Reviewerly/exHarmony huggingface dataset:
Improving Performance of the PRYSTINE Traffic Sign Classification by Using a Perturbation-Based Explainability Approach

Model understanding is critical in many domains, particularly those involved in high-stakes decisions, e.g., medicine, criminal justice, and autonomous driving. Explainable AI (XAI) methods are essential for working with black-box models such as convolutional neural networks. This paper evaluates the traffic sign classifier of the Deep Neural Network (DNN) from the Programmable Systems for Intelligence in Automobiles (PRYSTINE) project for explainability. The results of explanations were further used for the CNN PRYSTINE classifier vague kernels' compression. Then, the precision of the classifier was evaluated in different pruning scenarios. The proposed classifier performance methodology was realised by creating an original traffic sign and traffic light classification and explanation code. First, the status of the kernels of the network was evaluated for explainability. For this task, the post-hoc, local, meaningful perturbation-based forward explainable method was integrated into the model to evaluate each kernel status of the network. This method enabled distinguishing high- and low-impact kernels in the CNN. Second, the vague kernels of the classifier of the last layer before the fully connected layer were excluded by withdrawing them from the network. Third, the network's precision was evaluated in different kernel compression levels. It is shown that by using the XAI approach for network kernel compression, the pruning of 5% of kernels leads to a 2% loss in traffic sign and traffic light classification precision. The proposed methodology is crucial where execution time and processing capacity prevail.
Bringing Federated Semantic Queries to the GIS-Based Scenario

Geospatial data is increasingly being made available on the Web as knowledge graphs using Linked Data principles. This entails adopting the best practices for publishing, retrieving, and using data, providing relevant initiatives that play a prominent role in the Web of Data. Despite the appropriate progress related to the amount of geospatial data available, knowledge graphs still face significant limitations in the GIScience community since their use, consumption, and exploitation are scarce, especially considering that just a few developments retrieve and consume geospatial knowledge graphs from within GIS. To overcome these limitations and address some critical challenges of GIScience, standards and specific best practices for publishing, retrieving, and using geospatial data on the Web have appeared. Nevertheless, there are few developments and experiences that support the possibility of expressing queries across diverse knowledge graphs to retrieve and process geospatial data from different and distributed sources. In this scenario, we present an approach to request, retrieve, and consume (geospatial) knowledge graphs available at diverse and distributed platforms, prototypically implemented on Apache Marmotta, supporting SPARQL 1.1 and GeoSPARQL standards. Moreover, our approach enables the consumption of geospatial knowledge graphs through a lightweight web application or QGIS. The potential of this work is shown with two examples that use GeoSPARQL-based knowledge graphs.
Automatically Generating Models of IT Systems

Information technology system (ITS), informally, consists of hardware and software infrastructure (e.g., workstations, servers, laptops, installed software packages, databases, LANs, firewalls, etc.), along with physical and logical connections and inter-dependencies between various items. Nowadays, every company owns and operates an ITS, but detailed information about the system is rarely publicly available. However, there are many situations where the availability of such data would be beneficial. For example, cyber ranges need descriptions of complex realistic IT systems in order to provide an effective training and education platform. Furthermore, various algorithms in cybersecurity, in particular attack tree generation, need to be validated on realistic models of IT systems. In this paper, we describe a system we call the Generator that, based on the high-level requirements such as the number of employees and the business area the target company belongs to, generates a model of an ITS that satisfies the given requirements. We put special emphasis on the following two criteria: the generated ITS models a large amount of details, and ideally resembles a real system. Our survey of related literature found no sufficiently similar prior works, so we believe that this is the first attempt of building something like this. We created a proof-of-concept implementation of the Generator, validated it by generating ITS models for a simplified fictional financial institution, and analyzed the Generators performance with respect to the problem size. The research was done in an iterative manner, with coauthors continuously providing feedback on intermediate results. (...) We intend to extend this prototype to allow probabilistic generation of IT systems when only a subset of parameters is explicitly defined, and further develop and validate our approach with the help of domain experts.
ALBERT-BPF: a book purchase forecast model for university library by using ALBERT for text feature extraction

Purpose The purpose of this research is to achieve automatic and accurate book purchase forecasts for the university libraries and improve efficiency of manual book purchase. Design/methodology/approach The authors presented a Book Purchase Forecast model with A Lite BERT(ALBERT-BPF) to achieve their goals. First, the authors process all the book data to unify format of books' features, such as ISBN, title, authors, brief introduction and so on. Second, they exploit the book order data to label all books supplied by booksellers with “purchased” or “non-purchased”. The labelled data will be used for model training. Last, the authors regard the book purchase task as a text classification problem and present a model named ALBERT-BPF, which applies ALBERT to extract text features of books and BPF classification layer to forecast purchased books, to solve the problem. Findings The application of deep learning in book purchase task is effective. The data the authors exploited are the historical book purchase data from their university library. The authors’ experiments on the data show that ALBERT-BPF can seek out the books that need to be purchased with an accuracy of over 82%. And the highest accuracy reached is 88.06%. These indicate that the deep learning model is sufficient to assist the traditional manual book purchase way. Originality/value This research applies ALBERT, which is based on the latest Natural Language Processing (NLP) architecture Transformer, to library book purchase task.
Student Graduation Predictions Using Comparison of C5.0 Algorithm With Linear Regression

Technological advances supported by human knowledge have a very good influence on data and information storage technology, including in predicting student graduation (Graduation Prediction) on time, by applying several existing algorithms. In this study, researchers used the C5.0 Algorithm and Linear Regression. The concept of the research is to compare two algorithms, namely C5.0 and Linear Regression to the case of graduating students on time. Based on the length of study, students who graduated correctly amounted to 651 (91%) with a male gender of 427 students and a female gender of 224 students while those who did not pass (late) correctly amounted to 64 (9%) with a male gender totaling 53 students and female gender totaling 11 students from 2017-2020. Comparison results The R2 score from the C5.0 algorithm reached 96.85% (training) and 93.
Modified Adaptive Fusion Scheme for Kalman Filter Based on the Hypothesis Test

In the literature, the fading factor was constructed to overcome the shortage of model uncertainties in the Kalman filter. However, the a priori covariance matrix might be inflated abnormally by the fading factor once the measurement is unreliable. Thus, the fading factor may become invalid, and this problem is rarely discussed and tested. In this paper, squares of the Mahalanobis distance are introduced as the judging index, and the fading factor or the covariance inflation factor is adopted conditionally according to the hypothesis testing result. Therefore, an adaptive filtering scheme based on the Mahalanobis distance is put forward for the systems with model uncertainties. The proposed algorithm is implemented with the actual data collected by the integration of the global navigation satellite system (GNSS) and the inertial navigation system and INS (inertial navigation system) integrated systems (INS). For the systems with model uncertainties, experimental results demonstrate that the influences of both the outlying measurements and model errors are controlled effectively with the proposed scheme.
Investigating the application of Kalman Filters for real-time accountancy in fusion fuel cycles

Tritium accountancy in the fusion fuel cycle is a significant concern for the operation of commercial devices. It is expected that a limit on the maximum amount of tritium inventory in the system will be implemented, meaning that accountancy of the tritium inventory in the fuel cycle will need to be as accurate as possible. This is difficult since not all locations along the fuel cycle can benefit from the implementation of a tritium accountancy sensor and measurements will inherently contain error. Commercial fusion plants will also operate continuously, challenging current accountancy techniques that rely on static processes in well-controlled environments. In this paper a simple fusion fuel cycle concept is defined and the tritium inventory over time of each component is modelled using the Euler Approximation of a series of differential Equations in Python. This information is then used to simulate sensor measurements at specific points on the fuel cycle and then passed through a Kalman Filter (KF) to improve the accuracy of the true measurement of the sensors.
Prácticas docentes en la escuela digital: la inclusión como reto

Digital technologies are transforming life and organisational and didactic processes in schools. In this context, technologies have demonstrated their potential for improving education, but there are also questions of great social significance that remain to be answered and require in-depth analysis. An example of this is the degree of integration of technologies and digital teaching materials to find out what role they play in the inclusion of all pupils. To address this issue, a research project funded by Generalitat Valenciana has been developed based on a multiple case study in two schools. Interviews, focus groups and participant observation have been used as data collection techniques. The results show a progressive digitalisation of teaching practices, although without a clear underlying pedagogical perspective that would allow for an innovative use of technology in the service of inclusion. In contrast to this position, we claim the need to innovate in schools using technology, which implies the use of more complex processes that should be further investigated to promote inclusion.
NEW TENDENCY OF ECONOMIC AND FINANCIAL CRIME IN THE CONTEXT OF DIGITAL AGE. A LITERATURE REVIEW

This paper wraps together two big concepts. First of them is the economic and financial crime and the second one is the digitalization. Each one of them has its own sub-components. The economic and financial crime can be split in: corruption, shadow economy, money laundering or cybercrime. Also, the digitalization can be explained as being related to smaller components, such as: connectivity, use of internet or digital public services. We tried to identify the relationship between these two concepts. As time goes on, we notice that the classical methods of economic and financial crime are at the same level as they were in the past, but digitalization is increasingly making itself comfortable in the field, which is why these types of crime methods are blooming. The subject provides a long-term research because of the technological development. This literature review study paves the way for more extensive research in the form of an empirical research, in order to statistically identify which phenomenon affects the other and how it is actually happening.
Blockchain in Taxation

Blockchain is comprised of a scattered database of blocks containing information which is encrypted with special algorithms called hash, that enable data monitoring. The digitization of payment systems as well as goods and services has led to the development of cryptocurrencies such as Bitcoin. However, although it was originally created for bitcoin trading, the potential of blockchain extends beyond cryptocurrencies and is still evolving. The aim of this study is to investigate the use of blockchain technology in taxation. In the study, the use of blockchain technology in taxation was examined and analyzed for selected countries and Turkey. Legal and administrative studies continue to be carried out in countries around the world to reduce the administrative burden in tax systems, allow tax collection at low costs and reduce tax deficits. As a result, blockchain technology will lead to change and transformation of the world tax regimes due to its transparency.
Entangled two-photon absorption with Brownian-oscillator fluctuations

We theoretically investigate the two-photon absorption signals of a three-band (g, e, f) system diagonally coupled to an over-damped Brownian oscillator bath, which induces random Gaussian modulations of energy levels with an arbitrary degree of correlation. For fast modulation, extra 2ωeg and 2ωfe peaks may obscure the g-f transitions in the classical two-photon absorption (CTPA) spectra for nearly resonant e states. These peaks arise from one-photon resonant g-e or e-f transitions. In the slow modulation limit, these peaks vanish because of the short tails of the Gaussian line shape. CTPA strongly depends on the correlations between energy fluctuations. In entangled two-photon absorption, the extra peaks are eliminated because of the broad one-photon but narrow two-photon spectrum of the twin photons. The variation of the coherences between f states with the correlation between energy fluctuations is explored.
Enhanced Generative Adversarial Networks with Restart Learning Rate in Discriminator

A series of Generative Adversarial Networks (GANs) could effectively capture the salient features in the dataset in an adversarial way, thereby generating target data. The discriminator of GANs provides significant information to update parameters in the generator and itself. However, the discriminator usually becomes converged before the generator has been well trained. Due to this problem, GANs frequently fail to converge and are led to mode collapse. This situation can cause inadequate learning. In this paper, we apply restart learning in the discriminator of the GAN model, which could bring more meaningful updates for the training process. Based on CIFAR-10 and Align Celeba, the experiment results show that the proposed method could improve the performance of a DCGAN with a low FID score over a stable learning rate scheme. Compared with two other stable GANs—SNGAN and WGAN-GP—the DCGAN with a restart schedule had a satisfying performance. Compared with the Two Time-Scale Update Rule, the restart learning rate is more conducive to the training of DCGAN. The empirical analysis indicates four main parameters have varying degrees of influence on the proposed method and present an appropriate parameter setting.
Intelligent Objective Osteon Segmentation Based on Deep Learning

Histology is key to understand physiology, development, growth and even reproduction of extinct animals. However, the identification and interpretation of certain structures, such as osteons, medullary bone (MB), and Lines of Arrested Growth (LAGs), are not only based on personal judgments, but also require considerable labor for subsequent analysis. Due to the dearth of available specimens, only a few quantitative histological studies have been proceeded for limited dinosaur taxa, most of which focus primarily on their growth, namely, LAGs and other growth lines without much attention to other histological structures. Here we develop a deep convolutional neural network-based method for automated osteohistological segmentation. Raw images are firstly divided into sub-images and the borders are expanded to guarantee the osteon regions integrity. ResNet-50 is employed as feature extractor and atrous spatial pyramid pooling (ASPP) is used to capture multi-scale information. A dual-resolution segmentation strategy is designed to observe the primary and secondary osteon regions from the matrix background. Finally, a segmented map with different osteon regions is obtained. This deep convolutional neural network-based model is tested on a histological dataset derived from various taxa in Alvarezsauria, a highly specialized group of non-avian theropod dinosaurs. The results show that large-scale quantitative histological analysis can be achieved by neural network-based methods, and previously hidden information by traditional methods can be revealed. Phylogenetic mapping of osteon segmentation results suggests a developmental pathway towards miniaturized body sizes in the evolution of Alvarezsauria, which may resemble the transition from non-avian dinosaurs to birds.
The Adaptation of Internet of Things in the Indian Insurance Industry—Reviewing the Challenges and Potential Solutions

The concept of insurance was found several centuries before Christ. Correspondingly, Chinese and Babylonian traders practiced moving or dispensing risks in the second and third millennia BC. Nowadays, insurance is the backbone of the economy. The recent introduction of big data, IoT, and other forms of InsurTech led to the fourth industrial revolution in insurance in the developed world. The industry is looking to improve the ergonomics of remote sensing technology to improve the acceptability of the clients. The adaptation of IoT in developing economies may provide a solution in increasing insurance penetration. This study explores the challenges and solutions in adopting IoT to increase insurance penetration in India. This study applied a systematic literature review (SLR) to extract the themes/variables related to challenges and solutions in adopting IoT in India’s insurance sector. Several keywords were used to search the relevant literature from Google Scholar. Based on inclusion and exclusion criteria, the filtered studies were explored. This study listed several challenges and their solutions in the adaption of IoT in the Indian insurance industry. Policymakers could adapt the suggestions provided to improve the service delivery insurance sector. The authors listed several challenges and solutions in the adaption of IoT in the Indian insurance industry through a systematic literature review to facilitate the policymakers to make the right decisions.
Hyperinvariant multiscale entanglement renormalization ansatz: Approximate holographic error correction codes with power-law correlations

We consider a class of holographic tensor networks that are efficiently contractible variational ansatz, manifestly (approximate) quantum error correction codes, and can support power-law correlation functions. In the case when the network consists of a single type of tensor that also acts as an erasure correction code, we show that it cannot be both locally contractible and sustain power-law correlation functions. Motivated by this no-go theorem, and the desirability of local contractibility for an efficient variational ansatz, we provide guidelines for constructing networks consisting of multiple types of tensors that can support power-law correlation. We also provide an explicit construction of one such network, which approximates the holographic happy pentagon code in the limit where variational parameters are taken to be small.
Cross-modal distribution alignment embedding network for generalized zero-shot learning

Many approaches in generalized zero-shot learning (GZSL) rely on cross-modal mapping between the image feature space and the class embedding space, which achieves knowledge transfer from seen to unseen classes. However, these two spaces are completely different space and their manifolds are inconsistent, the existing methods suffer from highly overlapped semantic description of different classes, as in GZSL tasks unseen classes can be easily misclassified into seen classes. To handle these problems, we adopt a novel semantic embedding network which helps to encode more discriminative information from initial semantic attributes to semantic embeddings in visual space. Meanwhile, a distribution alignment constraint is adopted to help keep the distribution of the learned semantic embeddings consistent with the distribution of real image features. Moreover, an auxiliary classifier is adopted to strengthen the quality of the learned semantic embeddings. Finally, a relation network is used to classify the unseen images by computing the relation scores between the semantic embeddings and image features, which is much more flexible than the fixed distance metric functions. Experimental results demonstrate that our proposed method is superior to other state-of-the-arts.
Dynamic hand gesture recognition of Arabic sign language by using deep convolutional neural networks

&lt;p&gt;In computer vision, one of the most difficult problems is human gestures in videos recognition Because of certain irrelevant environmental variables. This issue has been solved by using single deep networks to learn spatiotemporal characteristics from video data, and this approach is still insufficient to handle both problems at the same time. As a result, the researchers fused various models to allow for the effective collection of important shape information as well as precise spatiotemporal variation of gestures. In this study, we collected the dynamic dataset for twenty meaningful words of Arabic sign language (ArSL) using a Microsoft Kinect v2 camera. The recorded data included 7350 red, green, and blue (RGB) videos and 7350 depth videos. We proposed four deep neural networks models using 2D and 3D convolutional neural network (CNN) to cover all feature extraction methods and then passing these features to the recurrent neural network (RNN) for sequence classification. Long short-term memory (LSTM) and gated recurrent unit (GRU) are two types of using RNN. Also, the research included evaluation fusion techniques for several types of multiple models. The experiment results show the best multi-model for the dynamic dataset of the ArSL recognition achieved 100% accuracy.&lt;/p&gt;
Phase-space simulations of feedback coherent Ising machines

A new, to the best of our knowledge, technique is demonstrated for carrying out exact positive-P phase-space simulations of the coherent Ising machine quantum computer. By suitable design of the coupling matrix, general hard optimization problems can be solved. Here, computational quantum simulations of a feedback type of photonic parametric network are carried out, which is the implementation of the coherent Ising machine. Results for success rates are obtained using this scalable phase-space algorithm for quantum simulations of quantum feedback devices.