Reviewerly/exHarmony huggingface dataset:
Secure key distribution exploiting error rate criticality for radio frequency links

We propose a method by which two radio frequency (RF) communication terminals exchange encryption keys or other data securely. This method draws on the approach developed for quantum key distribution (QKD) for detecting eavesdroppers but our method does not use any quantum properties at all. Instead, by exploiting the effects an eavesdropper has on channel stability, we explore a line-of-sight link radio in which data transfer rates are so high as to approach the Shannon limit. With very steep rises in bit error rate accompanying a small degradation of signal-to-noise limits for certain forward error correction codes, it becomes possible to infer the existence of an eavesdropper before they are able to obtain a complete key. We describe our method and analyse one possible implementation using low density parity check codes with quadrature phase shift keying modulation. The proposed technique is in principle far easier to implement than quantum-based approaches for RF and optical wireless links since the required hardware is readily available and the basic principles are well known and well understood. Finally, we show our method to have a higher key rate and spectral efficiency than those of QKD.
GEOMETRIC ACCURACY ANALYSIS BETWEEN NEURAL RADIANCE FIELDS (NERFS) AND TERRESTRIAL LASER SCANNING (TLS)

Abstract. Neural Radiance Fields (NeRFs) use a set of camera poses with associated images to represent a scene through a position-dependent density and radiance at given spatial location. Generating a geometric representation in form of a point cloud is gained by ray tracing and sampling 3D points with density and color along the rays. In this contribution we evaluate object reconstruction by NeRFs in 3D metric space against Terrestrial Laser Scanning (TLS) using ground truth data in form of a Structured Light Imaging (SLI) mesh and investigate the influence of the density to the reconstruction’s accuracy. We extend the accuracy assessment from 2D to 3D space and perform high resolution investigations on NeRFs by using camera images with 36MP resolution as well as comparison among point clouds of more than 20 million points against a 0.1mm ground truth mesh. TLS achieves the highest geometric accuracy results with a standard deviation of 1.68mm, while NeRFδt=300 diverges 18.61mm from the ground truth. All NeRF reconstructions contain 3D points inside the object which have the highest displacements from the ground truth, thus contribute the most to the accuracy results. NeRFs accuracy improves with increasing the density threshold as a consequence of completeness, since beside noise and outliers the object points are also being removed.
Privacy-preserving Naive Bayes classification based on secure two-party computation

&lt;abstract&gt;&lt;p&gt;With the proliferation of data and machine learning techniques, there is a growing need to develop methods that enable collaborative training and prediction of sensitive data while preserving privacy. This paper proposes a new protocol for privacy-preserving Naive Bayes classification using secure two-party computation (STPC). The key idea is to split the training data between two non-colluding servers using STPC to train the model without leaking information. The servers secretly share their data and the intermediate computations using cryptographic techniques like Beaver's multiplication triples and Yao's garbled circuits. We implement and evaluate our protocols on the MNIST dataset, demonstrating that they achieve the same accuracy as plaintext computation with reasonable overhead. A formal security analysis in the semi-honest model shows that the scheme protects the privacy of the training data. Our work advances privacy-preserving machine learning by enabling secure outsourced Naive Bayes classification with applications such as fraud detection, medical diagnosis, and predictive analytics on confidential data from multiple entities. The modular design allows embedding different secure matrix multiplication techniques, making the framework adaptable. This line of research paves the way for practical and secure data mining in a distributed manner, upholding stringent privacy regulations.&lt;/p&gt;&lt;/abstract&gt;
School Reading Lists and Understanding Written Information for Life and Work

School reading lists are an essential factor influencing understanding the content and form of various types of texts: artistic, non-artistic, and electronic texts of an everyday nature among Czech technical secondary school pupils. The research aims to determine by analysing eight key school documents whether recommended school lists of texts for study at secondary technical schools are published electronically on schools' websites in the Moravian-Bohemian and Olomouc Regions of the Czech Republic. A partial goal is to determine whether school reading lists contain different types of texts and whether pupils at technical secondary schools understand information with a professional, artistic, media, etc., focus. A quantitatively oriented questionnaire survey through the lens of 523 pupils from 5 Czech secondary technical schools investigated what factors influence the reader's understanding of printed and electronic texts. The research results showed that Czech secondary school pupils like working with informal texts on social networks, frequently require help understanding texts of an administrative nature, and rarely work with texts from everyday life at school. The average to below-average level of reading and digital literacy among Czech 15-year-old pupils was also highlighted by the PISA 2018 international research.
The MAXLab Aggression and Bystander Intervention Scenario Set (MAXLab_ABISS): A modular scenario set for studying decision making in situations of interpersonal violence in virtual reality (preprint)

This Research Note describes the MAXLab Aggression and Bystander Intervention Scenario Set (MAXLab_ABISS). This modular stimulus set contains various immersive 360Â° videos that can be used to study decision making in situations of interpersonal violence. The goal of MAXLab_ABISS is to provide the research community with a new method for collecting data involving immersive technology that allows for studying peoplesâ perceptions of, and reactions towards, violent situations. We describe the scenarios and how they can be used, elaborate on the production process,Â provide technical specifications, and explain how the scenario set can be obtained.
Warped Gradient-Enhanced Gaussian Process Surrogate Models for Exponential Family Likelihoods with Intractable Normalizing Constants

Markov chain Monte Carlo methods for exponential family models with intractable normalizing constant, such as the exchange algorithm, require simulations of the sufficient statistics at every iteration of the Markov chain, which often result in expensive computations. Surrogate models for the likelihood function have been developed to accelerate inference algorithms in this context. However, these surrogate models tend to be relatively inflexible, and often provide a poor approximation to the true likelihood function. In this article, we propose the use of a warped, gradient-enhanced, Gaussian process surrogate model for the likelihood function, which jointly models the sample means and variances of the sufficient statistics, and uses warping functions to capture covariance nonstationarity in the input parameter space. We show that both the consideration of nonstationarity and the inclusion of gradient information can be leveraged to obtain a surrogate model that outperforms the conventional stationary Gaussian process surrogate model when making inference, particularly in regions where the likelihood function exhibits a phase transition. We also show that the proposed surrogate model can be used to improve the effective sample size per unit time when embedded in exact inferential algorithms. The utility of our approach in speeding up inferential algorithms is demonstrated on simulated and real-world data.
Augmenting Craft with Mixed Reality

This paper discusses novel methods for and advantages of integrating augmented reality (AR) and photogrammetry in hand clay-sculpting workflows.These techniques permit nontrained users to achieve higher precision during the sculpting process by holographically overlaying instructions from digital 3D source geometry on top of the sculpting material.By employing alternative notational systems in design implementation methods, the research positions itself in a postdigital context aimed at humanizing digital technologies.Throughout history, devices have been developed to increase production, such as Henry Dexter's 1842 "Apparatus for Sculptors" for marble sculpting.Extrapolating from this, the workflow presented in this paper uses AR to overlay extracted information from 3D models directly onto the sculptor's field of vision.This information can then become an AR-driven guidance system that assists the sculptor.Using the Microsoft HoloLens, holographic instructions are introduced in the production sequence, connecting the analog sculpture fabrication directly with a digital environment, thus augmenting the craftspeople's agency.A series of AR-aided sculpting methods were developed and tested in a demonstrator case study project that created a small-scale clay copy of Henry Moore's Sheep Piece (1971)(1972).This paper demonstrates how user-friendly software and hardware tools have lowered the threshold for end users to develop new methods that straightforwardly facilitate and improve their crafts' effectiveness and agency.This shows that the fusion of computational design technology and AR visualization technology can innovate a specific craft's design and production workflow, opening the door for further application developments in more architecture-specific fabrication contexts.
Object detection: review and improvement based on deep learning

In recent years, with the quantum leap in deep learning, self-driving vehicle as one of its applications has been gaining tremendously increasing popularity as well as making a multitude of achievements. Object detection, which has made significant contribution to driver-less vehicle, has had been applied to a tremendously wide range of fields. However, reports relevant to automatic vehicle stating that accidents are caused by Automatic driving technology, present problems pointing out that existing target detection algorithms, which are already fairly well reliable, can probably be interfered by adverse conditions such as high temperature, raise dust and transmission loss, and be not capable of providing precise output. This paper recaps on these previous classic algorithms, and their large-scale application domain. Meanwhile, this paper presents improvements focusing on enhancing the robustness of these algorithms to overcome these problems caused by adverse conditions and improve the accuracy. Thus these improvements could augment the security of these driver-less vehicles, and eventually reduce traffic accident mortality relative to self-driving vehicles and safeguard road safety, and may potentially benefit to further research.
Enhanced glacial earthquake catalogues with supervised machine learning for more comprehensive analysis

SUMMARY Polar regions and Greenland in particular are highly sensitive to global warming. Impacts on Greenland’s glaciers may be observed through the increasing number of calving events. However, a direct assessment of the calving activity is limited due to the remoteness of polar regions and the cloudy weather which makes impossible a recurrent observation through satellite imagery. To tackle this issue, we exploit the seismological network deployed in Greenland which actively records seismic signals associated with calving events, hereinafter referred to as glacial earthquakes. These seismic signals present a broad frequency range and a wide diversity of waveform which make them difficult to discriminate from tectonic events as well as anthropogenic and natural noises. In this study, we start from two catalogues of known events, one for glacial earthquake events which occurred between 1993 and 2013 and one for earthquakes which occurred in the same time period, and we implement a detection algorithm based on the STA/LTA method to extract signals’ events from continuous data. Then, we train and test a machine learning processing chain based on the Random Forest algorithm which allows us to automatically associate the events respectively with calving and tectonic activity, with a certain probability. Finally, we investigate 844 selected days spanning time of continuous data from the Greenland regional seismic network which results in a new, more exhaustive, catalogue of glacial earthquakes expanded of 1633 newly detected glacial events. Moreover, we extensively discuss the choice of the features used to describe glacial earthquakes, in particular the 39 new features created in this study which have drastically improved our results with 7 of the 10 best features being in the added set. The perspective of further expansion of the glacial earthquake catalogue applying the processing chain discussed in this paper on different time spans highlights how combining seismology and machine learning can increase our understanding of the spatio-temporal evolution of calving activity in remote regions.
L’interazione tra la diatesi Non-attiva e le categorie di tempo e aspetto. Microvariazione nei dialetti italo-albanesi

This article deals with morphological non-active verbal voice, which characterises reflexive, middle and passive structures in Standard Albanian and Arbëresh dialects. The data, compared to those of Italian, show that the three different strategies for the realisation of Standard Albanian and Arbëresh non-active morphology interact with the categories of mood, tense and aspect. In particular, these morphological strategies determine a perfective/ imperfective split as well as a series of restrictions which convey different interpretations associated with a stative or eventive interpretation. Particular attention is paid to the possible influence of Italian structures on the Italo-Albanian varieties in the domain of non-active voice.
EAT: An Enhancer for Aesthetics-Oriented Transformers

Transformers have shown great potential in various vision tasks, but none of them have surpassed the best CNN model on image aesthetics assessment (IAA) tasks. IAA is a challenging task in multimedia systems that requires attention to both foreground and background, as well as robustness to noisy and redundant labels. The global and dense attention mechanism of Transformers, designed for saliency-oriented tasks, may miss important aesthetic information in the background, increase the computational cost and slow down the convergence on IAA tasks. To address these issues, we propose an Enhancer for Aesthetics-Oriented Transformers (EAT). EAT uses a deformable, sparse and data-dependent attention mechanism that learns where to focus and how to refine attention by offsets. EAT also guides the offsets to balance the attention between foreground and background according to dedicated rules. Our EAT-enhanced Transformers outperform the previous methods on four representative datasets with fewer training epochs. Code is available in https://github.com/woshidandan/Image-Aesthetics-Assessment
Stochastic declustering of earthquakes with the spatiotemporal renewal ETAS model

Modeling and forecasting earthquakes is challenging due to the complex interplay and clustering of main-shocks and aftershocks. The epidemic-type aftershock sequence (ETAS) model represents the conditional intensity of earthquakes as the superposition of a background and aftershock rate which allows for the declustering of the earthquakes. Its success has led to the development of numerous versions of the ETAS model. Among these extensions is the renewal ETAS (RETAS) model, which has shown promising potential. The RETAS model endows the main-shock arrival process with a renewal process, which serves as an alternative to the homogeneous Poisson process. Model fitting is performed using likelihood-based estimation by directly optimizing the exact likelihood. However, inferring the branching structure from the fitted RETAS model remains a challenging task since the declustering algorithm that is currently available for the ETAS model is not directly applicable. Therefore, this article develops an iterative algorithm to calculate the smoothed main- and aftershock probabilities, conditional on all available information contained in the catalog. Consequently, an estimate of the background spatial intensity function and model parameters can be obtained using an iterative semiparametric procedure with the smoothing parameters selected using information criteria. The methods proposed herein are illustrated on simulated data and a New Zealand earthquake catalog.
Mobile application for safflower technology transfer

Information and communication technologies brought significant change in agriculture development. Amongthe ICTs mobile technologies has better access to provide more information to the stakeholders. Towards this direction for effective technology transfer of safflower, an Andrioid based mobile application “ICARIIOR Safflower” mobile APP was developed at ICAR-IIOR to provide information on the safflower technologies to the stake holders. The information on different aspects of safflower viz., General information, Agronomic Practices, Preferred cultivars, Cropping systems, Insect pests, Diseases, AICRP centres and Commodity markets was compiled and categorised into major chapters with sub-topics within each. This APP supports English language and is available in Google play store.
Enhancing descriptive writing of secondary school students through digital lesson contents in Zanzibar

This study aimed to find out whether there was a significant difference in descriptive writing enhancement among secondary school students who were taught through digital lesson contents (DLCs) or not. A quasi-experimental pre-test and post-test through single group design was used for the study. One hundred twenty (120) students from three selected secondary schools were drawn using a purposive sampling technique. First, they were given a pre-test, and then teachers used DLCs for teaching descriptive writing within one month. After that, students were given post-tests to determine the variation of the two test scores. A mixed method approach was applied to focus group discussions and student achievement tests. The data of students’ tests were analysed by inferential statistics using a t-test with the help of SPSS program version 25. However, data from focus group discussions were analysed thematically. The findings revealed that the performance of the students improved after the use of DLCs for teaching and learning descriptive writing. There is a decrease in the proportion of students with low marks (&lt;2) from the pre-test to the post-test, while there is an increase in the proportion of students with high marks (2--5) from the pre-test to the post-test. The findings from participants revealed that DLCs had an impact on raising the students’ interest and motivation as well as improving students’ descriptive writing ability. The study concludes that using DLCs for teaching and learning is a catalyst because it supports and motivates students to achieve sufficient ability in descriptive writing. Based on the findings, the study recommends that effective availability of instructional materials such as computers, projectors and smart TVs should be highly considered for secondary schools to improve the quality of education.
Optimizing Multimodal Scene Recognition through Mutual Information-Based Feature Selection in Deep Learning Models

The field of scene recognition, which lies at the crossroads of computer vision and artificial intelligence, has experienced notable progress because of scholarly pursuits. This article introduces a novel methodology for scene recognition by combining convolutional neural networks (CNNs) with feature selection techniques based on mutual information (MI). The main goal of our study is to address the limitations inherent in conventional unimodal methods, with the aim of improving the precision and dependability of scene classification. The focus of our research is around the formulation of a comprehensive approach for scene detection, utilizing multimodal deep learning methodologies implemented on a solitary input image. Our work distinguishes itself by the innovative amalgamation of CNN- and MI-based feature selection. This integration provides distinct advantages and enhanced capabilities when compared to prevailing methodologies. In order to assess the effectiveness of our methodology, we performed tests on two openly accessible datasets, namely, the scene categorization dataset and the AID dataset. The results of these studies exhibited notable levels of precision, with accuracies of 100% and 98.83% achieved for the corresponding datasets. These findings surpass the performance of other established techniques. The primary objective of our end-to-end approach is to reduce complexity and resource requirements, hence creating a robust framework for the task of scene categorization. This work significantly advances the practical application of computer vision in various real-world scenarios, leading to a large improvement in the accuracy of scene recognition and interpretation.