Reviewerly/exHarmony huggingface dataset:
Tabib : Chatbot for Healthcare Automation with Audio Assistance using Artificial Intelligence

Individuals must prioritize health care in order to live a happy and healthy life. However, consulting a doctor in the event of a health problem is not always straightforward. The proposed concept is to use Artificial Intelligence to con- struct a chatbot for healthcare as a virtual assistant. Natural language pro- cessing and neural networks will be used to train the medical database in the healthcare chatbot system. The healthcare chatbot was created to lower total healthcare expenses and enhance access to medical knowledge. There are certain chatbots that operate as medical reference books, providing patients with additional knowledge about their ailment and assisting them in improving their health. In the proposed paper the chatbot system converses with patients about their health difficulties via voice-to-voice & text-to-text and offers a cus- tomized diagnosis based on their symptoms. In addition to text assistance this chat bot has audio assistance feature that massively help the disabled (visually impaired) patients to access the system. This feature eliminates the restric- tions that visually impaired patients face with currently available text enabled healthcare chat bots. We will be using the current location of the user and provide information about all doctors that are in and around the users loca- tion. Users can book appointments with different doctors in their locality. As a result, people will be aware of their health status and get immediate medication and will be aware of appropriate therapies.
Numerical evidence against advantage with quantum fidelity kernels on classical data

Quantum machine learning techniques are commonly considered one of the most promising candidates for demonstrating practical quantum advantage. In particular, quantum kernel methods have been demonstrated to be able to learn certain classically intractable functions efficiently if the kernel is well aligned with the target function. In the more general case, quantum kernels are known to suffer from exponential ``flattening'' of the spectrum as the number of qubits grows, preventing generalization and necessitating the control of the inductive bias by hyperparameters. We show that the general-purpose hyperparameter-tuning techniques proposed to improve the generalization of quantum kernels lead to the kernel becoming well approximated by a classical kernel, removing the possibility of quantum advantage. We provide extensive numerical evidence for this phenomenon utilizing multiple previously studied quantum feature maps and both synthetic and real data. Our results show that unless novel techniques are developed to control the inductive bias of quantum kernels, they are unlikely to provide a quantum advantage on classical data that lacks special structure.
Hazy Images Segmentation Method Based on Improved DeeplabV3+

To address the issue of decreased segmentation accuracy in foggy images and the difficulty in accurately segmenting heavy fog regions in traditional semantic segmentation models, we propose an improved segmentation model that combines image Levels adjustment and attention mechanisms.Firstly, the fog density of the image is estimated using an atmospheric scattering model, and the image is adjusted based on the estimated fog density to highlight the information that was originally obscured by the fog.A dual branch input (DB Input), is constructed for both heavy fog and light fog areas to enhance the feature learning of the model without destroying the initial information of the foggy image.An RCCA module, which is a spatial domain attention mechanism, is introduced at the end of the dual branch input to enhance the region attention ability of the module in different branches.Experiments are conducted on the datasets Foggy Cityscapes and Foggy Uavid.The results show that the improved model achieves 70.6% and 66.7% in the mIOU accuracy, respectively, which is a 5.2% and 5.4% improvement over the original model, indicating better segmentation results.
A detailed study of interpretability of deep neural network based top taggers

Abstract Recent developments in the methods of explainable artificial intelligence (XAI) allow researchers to explore the inner workings of deep neural networks (DNNs), revealing crucial information about input–output relationships and realizing how data connects with machine learning models. In this paper we explore interpretability of DNN models designed to identify jets coming from top quark decay in high energy proton–proton collisions at the Large Hadron Collider. We review a subset of existing top tagger models and explore different quantitative methods to identify which features play the most important roles in identifying the top jets. We also investigate how and why feature importance varies across different XAI metrics, how correlations among features impact their explainability, and how latent space representations encode information as well as correlate with physically meaningful quantities. Our studies uncover some major pitfalls of existing XAI methods and illustrate how they can be overcome to obtain consistent and meaningful interpretation of these models. We additionally illustrate the activity of hidden layers as neural activation pattern diagrams and demonstrate how they can be used to understand how DNNs relay information across the layers and how this understanding can help to make such models significantly simpler by allowing effective model reoptimization and hyperparameter tuning. These studies not only facilitate a methodological approach to interpreting models but also unveil new insights about what these models learn. Incorporating these observations into augmented model design, we propose the particle flow interaction network model and demonstrate how interpretability-inspired model augmentation can improve top tagging performance.
Adaptive Region-Specific Loss for Improved Medical Image Segmentation

Defining the loss function is an important part of neural network design and critically determines the success of deep learning modeling. A significant shortcoming of the conventional loss functions is that they weight all regions in the input image volume equally, despite the fact that the system is known to be heterogeneous (i.e., some regions can achieve high prediction performance more easily than others). Here, we introduce a region-specific loss to lift the implicit assumption of homogeneous weighting for better learning. We divide the entire volume into multiple sub-regions, each with an individualized loss constructed for optimal local performance. Effectively, this scheme imposes higher weightings on the sub-regions that are more difficult to segment, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vice versa</i> . Furthermore, the regional false positive and false negative errors are computed for each input image during a training step and the regional penalty is adjusted accordingly to enhance the overall accuracy of the prediction. Using different public and in-house medical image datasets, we demonstrate that the proposed regionally adaptive loss paradigm outperforms conventional methods in the multi-organ segmentations, without any modification to the neural network architecture or additional data preparation.
InstanceFormer: An Online Video Instance Segmentation Framework

Recent transformer-based offline video instance segmentation (VIS) approaches achieve encouraging results and significantly outperform online approaches. However, their reliance on the whole video and the immense computational complexity caused by full Spatio-temporal attention limit them in real-life applications such as processing lengthy videos. In this paper, we propose a single-stage transformer-based efficient online VIS framework named InstanceFormer, which is especially suitable for long and challenging videos. We propose three novel components to model short-term and long-term dependency and temporal coherence. First, we propagate the representation, location, and semantic information of prior instances to model short-term changes. Second, we propose a novel memory cross-attention in the decoder, which allows the network to look into earlier instances within a certain temporal window. Finally, we employ a temporal contrastive loss to impose coherence in the representation of an instance across all frames. Memory attention and temporal coherence are particularly beneficial to long-range dependency modeling, including challenging scenarios like occlusion. The proposed InstanceFormer outperforms previous online benchmark methods by a large margin across multiple datasets. Most importantly, InstanceFormer surpasses offline approaches for challenging and long datasets such as YouTube-VIS-2021 and OVIS. Code is available at https://github.com/rajatkoner08/InstanceFormer.
Towards Good Practices for Missing Modality Robust Action Recognition

Standard multi-modal models assume the use of the same modalities in training and inference stages. However, in practice, the environment in which multi-modal models operate may not satisfy such assumption. As such, their performances degrade drastically if any modality is missing in the inference stage. We ask: how can we train a model that is robust to missing modalities? This paper seeks a set of good practices for multi-modal action recognition, with a particular interest in circumstances where some modalities are not available at an inference time. First, we show how to effectively regularize the model during training (e.g., data augmentation). Second, we investigate on fusion methods for robustness to missing modalities: we find that transformer-based fusion shows better robustness for missing modality than summation or concatenation. Third, we propose a simple modular network, ActionMAE, which learns missing modality predictive coding by randomly dropping modality features and tries to reconstruct them with the remaining modality features. Coupling these good practices, we build a model that is not only effective in multi-modal action recognition but also robust to modality missing. Our model achieves the state-of-the-arts on multiple benchmarks and maintains competitive performances even in missing modality scenarios.
Compact Transformer Tracker with Correlative Masked Modeling

Transformer framework has been showing superior performances in visual object tracking for its great strength in information aggregation across the template and search image with the well-known attention mechanism. Most recent advances focus on exploring attention mechanism variants for better information aggregation. We find these schemes are equivalent to or even just a subset of the basic self-attention mechanism. In this paper, we prove that the vanilla self-attention structure is sufficient for information aggregation, and structural adaption is unnecessary. The key is not the attention structure, but how to extract the discriminative feature for tracking and enhance the communication between the target and search image. Based on this finding, we adopt the basic vision transformer (ViT) architecture as our main tracker and concatenate the template and search image for feature embedding. To guide the encoder to capture the invariant feature for tracking, we attach a lightweight correlative masked decoder which reconstructs the original template and search image from the corresponding masked tokens. The correlative masked decoder serves as a plugin for the compact transformer tracker and is skipped in inference. Our compact tracker uses the most simple structure which only consists of a ViT backbone and a box head, and can run at 40 fps. Extensive experiments show the proposed compact transform tracker outperforms existing approaches, including advanced attention variants, and demonstrates the sufficiency of self-attention in tracking tasks. Our method achieves state-of-the-art performance on five challenging datasets, along with the VOT2020, UAV123, LaSOT, TrackingNet, and GOT-10k benchmarks. Our project is available at https://github.com/HUSTDML/CTTrack.
Digital Audio Protection with Confusion and Diffusion Scheme Using Double-Scroll Chaotic Function

This article describes a new idea for protecting digital audio with a confusion and diffusion scheme based on the modification of the double-scroll function and SHA-256 function.In the first scheme, the confusion process is carried out by scrambling dual channels of plain audio using the keystream of the double-scroll function in the form of the proposed new nonlinear transformation function.The initial value of the double scroll function is obtained through the SHA-256 function.In the next scheme, the diffusion process is carried out by substituting the value of the dual channels based on the nonlinear transformation function, resulting in cipher audio.The proposed algorithm based on this new idea produces a very large key space of .This means that this algorithm is highly robust to brute force attacks.Simulation of the algorithm based on the test data used produces a histogram of uniformly distributed cipher audio, and the correlation coefficient is negligible (toward zero), which can be interpreted as uncorrelated.This means that this digital audio encryption algorithm is highly resistant to statistical attacks.Another performance resulting from the algorithm in this article is that it is excellent for doing cipher and decrypted audio with reference to the PSNR value of the cipher audio being negligible (4.66037-5.04020dB), and the PSNR value of the decrypted audio is much greater (69.28398-81.14115dB).
Contour Information-Guided Multi-Scale Feature Detection Method for Visible-Infrared Pedestrian Detection

Infrared pedestrian target detection is affected by factors such as the low resolution and contrast of infrared pedestrian images, as well as the complexity of the background and the presence of multiple targets occluding each other, resulting in indistinct target features. To address these issues, this paper proposes a method to enhance the accuracy of pedestrian target detection by employing contour information to guide multi-scale feature detection. This involves analyzing the shapes and edges of the targets in infrared images at different scales to more accurately identify and differentiate them from the background and other targets. First, we propose a preprocessing method to suppress background interference and extract color information from visible images. Second, we propose an information fusion residual block combining a U-shaped structure and residual connection to form a feature extraction network. Then, we propose an attention mechanism based on a contour information-guided approach to guide the network to extract the depth features of pedestrian targets. Finally, we use the clustering method of mIoU to generate anchor frame sizes applicable to the KAIST pedestrian dataset and propose a hybrid loss function to enhance the network’s adaptability to pedestrian targets. The extensive experimental results show that the method proposed in this paper outperforms other comparative algorithms in pedestrian detection, proving its superiority.
Subjective and Objective Analysis of Streamed Gaming Videos

The rising popularity of online User-Generated-Content (UGC) in the form of streamed and shared videos, has hastened the development of perceptual Video Quality Assessment (VQA) models, which can be used to help optimize their delivery. Gaming videos, which are a relatively new type of UGC videos, are created when skilled and casual gamers post videos of their gameplay. These kinds of screenshots of UGC gameplay videos have become extremely popular on major streaming platforms like YouTube and Twitch. Synthetically-generated gaming content presents challenges to existing VQA algorithms, including those based on natural scene/video statistics models. Synthetically generated gaming content presents different statistical behavior than naturalistic videos. A number of studies have been directed towards understanding the perceptual characteristics of professionally generated gaming videos arising in gaming video streaming, online gaming, and cloud gaming. However, little work has been done on understanding the quality of UGC gaming videos, and how it can be characterized and predicted. Towards boosting the progress of gaming video VQA model development, we conducted a comprehensive study of subjective and objective VQA models on UGC gaming videos. To do this, we created a novel UGC gaming video resource, called the LIVE-YouTube Gaming video quality (LIVE-YT-Gaming) database, comprised of 600 real UGC gaming videos. We conducted a subjective human study on this data, yielding 18,600 human quality ratings recorded by 61 human subjects. We also evaluated a number of state-of-the-art (SOTA) VQA models on the new database, including a new one, called GAME-VQP, based on both natural video statistics and CNN-learned features. To help support work in this field, we are making the new LIVE-YT-Gaming Database, along with code for GAME-VQP, publicly available through the link: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://live.ece.utexas.edu/research/LIVE-YT-Gaming/index.html</uri> .
Cite-seeing and reviewing: A study on citation bias in peer review

Citations play an important role in researchers' careers as a key factor in evaluation of scientific impact. Many anecdotes advice authors to exploit this fact and cite prospective reviewers to try obtaining a more positive evaluation for their submission. In this work, we investigate if such a citation bias actually exists: Does the citation of a reviewer's own work in a submission cause them to be positively biased towards the submission? In conjunction with the review process of two flagship conferences in machine learning and algorithmic economics, we execute an observational study to test for citation bias in peer review. In our analysis, we carefully account for various confounding factors such as paper quality and reviewer expertise, and apply different modeling techniques to alleviate concerns regarding the model mismatch. Overall, our analysis involves 1,314 papers and 1,717 reviewers and detects citation bias in both venues we consider. In terms of the effect size, by citing a reviewer's work, a submission has a non-trivial chance of getting a higher score from the reviewer: an expected increase in the score is approximately 0.23 on a 5-point Likert item. For reference, a one-point increase of a score by a single reviewer improves the position of a submission by 11% on average.
Exponential Shortcut to Measurement-Induced Entanglement Phase Transitions

Recently discovered measurement-induced entanglement phase transitions in monitored quantum circuits provide a novel example of far-from-equilibrium quantum criticality. Here, we propose a highly efficient strategy for experimentally accessing these transitions through fluctuations. Instead of directly measuring entanglement entropy, which requires an exponential number of measurements in the subsystem size, our method provides a scalable approach to entanglement transitions in the presence of conserved quantities. In analogy to entanglement entropy and mutual information, we illustrate how bipartite and multipartite fluctuations can both be employed to analyze the measurement-induced criticality. Remarkably, the phase transition can be revealed by measuring fluctuations of only a handful of qubits.
Faster RCNN Target Detection Algorithm Integrating CBAM and FPN

Small targets and occluded targets will inevitably appear in the image during the shooting process due to the influence of angle, distance, complex scene, illumination intensity, and other factors. These targets have few effective pixels, few features, and no apparent features, which makes extracting their efficient features difficult and easily leads to false detection, missed detection, and repeated detection, affecting the performance of target detection models. An improved faster region convolutional neural network (RCNN) algorithm (CF-RCNN) integrating convolutional block attention module (CBAM) and feature pyramid networks (FPN) is proposed to improve the detection and recognition accuracy of small-size objects, occluded or truncated objects in complex scenes. Firstly, the CBAM mechanism is integrated into the feature extraction network to improve the detection ability of occluded or truncated objects. Secondly, the FPN-featured pyramid structure is introduced to obtain high-resolution and vital semantic data to enhance the detection effect of small-size objects. The experimental results show that the mean average precision of target detection of the improved algorithm on PASCAL VOC2012 is improved to 76.1%, which is 13.8 percentage points higher than that of the commonly used Faster RCNN and other algorithms. Furthermore, it is better than the commonly used small sample target detection algorithm.
External Evaluation of a Mammography-based Deep Learning Model for Predicting Breast Cancer in an Ethnically Diverse Population

“Just Accepted” papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To externally evaluate a mammography-based deep learning (DL) model (Mirai) in a high-risk, racially diverse population and compare its performance with other mammographic measures. Materials and Methods The authors retrospectively evaluated 6,435 screening mammograms acquired from 2,096 female patients (median age, 56.4 years [SD: 11.2]) enrolled in a hospital-based case-control study between 2006–2020. Pathologically-confirmed breast cancer was the primary outcome. Mirai scores were the primary predictors. Breast density and Breast Imaging Reporting and Data System (BI-RADS) assessment categories were comparative predictors. Performance was evaluated using area under the receiver operating characteristic curve (AUC) and concordance-index analyses. Results Mirai achieved one-and five-year AUCs of 0.71 (95% CI: 0.68, 0.74) and 0.65 (0.64, 0.67), respectively. 1-year AUCs in nondense versus dense breasts were 0.72 versus 0.58 (P = .10). There was no evidence of a difference in near-term discrimination performance between BI-RADS and Mirai (1-year AUCs, 0.73 versus 0.68, P = .34). For longer-term prediction (2–5 years), Mirai outperformed BI-RADS assessment (5-year AUC, 0.63 versus 0.54; P < .001). Using only images of the unaffected breast reduced the discriminatory performance of the DL model (P < .001 at all timepoints), suggesting that its predictions are likely dependent on the detection of ipsilateral premalignant patterns. Conclusion A mammography DL model showed good performance in a high-risk external dataset enriched for African American patients, benign breast disease, and BRCA mutation carriers, and study findings suggest that its performance is likely driven by the detection of precancerous changes. ©RSNA, 2023