Tootfinder

Reddit user shares link to 'Cybersecurity Stash', a comprehensive directory of 600 security tools & resources, designed to aid both beginners & professionals. This platform aims to keep users updated with security trends, enable knowledge-sharing, and community interaction. https://reddit.com/r/cybersecurity/comments/1cjs276/

Optimizing Mobile-Friendly Viewport Prediction for Live 360-Degree Video Streaming
Lei Zhang, Tao Long, Weizhen Xu, Laizhong Cui, Jiangchuan Liu
https://arxiv.org/abs/2403.02693 <…

Optimizing Mobile-Friendly Viewport Prediction for Live 360-Degree Video Streaming
Viewport prediction is the crucial task for adaptive 360-degree video streaming, as the bitrate control algorithms usually require the knowledge of the user's viewing portions of the frames. Various methods are studied and adopted for viewport prediction from less accurate statistic tools to highly calibrated deep neural networks. Conventionally, it is difficult to implement sophisticated deep learning methods on mobile devices, which have limited computation capability. In this work, we propos…

CACTUS: Chemistry Agent Connecting Tool-Usage to Science
Andrew D. McNaughton, Gautham Ramalaxmi, Agustin Kruel, Carter R. Knutson, Rohith A. Varikoti, Neeraj Kumar
https://arxiv.org/abs/2405.00972

CACTUS: Chemistry Agent Connecting Tool-Usage to Science
Large language models (LLMs) have shown remarkable potential in various domains, but they often lack the ability to access and reason over domain-specific knowledge and tools. In this paper, we introduced CACTUS (Chemistry Agent Connecting Tool-Usage to Science), an LLM-based agent that integrates cheminformatics tools to enable advanced reasoning and problem-solving in chemistry and molecular discovery. We evaluate the performance of CACTUS using a diverse set of open-source LLMs, including Ge…

Decision Transformer as a Foundation Model for Partially Observable Continuous Control
Xiangyuan Zhang, Weichao Mao, Haoran Qiu, Tamer Ba\c{s}ar
https://arxiv.org/abs/2404.02407 <…

Decision Transformer as a Foundation Model for Partially Observable Continuous Control
Closed-loop control of nonlinear dynamical systems with partial-state observability demands expert knowledge of a diverse, less standardized set of theoretical tools. Moreover, it requires a delicate integration of controller and estimator designs to achieve the desired system behavior. To establish a general controller synthesis framework, we explore the Decision Transformer (DT) architecture. Specifically, we first frame the control task as predicting the current optimal action based on past …

Very interesting #V2G 🚗🔄⚡ network tariffs report for the 🇦🇺Australian Renewable Energy Agency
Dynamic energy and network pricing maximises grid benefits and consumer savings. They can amplify flex savings & help reduce grid peaks, but for some situations need for additional tools (e.g. dynamic operating envelopes, explicit DR)

ARENA - Vehicle to Grid - Network tariffs report
Report investigating electricity load and economic benefits of Vehicle to Grid operations with fleet electric vehicles.

DLAP: A Deep Learning Augmented Large Language Model Prompting Framework for Software Vulnerability Detection
Yanjing Yang, Xin Zhou, Runfeng Mao, Jinwei Xu, Lanxin Yang, Yu Zhangm, Haifeng Shen, He Zhang
https://arxiv.org/abs/2405.01202

DLAP: A Deep Learning Augmented Large Language Model Prompting Framework for Software Vulnerability Detection
Software vulnerability detection is generally supported by automated static analysis tools, which have recently been reinforced by deep learning (DL) models. However, despite the superior performance of DL-based approaches over rule-based ones in research, applying DL approaches to software vulnerability detection in practice remains a challenge due to the complex structure of source code, the black-box nature of DL, and the domain knowledge required to understand and validate the black-box res…

SpecstatOR: Speckle statistics-based iOCT Segmentation Network for Ophthalmic Surgery
Kristina Mach, Hessam Roodaki, Michael Sommersperger, Nassir Navab
https://arxiv.org/abs/2404.19481 https://arxiv.org/pdf/2404.19481
arXiv:2404.19481v1 Announce Type: new
Abstract: This paper presents an innovative approach to intraoperative Optical Coherence Tomography (iOCT) image segmentation in ophthalmic surgery, leveraging statistical analysis of speckle patterns to incorporate statistical pathology-specific prior knowledge. Our findings indicate statistically different speckle patterns within the retina and between retinal layers and surgical tools, facilitating the segmentation of previously unseen data without the necessity for manual labeling. The research involves fitting various statistical distributions to iOCT data, enabling the differentiation of different ocular structures and surgical tools. The proposed segmentation model aims to refine the statistical findings based on prior tissue understanding to leverage statistical and biological knowledge. Incorporating statistical parameters, physical analysis of light-tissue interaction, and deep learning informed by biological structures enhance segmentation accuracy, offering potential benefits to real-time applications in ophthalmic surgical procedures. The study demonstrates the adaptability and precision of using Gamma distribution parameters and the derived binary maps as sole inputs for segmentation, notably enhancing the model's inference performance on unseen data.

Special issue of #tgdk Transactions on Graph Data & Knowledge with a focus on
Resources (benchmarks, datasets, engines, models, tools, etc.)
Deadline: June 3rd, 2024.
https://mastodon.social/@tgdk…

Cutting-Edge Tools for Cutting Edges
Ruth Britto, Claude Duhr, Holmfridur S. Hannesdottir, Sebastian Mizera
https://arxiv.org/abs/2402.19415 https://

Cutting-Edge Tools for Cutting Edges
We review different notions of cuts appearing throughout the literature on scattering amplitudes. Despite similar names, such as unitarity cuts or generalized cuts, they often represent distinct computations and distinct physics. We consolidate this knowledge, summarize how cuts are used in various computational strategies, and explain their relations to other quantities including imaginary parts, discontinuities, and monodromies. Differences and nuances are illustrated on explicit examples.

What is needed is to use the tools with engaged people that are continually learning and adjusting the management system based on their increased understanding of the #organization_as_a_system . Using management tools effectively (if you are unsure of what those tools are, read the posts on this blog discussing many management improvement tools) supports gaining insight into the u…

Searches for "As of my last knowledge update", a phrase often used by ChatGPT, return 115 results on Google Scholar, suggesting the use of AI tools in papers (Emanuel Maiberg/404 Media)
https://www.404media.co/scientific-journals…

Augmenting Knowledge Graph Hierarchies Using Neural Transformers
Sanat Sharma, Mayank Poddar, Jayant Kumar, Kosta Blank, Tracy King
https://arxiv.org/abs/2404.08020

Augmenting Knowledge Graph Hierarchies Using Neural Transformers
Knowledge graphs are useful tools to organize, recommend and sort data. Hierarchies in knowledge graphs provide significant benefit in improving understanding and compartmentalization of the data within a knowledge graph. This work leverages large language models to generate and augment hierarchies in an existing knowledge graph. For small (<100,000 node) domain-specific KGs, we find that a combination of few-shot prompting with one-shot generation works well, while larger KG may require cyclic…

SoK: Exploring the Potential of Large Language Models for Improving Digital Forensic Investigation Efficiency
Akila Wickramasekara, Frank Breitinger, Mark Scanlon
https://arxiv.org/abs/2402.19366

SoK: Exploring the Potential of Large Language Models for Improving Digital Forensic Investigation Efficiency
The growing number of cases requiring digital forensic analysis raises concerns about law enforcement's ability to conduct investigations promptly. Consequently, this systemisation of knowledge paper delves into the potential and effectiveness of integrating Large Language Models (LLMs) into digital forensic investigation to address these challenges. A thorough literature review is undertaken, encompassing existing digital forensic models, tools, LLMs, deep learning techniques, and the utilisat…

This https://arxiv.org/abs/2309.13506 has been replaced.
link: https://scholar.google.com/scholar?q=a

Evaluating the Usability of Differential Privacy Tools with Data Practitioners
Differential privacy (DP) has become the gold standard in privacy-preserving data analytics, but implementing it in real-world datasets and systems remains challenging. Recently developed DP tools aim to make DP implementation easier, but limited research has investigated these DP tools' usability. Through a usability study with 24 US data practitioners with varying prior DP knowledge, we evaluated the usability of four Python-based open-source DP tools: DiffPrivLib, Tumult Analytics, PipelineD…

Canonical Decision Diagrams Modulo Theories
Massimo Michelutti, Gabriele Masina, Giuseppe Spallitta, Roberto Sebastiani
https://arxiv.org/abs/2404.16455 ht…

Canonical Decision Diagrams Modulo Theories
Decision diagrams (DDs) are powerful tools to represent effectively propositional formulas, which are largely used in many domains, in particular in formal verification and in knowledge compilation. Some forms of DDs (e.g., OBDDs, SDDs) are canonical, that is, (under given conditions on the atom list) they univocally represent equivalence classes of formulas. Given the limited expressiveness of propositional logic, a few attempts to leverage DDs to SMT level have been presented in the literatur…

This https://arxiv.org/abs/2308.14296 has been replaced.
link: https://scholar.google.com/scholar?q=a

RecMind: Large Language Model Powered Agent For Recommendation
While the recommendation system (RS) has advanced significantly through deep learning, current RS approaches usually train and fine-tune models on task-specific datasets, limiting their generalizability to new recommendation tasks and their ability to leverage external knowledge due to model scale and data size constraints. Thus, we designed an LLM-powered autonomous recommender agent, RecMind, which is capable of leveraging external knowledge, utilizing tools with careful planning to provide z…

Augmenting Knowledge Graph Hierarchies Using Neural Transformers
Sanat Sharma, Mayank Poddar, Jayant Kumar, Kosta Blank, Tracy King
https://arxiv.org/abs/2404.08020

Augmenting Knowledge Graph Hierarchies Using Neural Transformers
Knowledge graphs are useful tools to organize, recommend and sort data. Hierarchies in knowledge graphs provide significant benefit in improving understanding and compartmentalization of the data within a knowledge graph. This work leverages large language models to generate and augment hierarchies in an existing knowledge graph. For small (<100,000 node) domain-specific KGs, we find that a combination of few-shot prompting with one-shot generation works well, while larger KG may require cyclic…

This https://arxiv.org/abs/2403.14721 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDL_…

Automated Extraction and Maturity Analysis of Open Source Clinical Informatics Repositories from Scientific Literature
In the evolving landscape of clinical informatics, the integration and utilization of software tools developed through governmental funding represent a pivotal advancement in research and application. However, the dispersion of these tools across various repositories, with no centralized knowledge base, poses significant challenges to leveraging their full potential. This study introduces an automated methodology to bridge this gap by systematically extracting GitHub repository URLs from academ…

This https://arxiv.org/abs/2310.18053 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Phylogenetic invariants: straightforward from the general Markov to equivariant models
In the last decade, some algebraic tools have been successfully applied to phylogenetic reconstruction. These tools are mainly based on the knowledge of equations describing algebraic varieties associated to phylogenetic trees evolving under Markov processes of molecular substitution, the so called phylogenetic invariants. Although the theory involved allows to explicitly obtain these equations for all equivariant models (which include some of the most popular nucleotide substitution models), p…

This https://arxiv.org/abs/2402.16696 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Look Before You Leap: Towards Decision-Aware and Generalizable Tool-Usage for Large Language Models
Tool-augmented large language models (LLMs) are attracting widespread attention when accessing up-to-date knowledge and alleviating hallucination issues. Nowadays, advanced closed-source LLMs (e.g., ChatGPT) have demonstrated surprising tool-usage capabilities through prompting and in-context learning techniques. To empower the capabilities of open-source LLMs (e.g., LLaMA) in manipulating tools, current efforts focus on either template-driven or token-triggered tool-usage. However, the former …

This https://arxiv.org/abs/2402.15990 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

An Empirical Study of Challenges in Machine Learning Asset Management
In machine learning (ML), efficient asset management, including ML models, datasets, algorithms, and tools, is vital for resource optimization, consistent performance, and a streamlined development lifecycle. This enables quicker iterations, adaptability, reduced development-to-deployment time, and reliable outputs. Despite existing research, a significant knowledge gap remains in operational challenges like model versioning, data traceability, and collaboration, which are crucial for the succe…

Improving Welding Robotization via Operator Skill Identification, Modeling, and Human-Machine Collaboration: Experimental Protocol Implementation
Antoine L\'enatCETIM, LS2N, LS2N - \'equipe RoMas, LS2N - \'equipe PACCE, Olivier CheminatCETIM, Damien ChablatLS2N, LS2N - \'equipe RoMas, Camilo CharronLS2N, UR2
https://

Improving Welding Robotization via Operator Skill Identification, Modeling, and Human-Machine Collaboration: Experimental Protocol Implementation
The industry of the future, also known as Industry 5.0, aims to modernize production tools, digitize workshops, and cultivate the invaluable human capital within the company. Industry 5.0 can't be done without fostering a workforce that is not only technologically adept but also has enhanced skills and knowledge. Specifically, collaborative robotics plays a key role in automating strenuous or repetitive tasks, enabling human cognitive functions to contribute to quality and innovation. In manual…

A Comprehensive Analysis of Pegasus Spyware and Its Implications for Digital Privacy and Security
Karwan Kareem
https://arxiv.org/abs/2404.19677 https://arxiv.org/pdf/2404.19677
arXiv:2404.19677v1 Announce Type: new
Abstract: This paper comprehensively analyzes the Pegasus spyware and its implications for digital privacy and security. The Israeli cyber intelligence company NSO Group's Pegasus has gained recognition as a potent surveillance tool capable of hacking into smartphones and extracting data without the user's knowledge [49], [50]. The research emphasizes the technical aspects of this spyware, its deployment methods, and the controversies surrounding its use. The research also emphasizes the growing worries surrounding digital privacy and security as a result of the prevalent use of advanced spyware. By delving into legal, ethical, and policy issues, the objective of this study is to deliver a holistic understanding of the challenges posed by Pegasus and similar spyware tools. Through a comprehensive examination of the subject, the paper presents potential solutions to mitigate the threats and protect users from invasive surveillance techniques.

Co-Designing a wiki-based community knowledge management system for personal science
Katharina Kloppenborg, Mad Price Ball, Steven Jonas, Gary Isaac Wolf, Bastian Greshake Tzovaras
https://arxiv.org/abs/2402.09799

Co-Designing a wiki-based community knowledge management system for personal science
Personal science is the practice of addressing personally relevant health questions through self-research. Implementing personal science can be challenging, due to the need to develop and adopt research protocols, tools, and methods. While online communities can provide valuable peer support, tools for systematically accessing community knowledge are lacking. The objective of this study is to apply a participatory design process involving a community of personal science practitioners to develop…

Evaluation of Geographical Distortions in Language Models: A Crucial Step Towards Equitable Representations
R\'emy Decoupes, Roberto Interdonato, Mathieu Roche, Maguelonne Teisseire, Sarah Valentin
https://arxiv.org/abs/2404.17401

Evaluation of Geographical Distortions in Language Models: A Crucial Step Towards Equitable Representations
Language models now constitute essential tools for improving efficiency for many professional tasks such as writing, coding, or learning. For this reason, it is imperative to identify inherent biases. In the field of Natural Language Processing, five sources of bias are well-identified: data, annotation, representation, models, and research design. This study focuses on biases related to geographical knowledge. We explore the connection between geography and language models by highlighting thei…

From Keywords to Structured Summaries: Streamlining Scholarly Knowledge Access
Mahsa Shamsabadi, Jennifer D'Souza
https://arxiv.org/abs/2402.14622 http…

From Keywords to Structured Summaries: Streamlining Scholarly Knowledge Access
This short paper highlights the growing importance of information retrieval (IR) engines in the scientific community, addressing the inefficiency of traditional keyword-based search engines due to the rising volume of publications. The proposed solution involves structured records, underpinning advanced information technology (IT) tools, including visualization dashboards, to revolutionize how researchers access and filter articles, replacing the traditional text-heavy approach. This vision is …

pyKCN: A Python Tool for Bridging Scientific Knowledge
Zhenyuan Lu, Wei Li, Burcu Ozek, Haozhou Zhou, Srinivasan Radhakrishnan, Sagar Kamarthi
https://arxiv.org/abs/2403.16157

pyKCN: A Python Tool for Bridging Scientific Knowledge
The study of research trends is pivotal for understanding scientific development on specific topics. Traditionally, this involves keyword analysis within scholarly literature, yet comprehensive tools for such analysis are scarce, especially those capable of parsing large datasets with precision. pyKCN, a Python toolkit, addresses this gap by automating keyword cleaning, extraction and trend analysis from extensive academic corpora. It is equipped with modules for text processing, deduplication,…

This https://arxiv.org/abs/2202.12577 has been replaced.
link: https://scholar.google.com/scholar?q=a

Challenges and Opportunities for Machine Learning in Fluid Mechanics
Big data and machine learning are driving comprehensive economic and social transformations and are rapidly re-shaping the toolbox and the methodologies of applied scientists. Machine learning tools are designed to learn functions from data with little to no need of prior knowledge. As continuous developments in experimental and numerical methods improve our ability to collect high-quality data, machine learning tools become increasingly viable and promising also in disciplines rooted in physic…

An Empirical Study of Challenges in Machine Learning Asset Management
Zhimin Zhao, Yihao Chen, Abdul Ali Bangash, Bram Adams, Ahmed E. Hassan
https://arxiv.org/abs/2402.15990

An Empirical Study of Challenges in Machine Learning Asset Management
In machine learning (ML), efficient asset management, including ML models, datasets, algorithms, and tools, is vital for resource optimization, consistent performance, and a streamlined development lifecycle. This enables quicker iterations, adaptability, reduced development-to-deployment time, and reliable outputs. Despite existing research, a significant knowledge gap remains in operational challenges like model versioning, data traceability, and collaboration, which are crucial for the succe…

This https://arxiv.org/abs/2211.01421 has been replaced.
link: https://scholar.google.com/scholar?q=a

Modern Machine Learning for LHC Physicists
Modern machine learning is transforming particle physics fast, bullying its way into our numerical tool box. For young researchers it is crucial to stay on top of this development, which means applying cutting-edge methods and tools to the full range of LHC physics problems. These lecture notes lead students with basic knowledge of particle physics and significant enthusiasm for machine learning to relevant applications. They start with an LHC-specific motivation and a non-standard introduction…

Improving Welding Robotization via Operator Skill Identification, Modeling, and Human-Machine Collaboration: Experimental Protocol Implementation
Antoine L\'enatCETIM, LS2N, LS2N - \'equipe RoMas, LS2N - \'equipe PACCE, Olivier CheminatCETIM, Damien ChablatLS2N, LS2N - \'equipe RoMas, Camilo CharronLS2N, UR2
https://

Improving Welding Robotization via Operator Skill Identification, Modeling, and Human-Machine Collaboration: Experimental Protocol Implementation
The industry of the future, also known as Industry 5.0, aims to modernize production tools, digitize workshops, and cultivate the invaluable human capital within the company. Industry 5.0 can't be done without fostering a workforce that is not only technologically adept but also has enhanced skills and knowledge. Specifically, collaborative robotics plays a key role in automating strenuous or repetitive tasks, enabling human cognitive functions to contribute to quality and innovation. In manual…

The AffectToolbox: Affect Analysis for Everyone
Silvan Mertes, Dominik Schiller, Michael Dietz, Elisabeth Andr\'e, Florian Lingenfelser
https://arxiv.org/abs/2402.15195

The AffectToolbox: Affect Analysis for Everyone
In the field of affective computing, where research continually advances at a rapid pace, the demand for user-friendly tools has become increasingly apparent. In this paper, we present the AffectToolbox, a novel software system that aims to support researchers in developing affect-sensitive studies and prototypes. The proposed system addresses the challenges posed by existing frameworks, which often require profound programming knowledge and cater primarily to power-users or skilled developers.…

The Ebb and Flow of Brand Loyalty: A 28-Year Bibliometric and Content Analysis
Azin Yazdi, Sunder Ramachandran, Hoda Mohsenifard, Khaled Nawaser, Faraz Sasani, Behrooz Gharleghi
https://arxiv.org/abs/2402.13177

The Ebb and Flow of Brand Loyalty: A 28-Year Bibliometric and Content Analysis
Business research is facing the challenge of scattered knowledge, particularly in the realm of brand loyalty (BL). Although literature reviews on BL exist, they predominantly concentrate on the pre-sent state, neglecting future trends. Therefore, a comprehensive review is imperative to ascertain emerging trends in BL This study employs a bibliometric approach, analyzing 1,468 papers from the Scopus database. Various tools including R software, VOS viewer software, and Publish or Perish are util…

This https://arxiv.org/abs/2402.12372 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

HunFlair2 in a cross-corpus evaluation of named entity recognition and normalization tools
With the exponential growth of the life science literature, biomedical text mining (BTM) has become an essential technology for accelerating the extraction of insights from publications. Identifying named entities (e.g., diseases, drugs, or genes) in texts and their linkage to reference knowledge bases are crucial steps in BTM pipelines to enable information aggregation from different documents. However, tools for these two steps are rarely applied in the same context in which they were develop…

Automated Extraction and Maturity Analysis of Open Source Clinical Informatics Repositories from Scientific Literature
Jeremy R. Harper
https://arxiv.org/abs/2403.14721

Automated Extraction and Maturity Analysis of Open Source Clinical Informatics Repositories from Scientific Literature
In the evolving landscape of clinical informatics, the integration and utilization of software tools developed through governmental funding represent a pivotal advancement in research and application. However, the dispersion of these tools across various repositories, with no centralized knowledge base, poses significant challenges to leveraging their full potential. This study introduces an automated methodology to bridge this gap by systematically extracting GitHub repository URLs from academ…

This https://arxiv.org/abs/2308.14296 has been replaced.
link: https://scholar.google.com/scholar?q=a

RecMind: Large Language Model Powered Agent For Recommendation
While the recommendation system (RS) has advanced significantly through deep learning, current RS approaches usually train and fine-tune models on task-specific datasets, limiting their generalizability to new recommendation tasks and their ability to leverage external knowledge due to model scale and data size constraints. Thus, we designed an LLM-powered autonomous recommender agent, RecMind, which is capable of leveraging external knowledge, utilizing tools with careful planning to provide z…

This https://arxiv.org/abs/2310.09359 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Laser-driven ultrafast impedance spectroscopy for measuring complex ion hopping processes
Superionic conductors, or solid-state ion-conductors surpassing 0.01 S/cm in conductivity, can enable more energy dense batteries, robust artificial ion pumps, and optimized fuel cells. However, tailoring superionic conductors require precise knowledge of ion migration mechanisms that are still not well understood, due to limitations set by available spectroscopic tools. Most spectroscopic techniques do not probe ion hopping at its inherent picosecond timescale, nor the many-body correlations b…

This https://arxiv.org/abs/2403.16157 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDL_…

pyKCN: A Python Tool for Bridging Scientific Knowledge
The study of research trends is pivotal for understanding scientific development on specific topics. Traditionally, this involves keyword analysis within scholarly literature, yet comprehensive tools for such analysis are scarce, especially those capable of parsing large datasets with precision. pyKCN, a Python toolkit, addresses this gap by automating keyword cleaning, extraction and trend analysis from extensive academic corpora. It is equipped with modules for text processing, deduplication,…

This https://arxiv.org/abs/2211.01421 has been replaced.
link: https://scholar.google.com/scholar?q=a

Modern Machine Learning for LHC Physicists
Modern machine learning is transforming particle physics fast, bullying its way into our numerical tool box. For young researchers it is crucial to stay on top of this development, which means applying cutting-edge methods and tools to the full range of LHC physics problems. These lecture notes lead students with basic knowledge of particle physics and significant enthusiasm for machine learning to relevant applications. They start with an LHC-specific motivation and a non-standard introduction…

Supporting Experts with a Multimodal Machine-Learning-Based Tool for Human Behavior Analysis of Conversational Videos
Riku Arakawa, Kiyosu Maeda, Hiromu Yakura
https://arxiv.org/abs/2402.11145

Supporting Experts with a Multimodal Machine-Learning-Based Tool for Human Behavior Analysis of Conversational Videos
Multimodal scene search of conversations is essential for unlocking valuable insights into social dynamics and enhancing our communication. While experts in conversational analysis have their own knowledge and skills to find key scenes, a lack of comprehensive, user-friendly tools that streamline the processing of diverse multimodal queries impedes efficiency and objectivity. To solve it, we developed Providence, a visual-programming-based tool based on design considerations derived from a form…

Truth-Aware Context Selection: Mitigating the Hallucinations of Large Language Models Being Misled by Untruthful Contexts
Tian Yu, Shaolei Zhang, Yang Feng
https://arxiv.org/abs/2403.07556

Truth-Aware Context Selection: Mitigating the Hallucinations of Large Language Models Being Misled by Untruthful Contexts
Although large language models (LLMs) have demonstrated impressive text generation capabilities, they are easily misled by the untruthful context provided by users or knowledge argumentation tools, thereby producing hallucinations. To alleviate the LLMs from being misled by untruthful information and take advantage of knowledge argumentation, we propose Truth-Aware Context Selection (TACS), a lightweight method to shield untruthful context from the inputs. TACS begins by performing truth detect…

This https://arxiv.org/abs/2305.06902 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

REMaQE: Reverse Engineering Math Equations from Executables
Cybersecurity attacks on embedded devices for industrial control systems and cyber-physical systems may cause catastrophic physical damage as well as economic loss. This could be achieved by infecting device binaries with malware that modifies the physical characteristics of the system operation. Mitigating such attacks benefits from reverse engineering tools that recover sufficient semantic knowledge in terms of mathematical equations of the implemented algorithm. Conventional reverse engineer…

Truth-Aware Context Selection: Mitigating the Hallucinations of Large Language Models Being Misled by Untruthful Contexts
Tian Yu, Shaolei Zhang, Yang Feng
https://arxiv.org/abs/2403.07556

Truth-Aware Context Selection: Mitigating the Hallucinations of Large Language Models Being Misled by Untruthful Contexts
Although large language models (LLMs) have demonstrated impressive text generation capabilities, they are easily misled by the untruthful context provided by users or knowledge argumentation tools, thereby producing hallucinations. To alleviate the LLMs from being misled by untruthful information and take advantage of knowledge argumentation, we propose Truth-Aware Context Selection (TACS), a lightweight method to shield untruthful context from the inputs. TACS begins by performing truth detect…

This https://arxiv.org/abs/2402.17231 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

MATHSENSEI: A Tool-Augmented Large Language Model for Mathematical Reasoning
Tool-augmented Large Language Models (TALM) are known to enhance the skillset of large language models (LLM), thereby, leading to their improved reasoning abilities across many tasks. While, TALMs have been successfully employed in different question-answering benchmarks, their efficacy on complex mathematical reasoning benchmarks, and the potential complimentary benefits offered by tools for knowledge retrieval and mathematical equation solving, are open research questions. In this work, we pr…

RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems
Jianxun Lian, Yuxuan Lei, Xu Huang, Jing Yao, Wei Xu, Xing Xie
https://arxiv.org/abs/2403.06465

RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems
This paper introduces RecAI, a practical toolkit designed to augment or even revolutionize recommender systems with the advanced capabilities of Large Language Models (LLMs). RecAI provides a suite of tools, including Recommender AI Agent, Recommendation-oriented Language Models, Knowledge Plugin, RecExplainer, and Evaluator, to facilitate the integration of LLMs into recommender systems from multifaceted perspectives. The new generation of recommender systems, empowered by LLMs, are expected t…

Untangling Critical Interaction with AI in Students Written Assessment
Antonette Shibani, Simon Knight, Kirsty Kitto, Ajanie Karunanayake, Simon Buckingham Shum
https://arxiv.org/abs/2404.06955

Untangling Critical Interaction with AI in Students Written Assessment
Artificial Intelligence (AI) has become a ubiquitous part of society, but a key challenge exists in ensuring that humans are equipped with the required critical thinking and AI literacy skills to interact with machines effectively by understanding their capabilities and limitations. These skills are particularly important for learners to develop in the age of generative AI where AI tools can demonstrate complex knowledge and ability previously thought to be uniquely human. To activate effective…

Development of a Reliable and Accessible Caregiving Language Model (CaLM)
Bambang Parmanto, Bayu Aryoyudanta, Wilbert Soekinto, I Made Agus Setiawan, Yuhan Wang, Haomin Hu, Andi Saptono, Yong K. Choi
https://arxiv.org/abs/2403.06857

Development of a Reliable and Accessible Caregiving Language Model (CaLM)
Unlike professional caregivers, family caregivers often assume this role without formal preparation or training. Because of this, there is an urgent need to enhance the capacity of family caregivers to provide quality care. Large language models can potentially be used as a foundation technology for supporting caregivers as educational tools or as adjunct to care. This study aimed to develop a reliable Caregiving Language Model (CaLM) by using FMs and a caregiving knowledge base, develop an acc…