OpenRaiser/Intern-Atlas huggingface dataset:
SALAD: Smart AI Language Assistant Daily
Venue: arXiv cs.CL 2024
Year: 2024

SALAD is an AI-driven language-learning application designed to help foreigners learn Japanese. It offers translations in Kanji-Kana-Romaji, speech recognition, translated audio, vocabulary tracking, grammar explanations, and songs generated from newly learned words. The app targets beginners and intermediate learners, aiming to make language acquisition more accessible and enjoyable. SALAD uses daily translations to enhance fluency and comfort in communication with native speakers. The primary objectives include effective Japanese language learning, user engagement, and progress tracking. A survey by us found that 39% of foreigners in Japan face discomfort in conversations with Japanese speakers. Over 60% of foreigners expressed confidence in SALAD's ability to enhance their Japanese language skills. The app uses large language models, speech recognition, and diffusion models to bridge the language gap and foster a more inclusive community in Japan.
Fully Dynamic Geometric Vertex Cover and Matching
Venue: arXiv cs.CG 2024
Year: 2024

In this work, we study two fundamental graph optimization problems, minimum vertex cover (MVC) and maximum-cardinality matching (MCM), for intersection graphs of geometric objects, e.g., disks, rectangles, hypercubes, etc., in $d$-dimensional Euclidean space. We consider the problems in fully dynamic settings, allowing insertions and deletions of objects. We develop a general framework for dynamic MVC in intersection graphs, achieving sublinear amortized update time for most natural families of geometric objects. In particular, we show that - - For a dynamic collection of disks in $\mathbb{R}^2$ or hypercubes in $\mathbb{R}^d$ (for constant $d$), it is possible to maintain a $(1+\varepsilon)$-approximate vertex cover in polylog amortized update time. These results also hold in the bipartite case. - For a dynamic collection of rectangles in $\mathbb{R}^2$, it is possible to maintain a $(\frac{3}{2}+\varepsilon)$-approximate vertex cover in polylog amortized update time. Along the way, we obtain the first near-linear time static algorithms for MVC in the above two cases with the same approximation factors. Next, we turn our attention to the MCM problem. Although our MVC algorithms automatically allow us to approximate the size of the MCM in bipartite geometric intersection graphs, they do not produce a matching. We give another general framework to maintain an approximate maximum matching, and further extend the approach to handle non-bipartite intersection graphs. In particular, we show that - - For a dynamic collection of (bichromatic or monochromatic) disks in $\mathbb{R}^2$ or hypercubes in $\mathbb{R}^d$ (for constant $d$), it is possible to maintain a $(1+\varepsilon)$-approximate matching in polylog amortized update time.
A Hormetic Approach to the Value-Loading Problem: Preventing the Paperclip Apocalypse?
Venue: arXiv cs.AI 2024
Year: 2024

The value-loading problem is a significant challenge for researchers aiming to create artificial intelligence (AI) systems that align with human values and preferences. This problem requires a method to define and regulate safe and optimal limits of AI behaviors. In this work, we propose HALO (Hormetic ALignment via Opponent processes), a regulatory paradigm that uses hormetic analysis to regulate the behavioral patterns of AI. Behavioral hormesis is a phenomenon where low frequencies of a behavior have beneficial effects, while high frequencies are harmful. By modeling behaviors as allostatic opponent processes, we can use either Behavioral Frequency Response Analysis (BFRA) or Behavioral Count Response Analysis (BCRA) to quantify the hormetic limits of repeatable behaviors. We demonstrate how HALO can solve the 'paperclip maximizer' scenario, a thought experiment where an unregulated AI tasked with making paperclips could end up converting all matter in the universe into paperclips. Our approach may be used to help create an evolving database of 'values' based on the hedonic calculus of repeatable behaviors with decreasing marginal utility. This positions HALO as a promising solution for the value-loading problem, which involves embedding human-aligned values into an AI system, and the weak-to-strong generalization problem, which explores whether weak models can supervise stronger models as they become more intelligent. Hence, HALO opens several research avenues that may lead to the development of a computational value system that allows an AI algorithm to learn whether the decisions it makes are right or wrong.
One Train for Two Tasks: An Encrypted Traffic Classification Framework Using Supervised Contrastive Learning
Venue: arXiv cs.LG 2024
Year: 2024

As network security receives widespread attention, encrypted traffic classification has become the current research focus. However, existing methods conduct traffic classification without sufficiently considering the common characteristics between data samples, leading to suboptimal performance. Moreover, they train the packet-level and flow-level classification tasks independently, which is redundant because the packet representations learned in the packet-level task can be exploited by the flow-level task. Therefore, in this paper, we propose an effective model named a Contrastive Learning Enhanced Temporal Fusion Encoder (CLE-TFE). In particular, we utilize supervised contrastive learning to enhance the packet-level and flow-level representations and perform graph data augmentation on the byte-level traffic graph so that the fine-grained semantic-invariant characteristics between bytes can be captured through contrastive learning. We also propose cross-level multi-task learning, which simultaneously accomplishes the packet-level and flow-level classification tasks in the same model with one training. Further experiments show that CLE-TFE achieves the best overall performance on the two tasks, while its computational overhead (i.e., floating point operations, FLOPs) is only about 1/14 of the pre-trained model (e.g., ET-BERT). We release the code at https://github.com/ViktorAxelsen/CLE-TFE
Semantic Data for Humanities and Social Sciences (SDHSS): an Ecosystem of CIDOC CRM Extensions for Research Data Production and Reuse
Venue: arXiv cs.IT 2024
Year: 2024

Given the challenge of giant knowledge graphs created by major eco-nomic actors, which could virtually replace research in the Humani-ties and Social Sciences (HSS) in responding to public concerns, thequestion arises of how to increase the value of research data throughtheir publication and networking, applying the FAIR principles. Bothan epistemological and a semantic analysis show that the most rel-evant part of research data is factual information, understood as arepresentation of the objects observed by the scientific disciplines,their properties and their relationships.This rich universe of information will be made understandable andtherefore reusable through the application of foundational ontologiesand a methodology based on the distinction between different levelsof abstraction, allowing the collective development of one or moreshared and reusable domain ontologies. This vision is being carriedout around the CIDOC CRM, as core ontology, and Semantic Datafor Humanities and Social Sciences (SDHSS), as a high-level exten-sion of it, as well as an ecosystem of sub-domain extensions that canbe easily managed through the ontome.net application. This willresult in an interoperability that is semantically richer than the sim-ple alignment of ontologies and less costly in terms of resources, andabove all adapted to the scientific and humanistic project of the HSS.
UAV-assisted Visual SLAM Generating Reconstructed 3D Scene Graphs in GPS-denied Environments
Venue: arXiv cs.RO 2024
Year: 2024

Aerial robots play a vital role in various applications where the situational awareness of the robots concerning the environment is a fundamental demand. As one such use case, drones in GPS-denied environments require equipping with different sensors (e.g., vision sensors) that provide reliable sensing results while performing pose estimation and localization. In this paper, reconstructing the maps of indoor environments alongside generating 3D scene graphs for a high-level representation using a camera mounted on a drone is targeted. Accordingly, an aerial robot equipped with a companion computer and an RGB-D camera was built and employed to be appropriately integrated with a Visual Simultaneous Localization and Mapping (VSLAM) framework proposed by the authors. To enhance the situational awareness of the robot while reconstructing maps, various structural elements, including doors and walls, were labeled with printed fiducial markers, and a dictionary of the topological relations among them was fed to the system. The VSLAM system detects markers and reconstructs the map of the indoor areas enriched with higher-level semantic entities, including corridors and rooms. Another achievement is generating multi-layered vision-based situational graphs containing enhanced hierarchical representations of the indoor environment. In this regard, integrating VSLAM into the employed drone is the primary target of this paper to provide an end-to-end robot application for GPS-denied environments. To show the practicality of the system, various real-world condition experiments have been conducted in indoor scenarios with dissimilar structural layouts. Evaluations show the proposed drone application can perform adequately w.r.t. the ground-truth data and its baseline.
ASAP-Repair: API-Specific Automated Program Repair Based on API Usage Graphs
Venue: arXiv cs.SE 2024
Year: 2024

Modern software development relies on the reuse of code via Application Programming Interfaces (APIs). Such reuse relieves developers from learning and developing established algorithms and data structures anew, enabling them to focus on their problem at hand. However, there is also the risk of misusing an API due to a lack of understanding or proper documentation. While many techniques target API misuse detection, only limited efforts have been put into automatically repairing API misuses. In this paper, we present our advances on our technique API-Specific Automated Program Repair (ASAP-Repair). ASAP-Repair is intended to fix API misuses based on API Usage Graphs (AUGs) by leveraging API usage templates of state-of-the-art API misuse detectors. We demonstrate that ASAP-Repair is in principle applicable on an established API misuse dataset. Moreover, we discuss next steps and challenges to evolve ASAP-Repair towards a full-fledged Automatic Program Repair (APR) technique.
Digital Twins Below the Surface: Enhancing Underwater Teleoperation
Venue: arXiv cs.RO 2024
Year: 2024

Subsea exploration, inspection, and intervention operations heavily rely on remotely operated vehicles (ROVs). However, the inherent complexity of the underwater environment presents significant challenges to the operators of these vehicles. This paper delves into the challenges associated with navigation and maneuvering tasks in the teleoperation of ROVs, such as reduced situational awareness and heightened teleoperator workload. To address these challenges, we introduce an underwater Digital Twin (DT) system designed to enhance underwater teleoperation, enable autonomous navigation, support system monitoring, and facilitate system testing through simulation. Our approach involves a dynamic representation of the underwater robot and its environment using desktop virtual reality, as well as the integration of mapping, localization, path planning and simulation capabilities within the DT system. Our research demonstrates the system's adaptability, versatility and feasibility, highlighting significant challenges and, in turn, improving the teleoperators' situational awareness and reducing their workload.
Impact of spatial transformations on landscape features of CEC2022 basic benchmark problems
Venue: arXiv cs.NE 2024
Year: 2024

When benchmarking optimization heuristics, we need to take care to avoid an algorithm exploiting biases in the construction of the used problems. One way in which this might be done is by providing different versions of each problem but with transformations applied to ensure the algorithms are equipped with mechanisms for successfully tackling a range of problems. In this paper, we investigate several of these problem transformations and show how they influence the low-level landscape features of a set of 5 problems from the CEC2022 benchmark suite. Our results highlight that even relatively small transformations can significantly alter the measured landscape features. This poses a wider question of what properties we want to preserve when creating problem transformations, and how to fairly measure them.
Multi-Behavior Collaborative Filtering with Partial Order Graph Convolutional Networks
Venue: arXiv cs.IR 2024
Year: 2024

Representing information of multiple behaviors in the single graph collaborative filtering (CF) vector has been a long-standing challenge. This is because different behaviors naturally form separate behavior graphs and learn separate CF embeddings. Existing models merge the separate embeddings by appointing the CF embeddings for some behaviors as the primary embedding and utilizing other auxiliaries to enhance the primary embedding. However, this approach often results in the joint embedding performing well on the main tasks but poorly on the auxiliary ones. To address the problem arising from the separate behavior graphs, we propose the concept of Partial Order Recommendation Graphs (POG). POG defines the partial order relation of multiple behaviors and models behavior combinations as weighted edges to merge separate behavior graphs into a joint POG. Theoretical proof verifies that POG can be generalized to any given set of multiple behaviors. Based on POG, we propose the tailored Partial Order Graph Convolutional Networks (POGCN) that convolute neighbors' information while considering the behavior relations between users and items. POGCN also introduces a partial-order BPR sampling strategy for efficient and effective multiple-behavior CF training. POGCN has been successfully deployed on the homepage of Alibaba for two months, providing recommendation services for over one billion users. Extensive offline experiments conducted on three public benchmark datasets demonstrate that POGCN outperforms state-of-the-art multi-behavior baselines across all types of behaviors. Furthermore, online A/B tests confirm the superiority of POGCN in billion-scale recommender systems.
Large Language Models "Ad Referendum": How Good Are They at Machine Translation in the Legal Domain?
Venue: arXiv cs.CL 2024
Year: 2024

This study evaluates the machine translation (MT) quality of two state-of-the-art large language models (LLMs) against a tradition-al neural machine translation (NMT) system across four language pairs in the legal domain. It combines automatic evaluation met-rics (AEMs) and human evaluation (HE) by professional transla-tors to assess translation ranking, fluency and adequacy. The re-sults indicate that while Google Translate generally outperforms LLMs in AEMs, human evaluators rate LLMs, especially GPT-4, comparably or slightly better in terms of producing contextually adequate and fluent translations. This discrepancy suggests LLMs' potential in handling specialized legal terminology and context, highlighting the importance of human evaluation methods in assessing MT quality. The study underscores the evolving capabil-ities of LLMs in specialized domains and calls for reevaluation of traditional AEMs to better capture the nuances of LLM-generated translations.
Two Choices are Enough for P-LCPs, USOs, and Colorful Tangents
Venue: arXiv cs.CC 2024
Year: 2024

We provide polynomial-time reductions between three search problems from three distinct areas: the P-matrix linear complementarity problem (P-LCP), finding the sink of a unique sink orientation (USO), and a variant of the $α$-Ham Sandwich problem. For all three settings, we show that "two choices are enough", meaning that the general non-binary version of the problem can be reduced in polynomial time to the binary version. This specifically means that generalized P-LCPs are equivalent to P-LCPs, and grid USOs are equivalent to cube USOs. These results are obtained by showing that both the P-LCP and our $α$-Ham Sandwich variant are equivalent to a new problem we introduce, P-Lin-Bellman. This problem can be seen as a new tool for formulating problems as P-LCPs.
Privacy-Preserving Gaze Data Streaming in Immersive Interactive Virtual Reality: Robustness and User Experience
Venue: arXiv cs.HC 2024
Year: 2024

Eye tracking is routinely being incorporated into virtual reality (VR) systems. Prior research has shown that eye tracking data, if exposed, can be used for re-identification attacks. The state of our knowledge about currently existing privacy mechanisms is limited to privacy-utility trade-off curves based on data-centric metrics of utility, such as prediction error, and black-box threat models. We propose that for interactive VR applications, it is essential to consider user-centric notions of utility and a variety of threat models. We develop a methodology to evaluate real-time privacy mechanisms for interactive VR applications that incorporate subjective user experience and task performance metrics. We evaluate selected privacy mechanisms using this methodology and find that re-identification accuracy can be decreased to as low as 14% while maintaining a high usability score and reasonable task performance. Finally, we elucidate three threat scenarios (black-box, black-box with exemplars, and white-box) and assess how well the different privacy mechanisms hold up to these adversarial scenarios. This work advances the state of the art in VR privacy by providing a methodology for end-to-end assessment of the risk of re-identification attacks and potential mitigating solutions.
Optimizing Sparse Convolution on GPUs with CUDA for 3D Point Cloud Processing in Embedded Systems
Venue: arXiv cs.LG 2024
Year: 2024

In recent years, there has been a significant increase in the utilization of deep learning methods, particularly convolutional neural networks (CNNs), which have emerged as the dominant approach in various domains that involve structured grid data, such as picture analysis and processing. Nevertheless, the exponential growth in the utilization of LiDAR and 3D sensors across many domains has resulted in an increased need for the analysis of 3D point clouds. The utilization of 3D point clouds is crucial in various applications, including object recognition and segmentation, as they offer a spatial depiction of things within a three-dimensional environment. In contrast to photos, point clouds exhibit sparsity and lack a regular grid, hence posing distinct processing and computational issues.
VistaScenario: Interaction Scenario Engineering for Vehicles with Intelligent Systems for Transport Automation
Venue: arXiv cs.SE 2024
Year: 2024

Intelligent vehicles and autonomous driving systems rely on scenario engineering for intelligence and index (I&I), calibration and certification (C&C), and verification and validation (V&V). To extract and index scenarios, various vehicle interactions are worthy of much attention, and deserve refined descriptions and labels. However, existing methods cannot cope well with the problem of scenario classification and labeling with vehicle interactions as the core. In this paper, we propose VistaScenario framework to conduct interaction scenario engineering for vehicles with intelligent systems for transport automation. Based on the summarized basic types of vehicle interactions, we slice scenario data stream into a series of segments via spatiotemporal scenario evolution tree. We also propose the scenario metric Graph-DTW based on Graph Computation Tree and Dynamic Time Warping to conduct refined scenario comparison and labeling. The extreme interaction scenarios and corner cases can be efficiently filtered and extracted. Moreover, with naturalistic scenario datasets, testing examples on trajectory prediction model demonstrate the effectiveness and advantages of our framework. VistaScenario can provide solid support for the usage and indexing of scenario data, further promote the development of intelligent vehicles and transport automation.
TELLER: A Trustworthy Framework for Explainable, Generalizable and Controllable Fake News Detection
Venue: arXiv cs.CL 2024
Year: 2024

The proliferation of fake news has emerged as a severe societal problem, raising significant interest from industry and academia. While existing deep-learning based methods have made progress in detecting fake news accurately, their reliability may be compromised caused by the non-transparent reasoning processes, poor generalization abilities and inherent risks of integration with large language models (LLMs). To address this challenge, we propose {\methodname}, a novel framework for trustworthy fake news detection that prioritizes explainability, generalizability and controllability of models. This is achieved via a dual-system framework that integrates cognition and decision systems, adhering to the principles above. The cognition system harnesses human expertise to generate logical predicates, which guide LLMs in generating human-readable logic atoms. Meanwhile, the decision system deduces generalizable logic rules to aggregate these atoms, enabling the identification of the truthfulness of the input news across diverse domains and enhancing transparency in the decision-making process. Finally, we present comprehensive evaluation results on four datasets, demonstrating the feasibility and trustworthiness of our proposed framework. Our implementation is available at \url{https://github.com/less-and-less-bugs/Trust_TELLER}.
On Computationally Efficient Multi-Class Calibration
Venue: arXiv cs.LG 2024
Year: 2024

Consider a multi-class labelling problem, where the labels can take values in $[k]$, and a predictor predicts a distribution over the labels. In this work, we study the following foundational question: Are there notions of multi-class calibration that give strong guarantees of meaningful predictions and can be achieved in time and sample complexities polynomial in $k$? Prior notions of calibration exhibit a tradeoff between computational efficiency and expressivity: they either suffer from having sample complexity exponential in $k$, or needing to solve computationally intractable problems, or give rather weak guarantees. Our main contribution is a notion of calibration that achieves all these desiderata: we formulate a robust notion of projected smooth calibration for multi-class predictions, and give new recalibration algorithms for efficiently calibrating predictors under this definition with complexity polynomial in $k$. Projected smooth calibration gives strong guarantees for all downstream decision makers who want to use the predictor for binary classification problems of the form: does the label belong to a subset $T \subseteq [k]$: e.g. is this an image of an animal? It ensures that the probabilities predicted by summing the probabilities assigned to labels in $T$ are close to some perfectly calibrated binary predictor for that task. We also show that natural strengthenings of our definition are computationally hard to achieve: they run into information theoretic barriers or computational intractability. Underlying both our upper and lower bounds is a tight connection that we prove between multi-class calibration and the well-studied problem of agnostic learning in the (standard) binary prediction setting.