Reviewerly/exHarmony huggingface dataset:
Perceived Benefits of Future Teachers on the Usefulness of Virtual and Augmented Reality in the Teaching-Learning Process

Virtual and augmented reality technologies are increasingly being implemented in education and there is a growing body of evidence on their usefulness for training academic and non-academic skills among student with different education levels and diverse educational needs. To fully benefit from their use, teachers need to know the different existing tools and their potential, as well as be trained and motivated in their use. The current study aims to evaluate the factors that promote and hinder the use of virtual and augmented reality in future teaching professionals. Data were gathered by means of an online questionnaire from a total of 422 Spanish students from the Universidad Pontificia de Salamanca (Spain). Instrumental analyses (Cronbach’s Alpha, and CFA) were performed, together with descriptive, correlational, and inferential tests (Manova, Anova, and Student’s T test) to contrast the hypotheses. Results indicate that participants show favorable attitudes towards these technologies, but have little knowledge of them. Age, year of study, and knowledge are associated with a higher perception of usefulness as tools for teaching and learning and for promoting inclusion. The results support the relevance of providing knowledge, and enhancing skills, thus fostering positive attitudes towards these technologies.
Lithological Mapping of Kohat Basin in Pakistan Using Multispectral Remote Sensing Data: A Comparison of Support Vector Machine (SVM) and Artificial Neural Network (ANN)

Artificial intelligence (AI)-based multispectral remote sensing has been the best supporting tool using limited resources to enhance the lithological mapping abilities with accuracy, supported by ground truthing through traditional mapping techniques. The availability of the dataset, choice of algorithm, cost, accuracy, computational time, data labeling, and terrain features are some crucial considerations that researchers continue to explore. In this research, support vector machine (SVM) and artificial neural network (ANN) were applied to the Sentinel-2 MSI dataset for classifying lithologies having subtle compositional differences in the Kohat Basin’s remote, inaccessible regions within Pakistan. First, we used principal component analysis (PCA), minimum noise fraction (MNF), and available maps for reliable data annotation for training SVM and (ANN) models for mapping ten classes (nine lithological units + water). The ANN and SVM results were compared with the previously conducted studies in the area and ground truth survey to evaluate their accuracy. SVM mapped ten classes with an overall accuracy (OA) of 95.78% and kappa coefficient of 0.95, compared to 95.73% and 0.95 by ANN classification. The SVM algorithm was more efficient concerning computational efficiency, accuracy, and ease due to available features within Google Earth Engine (GEE). Contrarily, ANN required time-consuming data transformation from GEE to Google Cloud before application in Google Colab.
Quantum Key Distribution Based on Orthogonal State Encoding

Quantum key distribution(QKD) is one of the most significant areas in quantum information theory. For nearly four decades, substantial QKD schemes are developed. In early years, the security of QKD protocols is depend on switching different bases, namely based on non-orthogonal state encoding. The most famous example is the BB84 protocol. Later, other techniques were developed for orthogonal state encoding. Examples of such protocols include the GV protocol and order-rearrangement protocols. In this paper, we present two QKD protocols based on orthogonal state encoding. One of them does not need to employ order-rearrangement techniques while the other needs to. We provide analyses for them, demonstrating that they are highly efficient when considering consumptions of both qubits and classical bits. Furthermore, the employment of maximally entangled states could be less than previous ones and so the measurement efficiency could be increased. We also modify the protocols for implementing in noisy channels by applying the testing state method.
ADEV: Sound Automatic Differentiation of Expected Values of Probabilistic Programs

Optimizing the expected values of probabilistic processes is a central problem in computer science and its applications, arising in fields ranging from artificial intelligence to operations research to statistical computing. Unfortunately, automatic differentiation techniques developed for deterministic programs do not in general compute the correct gradients needed for widely used solutions based on gradient-based optimization. In this paper, we present ADEV, an extension to forward-mode AD that correctly differentiates the expectations of probabilistic processes represented as programs that make random choices. Our algorithm is a source-to-source program transformation on an expressive, higher-order language for probabilistic computation, with both discrete and continuous probability distributions. The result of our transformation is a new probabilistic program, whose expected return value is the derivative of the original program's expectation. This output program can be run to generate unbiased Monte Carlo estimates of the desired gradient, which can then be used within the inner loop of stochastic gradient descent. We prove ADEV correct using logical relations over the denotations of the source and target probabilistic programs. Because it modularly extends forward-mode AD, our algorithm lends itself to a concise implementation strategy, which we exploit to develop a prototype in just a few dozen lines of Haskell (https://github.com/probcomp/adev).
Geographical classification of malaria parasites through applying machine learning to whole genome sequence data

Malaria, caused by Plasmodium parasites, is a major global health challenge. Whole genome sequencing (WGS) of Plasmodium falciparum and Plasmodium vivax genomes is providing insights into parasite genetic diversity, transmission patterns, and can inform decision making for clinical and surveillance purposes. Advances in sequencing technologies are helping to generate timely and big genomic datasets, with the prospect of applying Artificial Intelligence analytical techniques (e.g., machine learning) to support programmatic malaria control and elimination. Here, we assess the potential of applying deep learning convolutional neural network approaches to predict the geographic origin of infections (continents, countries, GPS locations) using WGS data of P. falciparum (n = 5957; 27 countries) and P. vivax (n = 659; 13 countries) isolates. Using identified high-quality genome-wide single nucleotide polymorphisms (SNPs) (P. falciparum: 750 k, P. vivax: 588 k), an analysis of population structure and ancestry revealed clustering at the country-level. When predicting locations for both species, classification (compared to regression) methods had the lowest distance errors, and > 90% accuracy at a country level. Our work demonstrates the utility of machine learning approaches for geo-classification of malaria parasites. With timelier WGS data generation across more malaria-affected regions, the performance of machine learning approaches for geo-classification will improve, thereby supporting disease control activities.
Power mean based image segmentation in the presence of noise

In image segmentation and in general in image processing, noise and outliers distort contained information posing in this way a great challenge for accurate image segmentation results. To ensure a correct image segmentation in presence of noise and outliers, it is necessary to identify the outliers and isolate them during a denoising pre-processing or impose suitable constraints into a segmentation framework. In this paper, we impose suitable removing outliers constraints supported by a well-designed theory in a variational framework for accurate image segmentation. We investigate a novel approach based on the power mean function equipped with a well established theoretical base. The power mean function has the capability to distinguishes between true image pixels and outliers and, therefore, is robust against outliers. To deploy the novel image data term and to guaranteed unique segmentation results, a fuzzy-membership function is employed in the proposed energy functional. Based on qualitative and quantitative extensive analysis on various standard data sets, it has been observed that the proposed model works well in images having multi-objects with high noise and in images with intensity inhomogeneity in contrast with the latest and state-of-the-art models.
Interpretation and Transformation of Intrinsic Camera Parameters Used in Photogrammetry and Computer Vision

The precision modelling of intrinsic camera geometry is a common issue in the fields of photogrammetry (PH) and computer vision (CV). However, in both fields, intrinsic camera geometry has been modelled differently, which has led researchers to adopt different definitions of intrinsic camera parameters (ICPs), including focal length, principal point, radial distortion, decentring distortion, affinity and shear. These ICPs are indispensable for vision-based measurements. These differences can confuse researchers from one field when using ICPs obtained from a camera calibration software package developed in another field. This paper clarifies the ICP definitions used in each field and proposes an ICP transformation algorithm. The originality of this study lies in its use of least-squares adjustment, applying the image points involving ICPs defined in PH and CV image frames to convert a complete set of ICPs. This ICP transformation method is more rigorous than the simplified formulas used in conventional methods. Selecting suitable image points can increase the accuracy of the generated adjustment model. In addition, the proposed ICP transformation method enables users to apply mixed software in the fields of PH and CV. To validate the transformation algorithm, two cameras with different view angles were calibrated using typical camera calibration software packages applied in each field to obtain ICPs. Experimental results demonstrate that our proposed transformation algorithm can be used to convert ICPs derived from different software packages. Both the PH-to-CV and CV-to-PH transformation processes were executed using complete mathematical camera models. We also compared the rectified images and distortion plots generated using different ICPs. Furthermore, by comparing our method with the state of art method, we confirm the performance improvement of ICP conversions between PH and CV models.
JCBIE: a joint continual learning neural network for biomedical information extraction

Extracting knowledge from heterogeneous data sources is fundamental for the construction of structured biomedical knowledge graphs (BKGs), where entities and relations are represented as nodes and edges in the graphs, respectively. Previous biomedical knowledge extraction methods simply considered limited entity types and relations by using a task-specific training set, which is insufficient for large-scale BKGs development and downstream task applications in different scenarios. To alleviate this issue, we propose a joint continual learning biomedical information extraction (JCBIE) network to extract entities and relations from different biomedical information datasets. By empirically studying different joint learning and continual learning strategies, the proposed JCBIE can learn and expand different types of entities and relations from different datasets. JCBIE uses two separated encoders in joint-feature extraction, hence can effectively avoid the feature confusion problem comparing with using one hard-parameter sharing encoder. Specifically, it allows us to adopt entity augmented inputs to establish the interaction between named entity recognition and relation extraction. Finally, a novel evaluation mechanism is proposed for measuring cross-corpus generalization errors, which was ignored by traditional evaluation methods. Our empirical studies show that JCBIE achieves promising performance when continual learning strategy is adopted with multiple corpora.
Digital competence among university professors: analysis of the impact of the COVID crisis

The digital revolution and the consequences of COVID-19 have had a significant impact on higher education. The need to develop digital competences, especially among educators and within the entire university system, has become a priority. Furthermore, due to the change of habits required by technology, digitization has entailed a great effort by professors. Until March 2020, the development of digital competence had been increasing gradually. However, its evolution has experienced a qualitative leap due to the impact of the COVID crisis, which forced all education to be delivered initially online, and later in hybrid form. Several theoretical models have been used to determine the digital competence of professors and its impact on student learning. The European Framework for Digital Competence of Educators (DigCompEdu) model is applied in this research. It defines the digital competence of professors in six areas comprising 22 competences, all categorized under three main headings. A survey with 271 university professors has provided the quantitative data analysis for the findings. The results will help to analyze professors’ digital competence evolution showing that self-perception has improved by 51%, which is significant. Finally, some conclusions regarding training and investment in universities are considered from the study results.
Spatiotemporal information conversion machine for time-series forecasting

Making time-series forecasting in a robust way is a difficult task only based on the observed data of a nonlinear system. In this work, a neural network computing framework, the spatiotemporal information conversion machine (STICM), was developed to efficiently and accurately render a forecasting of a time series by employing a spatial-temporal information (STI) transformation. STICM combines the advantages of both the STI equation and the temporal convolutional network, which maps the high-dimensional/spatial data to the future temporal values of a target variable, thus naturally providing the forecasting of the target variable. From the observed variables, the STICM also infers the causal factors of the target variable in the sense of Granger causality, which are in turn selected as effective spatial information to improve the robustness of time-series forecasting. The STICM was successfully applied to both benchmark systems and real-world datasets, all of which show superior and robust performance in time-series forecasting, even when the data were perturbed by noise. From both theoretical and computational viewpoints, the STICM has great potential in practical applications in artificial intelligence (AI) or as a model-free method based only on the observed data, and also opens a new way to explore the observed high-dimensional data in a dynamical manner for machine learning.
Medical Image Encryption Based on Josephus Traversing and Hyperchaotic Lorenz System

This study proposes a new medical image encryption scheme based on Josephus traversing and hyperchaotic Lorenz system. First, a chaotic sequence is generated through hyperchaotic system. This hyperchaotic sequence is used in the scrambling and diffusion stages of the algorithm. Second, in the scrambling process, the image is initially confused by Josephus scrambling, and then the image is further confused by Arnold map. Finally, generated hyperchaos sequence and exclusive OR operation is used for the image to carry on the positive and reverse diffusion to change the pixel value of the image and further hide the effective information of the image. In addition, the information of the plaintext image is used to generate keys used in the algorithm, which increases the ability of resisting plaintext attack. Experimental results and security analysis show that the scheme can effectively hide plaintext image information according to the characteristics of medical images, and is resistant to common types of attacks. In addition, this scheme performs well in the experiments of robustness, which shows that the scheme can solve the problem of image damage in telemedicine. It has a positive significance for the future research.
STUDY ON IOT BASED WOMEN SAFETY DEVICES WITH SCREAMING DETECTION AND VIDEO CAPTURING

To propose an idea for implementing wearable IoT devices using the Raspberry Pi, which is equipped with a sound sensor, camera module, GPS, and GSM. When the sound sensor detects screaming, the SVM (support vector machine) algorithm in machine learning eliminates unnecessary noise and evaluates only the victim screaming. After assessing the victim's scream, the gadget promptly activates the camera module, which takes 30 seconds of footage. GPS will track the geographical coordinates and, using the GSM module, will send an alert message and an emergency call to the nearest police station. When the victim activates the switch in the second scenario, the camera module is immediately activated, and a 30-second clip is captured. The GPS will be active, and GSM will send an alert message with the status. The idea's major goal is to present a smart gadget for a woman that is entirely comfy and portable. When compared to other existing safety devices, the first and most crucial difference is the smart band's ability to shrink the size of gadgets.
Machine learning techniques for automated policy violation reporting

Citizens regularly face incidents or violations and even digital security incidents such as e-mail intrusion, system infiltration or damage caused by malicious software. These violations are usually left unreported because of the difficulties that the citizens face to get the incident reported. Another issue is that many complaints should be handled by different departments in different sectors, but due to a lack of cooperation between departments in different sectors, complaints are frequently misplaced. To solve this problem, we propose an automated client-server citizen reporting system framework based on machine learning techniques. The paper focuses on the design and implementation of an automated image feature-based classification framework that jointly uses feature extraction and deep learning to classify the images and forward them to the relevant department. In addition, the framework permits users to report about any cyber-crime incidents such as bank account intrusion (Alazab et al., 2011a, 2020b; Alazab, 2020), credit card fraud (Alazab et al., 2011b, 2012a, 2012c), phishing and pharming. The results show that complaints handling accuracy is up to 95.4%.
Chinese Word Sense Embedding with SememeWSD and Synonym Set

Word embedding is a fundamental natural language processing task which can learn feature of words. However, most word embedding methods assign only one vector to a word, even if polysemous words have multi-senses. To address this limitation, we propose SememeWSD Synonym (SWSDS) model to assign a different vector to every sense of polysemous words with the help of word sense disambiguation (WSD) and synonym set in OpenHowNet. We use the SememeWSD model, an unsupervised word sense disambiguation model based on OpenHowNet, to do word sense disambiguation and annotate the polysemous word with sense id. Then, we obtain top n synonyms of the word sense from OpenHowNet and calculate the average vector of synonyms as the vector of the word sense. In experiments, We evaluate the SWSDS model on semantic similarity calculation with Gensim’s wmdistance method. It achieves improvement of accuracy. We also examine the SememeWSD model on different BERT models to find the more effective model.
Block-Cipher-Based Tree Hashing

First of all we take a thorough look at an error in a paper by Daemen et al. (ToSC 2018) which looks at minimal requirements for tree-based hashing based on multiple primitives, including block ciphers. This reveals that the error is more fundamental than previously shown by Gunsing et al. (ToSC 2020), which is mainly interested in its effect on the security bounds. It turns out that the cause for the error is due to an essential oversight in the interaction between the different oracles used in the indifferentiability proofs. In essence, it reduces the claim from the normal indifferentiability setting to the weaker sequential indifferentiability one. As a matter of fact, this error appeared in multiple earlier indifferentiability papers, including the optimal indifferentiability of the sum of permutations (EUROCRYPT 2018) and the recent $$\textrm{ABR}^+$$ construction (EUROCRYPT 2021). We discuss in detail how this oversight is caused and how it can be avoided. We next demonstrate how the negative effects on the security bound of the construction by Daemen et al. can be resolved. Instead of only allowing a truncated output, we generalize the construction to allow for any finalization function and investigate the security of this for five different types of finalization. Our findings, among others, show that the security of the SHA-2 mode does not degrade if the feed-forward is dropped and that the modern BLAKE3 construction is secure in principle but that its use of the extendable output requires its counter used for random access to be public. Finally, we introduce the tree sponge, a generalization of the sequential sponge construction with parallel absorbing and squeezing.
Achieving Almost All Blockchain Functionalities with Polylogarithmic Storage

In current blockchain systems, full nodes that perform all of the available functionalities need to store the entire blockchain. In addition to the blockchain, full nodes also store a blockchain-summary, called the state, which is used to efficiently verify transactions. With the size of popular blockchains and their states growing rapidly, full nodes require massive storage resources in order to keep up with the scaling. This leads to a tug-of-war between scaling and decentralization since fewer entities can afford expensive resources. We present hybrid nodes for proof-of-work (PoW) cryptocurrencies which can validate transactions, validate blocks, validate states, mine, select the main chain, bootstrap new hybrid nodes, and verify payment proofs. With the use of a protocol called trimming, hybrid nodes only retain polylogarithmic number of blocks in the chain length in order to represent the proof-of-work of the blockchain. Hybrid nodes are also optimized for the storage of the state with the use of stateless blockchain protocols. The lowered storage requirements should enable more entities to join as hybrid nodes and improve the decentralization of the system. We define novel theoretical security models for hybrid nodes and show that they are provably secure. We also show that the storage requirement of hybrid nodes is near-optimal with respect to our security definitions.