Reviewerly/exHarmony huggingface dataset:
Exploring Histological Similarities Across Cancers From a Deep Learning Perspective

Histopathology image analysis is widely accepted as a gold standard for cancer diagnosis. The Cancer Genome Atlas (TCGA) contains large repositories of histopathology whole slide images spanning several organs and subtypes. However, not much work has gone into analyzing all the organs and subtypes and their similarities. Our work attempts to bridge this gap by training deep learning models to classify cancer vs. normal patches for 11 subtypes spanning seven organs (9,792 tissue slides) to achieve high classification performance. We used these models to investigate their performances in the test set of other organs (cross-organ inference). We found that every model had a good cross-organ inference accuracy when tested on breast, colorectal, and liver cancers. Further, high accuracy is observed between models trained on the cancer subtypes originating from the same organ (kidney and lung). We also validated these performances by showing the separability of cancer and normal samples in a high-dimensional feature space. We further hypothesized that the high cross-organ inferences are due to shared tumor morphologies among organs. We validated the hypothesis by showing the overlap in the Gradient-weighted Class Activation Mapping (GradCAM) visualizations and similarities in the distributions of nuclei features present within the high-attention regions.
3D-DesktopEye: A Desktop-Based Eye-Tracking System for Facility Management

Understanding the attentional patterns of construction professionals in design, construction, and facilities management processes is critical to identifying the performance bottlenecks and improving the current workflow designs in the Architecture, Engineering, Construction, and Facility Management (AEC/FM) industry. Recent developments in eye-tracking technologies have attracted an increasing attention of construction scholars as it is an effective tool for attentional analysis. However, the existing monitor-mounted eye trackers are designed for tracking gaze focus on 2D screens (such as attentions during a 2D drawing review) and thus are limited for understanding attentional patterns in 3D model reviews. To track gaze focus in 3D environments, scholars have to use expensive eye trackers integrated with glasses or headset displays such as Virtual Reality headsets. As an alternative, this paper proposes an effective solution for tracking gaze focus in 3D model reviews on 2D screens using the regular monitor-mounted eye trackers. The proposed 3D Desktop-Based Eye-Tracking System called "3D-DesktopEye" leverages the eye-tracking function of the Unity Game engine and a set of geometric conversations to project the 2D eye-tracking points into virtual 3D models. As a result, it can track the focus points of a user looking at a 3D model on a 2D screen. We tested the 3D-DesktopEye system in a facility management scenario—a pipe maintenance task. We also compared the effectiveness of the proposed 3D-DesktopEye system with the traditional 2D eye-tracking system. The results confirmed the usability and feasibility of the 3D-DesktopEye, and the proposed 3D-DesktopEye can help the user achieve better task performance.
Exploration, Analysis, and Practice of Sensor English Teaching Based on Immersive Augmented Reality Technology

With the continuous development of social economy, English teaching has become one of the inevitable choices of national communication, but how to improve the efficiency and quality of classroom teaching through English teaching is an extremely important research direction. In response to these shortcomings and needs, the immersive augmented reality technology is introduced in this paper. Exploratory analysis of English teaching is performed from various perspectives such as sensor experimental selection, experimental teaching content, sensor progress management, and sensor teaching assessment, through combing the principles and structural framework of sensor technology, to target students and teachers for performing multilevel and multitype analysis and practice, focus on the application analysis of sensors, and highlight manual practice. The simulation experiment results show that the immersive augmented reality technology is effective and can support the exploration, analysis, and practice of sensor English teaching.
An Adaptively Attention-Driven Cascade Part-Based Graph Embedding Framework for UAV Object Re-Identification

With the rapid development of unmanned aerial vehicles (UAVs), object re-identification (Re-ID) based on the UAV platforms has attracted increasing attention, and several excellent achievements have been shown in the traditional scenarios. However, object Re-ID in aerial imagery acquired from the UAVs is still a challenging task, which is mainly due to the reason that variable locations and diverse viewpoints in UAVs platform are always resulting in more appearance ambiguities among the intra-objects and inter-objects. To address the above issues, in this paper, we proposed an adaptively attention-driven cascade part-based graph embedding framework (AAD-CPGE) for UAV object Re-ID. The AAD-CPGE aims to optimally fuse node features and their topological characteristics on the multi-scale structured graphs of parts-based objects, and then adaptively learn the most correlated information for improving the object Re-ID performance. Specifically, we first executed GCNs on the parts-based cascade node feature graphs and topological feature graphs for acquiring multi-scale structured-graph feature representations. After that, we designed a self-attention-based module for adaptive node and topological features fusion on the constructed hierarchical parts-based graphs. Finally, these learning hybrid graph-structured features with the most correlation discriminative capability were applied for object Re-ID. Several experimental verifications on three widely used UAVs-based benchmark datasets were carried out, and comparison with some state-of-the-art object Re-ID approaches validated the effectiveness and benefits of our proposed AAD-CPGE Re-ID framework.
A Corpus-Based Sentence Classifier for Entity–Relationship Modelling

Automated creation of a conceptual data model based on user requirements expressed in the textual form of a natural language is a challenging research area. The complexity of natural language requires deep insight into the semantics buried in words, expressions, and string patterns. For the purpose of natural language processing, we created a corpus of business descriptions and an adherent lexicon containing all the words in the corpus. Thus, it was possible to define rules for the automatic translation of business descriptions into the entity–relationship (ER) data model. However, since the translation rules could not always lead to accurate translations, we created an additional classification process layer—a classifier which assigns to each input sentence some of the defined ER method classes. The classifier represents a formalized knowledge of the four data modelling experts. This rule-based classification process is based on the extraction of ER information from a given sentence. After the detailed description, the classification process itself was evaluated and tested using the standard multiclass performance measures: recall, precision and accuracy. The accuracy in the learning phase was 96.77% and in the testing phase 95.79%.
Fairness Metrics for Recommender Systems

Fairness is a hot topic in recommender system research in recent years. Researchers have resorted to regularization and other techniques to reduce fairness problems. However, a lot of research literature adopts classic evaluation metrics for recommender system results. There has been little attention paid to the fairness metrics for recommender system evaluation. In this paper and for the first time in the research history of recommender systems, we propose a set of fairness metrics based on extreme value theory. In the experiment section, we evaluate different classic algorithms and fair AI technologies with our newly invented fairness metrics.
Invariant rational functions, linear fractional transformations and irreducible polynomials over finite fields

For a subgroup of PGL(2,q) we show how some irreducible polynomials over Fq arise from the field of invariant rational functions. The proofs rely on combining two actions of PGL(2,F), one on the projective line over a field F and the other on the rational function field F(x). The invariant functions in F(x) are used to show that regular patterns exist in the factorization of certain polynomials into irreducible polynomials. We use some results about group actions and the orbit polynomial, whose proofs are included.
Statistical methods applied to geochemical analysis of data from the Guaritas Allogroup, Camaquã Basin – RS, Brazil

In this work, we use statistical methods for the analysis of geochemical data obtained through the technique of sample fusion mass spectrometry with inductively coupled plasma (FUS-ICP: Fusion-Inductively Coupled Plasma). The statistical treatment of geochemical data provides clusters and correlations of major, minor and trace chemical elements present in the chemical composition of the rocks of the Guaritas Allogroup, belonging to the Camaquã sedimentary basin, located in southern Brazil, in the state of Rio Grande do Sul. In addition to contributing to the consolidation of knowledge about the Camaquã Basin - RS/Brazil, the focus of this study is the statistical analysis of geochemical data available for the Guaritas Allogroup, from Denalle (2013). The following statistical methods were applied: straight correlation by approximating the points of one or more variables by the method of least squares, with calculation of the statistical significance of the correlation established for the sampled points and Principal Component Analysis – PCA. The statistical approach aims to optimize the data available for the Guaritas Allogroup, with the purpose to refining the information and possible interpretations of the data, which better establish the compositional characteristics and genesis relationships for these rocks. At the sampled points there are subtle variations in chemical composition, but no significant macroscopic variations were observed. The establishment of correlation lines helps to understand the specific relations of the composition of a chemical element, which can be a major element in the rock, for example, with respect to the variation of one or two minor elements or traces. The Principal Component Analysis method – PCA contributes to the understanding of the influence of which sets of chemical elements are decisive in the differentiation of lithological types. This technique proves to be advantageous for the analysis of extensive databases, as it reduces the excessive amount of data, eliminating overlaps, but with little loss of significant information. PCA points to choices of the most representative forms of data through linear combinations of the original variables. This technique provides advantages in the management of geochemical data or other areas of research in geosciences, and can be used in an analogous way in several available databases. For the Guaritas Allogroup, this technique proved to be useful in pointing out compositional relationships between major elements, main rock formers, such as SI, Ca, Al and Mg, and minor elements, such as Mn, Zn, Ti, Rb and Sr. Therefore, the preliminary results obtained represent an advantageous tool, used in an additional way, for the interpretation of geochemical data in support of the analytical techniques traditionally employed in chemical stratigraphy.
Anomaly Detection in Catalog Streams

Detecting anomalies with high accuracy and real time from large amounts of streaming data is a challenge for many real-world applications, such as smart city, astronomical observations, and remote sensing. This article focuses on a special kind of stream, catalog stream, whose high-level catalog structure can be used to analyze the stream effectively. We first formulate the anomaly detection in catalog streams as a constrained optimization problem based on a catalog stream matrix. Then, a novel filtering-identifying based anomaly detection algorithm ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FIAD</i> ) is proposed, which includes two complementary strategies, true event identifying and false alarm filtering, data-oriented general method and domain-oriented specific method together, to detect truly valuable anomalies. Furthermore, different kinds of attention windows are developed to provide corresponding data for various algorithm components. A scalable and lightweight catalog stream processing framework <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CSPF</i> is designed to support and implement the proposed method efficiently. A prototype system is developed to evaluate the proposed algorithm. Extensive experiments are conducted on the catalog stream data sets from an operational super large field-of-view high-cadence astronomy observation. The experimental results show that the proposed method can achieve a false-positive rate as low as 0.04%, reduces the false alarms by 98.6% compared with the existing methods, and the latency to handle each catalog is 2.1 seconds (much less than the required 15 seconds). Furthermore, a total of 36 transient candidates, including seven microlensing events, 27 superflares, and two dual-superflares, are detected from 21.67 million stars (involving 1.09 million catalogs) from one observation season.
The Effect of Using an Auto-Generated Anime Avatar on Player-Avatar Identification

Game avatars are a way to represent players within a virtual game world. Player-avatar identification can improve game enjoyment, where avatar customisation and player-avatar similarity are potential factors that contribute to identification. Recent emerging technologies, such as image translation algorithms, offer new alternatives for avatar customisation. However, its effectiveness remains unknown. Image translation algorithms enable users to automatically generate avatars that look similar to themselves from their self images (selfies). This research aims to investigate how player-avatar appearance similarity can affect identification by conducting three comparative experiments with different avatar appearance within an offline single-player game. Our results indicate a positive effect of using an auto-generated anime avatar compared to using a default avatar. No correlation was found between player-avatar appearance similarity and player-avatar identification in general. We therefore posit that avatar customisation acts as a greater contributor than player-avatar appearance similarity to avatar identification in offline single-player games.
Intellectual Property Legal Problem Based on the Big Data Algorithm

A series of intellectual property protection laws, such as trademark law, patent law, copyright law, anti fraud competition law, plant brand protection act, and integrated circuit Grape Design Protection Ordinance, were promulgated, and the intellectual property right system was established first. Through the protection of such a legal system, intellectual property rights can manifest themselves as a legally protected interest. This article discusses the legal aspects of intellectual property based on artificial intelligence algorithms.
Traffic pedestrian detection algorithm based on lightweight SSD

With the development of urbanization, pedestrian detection has become an important link in road traffic. Some existing pedestrian detection algorithm models are too large and the detection speed is too slow, while the accuracy of lightweight pedestrian detection can’t meet the detection requirements. Therefore, a detection algorithm that can meet the accuracy and real-time is designed. In this paper, SSD is selected as the basic model, MobileNetV2 is used as the backbone network, and deconvolution multi-scale feature fusion is added to the backbone network, ECA-Net high-efficiency channel attention module is added to the feature extraction network, and HFF multi-hierarchy structure is added to the auxiliary network to improve the detection ability of the model. In addition to ensuring the lightweight of the model, the inspection effect of the model can be improved. The experimental results show that the model designed in this paper obtains 78.6% mAP on Caltech dataset, the model size is 28M. Compared to the original model and other lightweight SSD models with higher accuracy, faster and smaller models.
Financial Asset Risk Measurement Based on Smart Sensor Big Data Security Analysis and Bayesian Posterior Probability Model

Following the speeding up of a process of financial globalization, the risks faced by financial markets have become more complex and diversified. Correlated patterns among financial assets exhibit characteristics of nonlinearity, asymmetry, and tail correlation. The original linear correlation analysis method is no longer suitable, but relevant information describing financial risks. In order to confirm whether an asset is safe, the key is to study and master its volatility, and this is based on our mastery of volatility measurement skills. This article is based on smart sensor big data security analysis and Bayesian analysis. The risk measurement of financial assets based on the empirical probability model is studied. The GARCH- <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><mi>t</mi></math> (1,1) model is selected according to the Akaike information criterion (AIC) after the generalized autoregressive conditional heteroskedasticity (GARCH) model is established by the EViews software. According to the results of probability integral transformation, a series of correlation coefficients and degrees of freedom of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"><mi>t</mi></math> -copula are obtained by the maximum likelihood estimation method. This paper uses the risk-adjusted return on capital (RAROC) method to evaluate the risk performance of financial assets. Financial institutions can only retain and absorb the financial market risks that cannot be avoided and transferred. The edge user node sends the service request to the edge server node. The edge server uses the model proposed in this paper to evaluate the user’s trust and selects the corresponding service level according to the trust level corresponding to the calculated credibility results. The data show that the edge calculation takes 0.2581 seconds, while the linear search takes about 64 seconds. The results show that intelligent edge computing improves the accuracy and efficiency of financial asset risk measurement.
Face Recognition Using Handcrafted Features Extraction Techniques Under Occlusion Condition

Over the past three decades, many face recognition methods have been proposed in computer vision, and most of the methods recognized faces under constrained scenarios where no occlusion, illumination and pose variation exist. In a real-world application such as video surveillance, human faces might be occluded by other objects or some face accessories and it is difficult to identify a person’s face. To address this, a new face recognition approach develop to recognize a person from their unoccluded part of the face. The proposed method extracts more relevant features than the existing handcrafted features methods, which helps to improve recognition accuracy. The validation of the proposed method has been verified using existing major datasets, i.e. AR and LFW public datasets.
Moving Object Detection and Tracking Algorithm Using Hybrid Decomposition Parallel Processing

Moving object detection, classification and tracking are more crucial and challenging task in most of the computer vision and machine vision applications such as robot navigation, human behavior analysis, traffic flow analysis and etc.However, most of object detection and tracking algorithms are not suitable for real time processing and causes slower processing speed due to the processing and analyzing of high resolution video from high-end multiple cameras.It requires more computation and storage.To address the aforementioned problem, this paper proposes a way of parallel processing of temporal frame differencing algorithm for object detection and contour tracking using the mixture of functional and domain decomposition parallel processing techniques.It has two main contributions.First, steps of frame differencing are parallelized using functional decomposition technique.Second, the processing of frames in each steps of frame differencing is again parallelized using domain decomposition technique.Finally, the performance is evaluated in Aneka Private Cloud platform and which yields to detect and track the object very swiftly and accurately.
Nonlinear Gaussian Filter with Multi-Step Colored Noise

Color noise is a special kind of noise often occurring in localization systems, and it is more suitable than the general Gaussian white noise to model time dependence due to time delay or high-frequency sampling. This paper derives a nonlinear Gaussian filtering framework for multi-step colored noise systems using noise whitening techniques and Bayes rule. Meanwhile, the cubature rule is used to solve the Gaussian-weighted integral in the proposed Gaussian filtering framework, resulting in an analytic form of posterior state estimate. Compared with the existing nonlinear filtering algorithms, the proposed method has obvious advantages in colored noise systems because it fully takes into account the time dependence of colored noise. Finally, the effectiveness and advantages of the proposed algorithm are verified with a classical target tracking system.
Modeling Several Optical Components Using Scalar Diffraction Theory

Compound systems are generally treated by geometrical optics, for instance, through the Gauss’ formalism. The objective is to simplify the process of image formation. However, this formalism does not include the wave characteristics of light and boundary effects. The treatment of diffraction is not straightforward. Thus, the extension of this formalism towards the scalar theory of diffraction is very desired. This work offers this extension and emphasizes its importance. Compound systems, including the human eye, are then modeled by Fresnel theory. For illustration, a lens-based model of the Fresnel transform is used to treat the human eye system.
PARKS-Gaze - A Precision-focused Gaze Estimation Dataset in the Wild under Extreme Head Poses

The performance of appearance-based gaze estimation systems that utilizes machine learning depends on training datasets. Most of the existing gaze estimation datasets were recorded in laboratory conditions. The datasets recorded in the wild conditions display limited head pose and intra-person variation. We proposed PARKS-Gaze, a gaze estimation dataset with 570 minutes of video data from 18 participants. We captured head pose range of ± 50, [-40,60] degrees in yaw and pitch directions respectively. We captured multiple images for a single Point of Gaze (PoG) enabling to carry out precision analysis of gaze estimation models. Our cross-dataset experiments revealed that the model trained on proposed dataset obtained lower mean test errors than existing datasets, indicating its utility for developing real-world interactive gaze controlled applications.