Reviewerly/exHarmony huggingface dataset:
Tools for Describing the Communicative Space of Scientific Schools

To manage research activities, it is necessary to develop up-to-date tools that allow to quickly and efficiently assess the effectiveness of the scientific space functioning. Contradictions between the new industry of scientific communication and traditional forms of preserving and multiplying intellectual capital have become more acute, new points of interaction between the institutional structures of science (scientific schools, “invisible colleges”, expert and experimental (real and virtual) laboratories, etc.) have appeared. The concepts of the cultural codes of representation and translation of the subjects (individual and collective) of scientific communication have changed, and there is a need to update the requirements for the culture of their identification and positioning in the age of digital technologies. A scientific school is a multi-purpose collective that simultaneously implements the functions of production, dissemination, protection of scientific knowledge and reproduction of scientists. The cultural traditions of its functioning, as well as the cultural norms of institutionalization, are being transformed. On the material of bibliographic description of the genesis and development of the cultural studies school of the Southern Urals (centered in the Chelyabinsk State Institute of Culture), the article shows opportunities for scientometric and informational analyses. The author offers to discuss and use two bibliographic publications (the catalog of the scientific school and the bibliographic navigator, which include information about research topics, successful scholars, scientific leaders, postgraduate students and applicants, opponents, leading organizations of the communicative field of cultural studies of the Southern Urals), as well as cultural researchers’ landmark publications, innovatively identified by the method of introspection. The publications are reviewed as tools in the system of cultural studies of the scientific school positioning. The article draws conclusions based on the study and comparison of the configurations of connections between persons (academic advisor, postgraduate student, opponent), leading organizations and scientific leaders and their geolocation, subject headings: “applicant — academic advisor — opponent”, “subject heading/topic — leading organization — opponent, specialty number”, etc. These connections are individual for the related concepts: “management — coordination”, “interpretation of a communicative challenge — adequacy of a communicative response”, “practical meaning — purpose of communication”. These areas of analysis of the scientific school functioning provide a vision of the scientific interaction communicative fields’ intersection, the increment and examination of scientific knowledge, the basis for making managerial decisions to organize science.
Low‐latency transformer model for streaming automatic speech recognition

Transformer models have made great progress in automatic speech recognition. However, it is challenging for streaming transformer models to make trade-off between output latency and recognition accuracy. In this letter, it is aimed to propose a low-latency transformer model with satisfactory recognition accuracy. First, a streaming transformer is designed and explain how it works streamingly. Second, the authors propose to use CTC during training to minimise the latency of transformer models. Finally, the authors also propose to utilise CTC as a backup during decoding to ensure that the low-latency characteristic is maintained. The authors fairly compare our streaming transformer model to existing streaming models, particularly the transducer model, which is a popular low-latency approach. The experiments show that, while having comparable output latency, the transformer model outperforms the transducer model by average relative character (or word) error rate reduction of 22.18%, 26.71% and 19.36% on HKUST, Switchboard and Call Home, respectively.
Classification Using Backpropagation Neural Network on Tweet Emotion Intensity

This Research presents text classification for tweet emotion intensity using Backpropagation Neural Network (BPNN) method. In this study, Twitter data is used for the research, where the data were categorized into four categories (fear, joy, anger and sadness). The dataset is included with emotion intensity with a scale from 0 to 1. The preprocessing steps consist of cleaning the data, normalization, tokenization, Bag Of Word (BoW) method, and steaming process. The intensity features in the dataset are analyzed and filtered with a certain thresholds to ensure only the right emotion is used for training. BPNN process is using 100 neuron for hidden layer, 4 neuron for the output layers with 50 epochs. This research can produce results with a value in accuracy and recall of 93.71&#x0025; and 94.3&#x0025;.
Off-Topic Detection of Business English Essay Based on Deep Learning Model

The automatic scoring system of business English essay has been widely used in the field of education, and it is indispensable for the task of off-topic detection of essay. Most of the traditional off-topic detection methods convert text into vector representation of vector space and then calculate the similarity between the text and the correct text to get the off-topic result. However, those methods only focus on the structure of the text, but ignore the semantic association. In addition, the traditional detection method has a low off-topic detection effect for essays with high divergence. In view of the above problems, this paper proposes an off-topic detection method for business English essay based on the deep learning model. Firstly, the word2vec model is used to represent words in sentences as word vectors. And, LDA is used to extract the vector of topic and text, respectively. Then, word vector and topic word vector are spliced together as the input of the convolutional neural network (CNN). CNN is used to extract and screen the features of sentences and perform similarity calculation. When the similarity is less than the threshold, the paper also maps the topic and the subject words in the coupling space and calculates their relevance. Finally, unsupervised off-topic detection is realized by the clustering method. The experimental results show that the off-topic detection method based on the deep learning model can improve the detection accuracy of both the essays with low divergence and the essays with high divergence to a certain extent, especially the essays with high divergence.
Fusion Shuffle Light Detector

The accuracy and speed of the one-stage object detection models based on the deep convolutional neural network in various detection tasks are in an advantageous position in comprehensive performance. However, in order to ensure accuracy, the models require a large number of trainable parameters as support, while the storage space occupied by the weight file obtained by training is too large. This makes it difficult to apply object detection models based on deep convolutional neural networks to embedded devices and mobile devices with limited storage space. To address this problem, we propose the new object detection model – Fusion Shuffle Light Detector(referred to as FSLDet). The model uses an improved ShuffleNet v2 network for feature extraction tasks, which improves the accuracy of the lightweight model. And in order to further improve the accuracy of the model, this paper uses the bidirectional feature pyramid(BiFPN) to improve the feature fusion operation, so that the deep-level feature map containing semantic information and the shallow-level feature map containing detailed information are fully integrated. According to the effectiveness of the improved method adopted by the FSLDet model, this paper conducts experiments on the PASCAL VOC 2007+2012 dataset. Compared with the model before the improvement, FSLDet has achieved a 2.97% improvement in the accuracy of the PASCAL VOC 2007+2012 dataset.
Evaluating Reliability in Explainable Search

Explainability in AI (XAI) is being investigated in various AI driven systems like search engines to promote trust and fairness in AI. In the context of search systems one major XAI goal is to explain the notion of similarity in text to a non-expert user like an avid reader searching for books in a digital library. What is the similarity between book A and B with respect to narrative time? How do the readers’ emotions change? This is often an important criteria for searching books. In this work we extend a recent explainable AI driven book search engine [1] with local explanations that visually support users to comprehend the similarity model. Each book is represented as a feature vector in an interpretable feature space with aspects such as sentence complexity, writing style. In the absence of ground truth similarities and explanations, the system is evaluated on the aspects of reliability of search results and explanations in user studies. Different baselines are created to compare with our back-end ranking model, keeping the user interface same for all. Experimental results on lab based user study with eye tracking mechanism indicate that the explanations help towards making the search system reliable for non expert users.
Using Digital Comics to Learn Indonesia's Geographical Characteristics: Social Studies Education Solutions for Elementary School Students during the Covid-19 Pandemic

The Covid-19 pandemic requires attractive learning media for online learning. According to the initial needs analysis, fifth- grade elementary school students were less interested in studying social sciences. As a result, the writer developed digital comics as an alternative to social studies learning material on Indonesia's geographical characteristics. The development of an ADDIE model was used toconduct this research. The data were collected using interviews and questionnaires. The digital comics developed in this study were validated by three experts, namely material, language, and media experts. The validation of material obtained a percentage of 83%, categorized as good category, 88% for language validation which was categorized as very good category, and media experts with the percentage of 80%, categorized as a good category. Furthermore, the developed digital comic was properly used and was an interesting learning media solution in social studies learning, notably during the Covid-19 pandemic.
The Application of Decision Tree and Data Analysis in Management System

Teaching and learning management in higher education institutions often requires handling a large amount of important data. We can apply the technologies of association analysis, classification and prediction, clustering and decision tree in data mining to help deal with the large amount of data work, such as the evaluation of students’ comprehensive quality, the evaluation of teachers, the reasonable setting of courses and the selection of teaching methods, the evaluation of test papers and the quality of test questions improves the quality and efficiency of teaching and administration.
Redundancy Removal Adversarial Active Learning Based on Norm Online Uncertainty Indicator

Active learning aims to select the most valuable unlabelled samples for annotation. In this paper, we propose a redundancy removal adversarial active learning (RRAAL) method based on norm online uncertainty indicator, which selects samples based on their distribution, uncertainty, and redundancy. RRAAL includes a representation generator, state discriminator, and redundancy removal module (RRM). The purpose of the representation generator is to learn the feature representation of a sample, and the state discriminator predicts the state of the feature vector after concatenation. We added a sample discriminator to the representation generator to improve the representation learning ability of the generator and designed a norm online uncertainty indicator (Norm-OUI) to provide a more accurate uncertainty score for the state discriminator. In addition, we designed an RRM based on a greedy algorithm to reduce the number of redundant samples in the labelled pool. The experimental results on four datasets show that the state discriminator, Norm-OUI, and RRM can improve the performance of RRAAL, and RRAAL outperforms the previous state-of-the-art active learning methods.
Radar based obstacle detection in unstructured scene

To solve the problem of obstacle detection in unstructured scene, we propose a novel obstacle detection method based on Radar. Compared with other sensors like 3D-Lidar and color camera, Radar has higher environmental robustness especially for sandy road with dust. According to the relative velocity information of detected obstacles, obstacles are divided into static ones and dynamic ones in the proposed method. For static obstacles, map reconstruction based on Bayesian probability model is used to overcome the problem of sparse obstacle detection result in single frame and obstacle retention in the blind area. The integrated navigation system of GNSS and IMU is used to provide position and posture information during map reconstruction. For dynamic obstacles, the historical trajectory of the same target is recorded in the global coordinate frame, then the Kalman predictor based trajectory tracking and prediction is used to predict the movement trend of moving obstacles. The proposed method has been verified in a variety of unstructured scene. The experimental results show that the proposed method can provide useful obstacle perception result and meet the autonomous navigation application of UGV in unstructured scene. We also compare the proposed method with that based on 3D-Lidar in terms of the same scene. And it has obvious advantages in environmental robustness.
A quality and popularity based ranking method for research datasets

Research outputs are the final products in the scientific research process and their quality is progressively being evaluated by various methods such as altmetrics, bibliometrics, impact factors and citation count etc. However, a significant component of scientific research involves creating/collecting/curating research datasets and globally, funding agencies and governments are mandating an open access policy on research datasets. Though repositories exist to store the datasets, there is no metricised guidance, indicating the quality of datasets for researchers wishing to reuse. We propose a novel method for ranking and visualising research datasets based on their quality and popularity, constructed through a normalised citation count since the year of origin, total cites and the impact factor of the journals which publish the articles citing the dataset. Additionally, we present the process flow for a proposed digital information system for the access of datasets according to their discipline and rank based on the variables. The proposed method is expected to assist researchers, globally, to choose the right datasets for their research, encourage researchers to share their datasets and promote interdisciplinary research.
Endoscopic Single Image Super-Resolution Based on Transformer and Convolutional Neural Network

As we all know, the image quality is related to the size of the image sensor. However, the texture of blood vessels displayed poorly which caused by the small size of image sensor in a limited internal space of disposable medical endoscope. Therefore, different strategies, which are principally using statistical analysis to infer the value of pixels in high-resolution (HR) images, have been developed to enhance the quality of endoscopic images. It is inevitable to bring some negative phenomena such as blurry, blocking and ill-posed. In this paper, we propose to combine the Transformer model with the convolutional neural network in Super-Resolution (SR) reconstruction of blood vessel texture. The algorithm is better than the existing SR algorithms based on the CNN model quantitatively and qualitatively. Compared with the existing methods, the proposed method has significantly improved PSNR and SSIM in the public dataset, which proves the effectiveness of the Transformer network in SR reconstruction.
Improving Sentence-Level Relation Classification via Machine Reading Comprehension and Reinforcement Learning

Distant supervision (DS) has been proposed to automatically annotate data and achieved significant success in relation classification. However, despite its efficiency, distant supervision often suffers from the noisy labeling problem. To solve the problem, existing methods can be divided into two major approaches: (1) Some works adopt multi-instance learning (MIL) for relation classification to reduce the impact of noisy data. However, they do not perform well at the sentence level. (2) Other works focus on finding the noisy instances directly. They mainly use reinforcement learning to filter out the noisy instances. The key component is the instance selector, which is used to select the correct instances from the noisy data. However, current instance selectors usually use simple neural network models and initialize the models with random parameters, which leads to limited improvement and slower convergence. In this paper, we propose a novel instance selector to directly select the high-quality instances from DS-generated data as the refined training data to improve the performance of sentence-level relation classification. Specifically, the instance selector consists of a machine reading comprehension (MRC) estimator and an instance sampler. The MRC estimator is used to evaluate the quality of the instances, and the instance sampler is used to select the high-quality instances. Moreover, due to the lack of explicit knowledge about which instances are mislabeled, we use reinforcement learning to train the MRC estimator. Experiments show that our method achieves state-of-the-art performance on two human-annotated NYT10 datasets. The source code of this paper can be found in https://github.com/xubodhu/MRCRL.
Trade-offs in Augmented Reality User Interfaces for Controlling a Smart Environment

Smart devices and Internet of Things (IoT) technologies are replacing or being incorporated into traditional devices at a growing pace. The use of digital interfaces to interact with these devices has become a common occurrence in homes, work spaces, and various industries around the world. The most common interfaces for these connected devices focus on mobile apps or voice control via intelligent virtual assistants. However, with augmented reality (AR) becoming more popular and accessible among consumers, there are new opportunities for spatial user interfaces to seamlessly bridge the gap between digital and physical affordances.
Content-Based Open Knowledge Graph Search: A Preliminary Study with OpenKG.CN

Users rely on open data portals and search engines to find open knowledge graphs (KGs). However, existing systems only provide metadata-based KG search but ignore the contents of KGs, i.e., triples. In this paper, we present one of the first content-based search engines for open KGs. Our system CKGSE supports keyword-based KG search, KG snippet generation, KG profiling and browsing, all computed over KGs' (large) contents rather than their (small) metadata. We implement a prototype with Chinese KGs crawled from OpenKG.CN and we report some preliminary results about the practicability of such a system.
Multi-task Text Normalization Approach for Speech Synthesis

Text normalization for Text-To-Speech includes several challenging tasks in natural language processing such as verbalizing abbreviations, Out-Of-Vocabulary (OOV) words, and chunking phrases for long sentences without punctuation. Instead of dealing with these tasks independently, we propose a multi-task end-to-end model based on a denoising auto-encoder. As enriching information via multi-task learning and text denoising, the proposed approach shows improvement in the text normalization task, especially for complicated cases which require context and language understanding. In addition, we also design a novel process of data prepossessing to leverage annotated data for training. According to experiments on a handcrafted test set of 200,000 sentences in Vietnamese, our model achieves an overall accuracy of 95.5%.
Comparison of Handcrafted and Deep Neural Network Feature Extraction for Recognizing Bangla Sign Digits Exploiting Machine Learning Classifiers

Sign Language is the main communication medium for people who have a difficulty with speaking or hearing. To communicate with other deaf or healthy people, they use sign language. Deaf and mute people of Bangladesh and India use Bangla sign language to fulfill their communication needs. But, the learning process of sign language is time consuming and arduous. Therefore, as the artificial intelligence technology advances, automatic recognition of Bangla sign language is gaining more attention from researchers. In this paper, the proposed research aims to develop an approach to automate the identification of Bangla Sign Digits comparing the efficacy of handcrafted and deep neural network features. The handcrafted feature extractors like Histogram of Oriented Gradient (HOG), Scale invariant feature transform (SIFT), and pre-trained deep neural network feature extractors such as InceptionV3, Xception, and DenseNet-121 was used on publicly available Ishara-Lipi dataset to extract features. Moreover, to recognize sign digits, we used three fine-tuned classification algorithms namely K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM) that were fed by extracted features. Experimental result shows that the deep neural network features outperformed the handcrafted features based approach. DenseNet-121 combined with SVM based approach achieved highest test accuracy of 99.53% on Ishara-Lipi dataset.
Improved Mean Shift Algorithm Based on Probability Distribution

The typical Mean Shift tracking algorithm was introduced and an improved Mean Shift algorithm based on probability distribution was presented. This algorithm uses color information and motion information to express target model. Compared with typical Mean Shift algorithm's, target expression and operation process are more simple and it is easy to integrate different target characteristics, which enhances tracking performance. Finally, tracking strategies were made aiming at problems of multi-objects and target lost.
Search-and-Attack: Temporally Sparse Adversarial Perturbations on Videos

Modern neural networks are known to be vulnerable to adversarial attacks in various domains. Although most attack methods usually densely change the input values, recent works have shown that deep neural networks (DNNs) are also vulnerable to sparse perturbations. Spatially sparse attacks on images or frames of a video are proven effective but the temporally sparse perturbations on videos have been less explored. In this paper, we present a novel framework to generate a temporally sparse adversarial attack, called Search-and-Attack scheme, on videos. The Search-and-Attack scheme first retrieves the most vulnerable frames and then attacks only those frames. Since identifying the most vulnerable set of frames involves an expensive combinatorial optimization problem, we introduce alternative definitions or surrogate objective functions: Magnitude of the Gradients (MoG) and Frame-wise Robustness Intensity (FRI). Combining them with iterative search schemes, extensive experiments on three public benchmark datasets (UCF, HMDB, and Kinetics) show that the proposed method achieves comparable performance to state-of-the-art dense attack methods.