Reviewerly/exHarmony huggingface dataset:
In the Eye of Transformer: Global–Local Correlation for Egocentric Gaze Estimation and Beyond

Predicting human's gaze from egocentric videos serves as a critical role for human intention understanding in daily activities. In this paper, we present the first transformer-based model to address the challenging problem of egocentric gaze estimation. We observe that the connection between the global scene context and local visual information is vital for localizing the gaze fixation from egocentric video frames. To this end, we design the transformer encoder to embed the global context as one additional visual token and further propose a novel global-local correlation module to explicitly model the correlation of the global token and each local token. We validate our model on two egocentric video datasets - EGTEA Gaze + and Ego4D. Our detailed ablation studies demonstrate the benefits of our method. In addition, our approach exceeds the previous state-of-the-art model by a large margin. We also apply our model to a novel gaze saccade/fixation prediction task and the traditional action recognition problem. The consistent gains suggest the strong generalization capability of our model. We also provide additional visualizations to support our claim that global-local correlation serves a key representation for predicting gaze fixation from egocentric videos. More details can be found in our website (https://bolinlai.github.io/GLC-EgoGazeEst).
Open Science 2.0: Towards a truly collaborative research ecosystem

Conversations about open science have reached the mainstream, yet many open science practices such as data sharing remain uncommon. Our efforts towards openness therefore need to increase in scale and aim for a more ambitious target. We need an ecosystem not only where research outputs are openly shared but also in which transparency permeates the research process from the start and lends itself to more rigorous and collaborative research. To support this vision, this Essay provides an overview of a selection of open science initiatives from the past 2 decades, focusing on methods transparency, scholarly communication, team science, and research culture, and speculates about what the future of open science could look like. It then draws on these examples to provide recommendations for how funders, institutions, journals, regulators, and other stakeholders can create an environment that is ripe for improvement.
In a Perfect World: Exploring the Desires and Realities for Digitized Historical Image Archives

ABSTRACT The primary goal of this paper is to explore users' desires for digitized historical image collections, examining their desires based on different use purposes and information interaction activities. In addition, we investigate the image attributes that users wish to search from the collection. To accomplish this, we conducted 21 qualitative interviews with active users of a digitized historical photograph archive. Our findings suggest that users' desires relate to three contexts: tools, collection, and socio‐organizational issues. Moreover, our results indicate that users require support for various information interaction activities, not just searching. We found that users' desires vary based on their specific use purposes, and that users prioritize conceptual access points that can already mostly be generated through automated annotation methods. Ultimately, this study contributes to a better understanding of users' real‐life image needs and offers implications for improving access to digital image collections.
Post-Hoc Explainability of BI-RADS Descriptors in a Multi-Task Framework for Breast Cancer Detection and Segmentation

Despite recent medical advancements, breast cancer remains one of the most prevalent and deadly diseases among women. Although machine learning-based Computer-Aided Diagnosis (CAD) systems have shown potential to assist radiologists in analyzing medical images, the opaque nature of the best-performing CAD systems has raised concerns about their trustworthiness and interpretability. This paper proposes MT-BI-RADS, a novel explainable deep learning approach for tumor detection in Breast Ultrasound (BUS) images. The approach offers three levels of explanations to enable radiologists to comprehend the decision-making process in predicting tumor malignancy. Firstly, the proposed model outputs the BI-RADS categories used for BUS image analysis by radiologists. Secondly, the model employs multitask learning to concurrently segment regions in images that correspond to tumors. Thirdly, the proposed approach outputs quantified contributions of each BI-RADS descriptor toward predicting the benign or malignant class using post-hoc explanations with Shapley Values.
Time series adversarial attacks: an investigation of smooth perturbations and defense approaches

Abstract Adversarial attacks represent a threat to every deep neural network. They are particularly effective if they can perturb a given model while remaining undetectable. They have been initially introduced for image classifiers, and are well studied for this task. For time series, few attacks have yet been proposed. Most that have are adaptations of attacks previously proposed for image classifiers. Although these attacks are effective, they generate perturbations containing clearly discernible patterns such as sawtooth and spikes. Adversarial patterns are not perceptible on images, but the attacks proposed to date are readily perceptible in the case of time series. In order to generate stealthier adversarial attacks for time series, we propose a new attack that produces smoother perturbations. We introduced a function to measure the smoothness for time series. Using it, we find that smooth perturbations are harder to detect both visually, by the naked eye and by deep learning models. We also show two ways of protection against adversarial attacks: the first one by detecting the attacks using a deep model; the second one by using adversarial training to improve the robustness of a model against a specific attack, thus making it less vulnerable.
The Open Access Week in the Scholarly Blogosphere

Happy Birthday, Open Access! Last Sunday, the Berlin Declaration turned twenty years old (Pampel &amp;amp; Kindling, 2023). Its anniversary coincides with the international Open Access Week. During this action week, events promoting Open Access are organized worldwide (an overview can be found at openaccessweek.org.
Biological age estimation using circulating blood biomarkers

Abstract Biological age captures physiological deterioration better than chronological age and is amenable to interventions. Blood-based biomarkers have been identified as suitable candidates for biological age estimation. This study aims to improve biological age estimation using machine learning models and a feature-set of 60 circulating biomarkers available from the UK Biobank ( n = 306,116). We implement an Elastic-Net derived Cox model with 25 selected biomarkers to predict mortality risk (C-Index = 0.778; 95% CI [0.767–0.788]), which outperforms the well-known blood-biomarker based PhenoAge model (C-Index = 0.750; 95% CI [0.739–0.761]), providing a C-Index lift of 0.028 representing an 11% relative increase in predictive value. Importantly, we then show that using common clinical assay panels, with few biomarkers, alongside imputation and the model derived on the full set of biomarkers, does not substantially degrade predictive accuracy from the theoretical maximum achievable for the available biomarkers. Biological age is estimated as the equivalent age within the same-sex population which corresponds to an individual’s mortality risk. Values ranged between 20-years younger and 20-years older than individuals’ chronological age, exposing the magnitude of ageing signals contained in blood markers. Thus, we demonstrate a practical and cost-efficient method of estimating an improved measure of Biological Age, available to the general population.
A systematic and comprehensive review and investigation of intelligent IoT-based healthcare systems in rural societies and governments

Healthcare needs in rural areas differ significantly from those in urban areas. Addressing the healthcare challenges in rural communities is of paramount importance, as these regions often lack access to adequate healthcare facilities. Moreover, technological advancements, particularly in the realm of the Internet of Things (IoT), have brought about significant changes in the healthcare industry. IoT involves connecting real-world objects to digital devices, opening up various possibilities for improving healthcare delivery. One promising application of IoT is its use in monitoring the spread of diseases in remote villages through interconnected sensors and devices. Surprisingly, there has been a noticeable absence of comprehensive research on this topic. Therefore, the primary objective of this study is to conduct a thorough and systematic review of intelligent IoT-based healthcare systems in rural communities and their governance. The analysis covers research papers published until December 2022 to provide valuable insights for future researchers. The selected articles have been categorized into three main groups: monitoring, intelligent services, and body sensor networks. The findings indicate that IoT research has garnered significant attention within the healthcare community. Furthermore, the results illustrate the potential benefits of IoT for governments, especially in rural areas, in improving public health and strengthening economic ties. It is worth noting that establishing a robust security infrastructure is essential for implementing IoT effectively, given its innovative operational principles. In summary, this review enhances scholars' understanding of the current state of IoT research in rural healthcare settings while highlighting areas that warrant further investigation. Additionally, it keeps healthcare professionals informed about the latest advancements and applications of IoT in rural healthcare.
A Comprehensive Survey on Deep Graph Representation Learning Methods

There has been a lot of activity in graph representation learning in recent years. Graph representation learning aims to produce graph representation vectors to represent the structure and characteristics of huge graphs precisely. This is crucial since the effectiveness of the graph representation vectors will influence how well they perform in subsequent tasks like anomaly detection, connection prediction, and node classification. Recently, there has been an increase in the use of other deep-learning breakthroughs for data-based graph problems. Graph-based learning environments have a taxonomy of approaches, and this study reviews all their learning settings. The learning problem is theoretically and empirically explored. This study briefly introduces and summarizes the Graph Neural Architecture Search (G-NAS), outlines several Graph Neural Networks’ drawbacks, and suggests some strategies to mitigate these challenges. Lastly, the study discusses several potential future study avenues yet to be explored.
Scale-aware dimension-wise attention network for small ship instance segmentation in synthetic aperture radar images

Small ship instance segmentation from synthetic aperture radar (SAR) images is a challenging task. Because small ships have smaller scales, indistinct contours, and weak feature response. In addition, background interference and clutter make feature extraction of small ships more difficult. To solve this issue, we propose a scale-aware dimension-wise attention network (SA-DWA-Net) for better small ship instance segmentation in SAR images. SA-DWA-Net has two subnetworks to ensure its desirable instance segmentation of small ships. The first is a scale-aware subnetwork that can fully use low-level location-sensitive information to achieve representative small ship features. The second is a dimension-wise attention subnetwork that can fully utilize high-level semantics-sensitive information for refined small ship feature expression. We perform experiments on two open SSDD and HRSID datasets to verify the effectiveness of the proposed method. Quantitative experimental results show the state-of-the-art SAR ship instance segmentation performance of the proposed SA-DWA-Net. Specifically, SA-DWA-Net surpasses the existing best model by 2.2% box detection average precision (AP) and 5.0% mask segmentation AP on SSDD and by 2.9% box detection AP and 3.7% mask segmentation AP on HRSID. Especially, the small ship mask segmentation AP of the proposed SA-DWA-Net is higher than the existing best model by 4.4% on SSDD and 3.7% on HRSID.
ChatGPT and the General Concepts of Education: Can Artificial Intelligence-Driven Chatbots Support the Process of Language Learning?

This paper explores the correspondence between the use of ChatGPT and the generally accepted definitions of learning. The study compares the data provided by ChatGPT with well-known definitions of learning in order to determine if the use of the chatbot supports language learning. The research questions focus on the alignment between ChatGPT and the definitions of learning, the potential behavioral changes resulting from learning via ChatGPT, the learning experience provided by the chatbot, and the support it offers for language learning and the development of linguistic and social skills. The methodology of the study involves a qualitative approach, collecting data from university students who used ChatGPT to complete writing assignments. The participants were observed during their interaction with ChatGPT, and interviews were conducted to gather their perspectives. The collected data were compared to a list of definitions and concepts of learning and language learning. The results indicate that learning via ChatGPT did not lead to immediate behavioral changes, as observed in the participants’ interactions. However, the use of ChatGPT, if repeated, could provide an experience that may contribute to learning, particularly in terms of motivation and engagement. The study also found that the learning process with ChatGPT lacked the traditional elements of student-teacher interaction and specific learning materials but offered a wide range of general material for consultation. Furthermore, the use of ChatGPT was seen to support language learning and the development of linguistic and social skills if it is used with other learning strategies. This paper sheds light on the potential of AI-driven chatbots, specifically ChatGPT, for language learning in relation to the use of writing composition and dialogues which leads to more laziness and dependence unless it is used with other learning techniques in language learning context. The use of ChatGPT serves more content knowledge in all disciplines more than oral skills of language learning. It highlights the need for further research to understand the limitations and risks associated with such technology and to explore its effective integration in educational settings.
Crypto Makes AI Evolve

Adopting cryptography has given rise to a significant?evolution in AI. This chapter studies the path and stages of this evolution. We start with reviewing existing relevant surveys, noting their shortcomings, especially the lack of a close look at the evolution process and solid future roadmap. These shortcomings justify the work of this chapter. Next, we identify, define, and discuss?five consequent stages in the evolution path, including Crypto-Sensitive AI, Crypto-Adapted AI, Crypto-Friendly AI, Crypto-Enabled AI, and Crypto-Protected AI. Then, we establish a future roadmap for further research in this area, focusing on the role of QiAI.
An overview of clinical machine learning applications in neurology

<h2>Abstract</h2> Machine learning techniques for clinical applications are evolving, and the potential impact this will have on clinical neurology is important to recognize. By providing a broad overview on this growing paradigm of clinical tools, this article aims to help healthcare professionals in neurology prepare to navigate both the opportunities and challenges brought on through continued advancements in machine learning. This narrative review first elaborates on how machine learning models are organized and implemented. Machine learning tools are then classified by clinical application, with examples of uses within neurology described in more detail. Finally, this article addresses limitations and considerations regarding clinical machine learning applications in neurology.
Analysis of Elliptic Curve Cryptography &amp; RSA

In today’s digital world, the Internet is an essential component of communication networks. It provides a platform for quickly exchanging information among communicating parties. There is a risk of unauthorized persons gaining access to our sensitive information while it is being transmitted. Cryptography is one of the most effective and efficient strategies for protecting our data and it are utilized all around the world. The efficiency of a cryptography algorithm is determined by a number of parameters, one of which is the length of the key. For cryptography, key (public/private) is an essential part. To provide robust security, RSA takes larger key size. If we use larger key size, the processing performance will be slowed. As a result, processing speed will decrease and memory consumption will increase. Due to this, cryptographic algorithms with smaller key size and higher security are becoming more popular. Out of the cryptographic algorithms, Elliptic Curve Cryptography (ECC) provides equivalent level of safety which RSA provides, but it takes smaller key size. On the basis of key size, our work focused on, studied, and compared the efficacy in terms of security among the well-known public key cryptography algorithms, namely ECC (Elliptic Curve Cryptography) and RSA (Rivets Shamir Adelman).
A deep learning approach for the detection and counting of colon cancer cells (HT-29 cells) bunches and impurities

HT-29 has an epithelial appearance as a human colorectal cancer cell line. Early detection of colorectal cancer can enhance survival rates. This study aims to detect and count HT-29 cells using a deep-learning approach (ResNet-50). The cell lines were procured from Procell Life Science &amp; Technology Co., Ltd. (Wuhan, China). Further, the dataset is self-prepared in lab experiments, cell culture, and collected 566 images. These images contain two classes; the HT-29 human colorectal adenocarcinoma cells (blue shapes in bunches) and impurities (tinny circular grey shapes). These images are annotated with the help of an image labeller as impurity and cancer cells. Then afterwards, the images are trained, validated, and tested against the deep learning approach ResNet50. Finally, in each image, the number of impurity and cancer cells are counted to find the accuracy of the proposed model. Accuracy and computational expense are used to gauge the network’s performance. Each model is tested ten times with a non-overlapping train and random test splits. The effect of data pre-processing is also examined and shown in several tasks. The results show an accuracy of 95.5% during training and 95.3% in validation for detecting and counting HT-29 cells. HT-29 cell detection and counting using deep learning is novel due to the scarcity of research in this area, the application of deep learning, and potential performance improvements over traditional methods. By addressing a gap in the literature, employing a unique dataset, and using custom model architecture, this approach contributes to advancing colon cancer understanding and diagnosis techniques.
Graph-guided Gaussian Process-based Diagnosis of CVD Severity with Uncertainty Measures

The severity of coronary artery disease can be assessed invasively using the Fractional Flow Reserve (FFR) index which is a useful diagnostic tool for the clinicians to select the treatment approach. The present work capitalizes a Gaussian process (GP) framework over graphs for the prediction of FFR index using only non-invasive imaging and clinical features. More specifically, taking the per-node one-hop connectivity vector as input, we employed a regression-based task by applying an ensemble of graph-adapted Gaussian process experts, with a data-adaptive fashion via online training. The main novelty of the work lies in the fact that for the first time in a medical field the inference model considers only the similarity condition of the patients, instead of their features. Our results demonstrate the impressive merits of the proposed medical EGP (MedEGP) method, in comparison to the single GP, and Linear Regression (LR) models to predict the FFR index, with well-calibrated uncertainty.Clinical Relevance- This paper establishes an accurate non-invasive approach to predict the FFR for the diagnosis of coronary artery disease.
Empowering Low-Light Image Enhancer through Customized Learnable Priors

Deep neural networks have achieved remarkable progress in enhancing low-light images by improving their brightness and eliminating noise. However, most existing methods construct end-to-end mapping networks heuristically, neglecting the intrinsic prior of image enhancement task and lacking transparency and interpretability. Although some unfolding solutions have been proposed to relieve these issues, they rely on proximal operator networks that deliver ambiguous and implicit priors. In this work, we propose a paradigm for low-light image enhancement that explores the potential of customized learnable priors to improve the transparency of the deep unfolding paradigm. Motivated by the powerful feature representation capability of Masked Autoencoder (MAE), we customize MAE-based illumination and noise priors and redevelop them from two perspectives: 1) structure flow: we train the MAE from a normal-light image to its illumination properties and then embed it into the proximal operator design of the unfolding architecture; and 2) optimization flow: we train MAE from a normal-light image to its gradient representation and then employ it as a regularization term to constrain noise in the model output. These designs improve the interpretability and representation capability of the model. Extensive experiments on multiple low-light image enhancement datasets demonstrate the superiority of our proposed paradigm over state-of-the-art methods. Code is available at https://github.com/zheng980629/CUE.
Exploring Temporal Frequency Spectrum in Deep Video Deblurring

Video deblurring aims to restore the latent video frames from their blurred counterparts. Despite the remarkable progress, most promising video deblurring methods only investigate the temporal priors in the spatial domain and rarely explore their its potential in the frequency domain. In this paper, we revisit the blurred sequence in the Fourier space and figure out some intrinsic frequency-temporal priors that imply the temporal blur degradation can be accessibly decoupled in the potential frequency domain. Based on these priors, we propose a novel Fourier-based frequency-temporal video deblurring solution, where the core design accommodates the temporal spectrum to a popular video deblurring pipeline of feature extraction, alignment, aggregation, and optimization. Specifically, we design a Spectrum Prior-guided Alignment module by leveraging enlarged blur information in the potential spectrum to mitigate the blur effects on the alignment. Then, Temporal Energy prior-driven Aggregation is implemented to replenish the original local features by estimating the temporal spectrum energy as the global sharpness guidance. In addition, the customized frequency loss is devised to optimize the proposed method for decent spectral distribution. Extensive experiments demonstrate that our model performs favorably against other state-of-the-art methods, thus confirming the effectiveness of frequency-temporal prior modeling.