Reviewerly/exHarmony huggingface dataset:
Opinion Prediction with User Fingerprinting

Opinion prediction is an emerging research area with diverse real-world applications, such as market research and situational awareness. We identify two lines of approaches to the problem of opinion prediction. One uses topic-based sentiment analysis with time-series modeling, while the other uses static embedding of text. The latter approaches seek user-specific solutions by generating user fingerprints. Such approaches are useful in predicting user’s reactions to unseen content. In this work, we propose a novel dynamic fingerprinting method that leverages contextual embedding of user’s comments conditioned on relevant user’s reading history. We integrate BERT variants with a recurrent neural network to generate predictions. The results show up to 13% improvement in micro F1-score compared to previous approaches. Experimental results show novel insights that were previously unknown such as better predictions for an increase in dynamic history length, the impact of the nature of the article on performance, thereby laying the foundation for further research.
A Clustering Method of Case-Involved News by Combining Topic Network and Multi-Head Attention Mechanism

Finding the news of same case from the large numbers of case-involved news is an important basis for public opinion analysis. Existing text clustering methods usually based on topic models which only use topic and case infomation as the global features of documents, so distinguishing between different cases with similar types remains a challenge. The contents of documents contain rich local features. Taking into account the internal features of news, the information of cases and the contributions provided by different topics, we propose a clustering method of case-involved news, which combines topic network and multi-head attention mechanism. Using case information and topic information to construct a topic network, then extracting the global features by graph convolution network, thus realizing the combination of case information and topic information. At the same time, the local features are extracted by multi-head attention mechanism. Finally, the fusion of global features and local features is realized by variational auto-encoder, and the learned latent representations are used for clustering. The experiments show that the proposed method significantly outperforms the state-of-the-art unsupervised clustering methods.
Construction of Prediction Model for Multi-Feature Fusion Time Sequence Data of Internet of Things Under VR and LSTM

The purpose of the study is to improve the utilization rate of time sequence data generated by the Internet of Things (IoT), and explore their hidden values. Based on the deep neural network of Long Short-Term Memory (LSTM), the prediction model of multi-feature fusion time sequence data under Virtual Reality (VR) is discussed. First, the application of VR in various fields and the application status of a deep learning algorithm to IoT are analyzed. Second, the preprocessing method of time sequence data of IoT and the demand of deep learning neural networks in predicting time sequence data are analyzed. Based on the above analysis, the prediction model for multi-feature fusion time sequence data of IoT based on the deep learning network of LSTM is proposed. Finally, the experiment are designed to test the performance of the model. The results show that the proposed model and the LSTM-based regression model show high accuracy in the prediction of electrcity consumption data, while the Multi-Layer Perceptron (MLP) regression model has many errors in the prediction of the data. The mean absolute percentage error (Mape) of the proposed model is the lowest, with a percentage of only 2.49%, indicating that the difference between the predicted value and the real value of the proposed model is the smallest. The Mape of the LSTM regression prediction model is 2.57%, only slightly higher than the recommended model. The Mape of the MLP regression model is much higher, with a difference of 9% compared with the real value. The R2 of the model is 0.873, which is the highest. This study provides a reference for the application of deep learning neural networks in IoT.
Scaling ASR Improves Zero and Few Shot Learning

With 4.5 million hours of English speech from 10 different sources across 120countries and models of up to 10 billion parameters, we explore the frontiersof scale for automatic speech recognition. We propose data selection techniquesto efficiently scale training data to find the most valuable samples in massivedatasets. To efficiently scale model sizes, we leverage various optimizationssuch as sparse transducer loss and model sharding. By training 1-10B parameteruniversal English ASR models, we push the limits of speech recognitionperformance across many domains. Furthermore, our models learn powerful speechrepresentations with zero and few-shot capabilities on novel domains and stylesof speech, exceeding previous results across multiple in-house and publicbenchmarks. For speakers with disorders due to brain damage, our best zero-shotand few-shot models achieve 22% and 60% relative improvement on the AphasiaBanktest set, respectively, while realizing the best performance on public socialmedia videos. Furthermore, the same universal model reaches equivalentperformance with 500x less in-domain data on the SPGISpeech financial-domaindataset.
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text

We present a framework for learning multimodal representations from unlabeled data using convolution-free Transformer architectures. Specifically, our Video-Audio-Text Transformer (VATT) takes raw signals as inputs and extracts multimodal representations that are rich enough to benefit a variety of downstream tasks. We train VATT end-to-end from scratch using multimodal contrastive losses and evaluate its performance by the downstream tasks of video action recognition, audio event classification, image classification, and text-to-video retrieval. Furthermore, we study a modality-agnostic single-backbone Transformer by sharing weights among the three modalities. We show that the convolution-free VATT outperforms state-of-the-art ConvNet-based architectures in the downstream tasks. Especially, VATT's vision Transformer achieves the top-1 accuracy of 82.1% on Kinetics-400, 83.6% on Kinetics-600,and 41.1% on Moments in Time, new records while avoiding supervised pre-training. Transferring to image classification leads to 78.7% top-1 accuracy on ImageNet compared to 64.7% by training the same Transformer from scratch, showing the generalizability of our model despite the domain gap between videos and images. VATT's audio Transformer also sets a new record on waveform-based audio event recognition by achieving the mAP of 39.4% on AudioSet without any supervised pre-training. VATT's source code is publicly available.
Oversampling Imbalanced Data Based on Convergent WGAN for Network Threat Detection

Class imbalance is a common problem in network threat detection. Oversampling the minority class is regarded as a popular countermeasure by generating enough new minority samples. Generative adversarial network (GAN) is a typical generative model that can generate any number of artificial minority samples, which are close to the real data. However, it is difficult to train GAN, and the Nash equilibrium is almost impossible to achieve. Therefore, in order to improve the training stability of GAN for oversampling to detect the network threat, a convergent WGAN-based oversampling model called convergent WGAN (CWGAN) is proposed in this paper. The training process of CWGAN contains multiple iterations. In each iteration, the training epochs of the discriminator are dynamic, which is determined by the convergence of discriminator loss function in the last two iterations. When the discriminator is trained to convergence, the generator will then be trained to generate new minority samples. The experiment results show that CWGAN not only improve the training stability of WGAN on the loss smoother and closer to 0 but also improve the performance of the minority class through oversampling, which means that CWGAN can improve the performance of network threat detection.
Domain-Lifelong Learning for Dialogue State Tracking via Knowledge Preservation Networks

Dialogue state tracking (DST), which estimates user goals given a dialogue context, is an essential component of task-oriented dialogue systems.Conventional DST models are usually trained offline, which requires a fixed dataset prepared in advance.This paradigm is often impractical in real-world applications since online dialogue systems usually involve continually emerging new data and domains.Therefore, this paper explores Domain-Lifelong Learning for Dialogue State Tracking (DLL-DST), which aims to continually train a DST model on new data to learn incessantly emerging new domains while avoiding catastrophically forgetting old learned domains.To this end, we propose a novel domainlifelong learning method, called Knowledge Preservation Networks (KPN), which consists of multi-prototype enhanced retrospection and multi-strategy knowledge distillation, to solve the problems of expression diversity and combinatorial explosion in the DLL-DST task.Experimental results show that KPN effectively alleviates catastrophic forgetting and outperforms previous state-of-the-art lifelong learning methods by 4.25% and 8.27% of whole joint goal accuracy on the MultiWOZ benchmark and the SGD benchmark, respectively.
When does Further Pre-training MLM Help? An Empirical Study on Task-Oriented Dialog Pre-training

Further pre-training language models on indomain data (domain-adaptive pre-training, DAPT) or task-relevant data (task-adaptive pretraining, TAPT) before fine-tuning has been shown to improve downstream tasks' performances.However, in task-oriented dialog modeling, we observe that further pre-training MLM does not always boost the performance on a downstream task.We find that DAPT is beneficial in the low-resource setting, but as the fine-tuning data size grows, DAPT becomes less beneficial or even useless, and scaling the size of DAPT data does not help.Through Representational Similarity Analysis, we conclude that more data for fine-tuning yields greater change of the model's representations and thus reduces the influence of initialization.
Cutting Events

Autonomous robots struggle with plan adaption in uncertain and changing environments. Although modern robots can make popcorn and pancakes, they are incapable of performing such tasks in unknown settings and unable to adapt action plans if ingredients or tools are missing. Humans are continuously aware of their surroundings. For robotic agents, real-time state updating is time-consuming and other methods for failure handling are required. Taking inspiration from human cognition, we propose a plan adaption method based on event segmentation of the image-schematic states of subtasks within action descriptors. For this, we reuse action plans of the robotic architecture CRAM and ontologically model the involved objects and image-schematic states of the action descriptor cutting. Our evaluation uses a robot simulation of the task of cutting bread and demonstrates that the system can reason about possible solutions to unexpected failures regarding tool use.
Enhancement of convolutional neural network for urban environment parking space classification

BACKGROUND AND OBJECTIVES: Soil or rock properties where buildings are situated play an important role in the ground shaking caused by an earthquake. The highly populated Banda Aceh city in the northernmost Sumatra is flanked by two active faults, the Seulimeum and the Aceh segment. Therefore, it is crucial to investigate the subsurface characteristics of the region to reduce the earthquake risk as there was no regional study has been conducted so far.METHODS: Characteristics of the soil or rock of the subsurface were derived from various seismic parameters. The seismic microtremors were recorded at 36 sites covering the highly populated city and the two active faults. The spatial autocorrelation method was used to obtain a dispersion curve based on the relationship between seismic frequencies and phase velocity from triangular geophones array to determine the shear wave velocity of the subsurface layer. The seismic amplification, dominant frequency and vulnerability value at each measurement point were measured using the horizontal-to-vertical spectral ratio method. The maps of velocity structure and HVSR parameters were generated from the interpolation of those seismic parameters.FINDING: Based on the variation of the four geophysical parameters: shear wave velocity, seismic amplification, dominant frequency, and seismic vulnerability, the study area can be clustered into five different groups: I) Banda Aceh, II) Jantho, III) Krueng Raya, IV) Lhoknga-Lhoong, and V) Seulawah, which classify the different types of rocks. The classification of soil properties from the combination of shear wave and horizontal-to-vertical spectral ratio data correlates with the geology of the study area.CONCLUSION: The Banda Aceh city, flanked by the two active faults, is characterized by low shear wave velocity and high amplification because the city stands on the sedimentary basin; thus, it requires a detailed investigation prior to constructing infrastructures. The other clusters are located on the relatively less vulnerable areas, indicated by moderate shear wave velocity and moderate to low seismic vulnerability indexes. The joint analysis shows that the combination of physical properties, including the shear wave velocity, seismic amplification, and dominant frequency, can be used to investigate lithology and seismic vulnerability into a specific cluster. The research results are essential for hazard mitigation and can be used for disaster risk management by the local government. A detailed investigation with denser measurement points needs to be conducted to comprehensively describe the types of rocks in Banda Aceh and its surrounding.
EXPERIENCE IN USING DISTANCE LEARNING SYSTEMS AT UNIVERSITIES OF UKRAINE AND MEXICO

The basic didactic principles of distance and traditional forms of education are considered. The use of learning management systems (Learning Management System) is relevant in the context of the COVID-19 pandemic. In the process of transition of educational institutions to distance work and learning during quarantine, there was an expansion of the range of tools used and the expansion of the functionality of existing new tools. The main functional modules of modern learning management systems like Moodle, Google Classroom, Microsoft Office 365 and their implementation in distance learning are analyzed. The main functional modules of modern distance learning management systems are identified, they include such modules as system administration, access to educational materials, means to ensure communication between participants in the distance learning process, etc. Distance education is based on the principles and features of traditional forms of learning, in addition, new features are added, which are the use of Internet technologies for access to educational materials, online interaction between students and more. The peculiarities of implementation of the principles of distance and traditional forms of education on the example of higher education institutions of Ukraine (Berdiansk State Pedagogical University, National University of Life and Environmental Sciences of Ukraine) and Mexico (Autonomous Honored University of Puebla) are considered in the article. Based on the results of the study, general recommendations were developed for the simultaneous use of several platforms, which allows you to work from any device; under modern pandemic conditions, you can also use the mobile Internet, which increases the convenience and productivity; at any time you can view the necessary documents, perform tasks, responds to changes in the workflow, which allows you to learn quickly about innovations and change ways to solve problems. Thanks to the obtained results, teachers will be able to develop more effectively online learning environment. Keywords: distance learning, didactic principles, distance learning management system.
Research on Education Management and Decision Optimization Based on Cloud Computing from the Perspective of Big Data

The development of economic globalization and information integration has spawned the arrival of big data environment, which is widely used in all fields of people's production and life. Big data has affected capital, technology, talent flow, etc., changed people's production and life, and also had a huge impact on the field of education, especially HE (Higher Education). In the education industry, information technology not only affects the educational means and technology, but also forms a new motive force and new idea for the development of education. Scientific and rational application of educational big data, optimization of educational management and teaching decision-making, and improvement of educational and teaching effects are important contents of information-based teaching reform and innovation, which can promote educational management and decision-making, and enhance the scientific level of educational management and teaching decision-making. This paper analyzes the positive and negative impacts of big data on HE management, studies the main causes of various problems, and puts forward ways to optimize HE management and decision-making based on cloud computing technology under the background of big data.
A Study of Sentiment Analysis Approaches in Short Text

Recently, the remarkable growth of Internet technology, particularly on social media networking sites, enables gathering data for analyzing and gaining insights. It is challenging to analyze such a huge amount of information that causes time-consuming. So, it is necessary to make an intelligent system that automatically analyzes a great amount of data. Sentiment analysis methods appear to analyze sentiments and opinions of people through what they write on social networking sites. Different sentiment analysis approaches have been proposed to understand the sentiments and opinions expressed by the individuals in the text. However, some methods produce an improper result when applied to short text due to text briefness and sparsity. In this paper, we present sentiment analysis models that analyzing people feelings and opinions in the short text such as tweets and instant messages. Then, we illustrate the evaluation metrics used to assess the quality of the generated hierarchical structure in extracting an ideal tree.
Effectiveness of a numeracy intelligent tutoring system in kindergarten: A conceptual replication

<p xmlns="http://www.ncbi.nlm.nih.gov/JATS1">Intelligent Tutoring Systems are a genre of highly adaptive software providing individualized instruction. The current study was a conceptual replication of a previous randomized control trial that incorporated the intelligent tutoring system Native Numbers, a program designed for early numeracy instruction. As a conceptual replication, we kept the method of instruction, the demographics, the number of kindergarten classrooms (n = 3), and the same numeracy and intrinsic motivation screeners as the original study. We changed the time of year of instruction, changed the control group to a wait-control group, added a maintenance assessment for the first group of participants, and included a mathematical language assessment. Analysis of within- and between-group differences using repeated measures ANOVA indicated gains of numeracy were significant only after using Native Numbers (Partial Eta Square = 0.147). Results of intrinsic motivation and mathematical language were not significant. The effect size of numeracy achievement did not reach that of the original study (Partial Eta Square = 0.622). Here, we compared the two studies, discussed plausible reasons for differences in the magnitude of effect sizes, and provided suggestions for future research.
Adaptive Decision Support System for On-Line Multi-Class Learning and Object Detection

A new online multi-class learning algorithm is proposed with three main characteristics. First, in order to make the feature pool fitter for the pattern pool, the adaptive feature pool is proposed to dynamically combine the three general features, Haar-like, Histogram of Oriented Gradient (HOG), and Local Binary Patterns (LBP). Second, the external model is integrated into the proposed model without re-training to enhance the efficacy of the model. Third, a new multi-class learning and updating mechanism are proposed that help to find unsuitable decisions and adjust them automatically. The performance of the proposed model is validated with multi-class detection and online learning system. The proposed model achieves a better score than other non-deep learning algorithms used in public pedestrian and multi-class databases. The multi-class databases contain data for pedestrians, faces, vehicles, motorcycles, bicycles, and aircraft.
Designing a Kubernetes Operator for Machine Learning Applications

Machine Learning workloads such as deep learning and hyperparameter tuning are compute-intensive by nature. Parallel execution is key to reducing the learning time. The Ray Framework is a distributed middleware that provides primitives to seamlessly parallelize machine learning code execution across a cluster of compute node. Launching a Ray managed machine learning application requires a Ray cluster that is diligently configured, well connected and easily scalable. Kubernetes, the container management middleware, satisfies all the requirements to create and scale ray clusters. However, setting up a cluster within Kubernetes, is a tedious and error prone task when done manually. In this paper we present KubeRay, an Operator and suite of tools designed, and built to create Ray cluster in Kubernetes with minimum effort. We present our architectural choices, our open-source implementation, and we analyze the performance of our solution.
How to evaluate classifier performance in the presence of additional effects: A new POD-based approach allowing certification of machine learning approaches

Classifiers are useful and well-known machine learning algorithms allowing classifications. A classifier may be suited for a specific task depending on the application and datasets. To select an approach for a task, performance evaluation may be imperative. Existing approaches like the receiver operating characteristic and precision–recall curves are popular in evaluating classifier performance, however both measures do not directly address the influence of additional and possibly unknown (process) parameters on the classification results. In this contribution, this limitation is discussed and addressed by adapting the Probability of Detection (POD) measure. The POD is a probabilistic method to quantify the reliability of a diagnostic procedure taking into account statistical variability of sensor and measurements properties. In this contribution the POD approach is adapted and extended. The introduced approach is implemented on driving behavior prediction data serving as illustrative example. Based on the introduced POD-related evaluation, different classifiers can be clearly distinguished with respect to their ability to predict the correct intended driver behavior as a function of remaining time (here assumed as process parameter) before the event itself. The introduced approach provides a new diagnostic and comprehensive interpretation of the quality of a classification model.