OpenRaiser/Intern-Atlas huggingface dataset:
A Binary Quantifier for Definite Descriptions in Nelsonian Free Logic
Venue: arXiv cs.LO 2024
Year: 2024

The method Kürbis used to formalise definite descriptions with a binary quantifier I, such that I$x[F,G]$ indicates `the F is G', is examined and improved upon in this work. Kürbis first looked at I in intuitionistic logic and its negative free form. It is well-known that intuitionistic reasoning approaches truth constructively. We also want to approach falsehood constructively, in Nelson's footsteps. Within the context of Nelson's paraconsistent logic N4 and its negative free variant, we examine I. We offer an embedding function from Nelson's (free) logic into intuitionistic (free) logic, as well as a natural deduction system for Nelson's (free) logic supplied with I and Kripke style semantics for it. Our method not only yields constructive falsehood, but also provides an alternate resolution to an issue pertaining to Russell's interpretation of definite descriptions. This comprehension might result in paradoxes. Free logic, which is often used to solve this issue, is insufficiently powerful to produce contradictions. Instead, we employ paraconsistent logic, which is made to function in the presence of contradicting data without devaluing the process of reasoning.
Nested-sequent Calculus for Modal Logic MB
Venue: arXiv cs.LO 2024
Year: 2024

Quantum logic (QL) is a non-classical logic for analyzing the propositions of quantum physics. Modal logic MB, which is a logic that handles the value of the inner product that appears in quantum mechanics, was constructed with the development of QL. Although the basic properties of this logic have already been analyzed in a previous study, some essential parts still need to be completed. They are concerned with the completeness theorem and the decidability of the validity problem of this logic. This study solves those problems by constructing a nested-sequent calculus for MB. In addition, new logic MB+ with the addition of new modal symbols is discussed.
The Power of Generalized Clemens Semantics
Venue: arXiv cs.LO 2024
Year: 2024

In this paper, we elaborate on the ordered-pair semantics originally presented by Matthew Clemens for LP (Priest's Logic of Paradox). For this purpose, we build on a generalization of Clemens semantics to the case of n-tuple semantics, for every n. More concretely, i) we deal with the case of a language with quantifiers, and ii) we consider philosophical implications of the semantics. The latter includes, first, a reading of the semantics in epistemic terms, involving multiple agents. Furthermore, we discuss the proper understanding of many-valued logics, namely LP and K3 (Kleene strong 3-valued logic), from the perspective of classical logic, along the lines suggested by Susan Haack. We will also discuss some applications of the semantics to issues related to informative contradictions, i.e. contradictions involving quantification over different respects a vague predicate may have, as advanced by Paul Égré, and also to the mixed consequence relations, promoted by Pablo Cobreros, Paul Égré, David Ripley and Robert van Rooij.
TinyHelen's First Curriculum: Training and Evaluating Tiny Language Models in a Simpler Language Environment
Venue: arXiv cs.CL 2024
Year: 2024

Training language models (LMs) and their application agents is increasingly costly due to large datasets and models, making test failures difficult to bear. Simplified language environments serve as primordial training and testing grounds, retaining essential commonsense and communication skills but in a more digestible form, potentially enhancing the learning efficiency of LMs, and thus reducing the required model size and data volume for effective training and evaluation. In these simplified language environments, workable strategies for small models, datasets, and agents may be adaptable to larger models, datasets, and agents in complex language environments. To create such environments, we focus on two aspects: i) minimizing language dataset noise and complexity, and ii) preserving the essential text distribution characteristics. Unlike previous methods, we propose a pipeline to refine text data by eliminating noise, minimizing vocabulary, and maintaining genre-specific patterns (e.g., for books, conversation, code, etc.). Implementing this pipeline with large LMs, we have created a leaner suite of LM training and evaluation datasets: 71M Leaner-Pretrain, 7M Leaner-Instruct, Leaner-Glue for assessing linguistic proficiency, and Leaner-Eval for testing instruction-following ability. Our experiments show that leaner pre-training boosts LM learning efficiency. Tiny LMs trained on these datasets outperform those trained on original datasets in instruction-following across different language granularity levels. Moreover, the Leaner-Pretrain dataset's alignment with conventional large LM training sets enables resource-optimized analysis of how learning objectives, model architectures, and training techniques impact performance on language modeling and downstream tasks. Our code and datasets are available at https://github.com/EmpathYang/TinyHelen.git.
"Dialogue" vs "Dialog" in NLP and AI research: Statistics from a Confused Discourse
Venue: arXiv cs.CL 2024
Year: 2024

Within computing research, there are two spellings for an increasingly important term - dialogue and dialog. We analyze thousands of research papers to understand this "dialog(ue) debacle". Among publications in top venues that use "dialog(ue)" in the title or abstract, 72% use "dialogue", 24% use "dialog", and 5% use both in the same title and abstract. This split distribution is more common in Computing than any other academic discipline. We investigate trends over ~20 years of NLP/AI research, not finding clear evidence of a shift over time. Author nationality is weakly correlated with spelling choice, but far from explains the mixed use. Many prolific authors publish papers with both spellings. We use several methods (such as syntactic parses and LM embeddings) to study how dialog(ue) context influences spelling, finding limited influence. Combining these results together, we discuss different theories that might explain the dialog(ue) divergence.
Efficient Standardization of Clinical Notes using Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Clinician notes are a rich source of patient information but often contain inconsistencies due to varied writing styles, colloquialisms, abbreviations, medical jargon, grammatical errors, and non-standard formatting. These inconsistencies hinder the extraction of meaningful data from electronic health records (EHRs), posing challenges for quality improvement, population health, precision medicine, decision support, and research. We present a large language model approach to standardizing a corpus of 1,618 clinical notes. Standardization corrected an average of $4.9 +/- 1.8$ grammatical errors, $3.3 +/- 5.2$ spelling errors, converted $3.1 +/- 3.0$ non-standard terms to standard terminology, and expanded $15.8 +/- 9.1$ abbreviations and acronyms per note. Additionally, notes were re-organized into canonical sections with standardized headings. This process prepared notes for key concept extraction, mapping to medical ontologies, and conversion to interoperable data formats such as FHIR. Expert review of randomly sampled notes found no significant data loss after standardization. This proof-of-concept study demonstrates that standardization of clinical notes can improve their readability, consistency, and usability, while also facilitating their conversion into interoperable data formats.
Controlled Causal Hallucinations Can Estimate Phantom Nodes in Multiexpert Mixtures of Fuzzy Cognitive Maps
Venue: arXiv cs.LG 2024
Year: 2024

An adaptive multiexpert mixture of feedback causal models can approximate missing or phantom nodes in large-scale causal models. The result gives a scalable form of \emph{big knowledge}. The mixed model approximates a sampled dynamical system by approximating its main limit-cycle equilibria. Each expert first draws a fuzzy cognitive map (FCM) with at least one missing causal node or variable. FCMs are directed signed partial-causality cyclic graphs. They mix naturally through convex combination to produce a new causal feedback FCM. Supervised learning helps each expert FCM estimate its phantom node by comparing the FCM's partial equilibrium with the complete multi-node equilibrium. Such phantom-node estimation allows partial control over these causal hallucinations and helps approximate the future trajectory of the dynamical system. But the approximation can be computationally heavy. Mixing the tuned expert FCMs gives a practical way to find several phantom nodes and thereby better approximate the feedback system's true equilibrium behavior.
UPC Sentinel: An Accurate Approach for Detecting Upgradeability Proxy Contracts in Ethereum
Venue: arXiv cs.SE 2024
Year: 2024

Software applications that run on a blockchain platform are known as DApps. DApps are built using smart contracts, which are immutable after deployment. Just like any real-world software system, DApps need to receive new features and bug fixes over time in order to remain useful and secure. However, Ethereum lacks native solutions for post-deployment smart contract maintenance, requiring developers to devise their own methods. A popular method is known as the upgradeability proxy contract (UPC), which involves implementing the proxy design pattern (as defined by the Gang of Four). In this method, client calls first hit a proxy contract, which then delegates calls to a certain implementation contract. Most importantly, the proxy contract can be reconfigured during runtime to delegate calls to another implementation contract, effectively enabling application upgrades. For researchers, the accurate detection of UPCs is a strong requirement in the understanding of how exactly real-world DApps are maintained over time. For practitioners, the accurate detection of UPCs is crucial for providing application behavior transparency and enabling auditing. In this paper, we introduce UPC Sentinel, a novel three-layer algorithm that utilizes both static and dynamic analysis of smart contract bytecode to accurately detect active UPCs. We evaluated UPC Sentinel using two distinct ground truth datasets. In the first dataset, our method demonstrated a near-perfect accuracy of 99%. The evaluation on the second dataset further established our method's efficacy, showing a perfect precision rate of 100% and a near-perfect recall of 99.3%, outperforming the state of the art. Finally, we discuss the potential value of UPC Sentinel in advancing future research efforts.
Survey on safe robot control via learning
Venue: arXiv cs.RO 2024
Year: 2024

Control systems are critical to modern technological infrastructure, spanning industries from aerospace to healthcare. This survey explores the landscape of safe robot learning, investigating methods that balance high-performance control with rigorous safety constraints. By examining classical control techniques, learning-based approaches, and embedded system design, the research seeks to understand how robotic systems can be developed to prevent hazardous states while maintaining optimal performance across complex operational environments.
Feedback Design and Implementation for Integrated Posture Manipulation and Thrust Vectoring
Venue: arXiv cs.RO 2024
Year: 2024

This MS thesis outlines my contributions to the closed loop control and system integration of two robotic platforms: 1) Aerobat, a flapping wing robot stabilized by air jets, and 2) Harpy, a bipedal robot equipped with dual thrusters. Both systems share a common theme of the integration of posture manipulation and thrust vectoring to achieve stability and controlled movement. For Aerobat, I developed the software and control architecture that enabled its first untethered flights. The control system combines flapping wing dynamics with multiple air jet stabilization to maintain roll, pitch and yaw stability. These results were published in the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). For Harpy, I implemented a closed-loop control framework that incorporates active thruster assisted frontal dynamics stabilization . My work led to preliminary untethered dynamic walking. This approach demonstrates how thrust assisted stability can enhance locomotion in legged robots which has not been explored before.
Human-AI Teaming Using Large Language Models: Boosting Brain-Computer Interfacing (BCI) and Brain Research
Venue: arXiv cs.HC 2024
Year: 2024

Recently, there is an increasing interest in using artificial intelligence (AI) to automate aspects of the research process, or even autonomously conduct the full research cycle from idea generation, over data analysis, to composing and evaluation of scientific manuscripts. Examples of working AI scientist systems have been demonstrated for computer science tasks and running molecular biology labs. While some approaches aim for full autonomy of the scientific AI, others rather aim for leveraging human-AI teaming. Here, we address how to adapt such approaches for boosting Brain-Computer Interface (BCI) development, as well as brain research resp. neuroscience at large. We argue that at this time, a strong emphasis on human-AI teaming, in contrast to fully autonomous AI BCI researcher will be the most promising way forward. We introduce the collaborative workspaces concept for human-AI teaming based on a set of Janusian design principles, looking both ways, to the human as well as to the AI side. Based on these principles, we present ChatBCI, a Python-based toolbox for enabling human-AI collaboration based on interaction with Large Language Models (LLMs), designed for BCI research and development projects. We show how ChatBCI was successfully used in a concrete BCI project on advancing motor imagery decoding from EEG signals. Our approach can be straightforwardly extended to broad neurotechnological and neuroscientific topics, and may by design facilitate human expert knowledge transfer to scientific AI systems in general.
A Survey on Food Ingredient Substitutions
Venue: arXiv cs.CY 2024
Year: 2024

Diet plays a crucial role in managing chronic conditions and overall well-being. As people become more selective about their food choices, finding recipes that meet dietary needs is important. Ingredient substitution is key to adapting recipes for dietary restrictions, allergies, and availability constraints. However, identifying suitable substitutions is challenging as it requires analyzing the flavor, functionality, and health suitability of ingredients. With the advancement of AI, researchers have explored computational approaches to address ingredient substitution. This survey paper provides a comprehensive overview of the research in this area, focusing on five key aspects: (i) datasets and data sources used to support ingredient substitution research; (ii) techniques and approaches applied to solve substitution problems (iii) contextual information of ingredients considered, such as nutritional content, flavor, and pairing potential; (iv) applications for which substitution models have been developed, including dietary restrictions, constraints, and missing ingredients; (v) safety and transparency of substitution models, focusing on user trust and health concerns. The survey also highlights promising directions for future research, such as integrating neuro-symbolic techniques for deep learning and utilizing knowledge graphs for improved reasoning, aiming to guide advancements in food computation and ingredient substitution.
Hawkes based Representation Learning for Reasoning over Scale-free Community-structured Temporal Knowledge Graphs
Venue: arXiv cs.SI 2024
Year: 2024

Temporal knowledge graph (TKG) reasoning has become a hot topic due to its great value in many practical tasks. The key to TKG reasoning is modeling the structural information and evolutional patterns of the TKGs. While great efforts have been devoted to TKG reasoning, the structural and evolutional characteristics of real-world networks have not been considered. In the aspect of structure, real-world networks usually exhibit clear community structure and scale-free (long-tailed distribution) properties. In the aspect of evolution, the impact of an event decays with the time elapsing. In this paper, we propose a novel TKG reasoning model called Hawkes process-based Evolutional Representation Learning Network (HERLN), which learns structural information and evolutional patterns of a TKG simultaneously, considering the characteristics of real-world networks: community structure, scale-free and temporal decaying. First, we find communities in the input TKG to make the encoding get more similar intra-community embeddings. Second, we design a Hawkes process-based relational graph convolutional network to cope with the event impact-decaying phenomenon. Third, we design a conditional decoding method to alleviate biases towards frequent entities caused by long-tailed distribution. Experimental results show that HERLN achieves significant improvements over the state-of-the-art models.
Graph Analysis of Citation and Co-authorship Networks of Egyptian Authors
Venue: arXiv cs.DL 2024
Year: 2024

The current research conducts a comprehensive analysis of citation networks focusing on publications by authors affiliated with Egyptian institutions. Leveraging the Semantic Scholar platform and its API, a citation network and a co-authorship network graphs are constructed to visualize the interconnections among these publications and their authors. This is done using the Python package for graph analysis (Networkx). The primary objective is to identify influential Egyptian publications and assess the centrality of nodes within the citation network. Through meticulous data collection including web scraping techniques, we obtained a cleaned dataset comprising publications by authors affiliated with Egyptian institutions. The analysis addresses challenges related to data quality, technical intricacies, and time constraints, resulting in a reliable and robust dataset. The findings provide valuable information on the impact of Egyptian publications, offering insights into the scholarly influence of authors associated with Egyptian institutions. This research equips researchers and academics interested in evaluating the impact of Egyptian publications with valuable data for future studies, collaborations, and policy decisions.
Upstream and Downstream AI Safety: Both on the Same River?
Venue: arXiv cs.CY 2024
Year: 2024

Traditional safety engineering assesses systems in their context of use, e.g. the operational design domain (road layout, speed limits, weather, etc.) for self-driving vehicles (including those using AI). We refer to this as downstream safety. In contrast, work on safety of frontier AI, e.g. large language models which can be further trained for downstream tasks, typically considers factors that are beyond specific application contexts, such as the ability of the model to evade human control, or to produce harmful content, e.g. how to make bombs. We refer to this as upstream safety. We outline the characteristics of both upstream and downstream safety frameworks then explore the extent to which the broad AI safety community can benefit from synergies between these frameworks. For example, can concepts such as common mode failures from downstream safety be used to help assess the strength of AI guardrails? Further, can the understanding of the capabilities and limitations of frontier AI be used to inform downstream safety analysis, e.g. where LLMs are fine-tuned to calculate voyage plans for autonomous vessels? The paper identifies some promising avenues to explore and outlines some challenges in achieving synergy, or a confluence, between upstream and downstream safety frameworks.
A Novel Method for Pignistic Information Fusion in the View of Z-number
Venue: arXiv cs.AI 2024
Year: 2024

How to properly fuse information from complex sources is still an open problem. Lots of methods have been put forward to provide a effective solution in fusing intricate information. Among them, Dempster-Shafer evidences theory (DSET) is one of the representatives, it is widely used to handle uncertain information. Based on DSET, a completely new method to fuse information from different sources based on pignistic transformation and Z-numbers is proposed in this paper which is able to handle separate situations of information and keeps high accuracy in producing rational and correct judgments on actual situations. Besides, in order to illustrate the superiority of the proposed method, some numerical examples and application are also provided to verify the validity and robustness of it.
Navigating Ethical Challenges in Generative AI-Enhanced Research: The ETHICAL Framework for Responsible Generative AI Use
Venue: arXiv cs.CY 2024
Year: 2024

The rapid adoption of generative artificial intelligence (GenAI) in research presents both opportunities and ethical challenges that should be carefully navigated. Although GenAI tools can enhance research efficiency through automation of tasks such as literature review and data analysis, their use raises concerns about aspects such as data accuracy, privacy, bias, and research integrity. This paper develops the ETHICAL framework, which is a practical guide for responsible GenAI use in research. Employing a constructivist case study examining multiple GenAI tools in real research contexts, the framework consists of seven key principles: Examine policies and guidelines, Think about social impacts, Harness understanding of the technology, Indicate use, Critically engage with outputs, Access secure versions, and Look at user agreements. Applying these principles will enable researchers to uphold research integrity while leveraging GenAI benefits. The framework addresses a critical gap between awareness of ethical issues and practical action steps, providing researchers with concrete guidance for ethical GenAI integration. This work has implications for research practice, institutional policy development, and the broader academic community while adapting to an AI-enhanced research landscape. The ETHICAL framework can serve as a foundation for developing AI literacy in academic settings and promoting responsible innovation in research methodologies.
AI-Enhanced Decision-Making for Sustainable Supply Chains: Reducing Carbon Footprints in the USA
Venue: arXiv cs.CY 2024
Year: 2024

Organizations increasingly need to reassess their supply chain strategies in the rapidly modernizing world towards sustainability. This is particularly true in the United States, where supply chains are very extensive and consume a large number of resources. This research paper discusses how AI can support decision-making for sustainable supply chains with a special focus on carbon footprints. These AI technologies, including machine learning, predictive analytics, and optimization algorithms, will enable companies to be more efficient, reduce emissions, and display regulatory and consumer demands for sustainability, among other aspects. The paper reviews challenges and opportunities regarding implementing AI-driven solutions to promote sustainable supply chain practices in the USA.