Reviewerly/exHarmony huggingface dataset:
Artificial intelligence and civil liability—do we need a new regime?

Abstract Artificial intelligence (AI) is almost ubiquitous, featuring innumerable facets of daily life. For all its advantages, however, it carries risks of harm. In this article, we discuss how the law of tort should deal with these risks. We take account of the need for any proposed scheme of liability to protect the existing values of tort law without acting as a barrier to innovation. To this end, we propose a strict liability regime in respect of personal injury and death, and a bespoke fault-based regime for dignitary or reputational injuries. For other losses, we take the view that there is no justification for introducing any new regime, on the basis that AI applications do not introduce substantial added degrees of risk that would justify departing from the existing scheme of liability arising under the current law of tort.
Signals from the Early Universe: Black Holes, Gravitational Waves and Particle Physics

We dedicate this thesis to the study of signatures coming from the primordial epochs of the universe. We will focus in particular on Primordial Black Holes (PBHs), which may be formed from perturbations generated during inflation and might comprise a fraction of the dark matter in the universe. In the first part of the thesis, we will address the PBH properties at the time of formation, that are their masses, spins and abundance, and investigate the generation of Gravitational Wave (GW) signals during their production. In the second part, we will describe the PBHs evolution across the cosmic history due to their assemble in binaries, phases of baryonic mass accretion and clustering effects. We will then discuss GW signatures coming from their coalescence, compare these predictions with present GW data detected by the LIGO/Virgo Collaboration (LVC) and assess the role of future GW experiments like 3G detectors and LISA in discovering these objects. Finally, in the third part, we will investigate some aspects of the interplay between black holes and fundamental physics in the early universe, focusing on the role of GWs to shed light on their properties.
Encrypted Email For Local Government Information Security In South Sumatra

This study obtains an overview of the conditions of the encryption email management workflow among local governments in South Sumatra. The workflow for managing email encryption for regular official news has been going very well. Meanwhile, the workflow for managing encryption of emails for confidential news is still experiencing problems, especially for local governments that do not have cipher expert personnel and official cipher equipment from the BSSN. In addition, this study also illustrates that the application of information security theory in the use of encrypted emails for confidential news between local governments in South Sumatra is not optimal. The main obstacle faced is that there are still local governments that do not have cipher expert personnel and official cipher equipment from the BSSN. The quality of the workflow is measured by looking at the fulfillment of the main elements contained in the Standard Operating Procedures (SOP) of the encryption email management workflow. The quality of using email encryption for information security is carried out by analyzing the fulfillment of the elements contained in the information security theory.
Unsupervised Pivot-based Neural Machine Translation for English to Kannada

Neural Machine Translation (NMT) is one of the approaches of Machine Translation (MT) that have marked its significant progress in recent years for Indic languages. Even though a lot of contributions have been done for many Indic language pairs, there is no still such model which can be considered as a benchmark due to scarcity in rich parallel corpus, syntactic, semantic and morphological divergence across languages, difference in sentence ordering and so on. The lack of a rich parallel corpus has created a great hindrance in generating efficient NMT systems. Unsupervised learning based NMT (UNMT) was introduced to cater this concern as it is based on monolingual corpus. In this paper, we propose pivot-based UNMT model for English to Kannada translation with Telugu as pivot language using monolingual corpus of 1 lakh sentences for each language. To further enhance the translation quality, mBART pre-trained model is used. Since partial corpus is taken a conventional UNMT model is built for the comparison of proposed models with state-of-art model. With the conventional UNMT model and proposed architecture, BLEU score of 0.2 and 0.5 is achieved respectively indicating that pivot-based approach with Telugu as pivot enhances the translation accuracy.
Saliency Detection From 4D Light Field Images Using an Optimized CNN-based Hybrid Model

Light Field Imaging, which captures a 4-dimensional scene representation, has numerous applications across multiple fields. This paper proposes an optimized hybrid saliency detection scheme that incorporates the goodness of handcrafted light field-specific features as well as deep features to create saliency maps out of 4D light field images. An optimized Convolutional Neural Network (CNN) architecture is proposed to extract deep features from light field all-in-focus images. The learning rate, momentum and encoder depth of the proposed CNN are optimized using the Bayesian optimization approach. More adversarial samples are being added to the training set to make it more varied and generalizable. Simulation results indicate that a proper hyperparameter tuning technique can improve the CNN performance and decrease the training time taken as it leads to faster convergence. Manual light field-specific feature extraction is often considered to be more reliable than CNN-based feature maps owing to the inherent ‘black box' nature of convolutional neural network architectures. But saliency maps relying only on manual light field-specific features fail to distinguish salient objects under challenging scenarios. To overcome this, a saliency map refinement step is employed wherein the effectiveness of both the CNN feature-based and manual feature-based saliency maps are combined to form a hybrid saliency map. The proposed optimized 4D hybrid saliency detection model has achieved an average increase of 20.90% and 19.01% in the F-measure and S-measure respectively as compared to the state-of-the-art algorithms considered here.
Local image descriptor developed from Fechner’s law

Human perception of visual stimulus and its physical characteristics have a nonlinear-logarithmic relation as stated in Fechner’s law, which is a psychophysical law of perception (i.e., subjective sensation). Inspired by Fechner’s law, we use the salient differences among pixels in images as the physical characteristics to mimic the human pattern perception and propose a Fechner local image descriptor (FLID) for face image representation. FLID is a nonlinear descriptor, so the represented image is more hierarchical than the general linear one. In addition, a threshold method is used to divide the features into four different intervals, which can effectively reduce the effects of noise and illumination. However, considering that FLID only extracts the local features of single-scale blocks, we incorporate multi-scale block into FLID and propose a multi-scale block Fechner local image descriptor (MB-FLID) to extract more global structural features. In the experimental part, we verified the performance of FLID and MB-FLID on the Face Recognition Technology (FERET), extended Yale-B, Olivetti Research Laboratories, Yale, and AR face databases, and compared them with a host of other local feature descriptors. The experimental results show that the proposed FLID and MB-FLID outperform the compared descriptors.
Towards Real-Time Online Unsupervised Domain Adaptation for Real-World Person Re-identification

Abstract Following the popularity of Unsupervised Domain Adaptation (UDA) in person re-identification, the recently proposed setting of Online Unsupervised Domain Adaptation (OUDA) attempts to bridge the gap towards practical applications by introducing a consideration of streaming data. However, this still falls short of truly representing real-world applications. This paper defines the setting of Real-world Real-time Online Unsupervised Domain Adaptation (R 2 OUDA) for Person Re-identification. The R 2 OUDA setting sets the stage for true real-world real-time OUDA, bringing to light four major limitations found in real-world applications that are often neglected in current research: system generated person images, subset distribution selection, time-based data stream segmentation, and a segment-based time constraint. To address all aspects of this new R 2 OUDA setting, this paper further proposes Real-World Real-Time Online Streaming Mutual Mean-Teaching (R 2 MMT), a novel multi-camera system for real-world person re-identification. Taking a popular person re-identification dataset, R 2 MMT was used to construct over 100 data subsets and train more than 3000 models, exploring the breadth of the R 2 OUDA setting to understand the training time and accuracy trade-offs and limitations for real-world applications. R 2 MMT, a real-world system able to respect the strict constraints of the proposed R 2 OUDA setting, achieves accuracies within 0 . 1% of comparable OUDA methods that cannot be applied directly to real-world applications.
MRI Brain Scans Classification Using Extreme Learning Machine on LBP and GLCM

The primary goal of this study is to predict the presence of a brain tumor using MRI brain images. These images are first pre-processed to remove the boundary borders and the undesired regions. Gray-Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern method (LBP) approaches are mixed for extracting multiple local and global features. The best features are selected using the ANOVA statistical approach, which is based on the largest variance. Then, the selected features are applied to many state of arts classifiers as well as to Extreme Learning Machine (ELM) neural network model, where the weights are optimized via the regularization of RELM using a suitable ratio of Cross Validation (CV) for the images' classification into one of two classes, namely normal (benign) and abnormal (malignant). The proposed ELM algorithm was trained and tested with 800 images of BRATS 2015 datasets types, and the experimental results demonstrated that this approach has better performance on several evaluation criteria, including accuracy, stability, and speedup. It reaches to 98.87% accuracy with extremely low classification time. ELM can improve the classification performance by raising the accuracy more than 2% and reducing the number of processes needed by speeding up the algorithm by a factor of 10 for an average of 20 trials.
Video Anomaly Detection Based on Attention Mechanism

Camera surveillance is widely used in residential areas, highways, schools and other public places. The monitoring and scanning of sudden abnormal events depend on humans. Human anomaly monitoring not only consumes a lot of manpower and time but also has a large error in anomaly detection. Video anomaly detection based on AE (Auto-Encoder) is currently the dominant research approach. The model has a highly symmetrical network structure in the encoding and decoding stages. The model is trained by learning standard video sequences, and the anomalous events are later determined in terms of reconstruction error and prediction error. However, in the case of limited computing power, the complex model will greatly reduce the detection efficiency, and unnecessary background information will seriously affect the detection accuracy of the model. This paper uses the AE loaded with dynamic prototype units as the basic model. We introduce an attention mechanism to improve the feature representation ability of the model. Deep separable convolution operation can effectively reduce the number of model parameters and complexity. Finally, we conducted experiments on three publicly available datasets of real scenarios (UCSD Ped1, UCSD Ped2 and CUHK Avenue). The experimental results show that compared with the baseline model, the accuracy of our model improved by 1.9%, 1.4% and 6.6%, respectively, across the three datasets. Compared with many popular models, the validity of our model in anomaly detection is verified.
A Probabilistic U-Net Workflow for Automatically Cell Counting in Digital Pathology

Abstract A common yet still manual task in basic biology research, high through-put drug screening, and digital pathology is identifying the number, location, and type of individual cells in microscopy images. Semantic segmentation-based methods can be useful for identifying individual cells as well as their morphological features in one step. However, the accuracy of segmentation is still low. As such, we propose a UNet-based neural network model to effectively improve the cell counting accuracy and efficiency for histology image analysis. In this work, we proposed a probabilistic U-Net workflow to bring cell counting automation into real practice. Our approach has proven to be competitive in terms of accuracy compared to state-of-the-art approaches, such as U-Net, ResU-net on three cell counting datasets. Moreover, experimental results on the CoNIC dataset show that our approach also outperforms the state-of-the-art under data imbalance scenario.
Research and Application of Generative-Adversarial-Network Attacks Defense Method Based on Federated Learning

In recent years, Federated Learning has attracted much attention because it solves the problem of data silos in machine learning to a certain extent. However, many studies have shown that attacks based on Generative Adversarial Networks pose a great threat to Federated Learning. This paper proposes Defense-GAN, a defense method against Generative Adversarial Network attacks under Federated Learning. Under this method, the attacker cannot learn the real image data distribution. Each Federated Learning participant uses SHAP to explain the model and masks the pixel features that have a greater impact on classification and recognition in their respective image data. The experimental results show that while attacking the federated training model using masked images, the attacker cannot always obtain the ground truth of the images. At the same time, this paper also uses CutMix to improve the generalization ability of the model, and the obtained model accuracy is only 1% different from that of the model trained with the original data. The results show that the defense method proposed in this paper can not only resist Generative Adversarial Network attacks in Federated Learning and protect client privacy, but also ensure that the model accuracy of the Federated model will not be greatly affected.
Performance analysis of quantum repeaters enabled by deterministically generated photonic graph states

By encoding logical qubits into specific types of photonic graph states, one can realize quantum repeaters that enable fast entanglement distribution rates approaching classical communication. However, the generation of these photonic graph states requires a formidable resource overhead using traditional approaches based on linear optics. Overcoming this challenge, a number of new schemes have been proposed that employ quantum emitters to deterministically generate photonic graph states. Although these schemes have the potential to significantly reduce the resource cost, a systematic comparison of the repeater performance among different encodings and different generation schemes is lacking. Here, we quantitatively analyze the performance of quantum repeaters based on two different graph states, i.e. the tree graph states and the repeater graph states. For both states, we compare the performance between two generation schemes, one based on a single quantum emitter coupled to ancillary matter qubits, and one based on a single quantum emitter coupled to a delayed feedback. We identify the numerically optimal scheme at different system parameters. Our analysis provides a clear guideline on the selection of the generation scheme for graph-state-based quantum repeaters, and lays out the parameter requirements for future experimental realizations of different schemes.
Exploring Math Anxiety Towards the Students’ Computer Self-Efficacy in Learning Mathematics

AbstrakDi tengah pandemi Covid-19, siswa dituntut menguasai penggunaan komputer dalam pembelajaran matematika daring. Tingkat efikasi diri yang tinggi dapat secara signifikan mengurangi kecemasan matematika siswa dan sebaliknya. Tujuan penelitian ini adalah untuk mengetahui tingkat kecemasan matematis terhadap efikasi diri komputer dalam pembelajaran matematika, dan mengetahui hubungan antara kecemasan matematis terhadap efikasi diri komputer. Penelitian kuantitatif ini menggunakan survei untuk mengumpulkan data. Partisipan dipilih dengan menggunakan probability sampling. Angket berisi 25 soal juga diberikan kepada 200 siswa kelas 8 MTSN 7 Model Ciracas. Hasil penelitian menunjukkan bahwa seluruh indikator pada variabel kecemasan matematika menunjukkan skor rata-rata sedang dengan skor rata-rata tertinggi (3,14). Rata-rata efikasi diri komputer masih tergolong sedang dengan indikator tingkat besaran (𝑥̅= 2,97) sebagai nilai rata-rata tertinggi. Nilai koefisien korelasi sebesar -0,193 menunjukkan adanya hubungan negatif yang signifikan dengan derajat korelasi yang dapat diabaikan. AbstractAmidst Covid-19 pandemic, students were required to master the use of computers in online mathematics learning. High level of self-efficacy could significantly reduce students’ math anxiety and vice versa. The purpose of this study was to determine the level of mathematical anxiety towards computer self-efficacy in learning mathematics, and investigate the relationship between mathematical anxiety towards computer self-efficacy. This quantitative study used survey to collect the data. The participants were selected by using a probability sampling. A questionnaire containing 25 questions were also given to 200 students in 8th grades at MTSN 7 Model Ciracas. The results revealed that all indicators in the mathematics anxiety variable showed a moderate average score with the highest average score (3.14). The average of computer self-efficacy was still moderate with a magnitude level indicator (𝑥̅= 2,97) as the highest average value. The correlation coefficient value was -0.193 implying that there was a significantly negative relationship with a negligible degree of correlation.
Construction of Smart Education Evaluation System: A Case Study of International Chinese Language Education

The enablement of intelligent technology and big data technology has provided an opportunity to transform and develop the evaluation of International Chinese Language Education. Based on the review of previous related research, this study constructs an overall framework for the International Chinese Language Smart Education evaluation system that contains five levels: user, application, information, data, and technology. The smart education evaluation model that contains four dimensions, namely smart learning environment, smart pedagogy, smart learning, and smart education support services, is constructed in this study. Meanwhile, some new features of evaluation on International Chinese Language Smart Education are assessed in this paper. Finally, taking an advanced Chinese writing course as a case, this paper adopts the Delphi and Analytic Hierarchy Process methods to determine the smart education evaluation index system, which includes 3 first-level indicators, 11 second-level indicators, 28 third-level indicators, and 120 fourth-level indicators.
Unsupervised learning of observation functions in state space models by nonparametric moment methods

We investigate the unsupervised learning of non-invertible observation functions in nonlinear state space models. Assuming abundant data of the observation process along with the distribution of the state process, we introduce a nonparametric generalized moment method to estimate the observation function via constrained regression. The major challenge comes from the non-invertibility of the observation function and the lack of data pairs between the state and observation. We address the fundamental issue of identifiability from quadratic loss functionals and show that the function space of identifiability is the closure of a RKHS that is intrinsic to the state process. Numerical results show that the first two moments and temporal correlations, along with upper and lower bounds, can identify functions ranging from piecewise polynomials to smooth functions, leading to convergent estimators. The limitations of this method, such as non-identifiability due to symmetry and stationarity, are also discussed.
An Image Classification Method Based on Adaptive Attention Mechanism and Feature Extraction Network

The convolution neural network (CNN) not only has high fault tolerance but also has high computing capacity. The image classification performance of CNN has an important relationship with its network depth. The network depth is deeper, and the fitting ability of CNN is stronger. However, a further increase in the depth of CNN will not improve the accuracy of the network but will produce higher training errors, which will reduce the image classification performance of CNN. In order to solve the above problems, this paper proposes a feature extraction network, AA-ResNet with an adaptive attention mechanism. The residual module of the adaptive attention mechanism is embedded for image classification. It consists of a feature extraction network guided by the pattern, a generator trained in advance, and a complementary network. The feature extraction network guided by the pattern is used to extract different levels of features to describe different aspects of an image. The design of the model effectively uses the image information of the whole level and the local level, and the feature representation ability is enhanced. The whole model is trained as a loss function, which is about a multitask problem and has a specially designed classification, which helps to reduce overfitting and make the model focus on easily confused categories. The experimental results show that the method in this paper performs well in image classification for the relatively simple Cifar-10 dataset, the moderately difficult Caltech-101 dataset, and the Caltech-256 dataset with large differences in object size and location. The fitting speed and accuracy are high.
Cybersecurity: Guiding Principles and Risk Management Advice for Healthcare Boards, Senior Leaders and Risk Managers

In recent years, the average cost of healthcare-related data breaches increased from approximately US$7 million in 2020 to over US$9 million in 2021. Moreover, breaches in healthcare have been consistently more costly than in other sectors for 11 consecutive years. With the frequency and costs of cyberattacks expected to rise, healthcare organizations must carefully plan for and identify strategies to mitigate cyber-related risks. This paper provides practical guidance for boards, senior leaders and risk managers in the development and implementation of organization-specific cybersecurity measures, with a focus on the identification, mitigation and management of risks.