OpenRaiser/Intern-Atlas huggingface dataset:
C4Q: A Chatbot for Quantum
Venue: arXiv cs.CL 2024
Year: 2024

Quantum computing is a growing field that promises many real-world applications such as quantum cryptography or quantum finance. The number of people able to use quantum computing is however still very small. This limitation comes from the difficulty to understand the concepts and to know how to start coding. Therefore, there is a need for tools that can assist non-expert in overcoming this complexity. One possibility would be to use existing conversational agents. Unfortunately ChatGPT and other Large-Language Models produce inaccurate results. This article presents C4Q, a chatbot that answers accurately basic questions and guides users when trying to code quantum programs. Contrary to other approaches C4Q uses a pre-trained large language model only to discover and classify user requests. It then generates an accurate answer using an own engine. Thanks to this architectural design, C4Q's answers are always correct, and thus C4Q can become a support tool that makes quantum computing more available to non-experts.
Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems
Venue: arXiv cs.NI 2024
Year: 2024

Large language models (LLMs) and foundation models have been recently touted as a game-changer for 6G systems. However, recent efforts on LLMs for wireless networks are limited to a direct application of existing language models that were designed for natural language processing (NLP) applications. To address this challenge and create wireless-centric foundation models, this paper presents a comprehensive vision on how to design universal foundation models that are tailored towards the deployment of artificial intelligence (AI)-native networks. Diverging from NLP-based foundation models, the proposed framework promotes the design of large multi-modal models (LMMs) fostered by three key capabilities: 1) processing of multi-modal sensing data, 2) grounding of physical symbol representations in real-world wireless systems using causal reasoning and retrieval-augmented generation (RAG), and 3) enabling instructibility from the wireless environment feedback to facilitate dynamic network adaptation thanks to logical and mathematical reasoning facilitated by neuro-symbolic AI. In essence, these properties enable the proposed LMM framework to build universal capabilities that cater to various cross-layer networking tasks and alignment of intents across different domains. Preliminary results from experimental evaluation demonstrate the efficacy of grounding using RAG in LMMs, and showcase the alignment of LMMs with wireless system designs. Furthermore, the enhanced rationale exhibited in the responses to mathematical questions by LMMs, compared to vanilla LLMs, demonstrates the logical and mathematical reasoning capabilities inherent in LMMs. Building on those results, we present a sequel of open questions and challenges for LMMs. We then conclude with a set of recommendations that ignite the path towards LMM-empowered AI-native systems.
Commercial AI, Conflict, and Moral Responsibility: A theoretical analysis and practical approach to the moral responsibilities associated with dual-use AI technology
Venue: arXiv cs.CY 2024
Year: 2024

This paper presents a theoretical analysis and practical approach to the moral responsibilities when developing AI systems for non-military applications that may nonetheless be used for conflict applications. We argue that AI represents a form of crossover technology that is different from previous historical examples of dual- or multi-use technology as it has a multiplicative effect across other technologies. As a result, existing analyses of ethical responsibilities around dual-use technologies do not necessarily work for AI systems. We instead argue that stakeholders involved in the AI system lifecycle are morally responsible for uses of their systems that are reasonably foreseeable. The core idea is that an agent's moral responsibility for some action is not necessarily determined by their intentions alone; we must also consider what the agent could reasonably have foreseen to be potential outcomes of their action, such as the potential use of a system in conflict even when it is not designed for that. In particular, we contend that it is reasonably foreseeable that: (1) civilian AI systems will be applied to active conflict, including conflict support activities, (2) the use of civilian AI systems in conflict will impact applications of the law of armed conflict, and (3) crossover AI technology will be applied to conflicts that fall short of armed conflict. Given these reasonably foreseeably outcomes, we present three technically feasible actions that developers of civilian AIs can take to potentially mitigate their moral responsibility: (a) establishing systematic approaches to multi-perspective capability testing, (b) integrating digital watermarking in model weight matrices, and (c) utilizing monitoring and reporting mechanisms for conflict-related AI applications.
Enriched Physics-informed Neural Networks for Dynamic Poisson-Nernst-Planck Systems
Venue: arXiv cs.LG 2024
Year: 2024

This paper proposes a meshless deep learning algorithm, enriched physics-informed neural networks (EPINNs), to solve dynamic Poisson-Nernst-Planck (PNP) equations with strong coupling and nonlinear characteristics. The EPINNs takes the traditional physics-informed neural networks as the foundation framework, and adds the adaptive loss weight to balance the loss functions, which automatically assigns the weights of losses by updating the parameters in each iteration based on the maximum likelihood estimate. The resampling strategy is employed in the EPINNs to accelerate the convergence of loss function. Meanwhile, the GPU parallel computing technique is adopted to accelerate the solving process. Four examples are provided to demonstrate the validity and effectiveness of the proposed method. Numerical results indicate that the new method has better applicability than traditional numerical methods in solving such coupled nonlinear systems. More importantly, the EPINNs is more accurate, stable, and fast than the traditional physics-informed neural networks. This work provides a simple and high-performance numerical tool for addressing PNPs with arbitrary boundary shapes and boundary conditions.
Harm Amplification in Text-to-Image Models
Venue: arXiv cs.CY 2024
Year: 2024

Text-to-image (T2I) models have emerged as a significant advancement in generative AI; however, there exist safety concerns regarding their potential to produce harmful image outputs even when users input seemingly safe prompts. This phenomenon, where T2I models generate harmful representations that were not explicit in the input prompt, poses a potentially greater risk than adversarial prompts, leaving users unintentionally exposed to harms. Our paper addresses this issue by formalizing a definition for this phenomenon which we term harm amplification. We further contribute to the field by developing a framework of methodologies to quantify harm amplification in which we consider the harm of the model output in the context of user input. We then empirically examine how to apply these different methodologies to simulate real-world deployment scenarios including a quantification of disparate impacts across genders resulting from harm amplification. Together, our work aims to offer researchers tools to comprehensively address safety challenges in T2I systems and contribute to the responsible deployment of generative AI models.
Analysis of Internet of Things Implementation Barriers in the Cold Supply Chain: An Integrated ISM-MICMAC and DEMATEL Approach
Venue: arXiv cs.CY 2024
Year: 2024

Integrating Internet of Things (IoT) technology inside the cold supply chain can enhance transparency, efficiency, and quality, optimizing operating procedures and increasing productivity. The integration of IoT in this complicated setting is hindered by specific barriers that need a thorough examination. Prominent barriers to IoT implementation in the cold supply chain are identified using a two-stage model. After reviewing the available literature on the topic of IoT implementation, a total of 13 barriers were found. The survey data was cross-validated for quality, and Cronbach's alpha test was employed to ensure validity. This research applies the interpretative structural modeling technique in the first phase to identify the main barriers. Among those barriers, "regularity compliance" and "cold chain networks" are key drivers for IoT adoption strategies. MICMAC's driving and dependence power element categorization helps evaluate the barrier interactions. In the second phase of this research, a decision-making trial and evaluation laboratory methodology was employed to identify causal relationships between barriers and evaluate them according to their relative importance. Each cause is a potential drive, and if its efficiency can be enhanced, the system as a whole benefits. The research findings provide industry stakeholders, governments, and organizations with significant drivers of IoT adoption to overcome these barriers and optimize the utilization of IoT technology to improve the effectiveness and reliability of the cold supply chain.
KS-Net: Multi-band joint speech restoration and enhancement network for 2024 ICASSP SSI Challenge
Venue: arXiv cs.SD 2024
Year: 2024

This paper presents the speech restoration and enhancement system created by the 1024K team for the ICASSP 2024 Speech Signal Improvement (SSI) Challenge. Our system consists of a generative adversarial network (GAN) in complex-domain for speech restoration and a fine-grained multi-band fusion module for speech enhancement. In the blind test set of SSI, the proposed system achieves an overall mean opinion score (MOS) of 3.49 based on ITU-T P.804 and a Word Accuracy Rate (WAcc) of 0.78 for the real-time track, as well as an overall P.804 MOS of 3.43 and a WAcc of 0.78 for the non-real-time track, ranking 1st in both tracks.
LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks
Venue: arXiv cs.AI 2024
Year: 2024

There is considerable confusion about the role of Large Language Models (LLMs) in planning and reasoning tasks. On one side are over-optimistic claims that LLMs can indeed do these tasks with just the right prompting or self-verification strategies. On the other side are perhaps over-pessimistic claims that all that LLMs are good for in planning/reasoning tasks are as mere translators of the problem specification from one syntactic format to another, and ship the problem off to external symbolic solvers. In this position paper, we take the view that both these extremes are misguided. We argue that auto-regressive LLMs cannot, by themselves, do planning or self-verification (which is after all a form of reasoning), and shed some light on the reasons for misunderstandings in the literature. We will also argue that LLMs should be viewed as universal approximate knowledge sources that have much more meaningful roles to play in planning/reasoning tasks beyond simple front-end/back-end format translators. We present a vision of {\bf LLM-Modulo Frameworks} that combine the strengths of LLMs with external model-based verifiers in a tighter bi-directional interaction regime. We will show how the models driving the external verifiers themselves can be acquired with the help of LLMs. We will also argue that rather than simply pipelining LLMs and symbolic components, this LLM-Modulo Framework provides a better neuro-symbolic approach that offers tighter integration between LLMs and symbolic components, and allows extending the scope of model-based planning/reasoning regimes towards more flexible knowledge, problem and preference specifications.
Leveraging Large Language Models for Analyzing Blood Pressure Variations Across Biological Sex from Scientific Literature
Venue: arXiv cs.CL 2024
Year: 2024

Hypertension, defined as blood pressure (BP) that is above normal, holds paramount significance in the realm of public health, as it serves as a critical precursor to various cardiovascular diseases (CVDs) and significantly contributes to elevated mortality rates worldwide. However, many existing BP measurement technologies and standards might be biased because they do not consider clinical outcomes, comorbidities, or demographic factors, making them inconclusive for diagnostic purposes. There is limited data-driven research focused on studying the variance in BP measurements across these variables. In this work, we employed GPT-35-turbo, a large language model (LLM), to automatically extract the mean and standard deviation values of BP for both males and females from a dataset comprising 25 million abstracts sourced from PubMed. 993 article abstracts met our predefined inclusion criteria (i.e., presence of references to blood pressure, units of blood pressure such as mmHg, and mention of biological sex). Based on the automatically-extracted information from these articles, we conducted an analysis of the variations of BP values across biological sex. Our results showed the viability of utilizing LLMs to study the BP variations across different demographic factors.
COMET: Generating Commit Messages using Delta Graph Context Representation
Venue: arXiv cs.SE 2024
Year: 2024

Commit messages explain code changes in a commit and facilitate collaboration among developers. Several commit message generation approaches have been proposed; however, they exhibit limited success in capturing the context of code changes. We propose Comet (Context-Aware Commit Message Generation), a novel approach that captures context of code changes using a graph-based representation and leverages a transformer-based model to generate high-quality commit messages. Our proposed method utilizes delta graph that we developed to effectively represent code differences. We also introduce a customizable quality assurance module to identify optimal messages, mitigating subjectivity in commit messages. Experiments show that Comet outperforms state-of-the-art techniques in terms of bleu-norm and meteor metrics while being comparable in terms of rogue-l. Additionally, we compare the proposed approach with the popular gpt-3.5-turbo model, along with gpt-4-turbo; the most capable GPT model, over zero-shot, one-shot, and multi-shot settings. We found Comet outperforming the GPT models, on five and four metrics respectively and provide competitive results with the two other metrics. The study has implications for researchers, tool developers, and software developers. Software developers may utilize Comet to generate context-aware commit messages. Researchers and tool developers can apply the proposed delta graph technique in similar contexts, like code review summarization.
Understanding Growth Mindset Practices in an Introductory Physical Computing Classroom: High School Students' Engagement with Debugging by Design Activities
Venue: arXiv cs.CY 2024
Year: 2024

Background and Context: While debugging is recognized as an essential practice, for many students, encountering bugs can generate emotional responses such as fear and anxiety that can lead to disengagement and the avoidance of computer programming. Growth mindsets can support perseverance and learning in these situations, yet few studies have investigated how growth mindsets emerge in practice amongst K-12 computing students facing physical computing debugging challenges. Objective: We seek to understand what (if any) growth mindset practices high school students exhibited when creating and exchanging buggy physical computing projects for their peers to solve during a Debugging by Design activity as part of their introductory computing course. Method: We focused on moment-to-moment microgenetic analysis of student interactions in designing and solving bugs for others to examine the practices students exhibited that demonstrated the development of a growth mindset and the contexts in which these practices emerged. Findings: We identified five emergent growth mindset practices: choosing challenges that lead to more learning, persisting after setbacks, giving and valuing praise for effort, approaching learning as constant improvement, and developing comfort with failure. Students most often exhibited these practices in peer-to-peer interactions and while making buggy physical computing projects for their peers to solve. Implications: Our analysis contributes to a more holistic understanding of students' social, emotional, and motivational approaches to debugging physical computing projects through the characterization of growth mindset practices. The presented inventory of growth mindset practices may be helpful to further study growth mindset in action in other computing settings.
Inverse Reinforcement Learning by Estimating Expertise of Demonstrators
Venue: arXiv cs.LG 2024
Year: 2024

In Imitation Learning (IL), utilizing suboptimal and heterogeneous demonstrations presents a substantial challenge due to the varied nature of real-world data. However, standard IL algorithms consider these datasets as homogeneous, thereby inheriting the deficiencies of suboptimal demonstrators. Previous approaches to this issue rely on impractical assumptions like high-quality data subsets, confidence rankings, or explicit environmental knowledge. This paper introduces IRLEED, Inverse Reinforcement Learning by Estimating Expertise of Demonstrators, a novel framework that overcomes these hurdles without prior knowledge of demonstrator expertise. IRLEED enhances existing Inverse Reinforcement Learning (IRL) algorithms by combining a general model for demonstrator suboptimality to address reward bias and action variance, with a Maximum Entropy IRL framework to efficiently derive the optimal policy from diverse, suboptimal demonstrations. Experiments in both online and offline IL settings, with simulated and human-generated data, demonstrate IRLEED's adaptability and effectiveness, making it a versatile solution for learning from suboptimal demonstrations.
The Role of Foundation Models in Neuro-Symbolic Learning and Reasoning
Venue: arXiv cs.AI 2024
Year: 2024

Neuro-Symbolic AI (NeSy) holds promise to ensure the safe deployment of AI systems, as interpretable symbolic techniques provide formal behaviour guarantees. The challenge is how to effectively integrate neural and symbolic computation, to enable learning and reasoning from raw data. Existing pipelines that train the neural and symbolic components sequentially require extensive labelling, whereas end-to-end approaches are limited in terms of scalability, due to the combinatorial explosion in the symbol grounding problem. In this paper, we leverage the implicit knowledge within foundation models to enhance the performance in NeSy tasks, whilst reducing the amount of data labelling and manual engineering. We introduce a new architecture, called NeSyGPT, which fine-tunes a vision-language foundation model to extract symbolic features from raw data, before learning a highly expressive answer set program to solve a downstream task. Our comprehensive evaluation demonstrates that NeSyGPT has superior accuracy over various baselines, and can scale to complex NeSy tasks. Finally, we highlight the effective use of a large language model to generate the programmatic interface between the neural and symbolic components, significantly reducing the amount of manual engineering required.
Carthago Delenda Est: Co-opetitive Indirect Information Diffusion Model for Influence Operations on Online Social Media
Venue: arXiv cs.SI 2024
Year: 2024

For a state or non-state actor whose credibility is bankrupt, relying on bots to conduct non-attributable, non-accountable, and seemingly-grassroots-but-decentralized-in-actuality influence/information operations (info ops) on social media can help circumvent the issue of trust deficit while advancing its interests. Planning and/or defending against decentralized info ops can be aided by computational simulations in lieu of ethically-fraught live experiments on social media. In this study, we introduce Diluvsion, an agent-based model for contested information propagation efforts on Twitter-like social media. The model emphasizes a user's belief in an opinion (stance) being impacted by the perception of potentially illusory popular support from constant incoming floods of indirect information, floods that can be cooperatively engineered in an uncoordinated manner by bots as they compete to spread their stances. Our model, which has been validated against real-world data, is an advancement over previous models because we account for engagement metrics in influencing stance adoption, non-social tie spreading of information, neutrality as a stance that can be spread, and themes that are analogous to media's framing effect and are symbiotic with respect to stance propagation. The strengths of the Diluvsion model are demonstrated in simulations of orthodox info ops, e.g., maximizing adoption of one stance; creating echo chambers; inducing polarization; and unorthodox info ops, e.g., simultaneous support of multiple stances as a Trojan horse tactic for the dissemination of a theme.
From PEFT to DEFT: Parameter Efficient Finetuning for Reducing Activation Density in Transformers
Venue: arXiv cs.LG 2024
Year: 2024

Pretrained Language Models (PLMs) have become the de facto starting point for fine-tuning on downstream tasks. However, as model sizes continue to increase, traditional fine-tuning of all the parameters becomes challenging. To address this, parameter-efficient fine-tuning (PEFT) methods have gained popularity as a means to adapt PLMs effectively. In parallel, recent studies have revealed the presence of activation sparsity within the intermediate outputs of the multilayer perceptron (MLP) blocks in transformers. Low activation density enables efficient model inference on sparsity-aware hardware. Building upon this insight, in this work, we propose a novel density loss that encourages higher activation sparsity (equivalently, lower activation density) in the pre-trained models. We demonstrate the effectiveness of our approach by utilizing mainstream PEFT techniques, including QLoRA, LoRA, Adapter, and Prompt/Prefix Tuning, to facilitate efficient model adaptation across diverse downstream tasks. Experiments show that our proposed method, \textbf{DEFT} (Density-Efficient Fine-Tuning), can consistently reduce activation density by up to \textbf{44.94\%} on RoBERTa$_\mathrm{Large}$ and by \textbf{53.19\%} (encoder density) and \textbf{90.60\%} (decoder density) on Flan-T5$_\mathrm{XXL}$ (\textbf{11B}) compared to PEFT, using GLUE and QA (SQuAD) benchmarks respectively. We also introduce \textbf{ADA-DEFT}, an adaptive variant of our DEFT approach, which achieves significant memory and runtime savings during inference. For instance, ADA-DEFT reduces runtime by \textbf{8.79\%}and memory usage by \textbf{17.46\%} in Flan-T5$_\mathrm{XL}$, and by \textbf{2.79\%} and \textbf{2.54\%} respectively in Flan-T5$_\mathrm{XXL}$. Additionally, we showcase that DEFT works complementarily with quantized and pruned models.