Miracle-dz/UniFAR-FTUs huggingface dataset:
Two is better than one: verification of the effect of praise from two robots on pre-school children’s learning time

This paper reports the effects of praise from two robots on children’s learning time, their performance, and their acceptance of the robots. Since praise from a single robot provides positive effects for children as educational support, we hypothesized that praise from two robots will increase such effects because two robots will enhance the social influences. To verify this hypothesis, we developed an English learning system that consisted of an e-learning application, a depth sensor for human-tracking, a tabletop humanoid robot, a doll-type robot, and an operator. We designed the robots to interactively praise children during their learning situations and experimented with a with-in participant design to compare the effects of praise from two robots and just one. 22 children participated in an experiment whose results showed that children learned more when two robots praised them than just one, even with an identical amount of praise. These results suggest the effectiveness and empirical evidence of using two robots for educational support. GRAPHICAL ABSTRACT
PseudoSloan: A perimetric-complexity and area-controlled font for vision and reading research

Artificial orthographies have long been used in studies of verbal learning and reading. These orthographies, also known as pseudo or false fonts, are designed to match the letters of an existing alphabet on a range of visual features, isolating effects of orthography from those owing to lexical processing. In a parallel line of research, there has been much interest in the design of optotypes for measuring visual acuity that have good properties in terms of character complexity and graceful degradation under blur. Here we merge these two traditions by designing a fully scalable pseudofont, “PseudoSloan,” that is based on the design rubric of the widely used Sloan optotypes. The font includes 26 Latin letters as well as two sets of letter-like symbols matching the Latin alphabet on a letter-by-letter basis. Quantitative matching of the pairs of Sloan and PseudoSloan glyphs is done on the basis of ink area and perimetric complexity. We provide the installable PseudoSloan font in TrueType and OpenType formats, plus a large number of PseudoSloan glyphs in . svg format that vary over wide ranges in their perimetric complexity and ink area (https://osf.io/qhj2b/).
The Effects of Advance Care Planning on Decision Conflict and Psychological Distress: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Purpose Advance care planning (ACP) is widely understood to improve end-of-life care. This systematic review and meta-analysis aimed to examine the effects of ACP interventions on decision conflict and psychological distress. Methods A search of PubMed, CINAHL, CENTRAL, EMBASE, KISS, KoreaMed, and RISS was conducted in November 2020. The study included randomized controlled trials. Data were pooled using fixed- and random-effects models. Results Fourteen studies were identified that cumulatively included 1,548 participants. ACP interventions were effective in alleviating decision conflict (d=-0.53; 95% CI -0.83 to -0.23), depression (d=-1.22; 95% CI -1.71 to -0.74) and anxiety (d=-0.76; 95% CI -1.12 to -0.39). Conclusion ACP interventions have significant positive effects on reducing decision conflict and psychological distress. A high level of bias was shown related to allocation concealment and blinding. The results of this study are expected to be useful for end-of-life care providers to improve the effectiveness of ACP interventions.
Bacterial Diversity in Calcium Carbonate Paleo Accretions (Tosca) in the Southern Pampas, Argentina

Abstract Paleosoil accretions of carbonates in the Tandilia system (Southern Pampas, Argentina) dated to the lower/middle Pleistocene age are locally referred to as tosca. The characterization of this indurated layer of carbonates were analyzed via a biophysicochemical approach, including, physicochemical analysis of soils, mineral x-ray diffraction of the tosca, and microbial diversity of modern soils and tosca layers. The minerals found within tosca were calcite, albite, muscovite, quartz, orthoclase and dolomite in order of most to least abundant. The microbial metataxonomics of tosca was described for the first time. The most abundant microorganisms in tosca were g_Geobacter, g_Pseudonocardia and p_Gemmatimonadetes2 and redundancy analysis of physicochemical parameters and relative microbial abundances revealed positive correlations between Nitrospirae and calcium ions, while mineral and microbial correlations associated Gemmatimonadetes and Firmicutes with calcite and dolomite presence. Magnetospirillum, Geobacter and Bacillus were present in the tosca and the soil above, indicating possible prenucleation sites for calcite and dolomite. Core microorganism abundance was >80% throughout horizons including tosca, hence either microbial entrapment via calcium carbonate precipitation or microbial leaching occurred within this layer.
Sunlight driven photocatalytic degradation of organic pollutants using a MnV2O6/BiVO4 heterojunction: mechanistic perception and degradation pathways

In the field of photocatalysis, fabrication of a heterojunction structure with effective charge separation at the interface and charge shift to enhance the photocatalytic activity has acquired extensive consideration. In the present investigation, MnV2O6/BiVO4 heterojunction samples with excellent photocatalytic performance under sunlight irradiation were conveniently synthesized by a hydrothermal technique, and characterized by UV-Vis, FTIR, XRD, FESEM, HRTEM, PL, BET and XPS techniques. The prepared samples were investigated as photocatalysts for degrading MB and RhB dyes under sunlight. Among various samples of MnV2O6/BiVO4, the S-V hetero-junction sample exhibited maximum photocatalytic activity with 98% and 96% degradation of MB and RhB dyes, respectively, in 6 and 35 min. The high photocatalytic activity of MnV2O6/BiVO4 may be due to the successful generation and shift of charges in the presence of visible light. The average reduction of chemical oxygen demand (COD) was found to be 75% after irradiation with direct sunlight. In the degradation process of dyes, superoxide anion radicals were the main responsive species, as revealed by trapping experiments. The degradation efficiency of MnV2O6/BiVO4 heterojunction did not diminish even after four cycles. In addition, the catalytic performance of the fabricated heterojunction was also explored for reducing 4-nitrophenols (4-NP) by using NaBH4. Absolute conversion of 4-NP to 4-aminophenol (4-AP) occurred without the production of intermediate byproducts.
Arable wildflowers have potential as living mulches for sustainable agriculture

ABSTRACT Background As agriculture has intensified, many once-common wildflowers have declined in arable landscapes, which has widespread implications for associated ecosystem services. The incorporation of sustainable practices, for example, growing living mulches (in-field, non-crop plant ground cover, maintained during the target crop growing season), can boost arable biodiversity, but few wildflower species have been utilised in this context. Aims Our aim was to determine the suitability of arable wildflower species, once considered weeds, for use as living mulches. Methods We first screened a number of arable wildflower species for germination when growing with a common cereal, barley (Hordeum vulgare). We then grew two (Centaurea cyanus and Scandix pecten-veneris) in pots in a glasshouse with and without barley, and grew barley alone to test the impact of the wildflowers on barley growth and biomass. Results Neither of the wildflowers significantly negatively impacted barley biomass. Barley initially facilitated germination in S. pecten-veneris, but ultimately suppressed the above-ground biomass of both wildflowers. However, both wildflower species were able to coexist alongside barley. Conclusions Our experiment provides evidence that wildflowers that were considered weeds in traditional agriculture have the potential to be grown alongside barley and could be incorporated as part of a living mulch.
Language Recognition Based on Unsupervised Pretrained Models

Unsupervised pretrained models have been proven to rival or even outperform supervised systems in various speech recognition tasks. However, their performance for language recognition is still left to be explored. In this paper, we construct several language recognition systems based on existing unsupervised pretraining approaches, and explore their credibility and performance to learn high-level generalization of language. We discover that unsupervised pretrained models capture expressive and highly linear-separable features. With these representations, language recognition can perform well even when the classiﬁers are relatively simple or only a small amount of labeled data is available. Although linear classiﬁers are usable, neural nets with RNN structures improve the results. Mean-while, unsupervised pretrained models are able to gain reﬁned representations on audio frame level that are strongly coupled with the acoustic features of the input sequence. Therefore these features contain redundant information of speakers and channels with few relations to the identity of the language. This nature of unsupervised pretrained models causes a performance degradation in language recognition tasks on cross-channel tests.
Research on Semi-Physical Simulation Testing of Inter-Satellite Laser Interference in the China Taiji Space Gravitational Wave Detection Program

To guarantee a smooth in-orbit space gravitational wave detection for the Taiji mission, a semi-physical simulation test of inter-satellite laser interference is carried out. The semi-physical simulation test consists of three aspects: the establishment of the inter-satellite laser link, interferometry of the inter-satellite ranging, and simulation of the space environment. With the designed specifications for the semi-physical simulation platform, the test results for the inter-satellite laser interference can be obtained. Based on the semi-physical simulation test, the risks of inter-satellite laser interference technology can be mitigated, laying a solid foundation for the successful detection of in-orbit gravitational waves.
Efficient DLT-Based Method for Solving PnP, PnPf, and PnPfr Problems

SUMMARY This paper presents an e ﬃ cient method for solving PnP, PnPf, and PnPfr problems, which are the problems of determining camera parameters from 2D–3D point correspondences. The proposed method is derived based on a simple usage of linear algebra, similarly to the classical DLT methods. Therefore, the new method is easier to understand, easier to implement, and several times faster than the state-of-the-art methods using Gr¨obner basis. Contrary to the existing Gr¨obner basis methods, the proposed method consists of three algorithms depending on the number of the points and the 3D point conﬁguration. Experimental results show that the proposed method is as accurate as the state-of-the-art methods even in near-planar scenes while achieving up to three times faster.
Fake News Detection Using Pos Tagging and Machine Learning

Abstract In this digital era, one major concern is not knowing what news to believe and not to believe. With the ever-growing progress being made in social media and technology, the problem has become more prominent. This also played a very important role in spreading fake information in this pandemic, creating chaos and worry throughout the world. Through the paper, I propose to understand and analyze the underlying writing style that can help in detecting fake news before it can be published, using a style-based approach in detection. An ensemble machine learning classification model was tried out to detect fake news.
Leveraging Non-Target Language Resources to Improve ASR Performance in a Target Language

This paper investigates approaches to improving automatic speech recognition (ASR) performance in a target language using resources in other languages. In particular, we assume that we have untranscribed speech in a different language and a well trained ASR system in yet another language. Concretely, we structure this as a multi-task problem, where the primary task is acoustic model training in the target language, and the secondary task is also acoustic model training but using a synthetic data set. The synthetic data set consists of pseudo transcripts generated by decoding the untranscribed speech using a well trained ASR model. We compare and contrast this with using labeled data sets, i.e. matched audio and human-generated transcripts, and show that our approach compares favorably. In most cases, we see performance improvements, and in some cases, depending on the selection of languages and nature of speech data, performance exceeds that of systems using labeled data sets as the secondary task. When extended to larger sets of data, we show that the mismatched data approach performs similarly to in-language semi-supervised training (SST) when the secondary task pseudo transcripts are generated by ASR models trained on large diverse data sets.
EVALUATION OF EVACUATION GUIDANCE PLAN FOR LARGE-SCALE UNDERGROUND MALL AT THE TIME OF FLOODING USING THE SIMULATION

This study aims to quantitatively evaluate the improvement of evacuation safety following the revision of the evacuation plan for tsunami disaster caused by a major earthquake in a large underground space. Several scenarios developed by the management (under consideration) were simulated at the most congested time of day (18:00 on weekdays), and a comparative study was conducted. As a result, we were able to evaluate part of the evacuation safety from the perspective of evacuation guidance by comparing each scenario (evacuation timeline) considered by the management company, using evacuation simulation. This study aims to quantitatively evaluate the improvement of evacuation safety following the revision of the evacuation plan for tsunami disaster caused by a major earthquake in a large underground space. Several scenarios developed by the management company (under consideration) were simulated at the most congested time of day (18:00 on weekdays), and a comparative study was conducted. As a result, we were able to evaluate part of the evacuation safety from the perspective of evacuation guidance by comparing each scenario (evacuation timeline) considered by the management company, using evacuation simulation. Underground spaces are widely developed in many large cities in Japan. Therefore, social and economic risk management of underground mall against tsunamis caused by major earthquakes is required. When the Great East Japan Earthquake struck in 2011, a tsunami of greater-than-expected proportions caused extensive flooding in large areas, and then forcing a fundamental review of disaster prevention measures. Administrators of underground malls and other facilities in the Tsunami Hazard Area were obliged to prepare and publish Tsunami Evacuation Plans (Act on Development of Areas Resilient to Tsunami Disasters ， 2011). Predicting the behavior of evacuees during a disaster is important to evaluate the safety of such evacuation plans. However, there are challenges in predicting evacuee behavior and verifying the safety of evacuation plans for various disaster situations. This study aims to quantitatively evaluate the improvement of evacuation safety following the revision of the evacuation plan for tsunami disaster in a large underground space. Several scenarios developed by the management company (under consideration) were simulated at the most congested time of day (18:00 on weekdays), and a comparative study was conducted. The main findings are as follows.
Understanding financial resilience from a resource-based view: Evidence from US state governments

ABSTRACT To sustain government services in times of disruptions, it is important to enhance the financial resilience of the government. This study explains the relationship between an organization’s financial resilience and its financial, human, and political resources using the framework of the resource-based view theory (RBV). The results show that the effect of government resources on financial resilience differs depending on the type of resources. Overall, while it is found that an increase in financial and human resources positively relates to financial resilience, an abundance of political resources negatively relates to resilience; and these findings are evident in the short-run model.
Self-Supervised Phonotactic Representations for Language Identification

Phonotactic constraints characterize the sequence of permissible phoneme structures in a language and hence form an important cue for language identiﬁcation (LID) task. As phonotactic constraints span across multiple phonemes, the short-term spectral analysis (20-30 ms) alone is not sufﬁcient to capture them. The speech signal has to be analyzed over longer contexts (100s of milliseconds) in order to extract features representing the phonotactic constraints. The supervised senone classiﬁers, aimed at modeling triphone context, have been used for extracting language-speciﬁc features for the LID task. However, it is difﬁcult to get large amounts of manually labeled data to train the supervised models. In this work, we explore a self-supervised approach to extract long-term contextual features for the LID task. We have used wav2vec architecture to extract contextualized representations from multiple frames of the speech signal. The contextualized representations extracted from the pre-trained wav2vec model are used for the LID task. The performance of the proposed features is evaluated on a dataset containing 7 Indian languages. The proposed self-supervised embeddings achieved 23% absolute improvement over the acoustic features and 3% absolute improvement over their supervised counterparts.
Automatic Detection of Shouted Speech Segments in Indian News Debates

Shouted speech detection is an essential pre-processing step in conventional speech processing systems such as speech and speaker recognition, speaker diarization, and others. Excitation source plays an important role in shouted speech production. This work explores feature computed from the Integrated Linear Prediction Residual (ILPR) signal for shouted speech detection in Indian news debates. The log spectrogram of ILPR signal provides time-frequency characteristics of excitation source signal. The proposed shouted speech detection system is deep network with CNN-based autoencoder and attention-based classi-ﬁer sub-modules. The Autoencoder sub-network aids the classi-ﬁer in learning discriminative deep embeddings for better clas-siﬁcation. The proposed classiﬁer is equipped with attention mechanism and Bidirectional Gated Recurrent Units. Classi-ﬁcation results show that the proposed system with excitation feature performs better than baseline log spectrogram computed from the pre-emphasized speech signal. A score-level fusion of the classiﬁers trained on the source feature and the baseline feature provides the best performance. The performance of the proposed shouted speech detection is also evaluated at various speech segment durations.
Absentee Landlords and Farm Management in Brazil During the 1960s

Remote control of land and labor by absentee landlords joined with other supportive elements of latifundismo to reinforce the discontinuity between rural poverty and urban wealth and power. Absentee ownership was self-perpetuating, as returns from latifundio agriculture were channeled into more remunerative commercial ventures. Not restricted in its distribution to a particular region, the incidence of absenteeism generally rose with increasing farm size. Three-quarters of the minifundiarios resided on their properties against one-half of the latifundiarios, and administrator-operators were concentrated heavily on larger farms. The economic double-life of the landed 61ite helped to sustain traditional practices of farm management that reduced opportunities for improved efficiency and productivity in Latin America's largest agrarian sector.
Mapping moral consciousness in research on historical consciousness and education - a summative content analysis of 512 research articles published between 1980 and 2020

ABSTRACT The purpose of this article is to provide a unique overview of how third-order concepts, linked to moral consciousness, are expressed in research articles on historical consciousness related to education, as well as to document how frequently the concepts are applied between 1980 and 2020. A count of word frequency says something about how popular (strong) a concept is during a particular period, while different themes of moral consciousness enable teachers, students, and researchers to broaden their perceptions and sharpen their (moral) judgment in their day-to-day reflections and practices. The following questions guide the study: 1. How do words signal good/bad and right/wrong in the texts about historical consciousness? 2. How frequently are the words mentioned? And 3. What kind of frames do the choices of words indicate for educational practice and purpose(s)? Very strong, strong, medium, and weak words have been located between 1980 and 2020 depending on how often the words are mentioned. Five themes were found and are reported on in this article: cosmopolitanism, democracy, emancipation, character building, and existential struggles, which all come with different frames for how to approach the past in relation to the present and future in history education.
Multi-Stream Gated and Pyramidal Temporal Convolutional Neural Networks for Audio-Visual Speech Separation in Multi-Talker Environments

Speech separation is the task of extracting target speech from noisy mixture. In applications like video telephones or video conferencing, lip movements of the target speaker are accessible, which can be leveraged for speech separation. This paper proposes a time-domain audio-visual speech separation model under multi-talker environments. The model receives audio-visual inputs including noisy mixture and speaker lip embedding, and reconstructs clean speech waveform for the target speaker. Once trained, the model can be ﬂexibly applied to unknown number of total speakers. This paper introduces and investigates the multi-stream gating mechanism and pyramidal convolution in temporal convolutional neural networks for audio-visual speech separation task. Speaker-and noise-independent multi-talker separation experiments are conducted on GRID benchmark dataset. The experimental results demonstrate the proposed method achieves 3.9 dB and 1.0 dB SI-SNRi improvement when compared with audio-only and audio-visual baselines respectively, showing effectiveness of the proposed method.
A Contribution to Knowledge of Gyroporus (Gyroporaceae, Boletales) in China: Three New Taxa and Amended Descriptions of Two Previous Species

Some species of Gyroporus (Gyroporaceae, Boletales) in China are investigated on the basis of morphology and molecular phylogenetic analyses. Six Chinese species are recognized in the present study. Among them, G. memnonius, G. porphyreus and G. subglobosus are new to science; G. longicystidiatus and G. paramjitii are previously described; and one ambiguous taxon tentatively named G. cf. castaneus. A key to known species of Gyroporus in China is also provided. A preliminary biogeographical analysis shows that Gyroporus in East Asia and Southeast/South Asia are closely related. Gyroporus longicystidiatus and G. paramjitii are geographically widespread species occurring in East Asia and Southeast/South Asia; G. cf. castaneus is a shared species between East Asia and Europe.