Reviewerly/exHarmony huggingface dataset:
Teaching Chinese Sign Language with a Smartphone

There is a large group of deaf-mutes in the world, and sign language is their major communication tool. Therefore, it’s necessary for the deaf-mutes to communicate with hearing-speech people and the hearing-speech people also have needed to understand sign language, which produces a great demand for sign language teaching. Even though there have already been a large number of books for sign language, it is low efficient to learn sign language with books, even teaching videos. To solve this problem, we develop a smartphone-based interactive Chinese sign language teaching systemfor sign language learning. The system provides a learner with some kinds of learning modes and captures the learner’s actions from its front camera of the smartphone. Right now the system provides a vocabulary set with 1000 frequently used words, and the learner can evaluate his/her sign action by subjective or objective comparison. In the mode of word recognition, the users can play any word within the vocabulary and the system will turn the top three retrieved candidates, so it can remind the learners what the sign is. This system provides interactive learning for a user to learn sign language high efficiently. The systemadopts an algorithm based on point cloud recognition to evaluate a user’s sign and costs about 700ms inference time for each sample, which meets the real-time requirements. This interactive learning system decreases the communication barriers between the deaf-mutes and hearing-speechers.
On the Characteristics and Structures of Dynamical Systems Suitable for Reservoir Computing

We present an overview of mathematical aspects of Reservoir Computing (RC). RC is a machine learning method suitable for physical implementation, which harnesses a type of synchronization, called Common-Signal-Induced Synchronization. A precise criterion for this synchronization is given by a quantity called the conditional Lyapunov exponent. We describe a class of dynamical systems (physical systems) that are utilizable for RC in terms of this quantity. Then, two notions characterizing the information processing performance of RC are illustrated: (i) Edge of Chaos and (ii) Memory-Nonlinearity Trade-off. Based on the notion (ii), a structure of dynamical systems suitable for RC has been proposed. This structure is called the mixture reservoir. We review the structure and show its remarkable information processing performance.
Learning Domain Invariant Representations for Generalizable Person Re-Identification

Generalizable person Re-Identification (ReID) aims to learn ready-to-use cross-domain representations for direct cross-data evaluation, which has attracted growing attention in the recent computer vision (CV) community. In this work, we construct a structural causal model (SCM) among identity labels, identity-specific factors (clothing/shoes color etc.), and domain-specific factors (background, viewpoints etc.). According to the causal analysis, we propose a novel Domain Invariant Representation Learning for generalizable person Re-Identification (DIR-ReID) framework. Specifically, we propose to disentangle the identity-specific and domain-specific factors into two independent feature spaces, based on which an effective backdoor adjustment approximate implementation is proposed for serving as a causal intervention towards the SCM. Extensive experiments have been conducted, showing that DIR-ReID outperforms state-of-the-art (SOTA) methods on large-scale domain generalization (DG) ReID benchmarks.
An Evaluation of SVM in Hand Gesture Detection Using IMU-Based Smartwatches for Smart Lighting Control

Hand gesture detection with a smartwatch can be used as a smart lighting control on the internet of things (IoT) environment using machine learning techniques such as support vector machine (SVM). However, several parameters affect the SVM model's performance and need to be evaluated. This study evaluates the parameters in building an SVM model for hand gesture detection in intelligent lighting control. In this study, eight gestures were defined to turn on and off four different lights, and then the data were collected through a smartwatch with an Inertial Measurement Unit (IMU) sensor. Feature selection using Pearson Correlation is then carried out on 36 features extracted from each gesture data. Finally, two sets of gestures were compared to evaluate the effect of gesture selection on model performance. The first set of gestures show that the accuracy of 10 features compared to the accuracy of 36 features is 94% compared to 71%, respectively. Furthermore, the second set of gestures has an accuracy lower than the first set of gestures, which is 64%. Results show that the lower the number of features, the better the accuracy. Then, the set of gestures that are not too distinctive show lower accuracy than the highly distinctive gesture sets. The conclusion is, in implementing gesture detection with SVM, low data dimensions need to be maintained through feature selection methods, and a distinctive set of gesture selection is required for a model with good performance.
AutoReCon: Neural Architecture Search-based Reconstruction for Data-free Compression

Data-free compression raises a new challenge because the original training dataset for a pre-trained model to be compressed is not available due to privacy or transmission issues. Thus, a common approach is to compute a reconstructed training dataset before compression. The current reconstruction methods compute the reconstructed training dataset with a generator by exploiting information from the pre-trained model. However, current reconstruction methods focus on extracting more information from the pre-trained model but do not leverage network engineering. This work is the first to consider network engineering as an approach to design the reconstruction method. Specifically, we propose the AutoReCon method, which is a neural architecture search-based reconstruction method. In the proposed AutoReCon method, the generator architecture is designed automatically given the pre-trained model for reconstruction. Experimental results show that using generators discovered by the AutoRecon method always improve the performance of data-free compression.
Algorithm optimization and anomaly detection simulation based on extended Jarvis-Patrick clustering and outlier detection

In this paper, the authors analyze the algorithm optimization and anomaly detection simulation based on extended jarvis-patrick clustering and outlier detection. We perform detection by using the jarvis-patrick graph-based clustering method. After that, to further improve the false alarm rate (FAR) of the algorithm, we use an extra outlier detection step combined with our proposed EJP to create a new anomaly detection method called LD-EJP. Using LD-EJP, the false alarm rate improved much (experiments show that the false alarm rate can reach 4.1% while the best JP clustering can reach is 7.4%). Then, we tested LD-EJP against two other anomaly detection methods using k-means and LGCCB, showing that our algorithm has a better detection rate and false alarm rate than these two clustering-based anomaly detection methods. In addition, the detection rate and false positives of the algorithm also have some room for improvement. In the labeling process, the proportion of anomaly clusters to normal clusters needs to be manually adjusted to find a better detection rate. In addition, the detection rate we chose can consume some of the detection rate gained in extended JP clustering to have the LD-EJP obtain a better FAR. Therefore, our future work contains finding or proposing another outlier detection algorithm with better performance than our LD-EJP method.
Single-shot fringe pattern phase retrieval using improved period-guided bidimensional empirical mode decomposition and Hilbert transform

Fringe pattern analysis is the central aspect of numerous optical measurement methods, e.g., interferometry, fringe projection, digital holography, quantitative phase microscopy. Experimental fringe patterns always contain significant features originating from fluctuating environment, optical system and illumination quality, and the sample itself that severely affect analysis outcome. Before the stage of phase retrieval (information decoding) interferogram needs proper filtering, which minimizes the impact of mentioned issues. In this paper we propose fully automatic and adaptive fringe pattern pre-processing technique - improved period guided bidimensional empirical mode decomposition algorithm (iPGBEMD). It is based on our previous work about PGBEMD which eliminated the mode-mixing phenomenon and made the empirical mode decomposition fully adaptive. In present work we overcame key problems of original PGBEMD – we have considerably increased algorithm’s application range and shortened computation time several-fold. We proposed three solutions to the problem of erroneous decomposition for very low fringe amplitude images, which limited original PGBEMD significantly and we have chosen the best one among them after comprehensive analysis. Several acceleration methods were also proposed and merged to ensure the best results. We combined our improved pre-processing algorithm with the Hilbert Spiral Transform to receive complete, consistent, and versatile fringe pattern analysis path. Quality and effectiveness evaluation, in comparison with selected reference methods, is provided using numerical simulations and experimental fringe data.
Learning with Different Amounts of Annotation: From Zero to Many Labels

Training NLP systems typically assumes access to annotated data that has a single human label per example.Given imperfect labeling from annotators and inherent ambiguity of language, we hypothesize that single label is not sufficient to learn the spectrum of language interpretation.We explore new annotation distribution schemes, assigning multiple labels per example for a small subset of training examples.Introducing such multi label examples at the cost of annotating fewer examples brings clear gains on natural language inference task and entity typing task, even when we simply first train with a single label data and then fine tune with multi label examples.Extending a MixUp data augmentation framework, we propose a learning algorithm that can learn from training examples with different amount of annotation (with zero, one, or multiple labels).This algorithm efficiently combines signals from uneven training data and brings additional gains in low annotation budget and cross domain settings.Together, our method achieves consistent gains in two tasks, suggesting distributing labels unevenly among training examples can be beneficial for many NLP tasks. 1
The Future of Instruction Media in Indonesian Education: Systematic Review

This study aimed to answer whether digital devices replace print media in education in Indonesia's digital era. Systematic studies and experts' findings can get a basic understanding of print media that can be replaced by digital media in education today and in the future. For this reason, the researchers have attempted to collect literature and publications related to this theme. Then it analyzes critically to prove whether our hypothesis is accepted or rejected. For example, the way we research seeks to understand the question's theme and matches the findings of several publications that we target from 2010 to 2020. Data sources come from Google Scholar, ERIC publications, and other search engines. This analytical process involves coding systems, content evaluation, in-depth interpretation, and concluding to obtain findings characterized by validity and trustworthiness. The results reveal that slowly the role of electronic media has begun to take over paper media. However, with the condition of Indonesian education in general, the role of print media continues to dominate the world of Indonesian education today, especially in remote areas of the country where modern educational facilities are not yet supported. Therefore, this finding is useful for policymakers and further study in a similar field.
Communication Cost Reduction with Partial Structure in Federated Learning

Federated learning is a distributed learning algorithm designed to train a single server model on a server using different clients and their local data. To improve the performance of the server model, continuous communication with clients is required, and since the number of clients is very large, the algorithm must be designed in consideration of the cost required for communication. In this paper, we propose a method for distributing a model with a structure different from that of the server model, distributing a model suitable for clients with different data sizes, and training a server model using the reconstructed model trained by the client. In this way, the server model deploys only a subset of the sequential model, collects gradient updates, and selectively applies updates to the server model. This method of delivering the server model at a lower cost to clients who only need smaller models can reduce the communication cost of training server models compared to standard methods. An image classification model was designed to verify the effectiveness of the proposed method via three data distribution situations and two datasets, and it was confirmed that training was accomplished only with a cost 0.229 times smaller than the standard method.
Relation classification via BERT with piecewise convolution and focal loss

Recent relation extraction models’ architecture are evolved from the shallow neural networks to natural language model, such as convolutional neural networks or recurrent neural networks to Bert. However, these methods did not consider the semantic information in the sequence or the distance dependence problem, the internal semantic information may contain the useful knowledge which can help relation classification. Focus on these problems, this paper proposed a BERT-based relation classification method. Compare with the existing Bert-based architecture, the proposed model can obtain the internal semantic information between entity pair and solve the distance semantic dependence better. The pre-trained BERT model after fine tuning is used in this paper to abstract the semantic representation of sequence, then adopt the piecewise convolution to obtain semantic information which influence the extraction results. Compare with the existing methods, the proposed method can achieve a better accuracy on relational extraction task because of the internal semantic information extracted in the sequence. While, the generalization ability is still a problem that cannot be ignored, and the numbers of the relationships are difference between different categories. In this paper, the focal loss function is adopted to solve this problem by assigning a heavy weight to less number or hard classify categories. Finally, comparing with the existing methods, the F1 metric of the proposed method can reach a superior result 89.95% on the SemEval-2010 Task 8 dataset.
Reinforce-Aligner: Reinforcement Alignment Search for Robust End-to-End Text-to-Speech

Text-to-speech (TTS) synthesis is the process of producing synthesized speech from text or phoneme input. Traditional TTS models contain multiple processing steps and require external aligners, which provide attention alignments of phoneme-to-frame sequences. As the complexity increases and efficiency decreases with every additional step, there is expanding demand in modern synthesis pipelines for end-to-end TTS with efficient internal aligners. In this work, we propose an end-to-end text-to-waveform network with a novel reinforcement learning based duration search method. Our proposed generator is feed-forward and the aligner trains the agent to make optimal duration predictions by receiving active feedback from actions taken to maximize cumulative reward. We demonstrate accurate alignments of phoneme-to-frame sequence generated from trained agents enhance fidelity and naturalness of synthesized audio. Experimental results also show the superiority of our proposed model compared to other state-of-the-art TTS models with internal and external aligners.
Entropy squeezing and atomic Wehrl density for the interaction between SU(1,1) Lie algebra and a three-level atom in presence of laser field

In this paper, we investigate a system Hamiltonian describing the interaction between SU(1,1) quantum system and a three-level atom in Λ configuration in presence of a classical external field. Appropriate conical transformations are performed for the atom matrices. The analytical solution of the model is obtained by using the Schrödinger equation. Then the atomic density operator of the proposed system is obtained. The effect of the detuning parameter and the classical external field on the atomic levels occupation, atomic entropy squeezing, variance squeezing, atomic Q-function and atomic Wehrl density are investigated for certain chosen values of the classical field parameter and the detuning parameter. The obtained results has a potential applications to topics related to ideal and real laser.
A keyphrase-based approach for interpretable ICD-10 code classification of Spanish medical reports

The 10th version of International Classification of Diseases (ICD-10) codification system has been widely adopted by the health systems of many countries, including Spain. However, manual code assignment of Electronic Health Records (EHR) is a complex and time-consuming task that requires a great amount of specialised human resources. Therefore, several machine learning approaches are being proposed to assist in the assignment task. In this work we present an alternative system for automatically recommending ICD-10 codes to be assigned to EHRs. Our proposal is based on characterising ICD-10 codes by a set of keyphrases that represent them. These keyphrases do not only include those that have literally appeared in some EHR with the considered ICD-10 codes assigned, but also others that have been obtained by a statistical process able to capture expressions that have led the annotators to assign the code. The result is an information model that allows to efficiently recommend codes to a new EHR based on their textual content. We explore an approach that proves to be competitive with other state-of-the-art approaches and can be combined with them to optimise results. In addition to its effectiveness, the recommendations of this method are easily interpretable since the phrases in an EHR leading to recommend an ICD-10 code are known. Moreover, the keyphrases associated with each ICD-10 code can be a valuable additional source of information for other approaches, such as machine learning techniques.
Experiences of Moving Quickly to Distance Teaching and Learning at All Levels of Education in Finland

Abstract In this chapter, the teaching and learning in Finnish compulsory education during the COVID-19 pandemic in the spring and autumn terms of 2020 will be analyzed and discussed. First, preconditions, such as teacher and student digi-competences and digi-infrastructure for switching to distance teaching and learning, will be analyzed. Second, the organization and experiences of teaching and learning during this time are described based on representative surveys conducted during and after the spring 2020 distance teaching period. Finally, teachers’, principals’ and students’ engagement and well-being during the pandemic will be analyzed based on survey data. Preconditions for organizing effective distance teaching and learning during the 2020 spring term were appropriate teacher and student digi-competences and digi-infrastructure and availability of digi-tools. During the pandemic, teachers’ digi-pedagogy and students’ digi-competences developed. Moreover, several digi-pedagogy and co-teaching innovations were created. However, at the student level, we identified decreased engagement during the pandemic, and at the teacher and principal levels, we identified not only decreased engagement but also increased stress and even burnout. Principals suffered from teachers’ stress, whereas teachers suffered from families’ inequality in coping with distance learning. All in all, the switch to distance teaching and learning was organized effectively, but the distance-learning period weakened the equality of teaching and the conditions for learning.
Småprat: DialoGPT for Natural Language Generation of Swedish Dialogue by Transfer Learning

Building open-domain conversational systems (or chatbots) that produce convincing responses is a recognized challenge. Recent state-of-the-art (SoTA) transformer-based models for the generation of natural language dialogue have demonstrated impressive performance in simulating human-like, single-turn conversations in English. This work investigates, by an empirical study, the potential for transfer learning of such models to Swedish language. DialoGPT, an English language pre-trained model, is adapted by training on three different Swedish language conversational datasets obtained from publicly available sources. Perplexity score (an automated intrinsic language model metric) and surveys by human evaluation were used to assess the performances of the fine-tuned models, with results that indicate that the capacity for transfer learning can be exploited with considerable success. Human evaluators asked to score the simulated dialogue judged over 57% of the chatbot responses to be human-like for the model trained on the largest (Swedish) dataset. We provide the demos and model checkpoints of our English and Swedish chatbots on the HuggingFace platform for public use.
In-Tool Motion Sensing for Evaluation of Violin Performance

A system that is able to measure motions is necessary for preserving the skills of experts. A previously reported study proposed a system that precisely measures and reproduces motions. However, this type of system must physically interact with the subject whose motions are measured. Such interaction can be troublesome when the measured motions require precise control of force or position or even a high degree of freedom. Therefore, this study proposes a novel method for sensing motions without interference and applies the approach to measure the skill of playing the violin. Using the data measured by the proposed system, the skills of an experienced violin player and a novice violin player are compared. This study attempts to visualize the differences between the two types of players by applying wavelet transform on the measured data and analyzing them in the frequency domain.
Use of Machine Learning–Based Software for the Screening of Thyroid Cytopathology Whole Slide Images

The use of whole slide images (WSIs) in diagnostic pathology presents special challenges for the cytopathologist. Informative areas on a direct smear from a thyroid fine-needle aspiration biopsy (FNAB) smear may be spread across a large area comprising blood and dead space. Manually navigating through these areas makes screening and evaluation of FNA smears on a digital platform time-consuming and laborious. We designed a machine learning algorithm that can identify regions of interest (ROIs) on thyroid fine-needle aspiration biopsy WSIs.To evaluate the ability of the machine learning algorithm and screening software to identify and screen for a subset of informative ROIs on a thyroid FNA WSI that can be used for final diagnosis.A representative slide from each of 109 consecutive thyroid fine-needle aspiration biopsies was scanned. A cytopathologist reviewed each WSI and recorded a diagnosis. The machine learning algorithm screened and selected a subset of 100 ROIs from each WSI to present as an image gallery to the same cytopathologist after a washout period of 117 days.Concordance between the diagnoses using WSIs and those using the machine learning algorithm-generated ROI image gallery was evaluated using pairwise weighted κ statistics. Almost perfect concordance was seen between the 2 methods with a κ score of 0.924.Our results show the potential of the screening software as an effective screening tool with the potential to reduce cytopathologist workloads.