pwc-archive/papers-with-abstracts huggingface dataset:
Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network
Authors: Ying Li, Haokui Zhang, Qiang Shen
Date: 2017-01-13 00:00:00
Proceeding: Remote Sensing 2017 1
Tasks: Classification, Few-Shot Image Classification, Hyperspectral Image Classification

Recent research has shown that using spectral–spatial information can considerably improve the performance of hyperspectral image (HSI) classification. HSI data is typically presented in the format of 3D cubes. Thus, 3D spatial filtering naturally offers a simple and effective method for simultaneously extracting the spectral–spatial features within such images. In this paper, a 3D convolutional neural network (3D-CNN) framework is proposed for accurate HSI classification. The proposed method views the HSI cube data altogether without relying on any preprocessing or post-processing, extracting the deep spectral–spatial-combined features effectively. In addition, it requires fewer parameters than other deep learning-based methods. Thus, the model is lighter, less likely to over-fit, and easier to train. For comparison and validation, we test the proposed method along with three other deep learning-based HSI classification methods—namely, stacked autoencoder (SAE), deep brief network (DBN), and 2D-CNN-based methods—on three real-world HSI datasets captured by different sensors. Experimental results demonstrate that our 3D-CNN-based method outperforms these state-of-the-art methods and sets a new record.
Deep supervised learning for hyperspectral data classification through convolutional neural networks
Authors: Konstantinos Makantasis, Konstantinos Karantzalos, Anastasios Doulamis, Nikolaos Doulamis
Date: 2015-07-26 00:00:00
Proceeding: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2015 7
Tasks: Classification, Few-Shot Image Classification, Hyperspectral Image Classification, Object Recognition

Spectral observations along the spectrum in many narrow spectral bands through hyperspectral imaging provides valuable information towards material and object recognition, which can be consider as a classification task. Most of the existing studies and research efforts are following the conventional pattern recognition paradigm, which is based on the construction of complex handcrafted features. However, it is rarely known which features are important for the problem at hand. In contrast to these approaches, we propose a deep learning based classification method that hierarchically constructs high-level features in an automated way. Our method exploits a Convolutional Neural Network to encode pixels' spectral and spatial information and a Multi-Layer Perceptron to conduct the classification task. Experimental results and quantitative validation on widely used datasets showcasing the potential of the developed approach for accurate hyperspectral data classification.
Feature Selection and Classification of Hyperspectral Images With Support Vector Machines
Authors: Rick Archibald, George Fann
Date: 2007-10-15 00:00:00
Proceeding: IEEE Geoscience and Remote Sensing Letters 2007 10
Tasks: Benchmarking, Classification, Classification Of Hyperspectral Images, feature selection, Few-Shot Image Classification, Hyperspectral Image Classification

Hyperspectral images consist of large number of bands which require sophisticated analysis to extract. One approach to reduce computational cost, information representation, and accelerate knowledge discovery is to eliminate bands that do not add value to the classification and analysis method which is being applied. In particular, algorithms that perform band elimination should be designed to take advantage of the structure of the classification method used. This letter introduces an embedded-feature-selection (EFS) algorithm that is tailored to operate with support vector machines (SVMs) to perform band selection and classification simultaneously. We have successfully applied this algorithm to determine a reasonable subset of bands without any user-defined stopping criteria on some sample AVIRIS images; a problem occurs in benchmarking recursive-feature-elimination methods for the SVMs.
Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering
Authors: Xudong Kang, Shutao Li, Jón Atli Benediktsson
Date: 2013-09-16 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2013 9
Tasks: Classification, Computational Efficiency, Few-Shot Image Classification, Hyperspectral Image Classification, image-classification, Image Classification

Feature extraction is known to be an effective way in both reducing computational complexity and increasing accuracy of hyperspectral image classification. In this paper, a simple yet quite powerful feature extraction method based on image fusion and recursive filtering (IFRF) is proposed. First, the hyperspectral image is partitioned into multiple subsets of adjacent hyperspectral bands. Then, the bands in each subset are fused together by averaging, which is one of the simplest image fusion methods. Finally, the fused bands are processed with transform domain recursive filtering to get the resulting features for classification. Experiments are performed on different hyperspectral images, with the support vector machines (SVMs) serving as the classifier. By using the proposed method, the accuracy of the SVM classifier can be improved significantly. Furthermore, compared with other hyperspectral classification methods, the proposed IFRF method shows outstanding performance in terms of classification accuracy and computational efficiency.
A framework for the identification and classification of homogeneous socioeconomic areas in the analysis of health care variation
Authors: Ludovico Pinzari, Soumya Mazumdar & Federico Girosi
Date: 2018-12-04 00:00:00
Proceeding: International Journal of Health Geographics 2018 12
Tasks: Descriptive, Unsupervised Spatial Clustering

Background
Detecting the variation of health indicators across similar areas or peer geographies is often useful if the spatial units are socially and economically meaningful, so that there is a degree of homogeneity in each unit. Indices are frequently constructed to generate summaries of socioeconomic status or other measures in geographic small areas. Larger areas may be built to be homogenous using regionalization algorithms. However, there are no explicit guidelines in the literature for the grouping of peer geographies based on measures such as area level socioeconomic indices. Moreover, the use of an index score becomes less meaningful as the size of an area increases. This paper introduces an easy to use statistical framework for the identification and classification of homogeneous areas. We propose the Homogeneity and Location indices to measure the concentration and central value respectively of an areas’ socioeconomic distribution. We also provide a transparent set of criteria that a researcher can follow to establish whether a set of proposed geographies are acceptably homogeneous or need further refining.
Results
We applied our framework to assess the socioeconomic homogeneity of the commonly used SA3 Australian census geography. These results showed that almost 60% of the SA3 census units are likely to be socioeconomically heterogeneous and hence inappropriate for presenting area level socioeconomic disadvantage. We also showed that the Location Index is a more robust descriptive measure of the distribution compared to other measures of central tendency. Finally, the methodology proposed was used to analyse the age-standardized variation of GP attenders in a metropolitan area. The results suggest that very high GP attenders (20+ visits) live in SA3s with the most socioeconomic disadvantage. The findings revealed that households with low income and families with children and jobless parents are the major drivers for discerning disadvantaged communities.
Conclusion
Reporting indicators rates for geographies grouped according to similarity may be useful for the analysis of geographic variation. The use of a framework for the identification of meaningful peer geographies would be beneficial to health planners and policy makers by providing realistic and valid peer group geographies.
GROBID: Combining Automatic Bibliographic Data Recognition and Term Extraction for Scholarship Publications
Authors: Patrice Lopez
Date: 2009-09-01 00:00:00
Proceeding: Research and Advanced Technology for Digital Libraries 2009 9
Tasks: Articles, Term Extraction

Based on state of the art machine learning techniques, GROBID (GeneRation Of BIbliographic Data) performs reliable bibliographic data extractions from scholar articles combined with multi-level term extractions. These two types of extraction present synergies and correspond to complementary descriptions of an article. This tool is viewed as a component for enhancing the existing and the future large repositories of technical and scientific publications.
V3CTRON | Data Retrieval & Access System For Flexible Semantic Search & Retrieval Of Proprietary Document Collections Using Natural Language Queries.
Authors: Devin Schumacher
Date: 2023-04-26 00:00:00
Proceeding: Social Science Research Network (SSRN) 2023 4
Tasks: Conversational Search, Information Retrieval, Natural Language Queries, Retrieval

V3CTRON is an open source vector database that allows users to upload text based documents & document collections, which are automatically embedded for super-accurate semantic search & retrieval using natural language queries. V3CTRON supports multiple vector database providers, including Milvus, LlamaIndex and Qdrant, giving developers flexibility & customization - and end users the ability to leverage the benefits of LLMs that can directly access their use-case specific data. A notable feature of V3CTRON is its memory capability, which allows ChatGPT to remember and retrieve information from previous conversations by saving conversation snippets to the vector database. V3CTRON is a practical solution for enhancing document accessibility and enabling context-aware and memory-relevant chat experiences with effective neural search capabilities.
Semantic bSDD: Improving the GraphQL, JSON and RDF Representations of buildingSmart Data Dictionary
Authors: Vladimir Alexiev, Mihail Radkov, Nataliya Keberle
Date: 2023-06-25 00:00:00
Proceeding: Linked Data in Architecture and Construction (LDAC 2023) 2023 6

The buildingSmart Data Dictionary (bSDD) is an important shared resource in the Architecture, Engineering, Construction, and Operations (AECO) domain. It is a collection of datasets ("domains") that define various classifications (objects representing building components, products, and materials), their properties, allowed values, etc. bSDD defines a GraphQL API, as well as REST APIs that return JSON and RDF representations. This improves the interoperability of bSDD and its easier deployment in architectural Computer Aided Design (CAD) and other AECO software. However, bSDD data is not structured as well as possible, and data retrieved via different APIs is not identical in content and structure. This lowers bSDD data quality, usability and trust. We conduct a thorough comparison and analysis of bSDD data related to fulfillment of FAIR (findable, accessible, interoperable, and reusable) principles. Based on this analysis, we suggest enhancements to make bSDD data better structured and more FAIR. We implement many of the suggestions by refactoring the original data to make it better structured/interconnected, and more "semantic". We provide a SPARQL endpoint using Ontotext GraphDB, and GraphQL endpoint using Ontotext Platform Semantic Objects. Our detailed work is available at https://github.com/Accord-Project/bsdd (open source) and https://bsdd.ontotext.com (home page, schemas, data, sample queries).
Recognizable Information Bottleneck
Authors: Yilin Lyu, Xin Liu, Mingyang Song, Xinyue Wang, Yaxin Peng, Tieyong Zeng, Liping Jing
Date: 2023-04-28 00:00:00

Information Bottlenecks (IBs) learn representations that generalize to unseen data by information compression. However, existing IBs are practically unable to guarantee generalization in real-world scenarios due to the vacuous generalization bound. The recent PAC-Bayes IB uses information complexity instead of information compression to establish a connection with the mutual information generalization bound. However, it requires the computation of expensive second-order curvature, which hinders its practical application. In this paper, we establish the connection between the recognizability of representations and the recent functional conditional mutual information (f-CMI) generalization bound, which is significantly easier to estimate. On this basis we propose a Recognizable Information Bottleneck (RIB) which regularizes the recognizability of representations through a recognizability critic optimized by density ratio matching under the Bregman divergence. Extensive experiments on several commonly used datasets demonstrate the effectiveness of the proposed method in regularizing the model and estimating the generalization gap.
LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
Authors: Peng Gao, Jiaming Han, Renrui Zhang, Ziyi Lin, Shijie Geng, Aojun Zhou, Wei zhang, Pan Lu, Conghui He, Xiangyu Yue, Hongsheng Li, Yu Qiao
Date: 2023-04-28 00:00:00
Tasks: Instruction Following, model, Optical Character Recognition (OCR), Video-based Generative Performance Benchmarking, Video-based Generative Performance Benchmarking (Consistency), Video-based Generative Performance Benchmarking (Contextual Understanding), Video-based Generative Performance Benchmarking (Correctness of Information), Video-based Generative Performance Benchmarking (Detail Orientation)), Video-based Generative Performance Benchmarking (Temporal Understanding), Video Question Answering, Visual Question Answering, Visual Question Answering (VQA), Zero-Shot Video Question Answer

How to efficiently transform large language models (LLMs) into instruction followers is recently a popular research direction, while training LLM for multi-modal reasoning remains less explored. Although the recent LLaMA-Adapter demonstrates the potential to handle visual inputs with LLMs, it still cannot generalize well to open-ended visual instructions and lags behind GPT-4. In this paper, we present LLaMA-Adapter V2, a parameter-efficient visual instruction model. Specifically, we first augment LLaMA-Adapter by unlocking more learnable parameters (e.g., norm, bias and scale), which distribute the instruction-following ability across the entire LLaMA model besides adapters. Secondly, we propose an early fusion strategy to feed visual tokens only into the early LLM layers, contributing to better visual knowledge incorporation. Thirdly, a joint training paradigm of image-text pairs and instruction-following data is introduced by optimizing disjoint groups of learnable parameters. This strategy effectively alleviates the interference between the two tasks of image-text alignment and instruction following and achieves strong multi-modal reasoning with only a small-scale image-text and instruction dataset. During inference, we incorporate additional expert models (e.g. captioning/OCR systems) into LLaMA-Adapter to further enhance its image understanding capability without incurring training costs. Compared to the original LLaMA-Adapter, our LLaMA-Adapter V2 can perform open-ended multi-modal instructions by merely introducing 14M parameters over LLaMA. The newly designed framework also exhibits stronger language-only instruction-following capabilities and even excels in chat interactions. Our code and models are available at https://github.com/ZrrSkywalker/LLaMA-Adapter.
Empirical Analysis of the Strengths and Weaknesses of PEFT Techniques for LLMs
Authors: George Pu, Anirudh Jain, Jihan Yin, Russell Kaplan
Date: 2023-04-28 00:00:00
Tasks: parameter-efficient fine-tuning

As foundation models continue to exponentially scale in size, efficient methods of adaptation become increasingly critical. Parameter-efficient fine-tuning (PEFT), a recent class of techniques that require only modifying a small percentage of the model parameters, is currently the most popular method for adapting large language models (LLMs). Several PEFT techniques have recently been proposed with varying tradeoffs. We provide a comprehensive and uniform benchmark of various PEFT techniques across a representative LLM, the FLAN-T5 model, and evaluate model performance across different data scales of classification and generation datasets. Based on this, we provide a framework for choosing the optimal fine-tuning techniques given the task type and data availability. Contrary to popular belief, we also empirically prove that PEFT techniques converge slower than full tuning in low data scenarios, and posit the amount of data required for PEFT methods to both perform well and converge efficiently. Lastly, we further optimize these PEFT techniques by selectively choosing which parts of the model to train, and find that these techniques can be applied with significantly fewer parameters while maintaining and even improving performance.
ChatGPT in the Classroom: An Analysis of Its Strengths and Weaknesses for Solving Undergraduate Computer Science Questions
Authors: Ishika Joshi, Ritvik Budhiraja, Harshal Dev, Jahnvi Kadia, M. Osama Ataullah, Sayan Mitra, Dhruv Kumar, Harshal D. Akolekar
Date: 2023-04-28 00:00:00
Tasks: Chatbot, Language Modeling, Language Modelling, Question Answering, Sentiment Analysis, Text Generation, Text Summarization

ChatGPT is an AI language model developed by OpenAI that can understand and generate human-like text. It can be used for a variety of use cases such as language generation, question answering, text summarization, chatbot development, language translation, sentiment analysis, content creation, personalization, text completion, and storytelling. While ChatGPT has garnered significant positive attention, it has also generated a sense of apprehension and uncertainty in academic circles. There is concern that students may leverage ChatGPT to complete take-home assignments and exams and obtain favorable grades without genuinely acquiring knowledge. This paper adopts a quantitative approach to demonstrate ChatGPT's high degree of unreliability in answering a diverse range of questions pertaining to topics in undergraduate computer science. Our analysis shows that students may risk self-sabotage by blindly depending on ChatGPT to complete assignments and exams. We build upon this analysis to provide constructive recommendations to both students and instructors.
Interpreting Vision and Language Generative Models with Semantic Visual Priors
Authors: Michele Cafagna, Lina M. Rojas-Barahona, Kees Van Deemter, Albert Gatt
Date: 2023-04-28 00:00:00
Tasks: Image to text

When applied to Image-to-text models, interpretability methods often provide token-by-token explanations namely, they compute a visual explanation for each token of the generated sequence. Those explanations are expensive to compute and unable to comprehensively explain the model's output. Therefore, these models often require some sort of approximation that eventually leads to misleading explanations. We develop a framework based on SHAP, that allows for generating comprehensive, meaningful explanations leveraging the meaning representation of the output sequence as a whole. Moreover, by exploiting semantic priors in the visual backbone, we extract an arbitrary number of features that allows the efficient computation of Shapley values on large-scale models, generating at the same time highly meaningful visual explanations. We demonstrate that our method generates semantically more expressive explanations than traditional methods at a lower compute cost and that it can be generalized over other explainability methods.
Optimal majority rules and quantitative Condorcet properties of setwise Kemeny voting schemes
Authors: Xuan Kien Phung, Sylvie Hamel
Date: 2023-04-28 00:00:00

The important Kemeny problem, which consists of computing median consensus rankings of an election with respect to the Kemeny voting rule, admits important applications in biology and computational social choice and was generalized recently via an interesting setwise approach by Gilbert et. al. Our first results establish optimal quantitative extensions of the Unanimity property and the well-known $3/4$-majority rule of Betzler et al. for the classical Kemeny median problem. Moreover, by elaborating an exhaustive list of quantified axiomatic properties (such as the Condorcet and Smith criteria, the $5/6$-majority rule, etc.) of the $3$-wise Kemeny rule where not only pairwise comparisons but also the discordance between the winners of subsets of three candidates are also taken into account, we come to the conclusion that the $3$-wise Kemeny voting scheme induced by the $3$-wise Kendall-tau distance presents interesting advantages in comparison with the classical Kemeny rule. For example, it satisfies several improved manipulation-proof properties. Since the $3$-wise Kemeny problem is NP-hard, our results also provide some of the first useful space reduction techniques by determining the relative orders of pairs of alternatives. Our works suggest similar interesting properties of higher setwise Kemeny voting schemes which justify and compensate for the more expensive computational cost than the classical Kemeny scheme.
MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks
Authors: Lei Zhang, Yuge Zhang, Kan Ren, Dongsheng Li, Yuqing Yang
Date: 2023-04-28 00:00:00
Tasks: AutoML, Code Generation

The field of machine learning (ML) has gained widespread adoption, leading to significant demand for adapting ML to specific scenarios, which is yet expensive and non-trivial. The predominant approaches towards the automation of solving ML tasks (e.g., AutoML) are often time-consuming and hard to understand for human developers. In contrast, though human engineers have the incredible ability to understand tasks and reason about solutions, their experience and knowledge are often sparse and difficult to utilize by quantitative approaches. In this paper, we aim to bridge the gap between machine intelligence and human knowledge by introducing a novel framework, which leverages the state-of-the-art large language models to develop ML solutions for novel tasks. We showcase the possibility of extending the capability of LLMs to comprehend structured inputs and perform thorough reasoning for solving novel ML tasks. And we find that, after some dedicated design, the LLM can (i) observe from the existing experiences of ML tasks and (ii) reason effectively to deliver promising results for new tasks. The solution generated can be used directly to achieve high levels of competitiveness. Examples and code available at https://github.com/microsoft/CoML.
A New Quantum Dempster Rule of Combination
Authors: Huaping He, Fuyuan Xiao
Date: 2023-04-28 00:00:00

Dempster rule of combination (DRC) is widely used for uncertainty reasoning in intelligent information system, which is generalized to complex domain recently. However, as the increase of identification framework elements, the computational complexity of Dempster Rule of Combination increases exponentially. To address this issue, we propose a novel quantum Dempster rule of combination (QDRC) by means of Toffoli gate. The QDRC combination process is completely implemented using quantum circuits.