Reviewerly/exHarmony huggingface dataset:
A study on smartphone sensor-based Human Activity Recognition using deep learning approaches

With the advent of smart devices like smartphones and smartwatches, it is becoming very easy to track our daily activities using the data captured by the sensors present in these devices. Human Activity Recognition (HAR) is a rapidly growing research field in the domain of computer vision, where a sequence of data for a specified time span is collected from the sensors like accelerometer and gyroscope present in these smart devices. HAR is not only limited to sensor-based observation but also helpful in detecting human activities from still images and videos. HAR plays a crucial role in detecting a user interaction with the environment, which helps in surveillance, health care, and building a smart environment based on human-computer interaction. In this work, a comparative study of the performances of various deep learning–based classification models applied to HAR has been presented. This study also reveals that the deep learning models described in our study provide many insights to predict human activities from sensor data. It is to be noted that designing a good set of handcrafted features from this vast amount of raw sensor data is challenging, thereby building a near-perfect predictive model from the heuristic of those features becomes difficult also. Hence, the use of deep learning–based models for this purpose is a viable alternative. To show the usefulness of deep learning models in handling HAR-related issues, in this work, an exhaustive comparative study of five existing deep learning models which include Convolutional Neural Network (1D-CNN), Recurrent Neural Network with Long-Short Term Memory (RNN-LSTM), CNN-LSTM, ConvLSTM, and Stacked-CNN is performed. Here, these deep-learning models can automatically infer meaningful information from raw sensor data. Three benchmark HAR datasets, namely RealWorld HAR, MHEALTH (Mobile HEALTH), and UCI HAR datasets have been used to demonstrate the task of activity recognition collected from smartphone sensor data. These models have achieved promising results on all the datasets mentioned above.
Automatic Subject Classification of Public Messages in E-government Affairs

Public messages on the Internet political inquiry platform rely on manual classification, which has the problems of heavy workload, low efficiency, and high error rate. A Bi-directional long short-term memory (Bi-LSTM) network model based on attention mechanism was proposed in this paper to realize the automatic classification of public messages. Considering the network political inquiry data set provided by the BdRace platform as samples, the Bi-LSTM algorithm is used to strengthen the correlation between the messages before and after the training process, and the semantic attention to important text features is strengthened in combination with the characteristics of attention mechanism. Feature weights are integrated through the full connection layer to carry out classification calculations. The experimental results show that the F1 value of the message classification model proposed here reaches 0.886 and 0.862, respectively, in the data set of long text and short text. Compared with three algorithms of long short-term memory (LSTM), logistic regression, and naive Bayesian, the Bi-LSTM model can achieve better results in the automatic classification of public message subjects.
WiC-TSV: An Evaluation Benchmark for Target Sense Verification of Words in Context

We present WiC-TSV, a new multi-domain evaluation benchmark for Word Sense Disambiguation.More specifically, we introduce a framework for Target Sense Verification of Words in Context which grounds its uniqueness in the formulation as binary classification task thus being independent of external sense inventories, and the coverage of various domains.This makes the dataset highly flexible for the evaluation of a diverse set of models and systems in and across domains.WiC-TSV provides three different evaluation settings, depending on the input signals provided to the model.We set baseline performance on the dataset using state-of-the-art language models.Experimental results show that even though these models can perform decently on the task, there remains a gap between machine and human performance, especially in outof-domain settings.WiC-TSV data is available at https://competitions.codalab.org/competitions/23683
Performance Ranking of Kalman Filter With Pre-Determined Initial State Prior

The Kalman filter has successful applications in many fields. The applicability of the standard Kalman filter critically hinges on the accurate prior knowledge of all the system model parameters. However, in practical applications, it can be difficult or unrealistic to obtain these parameters, in which case it is a common practice to employ some pre-determined alternatives for the unknown model parameters. This letter investigates the case of unknown initial state priors by assessing how their pre-determined alternatives affect the performance of the Kalman filter. The ranking of three types of mean squared errors is established. It is found that the definiteness of the initial state prior deviation is critical, which determine the ranking of the three types of mean squared errors. Such results provide guideline on the choice of the pre-determined initial state prior. Numerical examples are provided to validate the results.
Data Freshness in Ecology and Conservation

Evolving capabilities in environmental data collection, sharing, and processing, are enabling unprecedented use of data from a wide range of sources. Yet data freshness, an important quality dimension associated with the age of data, is a poorly reported aspect of data quality that can lead to additional uncertainty in research findings. Evolving capabilities in environmental data collection, sharing, and processing, are enabling unprecedented use of data from a wide range of sources. Yet data freshness, an important quality dimension associated with the age of data, is a poorly reported aspect of data quality that can lead to additional uncertainty in research findings.
Lossless Coding of Light Fields Based on 4D Minimum Rate Predictors

Common representations of light fields use four-dimensional data structures, where a given pixel is closely related not only to its spatial neighbours within the same view, but also to its angular neighbours, co-located in adjacent views. Such structure presents increased redundancy between pixels, when compared with regular single-view images. Then, these redundancies are exploited to obtain compressed representations of the light field, using prediction algorithms specifically tailored to estimate pixel values based on both spatial and angular references. This paper proposes new encoding schemes which take advantage of the four-dimensional light field data structures to improve the coding performance of Minimum Rate Predictors. The proposed methods expand previous research on lossless coding beyond the current state-of-the-art. The experimental results, obtained using both traditional datasets and others more challenging, show bit-rate savings no smaller than 10%, when compared with existing methods for lossless light field compression.
Intelligent Chatbot Adapted from Question and Answer System Using RNN-LSTM Model

Abstract In modern times, the chatbot is implemented to store data collected through a question and answer system, which can be applied in the Python program. The data to be used in this program is the Cornell Movie Dialog Corpus which is a dataset containing a corpus which contains a large collection of metadata-rich fictional conversations extracted from film scripts. The application of chatbot in the Python program can use various models, the one specifically used in this program is the LSTM. The output results from the chatbot program with the application of the LSTM model are in the form of accuracy, as well as a data set that matches the information that the user enters in the chatbot dialog box input. The choice of models that can be applied is based on data that can affect program performance, with the aim of the program which can determine the high or low level of accuracy that will be generated from the results obtained through a program, which can be a major factor in determining the selected model. Based on the application of the LSTM model into the chatbot, it can be concluded that with all program test results consisting of a variety of different parameter pairs, it is stated that Parameter Pair 1 (size_layer 512, num_layers 2, embedded_size 256, learning_rate 0.001, batch_size 32, epoch 20) from File 3 is the LSTM Chatbot with the avg accuracy value of 0.994869 which uses the LSTM model is the best parameter pair.
Tibetan text classification based on RNN

Abstract In this paper, a deep learning RNN model is used to classify Tibetan texts. The core idea is to first preprocess the Tibetan news corpus, and then use Tibetan syllables to construct a Tibetan syllable table based on the lexical and grammatical structure of Tibetan, embed the syllables in the sentence, and represent each syllable as a fixed Numerical vector. Secondly, the RNN cyclic neural network model is constructed. First, the text of different lengths is filled or truncated into a sequence length of uniform length. For each input text, the vector representation of text syllables is input on each time step of RNN to train the RNN model. The test samples were then used to evaluate the accuracy of model classification by introducing recall rate, precision rate and F-test. Finally, compared with traditional machine learning Logistic algorithm, polynomial naive Bayes algorithm and KNN algorithm, the results show that RNN model has better classification effect.
A Comparative Analysis of Feature Extraction Methods for Human Opinion Grouping Using Several Machine Learning Techniques

Opinion analysis is one kind of text mining that aims to find out human opinions and emotions towards any particular event, topic, or subject. Feature extraction is one of the most crucial steps in the text categorization process and an efficient feature extraction method can greatly enhance the accuracy of opinion mining. In this paper, we try to concentrate on comparing the performances of four well-known feature extraction techniques (i.e. BOW, TF-IDF, Skip-Gram, and CBOW) for binary opinion classification. To accomplish the task, two different datasets of hotel and restaurant have been considered and three machine learning models i.e. Naive Bayes (NB), Support Vector Machine (SVM), and Logistic Regression (LR) have been applied as supervised classifiers. In terms of classifier accuracy and mean squared error (MSE), the experimented result depicts that Skip-Gram technique shows the highest performance among the four feature extraction techniques for both datasets. In addition, SVM and LG classifiers perform the best for hotel and restaurant datasets respectively and our proposed approach comes out with an optimistic accuracy of 95.8%.
ROS-Det: Arbitrary-Oriented Ship Detection in High Resolution Optical Remote Sensing Images via Rotated One-Stage Detector

To address these problems, namely, arbitrary orientations, various sizes, and dense distributions of ship detection, we propose an arbitrary-oriented ship detection method via rotated single-stage detector (ROS-Det), which integrates a feature pyramid network (FPN) based on an improved ResNet50, rotated anchors, a classification network, and a regression network together. Firstly, to improve robustness against various sizes of ships, the FPN is used to fusion multiscale convolutional feature maps. Through several tweaks, the improved ResNet50 can receive more information and reduce the computational cost. Secondly, for the purpose of arbitrary-oriented ship detection, rotated anchors, skew intersection over union (IoU), and skew non-maximum suppression (NMS) are introduced to RetinaNet. Then, on account of the disadvantages that the arbitrary-oriented object detection methods usually cause loss discontinuity problem, we improve the traditional smooth L1 loss function by introducing an IoU constant factor. Finally, based on several techniques such as data augmentation and transfer learning, we achieve ship detection on a public ship dataset HRSC2016. Through comparison experiments, we have analyzed and discussed the validity of our proposed ROS-Det, which achieves the state-of-the-art performance.
Number-Based Strings And Degree-sequences Of Topological Cryptography

Based on the problems that quantum computer is not good at computing, various anti-quantum-computing ciphers have been designed to resist the threat of the forthcoming quantum computing to the public key cryptosystem. We design several topological authentications on number-based strings and degree-sequences in topological coding by sequence-type operations, leaf-type operations, vertex-type operations, cycle-type operations, where number-based strings are considered as public keys in topological authentications. The decomposition of degree-sequences of graphs are determined completely, and we build up the connections between degree-sequences and various graphic lattices, as well as the complexity of decomposing a number-based string into a degree-sequence is NP-hard.
Higher Order Crossings Analysis of Signals Over Graphs

We propose the extension of the Higher Order Crossings (HOC) analysis concept for signals over graphs. The HOC over graphs sequence, ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$HOC_g$</tex-math></inline-formula> ), is defined and its efficacy on graph signal discrimination is investigated. Compared with state of the art graph signal discrimination algorithms, the proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$HOC_g$</tex-math></inline-formula> combined with a Support Vector Machine classifier achieves fast and similar or better performance over five different benchmark data sets.
A new, enhanced EZW image codec with subband classification

In this paper, an enhanced version of Embedded zerotree wavelet (EZW) image coding algorithm is proposed, referred to as EZW-SC. By exploiting a new principle that relies on a subband classification concept, the enhanced algorithm allows the prediction of insignificant subbands at early passes, along with the use of an improved significance map. This reduces the redundancy of zerotree symbols, speeds up the coding process and improves the coding of significant coefficients. In fact, the EZW-SC algorithm scans only significant subbands and significantly improves the lossy compression performance with the conventional EZW. Moreover, new EZW-based schemes are presented to perform colour image coding by taking advantage of the interdependency of the colour components. Experimental results show clear superiority of the proposed algorithms over the conventional EZW as well as other related EZW schemes at various bit rates in both greyscale and colour image compression.
Comparing ROC Curve based Thresholding Methods in Online Transactions Fraud Detection System using Deep Learning

Data imbalance is one of the main issues in online transactions fraud detection systems where the majority class (i.e., genuine transactions) dominates the minority class (i.e., fraud transactions). Deep learning-based fraud detection systems have the capability to learn efficiently with imbalanced data. Thresholding with Deep Learning is still understudied. Thresholding or threshold moving alters the decision threshold of the learning model to change its output rather than changing the data distribution or learning of the model. A fraud detection system is considered efficient if it can detect the maximum number of frauds and maintain the overall performance of model. Thus, this paper presents a comparison of three thresholding methods based on Receiver Operating Characteristic (ROC) Curve i.e., Closest to (0,1) criteria, Youden Index (J), max -G-Mean in deep-learning based online transactions fraud detection system. The experimental results show that closest to (0,1) criterion has achieved maximum fraud detection rate i.e. True Positive Rate (TPR) as compared to the other two methods. Hence, selecting the right decisionthresholding method helps in achieving better results.
Conversations Powered by Cross-Lingual Knowledge

Today's open-domain conversational agents increase the informativeness of generated responses by leveraging external knowledge. Most of the existing approaches work only for scenarios with a massive amount of monolingual knowledge sources. For languages with limited availability of knowledge sources, it is not effective to use knowledge in the same language to generate informative responses. To address this problem, we propose the task of cross-lingual knowledge grounded conversation (CKGC), where we leverage large-scale knowledge sources in another language to generate informative responses. Two main challenges come with the task of cross-lingual knowledge grounded conversation: (1) knowledge selection and response generation in a cross-lingual setting; and (2) the lack of a test dataset for evaluation. To tackle the first challenge, we propose the curriculum self-knowledge distillation (CSKD) scheme, which utilizes a large-scale dialogue corpus in an auxiliary language to improve cross-lingual knowledge selection and knowledge expression in the target language via knowledge distillation. To tackle the second challenge, we collect a cross-lingual knowledge grounded conversation test dataset to facilitate relevant research in the future. Extensive experiments on the newly created dataset verify the effectiveness of our proposed curriculum self-knowledge distillation method for cross-lingual knowledge grounded conversation. In addition, we find that our proposed unsupervised method significantly outperforms the state-of-the-art baselines in cross-lingual knowledge selection.
Image Classification Using Tensor Flow

In this research article, a neural network model is created and trained so that, it will be able to predict digits from hand-written images with a high degree of accuracy. Tensor Flow syntax with Keras as its front end has been used for this purpose. The trained model can take an image of a handwritten digit as input and predicts the class of that digit, that is, it predicts the digit or it predicts the class of the input image. Nowadays, PCs can utilize machine vision advancements in blend with a camera and man-made reasoning programming to accomplish picture acknowledgment.
A Wearable Device used for Smart Doorbell in Home Automation System

Technology is very rapidly changing nowadays. Doorbells with a camera to check the visitor is an old way on Screen at home. This Paper talks about Smart Doorbells are used for unlocking the doors by Biometric Authentication. It introduces the idea of storing the Biometrics of the visitors in a local database and recognizes them later. When a Visitor comes and rang the bell, it captures all the essential information such as Face image, Iris image, and Voice of the visitor. It keeps the record of all the visitors that visit home. Also, it alerts about any motion outside the home and enables to interact with the visitor from anywhere. It can be controlled by Smart Ring. Smart Rings are valuable for monitoring and controlling the device from anywhere. Smart Rings are compatible with the device as they allow a quick notification to the user. The Doorbell Provides a Two-way Audio-Video communication between the User and Visitor at any place at any time.
High-Resolution Remote Sensing Image Classification with RmRMR-Enhanced Bag of Visual Words

A ReliefF improved mRMR (RmRMR) criterion-based bag of visual words (BoVW) algorithm is proposed to filter the visual words that are generated with high information redundancy for remote sensing image classification. First, the contribution degree of each word to the classification is represented by its weighting parameter, which is assigned using the ReliefF algorithm. Next, the relevance and redundancy of each word are calculated according to the mRMR criterion with the addition of a dictionary balance coefficient. Finally, a novel dictionary discriminant function is established, and the globally discriminative small-scale dictionary subsets are filtered and obtained. Experimental results show that the proposed algorithm effectively reduces the amount of redundant information in the dictionary and better balances the relevance and redundancy of words to improve the feature descriptive power of dictionary subsets and markedly increase the classification precision on a high-resolution remote sensing image.
Application of Artificial Intelligence in Higher Vocational English Teaching Mode

Abstract Science and technology has always been an indispensable force in the reform of English teaching(ET) mode. In recent years, the extensive application of AI technology has also accelerated the ET Reform under the background of AI. However, due to the fact that the research is still in the trial stage, there are still many problems in the practical application of many higher vocational colleges(HVC). Therefore, this paper puts forward the application research of AI in Higher Vocational ET mode. In this paper, the main problems and advantages of AI technology in ET are analyzed in depth. It is considered that the current reform has some typical problems, such as over reliance on multimedia, lack of innovation in multimedia teaching content, and no change in teaching thinking mode. In view of these problems, this paper puts forward the optimization and improvement measures, through the establishment of AI education platform and other means, to strengthen the intelligent work of English education. Based on the analysis of the changes of students and teachers in learning and teaching English according to AI, it is believed that because of the change of teaching methods, the ET in HVC has become much easier, and the students’ interest in learning has also been greatly improved. The results of the survey on the implementation of AI cloud teaching further confirm that AI has great help for ET. This paper holds that accelerating the reform of modern education is a major development trend of education in the future, and AI plays an important role in this aspect.
Efficient Creation of Japanese Tweet Emotion Dataset Using Sentence-Final Expressions

Emotion recognition in natural language text is one of the critical technologies in the human-computer interface in a wide range of fields, including health and well-being, and labeled data plays a significant role in developing such technology. This paper presents a method for efficiently collecting Japanese emotion tweets carrying the first-person's emotion using emotional expressions and sentence-final expressions. By exploiting sentence-final expressions, we can identify the targeted tweets even though the subjects of sentences are often omitted, and first-person pronouns are often not explicitly in Japanese. By applying the method to Japanese tweet data, we constructed a Japanese tweet dataset comprising 2,234 tweets with labels of emotion types and intensities for two types of emotions: joy and sadness. The evaluation results show that the proposed method can improve the collection efficiency of targeted tweets and the reliability of data labels. We developed classifiers from the dataset that recognize emotion intensities. We show that a classifier using a deep learning-based language model outperforms conventional baseline methods using a Bag of Words model and that the Japanese tweet emotion dataset constructed by our method is useful for the emotion intensity recognition.