pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Collaborative Sot Label Learning for Unsupervised Multi-view Feature Selection
Authors: Dan Shi, Shandong Normal University, China
Date: 2018-07-13 00:00:00
Proceeding: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18) 2018 7
Tasks: feature selection, graph construction, Pseudo Label

Unsupervised multi-view feature selection aims to select informative features with multi-view features and unsupervised
learning. It is a challenging problem due to the absence of explicit semantic supervision. Recently, graph theory and hard
pseudo-label learning have been adopted to solve multi-view feature selection problems under the unsupervised learning
paradigm. However, graph-based methods are diicult to support large-scale real scenarios due to the high computational
complexity of graph construction. Moreover, existing methods based on hard pseudo-label learning generally result in
signiicant information loss. In this paper, we propose an Adaptive Collaborative Soft Label Learning (ACSLL) model for
unsupervised multi-view feature selection. In this model, collaborative soft label learning and multi-view feature selection are
integrated into a uniied framework. Speciically, we learn the pseudo soft labels from each view feature by a simple and
eicient method and fuse them with an adaptive weighting strategy into a joint soft label matrix. This matrix is further used
for guiding the feature selection process to identify valuable features. An efective optimization strategy guaranteed with
proven convergence is derived to iteratively solve this problem. Experiments demonstrate the superiority of the proposed
method in both feature selection accuracy and eiciency.
Unveiling the potential of diffusion model-based framework with transformer for hyperspectral image classification
Authors: Neetu Sigger, Quoc-Tuan Vien, Sinh Van Nguyen, Gianluca Tozzi & Tuan Thanh Nguyen
Date: 2024-04-10 00:00:00
Proceeding: Nature Scientific Reports 2024 4
Tasks: Denoising, Hyperspectral Image Classification, image-classification, Image Classification

Hyperspectral imaging has gained popularity for analysing remotely sensed images in various fields such as agriculture and medical. However, existing models face challenges in dealing with the complex relationships and characteristics of spectral–spatial data due to the multi-band nature and data redundancy of hyperspectral data. To address this limitation, we propose a novel approach called DiffSpectralNet, which combines diffusion and transformer techniques. The diffusion method is able extract diverse and meaningful spectral–spatial features, leading to improvement in HSI classification. Our approach involves training an unsupervised learning framework based on the diffusion model to extract high-level and low-level spectral–spatial features, followed by the extraction of intermediate hierarchical features from different timestamps for classification using a pre-trained denoising U-Net. Finally, we employ a supervised transformer-based classifier to perform the HSI classification. We conduct comprehensive experiments on three publicly available datasets to validate our approach. The results demonstrate that our framework significantly outperforms existing approaches, achieving state-of-the-art performance. The stability and reliability of our approach are demonstrated across various classes in all datasets.
SmartQuant: CXL-based AI Model Store in Support of Runtime Configurable Weight Quantization
Authors: Rui Xie, Asad Ul Haq, Linsen Ma, Krystal Sun, Sanchari Sen, Swagath Venkataramani, Liu Liu, Tong Zhang
Date: 2024-07-17 00:00:00
Tasks: GPU, Quantization

Recent studies have revealed that, during the inference on generative AI models such as transformer, the importance of different weights exhibits substantial context-dependent variations. This naturally manifests a promising potential of adaptively configuring weight quantization to improve the generative AI inference efficiency. Although configurable weight quantization can readily leverage the hardware support of variable-precision arithmetics in modern GPU and AI accelerators, little prior research has studied how one could exploit variable weight quantization to proportionally improve the AI model memory access speed and energy efficiency. Motivated by the rapidly maturing CXL ecosystem, this work develops a CXL-based design solution to fill this gap. The key is to allow CXL memory controllers play an active role in supporting and exploiting runtime configurable weight quantization. Using transformer as a representative generative AI model, we carried out experiments that well demonstrate the effectiveness of the proposed design solution.
A Survey of AI-Powered Mini-Grid Solutions for a Sustainable Future in Rural Communities
Authors: Craig Pirie, Harsha Kalutarage, Muhammad Shadi Hajar, Nirmalie Wiratunga, Subodha Charles, Geeth Sandaru Madhushan, Priyantha Buddhika, Supun Wijesiriwardana, Akila Dimantha, Kithdara Hansamal, Shalitha Pathiranage
Date: 2024-07-17 00:00:00
Tasks: Management, Survey

This paper presents a comprehensive survey of AI-driven mini-grid solutions aimed at enhancing sustainable energy access. It emphasises the potential of mini-grids, which can operate independently or in conjunction with national power grids, to provide reliable and affordable electricity to remote communities. Given the inherent unpredictability of renewable energy sources such as solar and wind, the necessity for accurate energy forecasting and management is discussed, highlighting the role of advanced AI techniques in forecasting energy supply and demand, optimising grid operations, and ensuring sustainable energy distribution. This paper reviews various forecasting models, including statistical methods, machine learning algorithms, and hybrid approaches, evaluating their effectiveness for both short-term and long-term predictions. Additionally, it explores public datasets and tools such as Prophet, NeuralProphet, and N-BEATS for model implementation and validation. The survey concludes with recommendations for future research, addressing challenges in model adaptation and optimisation for real-world applications.
Performance Evaluation of Lightweight Open-source Large Language Models in Pediatric Consultations: A Comparative Analysis
Authors: Qiuhong Wei, Ying Cui, Mengwei Ding, Yanqin Wang, Lingling Xiang, Zhengxiong Yao, Ceran Chen, Ying Long, Zhezhen Jin, Ximing Xu
Date: 2024-07-16 00:00:00

Large language models (LLMs) have demonstrated potential applications in medicine, yet data privacy and computational burden limit their deployment in healthcare institutions. Open-source and lightweight versions of LLMs emerge as potential solutions, but their performance, particularly in pediatric settings remains underexplored. In this cross-sectional study, 250 patient consultation questions were randomly selected from a public online medical forum, with 10 questions from each of 25 pediatric departments, spanning from December 1, 2022, to October 30, 2023. Two lightweight open-source LLMs, ChatGLM3-6B and Vicuna-7B, along with a larger-scale model, Vicuna-13B, and the widely-used proprietary ChatGPT-3.5, independently answered these questions in Chinese between November 1, 2023, and November 7, 2023. To assess reproducibility, each inquiry was replicated once. We found that ChatGLM3-6B demonstrated higher accuracy and completeness than Vicuna-13B and Vicuna-7B (P < .001), but all were outperformed by ChatGPT-3.5. ChatGPT-3.5 received the highest ratings in accuracy (65.2%) compared to ChatGLM3-6B (41.2%), Vicuna-13B (11.2%), and Vicuna-7B (4.4%). Similarly, in completeness, ChatGPT-3.5 led (78.4%), followed by ChatGLM3-6B (76.0%), Vicuna-13B (34.8%), and Vicuna-7B (22.0%) in highest ratings. ChatGLM3-6B matched ChatGPT-3.5 in readability, both outperforming Vicuna models (P < .001). In terms of empathy, ChatGPT-3.5 outperformed the lightweight LLMs (P < .001). In safety, all models performed comparably well (P > .05), with over 98.4% of responses being rated as safe. Repetition of inquiries confirmed these findings. In conclusion, Lightweight LLMs demonstrate promising application in pediatric healthcare. However, the observed gap between lightweight and large-scale proprietary LLMs underscores the need for continued development efforts.
How Do Students Interact with an LLM-powered Virtual Teaching Assistant in Different Educational Settings?
Authors: Pratyusha Maiti, Ashok K. Goel
Date: 2024-07-15 00:00:00

Jill Watson, a virtual teaching assistant powered by LLMs, answers student questions and engages them in extended conversations on courseware provided by the instructors. In this paper, we analyze student interactions with Jill across multiple courses and colleges, focusing on the types and complexity of student questions based on Bloom's Revised Taxonomy and tool usage patterns. We find that, by supporting a wide range of cognitive demands, Jill encourages students to engage in sophisticated, higher-order cognitive questions. However, the frequency of usage varies significantly across deployments, and the types of questions asked depend on course-specific contexts. These findings pave the way for future work on AI-driven educational tools tailored to individual learning styles and course structure, potentially enhancing both the teaching and learning experience in classrooms.
AIR-Bench 2024: A Safety Benchmark Based on Risk Categories from Regulations and Policies
Authors: Yi Zeng, Yu Yang, Andy Zhou, Jeffrey Ziwei Tan, Yuheng Tu, Yifan Mai, Kevin Klyman, Minzhou Pan, Ruoxi Jia, Dawn Song, Percy Liang, Bo Li
Date: 2024-07-11 00:00:00
Tasks: Common Sense Reasoning

Foundation models (FMs) provide societal benefits but also amplify risks. Governments, companies, and researchers have proposed regulatory frameworks, acceptable use policies, and safety benchmarks in response. However, existing public benchmarks often define safety categories based on previous literature, intuitions, or common sense, leading to disjointed sets of categories for risks specified in recent regulations and policies, which makes it challenging to evaluate and compare FMs across these benchmarks. To bridge this gap, we introduce AIR-Bench 2024, the first AI safety benchmark aligned with emerging government regulations and company policies, following the regulation-based safety categories grounded in our AI risks study, AIR 2024. AIR 2024 decomposes 8 government regulations and 16 company policies into a four-tiered safety taxonomy with 314 granular risk categories in the lowest tier. AIR-Bench 2024 contains 5,694 diverse prompts spanning these categories, with manual curation and human auditing to ensure quality. We evaluate leading language models on AIR-Bench 2024, uncovering insights into their alignment with specified safety concerns. By bridging the gap between public benchmarks and practical AI risks, AIR-Bench 2024 provides a foundation for assessing model safety across jurisdictions, fostering the development of safer and more responsible AI systems.
Uncertainty-preserving deep knowledge tracing with state-space models
Authors: S. Thomas Christie, Carson Cook, Anna N. Rafferty
Date: 2024-07-09 00:00:00
Tasks: Knowledge Tracing, State Space Models

A central goal of both knowledge tracing and traditional assessment is to quantify student knowledge and skills at a given point in time. Deep knowledge tracing flexibly considers a student's response history but does not quantify epistemic uncertainty, while IRT and CDM compute measurement error but only consider responses to individual tests in isolation from a student's past responses. Elo and BKT could bridge this divide, but the simplicity of the underlying models limits information sharing across skills and imposes strong inductive biases. To overcome these limitations, we introduce Dynamic LENS, a modeling paradigm that combines the flexible uncertainty-preserving properties of variational autoencoders with the principled information integration of Bayesian state-space models. Dynamic LENS allows information from student responses to be collected across time, while treating responses from the same test as exchangeable observations generated by a shared latent state. It represents student knowledge as Gaussian distributions in high-dimensional space and combines estimates both within tests and across time using Bayesian updating. We show that Dynamic LENS has similar predictive performance to competing models, while preserving the epistemic uncertainty - the deep learning analogue to measurement error - that DKT models lack. This approach provides a conceptual bridge across an important divide between models designed for formative practice and summative assessment.
Media Manipulations in the Coverage of Events of the Ukrainian Revolution of Dignity: Historical, Linguistic, and Psychological Approaches
Authors: Ivan Khoma, Solomia Fedushko, Zoryana Kunch
Date: 2024-07-09 00:00:00

This article examines the use of manipulation in the coverage of events of the Ukrainian Revolution of Dignity in the mass media, namely in the content of the online newspaper Ukrainian Truth (Ukrainska pravda), online newspaper High Castle (Vysokyi Zamok), and online newspaper ZIK during the public protest, namely during the Ukrainian Revolution of Dignity. Contents of these online newspapers the historical, linguistic, and psychological approaches are used. Also media manipulations in the coverage of events of the Ukrainian Revolution of Dignity are studied. Internet resources that cover news are analyzed. Current and most popular Internet resources are identified. The content of online newspapers is analyzed and statistically processed. Internet content of newspapers by the level of significance of data (very significant data, significant data and insignificant data) is classified. The algorithm of detection of the media manipulations in the highlighting the course of the Ukrainian revolutions based on historical, linguistic, and psychological approaches is designed. Methods of counteracting information attacks in online newspapers are developed.
Unveiling Legitimacy in the unexpected events context : An Inquiry into Information System Consultancy companies and international organizations through Topic Modeling Analysis
Authors: Oussama Abidi
Date: 2024-07-08 00:00:00

In an increasingly dynamic and modern market, the recurrence of unexpected events necessitates proactive responses from information system (IS) stakeholders. Each IS actor strives to legitimize its actions and communicate its strategy. This study delves into the realm of IS legitimation, focusing on the communication of two key stakeholders: IS consultancy companies and international organizations, particularly in the context of unexpected events. To achieve this objective, we examined a diverse array of publications released by both actors. Employing a topic modeling methodology, we analyzed these documents to extract valuable insights regarding their methods of legitimation. Through this research, we aim to contribute to the legitimation discourse literature by offering an exploration of two key IS stakeholders responding to the challenges posed by unexpected events.
The Price of Prompting: Profiling Energy Use in Large Language Models Inference
Authors: Erik Johannes Husom, Arda Goknil, Lwin Khin Shar, Sagar Sen
Date: 2024-07-04 00:00:00

In the rapidly evolving realm of artificial intelligence, deploying large language models (LLMs) poses increasingly pressing computational and environmental challenges. This paper introduces MELODI - Monitoring Energy Levels and Optimization for Data-driven Inference - a multifaceted framework crafted to monitor and analyze the energy consumed during LLM inference processes. MELODI enables detailed observations of power consumption dynamics and facilitates the creation of a comprehensive dataset reflective of energy efficiency across varied deployment scenarios. The dataset, generated using MELODI, encompasses a broad spectrum of LLM deployment frameworks, multiple language models, and extensive prompt datasets, enabling a comparative analysis of energy use. Using the dataset, we investigate how prompt attributes, including length and complexity, correlate with energy expenditure. Our findings indicate substantial disparities in energy efficiency, suggesting ample scope for optimization and adoption of sustainable measures in LLM deployment. Our contribution lies not only in the MELODI framework but also in the novel dataset, a resource that can be expanded by other researchers. Thus, MELODI is a foundational tool and dataset for advancing research into energy-conscious LLM deployment, steering the field toward a more sustainable future.
Explainable Natural Language Processing for Corporate Sustainability Analysis
Authors: Keane Ong, Rui Mao, Ranjan Satapathy, Ricardo Shirota Filho, Erik Cambria, Johan Sulaeman, Gianmarco Mengaldo
Date: 2024-07-03 00:00:00

Sustainability commonly refers to entities, such as individuals, companies, and institutions, having a non-detrimental (or even positive) impact on the environment, society, and the economy. With sustainability becoming a synonym of acceptable and legitimate behaviour, it is being increasingly demanded and regulated. Several frameworks and standards have been proposed to measure the sustainability impact of corporations, including United Nations' sustainable development goals and the recently introduced global sustainability reporting framework, amongst others. However, the concept of corporate sustainability is complex due to the diverse and intricate nature of firm operations (i.e. geography, size, business activities, interlinks with other stakeholders). As a result, corporate sustainability assessments are plagued by subjectivity both within data that reflect corporate sustainability efforts (i.e. corporate sustainability disclosures) and the analysts evaluating them. This subjectivity can be distilled into distinct challenges, such as incompleteness, ambiguity, unreliability and sophistication on the data dimension, as well as limited resources and potential bias on the analyst dimension. Put together, subjectivity hinders effective cost attribution to entities non-compliant with prevailing sustainability expectations, potentially rendering sustainability efforts and its associated regulations futile. To this end, we argue that Explainable Natural Language Processing (XNLP) can significantly enhance corporate sustainability analysis. Specifically, linguistic understanding algorithms (lexical, semantic, syntactic), integrated with XAI capabilities (interpretability, explainability, faithfulness), can bridge gaps in analyst resources and mitigate subjectivity problems within data.
A Survey of Accessible Explainable Artificial Intelligence Research
Authors: Chukwunonso Henry Nwokoye, Maria J. P. Peixoto, Akriti Pandey, Lauren Pardy, Mahadeo Sukhai, Peter R. Lewis
Date: 2024-07-02 00:00:00
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), Survey, Systematic Literature Review

The increasing integration of Artificial Intelligence (AI) into everyday life makes it essential to explain AI-based decision-making in a way that is understandable to all users, including those with disabilities. Accessible explanations are crucial as accessibility in technology promotes digital inclusion and allows everyone, regardless of their physical, sensory, or cognitive abilities, to use these technologies effectively. This paper presents a systematic literature review of the research on the accessibility of Explainable Artificial Intelligence (XAI), specifically considering persons with sight loss. Our methodology includes searching several academic databases with search terms to capture intersections between XAI and accessibility. The results of this survey highlight the lack of research on Accessible XAI (AXAI) and stress the importance of including the disability community in XAI development to promote digital inclusion and accessibility and remove barriers. Most XAI techniques rely on visual explanations, such as heatmaps or graphs, which are not accessible to persons who are blind or have low vision. Therefore, it is necessary to develop explanation methods through non-visual modalities, such as auditory and tactile feedback, visual modalities accessible to persons with low vision, and personalized solutions that meet the needs of individuals, including those with multiple disabilities. We further emphasize the importance of integrating universal design principles into AI development practices to ensure that AI technologies are usable by everyone.
Reinforcement Learning from Human Feedback: Whose Culture, Whose Values, Whose Perspectives?
Authors: Kristian González Barman, Simon Lohse, Henk de Regt
Date: 2024-07-02 00:00:00
Tasks: Philosophy, reinforcement-learning, Reinforcement Learning

We argue for the epistemic and ethical advantages of pluralism in Reinforcement Learning from Human Feedback (RLHF) in the context of Large Language Models (LLM). Drawing on social epistemology and pluralist philosophy of science, we suggest ways in which RHLF can be made more responsive to human needs and how we can address challenges along the way. The paper concludes with an agenda for change, i.e. concrete, actionable steps to improve LLM development.
LLM4PM: A case study on using Large Language Models for Process Modeling in Enterprise Organizations
Authors: Clara Ziche, Giovanni Apruzzese
Date: 2024-07-01 00:00:00
Tasks: Chatbot, Management

We investigate the potential of using Large Language Models (LLM) to support process model creation in organizational contexts. Specifically, we carry out a case study wherein we develop and test an LLM-based chatbot, PRODIGY (PROcess moDellIng Guidance for You), in a multinational company, the Hilti Group. We are particularly interested in understanding how LLM can aid (human) modellers in creating process flow diagrams. To this purpose, we first conduct a preliminary user study (n=10) with professional process modellers from Hilti, inquiring for various pain-points they encounter in their daily routines. Then, we use their responses to design and implement PRODIGY. Finally, we evaluate PRODIGY by letting our user study's participants use PRODIGY, and then ask for their opinion on the pros and cons of PRODIGY. We coalesce our results in actionable takeaways. Through our research, we showcase the first practical application of LLM for process modelling in the real world, shedding light on how industries can leverage LLM to enhance their Business Process Management activities.
Toward Automated Detection of Biased Social Signals from the Content of Clinical Conversations
Authors: Feng Chen, Manas Satish Bedmutha, Ray-Yuan Chung, Janice Sabin, Wanda Pratt, Brian R. Wood, Nadir Weibel, Andrea L. Hartzler, Trevor Cohen
Date: 2024-07-01 00:00:00
Tasks: Fairness, speech-recognition, Speech Recognition

Implicit bias can impede patient-provider interactions and lead to inequities in care. Raising awareness is key to reducing such bias, but its manifestations in the social dynamics of patient-provider communication are difficult to detect. In this study, we used automated speech recognition (ASR) and natural language processing (NLP) to identify social signals in patient-provider interactions. We built an automated pipeline to predict social signals from audio recordings of 782 primary care visits that achieved 90.1% average accuracy across codes, and exhibited fairness in its predictions for white and non-white patients. Applying this pipeline, we identified statistically significant differences in provider communication behavior toward white versus non-white patients. In particular, providers expressed more patient-centered behaviors towards white patients including more warmth, engagement, and attentiveness. Our study underscores the potential of automated tools in identifying subtle communication signals that may be linked with bias and impact healthcare quality and equity.