Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring Teachers’ Social-Emotional Competence: Development and Validation of a Situational Judgment Test

Teachers’ social-emotional competence is considered important in order to master the social and emotional challenges inherent in their profession and to build positive teacher-student relationships. In turn, this is key to both teachers’ occupational well-being and positive student development. Nonetheless, an instrument assessing the profession-specific knowledge and skills that teachers need to master the social and emotional demands in the classroom is still lacking. Therefore, we developed the Test of Regulation in and Understanding of Social Situations in Teaching (TRUST), which is a theory-based situational judgment test measuring teachers’ knowledge about strategies for emotion regulation and relationship management in emotionally and socially challenging situations with students. Results from three studies (N = 166 in-service teachers, N = 73 in-service teachers, N = 107 pre-service teachers) showed satisfactory internal consistency for both the emotion regulation and relationship management subtests. Furthermore, confirmatory factor analyses supported the differentiation between the two facets of social-emotional competence. Regarding convergent validity, results from Study 3 revealed a positive association between the profession-specific TRUST and pre-service teachers’ general emotional intelligence. Furthermore, small to moderate correlations with the Big Five personality traits provided evidence for the discriminant validity of TRUST. In Studies 1 and 2, we found evidence for a correlation with external criteria, that is, teachers with higher test scores reported providing more emotional support for students and having better teacher-student relationships. For teachers’ occupational well-being, we found a link with symptoms of depersonalization and job satisfaction, but none for emotional exhaustion. We will discuss the use of TRUST in research, for the evaluation of interventions, in teacher education, and professional development and will illustrate ideas for enhancing the tool.
Temperature correction to dielectric modulus and activation energy prediction of oil-immersed cellulose insulation

The dielectric modulus Μ∗(ω) is used for studying the frequency domain response and activation energy (Ea) of transformer immersed cellulose insulation. Existing literatures show that the temperature is a crucial factor affecting the Μ∗(ω) and aging rate of transformer oil-paper insulation, while the Ea can be regarded as an indicator to reflect the average aging rate of the transformer immersed cellulose insulation. Therefore, the discussion of the temperature-dependence on both Μ∗(ω) and Ea is of great significance. This paper attempts to report an approach for understanding the temperature impact on both Μ∗(ω) and Ea. The obtained knowledge can be used to establish two available models for temperature correction on Μ∗(ω) and activation energy prediction of transform immersed cellulose insulation, respectively. These findings are expected to promote the reliability of the condition prediction result of transformer immersed cellulose insulation by using Μ∗(ω) and Ea technique.
Generative Text Summary Based on Enhanced Semantic Attention and Gain-Benefit Gate

Generative text summary is an important branch of natural language processing. Aiming at the problems of insufficient use of semantic information, insufficient summary precision and the problem of semantics-loss in the current generated text summary method, an enhanced semantic model is proposed based on dual-encoder, which can provide richer semantic information for sequence-to-sequence architecture through dual-encoder. The enhanced attention architecture with dual-channel semantics is optimized, and the empirical distribution and Gain-Benefit gate are built for decoding. In addition, the position embedding and word embedding are merged into the word embedding technology, and the TF-IDF(term frequency-inverse document frequency), part of speech, key score are added to word’s feature. Meanwhile, the optimal dimension of word embedding is optimized. This paper aims to optimize the traditional sequence mapping and word feature representation, enhance the model’s semantic understanding, and improve the quality of the summary. The LCSTS and SOGOU datasets are used to validate proposed method. The experimental results show that the proposed method can improve the performance of the ROUGE evaluation system by 10–13 percentage points compared with other listed algorithms. We can observe that the semantic understanding of the text summaries is more accurate and the generation effect is better, which has a better application prospect.
Eight-Element Compact UWB-MIMO/Diversity Antenna With WLAN Band Rejection for 3G/4G/5G Communications

An eight element, compact Ultra Wideband– Multiple Input Multiple Output (UWB-MIMO) antenna capable of providing high data rates for future Fifth Generation (5G) terminal equipments along with the provision of necessary bandwidth for Third Generation (3G) and Fourth Generation (4G) communications that accomplishes band rejection from 4.85 to 6.35 GHz by deploying a Inductor Capacitor (LC) stub on the ground plane is presented. The incorporated stub also provides flexibility to reject any selected band as well as bandwidth control. The orthogonal placement of the printed monopoles permits polarization diversity and provides high isolation. In the proposed eight element UWB-MIMO/diversity antenna, monopole pair 3–4 are 180° mirrored transform of monopole pair 1–2 which lie on the opposite corners of a planar $50\times50$ mm2 substrate. Four additional monopoles are then placed perpendicularly to the same board leading to a total size of $50\times 50\times25$ mm3 only. The simulated results are validated by comparing the measurements of a fabricated prototype. It was concluded that the design meets the target specifications over the entire bandwidth of 2 to 12 GHz with a reflection coefficient better than −10 dB (except the rejected band), isolation more than 17 dB, low envelope correlation, low gain variation, stable radiation pattern, and strong rejection of the signals in the Wireless Local Area Network (WLAN) band. Overall, compact and reduced complexity of the proposed eight element architecture, strengthens its practical viability for the diversity applications in future 5G terminal equipments amongst other MIMO antennas designs present in the literature.
Transmission Distance Extension of Directly Modulated Tunable V-cavity Laser Using AWG Wavelength Detuning

Directly modulated tunable lasers are highly desirable for constructing dense-wavelength division multiplexing (DWDM) access networks, owing to their advantages of low cost, compact size and low power consumption. However, the transmission distance for 10 Gbps is usually limited to about 10 km in the telecom C-band in standard single-mode fiber (SMF) due to wavelength chirp. Here we propose and demonstrate a simple technique for extending the transmission distance by detuning the wavelength of a tunable V-cavity laser with respect to DWDM multiplexers. Experiment results show that 10 Gbps signal can be transmitted error-free (BER<10−12) over 20 km SMF with a wavelength-detuning with respect to a Gaussian-type arrayed waveguide grating (AWG) without dispersion compensation. The power penalty is only 1 dB compared to the back-to-back transmission.
Utilizing Textual Information in Knowledge Graph Embedding: A Survey of Methods and Applications

Techniques that map the entities and relations of the knowledge graph (KG) into a low-dimensional continuous space are called KG embedding or knowledge representation learning. However, most existing techniques learn the embeddings based on the facts in KG alone, suffering from the issues of imperfection and spareness of KG. Recently, the research on textual information in KG embedding has attracted much attention due to the rich semantic information supplied by the texts. Thus, in this paper, a survey of techniques for textual information based KG embedding is proposed. Firstly, we introduce the techniques for encoding the textual information to represent the entities and relations from perspectives of encoding models and scoring functions, respectively. Secondly, methods for incorporating the textual information in the existing embedding techniques are summarized. Thirdly, we discuss the training procedure of textual information based KG embedding techniques. Finally, applications of KG embedding with textual information in the specific tasks such as KG completion in zero-shot scenario, multilingual entity alignment, relation extraction and recommender system are explored. We hope that this survey will give insights to researchers into textual information based KG embedding.
27 GHz Silicon-Contacted Waveguide-Coupled Ge/Si Avalanche Photodiode

We report a silicon-contacted Ge/Si avalanche photodiode with RF gain of 11 and a bandwidth of 27 GHz at -12 V operating at 1310 nm. The device is fabricated in an established Si photonics platform without additional process complexity and contacts only on Si. Wafer-scale performance data are presented confirming the reproducibility and the manufacturability of the device. Wide open eye diagrams are demonstrated for 25, 40, and 50 Gbps data-rates. The demonstration of such avalanche photodiode shows great potential for improving optical link margins for optical transceivers operating at 400 Gb/s and beyond.
The Use of Multimodal Resources by Technical Managers and Their Peers in Meetings Using English as the Business Lingua Franca

Background: Engineers increasingly work and advance their careers in international business settings. As technical managers, they need management and technical skills when working with different stakeholders with whom they may not share a common first language. Studies have revealed that informal oral communication skills are of prime importance for global engineers who face challenges in building shared meaning and formulating clear messages in meetings with non-native speakers of English. This article proposes that studying the use of multimodal resources (spoken language, gaze, gestures, and objects) in meetings can unpack how work tasks are accomplished in business through different communicative strategies. Literature review: This paper focuses on engineers’ and technical managers’ needs and challenges in professional and intercultural communication where English is used as a business lingua franca (BELF) in multimodal meetings. While multimodal conversation and discourse analytic studies highlight the dynamic nature of meeting interaction, previous technical and professional communication and BELF research on multimodality is limited. Research questions: 1. How do technical managers use multimodal resources to articulate their ideas in BELF meetings with their peers? 2. How does the use of multimodal resources contribute to the construction of shared meaning in explanatory, consensus-seeking, and solution-finding communication? Methodology: This study reports on two case studies and multimodal discourse analysis of video-recorded meetings among technical managers and their peers in four companies. The use of multimodal resources is analyzed in explanatory, consensus-seeking, and solution-finding communication. Results and conclusions: In BELF meetings, assemblages of spoken language, gestures, tools, whiteboard, and documents contribute to constructing shared meaning. This study has implications for global professional and engineering communication. Future research should further examine multimodality in BELF meetings.
Fully Planar Dual-Polarized Broadband Antenna for 3G, 4G and Sub 6-GHz 5G Base Stations

A fully planar dual polarized radiating element with internal embedded coupled dipoles is presented in this paper. The inclusion of these elements allows achieving three different goals: firstly, an extension of the bandwidth towards lower frequencies (from 1.69 GHz to 1.427 GHz) as required in new 5G standards; secondly, a reduction of the antenna size in comparison with other topologies for bandwidth extension and, finally, a reduction of the secondary lobes. The proposed dual polarized broadband antenna is based on two sets of dual polarized dipoles, with a fully planar structure. The objective of this paper is the design of an antenna working in the extended 5G band in order to fulfil the future 5G requirement in the microwave region, in which the new frequency bandwidth goes from 1.427 up to 2.69 GHz with the return losses lower than −14 dB and the crosspolar isolation larger than 28 dB.
Facial Age Estimation Using a Multi-Task Network Combining Classification and Regression

Age estimation of facial images is very challenging because of the complexity of face aging process and the difficulty of collecting and labeling data. A holistic regression model is subject to imbalanced training data, while a divide-and-conquer method highly depends on the effect of the age classification, which usually has boundary effect due to cross-age correlations. This paper proposes a simple but effective multi-task learning (MTL) network combining classification and regression for age estimation called CR-MT net, where classification acts as an auxiliary task to regression. MTL can boost the generalization performance of the age regression task by shared information representation learning of the two tasks. Compared to divide-and-conquer methods, our method performs MTL for two tasks with an end-to-end training, and has no error propagation from classification to regression. The holistic regression model in CR-MT net does not meet the boundary effect, and can fit the heterogeneous or unbalanced age data more accurately with the aid of a good age data partition in classification. We evaluate two age grouping techniques to find a good data partition, and diagnose various factors which can influence the performance of the CR-MT net by extensive experiments. The CR-MT net is verified in three public datasets, and achieves state-of-art results.
Ultra-Low Index-Contrast Polymeric Photonic Crystal Nanobeam Electro-Optic Modulator

A novel nanocavity-based polymer-on-insulator (POI) electro-optic modulator (EOM) is proposed. It consists of a polymeric photonic-crystal nanobeam cavity (PCNC) with ultra-low index-contrast (<italic>n<inline-formula><tex-math notation="LaTeX">$_{\text{cavity}}$</tex-math></inline-formula>/n<inline-formula><tex-math notation="LaTeX">$_{bg}$</tex-math></inline-formula></italic> = 1.17). Based on three-dimensional (3D) finite-difference time-domain (FDTD) method, the PCNC design and optimization are investigated theoretically. A high quality-factor (<italic>Q</italic>) of 3.4<inline-formula><tex-math notation="LaTeX">$\,\times\, 10^4$</tex-math></inline-formula> and small mode-volume of 22.8 <italic><inline-formula><tex-math notation="LaTeX">${({\lambda }/n_{SEO}})^{3}$</tex-math></inline-formula></italic> can be achieved. In order to judge the efficiency of the cavity design for electro-optic (EO) modulation, the optical modes and the electric field distribution are computed using an electromagnetic finite-element solver. Benefiting from the fast and strong EO effect in polymers, the modulator shows an EO modulation efficiency of up to 16 pm/V, which is over an order of magnitude higher than that in lithium niobate (LN) PCNCs. Moreover, the device is only 80 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>m in length, leading to a voltage-length product <italic><inline-formula><tex-math notation="LaTeX">$V_{\pi }$</tex-math></inline-formula> × L</italic> = 0.05 V<inline-formula><tex-math notation="LaTeX">$\cdot$</tex-math></inline-formula>cm, which is much smaller than those of Mach-Zehnder modulators. To the best of our knowledge, this is the first on-chip POI-based EOM that features both ultra-compact size and high modulation efficiency. Hence, it is potentially an ideal platform for applications in optical communications, electric-field sensing, and tunable photonic circuits.
Word Embedding Evaluation for Sinhala

This paper presents the first ever comprehensive evaluation of different types of word embeddings for Sinhala language. Three standard word embedding models, namely, Word2Vec (both Skipgram and CBOW), FastText, and Glove are evaluated under two types of evaluation methods: intrinsic evaluation and extrinsic evaluation. Word analogy and word relatedness evaluations were performed in terms of intrinsic evaluation, while sentiment analysis and part-of-speech (POS) tagging were conducted as the extrinsic evaluation tasks. Benchmark datasets used for intrinsic evaluations were carefully crafted considering specific linguistic features of Sinhala. In general, FastText word embeddings with 300 dimensions reported the finest accuracies across all the evaluation tasks, while Glove reported the lowest results.
AR-ASAG An ARabic Dataset for Automatic Short Answer Grading Evaluation

Automatic short answer grading is a significant problem in E-assessment. Several models have been proposed to deal with it. Evaluation and comparison of such solutions need the availability of Datasets with manual examples. In this paper, we introduce AR-ASAG, an Arabic Dataset for automatic short answer grading. The Dataset contains 2133 pairs of (Model Answer, Student Answer) in several versions (txt, xml, Moodle xml and .db). We explore then an unsupervised corpus based approach for automatic grading adapted to the Arabic Language. We use COALS (Correlated Occurrence Analogue to Lexical Semantic) algorithm to create semantic space for word distribution. The summation vector model is combined to term weighting and common words to achieve similarity between a teacher model answer and a student answer. The approach is particularly suitable for languages with scarce resources such as Arabic language where robust specific resources are not yet available. A set of experiments were conducted to analyze the effect of domain specificity, semantic space dimension and stemming techniques on the effectiveness of the grading model. The proposed approach gives promising results for Arabic language. The reported results may serve as baseline for future research work evaluation
Predicting Ratings of Real Dialogue Participants from Artificial Data and Ratings of Human Dialogue Observers

We collected a corpus of dialogues in a Wizard of Oz (WOz) setting in the Internet of Things (IoT) domain. We asked users participating in these dialogues to rate the system on a number of aspects, namely, intelligence, naturalness, personality, friendliness, their enjoyment, overall quality, and whether they would recommend the system to others. Then we asked dialogue observers, i.e., Amazon Mechanical Turkers (MTurkers), to rate these dialogues on the same aspects. We also generated simulated dialogues between dialogue policies and simulated users and asked MTurkers to rate them again on the same aspects. Using linear regression, we developed dialogue evaluation functions based on features from the simulated dialogues and the MTurkers’ ratings, the WOz dialogues and the MTurkers’ ratings, and the WOz dialogues and the WOz participants’ ratings. We applied all these dialogue evaluation functions to a held-out portion of our WOz dialogues, and we report results on the predictive power of these different types of dialogue evaluation functions. Our results suggest that for three conversational aspects (intelligence, naturalness, overall quality) just training evaluation functions on simulated data could be sufficient.
Developing a Twi (Asante) Dictionary from Akan Interlinear Glossed Texts

Traditionally, a lexicographer identifies the lexical items to be added to a dictionary. Here we present a corpus-based approach to dictionary compilation and describe a procedure that derives a Twi dictionary from a TypeCraft corpus of Interlinear Glossed Texts. We first extracted a list of unique words. We excluded words belonging to different dialects of Akan (mostly Fante and Abron). We corrected misspellings and distinguished English loan words to be integrated in our dictionary from instances of code switching. Next to the dictionary itself, one other resource arising from our work is a lexicographical model for Akan which represents the lexical resource itself, and the extended morphological and word class inventories that provide information to be aggregated. We also represent external resources such as the corpus that serves as the source and word level audio files. The Twi dictionary consists at present of 1367 words; it will be available online and from an open mobile app.
Mimicry by asx‐ and ST‐turns of the four main types of β‐turn in proteins

Hydrogen‐bonded β‐turns in proteins occur in four categories: type I (the most common), type II, type II', and type I'. Asx‐turns resemble β‐turns, in that both have an NH…OC hydrogen bond forming a ring of 10 atoms. Serine and threonine side chains also commonly form hydrogen‐bonded turns, here called ST‐turns. Asx‐turns and ST‐turns can be categorized into four classes, based on side chain rotamers and the conformation of the central turn residue, which are geometrically equivalent to the four types of β‐turns. We propose asx‐ and ST‐turns be named using the type I, II, I', and II' β‐turn nomenclature. Using this, the frequency of occurrence of both asx‐ and ST‐turns is: type II' > type I > type II > type I', whereas for β‐turns it is type I > type II > type I' > type II'. Almost all type II asx‐turns occur as a recently described three residue feature named an asx‐nest.
Eye4Ref: A Multimodal Eye Movement Dataset of Referentially Complex Situations

Eye4Ref is a rich multimodal dataset of eye-movement recordings collected from referentially complex situated settings where the linguistic utterances and their visual referential world were available to the listener. It consists of not only fixation parameters but also saccadic movement parameters that are time-locked to accompanying German utterances (with English translations). Additionally, it also contains symbolic knowledge (contextual) representations of the images to map the referring expressions onto the objects in corresponding images. Overall, the data was collected from 62 participants in three different experimental setups (86 systematically controlled sentence–image pairs and 1844 eye-movement recordings). Referential complexity was controlled by visual manipulations (e.g. number of objects, visibility of the target items, etc.), and by linguistic manipulations (e.g., the position of the disambiguating word in a sentence). This multimodal dataset, in which the three different sources of information namely eye-tracking, language, and visual environment are aligned, offers a test of various research questions not from only language perspective but also computer vision.
Swiss-AL: A Multilingual Swiss Web Corpus for Applied Linguistics

The Swiss Web Corpus for Applied Linguistics (Swiss-AL) is a multilingual (German, French, Italian) collection of texts from selected web sources. Unlike most other web corpora it is not intended for NLP purposes, but rather designed to support data-based and data-driven research on societal and political discourses in Switzerland. It currently contains 8 million texts (approx. 1.55 billion tokens), including news and specialist publications, governmental opinions, and parliamentary records, web sites of political parties, companies, and universities, statements from industry associations and NGOs, etc. A flexible processing pipeline using state-of-the-art components allows researchers in applied linguistics to create tailor-made subcorpora for studying discourse in a wide range of domains. So far, Swiss-AL has been used successfully in research on Swiss public discourses on energy and on antibiotic resistance.