OpenRaiser/Intern-Atlas huggingface dataset:
Designing Child-Centric AI Learning Environments: Insights from LLM-Enhanced Creative Project-Based Learning
Venue: arXiv cs.HC 2024
Year: 2024

Project-based learning (PBL) is an instructional method that is very helpful in nurturing students' creativity, but it requires significant time and energy from both students and teachers. Large language models (LLMs) have been proven to assist in creative tasks, yet much controversy exists regarding their role in fostering creativity. This paper explores the potential of LLMs in PBL settings, with a special focus on fostering creativity. We began with an exploratory study involving 12 middle school students and identified five design considerations for LLM applications in PBL. Building on this, we developed an LLM-empowered, 48-hour PBL program and conducted an instructional experiment with 31 middle school students. Our results indicated that LLMs can enhance every stage of PBL. Additionally, we also discovered ambivalent perspectives among students and mentors toward LLM usage. Furthermore, we explored the challenge and design implications of integrating LLMs into PBL and reflected on the program. By bridging AI advancements into educational practice, our work aims to inspire further discourse and investigation into harnessing AI's potential in child-centric educational settings.
Site-Specific Beam Alignment in 6G via Deep Learning
Venue: arXiv cs.IT 2024
Year: 2024

Beam alignment (BA) in modern millimeter wave standards such as 5G NR and WiGig (802.11ay) is based on exhaustive and/or hierarchical beam searches over pre-defined codebooks of wide and narrow beams. This approach is slow and bandwidth/power-intensive, and is a considerable hindrance to the wide deployment of millimeter wave bands. A new approach is needed as we move towards 6G. BA is a promising use case for deep learning (DL) in the 6G air interface, offering the possibility of automated custom tuning of the BA procedure for each cell based on its unique propagation environment and user equipment (UE) location patterns. We overview and advocate for such an approach in this paper, which we term site-specific beam alignment (SSBA). SSBA largely eliminates wasteful searches and allows UEs to be found much more quickly and reliably, without many of the drawbacks of other machine learning-aided approaches. We first overview and demonstrate new results on SSBA, then identify the key open challenges facing SSBA.
Behind the (Digital Crime) Scenes: An MSC Model
Venue: arXiv cs.CR 2024
Year: 2024

Criminal investigations are inherently complex as they typically involve interactions among various actors like investigators, prosecutors, and defendants. The pervasive integration of technology in daily life adds an extra layer of complexity, especially in crimes that involve a digital element. The establishment of digital forensics as a foundational discipline for extracting digital evidence further exacerbates the complex nature of criminal investigations, leading to the proliferation of multiple scenarios. Recognising the need to structure standard operating procedures for the handling of digital evidence, the representation of digital forensics as a protocol emerges as a valuable opportunity to identify security and privacy threats. In this paper, we delineate the protocols that compose digital forensics within a criminal case, formalise them as message sequence charts (MSCs), and identify their functional requirements.
FH-SSTNet: Forehead Creases based User Verification using Spatio-Spatial Temporal Network
Venue: arXiv cs.CV 2024
Year: 2024

Biometric authentication, which utilizes contactless features, such as forehead patterns, has become increasingly important for identity verification and access management. The proposed method is based on learning a 3D spatio-spatial temporal convolution to create detailed pictures of forehead patterns. We introduce a new CNN model called the Forehead Spatio-Spatial Temporal Network (FH-SSTNet), which utilizes a 3D CNN architecture with triplet loss to capture distinguishing features. We enhance the model's discrimination capability using Arcloss in the network's head. Experimentation on the Forehead Creases version 1 (FH-V1) dataset, containing 247 unique subjects, demonstrates the superior performance of FH-SSTNet compared to existing methods and pre-trained CNNs like ResNet50, especially for forehead-based user verification. The results demonstrate the superior performance of FH-SSTNet for forehead-based user verification, confirming its effectiveness in identity authentication.
Maximum Polygon Packing: The CG:SHOP Challenge 2024
Venue: arXiv cs.CG 2024
Year: 2024

We give an overview of the 2024 Computational Geometry Challenge targeting the problem \textsc{Maximum Polygon Packing}: Given a convex region $P$ in the plane, and a collection of simple polygons $Q_1, \ldots, Q_n$, each $Q_i$ with a respective value $c_i$, find a subset $S \subseteq \{1, \ldots,n\}$ and a feasible packing within $P$ of the polygons $Q_i$ (without rotation) for $i \in S$, maximizing $\sum_{i \in S} c_i$. Geometric packing problems, such as this, present significant computational challenges and are of substantial practical importance.
Dual-modal Prior Semantic Guided Infrared and Visible Image Fusion for Intelligent Transportation System
Venue: arXiv cs.CV 2024
Year: 2024

Infrared and visible image fusion (IVF) plays an important role in intelligent transportation system (ITS). The early works predominantly focus on boosting the visual appeal of the fused result, and only several recent approaches have tried to combine the high-level vision task with IVF. However, they prioritize the design of cascaded structure to seek unified suitable features and fit different tasks. Thus, they tend to typically bias toward to reconstructing raw pixels without considering the significance of semantic features. Therefore, we propose a novel prior semantic guided image fusion method based on the dual-modality strategy, improving the performance of IVF in ITS. Specifically, to explore the independent significant semantic of each modality, we first design two parallel semantic segmentation branches with a refined feature adaptive-modulation (RFaM) mechanism. RFaM can perceive the features that are semantically distinct enough in each semantic segmentation branch. Then, two pilot experiments based on the two branches are conducted to capture the significant prior semantic of two images, which then is applied to guide the fusion task in the integration of semantic segmentation branches and fusion branches. In addition, to aggregate both high-level semantics and impressive visual effects, we further investigate the frequency response of the prior semantics, and propose a multi-level representation-adaptive fusion (MRaF) module to explicitly integrate the low-frequent prior semantic with the high-frequent details. Extensive experiments on two public datasets demonstrate the superiority of our method over the state-of-the-art image fusion approaches, in terms of either the visual appeal or the high-level semantics.
Adversarially Masked Video Consistency for Unsupervised Domain Adaptation
Venue: arXiv cs.CV 2024
Year: 2024

We study the problem of unsupervised domain adaptation for egocentric videos. We propose a transformer-based model to learn class-discriminative and domain-invariant feature representations. It consists of two novel designs. The first module is called Generative Adversarial Domain Alignment Network with the aim of learning domain-invariant representations. It simultaneously learns a mask generator and a domain-invariant encoder in an adversarial way. The domain-invariant encoder is trained to minimize the distance between the source and target domain. The masking generator, conversely, aims at producing challenging masks by maximizing the domain distance. The second is a Masked Consistency Learning module to learn class-discriminative representations. It enforces the prediction consistency between the masked target videos and their full forms. To better evaluate the effectiveness of domain adaptation methods, we construct a more challenging benchmark for egocentric videos, U-Ego4D. Our method achieves state-of-the-art performance on the Epic-Kitchen and the proposed U-Ego4D benchmark.
Connecting the Dots: Inferring Patent Phrase Similarity with Retrieved Phrase Graphs
Venue: arXiv cs.CL 2024
Year: 2024

We study the patent phrase similarity inference task, which measures the semantic similarity between two patent phrases. As patent documents employ legal and highly technical language, existing semantic textual similarity methods that use localized contextual information do not perform satisfactorily in inferring patent phrase similarity. To address this, we introduce a graph-augmented approach to amplify the global contextual information of the patent phrases. For each patent phrase, we construct a phrase graph that links to its focal patents and a list of patents that are either cited by or cite these focal patents. The augmented phrase embedding is then derived from combining its localized contextual embedding with its global embedding within the phrase graph. We further propose a self-supervised learning objective that capitalizes on the retrieved topology to refine both the contextualized embedding and the graph parameters in an end-to-end manner. Experimental results from a unique patent phrase similarity dataset demonstrate that our approach significantly enhances the representation of patent phrases, resulting in marked improvements in similarity inference in a self-supervised fashion. Substantial improvements are also observed in the supervised setting, underscoring the potential benefits of leveraging retrieved phrase graph augmentation.
Object Detectors in the Open Environment: Challenges, Solutions, and Outlook
Venue: arXiv cs.CV 2024
Year: 2024

With the emergence of foundation models, deep learning-based object detectors have shown practical usability in closed set scenarios. However, for real-world tasks, object detectors often operate in open environments, where crucial factors (e.g., data distribution, objective) that influence model learning are often changing. The dynamic and intricate nature of the open environment poses novel and formidable challenges to object detectors. Unfortunately, current research on object detectors in open environments lacks a comprehensive analysis of their distinctive characteristics, challenges, and corresponding solutions, which hinders their secure deployment in critical real-world scenarios. This paper aims to bridge this gap by conducting a comprehensive review and analysis of object detectors in open environments. We initially identified limitations of key structural components within the existing detection pipeline and propose the open environment object detector challenge framework that includes four quadrants (i.e., out-of-domain, out-of-category, robust learning, and incremental learning) based on the dimensions of the data / target changes. For each quadrant of challenges in the proposed framework, we present a detailed description and systematic analysis of the overarching goals and core difficulties, systematically review the corresponding solutions, and benchmark their performance over multiple widely adopted datasets. In addition, we engage in a discussion of open problems and potential avenues for future research. This paper aims to provide a fresh, comprehensive, and systematic understanding of the challenges and solutions associated with open-environment object detectors, thus catalyzing the development of more solid applications in real-world scenarios. A project related to this survey can be found at https://github.com/LiangSiyuan21/OEOD_Survey.
Combined Task and Motion Planning Via Sketch Decompositions (Extended Version with Supplementary Material)
Venue: arXiv cs.RO 2024
Year: 2024

The challenge in combined task and motion planning (TAMP) is the effective integration of a search over a combinatorial space, usually carried out by a task planner, and a search over a continuous configuration space, carried out by a motion planner. Using motion planners for testing the feasibility of task plans and filling out the details is not effective because it makes the geometrical constraints play a passive role. This work introduces a new interleaved approach for integrating the two dimensions of TAMP that makes use of sketches, a recent simple but powerful language for expressing the decomposition of problems into subproblems. A sketch has width 1 if it decomposes the problem into subproblems that can be solved greedily in linear time. In the paper, a general sketch is introduced for several classes of TAMP problems which has width 1 under suitable assumptions. While sketch decompositions have been developed for classical planning, they offer two important benefits in the context of TAMP. First, when a task plan is found to be unfeasible due to the geometric constraints, the combinatorial search resumes in a specific sub-problem. Second, the sampling of object configurations is not done once, globally, at the start of the search, but locally, at the start of each subproblem. Optimizations of this basic setting are also considered and experimental results over existing and new pick-and-place benchmarks are reported.
Q-adaptive: A Multi-Agent Reinforcement Learning Based Routing on Dragonfly Network
Venue: arXiv cs.NI 2024
Year: 2024

High-radix interconnects such as Dragonfly and its variants rely on adaptive routing to balance network traffic for optimum performance. Ideally, adaptive routing attempts to forward packets between minimal and non-minimal paths with the least congestion. In practice, current adaptive routing algorithms estimate routing path congestion based on local information such as output queue occupancy. Using local information to estimate global path congestion is inevitably inaccurate because a router has no precise knowledge of link states a few hops away. This inaccuracy could lead to interconnect congestion. In this study, we present Q-adaptive routing, a multi-agent reinforcement learning routing scheme for Dragonfly systems. Q-adaptive routing enables routers to learn to route autonomously by leveraging advanced reinforcement learning technology. The proposed Q-adaptive routing is highly scalable thanks to its fully distributed nature without using any shared information between routers. Furthermore, a new two-level Q-table is designed for Q-adaptive to make it computational lightly and saves 50% of router memory usage compared with the previous Q-routing. We implement the proposed Q-adaptive routing in SST/Merlin simulator. Our evaluation results show that Q-adaptive routing achieves up to 10.5% system throughput improvement and 5.2x average packet latency reduction compared with adaptive routing algorithms. Remarkably, Q-adaptive can even outperform the optimal VALn non-minimal routing under the ADV+1 adversarial traffic pattern with up to 3% system throughput improvement and 75% average packet latency reduction.
Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis
Venue: arXiv cs.DL 2024
Year: 2024

Large Language Models (LLMs) have rapidly become important tools in Biomedical and Health Informatics (BHI), enabling new ways to analyze data, treat patients, and conduct research. This study aims to provide a comprehensive overview of LLM applications in BHI, highlighting their transformative potential and addressing the associated ethical and practical challenges. We reviewed 1,698 research articles from January 2022 to December 2023, categorizing them by research themes and diagnostic categories. Additionally, we conducted network analysis to map scholarly collaborations and research dynamics. Our findings reveal a substantial increase in the potential applications of LLMs to a variety of BHI tasks, including clinical decision support, patient interaction, and medical document analysis. Notably, LLMs are expected to be instrumental in enhancing the accuracy of diagnostic tools and patient care protocols. The network analysis highlights dense and dynamically evolving collaborations across institutions, underscoring the interdisciplinary nature of LLM research in BHI. A significant trend was the application of LLMs in managing specific disease categories such as mental health and neurological disorders, demonstrating their potential to influence personalized medicine and public health strategies. LLMs hold promising potential to further transform biomedical research and healthcare delivery. While promising, the ethical implications and challenges of model validation call for rigorous scrutiny to optimize their benefits in clinical settings. This survey serves as a resource for stakeholders in healthcare, including researchers, clinicians, and policymakers, to understand the current state and future potential of LLMs in BHI.
"It Is Easy Using My Apps:" Understanding Technology Use and Needs of Adults with Down Syndrome
Venue: arXiv cs.HC 2024
Year: 2024

Assistive technologies for adults with Down syndrome (DS) need designs tailored to their specific technology requirements. While prior research has explored technology design for individuals with intellectual disabilities, little is understood about the needs and expectations of adults with DS. Assistive technologies should leverage the abilities and interests of the population, while incorporating age- and context-considerate content. In this work, we interviewed six adults with DS, seven parents of adults with DS, and three experts in speech-language pathology, special education, and occupational therapy to determine how technology could support adults with DS. In our thematic analysis, four main themes emerged, including (1) community vs. home social involvement; (2) misalignment of skill expectations between adults with DS and parents; (3) family limitations in technology support; and (4) considerations for technology development. Our findings extend prior literature by including the voices of adults with DS in how and when they use technology.
A Novel exact algorithm for economic lot-sizing with piecewise linear production costs
Venue: arXiv cs.DS 2024
Year: 2024

In this paper, we study the single-item economic lot-sizing problem with production cost functions that are piecewise linear. The lot-sizing problem stands as a foundational cornerstone within the domain of lot-sizing problems. It is also applicable to a variety of important production planning problems which are special cases to it according to \cite{ou}. The problem becomes intractable when $m$, the number of different breakpoints of the production-cost function is variable as the problem was proven NP-hard by \cite{Florian1980}. For a fixed $m$ an $O(T^{2m+3})$ time algorithm was given by \cite{Koca2014} which was subsequently improved to $O(T^{m+2}\log(T))$ time by \cite{ou} where $T$ is the number of periods in the planning horizon.\newline We introduce a more efficient $O(T^{m+2})$ time algorithm for this problem which improves upon the previous state-of-the-art algorithm by Ou and which is derived using several novel algorithmic techniques that may be of independent interest.
Single-Motor Robotic Gripper with Multi-Surface Fingers for Variable Grasping Configurations
Venue: arXiv cs.RO 2024
Year: 2024

This study proposes a novel robotic gripper with variable grasping configurations for grasping various objects. The fingers of the developed gripper incorporate multiple different surfaces. The gripper possesses the function of altering the finger surfaces facing a target object by rotating the fingers in its longitudinal direction. In the proposed design equipped with two fingers, the two fingers incorporate three and four surfaces, respectively, resulting in the nine available grasping configurations by the combination of these finger surfaces. The developed gripper is equipped with the functions of opening/closing its fingers for grasping and rotating its fingers to alter the grasping configuration -all achieved with a single motor. To enable the two motions using a single motor, this study introduces a self-motion switching mechanism utilizing magnets. This mechanism automatically transitions between gripper motions based on the direction of the motor rotation when the gripper is fully opened. In this state, rotating the motor towards closing initiates the finger closing action, while further opening the fingers from the fully opened state activates the finger rotation. This letter presents the gripper design, the mechanics of the self-motion switching mechanism, the control method, and the grasping configuration selection strategy. The performance of the gripper is experimentally demonstrated.
Artificial Neural Microcircuits as Building Blocks: Concept and Challenges
Venue: arXiv cs.NE 2024
Year: 2024

Artificial Neural Networks (ANNs) are one of the most widely employed forms of bio-inspired computation. However the current trend is for ANNs to be structurally homogeneous. Furthermore, this structural homogeneity requires the application of complex training and learning tools that produce application specific ANNs, susceptible to pitfalls such as overfitting. In this paper, an new approach is explored, inspired by the role played in biology by Neural Microcircuits, the so called ``fundamental processing elements'' of organic nervous systems. How large neural networks, particularly Spiking Neural Networks (SNNs) can be assembled using Artificial Neural Microcircuits (ANMs), intended as off-the-shelf components, is articulated; the results of initial work to produce a catalogue of such Microcircuits though the use of Novelty Search is shown; followed by efforts to expand upon this initial work, including a discussion of challenges uncovered during these efforts and explorations of methods by which they might be overcome.
Learning Action-based Representations Using Invariance
Venue: arXiv cs.LG 2024
Year: 2024

Robust reinforcement learning agents using high-dimensional observations must be able to identify relevant state features amidst many exogeneous distractors. A representation that captures controllability identifies these state elements by determining what affects agent control. While methods such as inverse dynamics and mutual information capture controllability for a limited number of timesteps, capturing long-horizon elements remains a challenging problem. Myopic controllability can capture the moment right before an agent crashes into a wall, but not the control-relevance of the wall while the agent is still some distance away. To address this we introduce action-bisimulation encoding, a method inspired by the bisimulation invariance pseudometric, that extends single-step controllability with a recursive invariance constraint. By doing this, action-bisimulation learns a multi-step controllability metric that smoothly discounts distant state features that are relevant for control. We demonstrate that action-bisimulation pretraining on reward-free, uniformly random data improves sample efficiency in several environments, including a photorealistic 3D simulation domain, Habitat. Additionally, we provide theoretical analysis and qualitative results demonstrating the information captured by action-bisimulation.