Reviewerly/exHarmony huggingface dataset:
Facial Anonymization and Privacy Concerns in Total-Body PET/CT

Total-body PET/CT images can be rendered to produce images of a subject's face and body. In response to privacy and identifiability concerns when sharing data, we have developed and validated a workflow that obscures (defaces) a subject's face in 3-dimensional volumetric data. <b>Methods:</b> To validate our method, we measured facial identifiability before and after defacing images from 30 healthy subjects who were imaged with both [<sup>18</sup>F]FDG PET and CT at either 3 or 6 time points. Briefly, facial embeddings were calculated using Google's FaceNet, and an analysis of clustering was used to estimate identifiability. <b>Results:</b> Faces rendered from CT images were correctly matched to CT scans at other time points at a rate of 93%, which decreased to 6% after defacing. Faces rendered from PET images were correctly matched to PET images at other time points at a maximum rate of 64% and to CT images at a maximum rate of 50%, both of which decreased to 7% after defacing. We further demonstrated that defaced CT images can be used for attenuation correction during PET reconstruction, introducing a maximum bias of −3.3% in regions of the cerebral cortex nearest the face. <b>Conclusion:</b> We believe that the proposed method provides a baseline of anonymity and discretion when sharing image data online or between institutions and will help to facilitate collaboration and future regulatory compliance.
Distributed large-scale graph processing on FPGAs

Abstract Processing large-scale graphs is challenging due to the nature of the computation that causes irregular memory access patterns. Managing such irregular accesses may cause significant performance degradation on both CPUs and GPUs. Thus, recent research trends propose graph processing acceleration with Field-Programmable Gate Arrays (FPGA). FPGAs are programmable hardware devices that can be fully customised to perform specific tasks in a highly parallel and efficient manner. However, FPGAs have a limited amount of on-chip memory that cannot fit the entire graph. Due to the limited device memory size, data needs to be repeatedly transferred to and from the FPGA on-chip memory, which makes data transfer time dominate over the computation time. A possible way to overcome the FPGA accelerators’ resource limitation is to engage a multi-FPGA distributed architecture and use an efficient partitioning scheme. Such a scheme aims to increase data locality and minimise communication between different partitions. This work proposes an FPGA processing engine that overlaps, hides and customises all data transfers so that the FPGA accelerator is fully utilised. This engine is integrated into a framework for using FPGA clusters and is able to use an offline partitioning method to facilitate the distribution of large-scale graphs. The proposed framework uses Hadoop at a higher level to map a graph to the underlying hardware platform. The higher layer of computation is responsible for gathering the blocks of data that have been pre-processed and stored on the host’s file system and distribute to a lower layer of computation made of FPGAs. We show how graph partitioning combined with an FPGA architecture will lead to high performance, even when the graph has Millions of vertices and Billions of edges. In the case of the PageRank algorithm, widely used for ranking the importance of nodes in a graph, compared to state-of-the-art CPU and GPU solutions, our implementation is the fastest, achieving a speedup of 13 compared to 8 and 3 respectively. Moreover, in the case of the large-scale graphs, the GPU solution fails due to memory limitations while the CPU solution achieves a speedup of 12 compared to the 26x achieved by our FPGA solution. Other state-of-the-art FPGA solutions are 28 times slower than our proposed solution. When the size of a graph limits the performance of a single FPGA device, our performance model shows that using multi-FPGAs in a distributed system can further improve the performance by about 12x. This highlights our implementation efficiency for large datasets not fitting in the on-chip memory of a hardware device.
Comparing Prognostic Factors of Cancers Identified by Artificial Intelligence (AI) and Human Readers in Breast Cancer Screening

Invasiveness status, histological grade, lymph node stage, and tumour size are important prognostic factors for breast cancer survival. This evaluation aims to compare these features for cancers detected by AI and human readers using digital mammography. Women diagnosed with breast cancer between 2009 and 2019 from three UK double-reading sites were included in this retrospective cohort evaluation. Differences in prognostic features of cancers detected by AI and the first human reader (R1) were assessed using chi-square tests, with significance at p < 0.05. From 1718 screen-detected cancers (SDCs) and 293 interval cancers (ICs), AI flagged 85.9% and 31.7%, respectively. R1 detected 90.8% of SDCs and 7.2% of ICs. Of the screen-detected cancers detected by the AI, 82.5% had an invasive component, compared to 81.1% for R1 (p-0.374). For the ICs, this was 91.5% and 93.8% for AI and R1, respectively (p = 0.829). For the invasive tumours, no differences were found for histological grade, tumour size, or lymph node stage. The AI detected more ICs. In summary, no differences in prognostic factors were found comparing SDC and ICs identified by AI or human readers. These findings support a potential role for AI in the double-reading workflow.
SUnAA: Sparse Unmixing Using Archetypal Analysis

This paper introduces a new sparse unmixing technique using archetypal analysis (SUnAA). First, we design a new model based on archetypal analysis. We assume that the endmembers of interest are a convex combination of endmembers provided by a spectral library and that the number of endmembers of interest is known. Then, we propose a minimization problem. Unlike most conventional sparse unmixing methods, here the minimization problem is non-convex. We minimize the optimization objective iteratively using an active set algorithm. Our method is robust to the initialization and only requires the number of endmembers of interest. SUnAA is evaluated using two simulated datasets for which results confirm its better performance over other conventional and advanced techniques in terms of signal-to-reconstruction error. SUnAA is also applied to Cuprite dataset and the results are compared visually with the available geological map provided for this dataset. The qualitative assessment demonstrates the successful estimation of the minerals abundances and significantly improves the detection of dominant minerals compared to the conventional regression-based sparse unmixing methods. The Python implementation of SUnAA can be found at: https://github.com/BehnoodRasti/SUnAA.
Hierarchical Time-Aware Summarization with an Adaptive Transformer for Video Captioning

A coherent description is an ultimate goal regarding video captioning via a couple of sentences because it might also affect the consistency and intelligibility of the generated results. In this context, a paragraph describing a video is affected by the activities used to both produce its specific narrative and provide some clues that can also assist in decreasing textual repetition. This work proposes a model, named Hierarchical time-aware Summarization with an Adaptive Transformer (HSAT), that uses a strategy to enhance the frame selection reducing the amount of information that needed to be processed along with attention mechanisms to enhance a memory-augmented transformer. This new approach increases the coherence among the generated sentences, assessing data importance (about the video segments) contained in the self-attention results and uses that to improve readability using only a small fraction of time spent by the other methods. The test results show the potential of this new approach as it provides higher coherence among the various video segments, decreasing the repetition in the generated sentences and improving the description diversity in the ActivityNet Captions dataset.
A Survey on Differential Privacy for Medical Data Analysis

Machine learning methods promote the sustainable development of wise information technology of medicine (WITMED), and a variety of medical data brings high value and convenience to medical analysis. However, the applications of medical data have also been confronted with the risk of privacy leakage that is hard to avoid, especially when conducting correlation analysis or data sharing among multiple institutions. Data security and privacy preservation have recently played an essential role in the field of secure and private medical data analysis, where many differential privacy strategies are applied to medical data publishing and mining. In this paper, we survey research work on the applications of differential privacy for medical data analysis, discussing the necessity of medical privacy-preserving, the advantages of differential privacy, and their applications to typical medical data, such as genomic data and wearable device data. Furthermore, we discuss the challenges and potential future research directions for differential privacy in medical applications.
Temporal convolution attention model for sepsis clinical assistant diagnosis prediction

Sepsis is an organ failure disease caused by an infection acquired in an intensive care unit (ICU), which leads to a high mortality rate. Developing intelligent monitoring and early warning systems for sepsis is a key research area in the field of smart healthcare. Early and accurate identification of patients at high risk of sepsis can help doctors make the best clinical decisions and reduce the mortality rate of patients with sepsis. However, the scientific understanding of sepsis remains inadequate, leading to slow progress in sepsis research. With the accumulation of electronic medical records (EMRs) in hospitals, data mining technologies that can identify patient risk patterns from the vast amount of sepsis-related EMRs and the development of smart surveillance and early warning models show promise in reducing mortality. Based on the Medical Information Mart for Intensive Care Ⅲ, a massive dataset of ICU EMRs published by MIT and Beth Israel Deaconess Medical Center, we propose a Temporal Convolution Attention Model for Sepsis Clinical Assistant Diagnosis Prediction (TCASP) to predict the incidence of sepsis infection in ICU patients. First, sepsis patient data is extracted from the EMRs. Then, the incidence of sepsis is predicted based on various physiological features of sepsis patients in the ICU. Finally, the TCASP model is utilized to predict the time of the first sepsis infection in ICU patients. The experiments show that the proposed model achieves an area under the receiver operating characteristic curve (AUROC) score of 86.9% (an improvement of 6.4% ) and an area under the precision-recall curve (AUPRC) score of 63.9% (an improvement of 3.9% ) compared to five state-of-the-art models.
Listening Comprehension Difficulties Faced by EFL Students

This study analyzes the students’ difficulties in comprehending listening in English. The study used a quantitative method to investigate the problems faced by students in taking listening comprehension test. The data were collected by distributing the questionnaires and carrying out interview to the students. The study was carried out at an Islamic Boarding School in Aceh Besar District, Aceh Province. The findings showed that the students had limited chance to repeat the words, lack of listening length and speed, lack of contextual, culture and background knowledge, and poor grammar and vocabulary.
A Digital Camera-based Eye Movement Assessment Method for NeuroEye Examination

The ability to perform quantitative and automated neurological assessment could enhance diagnosis and treatment in the pre-hospital setting, such as during telemedicine or emergency medical services (EMS) encounters. Such a tool could be developed by adapting clinically significant information such as symmetry of eye movement or conjugate eye movement. Here we describe a digital camera-based eye tracking method “NeuroGaze” to capture the symmetry of eye movement while performing neurological eye examination. The proposed method was developed based on detecting the center of the pupil for both eyes from a given video and measuring eye conjugacy by transforming the pupil center coordinates to relative gaze. The method was tested on healthy volunteers while performing three neurological eye examinations <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sup>1</sup></xref> <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label><sup>1</sup></label> The NeuroEye dataset is made available at <uri>https://www.kaggle.com/datasets/mahassan8/neuroeye</uri> </fn> . We also compared our proposed approach to state-of-the-art digital camera-based eye-tracking methods and commercial off-the-shelf (COTS) eye trackers. NeuroGaze outperformed digital camera-based eye tracking methods by reporting a mean Spearman rank-order correlation coefficient of 0.86 for the H-test, 0.87 for the Dot-test, and 0.56 for the OKN-Test, and shows similarity in trends for the relative gaze trajectories with a noticeable offset in the scale of the relative gaze angle compared to COTS eye tracker (see Fig. 1). The study demonstrates that by using a pupil-center-based eye-tracking method, a digital camera can measure clinically relevant information regarding eye movement.
Insurtech, Proptech, and Fintech Environment: Sustainability, Global Trends and Opportunities

The Special Issue “Insurtech, Proptech, and Fintech Environment: Sustainability, Global Trends and Opportunities” is focused on the InsurTech, PropTech, and FinTech environments [...]
A novel four-dimensional prediction model of soil heavy metal pollution: Geographical explanations beyond artificial intelligence "black box"

The current artificial intelligence (AI)-based prediction approaches of soil pollutants are inadequate in estimating the geospatial source-sink processes and striking a balance between the interpretability and accuracy, resulting in poor spatial extrapolation and generalization. In this study, we developed and tested a geographically interpretable four-dimensional AI prediction model for soil heavy metal (Cd) contents (4DGISHM) in Shaoguan city of China from 2016 to 2030. The 4DGISHM approach characterized spatio-temporal changes in source-sink processes of soil Cd by estimating spatio-temporal patterns and the effects of drivers and their interactions of soil Cd at local to regional scales using TreeExplainer-based SHAP and parallel ensemble AI algorithms. The results demonstrate that the prediction model achieved MSE and R2 values of 0.012 and 0.938, respectively, at a spatial resolution of 1 km. The predicted areas exceeding the risk control values for soil Cd across Shaoguan from 2022 to 2030 increased by 22.92% at the baseline scenario. By 2030, enterprise and transportation emissions (SHAP values 0.23 and 0.12 mg/kg, respectively) were the major drivers. The influence of driver interactions on soil Cd was marginal. Our approach surpasses the limitations of the AI "black box" by integrating spatio-temporal source-sink explanation and accuracy. This advancement enables geographically precise prediction and control of soil pollutants.
Easy to Remember, Hard to Guess: A Password Generation Tool for the Digital Age

A brute force attack is a common method used by cybercriminals to gain unauthorized access to user accounts. It is essential for individuals and organizations to take proactive measures to protect themselves from such attacks. One way to do this is by improving their knowledge of cybersecurity and implementing measures to safeguard their online presence. Using programming languages like Python and web-frameworks like Django, websites can be developed to help individuals generate secure and memorable passwords that align with the latest password security standards. This can help anyone who wants to improve their password security, irrespective of whether they have been a victim of a cyber-attack or not.
Evaluating Feature Importance to Investigate Publishers Conduct for Detecting Click Fraud

Pay-Per-Click (PPC) model of online advertising is vulnerable to click frauds. Click frauds are repetitive clicks on a published advertisement by publishers, aimed to monetize the illegitimate clicks. Statistical features extracted from user-click dataset are fed to machine learning models for automated detection of fraudulent publisher’s conduct. However, the presence of many collinear, redundant and least significant features poses a challenge in investigating the publisher’s behavior and increases the model complexities. Therefore, this paper aims to use potential features to address the aforementioned issues for the identification and the classification of illegitimate publishers from legitimacy. Publisher’s conduct is identified by evaluating the feature importance from gradient tree boosting (GTB), decision tree (DT), random forest (RF), logistic regression (LR) and XGBoost (XGB), wherein relative scores provided by the classifiers highlights the features that are utmost relevant and at least relevant to the target. Features with highest scores are kept while discarding the rest with low scores. This plausible strategy simplifies the issue being modeled and speeds up the modeling procedure while improving the model’s performance. Experiments are performed on FDMA2012 user-click dataset in two folds, first with all extracted features and second with 10 significant features selected by classifiers. The performances of learners are evaluated using average precision, sensitivity, specificity and G-mean. The results demonstrate an improvement in the performance of learners with selected significant feature sets. Overall, the GTB performed superiorly by minimizing the loss of the model via merging the weak learners into a strong one iteratively with an average precision (AP) score of 64.86% with selected features and 57.05% with all features, respectively.
Realtime Detection of Table Objects and Text Areas for OCR Preprocessing

OCR (Optical Character Recognition) is a technology that automatically detects, recognizes, and digitally converts text into images. OCR has a variety of uses, including reducing human error when viewing and typing documents and helping people work more efficiently with documents. It can increase efficiency and save money by eliminating the need to manually type text, especially when scanning documents or digitizing images. OCR is divided into text object detection and text recognition in an image, and preprocessing techniques are used during the original document imaging process to increase the accuracy of OCR results. There are various preprocessing techniques. They are generally classified into image enhancement, binarization techniques, text alignment and correction, and segmentation techniques. In this paper, we propose a special-purpose preprocessing technique and application called Table Area Detection. Recently, table detection using deep learning has been actively researched, and the research results are helping to improve the performance of table recognition technology. Table detection will become an important preprocessing technology for text extraction and analysis in various documents, and it requires a lot of research and accuracy. While many previous studies have focused on improving the accuracy of OCR algorithms through various techniques, this study proposes a method to discover and exclude false positives by introducing a factor called Table Area Detection.
RMAFF-PSN: A Residual Multi-Scale Attention Feature Fusion Photometric Stereo Network

Predicting accurate normal maps of objects from two-dimensional images in regions of complex structure and spatial material variations is challenging using photometric stereo methods due to the influence of surface reflection properties caused by variations in object geometry and surface materials. To address this issue, we propose a photometric stereo network called a RMAFF-PSN that uses residual multiscale attentional feature fusion to handle the “difficult” regions of the object. Unlike previous approaches that only use stacked convolutional layers to extract deep features from the input image, our method integrates feature information from different resolution stages and scales of the image. This approach preserves more physical information, such as texture and geometry of the object in complex regions, through shallow-deep stage feature extraction, double branching enhancement, and attention optimization. To test the network structure under real-world conditions, we propose a new real dataset called Simple PS data, which contains multiple objects with varying structures and materials. Experimental results on a publicly available benchmark dataset demonstrate that our method outperforms most existing calibrated photometric stereo methods for the same number of input images, especially in the case of highly non-convex object structures. Our method also obtains good results under sparse lighting conditions.
Deep Learning Technique for Classification of Breast Cancer using Ultrasound Images

Breast cancer is a significant medical and social issue that draws attention from the global scientific community. The classification of ultrasound images of breast cancer plays a crucial role in computer-aided diagnostic systems. In this paper, we present a deep learning technique for the diagnosis of breast cancer using ultrasound images. The proposed system leverages Transfer Learning, a machine learning approach that allows the reuse of pre-trained models for new tasks. Four pre-trained models, namely VGG16, VGG19, MobileNetV2, and ResNet50V2, were considered for building the system. Our study uses a public dataset that contains 9016 ultrasound images related to benign and malignant breast cancers. Results indicate that the VGG19 network is the most accurate model in classifying breast cancer as benign or malignant. The proposed system showed remarkable results, with an accuracy rate of 98.44% as obtained from the VGG19 model. This study emphasizes the ability of Transfer Learning to improve the precision of breast tumor classification based on Ultrasound Images.
Pedestrian Identification and Tracking within Adaptive Collaboration Edge Computing*

Nowadays, video surveillance is widespread used to achieve security life in the construction of smart city. As a result, prevalence of video surveillance equipments and technologies enables pedestrian identification and tracking to be research hotspots in the field of computer vision, whose development and application are of great significance to the construction of a good social security environment. However, pedestrian identification and tracking in monitoring scenarios still have problems of low recognition accuracy and high model complexity, and computer vision based adaptive recognition methods still have a large room for improvement and development. To address this issue, real-time pedestrian identification and tracking within adaptive collaboration edge computing environment is investigated in this paper. Within the paradigm of edge computing, the Raspberry Pi 3B+ acting as the edge computing node is adopted to handle the related issues of pedestrian identification and tracking. The combination of HOG (Histogram of Oriented Gradient) and SVM (Support Vector Machine) is investigated to achieve pedestrian identification, where, HOG is employed as the feature descriptor and SVM is employed as the classification algorithm. Furthermore, the pixel-based visual tracking algorithm is investigated to achieve effective and uninterrupted pedestrian tracking. By implementing a prototype on Raspberry Pi 3B+ using OpenCV libraries, the experimental results finally have been shown to demonstrate the efficiency of the investigations.