Reviewerly/exHarmony huggingface dataset:
Learning to predict prostate cancer recurrence from tissue images

Roughly 30% of men with prostate cancer who undergo radical prostatectomy will suffer biochemical cancer recurrence (BCR). Accurately predicting which patients will experience BCR could identify who would benefit from increased surveillance or adjuvant therapy. Unfortunately, no current method can effectively predict this. We develop and evaluate PathCLR, a novel semi-supervised method that learns a model that can use hematoxylin and eosin (H&E)-stained tissue microarrays (TMAs) to predict prostate cancer recurrence within 5 years after diagnosis. The learning process involves 2 sequential steps: PathCLR (a) first employs self-supervised learning to generate effective feature representations of the input images, then (b) feeds these learned features into a fully supervised neural network classifier to learn a model for predicting BCR. We conducted training and evaluation using 2 large prostate cancer datasets: (1) the Cooperative Prostate Cancer Tissue Resource (CPCTR) with 374 patients, including 189 who experienced BCR, and (2) the Johns Hopkins University (JHU) prostate cancer dataset of 646 patients, with 451 patients having BCR. PathCLR’s (10-fold cross-validation) F1 score was 0.61 for CPCTR and 0.85 for JHU. This was statistically superior (paired t-test with P<.05) to the best-learned model that relied solely on clinicopathological features, including PSA level, primary and secondary Gleason Grade, etc. We attribute the improvement of PathCLR over models using only clinicopathological features to its utilization of both learned latent representations of tissue core images and clinicopathological features. This finding suggests that there is essential predictive information in tissue images at the time of surgery that goes beyond the knowledge obtained from reported clinicopathological features, helping predict the patient’s 5-year outcome.
Spatial Commonsense Reasoning for Machine Reading Comprehension

This paper studies the problem of spatial commonsense reasoning for the machine reading comprehension task. Spatial commonsense is the human-shared but latent knowledge of object shape, size, distance, and position. Reasoning this abstract knowledge can facilitate machines better perceive their surroundings, which is crucial for general intelligence. However, this valuable topic is challenging and has been less studied. To bridge this research gap, we focus on this topic and propose a new method to realize spatial reasoning. Given a text, we first build a potential reasoning graph based on its parsing tree. To better support spatial reasoning, we retrieve the related commonsense entities and relations from external knowledge sources, including the pre-trained language model (LM) and knowledge graph (KG). LM covers all kinds of factual knowledge and KG has abundant commonsense relations. We then propose a new fusion method called LEGRN (LM Edge-GNN Reasoner Networks) to fuse the text and graph. LEGRN adopts layer-based attention to integrate the LM text encoder and KG graph encoder, which can capture correlations between LM text context and KG graph structure. Considering that spatial relations involve a variety of attributes, we propose an attribute-aware inferential network to deduce the correct answers. To evaluate our approach, we construct a new large-scale dataset named CRCSpatial, consisting of 40k spatial reasoning questions. Experiment results illustrated the effectiveness of our approach.
CSFFNet: Lightweight cross‐scale feature fusion network for salient object detection in remote sensing images

Abstract Salient object detection (SOD), one of the most important applications in the field of computer vision, aims to extract the most visually appealing regions of scenes. However, the improvement of the accuracy of existing salient object detection in optical remote sensing images (ORSI‐SOD) is usually accompanied by an increase of network complexity, which affects the application of these models. Motivated by this, a novel lightweight edge‐supervised neural network for ORSI‐SOD is proposed, named CSFFNet. Specifically, the backbone (ResNet34) is first lightened by feature encoding module (FEM), building a lightweight subnet for feature extraction. Then, in the transformer‐based feature pyramid enhancement module (FPEM), the convolutional features obtained in the FEM are enhanced by long‐distance dependence to obtain multi‐scale features containing rich saliency cues. Based on this, the feature fusion module (FFM) is designed to capture cross‐scale long‐range dependencies and effectively fuse high‐level semantic information with low‐level detail information. Thus, the increase in network complexity due to multi‐level decoding is avoided. Finally, the segmentation results are optimized by using salient edges as auxiliary information, which effectively improves the contrast and completeness of the results. Experimental results on two public datasets demonstrate that the lightweight CSFFNet achieves competitive or even better performance compared with state‐of‐the‐art methods.
LiHiSTO: a comprehensive list of Hindi stopwords

Abstract A preliminary preprocessing step in text analytics is the removal of words with no semantic meaning, otherwise known as stopwords. English stopwords are very easily accessible and created due to the broad usability of the English language. However, a standard list of Hindi stopwords is still missing. This paper proposes an exhaustive list of generic Hindi stopwords and a Python package for easy distribution and usage. The methodology uses a dual mechanism for creating a list of Hindi stopwords. First, the famous English stopwords are collected and translated into meaningful Hindi words (group 1). Second, unique Hindi stopwords from multiple sources are fetched (group 2). Finally, the respective Hindi stopwords from groups 1 and 2 are combined, which resulted in a significantly large set of 820 Hindi stopwords. Additionally, the list of Hindi stopwords is made openly available for use at the Python Package Index (PyPI) repository as a Python package, which is named LiHiSTO . With the help of illustrative implementations, it is shown that LiHiSTO provides abstract and easy access to the list of stopwords for users to perform Hindi text analytics.
A Study of Deep Learning Methods for Automatic Cancer Detection and Classification in Histopathological Whole-Slide Images

Histopathological examination is the diagnostic and research of tissue disorders. An integrated suite of histopathological samples has greatly aided doctors and researchers in the world of clinical research. Identifying cancerous tissues is critical for clinicians in providing appropriate oncology treatments. A whole slide image (WSI) is a digitized scanning of the tissues on the glass slide that allows the samples taken to be stored digitally on the computer system in the form of a digital picture. Cancer is among the top causes of death whereas lung and colon cancers are considered to be the deadliest with a very low survival rate. Lung cancer and colon cancer are considered to be devastating among all cancers. People affected by cancer mostly die. The survival rate is very less than approximately 5% in lung cancer and it is caused by various factors like smoking, drinking, etc. It not only affects the lungs but can spread into other parts of the body as well. India is witnessing more than one million cases of breast cancer per year. However, since histological images contain multiple tissue types and characteristics, classification is still challenging. WSI processing and storage have greatly aided professionals while encouraging researchers to develop more reliable and efficient fully automated analysis diagnosing models—ML, specifically DL-based models. DL models have outperformed in various disciplines, along with clinical applications and profound features in healthcare. A Fine-tune based framework is proposed and evaluated on two famous datasets KimiaPath24C and LC25000. The Kimia Path24 dataset was particularly created for the classification and retrieval of histopathology images and the LC25000 dataset for the classification of lung and colon cancer. The deep learning models like MobileNetV2, Xception, NasaNetLarge, EfficientNetV2L, and Self-build CNN (Convolutional Neural Network) model of 15 layers have been proposed on both datasets.
Comparative Analysis of Business English and Everyday English Translation Stylistic Features Based on Keras Neural Networks

Abstract Based on the formal definition of semantic language and specific natural language, the research adopts the Word2vec model to map words into a low-dimensional dense space and performs semantic modeling by unsupervised learning through a large amount of unlabeled data to realize the vectorized representation of translated text. For the vectorized text, the text features are combined and filtered based on the TextCNN network, and the TextCNN network is implemented under the Keras framework. Based on the TextCNN network of Keras, the stylistic features of Business English and Daily English are compared at three levels: lexical, syntactic and discourse. Business English was rated 23.6 points higher in terms of conciseness of diction compared to everyday English, and 28.7 points improved the modesty of diction compared to everyday English. This study furthers the improvement of various English translation types.
Modelling and Analysing Cognition and Interaction

In this chapter we investigate how to formally model human cognition and how cognition drives the way people think and behave, thus linking people’s external behaviour, described in terms of their tasks, with people’s internal cognitive processing. This complements the physical and digital aspects of human-computer interaction considered in Chap. 1. Human memory is the basis of cognitive processing. The chapter starts presenting conceptual models of memory and the way they can be represented to best facilitate human understanding as well as to provide a formal, linear notation that can be used computationally but still does not prevent human understanding. Such a notation, the Behaviour and Reasoning Description Language (BRDL), is then used to illustrate how to model both factual and behavioural knowledge and how to use these two types of knowledge in cognitive processing and human activities. The last part of the chapter provides a light introduction to informal and formal approaches to the analysis of cognition and interaction.
A Deep Learning Model for Visual Sentiment Analysis of Social Media

Visual sentiment analysis seeks to comprehend the emotional responses that images elicit in viewers. Despite the fact that this topic is relatively new, a wide variety of strategies have been created for diverse data sources and issues, leading to a substantial body of study. This Thesis attempts to provide a thorough overview of the subject by reviewing key literature. The topic is covered under many primary headings after a description of the task and the relevant applications. The article describes the design principles of broad Visual Sentiment Analysis systems from the three main perspectives of emotional models, dataset specification, and feature design. Recent Deep FER systems have typically concentrated on two main problems: overfitting brought on by a lack of training data and expression-unrelated factors including illumination, head posture, and identification bias. Authors present a full evaluation of Deep FER in this study. This study presents details on FER dataset with DEEP Learning model i.e. CNN and comparative analysis with other models such as SVM, K-Means. Convolutional neural networks (CNNs), in particular, have among all FER techniques demonstrated enormous potential because to their robust automated feature extraction and computational efficiency. In this work, we achieve the highest classification accuracy on the FER2013 dataset.
Thermal image identification against pose and expression variations using deep learning

Thermal infrared face recognition systems have developed as an effective complement to visual systems for biometric identity purposes and military operations. With the help of deep learning and convolutional neural networks, we provide an efficient approach for thermal facial identification that can handle position fluctuations and expression dissimilarities in this study. As an essential deep learning model that has demonstrated its effectiveness in several computer vision and machine learning applications, we employ the ResNet-50 architecture which consists of 50 layers of convolution, activation, and pooling. The structures of those layers are discussed in detail to gain a profound insight about the operation of this architecture. In this regard, a deep and detailed mathematical analysis is furnished. The system is implemented on a dataset of 1500 thermal images, where we execute experiments in various setups and circumstances to address the issues with posture and expression variance. The experimental results show that the system achieves an accuracy rate of 99.4% when it is trained using 30% of the dataset after five epochs. With respect to other performance measures, the system attains 100% recall, precision, F-score, and specificity. In comparison to recently published works, the findings show that the suggested system offers improved discriminability, resilience against fluctuations, as well as high identification rates under diverse settings that mimic real-world scenarios.
Motion detail feature extraction of sports injury based on three-dimensional sensor tracking

In response to the issues of low extraction accuracy and prolonged extraction time associated with traditional methods of motion detail feature extraction in sports injury analysis, we propose a novel approach based on three-dimensional sensor tracking. This method leverages the principles of sports injury action image analysis, utilizing three-dimensional sensors to track and collect sports injury action images. These images are then subjected to wavelet transform for noise reduction and detail enhancement. Subsequent image preprocessing identifies the sports injury area, and a wavelet analysis extracts detailed characteristics from the sports injury action. Simulation experiments demonstrate the high accuracy and efficiency of our proposed method in motion detail feature extraction for sports injury.
STRFormer: Spatial–Temporal–ReTemporal Transformer for 3D human pose estimation

Transformer-based methods have emerged as the golden standard in 2D-3D human pose estimation from video sequences, largely thanks to their powerful spatial–temporal feature encoders. In the past, researchers have made concerted efforts to engineer spatial and temporal encoders using transformer blocks. This approach involved a dramatic reshaping of the input, transforming it from mere joint information to dynamic joint trajectories. Despite this, the inherent limitations of the spatial–temporal structure have resulted in an inadequate acquisition and subsequent utilization of temporal information. In an attempt to rectify this prevalent issue, our paper proposes a new model, dubbed Spatial–Temporal-ReTemporal Transformer (i.e., STRFormer). This model ingeniously employs two separate temporal transformer blocks to extract the essential temporal motion information from video sequences. Intriguingly, one temporal transformer block is dedicated to the original video sequence, while the other concerns itself with the reversed order video. This novel approach allows for a more thorough investigation and utilization of temporal information from the video sequences. In order to alternate the processing of these two blocks effectively with the spatial block, we focus on maximizing the extraction of temporal domain information. This method leads to a more comprehensive understanding of the pose estimation and its evolution over time. Furthermore, we introduce a novel error metric, Mean Per-Joint Position Acceleration Error (i.e., MPJAE). This advanced metric takes into account the body part velocity in adjacent predicted frames, allowing for a more detailed evaluation of the predicted poses. We conduct extensive experiments on various open benchmarks to evaluate the effectiveness of our proposed model. The results demonstrate that our STRFormer, coupled with the MPJAE loss, achieves highly competitive results when compared with other state-of-the-art models. This illustrates its promising potential and practical applicability in 2D-3D human pose estimation tasks. We plan to release our code publicly for further research.
Professional Differences in Use and Perceptions of an Augmented Reality Code Cart Application

Medication and equipment must be located and retrieved quickly during resuscitation to ensure good patient outcomes; code carts are often used to store commonly used items and may be standardized to support faster retrieval. An augmented reality (AR) application to teach clinicians about the contents and organization of a standardized pediatric code cart was developed for mobile devices to improve the speed and accuracy of retrieval of items from the code cart. In this study, we explore the use, usability, and satisfaction of users of that application. We conducted surveys (n = 56) with physicians, physicians in training, nurses and nurse educators who used the application. The surveys collected self-reported use, usability with the System Usability Scale (SUS) and satisfaction. We compared results from different clinical roles. The application had acceptable usability (average SUS score = 75.9) and average satisfaction of 74.9 on a scale from 0 to 100 reported after an average of nearly 3 h of application use, with no significant differences between clinical roles. While the application was acceptable, improving the interface design, features and function of the application could enhance the experience of users. Future work could include participants from other health care systems to gain a more generalizable understanding of user experience and compare the experience of users of the AR application with their experiences with other training methods.
Comparative analysis of effectiveness for distance and traditional learning technologies in the universities

The article presents a comparative analysis of the learning outcomes of higher education students using two different forms of learning: traditional offline learning and tele-learning. The features of tele-learning are considered. The study took into account the widespread introduction of digital technologies in both tele- and traditional learning. The subject of the study is the influence of various forms of organization of the educational process on the results of educational activities. Purpose of the study: to identify the most suitable forms, which are most effective in modern conditions. Author used comparative analysis of learning results of tele- and traditional forms of education obtained during the COVID-19 pandemic and after it. The following objective criteria were chosen: student grades, average score during certification, and the number of students who successfully passed the exams. The work takes into account the difficulties of objective comparison of learning results of tele- and traditional education formats. Despite this, it was possible to identify differences between different types of education, which are especially visible when conducting certification in a standardized form. In general, it was shown that students who were trained in the traditional format gained more professional skills and competencies compared to students who studied remotely. As a result of the study, preferred forms of training were identified as well as most effective forms. These forms currently include traditional learning, which uses the entire arsenal of modern digital content, as well as blended learning, which uses both traditional and tele-learning forms.
iPhone12 imagery in scene-referred computer graphics pipelines

With the release of the Apple iPhone 12 pro in 2020, various features were integrated that make it attractive as a recording device for scene-related computer graphics pipelines. The captured Apple RAW images have a much higher dynamic range than the standard 8-bit images. Since a scene-based workflow naturally has an extended dynamic range (HDR), the Apple RAW recordings can be well integrated. Another feature is the Dolby Vision HDR recordings, which are primarily adapted to the respective display of the source device. However, these recordings can also be used in the CG workflow since at least the basic HLG transfer function is integrated. The iPhone12pro's two Laser scanners can produce complex 3D models and textures for the CG pipeline. On the one hand, there is a scanner on the back that is primarily intended for capturing the surroundings for AR purposes. On the other hand, there is another scanner on the front for facial recognition. In addition, external software can read out the scanning data for integration in 3D applications. To correctly integrate the iPhone12pro Apple RAW data into a scene-related workflow, two command-line-based software solutions can be used, among others: dcraw and rawtoaces. Dcraw offers the possibility to export RAW images directly to ACES2065-1. Unfortunately, the modifiers for the four RAW color channels to address the different white points are unavailable. Experimental test series are performed under controlled studio conditions to retrieve these modifier values. Subsequently, these RAW-derived images are imported into computer graphics pipelines of various CG software applications (SideFx Houdini, The Foundry Nuke, Autodesk Maya) with the help of OpenColorIO (OCIO) and ACES. Finally, it will be determined if they can improve the overall color quality. Dolby Vision content can be captured using the native Camera app on an iPhone 12. It captures HDR video using Dolby Vision Profile 8.4, which contains a cross-compatible HLG Rec.2020 base layer and Dolby Vision dynamic metadata. Only the HLG base layer is passed on when exporting the Dolby Vision iPhone video without the corresponding metadata. It is investigated whether the iPhone12 videos transferred this way can increase the quality of the computer graphics pipeline. The 3D Scanner App software controls the two integrated Laser Scanners. In addition, the software provides a large number of export formats. Therefore, integrating the OBJ-3D data into industry-standard software like Maya and Houdini is unproblematic. Unfortunately, the models and the corresponding UV map are more or less machine-readable. So, manually improving the 3D geometry (filling holes, refining the geometry, setting up new topology) is cumbersome and time-consuming. It is investigated if standard techniques like using the ZRemesher in ZBrush, applying Texture- and UV-Projection in Maya, and VEX-snippets in Houdini can assemble these models and textures for manual editing.
Security Research: Program Analysis Meets Security

In this paper we present the key features of some of the security analysis tools developed at Oracle, Labs. These include Parfait, a static analyser, Affogato a dynamic analysis based on run-time instrumentation of Node.js applications and Gelato a dynamic analysis tool that inspects only the client-side code written in JavaScript. We show the how these tools can be integrated at different phases of the software development life-cycle. This paper is based on the presentation at the ICTAC school in 2021.