Reviewerly/exHarmony huggingface dataset:
Numerical analyses of emission of a single-photon pulse based on single-atom–cavity quantum electrodynamics

We numerically investigate an on-demand single-photon source, which is implemented with a strongly coupled atom–cavity system, proposed by A. Kuhn, M. Hennrich, T. Bondo, and G. Rempe, Appl. Phys. B 69, 373 (1999). In the scheme of Kuhn et al., a |$\Lambda$|-type three-level atom is captured in a single-mode optical cavity. Considering the three atomic levels, the ground state |$u$|⁠, the first excited state |$g$| accompanying the cavity mode, and the second excited state |$e$|⁠, in the |$\Lambda$|-configuration, we assume that a classical field and a quantized cavity field lead to the transition between |$u$| and |$e$| and that between |$e$| and |$g$|⁠, respectively. The classical light pulse rising sufficiently slowly triggers an adiabatic process of the system and lets a single photon of the cavity mode emerge. We simulate this adiabatic evolution and transmission of the single photon through an imperfect mirror of the cavity using the master equation. We concentrate on examining the physical properties of the efficiency of single-photon generation, the fluctuation of the duration of the photon emission, and the time of the emission measured from a peak of the trigger pulse. We find a function that approximates to the efficiency closely and the upper bound of the fluctuation of the duration.
Efficacy Verification of Team Learning Satisfaction, Problem Solving Ability, and Communication Ability of Problem Solving Process Classes Applying Action Learning, Problem-Based Learning, and Mentoring

The purpose of this study was i) to design a teaching and learning model for PSP class to which AL, PBL, and Mentoring are applied, ii) implement the model to verify the effects of AL’s on team learning, iii) analyze the enhancement of problem solving ability through PBL and iv) review development of communication skills through mentoring. The effect of team learning, improvement of problem solving ability within PBL, and improvement in communication through mentoring was verified through comparison of results from quantitative pre- and post-tests. Data was collected two times in the form of a questionnaire for 62 students at the beginning and at the end of the first semester of 2020. A paired samples t-test was among the methods of average comparison on the SPSS 18.0, used to determine differences in team learning satisfaction, problem solving ability, and communication skills based on experiences in AL, PBL, and Mentoring. The results of the test verified the efficacy of PSP class to which AL, PBL, and Mentoring were applied.
Theory-Inspired Path-Regularized Differential Network Architecture Search

Despite its high search efficiency, differential architecture search (DARTS) often selects network architectures with dominated skip connections which lead to performance degradation. However, theoretical understandings on this issue remain absent, hindering the development of more advanced methods in a principled way. In this work, we solve this problem by theoretically analyzing the effects of various types of operations, e.g. convolution, skip connection and zero operation, to the network optimization. We prove that the architectures with more skip connections can converge faster than the other candidates, and thus are selected by DARTS. This result, for the first time, theoretically and explicitly reveals the impact of skip connections to fast network optimization and its competitive advantage over other types of operations in DARTS. Then we propose a theory-inspired path-regularized DARTS that consists of two key modules: (i) a differential group-structured sparse binary gate introduced for each operation to avoid unfair competition among operations, and (ii) a path-depth-wise regularization used to incite search exploration for deep architectures that often converge slower than shallow ones as shown in our theory and are not well explored during the search. Experimental results on image classification tasks validate its advantages.
To Schedule or not to Schedule: Extracting Task Specific Temporal Entities and Associated Negation Constraints

State of the art research for date-time entity extraction from text is task agnostic. Consequently, while the methods proposed in literature perform well for generic date-time extraction from texts, they don’t fare as well on task specific date-time entity extraction where only a subset of the date-time entities present in the text are pertinent to solving the task. Furthermore, some tasks require identifying negation constraints associated with the date-time entities to correctly reason over time. We showcase a novel model for extracting task-specific date-time entities along with their negation constraints. We show the efficacy of our method on the task of date-time understanding in the context of scheduling meetings for an email-based digital AI scheduling assistant. Our method achieves an absolute gain of 19% f-score points compared to baseline methods in detecting the date-time entities relevant to scheduling meetings and a 4% improvement over baseline methods for detecting negation constraints over date-time entities.
Robust and Interpretable Grounding of Spatial References with Relation Networks

Learning representations of spatial references in natural language is a key challenge in tasks like autonomous navigation and robotic manipulation.Recent work has investigated various neural architectures for learning multi-modal representations for spatial concepts.However, the lack of explicit reasoning over entities makes such approaches vulnerable to noise in input text or state observations.In this paper, we develop effective models for understanding spatial references in text that are robust and interpretable, without sacrificing performance.We design a text-conditioned relation network whose parameters are dynamically computed with a cross-modal attention module to capture fine-grained spatial relations between entities.This design choice provides interpretability of learned intermediate outputs.Experiments across three tasks demonstrate that our model achieves superior performance, with a 17% improvement in predicting goal locations and a 15% improvement in robustness compared to state-of-the-art systems. 1
Classification and comparison of malignancy detection of cervical cells based on nucleus and textural features in microscopic images of uterine cervix

Cervical cancer is one of the major causes of death among women worldwide. Pap smear is the cytology based screening test which is used to detect abnormal cervical cells including pre-cancerous lesions. Accurate classification of Pap smear images is one of the challenging tasks in medical image processing and its performance can be enhanced by extracting and selecting the well-defined features and classifiers. The irregular chromatin structure is one of the prominent diagnostic features of abnormal cells. Classification is performed based on the extracted textural features and the benchmark Herlev dataset features. Radial basis function (RBF), polynomial and sigmoid SVM kernels are used for classification and comparison is performed with the features in benchmark database. Precision, recall and accuracy were calculated for all the combinations of the features. The classifier gives promising results when benchmark features are combined with textural features and also benchmark nucleus features with textural features. An effective integration of features for cervical cell classification had given good results for fast abnormal cell detection and primary Pap smear cell image classification.
Improving Low Compute Language Modeling with In-Domain Embedding Initialisation

Many NLP applications, such as biomedical data and technical support, have 10-100 million tokens of in-domain data and limited computational resources for learning from it.How should we train a language model in this scenario?Most language modeling research considers either a small dataset with a closed vocabulary (like the standard 1 million token Penn Treebank), or the whole web with bytepair encoding.We show that for our target setting in English, initialising and freezing input embeddings using in-domain data can improve language model performance by providing a useful representation of rare words, and this pattern holds across several different domains.In the process, we show that the standard convention of tying input and output embeddings does not improve perplexity when initializing with embeddings trained on in-domain data.
Comparing SAW and CPI Method in Decisions Systems Support to Evaluate Teachers Performance

At educational institutions like Junior High School, Human Resources especially teachers determines the quality of the school. To determine Junior High School have good quality teachers, then the best teacher selection is needed to spur the teacher's performance. However, the best teacher selection at Santa Maria 2 Junior High School which in Tangerang still doing direct observation and no method implements the calculation. To overcome those problems, then Decision Support System is needed to do a calculation and rating the teachers at ease and accurate. The proposed Decision Support System is using Simple Additive Weighting and Composite Performance Index methods, where’s the calculation is obtained from each alternatives score and value weight from each criterion. The criteria in best teacher selection are reviewed from the absence aspect, professionalism, solidarity corps, personality, involving in activities from inside or outside school events. The final result from this calculation formed to ranking. The execution time of the SAW method has a faster average time of 0.489005 than the CPI method with an average time of 0.62258 seconds. On Relative Standard Deviation Testing CPI percentage greater than SAW with 3.90% and CPI 6.48%.
NHK_STRL at WNUT-2020 Task 2: GATs with Syntactic Dependencies as Edges and CTC-based Loss for Text Classification

The outbreak of COVID-19 has greatly impacted our daily lives.In these circumstances, it is important to grasp the latest information to avoid causing too much fear and panic.To help grasp new information, extracting information from social networking sites is one of the effective ways.In this paper, we describe a method to identify whether a tweet related to COVID-19 is informative or not, which can help to grasp new information.The key features of our method are its use of graph attention networks to encode syntactic dependencies and word positions in the sentence, and a loss function based on connectionist temporal classification that can learn a label for each token without reference data for each token.Experimental results show that the proposed method achieved an F1 score of 0.9175, outperforming baseline methods.
Correctly Implementing Synchronous Message Passing in the Pi-Calculus By Concurrent Haskell's MVars

Comparison of concurrent programming languages and correctness of program transformations in concurrency are the focus of this research. As criterion we use contextual semantics adapted to concurrency, where may -- as well as should -- convergence are observed. We investigate the relation between the synchronous pi-calculus and a core language of Concurrent Haskell (CH). The contextual semantics is on the one hand forgiving with respect to the details of the operational semantics, and on the other hand implies strong requirements for the interplay between the processes after translation. Our result is that CH embraces the synchronous pi-calculus. Our main task is to find and prove correctness of encodings of pi-calculus channels by CH's concurrency primitives, which are MVars. They behave like (blocking) 1-place buffers modelling the shared-memory. The first developed translation uses an extra private MVar for every communication.We also automatically generate and check potentially correct translations that reuse the MVars where one MVar contains the message and two additional MVars for synchronization are used to model the synchronized communication of a single channel in the pi-calculus.Our automated experimental results lead to the conjecture that one additional MVar is insufficient.
THE PERCEPTION OF ROBOT'S EMOTIONAL GESTURES AND SPEECH BY CHILDREN SOLVING A SPATIAL PUZZLE

The emotional behavior of a companion robot is important for human-robot interaction in the situation of training tasks. We examined the influence of emotional gestures and emotional speech of the robot on its perception by primary school students (N=52, male, female, mean age 9.8) in the situation of joint solution of the spatial Tangram puzzle. It was shown that emotional gestures make a significant contribution to the attractiveness of the robot for the child. It was also found that test subjects prefer the robot with emotional gestures and speech over the robot with neutral gesture and speech behavior. The study also analyzed the communicative behavior of children, identified typical communicative signs that are typical for interaction start with the robot, for monitoring the game and for difficult situations. We described typical mistakes that children make when assembling a puzzle together with the robot.
Semi-supervised Learning From Demonstration Through Program Synthesis: An Inspection Robot Case Study

Semi-supervised learning improves the performance of supervised machine learning by leveraging methods from unsupervised learning to extract information not explicitly available in the labels.Through the design of a system that enables a robot to learn inspection strategies from a human operator, we present a hybrid semi-supervised system capable of learning interpretable and verifiable models from demonstrations.The system induces a controller program by learning from immersive demonstrations using sequential importance sampling.These visual servo controllers are parametrised by proportional gains and are visually verifiable through observation of the robot's position in the environment.Clustering and effective particle size filtering allows the system to discover goals in the state space.These goals are used to label the original demonstration for end-to-end learning of behavioural models.The behavioural models are used for autonomous model predictive control and scrutinised for explanations.We implement causal sensitivity analysis to identify salient objects and generate counterfactual conditional explanations.These features enable decision making interpretation and post hoc discovery of the causes of a failure.The proposed system expands on previous approaches to program synthesis by incorporating repellers in the attribution prior of the sampling process.We successfully learn the hybrid system from an inspection scenario where an unmanned ground vehicle has to inspect, in a specific order, different areas of the environment.The system induces an interpretable computer program of the demonstration that can be synthesised to produce novel inspection behaviours.Importantly, the robot successfully runs the synthesised program on an unseen configuration of the environment while presenting explanations of its autonomous behaviour.
A New Feature Extraction and Recognition Method for Microexpression Based on Local Non-negative Matrix Factorization

Micro-expression has the characteristics of short duration and small action range. The existing expression recognition algorithms are not suitable for micro-expression recognition. As a feature extraction method, NMF (nonnegative matrix factorization) can decompose the original data into different components, which has been successfully applied to facial macro-expression recognition. In this paper, local NMF (LNMF) is used to decompose micro-expression into some facial muscle action, extract features for micro-expression recognition. The micro-expression datasets have not enough samples to train a classifier with good generalization performance and the existing macro-to-micro (MtM) algorithm can’t meet nonnegative properties of NMF feature vectors. As for these problems, we propose an algorithm to generate new micro-expression samples by means of macro-expression samples based on LNMF. Our algorithm has a higher recognition rate compared with three related algorithms based on CK+48/CASME2 datasets.
The Learning of Arabic Alphabets for Special Needs Students in Elementary School

This research was motivated by the learning of Arabic letters in students with special needs as a basic stage of learning Arabic and the Qur’an. This study aimed to describe the learning of Arabic letters in students with special needs in elementary school Campurdarat Tulungagung East Java Indonesia.This type of research was descriptive qualitative. Data collection techniques by interview, observation, and documentation. Sources of data in this study were principals, teachers, students with special needs, and documents. The results of this study were descriptions of the learning of Arabic letters for students with special needs in elementary schools. (1) Learning planning included promissory notes, syllabus, and lesson plans like schools in general. (2) The implementation of learning included teaching materials with Islamic handbooks while learning methods included alphabetical methods, demonstrations, recitation, and lectures dominated by sign language, while the learning media included cards and Juz ‘Amma. (3) Evaluation of learning included evaluation of processes and results that show students with special abilities can identify Arabic letters, but difficulties in pronunciation.
Frequent pattern growth algorithm for maximizing display items

Products are goods that are available and provided in stores for sale. Products provided in stores must be arranged properly to order to attract the attention of consumers to buy. Products arranged in a store will depend on the type of store. The product arrangement at a retail store will be different from the product arrangement at a clothing store. Store display will reflect a picture that is in the store so consumers know the types of products sold by product arrangement. An attractive arrangement will stimulate the desire of consumers to buy. In data mining there are several types of methods by use including prediction, association, classification and estimation. In the prediction method there are several techniques including the frequent pattern growth (FP-growth) method. FP-growth algorithm is the development of the apriori algorithm. So, the shortcomings of the apriori algorithm are corrected by the FP-growth algorithm. FP-growth is one alternative algorithm that can be used to determine the set of data that most often appears (frequent itemset) in a data set. Results of research on the application of the FP-growth algorithm to maximizing the display of goods. It is hoped that this research can be used to adjust the product layout according to the level of frequency the product is sought by the customer so that the customer has no difficulty finding the product they want.
Abusive Language Detection using Syntactic Dependency Graphs

Automated detection of abusive language online has become imperative. Current sequential models (LSTM) do not work well for long and complex sentences while bi-transformer models (BERT) are not computationally efficient for the task. We show that classifiers based on syntactic structure of the text, dependency graphical convolutional networks (DepGCNs) can achieve state-of-the-art performance on abusive language datasets. The overall performance is at par with of strong baselines such as fine-tuned BERT. Further, our GCN-based approach is much more efficient than BERT at inference time making it suitable for real-time detection.
SOME ISSUES ON ORGANIZATION OF EDUCATIONAL PROCESS IN HIGHER EDUCATIONAL INSTITUTIONS DURING PANDEMIC

The endemic coronavirus (COVID-19) is growing exponentially around the world. The prevalence of the new coronavirus COVID-19 around the world has led to global changes in society, various organizations, and educational institutions. Primary school and university populations appear to be at a lower mortality risk than older adults, but precautions are still needed in a pandemic called "social distance" to reduce interpersonal contact and thereby minimize transmission of the virus. Approximately 264 million children and adolescents are out of school (UNESCO, 2017), and the pandemic has further exacerbated the situation. The more the pandemic grows, the more schools, colleges and universities are closed, contributing to the transition to online learning. The time has come to rethink and rebuild our education system based on the current situation. In the aftermath of the COVID-19 crisis, online education has been a pedagogical breakthrough from a traditional method to a more modern approach to teaching and learning, from class to Zoom, from personal to virtual, from seminars to webinars. Around the world, the entire education system, from primary to tertiary, was destroyed during the isolation of the new coronavirus disease 2019 (COVID-19). The pandemic has prompted a re-examination of the main points of online learning in education, as well as how existing educational resources can help transform formal education online through virtual classrooms and other online resources. The process of online teaching-learning modes is perceived by teachers and students in different ways, sometimes causing difficulties and controversy in the effectiveness of their use.
Hierarchical Region Learning for Nested Named Entity Recognition

Named Entity Recognition (NER) is deeply explored and widely used in various tasks.Usually, some entity mentions are nested in other entities, which leads to the nested NER problem.Leading region based models face both the efficiency and effectiveness challenge due to the high subsequence enumeration complexity.To tackle these challenges, we propose a hierarchical region learning framework to automatically generate a tree hierarchy of candidate regions with nearly linear complexity and incorporate structure information into the region representation for better classification.Experiments on benchmark datasets ACE-2005, GENIA and JNLPBA demonstrate competitive or better results than state-of-theart baselines.
Overcoming Adversarial Perturbations in Data-driven Prognostics Through Semantic Structural Context-driven Deep Learning

Deep learning has shown impressive performance across a variety of domains, including data-driven prognostics.However, research has shown that deep neural networks are susceptible to adversarial perturbations, which are small but specially designed modifications to normal data inputs that can adversely affect the quality of the machine learning predictor.We study the impact of such adversarial perturbations in data-driven prognostics where sensor readings are utilized for system health status prediction including status classification and remaining useful life regression.We find that we can introduce obvious errors in prognostics by adding imperceptible noise to a normal input and that the hybrid model with randomization and structural contexts is more robust to adversarial perturbations than the conventional deep neural network.Our work shows limitations of current deep learning techniques in pure data-driven prognostics, and indicates a potential technical path forward.To the best of our knowledge, this work is the first to investigate the implications of using randomization and semantic structural contexts against current adversarial attacks for deep learning-based prognostics.