Reviewerly/exHarmony huggingface dataset:
New opportunities for older adults care transition from traditional to personalised assistive care: vINCI platform

Today, we are witnessing profound changes in the provision of healthcare services, shifting from illness-centred systems to patient-centred ones, able to deliver a personalised and integrated experience. This new approach has been generated by the tendencies of recent decades regarding the continuous increase of chronic medical conditions associated with both the modern lifestyle and the accelerated rate of population ageing. Therefore, under the pressure of the three requirements that shape the future of care services (better quality care, improving patient experience and health, while reducing total costs), we are moving towards a new ecosystem-based model of care that incorporates the following key features: permanent innovation, self and continuous care based on emergent technologies (Internet of Things (IoT), Artificial Intelligence, Big data etc.) available nearby or at home, patient engagement, personalised assistance, data and analytics. In this regard, as part of the European research effort, in this paper it is presented a new integrated and validated framework using IoT to provide non-intrusive monitoring and personalised assistance services to older adults: the vINCI platform. This system is a clinically-validated environmental framework, in which several smart devices work together to aggregate a solution able to contribute at the improvement of the older adults' quality of life.
BIOMEX-DB: A Cognitive Audiovisual Dataset for Unimodal and Multimodal Biometric Systems

Multimodal biometric schemes arise as an interesting solution to the multidimensional reinforcement problem for biometric security systems. Along with the performance dimension, these systems should also comply with required levels for other conditions such as permanence, collectability, and circumvention, among others. In response to the demand for a multimodal and synchronous dataset, we introduce in this paper an open-access database of synchronously recorded electroencephalogram signals (EEG), voice signals, and video feed from 51 volunteers, 25 female, 26 male, captured for, but not limited to, biometric purposes. A total of 140 samples were collected from each user when pronouncing single digits in Spanish, giving a total of 7140 instances. EEG signals were captured using a 14-channel Emotiv™ Epoc headset. The resulting set becomes a valuable resource when working on unimodal biometric systems, but significantly more for the evaluation of multimodal variants. Furthermore, the usefulness of the collected signals extends to being exploited by projects in brain-computer interfaces and face recognition to name just a few. As an initial report on data separability of the related samples, five user recognition experiments are presented: a face recognition identifier with an accuracy of 99%, a speaker identification system with accuracy of 94.2%, a bimodal face-speech verification case with Equal Error Rate around 2.64, an EEG identification example, and a bimodal user identification exercise based on EEG and voice modalities with a registered accuracy of 97.6%.
Content Controlled Spectral Indices for Detection of Hydrothermal Alteration Minerals Based on Machine Learning and Lasso-Logistic Regression Analysis

This article introduced the quantity controlled spectral indices working at mineral contents higher than 5 wt.% for detection of sericite, chlorite, and pyrophyllite, which are the representative alteration minerals of phyllic, propylitic, and advanced argillic hydrothermal alterations. The X-ray diffraction analysis revealed that the samples are mostly pure with minor content of quartz. The absorption features of target minerals showed systematic decrease in absorption depth with decrease in the mineral content, and the changes varied by mineral types. A total of 1253 target mineral spectra and 605 nontarget mineral spectra were classified by a random forest model, which achieved an overall accuracy of 97% with mineral content above 5 wt.%. Least absolute shrinkage and selection operator logistic regressions employed spectral variables of 82 bands for sericite, 132 bands for chlorite, and 84 bands for pyrophyllite with minimal spectral overlap. The overall accuracies were higher than 93.6% with R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> values ranging from 0.57 to 0.71. Because both target minerals and nontarget minerals, these indices can reliably make mineral classification. To be compatible with remote sensing images, the water-absorption bands were excluded from the indices.
Exploring the Next Wave of Blockchain and Distributed Ledger Technology: The Overlooked Potential of Scenario Analysis

Blockchain is predicted to disrupt industries, economies, and societies. The properties of distributed ledgers allow the creation of immutable data structures that facilitate shared access in real time and enable a plethora of innovative applications. However, blockchain is not a uniform technology but rather a bundle of evolving components whose implications are notoriously hard to predict. At present, it is not clear how current trends will evolve, with technical evolution, legislation, and public policy being three contingency factors that make ongoing disruptive transformations particularly hard to predict. In light of blockchain’s potential disruptive impact, it is surprising that scenario analysis has hitherto been largely ignored in academic research. Therefore, in this paper, we introduce the technique, clarify several misconceptions, and provide examples illustrating how this method can help to overcome the limitations of existing technology impact research. We conclude that if applied correctly, scenario analysis represents the ideal tool to rigorously explore uncertain future developments and to create a comprehensive foundation for future research.
POSSIBILITIES OF USING PHOTOGRAMMETRY IN THE TEACHING PROCESS

Appears in: EDULEARN21 Proceedings Publication year: 2021Pages: 9237-9242ISBN: 978-84-09-31267-2ISSN: 2340-1117doi: 10.21125/edulearn.2021.1860Conference name: 13th International Conference on Education and New Learning TechnologiesDates: 5-6 July, 2021Location: Online Conference
DAAI at CASE 2021 Task 1: Transformer-based Multilingual Socio-political and Crisis Event Detection

Automatic socio-political and crisis event detection has been a challenge for natural language processing as well as social and political science communities, due to the diversity and nuance in such events and high accuracy requirements.In this paper, we propose an approach which can handle both document and cross-sentence level event detection in a multilingual setting using pretrained transformer models.Our approach became the winning solution in document level predictions and secured the 3 rd place in cross-sentence level predictions for the English language.We could also achieve competitive results for other languages to prove the effectiveness and universality of our approach.
The main causes of conflicts in higher educational institutions and the concept of their management

In the process of higher education, conflicts arise between leaders and professors, mainly due to the organization of the educational process, the organization of scientific work, the lack of good management of spiritual and educational work. The article discusses the main causes of conflicts in higher education institutions and the concept of their management. Conflicts between older professors and younger assistants are mainly due to the irresponsibility of young people, non-compliance with rules, lack of subordination to students, and shortcomings in the conduct of classes. It will be possible to solve this problem on the basis of regular evaluation at meetings of the department, the continuation of the tradition of "Teacher-Apprentice" with older qualified teachers, the organization of scientific seminars by qualified specialists.
Sublinear GMW-Style Compiler for MPC with Preprocessing

We consider the efficiency of protocols for secure multiparty computation (MPC) with a dishonest majority. A popular approach for the design of such protocols is to employ preprocessing. Before the inputs are known, the parties generate correlated secret randomness, which is consumed by a fast and possibly “information-theoretic” online protocol. A powerful technique for securing such protocols against malicious parties uses homomorphic MACs to authenticate the values produced by the online protocol. Compared to a baseline protocol, which is only secure against semi-honest parties, this involves a significant increase in the size of the correlated randomness, by a factor of up to a statistical security parameter. Different approaches for partially mitigating this extra storage cost come at the expense of increasing the online communication. In this work we propose a new technique for protecting MPC with preprocessing against malicious parties. We show that for circuit evaluation protocols that satisfy mild security and structural requirements, that are met by many standard protocols with semi-honest security, the extra additive storage and online communication costs are both logarithmic in the circuit size. This applies to Boolean circuits and to arithmetic circuits over fields or rings, and to both information-theoretic and computationally secure protocols. Our protocol can be viewed as a sublinear information-theoretic variant of the celebrated “GMW compiler” that applies to natural protocols for MPC with preprocessing. Our compiler makes a novel use of the techniques of Boneh et al. (Crypto 2019) for sublinear distributed zero knowledge, which were previously only used in the setting of honest-majority MPC.
Integrative 3D Geological Modeling Derived from SWIR Hyperspectral Imaging Techniques and UAV-Based 3D Model for Carbonate Rocks

This paper demonstrates an integrative 3D model of short-wave infrared (SWIR) hyperspectral mapping and unmanned aerial vehicle (UAV)-based digital elevation model (DEM) for a carbonate rock outcrop including limestone and dolostone in a field condition. The spectral characteristics in the target outcrop showed the limestone well coincided with the reference spectra, while the dolostone did not show clear absorption features compared to the reference spectra, indicating a mixture of clay minerals. The spectral indices based on SWIR hyperspectral images were derived for limestone and dolostone using aluminum hydroxide (AlOH), hydroxide (OH), iron hydroxide (FeOH), magnesium hydroxide (MgOH) and carbonate ion (CO32−) absorption features based on random forest and logistic regression models with an accuracy over 87%. Given that the indices were derived from field data with consideration of commonly occurring geological units, the indices have better applicability for real world cases. The integrative 3D geological model developed by co-registration between hyperspectral map and UAV-based DEM using best matching SIFT descriptor pairs showed the 3D rock formations between limestone and dolostone. Moreover, additional geological information of the outcrop was extracted including thickness, slope, rock classification, strike, and dip.
Opening Pandora’s Box: A Systematic Study of New Ways Microarchitecture Can Leak Private Data

Microarchitectural attacks have plunged Computer Architecture into a security crisis. Yet, as the slowing of Moore’s law justifies the use of ever more exotic microarchitecture, it is likely we have only seen the tip of the iceberg.To better anticipate this security crisis, this paper performs a systematic security-centric analysis of the Computer Architecture literature. Our rationale is that when implementing current and future processors, microarchitects will (quite reasonably) look to previously-proposed ideas. Our study uncovers seven classes of microarchitectural optimization with novel security implications, proposes a conceptual framework through which to study them and demonstrates several proofs-of-concept to show their efficacy. The optimizations we study range from those that leak as much privacy as Spectre/Meltdown (but without exploiting speculative execution) to those that otherwise undermine security-critical programs in a variety of ways. Many have storied histories— ranging from industry patents to media/3rd party speculation regarding current implementation status to recent renewed interest in the academic community. This paper’s goal is to perform an early (hopefully not too late) analysis to inform their development moving forward.
Development and Evaluation of a Mobile-Learning Application Based on the Felder-Silverman Learning Styles Model

Every student has a different learning style. Not all of these learning styles can be facilitated through classroom learning. Mobile learning (m-learning) is the use of mobile technology to support learning activities and facilitate a variety of student learning styles. However, no previous studies of the development of m-learning products have tested the possibility of facilitating different learning styles within a single M-learning application. This study describes the development and evaluation of an adaptive m-learning application that can facilitate the needs of different students. The Felder-Silverman Learning Style Model was used as the reference for learning styles. The prototype application was developed using the user-centered design method, which includes user needs analysis, interaction design, application development, and usability testing. The designers followed the best practices laid out in Shneiderman's Eight Golden Rules of Interface Design and Gagné’s Nine Events of Instruction. A usability test of the prototype involving ten participants. Usability testing is carried out by conducting usability testing and filling out the System Usability Scale (SUS) questionnaire by all participants. The result found that it had good usability.
Asynchronous Active Learning with Distributed Label Querying

Active learning tries to learn an effective model with lowest labeling cost. Most existing active learning methods work in a synchronous way, which implies that the label querying can be performed only after the model updating in each iteration. While training models is usually time-consuming, it may lead to serious latency between two queries, especially in the crowdsourcing environments where there are many online annotators working simultaneously. This will significantly decrease the labeling efficiency and strongly limit the application of active learning in real tasks. To overcome this challenge, we propose a multi-server multi-worker framework for asynchronous active learning in the distributed environment. By maintaining two shared pools of candidate queries and labeled data respectively, the servers, the workers and the annotators efficiently corporate with each other without synchronization. Moreover, diverse sampling strategies from distributed workers are incorporated to select the most useful instances for model improving. Both theoretical analysis and experimental study validate the effectiveness of the proposed approach.
Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks

Self-supervised learning has gradually emerged as a powerful technique for graph representation learning. However, transferable, generalizable, and robust representation learning on graph data still remains a challenge for pre-training graph neural networks. In this paper, we propose a simple and effective self-supervised pre-training strategy, named Pairwise Half-graph Discrimination (PHD), that explicitly pre-trains a graph neural network at graph-level. PHD is designed as a simple binary classification task to discriminate whether two half-graphs come from the same source. Experiments demonstrate that the PHD is an effective pre-training strategy that offers comparable or superior performance on 13 graph classification tasks compared with state-of-the-art strategies, and achieves notable improvements when combined with node-level strategies. Moreover, the visualization of learned representation revealed that PHD strategy indeed empowers the model to learn graph-level knowledge like the molecular scaffold. These results have established PHD as a powerful and effective self-supervised learning strategy in graph-level representation learning.
An Iterative Co-Training Transductive Framework for Zero Shot Learning

In zero-shot learning (ZSL) community, it is generally recognized that transductive learning performs better than inductive one as the unseen-class samples are also used in its training stage. How to generate pseudo labels for unseen-class samples and how to use such usually noisy pseudo labels are two critical issues in transductive learning. In this work, we introduce an iterative co-training framework which contains two different base ZSL models and an exchanging module. At each iteration, the two different ZSL models are co-trained to separately predict pseudo labels for the unseen-class samples, and the exchanging module exchanges the predicted pseudo labels, then the exchanged pseudo-labeled samples are added into the training sets for the next iteration. By such, our framework can gradually boost the ZSL performance by fully exploiting the potential complementarity of the two models' classification capabilities. In addition, our co-training framework is also applied to the generalized ZSL (GZSL), in which a semantic-guided OOD detector is proposed to pick out the most likely unseen-class samples before class-level classification to alleviate the bias problem in GZSL. Extensive experiments on three benchmarks show that our proposed methods could significantly outperform about 31 state-of-the-art ones.
IoT-based Cyber-security of Drones using the Naïve Bayes Algorithm

Recent advancements in drone technology are opening new opportunities and applications: in various fields of life especially in the form of small drones.However, these advancements are also causing new challenges in terms of security, adaptability, and consistency.Small drones are proving to be a new opportunity for the civil and military industries.The small drones are suffering from architectural issues and the definition of security and safety issues.The rapid growth of the Internet of things opens new dimensions for drone technology but posing new threats as well.The tiny flying intelligent devices are challenging for the security and privacy of data.The design of these small drones is yet not matured to fulfill the domain requirements.The basic design issues also need security mechanisms, privacy mechanisms, and data transformations.The aspects like intrusion and interception in the domain of the Internet of Drones (IoD) need to be investigated to make these timely drones more secure and more adaptable.In this paper, we have used intelligent machine learning approach to design an IoT aided drone.This approach will provide intelligent cyber security system which will help in detecting network security threats using Blockchain.
An Ontology to Model the International Rules for Multiple Primary Malignant Tumours in Cancer Registration

Population-based cancer registry data provide a key epidemiological resource for monitoring cancer in defined populations. Validation of the data variables contributing to a common data set is necessary to remove statistical bias; the process is currently performed centrally. An ontology-based approach promises advantages in devolving the validation process to the registry level but the checks regarding multiple primary tumours have presented a hurdle. This work presents a solution by modelling the international rules for multiple primary cancers in description logic. Topography groupings described in the rules had to be further categorised in order to simplify the axioms. Description logic expressivity was constrained as far as possible for reasons of automatic reasoning performance. The axioms were consistently able to trap all the different types of scenarios signalling violation of the rules. Batch processing of many records were performed using the Web Ontology Language application programme interface. Performance issues were circumvented for large data sets using the software interface to perform the reasoning operations on the basis of the axioms encoded in the ontology. These results remove one remaining hurdle in developing a purely ontology-based solution for performing the European harmonised data-quality checks, with a number of inherent advantages including the formalisation and integration of the validation rules within the domain data model itself.
Interfaces, mismatches, and the architecture of Functional Discourse Grammar

This chapter describes and delimits the nature of interfaces in FDG.The different interface conditions across languages are defined in terms of wellestablished typological hierarchies: for each language, a basic setting on these hierarchies predicts the overall working of the interfaces.In addition, the Fund states numerous language-specific settings, such as its alignment system, the permissibility of zero anaphora, the presence of cliticization, etc.It is also argued that within the Fund there are compartments corresponding to each of the Levels in the grammar: for every lexical item and for every construction, its interpersonal, representational, morphosyntactic, and phonological aspects are stored separately.although the overall model is strongly top-down, some bottom-up processes are proposed, but these are restricted to the Fund and the Contextual Component.On this foundation, the chapter discusses mismatches across FDG's four levels of organization and shows that all possible mismatches may occur.Some mismatches follow from well-established typological hierarchies while others are the result of basic choices a language makes among various typological options.Bottom-up processes in the Fund are needed in order to account for certain types of mismatches, especially, but not exclusively, those involving feedback from the Phonological Level to higher levels.
HDR-Net-Fusion: Real-time 3D dynamic scene reconstruction with a hierarchical deep reinforcement network

Abstract Reconstructing dynamic scenes with commodity depth cameras has many applications in computer graphics, computer vision, and robotics. However, due to the presence of noise and erroneous observations from data capturing devices and the inherently ill-posed nature of non-rigid registration with insufficient information, traditional approaches often produce low-quality geometry with holes, bumps, and misalignments. We propose a novel 3D dynamic reconstruction system, named HDR-Net-Fusion, which learns to simultaneously reconstruct and refine the geometry on the fly with a sparse embedded deformation graph of surfels, using a hierarchical deep reinforcement (HDR) network. The latter comprises two parts: a global HDR-Net which rapidly detects local regions with large geometric errors, and a local HDR-Net serving as a local patch refinement operator to promptly complete and enhance such regions. Training the global HDR-Net is formulated as a novel reinforcement learning problem to implicitly learn the region selection strategy with the goal of improving the overall reconstruction quality. The applicability and efficiency of our approach are demonstrated using a large-scale dynamic reconstruction dataset. Our method can reconstruct geometry with higher quality than traditional methods.
Duped: Truth-default Theory and The Social Science of Lying and Deception

"Duped: Truth-default Theory and The Social Science of Lying and Deception." Southern Communication Journal, 86(5), pp. 511–512