Reviewerly/exHarmony huggingface dataset:
Research on Online Course Teaching of Public Physical Education in Universities Under the Pandemic Situation

After the implementation, the online and offline teaching modes are compared and analyzed. In the special social environment, in order to ensure the normal teaching order, the university public sports online course is constructed. The construction research mainly includes the necessity of online physical education course, the design of teaching scheme, teaching objectives, teaching mode, extracurricular sports activities, assessment methods, the connection and comparison of online and offline courses, etc. The purpose is to build a set of practical online course system of university public physical education, make up for the deficiency of offline course teaching, and realize the sharing of educational resources among students in different regions at the same time In order to provide reference for the normal development of physical education in special environment such as epidemic situation.
The variable use of diacritics in synchronous computer-mediated French discourse: Replication research

Abstract This study promotes replication research as a methodological approach that is needed in order to compare earlier and more recent analyses of digital discourse. When much of the existing research was conducted, the primary means of communication included the use of a computer keyboard, (presumably) less bandwidth, and fewer devices. However, with an increase of the range of device types, the study of diacritics deserves another look within the Digital Media landscape. The present study examines the variable use of diacritics in synchronous (i.e., real-time) French chat discourse. We have replicated a study with different data sets from the same chat corpus, which is composed of data from a European chat server. We have also compared the data from the 2008 half of the corpus to data from the same chat channels collected in 2016 (just over 60,000 words in each half of the corpus, which included a total of 7,569 tokens that were coded). Our analysis of the 2008 corpus showed that one main finding was not the same as ours (from a different part of the 2008 corpus). Moreover, a diachronic analysis (2008 vs. 2016) revealed reversed trends between the two age-based channels (i.e., 20s vs. 50s).
Effective creation of interactive educational objects for on-line teaching

Interactive educational objects, or interactive elements in short, are the cornerstones of interactive teaching. Their use distinguishes "real" on-line teaching, that is teaching that requires interaction of the consumer of education with the educational material in real time, from teaching that only offers the distribution of "electronic (university) textbooks".Recently, we have been involved in both individual and routine creation of interactive elements for hundreds of interactive educational materials.The paper focuses on the philosophy of using interactive elements in educational materials as well as didactical and pedagogical contexts and impacts. The core of the article focuses on explaining the possibilities of different types of interactive elements usage in on-line teaching as well as their advantages and limitations. The article also describes the technical processing of interactive elements in way, that enables their production in large quantities and excellent quality while keeping low costs.
A Chemistry Inclusive and Educational Serious Game

Promoting equal opportunities and social inclusion of disabled people is a concern of modern society and a key issue in European Education. The evolution of science and the emerging new technologies promote social inclusion and might gear traditional learning environments with the required features to assure disabled people face the same learning opportunities as all. Our current work brings in R&D world-class results in the field of Educational Serious Games, to help students to learn and to assimilate subjects related to the Chemistry study field. Chemistry subjects pose significant challenges to junior and high-school students. Chemistry is a hard learning subject that students are usually reluctant to learn through the traditional teaching/learning methods. Reading Chemistry class books for long hours is not very effective; this drawback is even more serious when it comes to students that do not master oral languages or face other disabilities. In this paper we describe a developed serious game meant to be educational and inclusive, to help high school Chemistry students to learn and explore the Table of Periodic Elements and the composition of molecules. The inclusive components of the game address several disabilities, such as color blindness, hearing, and mobility impairments. Adequate color themes, to enable color blind students, sign language, to enable hearing-impaired students, sensorial devices to enable mobility impaired students, will be provided to play the game without any stigma. High-Level Requirements will be established. The game has been required to be supported by a Serious Games architecture and Inclusive Games Standards. To meet these needs, several requirements must be met as part of the development such as Biometric Sensor; Beacons; Augmented Reality; Georeferencing; Multiplatform; Multiplayer; Closed source; DLC; and DDA.
Scalable low-rank factorization using a task-based runtime system with distributed memory

We present a new parallel task-based algorithm for randomized low-rank factorizations of a matrix and its application to fast hierarchical solvers. TaskTorrent is a lightweight, distributed, task-based runtime in C++. We explain how the randomized factorization can be expressed as a task graph in TaskTorrent. We compare the key steps in the factorization with similar implementations using StarPU, PaRSEC, and in ScaLAPACK. The overall randomized algorithm in TaskTorrent outperforms the column pivoted QR routine from ScaLAPACK by far. The weak and strong scaling experiments demonstrate excellent scalability up to our largest available configuration with 1,024 cores. Finally, we incorporate our distributed low-rank factorization into spaND, a fast hierarchical solver for solving large sparse linear systems. We demonstrate gains in performance and scalability of the spaND solver.
A Novel SOM Ensemble Classification Technique for Limited Data

Small datasets are common in research areas where measurement of data is expensive or difficult (e.g., healthcare). For small datasets, training of a data model and classification using the same remains a challenge. To address the issue of limited data training and classification, the authors propose to use a novel ensemble of self-organizing maps (SOMs). SOMs are shallow neural networks, and as a result, the number of parameters is not very high. This avoids over-fitting and over-parameterization of the network. For the SOM ensemble, a novel early stopping training technique is proposed that requires a small number of samples for training. In the ensemble, one SOM is used for every class. For benchmarking the proposed technique, its performance is compared with that of other state-of-the-art techniques like deep learning, CATBOOST decision tree approach, etc. For reproducibility of results, three popular and extensively benchmarked digit image datasets have been used (i.e., MNIST, USPS+, and MADB). The proposed technique generally outperforms all other techniques with which it is compared.
Evaluation and Visualization of Blockchain-Based Healthcare

Blockchain is a distributed ledger apparatus that utilizes timestamped blocks to manage and verify various transactions between two parties in a network with the help of cryptographic algorithms. As compared to popular opinion, blockchain not only serves as a backbone to cryptocurrencies but it can also serve as a pioneering solution to various disciplinary fields such as healthcare, logistics, and education. The approach taken by this chapter to tackle the various problems faced by the healthcare sector utilizes blockchain technology. It avails smart contracts as well as the decentralized nature of the blockchain and smart tokens to ensure smooth interactions between the patients and the physicians. Protection and maintaining the integrity of healthcare data without overlooking any security threats are the primary objectives of this chapter. This chapter discusses a model that uses a cloud-based decentralized model for the storage of patient data.
Fast Canonical Huffman Decoder

We propose a novel method to decode canonical Huffman codes specifically designed to decode multiple symbols in one decoding cycle. The encoding method used is the classic canonical Huffman encoding. Our decoding method is specifically designed for speed and provides big throughput of decoding data; within our tests, we have achieved a decompression output of more than 2.1GiB/s when decompressing highly redundant data. Our method holds minimal storage requirements for decoding tables while still operating at very high speeds. We will also show that for limited-length canonical Huffman codewords that extend up to 12 bits, the memory size and construction times of the decoding tables are negligible.
RoSAC: A Speech Corpus for Transcribing Romanian Emergency Calls

Publicly available speech datasets for Romanian are still scarce, being far from enough for obtaining state-of-the-art performance with modern deep neural networks. As a response to this issue, during the development of an internal automatic speech recognition system for the national emergency call center, we have created the Romanian Speech Alert Corpus, a new Romanian speech corpus that was obtained by crowd-sourcing the reading of sentences in our institution. This paper describes the data acquisition process, several statistics about the resulted corpus and the algorithm we employed for removing the inadequate recordings.
Revisiting the idea of a representative linguistic corpus

The paper revisits the notion of representativeness of a linguistic corpus obtained through successive concatenations of author corpora. The means for investigating this concept is statistical probability estimation by piecing together multiple author corpora and evaluating the consistency of the resulted construction in comparison with the overall corpus consisting of full texts for all the authors considered. Essential for this investigation are the concepts of statistical probability estimation and the associated confidence intervals. Furthermore, we also made use of previous results and we made correlations with the notions of minimum statistical independence distance, Zipf's Law priority areas and common words, starting with the new methodology proposed in this paper. The experimental results obtained shows that when data from the analyzed corpus were collected in an i.i.d. manner (independent and identically distributed), the analyzed word sets had a very good overlap with the set of Zipf Area 1 words (99%) and common words among author corpora (75%). These results can be aligned with the idea of representativeness of a linguistic corpus.
Multi-scale Feature Recovery of Low-Light Enhancement Algorithm Based on U-net Network and Perceptual Loss

How to handle low-light images is a challenging problem in the field of image processing. A mature low-light enhancement technique not only benefits human visual perception, but also can lay a good foundation for subsequent advanced tasks such as target detection and image classification. To balance the visual effect of an image and its contribution to subsequent tasks, we propose an innovative multi-scale feature recovery low-light enhancement model combining U-net network and perceptual loss. It first passes the image through a denoising module to reduce the uncertainty of the subsequent enhancement process, and then trains a tandem “end-to-end” network with a synthetic image training dataset to complete the entire enhancement process. The unique U-shaped structure with multiple loss function constraints allows the algorithm to better recover feature information at different scales of the image, resulting in a more distinctive resultant image with normal illumination. Through extensive experiments on synthetic low-light images and real low-light images, the analysis results show that the algorithm can not only make the low-light image enhancement results with more natural colors, which can bring a pleasant visual experience to the observer and satisfy the basic low-light enhancement needs, but also recover as many features and image details as possible, which is beneficial to the subsequent image vision tasks.
Consortium Blockchain Distributed Storage Protocol

Since requesting data will fetch the data sent by all nodes that own the data, too small data chunks lead to too many chunks, which leads to the problems of data redundancy, high consumption of reconstruction, high latency of I/O operation, low performance of file request, and bottleneck of download in IPFS. In addition, IPFS lacks data security guarantee mechanism as long as it owns the data storage address to fetch data. To address the above problems, this paper proposes an IPFS-CBSM consortium blockchain distributed storage protocol based on IPFS. Data is first encrypted with SM4 algorithm and stored on IPFS private network constructed for the consortium blockchain. Then, a data block optimization algorithm and a data request negotiation mechanism are designed to solve the shortage of IPFS data persistence and security guarantee mechanism, and to protect the privacy of the request and improve the search efficiency. Security analysis and experimental results show that the protocol is safe and efficient.
Pulse and Signal Data Classification Using Conventional and Few-Shot Machine Learning

Signal detection is a key component in a radar system; however, signals are often muddled with noise and interference, which can make singling out the pure signals difficult. Also, signals are often interleaved with other signals, which makes it difficult to tell from first glance where a signal starts and ends. This paper will focus on classifying pulses and signals that were generated in MATLAB using few-shot machine learning and conventional machine learning techniques. The signals will be filtered using the Hilbert transform, and the envelope will be taken in order for the data to be used to train machine learning models. The few-shot learning method used in this study involves meta-learning and utilizes an algorithm that was adapted to handle data rather than images. Specifically, the models will be trained using pure time domain data, and the validation accuracies of each model will be compared to see which technique fares best when using minimal data. The trained models will then be used to try to classify a test set and observe if they correctly classify whether a given sample of data represents a pulse or a signal. In the second portion of this experiment, the data will also be labeled based on the number of pulses or signals present in the given sample, using the same methodology but with eight classes instead of two. These results will be compared to see not only how the models fare against one another, but also how having a larger number of classes with a certain attribute to identify affects the accuracy as well.
An Auditing Framework for Analyzing Fairness of Spatial-Temporal Federated Learning Applications

Federated learning enables remote devices such as smartphones to train statistical models while ensuring that data remains private and secure. Performing privacy-preserving data analysis becomes increasingly crucial as our model is potentially being trained within heterogeneous and massive networks. While federated learning offers the potential to boost diversity in many existing models through on-device learning and enabling a wider range of users to participate, developing fair federated learning models is a challenging task. Throughout this paper, we propose a fairness auditing system for FL models that rely on spatial-temporal data. Borrowing tenets from mobility literature, we propose a set of metrics to define individual fairness using spatial-temporal data. We also introduce a set of approaches for measuring these metrics in distributed settings, as well as building a framework that can monitor the fairness of FL models dynamically.
An Automatic Speech Segmentation Algorithm of Portuguese based on Spectrogram Windowing

Sentence segmentation is important for improving the human readability of Automatic Speech Recognition (ASR) systems. Although it has been explored through numerous interdisciplinary studies, segmentation of Portuguese is still time-consuming due to the lack of efficient automatic segmentation methods and the reliance on qualified phonetic experts. This paper presents a novel algorithm that efficiently segments speech into sentences by learning the spectrogram of sentences through windows using a classification model developed with an Artificial Neural Network (ANN). Based on our experiments, the beginning part of a European Portuguese (EP) sentence enables better identification of the sentence's boundaries. In addition, a window frame of spectrogram constructed by the previous ending of 100 milliseconds (ms) and the subsequent beginning of 300 ms presents the best performance in the automatic sentence segmentation. As a result, the proposed algorithm can automatically segment Portuguese speech into sentences by analyzing its spectrogram without knowing the speech semantics.
Comparison of Task Performance and User Satisfaction Between Holographic and Standard QWERTY Keyboard

Advancements in Internet of things (IoT) and sensor technologies along with deep learning techniques have enabled the design of several types of virtual keyboards including holographic keyboards. However, there is a need to better understand how its performance compares against the standard QWERTY keyboard in order to identify specific aspects of performance that are better as well as others than need to be further optimized. We conducted an initial two-part study. The first part was a controlled experimental study with 12 participants to see how a holographic keyboard compares with a QWERTY keyboard as a text entry tool on various metrics including task success, speed, and user satisfaction. The second part consisted of a short semi-structured interview with the participants, specifically based on the observations made by the researchers during the first part. Our initial findings indicate that although there is a speed advantage when users initially use standard QWERTY keyboard, there is a learning effect on speed with holographic keyboard and participants improve over time. Participants also indicated they would learn the holographic Keyboard quickly on a system usability sub-scale. This is still a work in progress and we aim to improve the design of the holographic keyboard based on the feedback from participants, and conduct a final summative evaluation.
Graph Attention Neural Network Distributed Model Training

The scale of neural language models has been increasing significantly over recent years. As a result, the time complexity of training larger language models and resource utilization has been increasing at a higher rate as well. In this research, we propose a distributed implementation of a Graph Attention Neural Network model with 120 million parameters and train it on a cluster of eight GPUs. We demonstrate three times speedup in model training while keeping the stability of accuracy and loss rates during training and testing compared to single GPU instance training.
Facial Detection in Low Light Environments Using OpenCV

Detecting faces in low-light environments is an important new technology and have been under development for years. In surveillance, some security cameras with thermal technology can recognize humans based on the heat that the object radiates. However, with only thermal techniques, it is still challenging to recognize specific people. Recognizing human with heat vision makes it hard to tell the identity of the person with existing Computer Vision techniques such as CNN. In this research, we present a system to recognize human faces in a low-light environment by enhancing low-light images and applying facial detection to them. Another technique of image super-resolution will also be applied to enhance the quality of the images for better detection.
An Improved Particle Filter Target Tracking Algorithm Using Online Learning Model

An improved particle filter target tracking algorithm is proposed which using optimization observation likelihood and Online Learning Model in this paper. Focusing on uncertain features, the detection of dynamic targets is realized by laser range finder observation, and an online learning algorithm of the proposed geometry model of the vehicle can automatically adapt to the dimensions of various vehicle body targets without any prior information. The experiment results on the Navlab and Standford Campus datasets show that the proposed method can achieve good autonomous navigation performance.
A*-FastIsomap: An Improved Performance of Classical Isomap Based on A* Search Algorithm

Nonlinear Dimensionality Reduction (NLDR) is a well-known approach of manifold learning to transform the data from high to low dimensional space. After studying various techniques proposed for the NLDR, we find that performance improvement is still required. Therefore, we adopt classical Isomap, which faces Shortest Path Distance (SPD) and high computational time cost problems. These problems are occurring due to the Dijkstra algorithm. This paper presents the A*FastIsomap method for SPD issues, which is based on the A* Search Algorithm with the Double Buckets algorithm. We executed a comparison of the A*-FastIsomap with classical Isomap to verify its better efficiency and results for high dimensional datasets with much higher accuracy. The outcome of our current study demonstrates that as compared to classical Isomap, our proposed A*-FastIsomap is faster and more accurate. Furthermore, our proposed method can reduce the computation time for high and large dimensional datasets.
Constructing Dynamic Knowledge Graph Based on Ontology Modeling and Neo4j Graph Database

At present, there are several issues with large-scale domain dynamic knowledge graphs including incomplete acquisition of original data, low accuracy with knowledge extraction and knowledge fusion, as well as un nonuniform semantic relations between entities. This paper constructs dynamic knowledge graph based on ontology modeling and Neo4j graph database. The ontology data model built based on the “seven-step method” effectively avoids the filling of instances without concept classes in the original data, while removing concepts with low user attention or learning value, which ensures integrity of original data acquisition, efficiency and accuracy of knowledge extraction and fusion, as well as rationality of logical relations between classes. Based on the ontology constraints and the mapping between the ontology model and Neo4j graph database, large-scale domain dynamic knowledge graph is achieved. We apply this scheme in the field of agricultural informatization and receive satisfying experimental results. In future work, we plan to explore multi-modal dynamic knowledge graph.