pwc-archive/papers-with-abstracts huggingface dataset:
Correlating Power Outage Spread with Infrastructure Interdependencies During Hurricanes
Authors: Avishek Bose, SangKeun Lee, Narayan Bhusal, Supriya Chinthavali
Date: 2024-07-13 00:00:00

Power outages caused by extreme weather events, such as hurricanes, can significantly disrupt essential services and delay recovery efforts, underscoring the importance of enhancing our infrastructure's resilience. This study investigates the spread of power outages during hurricanes by analyzing the correlation between the network of critical infrastructure and outage propagation. We leveraged datasets from Hurricanemapping.com, the North American Energy Resilience Model Interdependency Analysis (NAERM-IA), and historical power outage data from the Oak Ridge National Laboratory (ORNL)'s EAGLE-I system. Our analysis reveals a consistent positive correlation between the extent of critical infrastructure components accessible within a certain number of steps (k-hop distance) from initial impact areas and the occurrence of power outages in broader regions. This insight suggests that understanding the interconnectedness among critical infrastructure elements is key to identifying areas indirectly affected by extreme weather events.
Popular News Always Compete for the User's Attention! POPK: Mitigating Popularity Bias via a Temporal-Counterfactual
Authors: Igor L. R. Azevedo, Toyotaro Suzumura, Yuichiro Yasui
Date: 2024-07-13 00:00:00
Tasks: Articles, counterfactual, Counterfactual Reasoning, Diversity, News Recommendation, Recommendation Systems

In news recommendation systems, reducing popularity bias is essential for delivering accurate and diverse recommendations. This paper presents POPK, a new method that uses temporal-counterfactual analysis to mitigate the influence of popular news articles. By asking, "What if, at a given time $t$, a set of popular news articles were competing for the user's attention to be clicked?", POPK aims to improve recommendation accuracy and diversity. We tested POPK on three different language datasets (Japanese, English, and Norwegian) and found that it successfully enhances traditional methods. POPK offers flexibility for customization to enhance either accuracy or diversity, alongside providing distinct ways of measuring popularity. We argue that popular news articles always compete for attention, even if they are not explicitly present in the user's impression list. POPK systematically eliminates the implicit influence of popular news articles during each training step. We combine counterfactual reasoning with a temporal approach to adjust the negative sample space, refining understanding of user interests. Our findings underscore how POPK effectively enhances the accuracy and diversity of recommended articles while also tailoring the approach to specific needs.
CUSIDE-array: A Streaming Multi-Channel End-to-End Speech Recognition System with Realistic Evaluations
Authors: Xiangzhu Kong, Tianqi Ning, Hao Huang, Zhijian Ou
Date: 2024-07-13 00:00:00
Tasks: Chunking, speech-recognition, Speech Recognition

Recently multi-channel end-to-end (ME2E) ASR systems have emerged. While streaming single-channel end-to-end ASR has been extensively studied, streaming ME2E ASR is limited in exploration. Additionally, recent studies call attention to the gap between in-distribution (ID) and out-of-distribution (OOD) tests and doing realistic evaluations. This paper focuses on two research problems: realizing streaming ME2E ASR and improving OOD generalization. We propose the CUSIDE-array method, which integrates the recent CUSIDE methodology (Chunking, Simulating Future Context and Decoding) into the neural beamformer approach of ME2E ASR. It enables streaming processing of both front-end and back-end with a total latency of 402ms. The CUSIDE-array ME2E models are shown to achieve superior streaming results in both ID and OOD tests. Realistic evaluations confirm the advantage of CUSIDE-array in its capability to consume single-channel data to improve OOD generalization via back-end pre-training and ME2E fine-tuning.
Learning to Extract Attribute Value from Product via Question Answering: A Multi-task Approach
Authors: Qifan Wang, Li Yang, Bhargav Kanagal, Sumit Sanghai, D. Sivakumar, Bin Shu, Zac Yu, Jon Elsas
Date: 2020-08-20 00:00:00
Proceeding: ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2020 8
Tasks: Attribute, Attribute Value Extraction, Knowledge Distillation, Language Modeling, Language Modelling, Question Answering

Attribute value extraction refers to the task of identifying values of an attribute of interest from product information. It is an important research topic which has been widely studied in e-Commerce and relation learning. There are two main limitations in existing attribute value extraction methods: scalability and generalizability. Most existing methods treat each attribute independently and build separate models for each of them, which are not suitable for large scale attribute systems in real-world applications. Moreover, very limited research has focused on generalizing extraction to new attributes.
In this work, we propose a novel approach for Attribute Value Extraction via Question Answering (AVEQA) using a multi-task framework. In particular, we build a question answering model which treats each attribute as a question and identifies the answer span corresponding to the attribute value in the product context. A unique BERT contextual encoder is adopted and shared across all attributes to encode both the context and the question, which makes the model scalable. A distilled masked language model with knowledge distillation loss is introduced to improve the model generalization ability. In addition, we employ a no-answer classifier to explicitly handle the cases where there are no values for a given attribute in the product context. The question answering, distilled masked language model and the no answer classification are then combined into a unified multi-task framework. We conduct extensive experiments on a public dataset. The results demonstrate that the proposed approach outperforms several state-of-the-art methods with large margin
MixPAVE: Mix-Prompt Tuning for Few-shot Product Attribute Value Extraction
Authors: Li Yang, Qifan Wang, Jingang Wang, Xiaojun Quan, Fuli Feng, Yu Chen, Madian Khabsa, Sinong Wang, Zenglin Xu, Dongfang Liu
Date: 2023-07-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2023 7
Tasks: Attribute, Attribute Value Extraction

The task of product attribute value extraction is to identify values of an attribute from product information. Product attributes are important features, which help improve online shopping experience of customers, such as product search, recommendation and comparison. Most existing works only focus on extracting values for a set of known attributes with sufficient training data. However, with the emerging nature of e-commerce, new products with their unique set of new attributes are constantly generated from different retailers and merchants. Collecting a large number of annotations for every new attribute is costly and time consuming. Therefore, it is an important research problem for product attribute value extraction with limited data. In this work, we propose a novel prompt tuning approach with Mixed Prompts for few-shot Attribute Value Extraction, namely MixPAVE. Specifically, MixPAVE introduces only a small amount (< 1%) of trainable parameters, i.e., a mixture of two learnable prompts, while keeping the existing extraction model frozen. In this way, MixPAVE not only benefits from parameter-efficient training, but also avoids model overfitting on limited training examples. Experimental results on two product benchmarks demonstrate the superior performance of the proposed approach over several state-of-the-art baselines. A comprehensive set of ablation studies validate the effectiveness of the prompt design, as well as the efficiency of our approach.
A branch-and-cut algorithm for the latent-class logit assortment problem
Authors: Isabel Méndez-Díaz, Juan José Miranda-Bront, Gustavo Vulcano, Paula Zabala
Date: 2014-02-14 00:00:00
Proceeding: Discrete Applied Mathematics 2014 2
Tasks: Assortment Optimization, Discrete Choice Models

We study the product assortment problem of a retail operation that faces a stream of customers who are heterogeneous with respect to preferences. Each customer belongs to a market segment characterized by a consideration set that includes the alternatives viewed as options, and by the preference weights that the segment assigns to each of those alternatives. Upon arrival, he checks the offer set displayed by the firm, and either chooses one of those products or quits without purchasing according to a multinomial-logit (MNL) criterion. The firm’s goal is to maximize the expected revenue extracted during a fixed time horizon. This problem also arises in the growing area of choice-based, network revenue management, where computational speed is a critical factor for the practical viability of a solution approach.

This so-called latent-class, logit assortment problem is known to be NP-Hard. In this paper, we analyze unconstrained and constrained (i.e., with a limited number of products to display) versions of it, and propose a branch-and-cut algorithm that is computationally fast and leads to (nearly) optimal solutions.
Low-Light Image Enhancement Based on Cell Vibration Energy Model and Lightness Difference
Authors: Xiaozhou Lei, Zixiang Fei, Wenju Zhou, Huiyu Zhou, Minrui Fei
Date: 2024-07-14 00:00:00
Proceeding: journal 2024 7
Tasks: Image Enhancement, Low-Light Image Enhancement

Low-light image enhancement algorithms play a crucial role in revealing details obscured by darkness in images and substantially improving overall image quality. However, existing methods often suffer from issues like color or lightness distortion and possess limited scalability. In response to these challenges, we introduce a novel low-light image enhancement algorithm leveraging a cell vibration energy model and lightness difference. Initially, a new low-light image enhancement framework is proposed, building upon a comprehensive understanding and analysis of the cell vibration energy model and its statistical properties. Subsequently, to achieve pixel-level multi-lightness difference adjustment and exert control over the lightness level of each pixel independently, a lightness difference adjustment strategy is introduced utilizing Weibull distribution and linear mapping. Furthermore, to expand the adaptive range of the algorithm, we consider the disparities between HSV space and RGB space. Two enhanced image output modes are designed, accompanied by a thorough analysis and deduction of the relevant image layer mapping formulas. Finally, to enhance the reliability of experimental results, certain image faults in the SICE database are rectified using the feature matching method. Experimental results showcase the superiority of the proposed algorithm over twelve state-of-the-art algorithms. The resource code of this article will be released at 
https://github.com/leixiaozhou/CDEGmethod
LDRE: LLM-based Divergent Reasoning and Ensemble for Zero-Shot Composed Image Retrieval
Authors: Zhenyu Yang, Dizhan Xue, Shengsheng Qian, WeiMing Dong, Changsheng Xu
Date: 2024-07-11 00:00:00
Proceeding: SIGIR 2024 7
Tasks: Image Retrieval, Image to text, Retrieval, Zero-Shot Composed Image Retrieval (ZS-CIR)

Zero-Shot Composed Image Retrieval (ZS-CIR) has garnered increasing interest in recent years, which aims to retrieve a target image based on a query composed of a reference image and a modification text without training samples. Specifically, the modification text describes the distinction between the two images. To conduct ZS-CIR, the prevailing methods employ pre-trained image-to-text models to transform the query image and text into a single text, which is then projected into the common feature space by CLIP to retrieve the target image. However, these methods neglect that ZS-CIR is a typical fuzzy retrieval task, where the semantics of the target image are not strictly defined by the query image and text. To overcome this limitation, this paper proposes a training-free LLM-based Divergent Reasoning and Ensemble (LDRE) method for ZS-CIR to capture diverse possible semantics of the composed result. Firstly, we employ a pre-trained captioning model to generate dense captions for the reference image, focusing on different semantic perspectives of the reference image. Then, we prompt Large Language Models (LLMs) to conduct divergent compositional reasoning based on the dense captions and modification text, deriving divergent edited captions that cover the possible semantics of the composed target. Finally, we design a divergent caption ensemble to obtain the ensemble caption feature weighted by semantic relevance scores, which is subsequently utilized to retrieve the target image in the CLIP feature space. Extensive experiments on three public datasets demonstrate that our proposed LDRE achieves the new state-of-the-art performance.
Prompting Whole Slide Image Based Genetic Biomarker Prediction
Authors: Ling Zhang, Boxiang Yun, Xingran Xie, Qingli Li, Xinxing Li, Yan Wang
Date: 2024-06-26 00:00:00
Tasks: Decision Making, Prediction

Prediction of genetic biomarkers, e.g., microsatellite instability and BRAF in colorectal cancer is crucial for clinical decision making. In this paper, we propose a whole slide image (WSI) based genetic biomarker prediction method via prompting techniques. Our work aims at addressing the following challenges: (1) extracting foreground instances related to genetic biomarkers from gigapixel WSIs, and (2) the interaction among the fine-grained pathological components in WSIs.Specifically, we leverage large language models to generate medical prompts that serve as prior knowledge in extracting instances associated with genetic biomarkers. We adopt a coarse-to-fine approach to mine biomarker information within the tumor microenvironment. This involves extracting instances related to genetic biomarkers using coarse medical prior knowledge, grouping pathology instances into fine-grained pathological components and mining their interactions. Experimental results on two colorectal cancer datasets show the superiority of our method, achieving 91.49% in AUC for MSI classification. The analysis further shows the clinical interpretability of our method. Code is publicly available at https://github.com/DeepMed-Lab-ECNU/PromptBio.
The Blockchain Risk Parity Line: Moving From The Efficient Frontier To The Final Frontier Of Investments
Authors: Ravi Kashyap
Date: 2024-06-26 00:00:00

We engineer blockchain based risk managed portfolios by creating three funds with distinct risk and return profiles: 1) Alpha - high risk portfolio; 2) Beta - mimics the wider market; and 3) Gamma - represents the risk free rate adjusted to beat inflation. Each of the sub-funds (Alpha, Beta and Gamma) provides risk parity because the weight of each asset in the corresponding portfolio is set to be inversely proportional to the risk derived from investing in that asset. This can be equivalently stated as equal risk contributions from each asset towards the overall portfolio risk. We provide detailed mechanics of combining assets - including mathematical formulations - to obtain better risk managed portfolios. The descriptions are intended to show how a risk parity based efficient frontier portfolio management engine - that caters to different risk appetites of investors by letting each individual investor select their preferred risk-return combination - can be created seamlessly on blockchain. Any Investor - using decentralized ledger technology - can select their desired level of risk, or return, and allocate their wealth accordingly among the sub funds, which balance one another under different market conditions. This evolution of the risk parity principle - resulting in a mechanism that is geared to do well under all market cycles - brings more robust performance and can be termed as conceptual parity. We have given several numerical examples that illustrate the various scenarios that arise when combining Alpha, Beta and Gamma to obtain Parity. The final investment frontier is now possible - a modification to the efficient frontier, thus becoming more than a mere theoretical construct - on blockchain since anyone from anywhere can participate at anytime to obtain wealth appreciation based on their financial goals.
Partner in Crime: Boosting Targeted Poisoning Attacks against Federated Learning
Authors: Shihua Sun, Shridatt Sugrim, Angelos Stavrou, Haining Wang
Date: 2024-07-13 00:00:00
Tasks: Data Poisoning, Federated Learning, Model Poisoning

Federated Learning (FL) exposes vulnerabilities to targeted poisoning attacks that aim to cause misclassification specifically from the source class to the target class. However, using well-established defense frameworks, the poisoning impact of these attacks can be greatly mitigated. We introduce a generalized pre-training stage approach to Boost Targeted Poisoning Attacks against FL, called BoTPA. Its design rationale is to leverage the model update contributions of all data points, including ones outside of the source and target classes, to construct an Amplifier set, in which we falsify the data labels before the FL training process, as a means to boost attacks. We comprehensively evaluate the effectiveness and compatibility of BoTPA on various targeted poisoning attacks. Under data poisoning attacks, our evaluations reveal that BoTPA can achieve a median Relative Increase in Attack Success Rate (RI-ASR) between 15.3% and 36.9% across all possible source-target class combinations, with varying percentages of malicious clients, compared to its baseline. In the context of model poisoning, BoTPA attains RI-ASRs ranging from 13.3% to 94.7% in the presence of the Krum and Multi-Krum defenses, from 2.6% to 49.2% under the Median defense, and from 2.9% to 63.5% under the Flame defense.
LFFR: Logistic Function For (single-output) Regression
Authors: John Chiang
Date: 2024-07-13 00:00:00
Tasks: Privacy Preserving, regression

Privacy-preserving regression in machine learning is a crucial area of research, aimed at enabling the use of powerful machine learning techniques while protecting individuals' privacy. In this paper, we implement privacy-preserving regression training using data encrypted under a fully homomorphic encryption scheme. We first examine the common linear regression algorithm and propose a (simplified) fixed Hessian for linear regression training, which can be applied for any datasets even not normalized into the range $[0, 1]$. We also generalize this constant Hessian matrix to the ridge regression version, namely linear regression which includes a regularization term to penalize large coefficients. However, our main contribution is to develop a novel and efficient algorithm called LFFR for homomorphic regression using the logistic function, which could model more complex relations between input values and output prediction in comparison with linear regression. We also find a constant simplified Hessian to train our LFFR algorithm using the Newton-like method and compare it against to with our new fixed Hessian linear regression training over two real-world datasets. We suggest normalizing not only the data but also the target predictions even for the original linear regression used in a privacy-preserving manner, which is helpful to remain weights in a small range, say $[-5, +5]$ good for refreshing ciphertext setting parameters, and avoid tuning the regularization parameter $\lambda$ via cross validation. The linear regression with normalized predictions could be a viable alternative to ridge regression.
UQE: A Query Engine for Unstructured Databases
Authors: Hanjun Dai, Bethany Yixin Wang, Xingchen Wan, Bo Dai, Sherry Yang, Azade Nova, Pengcheng Yin, Phitchaya Mangpo Phothilimthana, Charles Sutton, Dale Schuurmans
Date: 2024-06-23 00:00:00
Tasks: Semantic Retrieval

Analytics on structured data is a mature field with many successful methods. However, most real world data exists in unstructured form, such as images and conversations. We investigate the potential of Large Language Models (LLMs) to enable unstructured data analytics. In particular, we propose a new Universal Query Engine (UQE) that directly interrogates and draws insights from unstructured data collections. This engine accepts queries in a Universal Query Language (UQL), a dialect of SQL that provides full natural language flexibility in specifying conditions and operators. The new engine leverages the ability of LLMs to conduct analysis of unstructured data, while also allowing us to exploit advances in sampling and optimization techniques to achieve efficient and accurate query execution. In addition, we borrow techniques from classical compiler theory to better orchestrate the workflow between sampling methods and foundation model calls. We demonstrate the efficiency of UQE on data analytics across different modalities, including images, dialogs and reviews, across a range of useful query types, including conditional aggregation, semantic retrieval and abstraction aggregation.
Refining Boolean models with the partial most permissive scheme
Authors: Nadine Ben Boina, Brigitte Mossé, Anaïs Baudot, Élisabeth Remy
Date: 2024-07-13 00:00:00

Motivation: In systems biology, modelling strategies aim to decode how molecular components interact to generate dynamical behaviour. Boolean modelling is more and more used, but the description of the dynamics from two-levels components may be too limited to capture certain dynamical properties. %However, in Boolean models, the description of the dynamics may be too limited to capture certain dynamical properties. Multivalued logical models can overcome this limitation by allowing more than two levels for each component. However, multivaluing a Boolean model is challenging. Results: We present MRBM, a method for efficiently identifying the components of a Boolean model to be multivalued in order to capture specific fixed-point reachabilities in the asynchronous dynamics. To this goal, we defined a new updating scheme locating reachability properties in the most permissive dynamics. MRBM is supported by mathematical demonstrations and illustrated on a toy model and on two models of stem cell differentiation.
AI-Based Copyright Detection Of An Image In a Video Using Degree Of Similarity And Image Hashing
Authors: Ashutosh, Rahul Jashvantbhai Pandya
Date: 2024-06-14 00:00:00
Tasks: image-classification, Image Classification

The expanse of information available over the internet makes it difficult to identify whether a specific work is a replica or a duplication of a protected work, especially if we talk about visual representations. Strategies are planned to identify the utilization of the copyrighted image in a report. Still, we want to resolve the issue of involving a copyrighted image in a video and a calculation that could recognize the degree of similarity of the copyrighted picture utilized in the video, even for the pieces of the video that are not featured a lot and in the end perform characterization errands on those edges. Machine learning (ML) and artificial intelligence (AI) are vital to address this problem. Numerous associations have been creating different calculations to screen the identification of copyrighted work. This work means concentrating on those calculations, recognizing designs inside the information, and fabricating a more reasonable model for copyrighted image classification and detection. We have used different algorithms like- Image Processing, Convolutional Neural Networks (CNN), Image hashing, etc. Keywords- Copyright, Artificial Intelligence(AI), Copyrighted Image, Convolutional Neural Network(CNN), Image processing, Degree of similarity, Image Hashing.