Reviewerly/exHarmony huggingface dataset:
Improved RANSAC Point Cloud Spherical Target Detection and Parameter Estimation Method Based on Principal Curvature Constraint

Spherical targets are widely used in coordinate unification of large-scale combined measurements. Through its central coordinates, scanned point cloud data from different locations can be converted into a unified coordinate reference system. However, point cloud sphere detection has the disadvantages of errors and slow detection time. For this reason, a novel method of spherical object detection and parameter estimation based on an improved random sample consensus (RANSAC) algorithm is proposed. The method is based on the RANSAC algorithm. Firstly, the principal curvature of point cloud data is calculated. Combined with the k-d nearest neighbor search algorithm, the principal curvature constraint of random sampling points is implemented to improve the quality of sample points selected by RANSAC and increase the detection speed. Secondly, the RANSAC method is combined with the total least squares method. The total least squares method is used to estimate the inner point set of spherical objects obtained by the RANSAC algorithm. The experimental results demonstrate that the method outperforms the conventional RANSAC algorithm in terms of accuracy and detection speed in estimating sphere parameters.
Evolution towards Hybrid Software Development Methods and Information Systems Audit Challenges

The key objective of this paper is to investigate the evolution of hybrid software development methods and highlight the main difficulties that arise with regard to information systems (IS) auditing. While technology firms today are under constant pressure to deliver software faster due to emerging needs worldwide, this continuous effort leads to innovative development models, apparently driven by practice. Since modern software development is neither pure linear phases progression nor agile, a challenge arises with regards to selecting the appropriate combination of approaches that serve to reach goals and assure value creation for organizations.
Object-Guided and Motion-Refined Attention Network for Video Anomaly Detection

Video anomaly detection is challenging due to the lack of abnormal videos and ambiguity of anomaly definition. Context information is important to identify anomalous events and can be reflected from the salient objects and related background. In this paper, we propose a new multi-level attention network consisting of an Object-Guided Attention Module (OGAM) and a Motion-Refined Attention Module (MRAM) to fully exploit context by leveraging both frame-level and object-level semantics. Specifically, OGAM highlights features of salient objects on frame features for future frame prediction. MRAM further leverages global positions and optical flow of objects to improve the prediction loss. By multi-level attention, objects are highlighted in the predicted frame and necessary context information is preserved at both feature level and pixel level to identify anomalies. Experiments demonstrate our method achieves state-of-the-art performance on benchmark datasets including Avenue, UCSD Ped2 and ShanghaiTech, specifically obtaining a frame-level AUC of 92.6% on Avenue.
Multi-Scale Continuity-Aware Refinement Network for Weakly Supervised Video Anomaly Detection

In many previous work, weakly supervised video anomaly detection is formulated as a multiple instance learning (MIL) problem, which represents the video as a bag of multiple instances. However, most MIL-based frameworks only focused on identifying anomalous events from the given instances, without considering the event continuity. Motivated by the fact that abnormal events tend to be more continuous in real-world videos, a Multi-scale Continuity-aware Refinement Network (MCR) is proposed in this paper. It utilizes the property of multi-scale continuity to refine anomaly scores by introducing differential contextual information of instances. At the same time, multi-scale attention is designed to produce a video-level weights in order to select the proper scale and fuse all scores at different scales. Experimental results of MCR show noticeable improvement on two public datasets, specifically obtaining a frame-level AUC 94.92% on ShanghaiTech dataset.
A self-supervised contrastive learning approach for whole slide image representation in digital pathology

Image analysis in digital pathology has proven to be one of the most challenging fields in medical imaging for AI-driven classification and search tasks. Due to their gigapixel dimensions, whole slide images (WSIs) are difficult to represent for computational pathology. Self-supervised learning (SSL) has recently demonstrated excellent performance in learning effective representations on pretext objectives, which may improve the generalizations of downstream tasks. Previous self-supervised representation methods rely on patch selection and classification such that the effect of SSL on end-to-end WSI representation is not investigated. In contrast to existing augmentation-based SSL methods, this paper proposes a novel self-supervised learning scheme based on the available primary site information. We also design a fully supervised contrastive learning setup to increase the robustness of the representations for WSI classification and search for both pretext and downstream tasks. We trained and evaluated the model on more than 6000 WSIs from The Cancer Genome Atlas (TCGA) repository provided by the National Cancer Institute. The proposed architecture achieved excellent results on most primary sites and cancer subtypes. We also achieved the best result on validation on a lung cancer classification task.
Cyberinsurance Policy

Why cyberinsurance has not improved cybersecurity and what governments can do to make it a more effective tool for cyber risk management. As cybersecurity incidents—ranging from data breaches and denial-of-service attacks to computer fraud and ransomware—become more common, a cyberinsurance industry has emerged to provide coverage for any resulting liability, business interruption, extortion payments, regulatory fines, or repairs. In this book, Josephine Wolff offers the first comprehensive history of cyberinsurance, from the early “Internet Security Liability” policies in the late 1990s to the expansive coverage offered today. Drawing on legal records, government reports, cyberinsurance policies, and interviews with regulators and insurers, Wolff finds that cyberinsurance has not improved cybersecurity or reduced cyber risks. Wolff examines the development of cyberinsurance, comparing it to other insurance sectors, including car and flood insurance; explores legal disputes between insurers and policyholders about whether cyber-related losses were covered under policies designed for liability, crime, or property and casualty losses; and traces the trend toward standalone cyberinsurance policies and government efforts to regulate and promote the industry. Cyberinsurance, she argues, is ineffective at curbing cybersecurity losses because it normalizes the payment of online ransoms, whereas the goal of cybersecurity is the opposite—to disincentivize such payments to make ransomware less profitable. An industry built on modeling risk has found itself confronted by new technologies before the risks posed by those technologies can be fully understood.
Privacy preservation and security challenges: a new frontier multimodal machine learning research

Multimodal machine learning is a vibrant multi-disciplinary field and achieved much attention due to its wide range of applications. A research problem is multimodal, for the impact of privacy preservation. It shields sensitive data in the cloud by using a single modality-based privacy system. The user's biometric features are always stored in the database, primarily present in the cloud server to validate the user and his access. This facet provides a beneficial quality but at the same time has raised crucial affairs in security and privacy of biometric feature set. The main concern is to manage and stop the privacy breaches in clouds. The article discusses the detailed analysis of security schemes with a multimodal-based learning framework over sensitive data and systems at both ends. The article also accentuates frameworks and schemes that may apply in various applications to ensure privacy preservation of individuals and data security by multimodal algorithms.
Infinite hidden Markov model for short-term solar irradiance forecasting

Hidden state models are among the most widely used and efficient schemes for solar irradiance modeling in general and forecasting in particular. However, the complexity of such models – in terms of the number of states – is usually needed to be specified a priori. For solar irradiance data this assumption is very difficult to justify. In this paper, an infinite hidden Markov model (InfHMM) is introduced for short-term probabilistic forecasting of solar irradiance, where the assumption of fixed number of states a priori is relaxed and model complexity is determined during the model training. InfHMM is a non-parametric Bayesian model (NPB) indexed with an infinite dimensional parameter space which allows the automatic adaptation of the model to the “correct” complexity. This facilitates the automatic adaptation of the model to all weather conditions and locations. Posterior inference for InfHMM is performed using the Markov chain Monte Carlo algorithm, namely the beam sampler. Data from 13 different sources are used to validate the proposed model and subsequently it is compared to two well-established models in the literature: Markov-chain mixture distribution (MCM) and complete-history persistence ensemble (CH-PeEn) models. Important results are found, that cannot be derived from the existing finite models, such as the variation of the number of states within and across sites. The comparison of the models shows that the InfHMM is more consistent in term of the forecasting horizon. For reproducibility of the methodology presented in this paper, we have provided an R script for the InfHMM as supplementary material.
The role of university teachers’ 21st-century digital competence in their attitudes toward ICT integration in higher education: Extending the theory of planned behavior

Amir Reza Rahimi , Dara Tafazoli - Due to the massive integration of Information and Communication Technologies (ICTs) in higher education, teachers need to be highly digital competent and have positive attitudes to manage their classes effectively.
Shortest Route to Non-Abelian Topological Order on a Quantum Processor

A highly coveted goal is to realize emergent non-Abelian gauge theories and their anyonic excitations, which encode decoherence-free quantum information. While measurements in quantum devices provide new hope for scalably preparing such long-range entangled states, existing protocols using the experimentally established ingredients of a finite-depth circuit and a single round of measurement produce only Abelian states. Surprisingly, we show there exists a broad family of non-Abelian states -- namely those with a Lagrangian subgroup -- which can be created using these same minimal ingredients, bypassing the need for new resources such as feed-forward. To illustrate that this provides realistic protocols, we show how $D_4$ non-Abelian topological order can be realized, e.g., on Google's quantum processors using a depth-11 circuit and a single layer of measurements. Our work opens the way towards the realization and manipulation of non-Abelian topological orders, and highlights counter-intuitive features of the complexity of non-Abelian phases.
Chinese Named Entity Recognition of Epidemiological Investigation of Information on COVID-19 Based on BERT

The named entity recognition based on the epidemiological investigation of information on COVID-19 can help analyze the source and route of transmission of the epidemic to control the spread of the epidemic better. Therefore, this paper proposes a Chinese named entity recognition model BERT-BiLSTM-IDCNN-ELU-CRF (BBIEC) based on the epidemiological investigation of information on COVID-19 of the BERT pre-training model. The model first processes the unlabeled epidemiological investigation of information on COVID-19 into the character-level corpus and annotates it with artificial entities according to the BIOES character-level labeling system and then uses the BERT pre-training model to obtain the word vector with position information; then, through the bidirectional long-short term memory neural network (BiLSTM) and the improved iterated dilated convolutional neural network (IDCNN) extract global context and local features from the generated word vectors and concatenate them serially; output all possible label sequences to the conditional random field (CRF); finally pass the condition random The airport decodes and generates the entity tag sequence. The experimental results show that the model is better than other traditional models in recognizing the entity of the epidemiological investigation of information on COVID-19.
Predicting Future Laboratory Fault Friction Through Deep Learning Transformer Models

Abstract Machine learning models using seismic emissions as input can predict instantaneous fault characteristics such as displacement and friction in laboratory experiments, and slow slip in Earth. Here, we address whether the seismic/acoustic emission (AE) from laboratory experiments contains information about future frictional behavior. The approach uses a convolutional encoder‐decoder containing a transformer model in the latent space, similar to models used for natural language processing. We test the model limits using progressively larger AE input time windows and progressively larger output friction time windows. The results demonstrate that very near‐term friction predictions are indeed contained in the AE signal, and predictions are progressively worse farther into the future. The future predictions by the model of impending failure in the near‐term are remarkably robust. This first effort predicting future fault frictional behavior with machine learning will aid in guiding efforts for applications in Earth.
CFNet: A medical image segmentation method using the multi-view attention mechanism and adaptive fusion strategy

The image feature extraction method based on the attention mechanism has contributed significantly to the accuracy of medical image segmentation. However, the current attention mechanism is based on the single-view information for feature extraction which imposes certain limitations for extracting efficient features. In this study, we propose the encoder-decoder structure of the U-Net as the basic network structure to construct a medical image segmentation method based on the multi-view attention mechanism and adaptive fusion strategy. We will refer to this new network as CFNet. The first component of CFNet is a cross-scale feature fusion method (CFF) employing a new multi-view attention mechanism (MAM) for feature extraction. This can effectively extract features in the multi-receptive field space and obtain more effective cross-scale fusion features in skip-connection. The second component is a fusion weight adaptive allocation strategy (FAS), which can guide the cross-scale fusion features to effectively connect to the decoder features for solving the semantic gap. We evaluated the CFNet using two publicly available medical image segmentation datasets: MoNuSeg and LGG. The experimental results show that the CFNet can achieve better performance compared with the current state-of-the-art methods in medical image segmentation. We then perform extensive ablation studies to validate our method.
Affective Learning Objectives for Communicative Visualizations

When designing communicative visualizations, we often focus on goals that seek to convey patterns, relations, or comparisons (cognitive learning objectives). We pay less attention to affective intents-those that seek to influence or leverage the audience's opinions, attitudes, or values in some way. Affective objectives may range in outcomes from making the viewer care about the subject, strengthening a stance on an opinion, or leading them to take further action. Because such goals are often considered a violation of perceived 'neutrality' or are 'political,' designers may resist or be unable to describe these intents, let alone formalize them as learning objectives. While there are notable exceptions-such as advocacy visualizations or persuasive cartography-we find that visualization designers rarely acknowledge or formalize affective objectives. Through interviews with visualization designers, we expand on prior work on using learning objectives as a framework for describing and assessing communicative intent. Specifically, we extend and revise the framework to include a set of affective learning objectives. This structured taxonomy can help designers identify and declare their goals and compare and assess designs in a more principled way. Additionally, the taxonomy can enable external critique and analysis of visualizations. We illustrate the use of the taxonomy with a critical analysis of an affective visualization.
Analysis of book circulation data and a book recommendation system in academic libraries using data mining techniques

The use of data mining modern technology in library management systems and information centers is of great importance. With the increasing availability of a large quantity of information, traditional tools and practices without wasting time and cost cannot respond to users accurately and quickly. The present study aims to analyze book circulation transactions and discover the user's book loan patterns to develop a recommender system. The data included 109,639 transactions and information from 8636 user records. Microsoft SQL Server and Matlab software were applied to analyze the data. Item-based collaborative filtering algorithms and decision tree methods were also applied. The results led to the extraction of rules for suggesting books to users. Analysis of the circulation data could be applied to address many issues like evaluation, collection acquisition policies, allocating funding for materials, and suggesting approaches to deselecting and allocating physical space for materials.
Complexity trade-offs and equi-complexity in natural languages: a meta-analysis

Abstract In linguistics, there is little consensus on how to define, measure, and compare complexity across languages. We propose to take the diversity of viewpoints as a given, and to capture the complexity of a language by a vector of measurements, rather than a single value. We then assess the statistical support for two controversial hypotheses: the trade-off hypothesis and the equi-complexity hypothesis. We furnish meta-analyses of 28 complexity metrics applied to texts written in overall 80 typologically diverse languages. The trade-off hypothesis is partially supported, in the sense that around one third of the significant correlations between measures are negative. The equi-complexity hypothesis, on the other hand, is largely confirmed. While we find evidence for complexity differences in the domains of morphology and syntax, the overall complexity vectors of languages turn out virtually indistinguishable.
A parameter-free KNN for rating prediction

Among the most popular collaborative filtering algorithms are methods based on the K nearest neighbors (KNN). In their basic operation, KNN methods consider a fixed number of neighbors to make recommendations. However, it is not easy to choose an appropriate number of neighbors. Thus, it is generally fixed by calibration to avoid inappropriate values which would negatively affect the accuracy of the recommendations. In the literature, some authors have addressed the problem of dynamically finding an appropriate number of neighbors. But they use additional parameters which limit their proposals because these parameters also require calibration. In this paper, we propose a parameter-free KNN method for rating prediction. It is able to dynamically select an appropriate number of neighbors to use. The experiments that we did on four publicly available datasets demonstrate the efficiency of our proposal. It rivals those of the state of the art in their best configurations.
An Integrated Framework with Deep Learning for Segmentation and Classification of Cancer Disease

This paper addresses radiologists’ specific diagnosis of cancer disease effectively using integrated framework of deep learning model. Although several existing diagnosis systems have been adopted by a physician, in few cases, it is not so practical to see the infected area from images in the normal eye. Thus, a fully integrated diagnosis framework for disease detection is proposed to find out the infected area from image using deep learning approaches in this paper. In this proposed framework, various components are designed through deep learning approaches such as detection, segmentation, classification etc. based on mass region. The classification technique is used to classify the disease as either benign or malignant. The vital part of this framework is developed by using a full resolution convolutional network (FrCN) that supports different stages of image processing, especially breast cancer disease. Different experimental evaluation is taken to perform on the accuracy, cross-validation tests, and the comparative testing. Since we have taken 4-fold evaluation, the FrCN performs with an average 98.7% Dice index, 97.8% TS/CSI coefficient, 99.1% overall accuracy, and 98.15% MCC. Our experiments demonstrated that the proposed diagnosis system performs on the deep learning approaches at each segmentation stage and classification with good results.
Dynamic obstacle avoidance for Multi-rotor UAV using chance-constraints based on obstacle velocity

To ensure the safety of autonomous Multi-rotor UAVs flying in urban airspace, they should be capable of avoiding collisions with unpredictable dynamic obstacles, such as birds. UAVs must consider both relative position and relative velocity to avoid moving obstacles. Model predictive control (MPC) can consider the multiple collision avoidance constraints in a constrained optimisation framework. This study proposes a chance-constraints based on obstacle velocity (CCOV) method, which can be combined with previous positional chance constraint methods to account for uncertainty in both position and velocity. This effectively prevents collision with high-velocity obstacles, even in a noisy environment. The proposed method has been performed on a numerical simulation built in MATLAB.