OpenRaiser/Intern-Atlas huggingface dataset:
Sample-Specific Debiasing for Better Image-Text Models
Venue: arXiv cs.LG 2023
Year: 2023

Self-supervised representation learning on image-text data facilitates crucial medical applications, such as image classification, visual grounding, and cross-modal retrieval. One common approach involves contrasting semantically similar (positive) and dissimilar (negative) pairs of data points. Drawing negative samples uniformly from the training data set introduces false negatives, i.e., samples that are treated as dissimilar but belong to the same class. In healthcare data, the underlying class distribution is nonuniform, implying that false negatives occur at a highly variable rate. To improve the quality of learned representations, we develop a novel approach that corrects for false negatives. Our method can be viewed as a variant of debiased contrastive learning that uses estimated sample-specific class probabilities. We provide theoretical analysis of the objective function and demonstrate the proposed approach on both image and paired image-text data sets. Our experiments illustrate empirical advantages of sample-specific debiasing.
Connector 0.5: A unified framework for graph representation learning
Venue: arXiv cs.LG 2023
Year: 2023

Graph representation learning models aim to represent the graph structure and its features into low-dimensional vectors in a latent space, which can benefit various downstream tasks, such as node classification and link prediction. Due to its powerful graph data modelling capabilities, various graph embedding models and libraries have been proposed to learn embeddings and help researchers ease conducting experiments. In this paper, we introduce a novel graph representation framework covering various graph embedding models, ranging from shallow to state-of-the-art models, namely Connector. First, we consider graph generation by constructing various types of graphs with different structural relations, including homogeneous, signed, heterogeneous, and knowledge graphs. Second, we introduce various graph representation learning models, ranging from shallow to deep graph embedding models. Finally, we plan to build an efficient open-source framework that can provide deep graph embedding models to represent structural relations in graphs. The framework is available at https://github.com/NSLab-CUK/Connector.
Making Models Shallow Again: Jointly Learning to Reduce Non-Linearity and Depth for Latency-Efficient Private Inference
Venue: arXiv cs.LG 2023
Year: 2023

Large number of ReLU and MAC operations of Deep neural networks make them ill-suited for latency and compute-efficient private inference. In this paper, we present a model optimization method that allows a model to learn to be shallow. In particular, we leverage the ReLU sensitivity of a convolutional block to remove a ReLU layer and merge its succeeding and preceding convolution layers to a shallow block. Unlike existing ReLU reduction methods, our joint reduction method can yield models with improved reduction of both ReLUs and linear operations by up to 1.73x and 1.47x, respectively, evaluated with ResNet18 on CIFAR-100 without any significant accuracy-drop.
Understanding the Security and Performance of the Web Presence of Hospitals: A Measurement Study
Venue: arXiv cs.CR 2023
Year: 2023

Using a total of 4,774 hospitals categorized as government, non-profit, and proprietary hospitals, this study provides the first measurement-based analysis of hospitals' websites and connects the findings with data breaches through a correlation analysis. We study the security attributes of three categories, collectively and in contrast, against domain name, content, and SSL certificate-level features. We find that each type of hospital has a distinctive characteristic of its utilization of domain name registrars, top-level domain distribution, and domain creation distribution, as well as content type and HTTP request features. Security-wise, and consistent with the general population of websites, only 1\% of government hospitals utilized DNSSEC, in contrast to 6\% of the proprietary hospitals. Alarmingly, we found that 25\% of the hospitals used plain HTTP, in contrast to 20\% in the general web population. Alarmingly too, we found that 8\%-84\% of the hospitals, depending on their type, had some malicious contents, which are mostly attributed to the lack of maintenance. We conclude with a correlation analysis against 414 confirmed and manually vetted hospitals' data breaches. Among other interesting findings, our study highlights that the security attributes highlighted in our analysis of hospital websites are forming a very strong indicator of their likelihood of being breached. Our analyses are the first step towards understanding patient online privacy, highlighting the lack of basic security in many hospitals' websites and opening various potential research directions.
Machine Vision-Based Crop-Load Estimation Using YOLOv8
Venue: arXiv cs.RO 2023
Year: 2023

Labor shortages in fruit crop production have prompted the development of mechanized and automated machines as alternatives to labor-intensive orchard operations such as harvesting, pruning, and thinning. Agricultural robots capable of identifying tree canopy parts and estimating geometric and topological parameters, such as branch diameter, length, and angles, can optimize crop yields through automated pruning and thinning platforms. In this study, we proposed a machine vision system to estimate canopy parameters in apple orchards and determine an optimal number of fruit for individual branches, providing a foundation for robotic pruning, flower thinning, and fruitlet thinning to achieve desired yield and quality.Using color and depth information from an RGB-D sensor (Microsoft Azure Kinect DK), a YOLOv8-based instance segmentation technique was developed to identify trunks and branches of apple trees during the dormant season. Principal Component Analysis was applied to estimate branch diameter (used to calculate limb cross-sectional area, or LCSA) and orientation. The estimated branch diameter was utilized to calculate LCSA, which served as an input for crop-load estimation, with larger LCSA values indicating a higher potential fruit-bearing capacity.RMSE for branch diameter estimation was 2.08 mm, and for crop-load estimation, 3.95. Based on commercial apple orchard management practices, the target crop-load (number of fruit) for each segmented branch was estimated with a mean absolute error (MAE) of 2.99 (ground truth crop-load was 6 apples per LCSA). This study demonstrated a promising workflow with high performance in identifying trunks and branches of apple trees in dynamic commercial orchard environments and integrating farm management practices into automated decision-making.
Systems Modeling for novice engineers to comprehend software products better
Venue: arXiv cs.SE 2023
Year: 2023

One of the key challenges for a novice engineer in a product company is to comprehend the product sufficiently and quickly. It can take anywhere from six months to several years for them to attain mastery but they need to start delivering results much before. SaaS (Software-as-a-Service) products have sophisticated system architecture which adds to the time and effort of understanding them. On the other hand, time available to new hires for product understanding continues to be short and getting shorter, given the pressure to deliver more in less time. Constructivist theory views learning as a personal process in which the learner constructs new knowledge for themselves. Building and refining a mental model is the key way in which they learn, similar to how the brain operates. This paper presents an approach to improve system comprehension process by using a system model that a) acts as a transitional object to aid and refine the mental model of the learner, and b) captures the current understanding of the dynamics of the software system in a way that can be reasoned with and simulated. We have adapted discrete systems modeling techniques and used a transition system as a lightweight modeling language. Such a model can be used by novice engineers during their product ramp-up phase to build a model of the software system that captures their knowledge of the system and aid their mental model. The paper also presents a learning approach in which the learners create and refine these models iteratively using the available and newly uncovered knowledge about the software system. We hypothesize that by leveraging this modeling language and approach, novice engineers can reduce the time it takes them to achieve desired proficiency level of system comprehension. This paper presents early ideas on this language and approach.
The physical Church thesis and the sensitivity to initial conditions
Venue: arXiv cs.LO 2023
Year: 2023

The physical Church thesis is a thesis about nature that expresses that all that can be computed by a physical system-a machine-is computable in the sense of computability theory. At a first look, this thesis seems contradictory with the existence, in nature, of chaotic dynamical systems, that is systems whose evolution cannot be ''computed'' because of their sensitivity to initial conditions. The goal of this note is to show that there exist dynamical systems that are both computable and chaotic, and thus that the existence in nature of chaotic dynamical system is not, per se, a refutation of the physical Church thesis. Thus, chaos seems to be compatible with computability, in the same way as it is compatible with determinism.
Streamlined Global and Local Features Combinator (SGLC) for High Resolution Image Dehazing
Venue: arXiv cs.CV 2023
Year: 2023

Image Dehazing aims to remove atmospheric fog or haze from an image. Although the Dehazing models have evolved a lot in recent years, few have precisely tackled the problem of High-Resolution hazy images. For this kind of image, the model needs to work on a downscaled version of the image or on cropped patches from it. In both cases, the accuracy will drop. This is primarily due to the inherent failure to combine global and local features when the image size increases. The Dehazing model requires global features to understand the general scene peculiarities and the local features to work better with fine and pixel details. In this study, we propose the Streamlined Global and Local Features Combinator (SGLC) to solve these issues and to optimize the application of any Dehazing model to High-Resolution images. The SGLC contains two successive blocks. The first is the Global Features Generator (GFG) which generates the first version of the Dehazed image containing strong global features. The second block is the Local Features Enhancer (LFE) which improves the local feature details inside the previously generated image. When tested on the Uformer architecture for Dehazing, SGLC increased the PSNR metric by a significant margin. Any other model can be incorporated inside the SGLC process to improve its efficiency on High-Resolution input data.
GENIE-NF-AI: Identifying Neurofibromatosis Tumors using Liquid Neural Network (LTC) trained on AACR GENIE Datasets
Venue: arXiv cs.LG 2023
Year: 2023

In recent years, the field of medicine has been increasingly adopting artificial intelligence (AI) technologies to provide faster and more accurate disease detection, prediction, and assessment. In this study, we propose an interpretable AI approach to diagnose patients with neurofibromatosis using blood tests and pathogenic variables. We evaluated the proposed method using a dataset from the AACR GENIE project and compared its performance with modern approaches. Our proposed approach outperformed existing models with 99.86% accuracy. We also conducted NF1 and interpretable AI tests to validate our approach. Our work provides an explainable approach model using logistic regression and explanatory stimulus as well as a black-box model. The explainable models help to explain the predictions of black-box models while the glass-box models provide information about the best-fit features. Overall, our study presents an interpretable AI approach for diagnosing patients with neurofibromatosis and demonstrates the potential of AI in the medical field.
A Comparative Analysis of Multiple Methods for Predicting a Specific Type of Crime in the City of Chicago
Venue: arXiv cs.LG 2023
Year: 2023

Researchers regard crime as a social phenomenon that is influenced by several physical, social, and economic factors. Different types of crimes are said to have different motivations. Theft, for instance, is a crime that is based on opportunity, whereas murder is driven by emotion. In accordance with this, we examine how well a model can perform with only spatiotemporal information at hand when it comes to predicting a single crime. More specifically, we aim at predicting theft, as this is a crime that should be predictable using spatiotemporal information. We aim to answer the question: "How well can we predict theft using spatial and temporal features?". To answer this question, we examine the effectiveness of support vector machines, linear regression, XGBoost, Random Forest, and k-nearest neighbours, using different imbalanced techniques and hyperparameters. XGBoost showed the best results with an F1-score of 0.86.
Effect of latent space distribution on the segmentation of images with multiple annotations
Venue: arXiv cs.CV 2023
Year: 2023

We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net by allowing more general forms of the Gaussian distribution as the latent space distribution that can better approximate the uncertainty in the reference segmentations. We study the effect the choice of latent space distribution has on capturing the variation in the reference segmentations for lung tumors and white matter hyperintensities in the brain. We show that the choice of distribution affects the sample diversity of the predictions and their overlap with respect to the reference segmentations. We have made our implementation available at https://github.com/ishaanb92/GeneralizedProbabilisticUNet
Towards clinical AI fairness: A translational perspective
Venue: arXiv cs.CY 2023
Year: 2023

Artificial intelligence (AI) has demonstrated the ability to extract insights from data, but the issue of fairness remains a concern in high-stakes fields such as healthcare. Despite extensive discussion and efforts in algorithm development, AI fairness and clinical concerns have not been adequately addressed. In this paper, we discuss the misalignment between technical and clinical perspectives of AI fairness, highlight the barriers to AI fairness' translation to healthcare, advocate multidisciplinary collaboration to bridge the knowledge gap, and provide possible solutions to address the clinical concerns pertaining to AI fairness.
A Secure Medical Record Sharing Scheme Based on Blockchain and Two-fold Encryption
Venue: arXiv cs.CR 2023
Year: 2023

Usually, a medical record (MR) contains the patients disease-oriented sensitive information. In addition, the MR needs to be shared among different bodies, e.g., diagnostic centres, hospitals, physicians, etc. Hence, retaining the privacy and integrity of MR is crucial. A blockchain based secure MR sharing system can manage these aspects properly. This paper proposes a blockchain based electronic (e-) MR sharing scheme that (i) considers the medical image and the text as the input, (ii) enriches the data privacy through a two-fold encryption mechanism consisting of an asymmetric cryptosystem and the dynamic DNA encoding, (iii) assures data integrity by storing the encrypted e-MR in the distinct block designated for each user in the blockchain, and (iv) eventually, enables authorized entities to regain the e-MR through decryption. Preliminary evaluations, analyses, comparisons with state-of-the-art works, etc., imply the efficacy of the proposed scheme.
A Secure Land Record Management System using Blockchain Technology
Venue: arXiv cs.CR 2023
Year: 2023

A land record (LR) contains very sensitive information related to land e.g. owner, buyer, etc. Currently, almost all over the world, the LR is maintained by different governmental offices and most of them maintain the LR with paper-based approach. Some of the works focus to digitalize the existing land record management system (LRMS) but with some security concerns. A blockchain-based LRMS can be effective enough to solve the existing issues. This paper proposes a blockchain-based LRMS that (i) digitalizes the existing paper-based system, (ii) ensures LR privacy using an asymmetric cryptosystem, (iii) preserves LR integrity, (iv) facilitates a platform for trading land through an advertising agency, and (v) accelerates the process of changing ownership that saves time significantly. Besides, this paper also proposes a new way of character to integer mapping named C2I table that reduces around 33% overhead of text to integer conversion compared to ASCII table. The experimental results, analyses, and comparisons indicate the effectiveness of the proposed LRMS over the state-of-the-art systems.
Oracle R12 EBusiness Suite Role Based Access Control and Roles Lifecycle Management
Venue: arXiv cs.CR 2023
Year: 2023

Oracle E-Business Suite R12 is a widely used ERP solution that provides integrated view of information across multiple functions and sources. It allows for simplified business process tools for Shared service model e.g. Centralized Operation where multiple operating units can be supported. Security considerations are vital for such operations in large enterprises. R12 introduced Role Based Access Control security based on ANSI RBAC standard. R12 RBAC implementation is challenged with lack of Roles Lifecycle Management (RLM) process which also contributes to challenges such as Segregation of duty (SOD), and controlling access to PII for multi-country operation for common functional areas. The paper will propose a possible Roles Lifecycle Management process.
Key-value information extraction from full handwritten pages
Venue: arXiv cs.CV 2023
Year: 2023

We propose a Transformer-based approach for information extraction from digitized handwritten documents. Our approach combines, in a single model, the different steps that were so far performed by separate models: feature extraction, handwriting recognition and named entity recognition. We compare this integrated approach with traditional two-stage methods that perform handwriting recognition before named entity recognition, and present results at different levels: line, paragraph, and page. Our experiments show that attention-based models are especially interesting when applied on full pages, as they do not require any prior segmentation step. Finally, we show that they are able to learn from key-value annotations: a list of important words with their corresponding named entities. We compare our models to state-of-the-art methods on three public databases (IAM, ESPOSALLES, and POPP) and outperform previous performances on all three datasets.
Integrated Architecture for Neural Networks and Security Primitives using RRAM Crossbar
Venue: arXiv cs.ET 2023
Year: 2023

This paper proposes an architecture that integrates neural networks (NNs) and hardware security modules using a single resistive random access memory (RRAM) crossbar. The proposed architecture enables using a single crossbar to implement NN, true random number generator (TRNG), and physical unclonable function (PUF) applications while exploiting the multi-state storage characteristic of the RRAM crossbar for the vector-matrix multiplication operation required for the implementation of NN. The TRNG is implemented by utilizing the crossbar's variation in device switching thresholds to generate random bits. The PUF is implemented using the same crossbar initialized as an entropy source for the TRNG. Additionally, the weights locking concept is introduced to enhance the security of NNs by preventing unauthorized access to the NN weights. The proposed architecture provides flexibility to configure the RRAM device in multiple modes to suit different applications. It shows promise in achieving a more efficient and compact design for the hardware implementation of NNs and security primitives.
Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models
Venue: arXiv cs.LG 2023
Year: 2023

Recent work in XAI for eye tracking data has evaluated the suitability of feature attribution methods to explain the output of deep neural sequence models for the task of oculomotric biometric identification. These methods provide saliency maps to highlight important input features of a specific eye gaze sequence. However, to date, its localization analysis has been lacking a quantitative approach across entire datasets. In this work, we employ established gaze event detection algorithms for fixations and saccades and quantitatively evaluate the impact of these events by determining their concept influence. Input features that belong to saccades are shown to be substantially more important than features that belong to fixations. By dissecting saccade events into sub-events, we are able to show that gaze samples that are close to the saccadic peak velocity are most influential. We further investigate the effect of event properties like saccadic amplitude or fixational dispersion on the resulting concept influence.
Tensor Decomposition for Model Reduction in Neural Networks: A Review
Venue: arXiv cs.LG 2023
Year: 2023

Modern neural networks have revolutionized the fields of computer vision (CV) and Natural Language Processing (NLP). They are widely used for solving complex CV tasks and NLP tasks such as image classification, image generation, and machine translation. Most state-of-the-art neural networks are over-parameterized and require a high computational cost. One straightforward solution is to replace the layers of the networks with their low-rank tensor approximations using different tensor decomposition methods. This paper reviews six tensor decomposition methods and illustrates their ability to compress model parameters of convolutional neural networks (CNNs), recurrent neural networks (RNNs) and Transformers. The accuracy of some compressed models can be higher than the original versions. Evaluations indicate that tensor decompositions can achieve significant reductions in model size, run-time and energy consumption, and are well suited for implementing neural networks on edge devices.