Reviewerly/exHarmony huggingface dataset:
Transforming Term Extraction: Transformer-Based Approaches to Multilingual Term Extraction Across Domains

Automated Term Extraction (ATE), even though well-investigated, continues to be a challenging task.Approaches conventionally extract terms on corpus or document level and the benefits of neural models still remain underexplored with very few exceptions.We introduce three transformer-based term extraction models operating on sentence level: a language model for token classification, one for sequence classification, and an innovative use of Neural Machine Translation (NMT), which learns to reduce sentences to terms.All three models are trained and tested on the dataset of the ATE challenge TermEval 2020 in English, French, and Dutch across four specialized domains.The two best performing approaches are also evaluated on the ACL RD-TEC 2.0 dataset.Our models outperform previous baselines, one of which is BERT-based, by a substantial margin, with the token-classifier language model performing best.
WikiTableT: A Large-Scale Data-to-Text Dataset for Generating Wikipedia Article Sections

Datasets for data-to-text generation typically focus either on multi-domain, single-sentence generation or on single-domain, long-form generation.In this work, we cast generating Wikipedia sections as a data-to-text generation task and create a large-scale dataset, WIKITABLET, that pairs Wikipedia sections with their corresponding tabular data and various metadata.WIKITABLET contains millions of instances, covering a broad range of topics, as well as a variety of flavors of generation tasks with different levels of flexibility.We benchmark several training and decoding strategies on WIKITABLET.Our qualitative analysis shows that the best approaches can generate fluent and high quality texts but they struggle with coherence and factuality, showing the potential for our dataset to inspire future work on long-form generation.
Keep the Primary, Rewrite the Secondary: A Two-Stage Approach for Paraphrase Generation

Paraphrase generation is an important and challenging NLG problem.In this work, we propose a new Identification-then-Aggregation (IA) framework to tackle this task.In the identification step, the input tokens are sorted into two groups by a novel Primary/Secondary Identification (PSI) algorithm.In the aggregation step, these groups are separately encoded, before being aggregated by a custom designed decoder, which autoregressively generates the paraphrased sentence.In extensive experiments on two benchmark datasets, we demonstrate that our model outperforms previous studies by a notable margin.We also show that the proposed approach can generate paraphrases in an interpretable and controllable way.
Structure-aware Person Image Generation with Pose Decomposition and Semantic Correlation

In this paper we tackle the problem of pose guided person image generation, which aims to transfer a person image from the source pose to a novel target pose while maintaining the source appearance. Given the inefficiency of standard CNNs in handling large spatial transformation, we propose a structure-aware flow based method for high-quality person image generation. Specifically, instead of learning the complex overall pose changes of human body, we decompose the human body into different semantic parts (e.g., head, torso, and legs) and apply different networks to predict the flow fields for these parts separately. Moreover, we carefully design the network modules to effectively capture the local and global semantic correlations of features within and among the human parts respectively. Extensive experimental results show that our method can generate high-quality results under large pose discrepancy and outperforms state-of-the-art methods in both qualitative and quantitative comparisons.
PASSLEAF: A Pool-bAsed Semi-Supervised LEArning Framework for Uncertain Knowledge Graph Embedding

In this paper, we study the problem of embedding uncertain knowledge graphs, where each relation between entities is associated with a confidence score. Observing the existing embedding methods may discard the uncertainty information, only incorporate a specific type of score function, or cause many false-negative samples in the training, we propose the PASSLEAF framework to solve the above issues. PASSLEAF consists of two parts, one is a model that can incorporate different types of scoring functions to predict the relation confidence scores and the other is the semi-supervised learning model by exploiting both positive and negative samples associated with the estimated confidence scores. Furthermore, PASSLEAF leverages a sample pool as a relay of generated samples to further augment the semi-supervised learning. Experiment results show that our proposed framework can learn better embedding in terms of having higher accuracy in both the confidence score prediction and tail entity prediction.
Coherent dynamics of Floquet-Bloch states in monolayer <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi mathvariant="normal">W</mml:mi><mml:msub><mml:mi mathvariant="normal">S</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math> reveals fast adiabatic switching

Floquet engineering offers a path to optically-controlled materials, but experimental implementations have frequently relied on femtosecond pulses to achieve the high peak fields required to maximise interactions and obtain a measurable response. The Floquet formalism, however, is based on a continuous, periodic drive. Here we use femtosecond laser pulses to drive the optical Stark effect, a simple realization of Floquet engineering, in monolayer WS2. By monitoring the coherent evolution of the free-induction decay, we show that the system evolves adiabatically, and that the finite duration of the pulses does not introduce any effects beyond Floquet theory. Furthermore, we demonstrate that the induced energy shift follows a linear dependence on instantaneous intensity, even for ultrafast driving fields of fewer than 15 optical cycles.
A Comprehensive Program of activities to develop sustainable core skills in novice scientists

Abstract This paper is aimed at studying scientific communication as an integral part of a scientists activity. The authors of this article analysed the development of informational technologies, which gave rise to a new paradigm of scientific communication Research 2.0. In the present study the analysis of research papers, describing models of scientific communication is done. The findings allow to define the structure and content of a comprehensive program of activities, connected to scientific communication in compliance with the Scientific Communication Life Cycle Model. In order to implement the program, aimed at developing core skills through scientific communication of scientists, a target audience, comprising postgraduate students and young researchers in Mathematics and Teaching Methods was engaged. A five-stage program of activities, which was developed, prompted scientific activity of young researchers and gave them an opportunity to learn about means of presenting research results, elements of management, mechanisms for applying the findings. A constructive description of each module of the program is done, actions and a strategy are described, communication between participants and tutors through the platform Higher School Mathematics Teacher is arranged in this research. In order to assess the efficiency of implementing the program, Researcher Development Framework (RDF) is used. The study also presents the results of the activity of young researchers, who were engaged in the program. Following the change in the phase of the development of researchers characteristic features and in compliance with RDF, a conclusion is made about a positive impact of the program on the development of career skills of young scientists, their interaction skills, awareness of professional behavior procedure.
An integrated ASTER-based approach for mapping carbonatite and iron oxide-apatite deposits

Mapping of carbonatites and related mineral deposits has occupied prominent place in mineral resource exploration programs given their potential to host valuable concentrations of critical metals such as rare earth elements and niobium. Based on spectral characteristics of most indicative minerals for these rocks, a mapping approach was developed using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. The combination of band rationing outcomes with components from the principal component analysis and minimum noise fraction techniques highlighted the targeted rocks, with the excellent prospective zone representing ∼ 0.2% of the total investigated area. This approach was successfully applied to the Gleibat Lafhouda complex to rapidly delineate carbonatites and iron oxide-apatite ore outcrops. Results were validated through field observations and in-situ geochemical analysis using a portable X-ray fluorescence analyzer. Field data have also served as training data to perform a supervised classification, allowing further improvement of the mapping results.
Usage of Prosody Modification and Acoustic Adaptation for Robust Automatic Speech Recognition (ASR) System

Most of the automatic speech recognition (ASR) systems are trained using adult speech due to the less availability of the children's speech dataset. The speech recognition rate of such systems is very less when tested using the children's speech, due to the presence of the inter-speaker acoustic variabilities between the adults and children's speech. These inter-speaker acoustic variabilities are mainly because of the higher pitch and lower speaking rate of the children. Thus, the main objective of the research work is to increase the speech recognition rate of the Punjabi-ASR system by reducing these inter-speaker acoustic variabilities with the help of prosody modification and speaker adaptive training. The pitch period and duration (speaking rate) of the speech signal can be altered with prosody modification without influencing the naturalness, message of the signal and helps to overcome the acoustic variations present in the adult's and children's speech. The developed Punjabi-ASR system is trained with the help of adult speech and prosody-modified adult speech. This prosody modified speech overcomes the massive need for children's speech for training the ASR system and improves the recognition rate. Results show that prosody modification and speaker adaptive training helps to minimize the word error rate (WER) of the Punjabi-ASR system to 8.79% when tested using children's speech.
UBLS: User-Based Location Selection Scheme for Preserving Location Privacy

Due to the wide availability of location-based services (LBSs) that enable many applications to provide usertailored services, it becomes possible to trace the locations of an individual by an adversary, especially when the LBS server is distrusted, which violates the user's privacy. Therefore, we propose, in this paper, a user-based location selection scheme (UBLS) to hide the users' locations using k-anonymity to preserve users' privacy. The proposed scheme uses the concept of dummy locations to hide the real locations of the users, but on top of that, it selects the dummy locations based on the users that exist in these locations. Moreover, we propose an attacker location exclusion (ALE) algorithm that can be used to attack the existing location privacy-preserving schemes. We also propose a new metric, namely location privacy level (LPL), to qualify the ability of the malicious LBS server to reduce the privacy level of the requester. Our envisioned UBLS scheme is evaluated with extensive computer-based simulations. Comparing to the existing schemes in the literature that preserve location privacy, our proposed UBLS demonstrates performance improvement in terms of entropy, cloaking region, and location privacy level metrics.
Online Data Migration Model and ID3 Algorithm in Sports Competition Action Data Mining Application

The ID3 algorithm is a key and important method in existing data mining, and its rules are simple and easy to understand and have high application value. If the decision tree algorithm is applied to the online data migration of sports competition actions, it can grasp the sports competition rules in the relationship between massive data to guide sports competition. This paper analyzes the application performance of the traditional ID3 algorithm in online data migration of sports competition actions; realizes the application steps and data processing process of the traditional ID3 algorithm, including original data collection, original data preprocessing, data preparation, constructing a decision tree, data mining, and making a comprehensive evaluation of the traditional ID3 algorithm; and clarifies the problems of the traditional ID3 algorithm. Mainly, the problems of missing attributes and overfitting are clarified, which provide directions for the subsequent algorithm optimization. Then, this paper proposes a <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>k</mi> </math> -nearest neighbor-based ID3 optimization algorithm, which selects values similar to <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>k</mi> </math> -nearest neighbors to fill in the missing values for the attribute missing problem of the traditional ID3 algorithm. Based on this, the improved algorithm is applied to the online data migration of sports competition actions, and the application effect is evaluated. The results show that the performance of the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>k</mi> </math> -nearest neighbor-based ID3 optimization algorithm is significantly improved, and it can also solve the overfitting problem existing in the traditional ID3 algorithm. For the overall classification problem of six types of samples of travel patterns, the experimental data samples have the characteristics of high data quality, a considerable number of samples, and obvious sample differentiation. Therefore, this paper also uses the deep factorization machine algorithm based on deep learning to classify the six classes of travel patterns of sports competition action data using the previously extracted relevant features. The research in this paper provides a more accurate method and a higher-performance online data migration model for sports competition action data mining.
Successful control of COVID-19 outbreak through tracing, testing, and isolation: Lessons learned from the outbreak control efforts made in a metropolitan city of South Korea

The first surge of coronavirus disease 2019 (COVID-19) cases began on June 27, 2020 in Gwangju metropolitan city, located in the southwestern part of South Korea, with a population of 1,501,000. Local governments and the Korean Center for Disease Control and Prevention immediately started an epidemiologic investigation and traced the contacts of patients using a wide variety of data sources, including location data from mobile devices, credit card transaction, and closed-circuit television footage. Until July 16, 2020, 138 community transmission cases and 10 infection clusters were identified across the city. Through contact tracing, epidemiologic relatedness was found in 136 (98.6%) of 138 cases. Our investigation showed how the extensive and meticulous contact tracing suppressed COVID-19 outbreak in a populated city.
Upper Bounds on Device-Independent Quantum Key Distribution Rates in Static and Dynamic Scenarios

In this work, we develop upper bounds on key rates for device-independent quantum key distribution (DI-QKD) protocols and devices. We study the reduced $\mathrm{cc}$-squashed entanglement and show that it is a convex functional. As a result, we show that the convex hull of the currently known bounds is a tighter upper bound on the device-independent key rates of the standard Clauser-Horne-Shimony-Holt (CHSH)-based protocol. We further provide tighter bounds for DI-QKD key rates achievable by any protocol applied to the CHSH-based device. This bound is based on reduced relative entropy of entanglement optimized over decompositions into local and nonlocal parts. In the dynamical scenario of quantum channels, we obtain upper bounds for device-independent private capacity for the CHSH-based protocols. We show that the device-independent private capacity for the CHSH-based protocols on depolarizing and erasure channels is limited by the secret key capacity of dephasing channels.
Design and Implementation of Intelligent Inspection and Alarm Flight System for Epidemic Prevention

Unmanned aerial vehicles (UAV) and related technologies have played an active role in the prevention and control of novel coronaviruses at home and abroad, especially in epidemic prevention, surveillance, and elimination. However, the existing UAVs have a single function, limited processing capacity, and poor interaction. To overcome these shortcomings, we designed an intelligent anti-epidemic patrol detection and warning flight system, which integrates UAV autonomous navigation, deep learning, intelligent voice, and other technologies. Based on the convolution neural network and deep learning technology, the system possesses a crowd density detection method and a face mask detection method, which can detect the position of dense crowds. Intelligent voice alarm technology was used to achieve an intelligent alarm system for abnormal situations, such as crowd-gathering areas and people without masks, and to carry out intelligent dissemination of epidemic prevention policies, which provides a powerful technical means for epidemic prevention and delaying their spread. To verify the superiority and feasibility of the system, high-precision online analysis was carried out for the crowd in the inspection area, and pedestrians’ faces were detected on the ground to identify whether they were wearing a mask. The experimental results show that the mean absolute error (MAE) of the crowd density detection was less than 8.4, and the mean average precision (mAP) of face mask detection was 61.42%. The system can provide convenient and accurate evaluation information for decision-makers and meets the requirements of real-time and accurate detection.
Robust recognition technique for handwritten Kannada character recognition using capsule networks

&lt;p&gt;Automated reading of handwritten Kannada documents is highly challenging due to the presence of vowels, consonants and its modifiers. The variable nature of handwriting styles aggravates the complexity of machine based reading of handwritten vowels and consonants. In this paper, our investigation is inclined towards design of a deep convolution network with capsule and routing layers to efficiently recognize Kannada handwritten characters. Capsule network architecture is built of an input layer, two convolution layers, primary capsule, routing capsule layers followed by tri-level dense convolution layer and an output layer. For experimentation, datasets are collected from more than 100 users for creation of training data samples of about 7769 comprising of 49 classes. Test samples of all the 49 classes are again collected separately from 3 to 5 users creating a total of 245 samples for novel patterns. It is inferred from performance evaluation; a loss of 0.66% is obtained in the classification process and for 43 classes precision of 100% is achieved with an accuracy of 99%. An average accuracy of 95% is achieved for all remaining 6 classes with an average precision of 89%.&lt;/p&gt;
Students as Designers of Augmented Reality: Impact on Learning and Motivation in Computer Science

In this study, we report findings from the PCBuildAR project, in which students developed augmented reality (AR) artifacts following a guided design-based learning (DBL) approach. Sixty-two students participated in the study and were either in their first year to learn about computer science or were more experienced computer science students. In terms of learning performance, only the first-year students benefited from our guided DBL approach. In contrast, the experienced students were highly motivated to learn computer science not only immediately after the intervention, but also in the long term. For first-year students, this effect was only evident directly after the intervention. Overall, the guided DBL design proved to be effective for both motivation and learning, especially for younger students. For older learners, a better balance between guidance and autonomy is recommended.
A WSQ-based mobile peer assessment approach to enhancing university students’ vocal music skills and learning perceptions

Peer assessment has been regarded as an effective learning strategy in art education, such as in music, dance and art design. For music education, technology-supported peer assessment makes it easier for learners to reflect on their learning performance. However, the process of reflection needs solid and systematic theoretical knowledge. In terms of vocal music, which develops students’ singing skills, it is difficult for learners to compare their own works with those of others because of a lack of sufficient theoretical knowledge. Therefore, a WSQ (watch-summary-question)-based mobile peer assessment approach was used to help learners reflect based on their theoretical knowledge with the support of WSQ learning sheets. To investigate the effectiveness of the approach, an experiment was carried out in a Chinese university. The experimental group (N = 22) learned with the WSQ approach, whereas the control group (N = 22) adopted a mobile peer assessment approach without WSQ. Vocal music skills, learning attitude and learning motivation were assessed. The experimental results indicate that the approach effectively enhanced the students’ vocal music skills but did not improve their learning motivation or attitude. In addition, a higher correlation between teacher scoring and peer scoring was found for the experimental group.&#x0D; Implications for practice or policy:&#x0D; &#x0D; Technology has a great potential in supporting art education and integrating technology into vocal music education should follow the rules of music education.&#x0D; Developing students’ reflective thinking skills in vocal music education is challenging, but attainable; it needs considered instructional design and sustained efforts from students.&#x0D;