OpenRaiser/Intern-Atlas huggingface dataset:
From Imitation to Refinement -- Residual RL for Precise Assembly
Venue: arXiv cs.RO 2024
Year: 2024

Recent advances in Behavior Cloning (BC) have made it easy to teach robots new tasks. However, we find that the ease of teaching comes at the cost of unreliable performance that saturates with increasing data for tasks requiring precision. The performance saturation can be attributed to two critical factors: (a) distribution shift resulting from the use of offline data and (b) the lack of closed-loop corrective control caused by action chucking (predicting a set of future actions executed open-loop) critical for BC performance. Our key insight is that by predicting action chunks, BC policies function more like trajectory "planners" than closed-loop controllers necessary for reliable execution. To address these challenges, we devise a simple yet effective method, ResiP (Residual for Precise Manipulation), that overcomes the reliability problem while retaining BC's ease of teaching and long-horizon capabilities. ResiP augments a frozen, chunked BC model with a fully closed-loop residual policy trained with reinforcement learning (RL) that addresses distribution shifts and introduces closed-loop corrections over open-loop execution of action chunks predicted by the BC trajectory planner. Videos, code, and data: https://residual-assembly.github.io.
Aster: Fixing the Android TEE Ecosystem with Arm CCA
Venue: arXiv cs.CR 2024
Year: 2024

The Android ecosystem relies on either TrustZone (e.g., OP-TEE, QTEE, Trusty) or trusted hypervisors (pKVM, Gunyah) to isolate security-sensitive services from malicious apps and Android bugs. TrustZone allows any secure world code to access the normal world that runs Android. Similarly, a trusted hypervisor has full access to Android running in one VM and security services in other VMs. In this paper, we motivate the need for mutual isolation, wherein Android, hypervisors, and the secure world are isolated from each other. Then, we propose a sandboxed service abstraction, such that a sandboxed execution cannot access any other sandbox, Android, hypervisor, or secure world memory. We present Aster which achieves these goals while ensuring that sandboxed execution can still communicate with Android to get inputs and provide outputs securely. Our main insight is to leverage the hardware isolation offered by Arm Confidential Computing Architecture (CCA). However, since CCA does not satisfy our sandboxing and mutual isolation requirements, Aster repurposes its hardware enforcement to meet its goals while addressing challenges such as secure interfaces, virtio, and protection against interrupts. We implement Aster to demonstrate its feasibility and assess its compatibility. We take three case studies, including one currently deployed on Android phones and insufficiently secured using a trusted hypervisor, to demonstrate that they can be protected by Aster.
Architectural Tactics to Improve the Environmental Sustainability of Microservices: A Rapid Review
Venue: arXiv cs.SE 2024
Year: 2024

Microservices are a popular architectural style adopted by the industry when it comes to deploying software that requires scalability, maintainability, and agile development. There is an increasing demand for improving the sustainability of microservice systems in the industry. This rapid review gathers 22 peer-reviewed studies and synthesizes architectural tactics that improve the environmental sustainability of microservices from them. We list 6 tactics that are presented in an actionable way and categorized according to their sustainability aspects and context. The sustainability aspects include energy efficiency, carbon efficiency, and resource efficiency, among which resource efficiency is the most researched one while energy efficiency and carbon efficiency are still in the early stage of study. The context categorization, including serverless platforms, decentralized networks, etc., helps to identify the tactics that we can use in a specific setting. Additionally, we present how the evidence of optimization after adopting these tactics is presented, like the measurement unit and statistical methods, and how experiments are generally set up so that this review is both instructive for our future study and our industrial practitioners' interest. We further study the insufficiencies of the current study and hope to provide insight for other researchers and the industry.
Container Morphisms for Composable Interactive Systems
Venue: arXiv cs.SE 2024
Year: 2024

This paper provides a mathematical framework for client-server communication that results in a modular and type-safe architecture. It is informed and motivated by the software engineering practice of developing server backends with a database layer and a frontend, all of which communicate with a notion of request/response. I make use of dependent types to ensure the request/response relation matches and show how this idea fits in the broader context of containers and their morphisms. Using the category of containers and their monoidal products, I define monads on containers that mimic their functional programming counterparts, and using the Kleene star, I describe stateful protocols in the same system.
In Search for Architectures and Loss Functions in Multi-Objective Reinforcement Learning
Venue: arXiv cs.LG 2024
Year: 2024

Multi-objective reinforcement learning (MORL) is essential for addressing the intricacies of real-world RL problems, which often require trade-offs between multiple utility functions. However, MORL is challenging due to unstable learning dynamics with deep learning-based function approximators. The research path most taken has been to explore different value-based loss functions for MORL to overcome this issue. Our work empirically explores model-free policy learning loss functions and the impact of different architectural choices. We introduce two different approaches: Multi-objective Proximal Policy Optimization (MOPPO), which extends PPO to MORL, and Multi-objective Advantage Actor Critic (MOA2C), which acts as a simple baseline in our ablations. Our proposed approach is straightforward to implement, requiring only small modifications at the level of function approximator. We conduct comprehensive evaluations on the MORL Deep Sea Treasure, Minecart, and Reacher environments and show that MOPPO effectively captures the Pareto front. Our extensive ablation studies and empirical analyses reveal the impact of different architectural choices, underscoring the robustness and versatility of MOPPO compared to popular MORL approaches like Pareto Conditioned Networks (PCN) and Envelope Q-learning in terms of MORL metrics, including hypervolume and expected utility.
Trust Your Gut: Comparing Human and Machine Inference from Noisy Visualizations
Venue: arXiv cs.HC 2024
Year: 2024

People commonly utilize visualizations not only to examine a given dataset, but also to draw generalizable conclusions about the underlying models or phenomena. Prior research has compared human visual inference to that of an optimal Bayesian agent, with deviations from rational analysis viewed as problematic. However, human reliance on non-normative heuristics may prove advantageous in certain circumstances. We investigate scenarios where human intuition might surpass idealized statistical rationality. In two experiments, we examine individuals' accuracy in characterizing the parameters of known data-generating models from bivariate visualizations. Our findings indicate that, although participants generally exhibited lower accuracy compared to statistical models, they frequently outperformed Bayesian agents, particularly when faced with extreme samples. Participants appeared to rely on their internal models to filter out noisy visualizations, thus improving their resilience against spurious data. However, participants displayed overconfidence and struggled with uncertainty estimation. They also exhibited higher variance than statistical machines. Our findings suggest that analyst gut reactions to visualizations may provide an advantage, even when departing from rationality. These results carry implications for designing visual analytics tools, offering new perspectives on how to integrate statistical models and analyst intuition for improved inference and decision-making. The data and materials for this paper are available at https://osf.io/qmfv6
Fostering Microservice Maintainability Assurance through a Comprehensive Framework
Venue: arXiv cs.SE 2024
Year: 2024

Cloud-native systems represent a significant leap in constructing scalable, large systems, employing microservice architecture as a key element in developing distributed systems through self-contained components. However, the decentralized nature of these systems, characterized by separate source codes and deployments, introduces challenges in assessing system qualities. Microservice-based systems, with their inherent complexity and the need for coordinated changes across multiple microservices, lack established best practices and guidelines, leading to difficulties in constructing and comprehending the holistic system view. This gap can result in performance degradation and increased maintenance costs, potentially requiring system refactoring. The main goal of this project is to offer maintainability assurance for microservice practitioners. It introduces an automated assessment framework tailored to microservice architecture, enhancing practitioners' understanding and analytical capabilities of the multiple system perspectives. The framework addresses various granularity levels, from artifacts to constructing holistic views of static and dynamic system characteristics. It integrates diverse perspectives, encompassing human-centric elements like architectural visualization and automated evaluations, including coupling detection, testing coverage measurement, and semantic clone identification. Validation studies involving practitioners demonstrate the framework's effectiveness in addressing diverse quality and maintainability issues, revealing insights not apparent when analyzing individual microservices in isolation.
Why Machines Can't Be Moral: Turing's Halting Problem and the Moral Limits of Artificial Intelligence
Venue: arXiv cs.CY 2024
Year: 2024

In this essay, I argue that explicit ethical machines, whose moral principles are inferred through a bottom-up approach, are unable to replicate human-like moral reasoning and cannot be considered moral agents. By utilizing Alan Turing's theory of computation, I demonstrate that moral reasoning is computationally intractable by these machines due to the halting problem. I address the frontiers of machine ethics by formalizing moral problems into 'algorithmic moral questions' and by exploring moral psychology's dual-process model. While the nature of Turing Machines theoretically allows artificial agents to engage in recursive moral reasoning, critical limitations are introduced by the halting problem, which states that it is impossible to predict with certainty whether a computational process will halt. A thought experiment involving a military drone illustrates this issue, showing that an artificial agent might fail to decide between actions due to the halting problem, which limits the agent's ability to make decisions in all instances, undermining its moral agency.
Cultural Value Differences of LLMs: Prompt, Language, and Model Size
Venue: arXiv cs.CY 2024
Year: 2024

Our study aims to identify behavior patterns in cultural values exhibited by large language models (LLMs). The studied variants include question ordering, prompting language, and model size. Our experiments reveal that each tested LLM can efficiently behave with different cultural values. More interestingly: (i) LLMs exhibit relatively consistent cultural values when presented with prompts in a single language. (ii) The prompting language e.g., Chinese or English, can influence the expression of cultural values. The same question can elicit divergent cultural values when the same LLM is queried in a different language. (iii) Differences in sizes of the same model (e.g., Llama2-7B vs 13B vs 70B) have a more significant impact on their demonstrated cultural values than model differences (e.g., Llama2 vs Mixtral). Our experiments reveal that query language and model size of LLM are the main factors resulting in cultural value differences.
Free to play: UN Trade and Development's experience with developing its own open-source Retrieval Augmented Generation Large Language Model application
Venue: arXiv cs.CY 2024
Year: 2024

Generative artificial intelligence (AI), and in particular Large Language Models (LLMs), have exploded in popularity and attention since the release to the public of ChatGPT's Generative Pre-trained Transformer (GPT)-3.5 model in November of 2022. Due to the power of these general purpose models and their ability to communicate in natural language, they can be useful in a range of domains, including the work of official statistics and international organizations. However, with such a novel and seemingly complex technology, it can feel as if generative AI is something that happens to an organization, something that can be talked about but not understood, that can be commented on but not contributed to. Additionally, the costs of adoption and operation of proprietary solutions can be both uncertain and high, a barrier for often cost-constrained international organizations. In the face of these challenges, United Nations Trade and Development (UNCTAD), through its Global Crisis Response Group (GCRG), has explored and developed its own open-source Retrieval Augmented Generation (RAG) LLM application. RAG makes LLMs aware of and more useful for the organization's domain and work. Developing in-house solutions comes with pros and cons, with pros including cost, flexibility, and fostering institutional knowledge. Cons include time and skill investments and gaps and application polish and power. The three libraries developed to produce the app, nlp_pipeline for document processing and statistical analysis, local_rag_llm for running a local RAG LLM, and streamlit_rag for the user interface, are publicly available on PyPI and GitHub with Dockerfiles. A fourth library, local_llm_finetune, is also available for fine-tuning existing LLMs which can then be used in the application.
HexTiles and Semantic Icons for MAUP-Aware Multivariate Geospatial Visualizations
Venue: arXiv cs.HC 2024
Year: 2024

We introduce HexTiles, a domain-agnostic hexagonal-tiling based visual encoding design for multivariate geospatial data. Multivariate geospatial data have presented a challenge due to the graph schema associated with geospatial maps, on which most geospatial data is presented. With HexTiles, we design a multivariate geospatial visualization design that leverages semantic icons to (1) simplify the process of interpreting interactions between multivariate geospatial data, and (2) put the visualization designer in the driver's seat to guide user attention to specific variables and interactions. Additionally with HexTiles, we attempt to explicitly mitigate effects of the Modifiable Areal Unit Problem (MAUP) for interpreting geospatial data, by proposing a confidence encoding for each of the information channels in HexTiles. We calculate weighted variances of the variables in each HexTile to provide a confidence value for each tile, which can be used to interpret the variability of the data within the corresponding geospatial area, an information that can be lost in geospatial visualizations. To validate our approach, we gather quantitative and qualitative feedback from a user study and document domain expert feedback from ecologists and hydrologists experienced in designing geospatial visualizations.
Introducing Individuality into Students' High School Timetables
Venue: arXiv cs.CY 2024
Year: 2024

In a perfect world, each high school student could pursue their interests through a personalized timetable that supports their strengths, weaknesses, and curiosities. While recent research has shown that school systems are evolving to support those developments by strengthening modularity in their curricula, there is often a hurdle that prevents the complete success of such a system: the scheduling process is too complex. While there are many tools that assist with scheduling timetables in an effective way, they usually arrange students into groups and classes with similar interests instead of handling each student individually. In this paper, we propose an extension of the popular XHSTT framework that adds two new constraints to model the individual student choices as well as the requirements for group formation that arise from them. Those two constraints were identified through extensive interviews with school administrators and other school timetabling experts from six European countries. We propose a corresponding ILP formulation and show first optimization results for real-world instances from schools in Germany.
How Video Passthrough Headsets Influence Perception of Self and Others
Venue: arXiv cs.HC 2024
Year: 2024

With the increasing adoption of mixed reality headsets with video passthrough functionality, concerns over perceptual and social effects have surfaced. Building on prior qualitative findings, this study quantitatively investigates the impact of video passthrough on users. Forty participants completed a body transfer task twice, once while wearing a headset in video passthrough and once without a headset. Results indicate that using video passthrough induces simulator sickness, creates social absence, (another person in the physical room feels less present), alters self-reported body schema, and distorts distance perception. On the other hand, compared to past research which showed perceptual aftereffects from video passthrough, the current study found none. We discuss the broader implications for the widespread adoption of mixed reality headsets and their impact on theories surrounding presence and body transfer.
Synthetic Data, Similarity-based Privacy Metrics, and Regulatory (Non-)Compliance
Venue: arXiv cs.CR 2024
Year: 2024

In this paper, we argue that similarity-based privacy metrics cannot ensure regulatory compliance of synthetic data. Our analysis and counter-examples show that they do not protect against singling out and linkability and, among other fundamental issues, completely ignore the motivated intruder test.
Automatic Categorization of GitHub Actions with Transformers and Few-shot Learning
Venue: arXiv cs.SE 2024
Year: 2024

In the GitHub ecosystem, workflows are used as an effective means to automate development tasks and to set up a Continuous Integration and Delivery (CI/CD pipeline). GitHub Actions (GHA) have been conceived to provide developers with a practical tool to create and maintain workflows, avoiding reinventing the wheel and cluttering the workflow with shell commands. Properly leveraging the power of GitHub Actions can facilitate the development processes, enhance collaboration, and significantly impact project outcomes. To expose actions to search engines, GitHub allows developers to assign them to one or more categories manually. These are used as an effective means to group actions sharing similar functionality. Nevertheless, while providing a practical way to execute workflows, many actions have unclear purposes, and sometimes they are not categorized. In this work, we bridge such a gap by conceptualizing Gavel, a practical solution to increasing the visibility of actions in GitHub. By leveraging the content of README.MD files for each action, we use Transformer--a deep learning algorithm--to assign suitable categories to the action. We conducted an empirical investigation and compared Gavel with a state-of-the-art baseline. The experimental results show that our proposed approach can assign categories to GitHub actions effectively, thus outperforming the state-of-the-art baseline.
Profitable Manipulations of Cryptographic Self-Selection are Statistically Detectable
Venue: arXiv cs.GT 2024
Year: 2024

Cryptographic Self-Selection is a common primitive underlying leader-selection for Proof-of-Stake blockchain protocols. The concept was first popularized in Algorand [CM19], who also observed that the protocol might be manipulable. [FHWY22] provide a concrete manipulation that is strictly profitable for a staker of any size (and also prove upper bounds on the gains from manipulation). Separately, [YSZ23, BM24] initiate the study of undetectable profitable manipulations of consensus protocols with a focus on the seminal Selfish Mining strategy [ES14] for Bitcoin's Proof-of-Work longest-chain protocol. They design a Selfish Mining variant that, for sufficiently large miners, is strictly profitable yet also indistinguishable to an onlooker from routine latency (that is, a sufficiently large profit-maximizing miner could use their strategy to strictly profit over being honest in a way that still appears to the rest of the network as though everyone is honest but experiencing mildly higher latency. This avoids any risk of negatively impacting the value of the underlying cryptocurrency due to attack detection). We investigate the detectability of profitable manipulations of the canonical cryptographic self-selection leader selection protocol introduced in [CM19] and studied in [FHWY22], and establish that for any player with $α< \frac{3-\sqrt{5}}{2} \approx 0.38$ fraction of the total stake, every strictly profitable manipulation is statistically detectable. Specifically, we consider an onlooker who sees only the random seed of each round (and does not need to see any other broadcasts by any other players). We show that the distribution of the sequence of random seeds when any player is profitably manipulating the protocol is inconsistent with any distribution that could arise by honest stakers being offline or timing out (for a natural stylized model of honest timeouts).
Pose Estimation from Camera Images for Underwater Inspection
Venue: arXiv cs.CV 2024
Year: 2024

High-precision localization is pivotal in underwater reinspection missions. Traditional localization methods like inertial navigation systems, Doppler velocity loggers, and acoustic positioning face significant challenges and are not cost-effective for some applications. Visual localization is a cost-effective alternative in such cases, leveraging the cameras already equipped on inspection vehicles to estimate poses from images of the surrounding scene. Amongst these, machine learning-based pose estimation from images shows promise in underwater environments, performing efficient relocalization using models trained based on previously mapped scenes. We explore the efficacy of learning-based pose estimators in both clear and turbid water inspection missions, assessing the impact of image formats, model architectures and training data diversity. We innovate by employing novel view synthesis models to generate augmented training data, significantly enhancing pose estimation in unexplored regions. Moreover, we enhance localization accuracy by integrating pose estimator outputs with sensor data via an extended Kalman filter, demonstrating improved trajectory smoothness and accuracy.