OpenRaiser/Intern-Atlas huggingface dataset:
Personalised Feedback Framework for Online Education Programmes Using Generative AI
Venue: arXiv cs.HC 2024
Year: 2024

AI tools, particularly large language modules, have recently proven their effectiveness within learning management systems and online education programmes. As feedback continues to play a crucial role in learning and assessment in schools, educators must carefully customise the use of AI tools in order to optimally support students in their learning journey. Efforts to improve educational feedback systems have seen numerous attempts reflected in the research studies but mostly have been focusing on qualitatively benchmarking AI feedback against human-generated feedback. This paper presents an exploration of an alternative feedback framework which extends the capabilities of ChatGPT by integrating embeddings, enabling a more nuanced understanding of educational materials and facilitating topic-targeted feedback for quiz-based assessments. As part of the study, we proposed and developed a proof of concept solution, achieving an efficacy rate of 90% and 100% for open-ended and multiple-choice questions, respectively. The results showed that our framework not only surpasses expectations but also rivals human narratives, highlighting the potential of AI in revolutionising educational feedback mechanisms.
Development and Testing of a Wood Panels Bark Removal Equipment Based on Deep Learning
Venue: arXiv cs.CV 2024
Year: 2024

Attempting to apply deep learning methods to wood panels bark removal equipment to enhance the quality and efficiency of bark removal is a significant and challenging endeavor. This study develops and tests a deep learning-based wood panels bark removal equipment. In accordance with the practical requirements of sawmills, a wood panels bark removal equipment equipped with a vision inspection system is designed. Based on a substantial collection of wood panel images obtained using the visual inspection system, the first general wood panels semantic segmentation dataset is constructed for training the BiSeNetV1 model employed in this study. Furthermore, the calculation methods and processes for the essential key data required in the bark removal process are presented in detail. Comparative experiments of the BiSeNetV1 model and tests of bark removal effectiveness are conducted in both laboratory and sawmill environments. The results of the comparative experiments indicate that the application of the BiSeNetV1 segmentation model is rational and feasible. The results of the bark removal effectiveness tests demonstrate a significant improvement in both the quality and efficiency of bark removal. The developed equipment fully meets the sawmill's requirements for precision and efficiency in bark removal processing.
Guideline for Manual Process Discovery in Industrial IoT
Venue: arXiv cs.SE 2024
Year: 2024

In industry, the networking and automation of machines through the Internet of Things (IoT) continues to increase, leading to greater digitalization of production processes. Traditionally, business and production processes are controlled, optimized and monitored using business process management methods that require process discovery. However, these methods cannot be fully applied to industrial production processes. Nevertheless, processes in the industry must also be monitored and discovered for this purpose. The aim of this paper is to develop an approach for process discovery methods and to adapt existing process discovery methods for application to industrial processes. The adaptations of classic discovery methods are presented as universally applicable guidelines specifically for the Industrial Internet of Things (IIoT). In order to create an optimal process model based on process evaluation, different methods are combined into a standardized discovery approach that is both efficient and cost-effective.
Min-Max Gathering on Infinite Grid
Venue: arXiv cs.DC 2024
Year: 2024

Gathering is a fundamental coordination problem in swarm robotics, where the objective is to bring robots together at a point not known to them at the beginning. While most research focuses on continuous domains, some studies also examine the discrete domain. This paper addresses the optimal gathering problem on an infinite grid, aiming to improve the energy efficiency by minimizing the maximum distance any robot must travel. The robots are autonomous, anonymous, homogeneous, identical, and oblivious. We identify all initial configurations where the optimal gathering problem is unsolvable. For the remaining configurations, we introduce a deterministic distributed algorithm that effectively gathers $n$ robots ($n\ge 9$). The algorithm ensures that the robots gathers at one of the designated min-max nodes in the grid. Additionally, we provide a comprehensive characterization of the subgraph formed by the min-max nodes in this infinite grid model.
Subquadratic algorithms in minor-free digraphs: (weighted) distance oracles, decremental reachability, and more
Venue: arXiv cs.DS 2024
Year: 2024

Le and Wulff-Nilsen [SODA '24] initiated a systematic study of VC set systems to unweighted $K_h$-minor-free directed graphs. We extend their results in the following ways: $\bullet$ We present the first application of VC set systems for real-weighted minor-free digraphs to build the first exact subquadratic-space distance oracle with $O(\log n)$ query time. Prior work using VC set systems only applied in unweighted and integer weighted digraphs. $\bullet$ We describe a unified system for analyzing the VC dimension of balls and the LP set system (based on Li--Parter [STOC '19]) of Le--Wulff-Nilsen [SODA '24] using pseudodimension. This is a major conceptual contribution that allows for both improving our understanding of set systems in digraphs as well as improving the bound of the LP set system in directed graphs to $h-1$. $\bullet$ We present the first application of these set systems in a dynamic setting. Specifically, we construct decremental reachability oracles with subquadratic total update time and constant query time. Prior to this work, it was not known if this was possible to construct oracles with subquadratic total update time and polylogarithmic query time, even in planar digraphs. $\bullet$ We describe subquadratic time algorithms for unweighted digraphs including (1) constructions of exact distance oracles, (2) computation of vertex eccentricities and Wiener index. The main innovation in obtaining these results is the use of dynamic string data structures.
A Lyapunov-Based Switching Scheme for Selecting the Stable Closed-Loop Fixed Attitude-Error Quaternion During Flight
Venue: arXiv cs.RO 2024
Year: 2024

We present a switching scheme, which uses both the attitude-error quaternion (AEQ) and the angular-velocity error, for controlling the rotational degrees of freedom of an uncrewed aerial vehicle (UAV) during flight. In this approach, the proposed controller continually selects the stable closed-loop (CL) equilibrium AEQ corresponding to the smallest cost between those computed with two energy-based Lyapunov functions. To analyze and enforce the stability of the CL switching dynamics, we use basic nonlinear theory. This research problem is relevant because the selection of the stable CL equilibrium AEQ directly determines the power and energy requirements of the controlled UAV during flight. To test and demonstrate the implementation, suitability, functionality, and performance of the proposed approach, we present experimental results obtained using a 31-gram quadrotor, which was controlled to execute high-speed yaw maneuvers in flight. These flight tests show that the proposed switching controller can respectively reduce the control effort and rotational power by as much as 49.75 % and 28.14 %, on average, compared to those corresponding to an often-used benchmark controller.
SplatPose+: Real-time Image-Based Pose-Agnostic 3D Anomaly Detection
Venue: arXiv cs.CV 2024
Year: 2024

Image-based Pose-Agnostic 3D Anomaly Detection is an important task that has emerged in industrial quality control. This task seeks to find anomalies from query images of a tested object given a set of reference images of an anomaly-free object. The challenge is that the query views (a.k.a poses) are unknown and can be different from the reference views. Currently, new methods such as OmniposeAD and SplatPose have emerged to bridge the gap by synthesizing pseudo reference images at the query views for pixel-to-pixel comparison. However, none of these methods can infer in real-time, which is critical in industrial quality control for massive production. For this reason, we propose SplatPose+, which employs a hybrid representation consisting of a Structure from Motion (SfM) model for localization and a 3D Gaussian Splatting (3DGS) model for Novel View Synthesis. Although our proposed pipeline requires the computation of an additional SfM model, it offers real-time inference speeds and faster training compared to SplatPose. Quality-wise, we achieved a new SOTA on the Pose-agnostic Anomaly Detection benchmark with the Multi-Pose Anomaly Detection (MAD-SIM) dataset.
Juggernaut: Efficient Crypto-Agnostic Byzantine Agreement
Venue: arXiv cs.DC 2024
Year: 2024

It is well known that a trusted setup allows one to solve the Byzantine agreement problem in the presence of $t<n/2$ corruptions, bypassing the setup-free $t<n/3$ barrier. Alas, the overwhelming majority of protocols in the literature have the caveat that their security crucially hinges on the security of the cryptography and setup, to the point where if the cryptography is broken, even a single corrupted party can violate the security of the protocol. Thus these protocols provide higher corruption resilience ($n/2$ instead of $n/3$) for the price of increased assumptions. Is this trade-off necessary? We further the study of crypto-agnostic Byzantine agreement among $n$ parties that answers this question in the negative. Specifically, let $t_s$ and $t_i$ denote two parameters such that (1) $2t_i + t_s < n$, and (2) $t_i \leq t_s < n/2$. Crypto-agnostic Byzantine agreement ensures agreement among honest parties if (1) the adversary is computationally bounded and corrupts up to $t_s$ parties, or (2) the adversary is computationally unbounded and corrupts up to $t_i$ parties, and is moreover given all secrets of all parties established during the setup. We propose a compiler that transforms any pair of resilience-optimal Byzantine agreement protocols in the authenticated and information-theoretic setting into one that is crypto-agnostic. Our compiler has several attractive qualities, including using only $O(λn^2)$ bits over the two underlying Byzantine agreement protocols, and preserving round and communication complexity in the authenticated setting. In particular, our results improve the state-of-the-art in bit complexity by at least two factors of $n$ and provide either early stopping (deterministic) or expected constant round complexity (randomized). We therefore provide fallback security for authenticated Byzantine agreement for free for $t_i \leq n/4$.
Proof of Team Sprint: A Collaborative Consensus Algorithm for Reducing Energy Consumption in Blockchain Systems
Venue: arXiv cs.DC 2024
Year: 2024

This paper introduces Proof of Team Sprint (PoTS), a novel consensus algorithm designed to address the significant energy inefficiencies inherent in traditional Proof of Work (PoW) systems. PoTS shifts the consensus mechanism from an individual competition model to a collaborative team-based approach. Participants are organized into groups, with each group collaboratively working to solve cryptographic puzzles required to validate transactions and add new blocks to the blockchain. This collaborative approach significantly reduces the overall energy consumption of the network while maintaining high levels of security and decentralization. Our analysis shows that PoTS can reduce energy consumption by a factor of 1/N, where N is the number of participants in each group, compared to PoW. Furthermore, PoTS maintains a fair and equitable reward distribution among participants, ensuring continued engagement and network integrity. The paper also discusses the scalability, security implications, and potential challenges of adopting PoTS, positioning it as a promising alternative for sustainable blockchain technology.
Mixed or Misperceived Reality? Flusserian Media Freedom through Surreal Me
Venue: arXiv cs.HC 2024
Year: 2024

This paper delves into Vilém Flusser's critique of media as mediators that distort the human perception of reality and diminish freedom, particularly within the Mixed Reality context, i.e., Misperceived Reality. It introduces an artistic inquiry through Surreal Me, which engages participants to experience a two-phase virtual embodying process and reveal the "Misperceived Reality." The process examines the obfuscating nature of media; as the Sense of Embodiment inevitably breaks down, users can discover the constructed nature of media-projected reality. When users reflect on reality's authentic and mediated experiences in MR, this work fosters a critical discourse on Flusserian media freedom addressing emerging immersive technologies.
Reinforcement Learning with LTL and $ω$-Regular Objectives via Optimality-Preserving Translation to Average Rewards
Venue: arXiv cs.LG 2024
Year: 2024

Linear temporal logic (LTL) and, more generally, $ω$-regular objectives are alternatives to the traditional discount sum and average reward objectives in reinforcement learning (RL), offering the advantage of greater comprehensibility and hence explainability. In this work, we study the relationship between these objectives. Our main result is that each RL problem for $ω$-regular objectives can be reduced to a limit-average reward problem in an optimality-preserving fashion, via (finite-memory) reward machines. Furthermore, we demonstrate the efficacy of this approach by showing that optimal policies for limit-average problems can be found asymptotically by solving a sequence of discount-sum problems approximately. Consequently, we resolve an open problem: optimal policies for LTL and $ω$-regular objectives can be learned asymptotically.
Challenges in Adopting Companion Robots: An Exploratory Study of Robotic Companionship Conducted with Chinese Retirees
Venue: arXiv cs.HC 2024
Year: 2024

Companion robots hold immense potential in providing emotional support to older adults in the rapidly aging world. However, questions have been raised regarding whether having a robotic companion benefits healthy older adults, how they perceive the value of companion robots, and what their relationship with companion robots would be like. To understand healthy older adults' perceptions, attitudes, and relationships toward companion robots, we conducted multiple focus groups with eighteen retirees. Our findings underscore the social context encountered by older adults in China and reveal the mismatch between the current value proposition of companion robots and healthy older adults' needs. We further identify factors influencing the adoption of robotic companionship, which include individuals' self-disclosure tendencies, quality of companionship, differentiated value, and seamless collaboration with aging-in-community infrastructure and services.
fAmulet: Finding Finalization Failure Bugs in Polygon zkRollup
Venue: arXiv cs.CR 2024
Year: 2024

Zero-knowledge layer 2 protocols emerge as a compelling approach to overcoming blockchain scalability issues by processing transactions through the transaction finalization process. During this process, transactions are efficiently processed off the main chain. Besides, both the transaction data and the zero-knowledge proofs of transaction executions are reserved on the main chain, ensuring the availability of transaction data as well as the correctness and verifiability of transaction executions. Hence, any bugs that cause the transaction finalization failure are crucial, as they impair the usability of these protocols and the scalability of blockchains. In this work, we conduct the first systematic study on finalization failure bugs in zero-knowledge layer 2 protocols, and define two kinds of such bugs. Besides, we design fAmulet, the first tool to detect finalization failure bugs in Polygon zkRollup, a prominent zero-knowledge layer 2 protocol, by leveraging fuzzing testing. To trigger finalization failure bugs effectively, we introduce a finalization behavior model to guide our transaction fuzzer to generate and mutate transactions for inducing diverse behaviors across each component (e.g., Sequencer) in the finalization process. Moreover, we define bug oracles according to the distinct bug definitions to accurately detect bugs. Through our evaluation, fAmulet can uncover twelve zero-day finalization failure bugs in Polygon zkRollup, and cover at least 20.8% more branches than baselines. Furthermore, through our preliminary study, fAmulet uncovers a zero-day finalization failure bug in Scroll zkRollup, highlighting the generality of fAmulet to be applied to other zero-knowledge layer 2 protocols. At the time of writing, all our uncovered bugs have been confirmed and fixed by Polygon zkRollup and Scroll zkRollup teams.
Leveraging Spatial Attention and Edge Context for Optimized Feature Selection in Visual Localization
Venue: arXiv cs.CV 2024
Year: 2024

Visual localization determines an agent's precise position and orientation within an environment using visual data. It has become a critical task in the field of robotics, particularly in applications such as autonomous navigation. This is due to the ability to determine an agent's pose using cost-effective sensors such as RGB cameras. Recent methods in visual localization employ scene coordinate regression to determine the agent's pose. However, these methods face challenges as they attempt to regress 2D-3D correspondences across the entire image region, despite not all regions providing useful information. To address this issue, we introduce an attention network that selectively targets informative regions of the image. Using this network, we identify the highest-scoring features to improve the feature selection process and combine the result with edge detection. This integration ensures that the features chosen for the training buffer are located within robust regions, thereby improving 2D-3D correspondence and overall localization performance. Our approach was tested on the outdoor benchmark dataset, demonstrating superior results compared to previous methods.
iFuzzyTL: Interpretable Fuzzy Transfer Learning for SSVEP BCI System
Venue: arXiv cs.HC 2024
Year: 2024

The rapid evolution of Brain-Computer Interfaces (BCIs) has significantly influenced the domain of human-computer interaction, with Steady-State Visual Evoked Potentials (SSVEP) emerging as a notably robust paradigm. This study explores advanced classification techniques leveraging interpretable fuzzy transfer learning (iFuzzyTL) to enhance the adaptability and performance of SSVEP-based systems. Recent efforts have strengthened to reduce calibration requirements through innovative transfer learning approaches, which refine cross-subject generalizability and minimize calibration through strategic application of domain adaptation and few-shot learning strategies. Pioneering developments in deep learning also offer promising enhancements, facilitating robust domain adaptation and significantly improving system responsiveness and accuracy in SSVEP classification. However, these methods often require complex tuning and extensive data, limiting immediate applicability. iFuzzyTL introduces an adaptive framework that combines fuzzy logic principles with neural network architectures, focusing on efficient knowledge transfer and domain adaptation. iFuzzyTL refines input signal processing and classification in a human-interpretable format by integrating fuzzy inference systems and attention mechanisms. This approach bolsters the model's precision and aligns with real-world operational demands by effectively managing the inherent variability and uncertainty of EEG data. The model's efficacy is demonstrated across three datasets: 12JFPM (89.70% accuracy for 1s with an information transfer rate (ITR) of 149.58), Benchmark (85.81% accuracy for 1s with an ITR of 213.99), and eldBETA (76.50% accuracy for 1s with an ITR of 94.63), achieving state-of-the-art results and setting new benchmarks for SSVEP BCI performance.
Kallini et al. (2024) do not compare impossible languages with constituency-based ones
Venue: arXiv cs.CL 2024
Year: 2024

A central goal of linguistic theory is to find a precise characterization of the notion "possible human language", in the form of a computational device that is capable of describing all and only the languages that can be acquired by a typically developing human child. The success of recent large language models (LLMs) in NLP applications arguably raises the possibility that LLMs might be computational devices that meet this goal. This would only be the case if, in addition to succeeding in learning human languages, LLMs struggle to learn "impossible" human languages. Kallini et al. (2024; "Mission: Impossible Language Models", Proc. ACL) conducted experiments aiming to test this by training GPT-2 on a variety of synthetic languages, and found that it learns some more successfully than others. They present these asymmetries as support for the idea that LLMs' inductive biases align with what is regarded as "possible" for human languages, but the most significant comparison has a confound that makes this conclusion unwarranted. In this paper I explain the confound and suggest some ways forward towards constructing a comparison that appropriately tests the underlying issue.
Two Birds with One Stone: Multi-Task Semantic Communications Systems over Relay Channel
Venue: arXiv cs.IT 2024
Year: 2024

In this paper, we propose a novel multi-task, multi-link relay semantic communications (MTML-RSC) scheme that enables the destination node to simultaneously perform image reconstruction and classification with one transmission from the source node. In the MTML-RSC scheme, the source node broadcasts a signal using semantic communications, and the relay node forwards the signal to the destination. We analyze the coupling relationship between the two tasks and the two links (source-to-relay and source-to-destination) and design a semantic-focused forward method for the relay node, where it selectively forwards only the semantics of the relevant class while ignoring others. At the destination, the node combines signals from both the source node and the relay node to perform classification, and then uses the classification result to assist in decoding the signal from the relay node for image reconstructing. Experimental results demonstrate that the proposed MTML-RSC scheme achieves significant performance gains, e.g., $1.73$ dB improvement in peak-signal-to-noise ratio (PSNR) for image reconstruction and increasing the accuracy from $64.89\%$ to $70.31\%$ for classification.