match. Practical code examples in Python illustrate to prospective practitioners and researchers how VPR is implemented and evaluated.
The Wasserstein Believer: Learning Belief Updates for Partially Observable Environments through Reliable Latent Space Models
Venue: arXiv cs.LG 2023
Year: 2023

Partially Observable Markov Decision Processes (POMDPs) are used to model environments where the full state cannot be perceived by an agent. As such the agent needs to reason taking into account the past observations and actions. However, simply remembering the full history is generally intractable due to the exponential growth in the history space. Maintaining a probability distribution that models the belief over what the true state is can be used as a sufficient statistic of the history, but its computation requires access to the model of the environment and is often intractable. While SOTA algorithms use Recurrent Neural Networks to compress the observation-action history aiming to learn a sufficient statistic, they lack guarantees of success and can lead to sub-optimal policies. To overcome this, we propose the Wasserstein Belief Updater, an RL algorithm that learns a latent model of the POMDP and an approximation of the belief update. Our approach comes with theoretical guarantees on the quality of our approximation ensuring that our outputted beliefs allow for learning the optimal value function.
Time series anomaly detection with reconstruction-based state-space models
Venue: arXiv cs.LG 2023
Year: 2023

Recent advances in digitization have led to the availability of multivariate time series data in various domains, enabling real-time monitoring of operations. Identifying abnormal data patterns and detecting potential failures in these scenarios are important yet rather challenging. In this work, we propose a novel unsupervised anomaly detection method for time series data. The proposed framework jointly learns the observation model and the dynamic model, and model uncertainty is estimated from normal samples. Specifically, a long short-term memory (LSTM)-based encoder-decoder is adopted to represent the mapping between the observation space and the latent space. Bidirectional transitions of states are simultaneously modeled by leveraging backward and forward temporal information. Regularization of the latent space places constraints on the states of normal samples, and Mahalanobis distance is used to evaluate the abnormality level. Empirical studies on synthetic and real-world datasets demonstrate the superior performance of the proposed method in anomaly detection tasks.
Tight Bounds for $γ$-Regret via the Decision-Estimation Coefficient
Venue: arXiv cs.LG 2023
Year: 2023

In this work, we give a statistical characterization of the $γ$-regret for arbitrary structured bandit problems, the regret which arises when comparing against a benchmark that is $γ$ times the optimal solution. The $γ$-regret emerges in structured bandit problems over a function class $\mathcal{F}$ where finding an exact optimum of $f \in \mathcal{F}$ is intractable. Our characterization is given in terms of the $γ$-DEC, a statistical complexity parameter for the class $\mathcal{F}$, which is a modification of the constrained Decision-Estimation Coefficient (DEC) of Foster et al., 2023 (and closely related to the original offset DEC of Foster et al., 2021). Our lower bound shows that the $γ$-DEC is a fundamental limit for any model class $\mathcal{F}$: for any algorithm, there exists some $f \in \mathcal{F}$ for which the $γ$-regret of that algorithm scales (nearly) with the $γ$-DEC of $\mathcal{F}$. We provide an upper bound showing that there exists an algorithm attaining a nearly matching $γ$-regret. Due to significant challenges in applying the prior results on the DEC to the $γ$-regret case, both our lower and upper bounds require novel techniques and a new algorithm.
Efficient Skill Acquisition for Complex Manipulation Tasks in Obstructed Environments
Venue: arXiv cs.RO 2023
Year: 2023

Data efficiency in robotic skill acquisition is crucial for operating robots in varied small-batch assembly settings. To operate in such environments, robots must have robust obstacle avoidance and versatile goal conditioning acquired from only a few simple demonstrations. Existing approaches, however, fall short of these requirements. Deep reinforcement learning (RL) enables a robot to learn complex manipulation tasks but is often limited to small task spaces in the real world due to sample inefficiency and safety concerns. Motion planning (MP) can generate collision-free paths in obstructed environments, but cannot solve complex manipulation tasks and requires goal states often specified by a user or object-specific pose estimator. In this work, we propose a system for efficient skill acquisition that leverages an object-centric generative model (OCGM) for versatile goal identification to specify a goal for MP combined with RL to solve complex manipulation tasks in obstructed environments. Specifically, OCGM enables one-shot target object identification and re-identification in new scenes, allowing MP to guide the robot to the target object while avoiding obstacles. This is combined with a skill transition network, which bridges the gap between terminal states of MP and feasible start states of a sample-efficient RL policy. The experiments demonstrate that our OCGM-based one-shot goal identification provides competitive accuracy to other baseline approaches and that our modular framework outperforms competitive baselines, including a state-of-the-art RL algorithm, by a significant margin for complex manipulation tasks in obstructed environments.
Fixpoint operators for 2-categorical structures
Venue: arXiv cs.LO 2023
Year: 2023

Fixpoint operators are tools to reason on recursive programs and data types obtained by induction (e.g. lists, trees) or coinduction (e.g. streams). They were given a categorical treatment with the notion of categories with fixpoints. A theorem by Plotkin and Simpson characterizes existence and uniqueness of fixpoint operators for categories satisfying some conditions on bifree algebras and recovers the standard examples of the category Cppo ($ω$-complete pointed partial orders and continuous functions) in domain theory and the relational model in linear logic. We present a categorification of this result and develop the theory of 2-categorical fixpoint operators where the 2-dimensional framework allows to model the execution steps for languages with (co)inductive principles. We recover the standard categorical constructions of initial algebras and final coalgebras for endofunctors as well as fixpoints of generalized species and polynomial functors.
Spelling convention sensitivity in neural language models
Venue: arXiv cs.CL 2023
Year: 2023

We examine whether large neural language models, trained on very large collections of varied English text, learn the potentially long-distance dependency of British versus American spelling conventions, i.e., whether spelling is consistently one or the other within model-generated strings. In contrast to long-distance dependencies in non-surface underlying structure (e.g., syntax), spelling consistency is easier to measure both in LMs and the text corpora used to train them, which can provide additional insight into certain observed model behaviors. Using a set of probe words unique to either British or American English, we first establish that training corpora exhibit substantial (though not total) consistency. A large T5 language model does appear to internalize this consistency, though only with respect to observed lexical items (not nonce words with British/American spelling patterns). We further experiment with correcting for biases in the training data by fine-tuning T5 on synthetic data that has been debiased, and find that finetuned T5 remains only somewhat sensitive to spelling consistency. Further experiments show GPT2 to be similarly limited.
Can an Embodied Agent Find Your "Cat-shaped Mug"? LLM-Guided Exploration for Zero-Shot Object Navigation
Venue: arXiv cs.RO 2023
Year: 2023

We present LGX (Language-guided Exploration), a novel algorithm for Language-Driven Zero-Shot Object Goal Navigation (L-ZSON), where an embodied agent navigates to a uniquely described target object in a previously unseen environment. Our approach makes use of Large Language Models (LLMs) for this task by leveraging the LLM's commonsense reasoning capabilities for making sequential navigational decisions. Simultaneously, we perform generalized target object detection using a pre-trained Vision-Language grounding model. We achieve state-of-the-art zero-shot object navigation results on RoboTHOR with a success rate (SR) improvement of over 27% over the current baseline of the OWL-ViT CLIP on Wheels (OWL CoW). Furthermore, we study the usage of LLMs for robot navigation and present an analysis of various prompting strategies affecting the model output. Finally, we showcase the benefits of our approach via \textit{real-world} experiments that indicate the superior performance of LGX in detecting and navigating to visually unique objects.
Understanding Human Intervention in the Platform Economy: A case study of an indie food delivery service
Venue: arXiv cs.HC 2023
Year: 2023

This paper examines the sociotechnical infrastructure of an "indie" food delivery platform. The platform, Nosh, provides an alternative to mainstream services, such as Doordash and Uber Eats, in several communities in the Western United States. We interviewed 28 stakeholders including restauranteurs, couriers, consumers, and platform administrators. Drawing on infrastructure literature, we learned that the platform is a patchwork of disparate technical systems held together by human intervention. Participants join this platform because they receive greater agency, financial security, and local support. We identify human intervention's key role in making food delivery platform users feel respected. This study provides insights into the affordances, limitations, and possibilities of food delivery platforms designed to prioritize local contexts over transnational scales.
Judging Adam: Studying the Performance of Optimization Methods on ML4SE Tasks
Venue: arXiv cs.SE 2023
Year: 2023

Solving a problem with a deep learning model requires researchers to optimize the loss function with a certain optimization method. The research community has developed more than a hundred different optimizers, yet there is scarce data on optimizer performance in various tasks. In particular, none of the benchmarks test the performance of optimizers on source code-related problems. However, existing benchmark data indicates that certain optimizers may be more efficient for particular domains. In this work, we test the performance of various optimizers on deep learning models for source code and find that the choice of an optimizer can have a significant impact on the model quality, with up to two-fold score differences between some of the relatively well-performing optimizers. We also find that RAdam optimizer (and its modification with the Lookahead envelope) is the best optimizer that almost always performs well on the tasks we consider. Our findings show a need for a more extensive study of the optimizers in code-related tasks, and indicate that the ML4SE community should consider using RAdam instead of Adam as the default optimizer for code-related deep learning tasks.
Expressivity of Shallow and Deep Neural Networks for Polynomial Approximation
Venue: arXiv cs.LG 2023
Year: 2023

This study explores the number of neurons required for a Rectified Linear Unit (ReLU) neural network to approximate multivariate monomials. We establish an exponential lower bound on the complexity of any shallow network approximating the product function over a general compact domain. We also demonstrate this lower bound doesn't apply to normalized Lipschitz monomials over the unit cube. These findings suggest that shallow ReLU networks experience the curse of dimensionality when expressing functions with a Lipschitz parameter scaling with the dimension of the input, and that the expressive power of neural networks is more dependent on their depth rather than overall complexity.
ISC-FLAT: On the Conflict Between Control Flow Attestation and Real-Time Operations
Venue: arXiv cs.CR 2023
Year: 2023

The wide adoption of IoT gadgets and Cyber-Physical Systems (CPS) makes embedded devices increasingly important. While some of these devices perform mission-critical tasks, they are usually implemented using Micro-Controller Units (MCUs) that lack security mechanisms on par with those available to general-purpose computers, making them more susceptible to remote exploits that could corrupt their software integrity. Motivated by this problem, prior work has proposed techniques to remotely assess the trustworthiness of embedded MCU software. Among them, Control Flow Attestation (CFA) enables remote detection of runtime abuses that illegally modify the program's control flow during execution. Despite these advances, current CFA methods share a fundamental limitation: they preclude interrupts during the execution of the software operation being attested. Simply put, existing CFA techniques are insecure unless interrupts are disabled on the MCU. On the other hand, we argue that the lack of interruptability can obscure CFA usefulness, as most embedded applications depend on interrupts to process asynchronous events in real-time. To address this limitation, we propose Interrupt-Safe Control Flow Attestation (ISC-FLAT): a CFA technique that is compatible with existing MCUs and enables interrupt handling without compromising the authenticity of CFA reports. Similar to other CFA techniques that do not require customized hardware modifications, ISC-FLAT leverages a Trusted Execution Environment (TEE) (in particular, our prototype is built on ARM TrustZone-M) to securely generate unforgeable CFA reports without precluding applications from processing interrupts. We implement a fully functional ISC-FLAT prototype on the ARM Cortex-M33 MCU and demonstrate that it incurs minimal runtime overhead when compared to existing TEE-based CFA methods that do not support interrupts.
FSVVD: A Dataset of Full Scene Volumetric Video
Venue: arXiv cs.MM 2023
Year: 2023

Recent years have witnessed a rapid development of immersive multimedia which bridges the gap between the real world and virtual space. Volumetric videos, as an emerging representative 3D video paradigm that empowers extended reality, stand out to provide unprecedented immersive and interactive video watching experience. Despite the tremendous potential, the research towards 3D volumetric video is still in its infancy, relying on sufficient and complete datasets for further exploration. However, existing related volumetric video datasets mostly only include a single object, lacking details about the scene and the interaction between them. In this paper, we focus on the current most widely used data format, point cloud, and for the first time release a full-scene volumetric video dataset that includes multiple people and their daily activities interacting with the external environments. Comprehensive dataset description and analysis are conducted, with potential usage of this dataset. The dataset and additional tools can be accessed via the following website: https://cuhksz-inml.github.io/full_scene_volumetric_video_dataset/.