Miracle-dz/UniFAR-FTUs huggingface dataset:
Reading to dogs in schools: an exploratory study of teacher perspectives

ABSTRACT Background Reading to Dogs (RTD) interventions have become increasingly prevalent in UK primary schools. However, there is a need for research examining teachers’ perspectives on RTD, as this could be key in influencing the uptake and adherence to school RTD interventions. Purpose This study sought to examine primary school teachers’ views of RTD in schools, exploring perceived benefits and challenges, in addition to their experience of RTD interventions. Methods The sample was gathered through voluntary/self-selecting participation in an open questionnaire-based survey shared through UK online teaching forums. In total, 253 UK primary school teachers (with varying knowledge and experience of RTD) completed the questionnaire, which focused on benefits and challenges associated with RTD identified in existing literature. More specifically, teachers’ perspectives of reading, social, emotional and behavioural benefits, and challenges (e.g. paperwork, time commitment, allergies, child/staff/dog welfare) were examined. Also, 59 teachers provided additional written comments regarding benefits and challenges associated with RTD. Results Teachers’ perspectives on RTD were generally very positive; perceptions of benefits to social, emotional and behavioural outcomes were more positive than those associated with reading outcomes. Furthermore, teachers perceived greater benefits to children’s reading affect (e.g. motivation, confidence) than their reading frequency or skill. In general, teachers reported low concerns about the challenges associated with RTD; qualitative responses suggested that, while these challenges were real, they were not seen as insurmountable. Finally, teachers with greater knowledge and/or experience of RTD were more positive about its benefits and had fewer concerns about the challenges, although there were some exceptions. Additional written responses provided qualitative insights into teachers’ experiences of RTD. Implications and conclusion Overall, UK primary school teachers in our sample were very positive about RTD; while they acknowledged challenges, these were not regarded as barriers to implementation. Furthermore, teachers could provide useful insights into the benefits and challenges associated with RTD from a practical and pedagogical perspective. Indeed, this study highlights the importance of gaining teachers’ perspectives of interventions that affect them and their pupils. Understanding teachers’ varied perspectives, and experiences, of educational interventions is essential to ensure that their professional and pedagogical knowledge feeds into future intervention design and implementation, in addition to future research and evaluation.
A preliminary assessment of midhaul links at 140 GHz using ray-tracing

The ever-growing demand for mobile data necessitates a transport network architecture that can withstand the 5G-and-beyond multi-Gbps traffic requirements. To cater for such unprecedented demand, studies are being conducted to incorporate TeraHertz (THz) communications in future mobile networks. In this paper, we consider an urban environment and evaluate the feasibility of THz wireless midhaul links for the transport networks between the Central Units (CU) and Distributed Units (DU) in a disaggregated 5G network architecture with functional splits. Our goal is to study the feasibility of midhaul links at 140 GHz by minimizing the number of required CUs to serve all the DUs. To this end, we define several policies for selecting CU and DU nodes in order to determine the peak data rate that can be supported over each link between a CU and DU. Our numerical results based on ray-tracing suggest that wireless links at 140 GHz with 3GPP option 2 as High Layer Split (HLS) represents a promising technology for midhaul transport networks.
XGest: Enabling Cross-Label Gesture Recognition with RF Signals

Extensive efforts have been devoted to human gesture recognition with radio frequency (RF) signals. However, their performance degrades when applied to novel gesture classes that have never been seen in the training set. To handle unseen gestures, extra efforts are inevitable in terms of data collection and model retraining. In this article, we present XGest, a cross-label gesture recognition system that can accurately recognize gestures outside of the predefined gesture set with zero extra training effort. The key insight of XGest is to build a knowledge transfer framework between different gesture datasets. Specifically, we design a novel deep neural network to embed gestures into a high-dimensional Euclidean space. Several techniques are designed to tackle the spatial resolution limits imposed by RF hardware and the specular reflection effect of RF signals in this model. We implement XGest on a commodity mmWave device, and extensive experiments have demonstrated the significant recognition performance.
The Malagasy enigmatic genus Apodocephala (Asteraceae), a new member of the tribe Athroismeae

Abstract: Apodocephala is a small genus of nine species of shrubs and trees endemic to Madagascar and currently classified in the tribe Astereae (Asteraceae). However, its present tribal position has been questioned, as it lacks some of the salient morphological features of that tribe. This study includes for the first time DNA sequences from Apodocephala (two species including the type of the generic name) to test its phylogenetic position. Phylogenetic analyses based on sequence data from nuclear ribosomal (ETS and ITS) and plastid (ndhF and trnL–trnF) DNA regions, using Bayesian inference and maximum parsimony methods, strongly reject the placement of Apodocephala in the Astereae. Our results, instead, support its position as sister to the Malagasy monospecific genus Lowryanthus within the tribe Athroismeae. Morphological comparison of these sister genera reveal similarities in cypsela morphology, and the inclusion of Apodocephala in the subtribe Lowryanthinae is proposed here. The tribe Athroismeae is now represented by ten genera (Anisochaeta, Anisopappus, Apodocephala, Artemisiopsis, Athroisma, Blepharispermum, Centipeda, Leucoblepharis, Lowryanthus and Symphyllocarpus), six of which occur in Madagascar. The Malagasy Athroismeae occur in all Malagasy terrestrial ecosystems (rainforests, deciduous dry forests, thicket spiny forests and savannas) and are likely the result of multiple independent colonization events mostly from mainland Africa. Citation: Bengtson A. , Anderberg A. A. & Razafimandimbison S. G. 2021: The Malagasy enigmatic genus Apodocephala (Asteraceae), a new member of the tribe Athroismeae. – Willdenowia 51: 221–230. Version of record first published online on 10 August 2021 ahead of inclusion in August 2021 issue.
A pediatric case of EDTA-related pseudothrombocytopenia and implementation of the histograms by the automated cell counters

Abstract Objectives Platelet aggregation in the presence of ethylenediaminetetraacetic acid (EDTA), is called EDTA-related pseudothrombocytopenia (EDTA-PTP), resulting in low platelet count by automatic cell counters. Case presentation Herein, we present a case of a 5-year-old female, who was referred to our laboratory due to persistent thrombocytopenia. Conclusion Our case report discusses the efficiency of the histograms and flag warnings of the cell counter, indicating the importance of these variables.
Gender and Social Institutions in the Labour Markets: An Analytical Perspective on the Covid-19 Disruptions in Northeast India

This article examines the trends and patterns of unpaid work performed by women in India’s North Eastern States and account for the factors that underlie these trends. It uses the two unit-level datasets from the National Sample Survey Office Employment and Unemployment Survey 2011–2012 and Periodic Labour Force Survey 2018–2019. The multinomial regression results found that illiterate and lower social stratum have more chances to engage in unpaid activities. It then explores the impact of COVID-19 on unpaid work activities among women in the northeast states. The telephonic conversation and informal interviews with different regional stakeholders have been substantiated along with the utilisation of the Centre for Monitoring Indian Economy report on employment and unemployment for the second quarter of 2020 for nuanced analysis. The study found that women are losing their livelihood very fast during the pandemic and the effects are likely to linger for a more extended period. JEL Codes: J16, J21, J22, R23
5G Security Challenges and Solutions: A Review by OSI Layers

The Fifth Generation of Communication Networks (5G) envisions a broader range of services compared to previous generations, supporting an increased number of use cases and applications. The broader application domain leads to increase in consumer use and, in turn, increased hacker activity. Due to this chain of events, strong and efficient security measures are required to create a secure and trusted environment for users. In this paper, we provide an objective overview of 5G security issues and the existing and newly proposed technologies designed to secure the 5G environment. We categorize security technologies using Open Systems Interconnection (OSI) layers and, for each layer, we discuss vulnerabilities, threats, security solutions, challenges, gaps and open research issues. While we discuss all seven OSI layers, the most interesting findings are in layer one, the physical layer. In fact, compared to other layers, the physical layer between the base stations and users’ device presents increased opportunities for attacks such as eavesdropping and data fabrication. However, no single OSI layer can stand on its own to provide proper security. All layers in the 5G must work together, providing their own unique technology in an effort to ensure security and integrity for 5G data.
Wideband Single-Layer Substrate Integrated Waveguide Filtering Antenna With U-Shaped Slots

A wideband single-layer substrate integrated waveguide (SIW) filtering slot antenna with compact structure is investigated, which is originated from a SIW resonant cavity operating at its TE110 mode. A straight radiating slot is integrated on the top surface of the cavity. The resonator can be splitted into two half-mode resonators, generating a radiation null at higher edge of the passband. At same time, two U-shaped slots are etched on the bottom surface of the cavity. One of the U-shaped slots is used to introduce a radiation null at the left edge of the passband while the other U-shaped slot introduces the modified half-TE110 mode in the right half cavity, broadening the bandwidth of the antenna. Finally, the third-order filtering antenna is obtained with 11.84% impedance bandwidth in only one SIW resonant cavity. To validate design, a prototype is fabricated and measured. Compared with other designs, the proposed antenna owns features of simple structure, wide bandwidth, and compact size, satisfying the increasing requirement for the miniaturization and integration of RF devices.
“DELIVER AFRICA FROM DEBTS”: Good Governance Alone is not Enough to Save the Continent From Debt Onslaught

The present article analyzes the debt–economic growth nexus in African countries while controlling for the impact of good governance indicators. In contrast to a long tradition of scholarship that has consistently suggested that government debt has a detrimental impact on economic growth in sub-Saharan Africa, recent studies have actually shown that government debt, when coupled with improvements in the quality of government, is actually a driver of economic growth. By analyzing an original dataset that covers the 2002–15 period and additional debt–economic growth data going up to the year 2020, we are able to suggest three conclusions. First, in the absence of debt, good governance matters in improving economic growth. Second, some dimensions of governance are better predictors of economic performance than others—as the “good enough governance” literature has in recent years suggested. Third, under no circumstances is debt government growth beneficial for the economic performance of African countries. Building on this evidence, we suggest that the COVID-19 pandemic—which has already slowed down African economies and increased their debt exposure—may prevent African countries from making greater progress along the developmental path.
Assessing Differential Item Functioning in a Teacher Self-Assessment of Cultural Responsiveness

The Assessment of Culturally and Contextually Relevant Supports (ACCReS) was developed in response to the need for well-constructed instruments to measure teachers’ cultural responsiveness and guide decision-making related to professional development needs. The current study sought to evaluate the presence of differential item functioning (DIF) in ACCReS items and the magnitude of DIF, if detected. With a national sample of 999 grade K-12 teachers in the United States, we examined measurement invariance of ACCReS items in relation to responses from (a) racially and ethnically minoritized (REM) youth and white teachers (teacher race), (b) teachers in schools with 0–50% and 51–100% REM youth (student race), and (c) teachers with <1–5 years of teaching experience and teachers with >5 years of experience. Findings suggested that ACCReS items exhibited negligible levels of DIF. The lack of DIF found provides additional evidence for the validity of scores from the ACCReS to assess teachers’ cultural responsiveness. Furthermore, descriptive analyses revealed that teachers were more likely to agree with items pertaining to their own classroom practice than items related to access to adequate training and support. Results inform implications for future educational and measurement research.
The digital resurrection of Margaret Thatcher: Creative, technological and legal dilemmas in the use of deepfakes in screen drama

This article develops from the findings of an interdisciplinary research project that has linked film practice research with computer science and law, in an exercise that seeks to digitally resurrect Margaret Thatcher to play herself in a contemporary film drama. The article highlights the imminent spread of machine learning techniques for digital face replacement across fiction content production, with central research questions concerning the ethical and legal issues that arise from the appropriation of the facial image of a deceased person for use in drama.
Solving Doppler-Angle Ambiguity of BPSK-MIMO FMCW Radar System

In a multiple-input multiple-output (MIMO) radar system, binary phase-shift keying (BPSK) modulation is widely used to separate signals transmitted from multiple transmit (Tx) antennas. However, when the BPSK modulation is applied to the frequency-modulated continuous-wave (FMCW) radar system, there is an occurrence of Doppler-angle ambiguity due to the properties of the modulation scheme. In other words, the maximum detectable velocity is reduced in proportion to the number of Tx antennas, and the angle estimation performance can be degraded when a moving target induces a Doppler phase shift. Therefore, in this paper, we propose a method for solving the Doppler-angle ambiguity in the BPSK-MIMO FMCW radar system. The proposed method involves comparing and arranging the phases of signals corresponding to the same target in the range-velocity detection results. By arranging the phases to form an arithmetic sequence, the signals from each Tx antenna can be distinguished and the unambiguous velocity and angle of the target can be estimated. In addition, by using a general phase-shift keying (PSK) modulation scheme, the proposed method can be applied to an arbitrary MIMO system regardless of the number of Tx antennas.
Vehicle Lane Change Prediction on Highways Using Efficient Environment Representation and Deep Learning

This paper introduces a novel method of lane-change and lane-keeping detection and prediction of surrounding vehicles based on Convolutional Neural Network (CNN) classification approach. Context, interaction, vehicle trajectories, and scene appearance are efficiently combined into a single RGB image that is fed as input for the classification model. Several state-of-the-art classification-CNN models of varying complexity are evaluated to find out the most suitable one in terms of anticipation and prediction. The model has been trained and evaluated using the PREVENTION dataset, a specific dataset oriented to vehicle maneuver and trajectory prediction. The proposed model can be trained and used to detect lane changes as soon as they are observed, and to predict them before the lane change maneuver is initiated. Concurrently, a study on human performance in predicting lane-change maneuvers using visual inputs has been conducted, so as to establish a solid benchmark for comparison. The empirical study reveals that humans are able to detect the 83.9% of lane changes on average 1.66 seconds in advance. The proposed automated maneuver detection model increases anticipation by 0.43 seconds and accuracy by 2.5% compared to human results, while the maneuver prediction model increases anticipation by 1.03 seconds with an accuracy decrease of only 0.5%.
Novel Reducts in Pythagorean Fuzzy β-covering Approximation Spaces

The pythagorean fuzzy (PF) β-covering has been constructed to extend the fuzzy β-covering, and a type of reducts in pythagorean fuzzy (PF) β-coverings has been proposed based on the union operation. However, for svveral statements, there will be no reducts in PF β-coverings. Hence, this concept should be replenished by other concepts. Hence, we propose the new type of reducts in PF β-coverings in this paper, and we call it type-2 PF reduct. Moreover, the type-2 PF reducts in PF β-covering approximation spaces are studied while adding and removing several objects of the universe. First, the notion of the type-2 PF reduct in a PF β-covering approximation space is presented. Then, some properties of type-2 reducts of PF β-coverings are investigated while adding and removing some objects.
Convolutional Neural Networks’ Applications in Automatic Target Recognition for Synthetic Aperture Images

Automatic Target Recognition (ATR) is an important research area in the field of radar information processing. Because the deep Convolution Neural Network (CNN) does not need to carry out feature engineering and the performance of image classification is superior, it attracts more and more attention in the field of radar automatic target recognition. The application of CNN to radar image processing is reviewed in this paper. Firstly, the related knowledges including the characteristics of the radar image is introduced, and the limitations of traditional radar automatic target recognition methods are pointed out. The principle, composition and development of CNN the field of computer vision are introduced. Then, the research status of CNN in radar automatic target recognition is provided. The detection and recognition method of SAR image are presented in detail. Then, the challenge of radar automatic target recognition is analyzed. Finally, the new theory and model of convolution neural network, the new imaging technology of radar and the application to complex environments in the future are prospected.
Overview of trajectory planning methods for robot systems

With the advancement of science and technology, people have gradually realized the power of robots, and robots have been fully applied in many fields. The functional ability depends on its degree of intelligence, and trajectory planning is the core of intelligence. This article mainly summarizes the trajectory planning methods of common types of robot systems in recent years, and at the same time, outlines the critical issues of the trajectory planning technology of robot systems and a future development trend.
Synthesis of HgTe Quantum Dots and Its Application in Broadband Photodetection

Broadband photodetection from visible to infrared spectrum region are of increasing importance in military and civilian applications. Recently, mercury telluride (HgTe) colloidal quantum dots(CQDs) have gradually become an attractive alternative for traditional narrow band gap semiconductors owing to their tunable spectral response. In this paper, HgTe QDs have been successfully synthesized. By simply spin-coating and ligand exchange treatment, the broadband photodetection based on HgTe CQDs up to near Infrared can be achieved.
Quantification of Uncertainties in Deep Learning-based Environment Perception

In this work, we introduce a novel Deep Learning-based approach to perceive the environment of a vehicle based on radar scans while accounting for uncertainties in the model predictions. We segment the environment of the vehicle into equally sized grid cells, which we classify individually. Our algorithm is capable of differentiating uncertainties in the system output as being related to an inadequate model (epistemic uncertainty) or noisy data (aleatoric uncertainty). To this end, we utilize probability distributions on the weights that describe uncertainties in the model parameters and that can be learned in a supervised fashion using gradient descent. We show that the uncertainties in our model output correlate with the precision of the model predictions. Compared to previous concepts that estimate uncertainties by utilizing Monte Carlo Dropout layers, we show the superior performance of our approach to reliably perceive the environment of a vehicle.
Techno-Economic Framework for Optimal Capacity Expansion of Active Microgrid in the Mediterranean: A Case Study of MCAST

The purpose of microgrids is to improve system flexibility and resilience during normal and emergency conditions. The ceaseless load growth mandates to increase microgrid’s capacity, thereby improving the system flexibility and resilience. However, capacity expansion requires significant investments, making it essential to identify the optimal capacity of energy resources. The methodologies proposed in the literature identifies the microgrid’s capacity with an assumption of investments with a single installment. This way of theoretical approach leads to unrealistic solutions. Besides, microgrid’s participation in a flexible market will enhance its performance both in commercial and technical aspects. Therefore, this paper proposes a realistic framework with the concept “expansion through time” inspired by “Real Options Theory. ” This framework includes practical parameters like resource & load uncertainty, physical space required to install, revenue generated by resources, and maximum demand penalty, on top of electrical parameters; constrained with significant return in investments to improve the overall savings. In addition, this paper proposes a market participation model for microgrid, which defines a bidding process with two components, such as regular and flexible portions under both normal and extreme conditions. This study considers renewable-based energy resources like solar-photovoltaic plants (SPPs) and battery energy storage systems (BESSs) as microgrids’ energy resources. The system chosen for testing the efficacy of the proposed framework is a real-world active-microgrid of Malta College of Arts, Science and Technology (MCAST), located on an island.