pwc-archive/papers-with-abstracts huggingface dataset:
BOXREC: Recommending a Box of Preferred Outfits in Online Shopping
Authors: Debopriyo Banerjee, Krothapalli Sreenivasa Rao, Shamik Sural, Niloy Ganguly
Date: 2024-02-26 00:00:00
Tasks: Recommendation Systems

Over the past few years, automation of outfit composition has gained much attention from the research community. Most of the existing outfit recommendation systems focus on pairwise item compatibility prediction (using visual and text features) to score an outfit combination having several items, followed by recommendation of top-n outfits or a capsule wardrobe having a collection of outfits based on user's fashion taste. However, none of these consider user's preference of price-range for individual clothing types or an overall shopping budget for a set of items. In this paper, we propose a box recommendation framework - BOXREC - which at first, collects user preferences across different item types (namely, top-wear, bottom-wear and foot-wear) including price-range of each type and a maximum shopping budget for a particular shopping session. It then generates a set of preferred outfits by retrieving all types of preferred items from the database (according to user specified preferences including price-ranges), creates all possible combinations of three preferred items (belonging to distinct item types) and verifies each combination using an outfit scoring framework - BOXREC-OSF. Finally, it provides a box full of fashion items, such that different combinations of the items maximize the number of outfits suitable for an occasion while satisfying maximum shopping budget. Empirical results show superior performance of BOXREC-OSF over the baseline methods.
CycleNet: Rethinking Cycle Consistency in Text-Guided Diffusion for Image Manipulation
Authors: Sihan Xu, Ziqiao Ma, Yidong Huang, Honglak Lee, Joyce Chai
Date: 2023-10-19 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: 2k, GPU, Image Generation, Image Manipulation, Translation

Diffusion models (DMs) have enabled breakthroughs in image synthesis tasks but lack an intuitive interface for consistent image-to-image (I2I) translation. Various methods have been explored to address this issue, including mask-based methods, attention-based methods, and image-conditioning. However, it remains a critical challenge to enable unpaired I2I translation with pre-trained DMs while maintaining satisfying consistency. This paper introduces Cyclenet, a novel but simple method that incorporates cycle consistency into DMs to regularize image manipulation. We validate Cyclenet on unpaired I2I tasks of different granularities. Besides the scene and object level translation, we additionally contribute a multi-domain I2I translation dataset to study the physical state changes of objects. Our empirical studies show that Cyclenet is superior in translation consistency and quality, and can generate high-quality images for out-of-domain distributions with a simple change of the textual prompt. Cyclenet is a practical framework, which is robust even with very limited training data (around 2k) and requires minimal computational resources (1 GPU) to train. Project homepage: https://cyclenetweb.github.io/
Energy-Based Cross Attention for Bayesian Context Update in Text-to-Image Diffusion Models
Authors: Geon Yeong Park, Jeongsol Kim, Beomsu Kim, Sang Wan Lee, Jong Chul Ye
Date: 2023-06-16 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Denoising, Image Generation, Image Inpainting

Despite the remarkable performance of text-to-image diffusion models in image generation tasks, recent studies have raised the issue that generated images sometimes cannot capture the intended semantic contents of the text prompts, which phenomenon is often called semantic misalignment. To address this, here we present a novel energy-based model (EBM) framework for adaptive context control by modeling the posterior of context vectors. Specifically, we first formulate EBMs of latent image representations and text embeddings in each cross-attention layer of the denoising autoencoder. Then, we obtain the gradient of the log posterior of context vectors, which can be updated and transferred to the subsequent cross-attention layer, thereby implicitly minimizing a nested hierarchy of energy functions. Our latent EBMs further allow zero-shot compositional generation as a linear combination of cross-attention outputs from different contexts. Using extensive experiments, we demonstrate that the proposed method is highly effective in handling various image generation tasks, including multi-concept generation, text-guided image inpainting, and real and synthetic image editing. Code: https://github.com/EnergyAttention/Energy-Based-CrossAttention.
Multi-scale Target-Aware Framework for Constrained Splicing Detection and Localization
Authors: Yuxuan Tan, Yuanman Li∗
Date: 2023-11-29 00:00:00
Proceeding: ACM 2023 11

ABSTRACT
Constrained image splicing detection and localization (CISDL) is a
fundamental task of multimedia forensics, which detects splicing
operation between two suspected images and localizes the spliced
region on both images. Recent works regard it as a deep matching
problem and have made significant progress. However, existing
frameworks typically perform feature extraction and correlation
matching as separate processes, which may hinder the model’s
ability to learn discriminative features for matching and can be susceptible to interference from ambiguous background pixels. In this
work, we propose a multi-scale target-aware framework to couple
feature extraction and correlation matching in a unified pipeline. In
contrast to previous methods, we design a target-aware attention
mechanism that jointly learns features and performs correlation
matching between the probe and donor images. Our approach can
effectively promote the collaborative learning of related patches,
and perform mutual promotion of feature learning and correlation
matching. Additionally, in order to handle scale transformations,
we introduce a multi-scale projection method, which can be readily integrated into our target-aware framework that enables the
attention process to be conducted between tokens containing information of varying scales. Our experiments demonstrate that our
model, which uses a unified pipeline, outperforms state-of-the-art
methods on several benchmark datasets and is robust against scale
transformations
Quantifying the Resolution of a Template after Image Registration
Authors: Matthias Glock, Thomas Hotz
Date: 2024-02-27 00:00:00
Tasks: Affine Image Registration, Anatomy, Image Registration, Medical Image Registration, Template Matching

In many image processing applications (e.g. computational anatomy) a groupwise registration is performed on a sample of images and a template image is simultaneously generated. From the template alone it is in general unclear to which extent the registered images are still misaligned, which means that some regions of the template represent the structural features in the sample images less reliably than others. In a sense, the template exhibits a lower resolution there. Guided by characteristic examples of misaligned image features in one dimension, we develop a visual measure to quantify the resolution at each location of a template which is based on the observation that misalignments between the registered sample images are reduced by smoothing with the strength of the smoothing being related to the magnitude of the misalignment. Finally the resulting resolution measure is applied to example datasets in two and three dimensions. The corresponding code is publicly available on GitHub.
Screw defect detection system based on AI image recognition technology
Authors: HangHong Kuo, JuinMing Xu, ChaoTang Yu, JunJuh Yan
Date: 2021-04-08 00:00:00
Proceeding: 2020 2021 4
Tasks: Defect Detection

In the past ten years, smart manufacturing has been widely discussed and gradually introduced into various manufacturing fields. Since Germany proposed the concept of “Industry 4.0” in 2011, it has been spreading and feverish all over the world. For Industry 4.0, information digitization, intelligent defect detection and database platform management are their main core technologies. Aiming at a large screw industry manufacturing field in central and southern Taiwan, this paper proposes a screw defect detection system based on AI image recognition technology to detect damage to the nut during the “molding” process in the screw production process, and it is determined whether the inspected screw passes the inspection. The recognition result is given as shown in Figure 1. This paper uses 500 non-defective screw samples and 20 defective screw samples provided by the screw factory. The above samples collected real-time images through the sampling structure designed in this article, and we adopt Microsoft Corporation's ML.NET suite to model AI images, and uses the following four deep learning models: ResNetV2 50, ResNetV2 101, InceptionV3, MoblieNetV2 for learning; in the process of learning, this article divides the data set into three types of data sets (one is the unknown set that is not used for training but mixed with correct and defective samples, and the other is used for post-training verification of mixed samples with correct and defective samples. The third is a training set for training a mixture of correct and defective samples) This arrangement is used for subsequent verification models; after training, a PC-based screw defect detection system is implemented as shown in Figure 2; finally, with Detect screw defects in the form of instant photography. After the experiment, in 1,000 repeated tests, the success rate of defect detection reached 97%, while the false positive rate was only 2%.
Enhance Eye Disease Detection using Learnable Probabilistic Discrete Latents in Machine Learning Architectures
Authors: Anirudh Prabhakaran, YeKun Xiao, Ching-Yu Cheng, Dianbo Liu
Date: 2024-01-21 00:00:00
Tasks: Decision Making

Ocular diseases, including diabetic retinopathy and glaucoma, present a significant public health challenge due to their high prevalence and potential for causing vision impairment. Early and accurate diagnosis is crucial for effective treatment and management. In recent years, deep learning models have emerged as powerful tools for analysing medical images, such as retina imaging. However, challenges persist in model relibability and uncertainty estimation, which are critical for clinical decision-making. This study leverages the probabilistic framework of Generative Flow Networks (GFlowNets) to learn the posterior distribution over latent discrete dropout masks for the classification and analysis of ocular diseases using fundus images. We develop a robust and generalizable method that utilizes GFlowOut integrated with ResNet18 and ViT models as the backbone in identifying various ocular conditions. This study employs a unique set of dropout masks - none, random, bottomup, and topdown - to enhance model performance in analyzing these fundus images. Our results demonstrate that our learnable probablistic latents significantly improves accuracy, outperforming the traditional dropout approach. We utilize a gradient map calculation method, Grad-CAM, to assess model explainability, observing that the model accurately focuses on critical image regions for predictions. The integration of GFlowOut in neural networks presents a promising advancement in the automated diagnosis of ocular diseases, with implications for improving clinical workflows and patient outcomes.
FairER: Entity Resolution with Fairness Constraints
Authors: Vasilis Efthymiou, Kostas Stefanidis, Evaggelia Pitoura, Vassilis Christophides
Date: 2021-10-30 00:00:00
Proceeding: CIKM 2021 10
Tasks: Diversity, Entity Resolution, Fairness

One of the most critical tasks for improving data quality and increasing the reliability of data analytics is Entity Resolution (ER), which aims to identify different descriptions that refer to the same real-world entity. Despite several decades of research, ER remains a challenging problem. In this survey, we highlight the novel aspects of resolving Big Data entities when we should satisfy more than one of the Big Data characteristics simultaneously (i.e., Volume and Velocity with Variety). We present the basic concepts, processing steps, and execution strategies that have been proposed by database, semantic Web, and machine learning communities in order to cope with the loose structuredness, extreme diversity, high speed, and large scale of entity descriptions used by real-world applications. We provide an end-to-end view of ER workflows for Big Data, critically review the pros and cons of existing methods, and conclude with the main open research directions.
Securing the Future: Innovations in Privacy-Preserving Integrated Sensing for Industrial Cyber-Physical Systems
Authors: Ali Ranjha
Date: 2024-01-01 00:00:00
Proceeding: IEEE Transactions on Industrial Cyber-Physical Systems (CFP) 2024 1
Tasks: Management, Privacy Preserving

In the rapidly evolving landscape of intelligent mobile devices and the impending 6G networks, the Integrated Sensing Digital Framework (ISDF) emerges as a transformative force within the Internet of Things (IoT), offering unprecedented opportunities for real-time data collection. Comprising the Data Requester (DR), Sensing-Computing Provider (SCP), and Framework Executor (FE), ISDF revolutionizes data gathering by harnessing device intelligence and eliminating the need for specialized sensors, providing a rich source of real-time, diverse data at the convergence of physical and digital realms. However, the integration of ISDF presents challenges, particularly in privacy and reliability. Privacy threats such as data content, task content, and location and identity risks must be addressed for the framework's success. Additionally, the strain on network capacity due to ISDF's reliance on intelligent mobile devices requires careful management for effective support of Ultra-Reliable and Low Latency Communications (URLLC). This proposal focuses on innovative solutions that integrate principles from Integrated Sensing, Computing, and Communications (ISCC) and Zero Touch Network and Service Management (ZSM) to establish a secure and dependable ISDF framework, meeting the evolving needs of 6G systems.
Photolithography Control System : A Case Study For Cyber-Physical System
Authors: Youbao Zhang, Huijie Huang
Date: 2024-02-24 00:00:00

Photolithography control system (PCS) is an extremely complex distributed control system, which is composed of dozens of networked microprocessors, hundreds of actuators, hundreds of thousands of sensors, and millions of lines of code. Cyber-physical system (CPS), which deeply merges computation with physical processes together, copes with complex system from a higher level of abstraction. PCS is a representative CPS. This work points out that thinking under the framework of CPS, which includes holistic perspective, model-based design, hardware/software co-design and continuous integration, could solve the issues presented in the current PCS. Although the traditional embedded system approach and the CPS approach would be coexisting in the PCS for a long time, the CPS approach is definitely the future of the PCS development.
The Random Forest Model for Analyzing and Forecasting the US Stock Market in the Context of Smart Finance
Authors: Jiajian Zheng, Duan Xin, Qishuo Cheng, Miao Tian, Le Yang
Date: 2024-02-27 00:00:00

The stock market is a crucial component of the financial market, playing a vital role in wealth accumulation for investors, financing costs for listed companies, and the stable development of the national macroeconomy. Significant fluctuations in the stock market can damage the interests of stock investors and cause an imbalance in the industrial structure, which can interfere with the macro level development of the national economy. The prediction of stock price trends is a popular research topic in academia. Predicting the three trends of stock pricesrising, sideways, and falling can assist investors in making informed decisions about buying, holding, or selling stocks. Establishing an effective forecasting model for predicting these trends is of substantial practical importance. This paper evaluates the predictive performance of random forest models combined with artificial intelligence on a test set of four stocks using optimal parameters. The evaluation considers both predictive accuracy and time efficiency.
On Trojan Signatures in Large Language Models of Code
Authors: Aftab Hussain, Md Rafiqul Islam Rabin, Mohammad Amin Alipour
Date: 2024-02-23 00:00:00
Tasks: Binary Classification, Defect Detection

Trojan signatures, as described by Fields et al. (2021), are noticeable differences in the distribution of the trojaned class parameters (weights) and the non-trojaned class parameters of the trojaned model, that can be used to detect the trojaned model. Fields et al. (2021) found trojan signatures in computer vision classification tasks with image models, such as, Resnet, WideResnet, Densenet, and VGG. In this paper, we investigate such signatures in the classifier layer parameters of large language models of source code. Our results suggest that trojan signatures could not generalize to LLMs of code. We found that trojaned code models are stubborn, even when the models were poisoned under more explicit settings (finetuned with pre-trained weights frozen). We analyzed nine trojaned models for two binary classification tasks: clone and defect detection. To the best of our knowledge, this is the first work to examine weight-based trojan signature revelation techniques for large-language models of code and furthermore to demonstrate that detecting trojans only from the weights in such models is a hard problem.
Generative Models are Self-Watermarked: Declaring Model Authentication through Re-Generation
Authors: Aditya Desu, Xuanli He, Qiongkai Xu, Wei Lu
Date: 2024-02-23 00:00:00
Tasks: Misinformation

As machine- and AI-generated content proliferates, protecting the intellectual property of generative models has become imperative, yet verifying data ownership poses formidable challenges, particularly in cases of unauthorized reuse of generated data. The challenge of verifying data ownership is further amplified by using Machine Learning as a Service (MLaaS), which often functions as a black-box system. Our work is dedicated to detecting data reuse from even an individual sample. Traditionally, watermarking has been leveraged to detect AI-generated content. However, unlike watermarking techniques that embed additional information as triggers into models or generated content, potentially compromising output quality, our approach identifies latent fingerprints inherently present within the outputs through re-generation. We propose an explainable verification procedure that attributes data ownership through re-generation, and further amplifies these fingerprints in the generative models through iterative data re-generation. This methodology is theoretically grounded and demonstrates viability and robustness using recent advanced text and image generative models. Our methodology is significant as it goes beyond protecting the intellectual property of APIs and addresses important issues such as the spread of misinformation and academic misconduct. It provides a useful tool to ensure the integrity of sources and authorship, expanding its application in different scenarios where authenticity and ownership verification are essential.
Detecting misinformation through Framing Theory: the Frame Element-based Model
Authors: Guan Wang, Rebecca Frederick, Jinglong Duan, William Wong, Verica Rupar, Weihua Li, Quan Bai
Date: 2024-02-19 00:00:00
Tasks: Misinformation

In this paper, we delve into the rapidly evolving challenge of misinformation detection, with a specific focus on the nuanced manipulation of narrative frames - an under-explored area within the AI community. The potential for Generative AI models to generate misleading narratives underscores the urgency of this problem. Drawing from communication and framing theories, we posit that the presentation or 'framing' of accurate information can dramatically alter its interpretation, potentially leading to misinformation. We highlight this issue through real-world examples, demonstrating how shifts in narrative frames can transmute fact-based information into misinformation. To tackle this challenge, we propose an innovative approach leveraging the power of pre-trained Large Language Models and deep neural networks to detect misinformation originating from accurate facts portrayed under different frames. These advanced AI techniques offer unprecedented capabilities in identifying complex patterns within unstructured data critical for examining the subtleties of narrative frames. The objective of this paper is to bridge a significant research gap in the AI domain, providing valuable insights and methodologies for tackling framing-induced misinformation, thus contributing to the advancement of responsible and trustworthy AI technologies. Several experiments are intensively conducted and experimental results explicitly demonstrate the various impact of elements of framing theory proving the rationale of applying framing theory to increase the performance in misinformation detection.
Lightweight, error-tolerant edge detection using memristor-enabled stochastic logics
Authors: Lekai Song, Pengyu Liu, Jingfang Pei, Yang Liu, Songwei Liu, Shengbo Wang, Leonard W. T. Ng, Tawfique Hasan, Kong-Pang Pun, Shuo Gao, Guohua Hu
Date: 2024-02-25 00:00:00
Tasks: Autonomous Driving, Edge Detection, Medical Diagnosis

The demand for efficient edge vision has spurred the interest in developing stochastic computing approaches for performing image processing tasks. Memristors with inherent stochasticity readily introduce probability into the computations and thus enable stochastic image processing computations. Here, we present a stochastic computing approach for edge detection, a fundamental image processing technique, facilitated with memristor-enabled stochastic logics. Specifically, we integrate the memristors with logic circuits and harness the stochasticity from the memristors to realize compact stochastic logics for stochastic number encoding and processing. The stochastic numbers, exhibiting well-regulated probabilities and correlations, can be processed to perform logic operations with statistical probabilities. This can facilitate lightweight stochastic edge detection for edge visual scenarios characterized with high-level noise errors. As a practical demonstration, we implement a hardware stochastic Roberts cross operator using the stochastic logics, and prove its exceptional edge detection performance, remarkably, with 95% less computational cost while withstanding 50% bit-flip errors. The results underscore the great potential of our stochastic edge detection approach in developing lightweight, error-tolerant edge vision hardware and systems for autonomous driving, virtual/augmented reality, medical imaging diagnosis, industrial automation, and beyond.