OpenRaiser/Intern-Atlas huggingface dataset:
ELMO2EDS: Transforming Educational Credentials into Self-Sovereign Identity Paradigm
Venue: arXiv cs.CY 2024
Year: 2024

Digital credentials in education make it easier for students to apply for a course of study, a new job, or change a higher education institute. Academic networks, such as EMREX, support the exchange of digital credentials between students and education institutes. Students can fetch results from one educational institute and apply for a course of study at another educational institute. Digital signatures of the issuing institution can verify the authenticity of digital credentials. Each institution must provide the integration of EMREX using its identity management system. In this paper, we investigate how digital credentials can be integrated into the Self-Sovereign Identity ecosystem to overcome the known issues of academic networks. We examine known issues such as the authentication of students. Self-Sovereign Identity is a paradigm that gives individuals control of their digital identities. Based on our findings, we propose ELMO2EDS, a solution that 1) converts digital credentials from EMREX to a suitable Self-Sovereign Identy data format, 2) enables authenticating a student, and 3) enables issuing, storing, and verification of achieved study.
Inpainting the Gaps: A Novel Framework for Evaluating Explanation Methods in Vision Transformers
Venue: arXiv cs.CV 2024
Year: 2024

The perturbation test remains the go-to evaluation approach for explanation methods in computer vision. This evaluation method has a major drawback of test-time distribution shift due to pixel-masking that is not present in the training set. To overcome this drawback, we propose a novel evaluation framework called \textbf{Inpainting the Gaps (InG)}. Specifically, we propose inpainting parts that constitute partial or complete objects in an image. In this way, one can perform meaningful image perturbations with lower test-time distribution shifts, thereby improving the efficacy of the perturbation test. InG is applied to the PartImageNet dataset to evaluate the performance of popular explanation methods for three training strategies of the Vision Transformer (ViT). Based on this evaluation, we found Beyond Intuition and Generic Attribution to be the two most consistent explanation models. Further, and interestingly, the proposed framework results in higher and more consistent evaluation scores across all the ViT models considered in this work. To the best of our knowledge, InG is the first semi-synthetic framework for the evaluation of ViT explanation methods.
Intersymbolic AI: Interlinking Symbolic AI and Subsymbolic AI
Venue: arXiv cs.AI 2024
Year: 2024

This perspective piece calls for the study of the new field of Intersymbolic AI, by which we mean the combination of symbolic AI, whose building blocks have inherent significance/meaning, with subsymbolic AI, whose entirety creates significance/effect despite the fact that individual building blocks escape meaning. Canonical kinds of symbolic AI are logic, games and planning. Canonical kinds of subsymbolic AI are (un)supervised machine and reinforcement learning. Intersymbolic AI interlinks the worlds of symbolic AI with its compositional symbolic significance and meaning and of subsymbolic AI with its summative significance or effect to enable culminations of insights from both worlds by going between and across symbolic AI insights with subsymbolic AI techniques that are being helped by symbolic AI principles. For example, Intersymbolic AI may start with symbolic AI to understand a dynamic system, continue with subsymbolic AI to learn its control, and end with symbolic AI to safely use the outcome of the learned subsymbolic AI controller in the dynamic system. The way Intersymbolic AI combines both symbolic and subsymbolic AI to increase the effectiveness of AI compared to either kind of AI alone is likened to the way that the combination of both conscious and subconscious thought increases the effectiveness of human thought compared to either kind of thought alone. Some successful contributions to the Intersymbolic AI paradigm are surveyed here but many more are considered possible by advancing Intersymbolic AI.
Quaternion Generative Adversarial Neural Networks and Applications to Color Image Inpainting
Venue: arXiv cs.CV 2024
Year: 2024

Color image inpainting is a challenging task in imaging science. The existing method is based on real operation, and the red, green and blue channels of the color image are processed separately, ignoring the correlation between each channel. In order to make full use of the correlation between each channel, this paper proposes a Quaternion Generative Adversarial Neural Network (QGAN) model and related theory, and applies it to solve the problem of color image inpainting with large area missing. Firstly, the definition of quaternion deconvolution is given and the quaternion batch normalization is proposed. Secondly, the above two innovative modules are applied to generate adversarial networks to improve stability. Finally, QGAN is applied to color image inpainting and compared with other state-of-the-art algorithms. The experimental results show that QGAN has superiority in color image inpainting with large area missing.
YOLO-FEDER FusionNet: A Novel Deep Learning Architecture for Drone Detection
Venue: arXiv cs.CV 2024
Year: 2024

Predominant methods for image-based drone detection frequently rely on employing generic object detection algorithms like YOLOv5. While proficient in identifying drones against homogeneous backgrounds, these algorithms often struggle in complex, highly textured environments. In such scenarios, drones seamlessly integrate into the background, creating camouflage effects that adversely affect the detection quality. To address this issue, we introduce a novel deep learning architecture called YOLO-FEDER FusionNet. Unlike conventional approaches, YOLO-FEDER FusionNet combines generic object detection methods with the specialized strength of camouflage object detection techniques to enhance drone detection capabilities. Comprehensive evaluations of YOLO-FEDER FusionNet show the efficiency of the proposed model and demonstrate substantial improvements in both reducing missed detections and false alarms.
R-Eval: A Unified Toolkit for Evaluating Domain Knowledge of Retrieval Augmented Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Large language models have achieved remarkable success on general NLP tasks, but they may fall short for domain-specific problems. Recently, various Retrieval-Augmented Large Language Models (RALLMs) are proposed to address this shortcoming. However, existing evaluation tools only provide a few baselines and evaluate them on various domains without mining the depth of domain knowledge. In this paper, we address the challenges of evaluating RALLMs by introducing the R-Eval toolkit, a Python toolkit designed to streamline the evaluation of different RAG workflows in conjunction with LLMs. Our toolkit, which supports popular built-in RAG workflows and allows for the incorporation of customized testing data on the specific domain, is designed to be user-friendly, modular, and extensible. We conduct an evaluation of 21 RALLMs across three task levels and two representative domains, revealing significant variations in the effectiveness of RALLMs across different tasks and domains. Our analysis emphasizes the importance of considering both task and domain requirements when choosing a RAG workflow and LLM combination. We are committed to continuously maintaining our platform at https://github.com/THU-KEG/R-Eval to facilitate both the industry and the researchers.
Secure Cross-Chain Provenance for Digital Forensics Collaboration
Venue: arXiv cs.CR 2024
Year: 2024

In digital forensics and various sectors like medicine and supply chain, blockchains play a crucial role in providing a secure and tamper-resistant system that meticulously records every detail, ensuring accountability. However, collaboration among different agencies, each with its own blockchains, creates challenges due to diverse protocols and a lack of interoperability, hindering seamless information sharing. Cross-chain technology has been introduced to address these challenges. Current research about blockchains in digital forensics, tends to focus on individual agencies, lacking a comprehensive approach to collaboration and the essential aspect of cross-chain functionality. This emphasizes the necessity for a framework capable of effectively addressing challenges in securely sharing case information, implementing access controls, and capturing provenance data across interconnected blockchains. Our solution, ForensiCross, is the first cross-chain solution specifically designed for digital forensics and provenance. It includes BridgeChain and features a unique communication protocol for cross-chain and multi-chain solutions. ForensiCross offers meticulous provenance capture and extraction methods, mathematical analysis to ensure reliability, scalability considerations for a distributed intermediary in collaborative blockchain contexts, and robust security measures against potential vulnerabilities and attacks. Analysis and evaluation results indicate that ForensiCross is secure and, despite a slight increase in communication time, outperforms in node count efficiency and has secure provenance extraction. As an all-encompassing solution, ForensiCross aims to simplify collaborative investigations by ensuring data integrity and traceability.
Matching Query Image Against Selected NeRF Feature for Efficient and Scalable Localization
Venue: arXiv cs.CV 2024
Year: 2024

Neural implicit representations such as NeRF have revolutionized 3D scene representation with photo-realistic quality. However, existing methods for visual localization within NeRF representations suffer from inefficiency and scalability issues, particularly in large-scale environments. This work proposes MatLoc-NeRF, a novel matching-based localization framework using selected NeRF features. It addresses efficiency by employing a learnable feature selection mechanism that identifies informative NeRF features for matching with query images. This eliminates the need for all NeRF features or additional descriptors, leading to faster and more accurate pose estimation. To tackle large-scale scenes, MatLoc-NeRF utilizes a pose-aware scene partitioning strategy. It ensures that only the most relevant NeRF sub-block generates key features for a specific pose. Additionally, scene segmentation and a place predictor provide fast coarse initial pose estimation. Evaluations on public large-scale datasets demonstrate that MatLoc-NeRF achieves superior efficiency and accuracy compared to existing NeRF-based localization methods.
Deep Learning methodology for the identification of wood species using high-resolution macroscopic images
Venue: arXiv cs.CV 2024
Year: 2024

Significant advancements in the field of wood species identification are needed worldwide to support sustainable timber trade. In this work we contribute to automate the identification of wood species via high-resolution macroscopic images of timber. The main challenge of this problem is that fine-grained patterns in timber are crucial in order to accurately identify wood species, and these patterns are not properly learned by traditional convolutional neural networks (CNNs) trained on low/medium resolution images. We propose a Timber Deep Learning Identification with Patch-based Inference Voting methodology, abbreviated TDLI-PIV methodology. Our proposal exploits the concept of patching and the availability of high-resolution macroscopic images of timber in order to overcome the inherent challenges that CNNs face in timber identification. The TDLI-PIV methodology is able to capture fine-grained patterns in timber and, moreover, boosts robustness and prediction accuracy via a collaborative voting inference process. In this work we also introduce a new data set of marcroscopic images of timber, called GOIMAI-Phase-I, which has been obtained using optical magnification in order to capture fine-grained details, which contrasts to the other datasets that are publicly available. More concretely, images in GOIMAI-Phase-I are taken with a smartphone with a 24x magnifying lens attached to the camera. Our data set contains 2120 images of timber and covers 37 legally protected wood species. Our experiments have assessed the performance of the TDLI-PIV methodology, involving the comparison with other methodologies available in the literature, exploration of data augmentation methods and the effect that the dataset size has on the accuracy of TDLI-PIV.
PhyBench: A Physical Commonsense Benchmark for Evaluating Text-to-Image Models
Venue: arXiv cs.CV 2024
Year: 2024

Text-to-image (T2I) models have made substantial progress in generating images from textual prompts. However, they frequently fail to produce images consistent with physical commonsense, a vital capability for applications in world simulation and everyday tasks. Current T2I evaluation benchmarks focus on metrics such as accuracy, bias, and safety, neglecting the evaluation of models' internal knowledge, particularly physical commonsense. To address this issue, we introduce PhyBench, a comprehensive T2I evaluation dataset comprising 700 prompts across 4 primary categories: mechanics, optics, thermodynamics, and material properties, encompassing 31 distinct physical scenarios. We assess 6 prominent T2I models, including proprietary models DALLE3 and Gemini, and demonstrate that incorporating physical principles into prompts enhances the models' ability to generate physically accurate images. Our findings reveal that: (1) even advanced models frequently err in various physical scenarios, except for optics; (2) GPT-4o, with item-specific scoring instructions, effectively evaluates the models' understanding of physical commonsense, closely aligning with human assessments; and (3) current T2I models are primarily focused on text-to-image translation, lacking profound reasoning regarding physical commonsense. We advocate for increased attention to the inherent knowledge within T2I models, beyond their utility as mere image generation tools. The data will be available soon.
Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations
Venue: arXiv cs.LG 2024
Year: 2024

We study the computational and sample complexity of learning a target function $f_*:\mathbb{R}^d\to\mathbb{R}$ with additive structure, that is, $f_*(x) = \frac{1}{\sqrt{M}}\sum_{m=1}^M f_m(\langle x, v_m\rangle)$, where $f_1,f_2,...,f_M:\mathbb{R}\to\mathbb{R}$ are nonlinear link functions of single-index models (ridge functions) with diverse and near-orthogonal index features $\{v_m\}_{m=1}^M$, and the number of additive tasks $M$ grows with the dimensionality $M\asymp d^γ$ for $γ\ge 0$. This problem setting is motivated by the classical additive model literature, the recent representation learning theory of two-layer neural network, and large-scale pretraining where the model simultaneously acquires a large number of "skills" that are often localized in distinct parts of the trained network. We prove that a large subset of polynomial $f_*$ can be efficiently learned by gradient descent training of a two-layer neural network, with a polynomial statistical and computational complexity that depends on the number of tasks $M$ and the information exponent of $f_m$, despite the unknown link function and $M$ growing with the dimensionality. We complement this learnability guarantee with computational hardness result by establishing statistical query (SQ) lower bounds for both the correlational SQ and full SQ algorithms.
The Ethics of AI in Education
Venue: arXiv cs.CY 2024
Year: 2024

The transition of Artificial Intelligence (AI) from a lab-based science to live human contexts brings into sharp focus many historic, socio-cultural biases, inequalities, and moral dilemmas. Many questions that have been raised regarding the broader ethics of AI are also relevant for AI in Education (AIED). AIED raises further specific challenges related to the impact of its technologies on users, how such technologies might be used to reinforce or alter the way that we learn and teach, and what we, as a society and individuals, value as outcomes of education. This chapter discusses key ethical dimensions of AI and contextualises them within AIED design and engineering practices to draw connections between the AIED systems we build, the questions about human learning and development we ask, the ethics of the pedagogies we use, and the considerations of values that we promote in and through AIED within a wider socio-technical system.
Decoding the Sociotechnical Dimensions of Digital Misinformation: A Comprehensive Literature Review
Venue: arXiv cs.CY 2024
Year: 2024

This paper presents a systematic literature review in Computer Science that provide an overview of the initiatives related to digital misinformation. This is an exploratory study that covers research from 1993 to 2020, focusing on the investigation of the phenomenon of misinformation. The review consists of 788 studies from SCOPUS, IEEE, and ACM digital libraries, synthesizing the primary research directions and sociotechnical challenges. These challenges are classified into Physical, Empirical, Syntactic, Semantic, Pragmatic, and Social dimensions, drawing from Organizational Semiotics. The mapping identifies issues related to the concept of misinformation, highlights deficiencies in mitigation strategies, discusses challenges in approaching stakeholders, and unveils various sociotechnical aspects relevant to understanding and mitigating the harmful effects of digital misinformation. As contributions, this study present a novel categorization of mitigation strategies, a sociotechnical taxonomy for classifying types of false information and elaborate on the inter-relation of sociotechnical aspects and their impacts.
Attributions toward Artificial Agents in a modified Moral Turing Test
Venue: arXiv cs.CY 2024
Year: 2024

Advances in artificial intelligence (AI) raise important questions about whether people view moral evaluations by AI systems similarly to human-generated moral evaluations. We conducted a modified Moral Turing Test (m-MTT), inspired by Allen and colleagues' (2000) proposal, by asking people to distinguish real human moral evaluations from those made by a popular advanced AI language model: GPT-4. A representative sample of 299 U.S. adults first rated the quality of moral evaluations when blinded to their source. Remarkably, they rated the AI's moral reasoning as superior in quality to humans' along almost all dimensions, including virtuousness, intelligence, and trustworthiness, consistent with passing what Allen and colleagues call the comparative MTT. Next, when tasked with identifying the source of each evaluation (human or computer), people performed significantly above chance levels. Although the AI did not pass this test, this was not because of its inferior moral reasoning but, potentially, its perceived superiority, among other possible explanations. The emergence of language models capable of producing moral responses perceived as superior in quality to humans' raises concerns that people may uncritically accept potentially harmful moral guidance from AI. This possibility highlights the need for safeguards around generative language models in matters of morality.
Law and the Emerging Political Economy of Algorithmic Audits
Venue: arXiv cs.CY 2024
Year: 2024

For almost a decade now, scholarship in and beyond the ACM FAccT community has been focusing on novel and innovative ways and methodologies to audit the functioning of algorithmic systems. Over the years, this research idea and technical project has matured enough to become a regulatory mandate. Today, the Digital Services Act (DSA) and the Online Safety Act (OSA) have established the framework within which technology corporations and (traditional) auditors will develop the `practice' of algorithmic auditing thereby presaging how this `ecosystem' will develop. In this paper, we systematically review the auditing provisions in the DSA and the OSA in light of observations from the emerging industry of algorithmic auditing. Who is likely to occupy this space? What are some political and ethical tensions that are likely to arise? How are the mandates of `independent auditing' or `the evaluation of the societal context of an algorithmic function' likely to play out in practice? By shaping the picture of the emerging political economy of algorithmic auditing, we draw attention to strategies and cultures of traditional auditors that risk eroding important regulatory pillars of the DSA and the OSA. Importantly, we warn that ambitious research ideas and technical projects of/for algorithmic auditing may end up crashed by the standardising grip of traditional auditors and/or diluted within a complex web of (sub-)contractual arrangements, diverse portfolios, and tight timelines.
"Sora is Incredible and Scary": Emerging Governance Challenges of Text-to-Video Generative AI Models
Venue: arXiv cs.CY 2024
Year: 2024

Text-to-video generative AI models such as Sora OpenAI have the potential to disrupt multiple industries. In this paper, we report a qualitative social media analysis aiming to uncover people's perceived impact of and concerns about Sora's integration. We collected and analyzed comments (N=292) under popular posts about Sora-generated videos, comparison between Sora videos and Midjourney images, and artists' complaints about copyright infringement by Generative AI. We found that people were most concerned about Sora's impact on content creation-related industries. Emerging governance challenges included the for-profit nature of OpenAI, the blurred boundaries between real and fake content, human autonomy, data privacy, copyright issues, and environmental impact. Potential regulatory solutions proposed by people included law-enforced labeling of AI content and AI literacy education for the public. Based on the findings, we discuss the importance of gauging people's tech perceptions early and propose policy recommendations to regulate Sora before its public release.
Artificial Intelligence in Everyday Life 2.0: Educating University Students from Different Majors
Venue: arXiv cs.CY 2024
Year: 2024

With the surge in data-centric AI and its increasing capabilities, AI applications have become a part of our everyday lives. However, misunderstandings regarding their capabilities, limitations, and associated advantages and disadvantages are widespread. Consequently, in the university setting, there is a crucial need to educate not only computer science majors but also students from various disciplines about AI. In this experience report, we present an overview of an introductory course that we offered to students coming from different majors. Moreover, we discuss the assignments and quizzes of the course, which provided students with a firsthand experience of AI processes and insights into their learning patterns. Additionally, we provide a summary of the course evaluation, as well as students' performance. Finally, we present insights gained from teaching this course and elaborate on our future plans.