Reviewerly/exHarmony huggingface dataset:
Based on the improved Deeplabv3 + remote sensing image semantic segmentation algorithm

As a new technology, remote sensing originated in the 1960s. In recent years, with the rapid progress of remote sensing satellite and computer hardware equipment, the number and ways of remote sensing image acquisition are increasing day by day. Due to the different sizes of remote sensing images, which are easily affected by the complex environment, this paper proposes a semantic segmentation algorithm based on the improved deeplabV3 +. In this paper, we use the lightweight network mobilenetv3 to extract features, then use the hollow pyramid structure to expand the receptive field, and then use the global context to enhance the fusion of features. Finally, bilinear interpolation method is used to upsample the output feature image to obtain the pixel level prediction segmentation map, so as to ensure the accuracy of image segmentation. Experiments show that the data provided by CCF big data competition (high-definition remote sensing image of a city in southern China in 2015) has good segmentation results. The total accuracy is achieved and the ratio of intersection and merging is achieved.
EMBERT: A Pre-trained Language Model for Chinese Medical Text Mining

Medical text mining aims to learn models to extract useful information from medical sources. A major challenge is obtaining large-scale labeled data in the medical domain for model training, which is highly expensive. Recent studies show that leveraging massive unlabeled corpora for pre-training language models alleviates this problem by self-supervised learning. In this paper, we propose EMBERT, an entity-level knowledge-enhanced pre-trained language model, which leverages several distinct self-supervised tasks for Chinese medical text mining. EMBERT captures fine-grained semantic relations among medical terms by three self-supervised tasks, including i) context-entity consistency prediction (whether entities are of equivalence in meanings given certain contexts), ii) entity segmentation (segmenting entities into fine-grained semantic parts) and iii) bidirectional entity masking (predicting the atomic or adjective terms of long entities). The experimental results demonstrate that our model achieves significant improvements over five strong baselines on six public Chinese medical text mining datasets.
Improving Data Augmentation for Low-Resource NMT Guided by POS-Tagging and Paraphrase Embedding

Data augmentation is an approach for several text generation tasks. Generally, in the machine translation paradigm, mainly in low-resource language scenarios, many data augmentation methods have been proposed. The most used approaches for generating pseudo data mainly lay in word omission, random sampling, or replacing some words in the text. However, previous methods barely guarantee the quality of augmented data. In this work, we try to build the data by using paraphrase embedding and POS-Tagging. Namely, we generate the fake monolingual corpus by replacing the main four POS-Tagging labels, such as noun, adjective, adverb, and verb, based on both the paraphrase table and their similarity. We select the bigger corpus size of the paraphrase table with word level and obtain the word embedding of each word in the table, then calculate the cosine similarity between these words and tagged words in the original sequence. In addition, we exploit the ranking algorithm to choose highly similar words to reduce semantic errors and leverage the POS-Tagging replacement to mitigate syntactic error to some extent. Experimental results show that our augmentation method consistently outperforms all previous SOTA methods on the low-resource language pairs in seven language pairs from four corpora by 1.16 to 2.39 BLEU points.
Visual Chirality Meets Freehand Sketches

Visual chirality measures the distribution variation of visual data under transformation, while it has not been explored in freehand sketches yet. In this paper, we investigate the vertical flipping associated with visual chirality in freehand sketches. Our analysis of investigation results reveals that the vertical flipping shows a high degree of visual chirality. To utilize the high-level cues automatically discovered by predicting the vertical flipping, we propose a Visual Chirality Attention (VCA) module for deep CNNs, which consists of two sequential sub-modules: channel and chirality attention. Experimental results of sketch recognition on TU-Berlin dataset show that our method performs more favorably against state-of-the-art attention-based methods. Our code can be found at https://github.com/zhengyinghit/VCANet.
A Deep Learning based Self-Assessment Tool for Personality Traits and Interview Preparations

Many people face difficulty in analysing their own personality and to see whether they fit a particular job profile. Analysing our personality is very crucial, especially as a part of preparing for various types of interviews, as our responses reflects how we think and act, thus imprinting our first impression on the panelists. Studying personality traits has been proved to be an emerging stream using Machine Learning and Artificial Intelligence. Our idea is to create a platform to identify the personality traits of an individual and provide aid in suggesting changes, if required, in these traits. Our aim is to provide a helping hand for analysing how a person performs in various types of interviews like Video interview, Personal Interview, Group Discussions, etc. to ensure high-geared performance in final interviews. In our approach, we have used the Natural Language Processing (NLP) algorithm to analyse user's input in Group Discussion module, so as to provide additional context to the user. Sentiment Analysis of user's responses in Scenario Based Questions module results in how affirmative or negative the user's response is with respect to the expected solutions. For Video and Telephonic Interview modules, we have used MobileNet architecture and CNN algorithm to predict user's confidence level based on his/her facial expressions and voice modulation
Kalman Filter and Correlated Measurement Noise: The Variance Inflation Factor

Observations from high rate sensors are known to be time-correlated. When processed in a Kalman filter (KF) without accounting for correlations, i) a discrepancy with the true solution occurs, up to a divergence of the filter and ii) the covariance estimates are overestimated, which is equally problematic. Two main solutions exist to account for correlations in a KF: The time differenced and the state augmentation approach. They both model the correlated noise corresponding to an autoregressive process of the first order (AR(1), also called the Gauss Markov process) as it has a short memory, is linear and can be easily implemented. In this contribution, we propose a new method to account for measurement correlations by means of a variance inflation factor (VIF). The latter is derived from an AR(1) assumption for the measurement noise, and its parameters can be estimated by whitening the KF residuals. Our proposal, called the KF_VIF, is further extended to an AR(p) model to account for finer correlation structures. We compare the different approaches and address the impact of noise mismodeling. We use simulations to test the sensitivity of the KF solution to misspecifications and show that the KF_VIF proposed is a powerful answer to account for correlated measurement noise. A real case analysis corresponding to a precomputed flight trajectory with a constant velocity validates the results of the simulations.
Modified quantum delayed-choice experiment without quantum control or entanglement assistance

Wheeler's delayed-choice experiment delays the decision to observe either the wave or particle behavior of a photon until after it has entered the interferometer, and the quantum delayed-choice experiment provides the possibility of observing the wave and particle behaviors with a single experimental setup by introducing quantum control. We here propose a modified quantum delayed-choice experiment without quantum control or entanglement assistance, in which a photon can be prepared in a wave-particle superposition state and the morphing behavior of wave-to-particle transition can be observed easily. It is demonstrated that the presented scheme can allow us to rule out classical hidden-variable models in a device-independent manner via violating dimension witness. We also extend the scheme to the situation of two degrees of freedom, first constructing a hybrid quantum delayed-choice experiment which enables a photon respectively exhibits wave and particle behaviors in different degrees of freedom, and then proposing a scheme to prepare the single-photon wave-particle entanglement. This study is not only meaningful to explore the wave-particle complementarity of photons, but also provides potential for the research of the single-particle nonlocality from the perspective of the wave-particle degree of freedom.
Customized Instruction on RISC-V for Winograd-Based Convolution Acceleration

Convolution operation accounts for the major work-load in convolutional neural networks (CNN). However, standard instruction set for RISC-V processor cannot efficiently perform the matrix convolution between kernel and input matrices. In this paper, we construct a custom instruction under the RISC-V ISA that can perform the F(2&#x00D7;2,3&#x00D7;3) convolution within one single execution. Particularly, optimized by the Winograd algorithm, the operation only needs 16 multiplications instead of 36 multiplications as needed by standard ISA. Benefit from this cycles, as compared to 140 cycles using standard instructions. Thenew instruction, F(2&#x00D7;2,3&#x00D7;3) can be calculated within 19 clock power consumed during convolution operation is also reduced significantly.
A test for independence via Bayesian nonparametric estimation of mutual information

Mutual information is a well‐known tool to measure the mutual dependence between variables. In this article, a Bayesian nonparametric estimator of mutual information is established by means of the Dirichlet process and the k ‐nearest neighbour distance. As a result, an easy‐to‐implement test of independence is introduced through the relative belief ratio. Several theoretical properties of the approach are presented. The procedure is illustrated through various examples and is compared with its frequentist counterpart.
Calculating dependently-typed compilers (functional pearl)

Compilers are difficult to write, and difficult to get right. Bahr and Hutton recently developed a new technique for calculating compilers directly from specifications of their correctness, which ensures that the resulting compilers are correct-by-construction. To date, however, this technique has only been applicable to source languages that are untyped. In this article, we show that moving to a dependently-typed setting allows us to naturally support typed source languages, ensure that all compilation components are type-safe, and make the resulting calculations easier to mechanically check using a proof assistant.
Towards a Personalized Movie Recommendation System: A Deep Learning Approach

In view of the limitations of existing personalized movie recommendation algorithms, in this article, we propose a personalized movie recommendation system based on deep neural networks. The system uses a deep neural network to process discrete features to fully dig out the various features between the user and the movie, and construct a user feature vector model and a movie feature vector model with demographic characteristics. Specifically, we introduce an attention mechanism into the model to learn the user's behavioral preferences and integrate user feature vectors to obtain the user's dynamic short-term interest features and static long-term historical interest features. This method enhances the recommendation performance of the model. We conduct performance exploration on the public dataset MovieLens, and the proposed recommendation model is superior to traditional recommendation algorithms in terms of accuracy and recall, and has higher recommendation performance.
Knowledge-based sentence semantic similarity: algebraical properties

Abstract Determining the extent to which two text snippets are semantically equivalent is a well-researched topic in the areas of natural language processing, information retrieval and text summarization. The sentence-to-sentence similarity scoring is extensively used in both generic and query-based summarization of documents as a significance or a similarity indicator. Nevertheless, most of these applications utilize the concept of semantic similarity measure only as a tool, without paying importance to the inherent properties of such tools that ultimately restrict the scope and technical soundness of the underlined applications. This paper aims to contribute to fill in this gap. It investigates three popular WordNet hierarchical semantic similarity measures, namely path-length, Wu and Palmer and Leacock and Chodorow, from both algebraical and intuitive properties, highlighting their inherent limitations and theoretical constraints. We have especially examined properties related to range and scope of the semantic similarity score, incremental monotonicity evolution, monotonicity with respect to hyponymy/hypernymy relationship as well as a set of interactive properties. Extension from word semantic similarity to sentence similarity has also been investigated using a pairwise canonical extension. Properties of the underlined sentence-to-sentence similarity are examined and scrutinized. Next, to overcome inherent limitations of WordNet semantic similarity in terms of accounting for various Part-of-Speech word categories, a WordNet “All word-To-Noun conversion” that makes use of Categorial Variation Database (CatVar) is put forward and evaluated using a publicly available dataset with a comparison with some state-of-the-art methods. The finding demonstrates the feasibility of the proposal and opens up new opportunities in information retrieval and natural language processing tasks.
Video Quality Assessment of User Generated Content: A Benchmark Study and a New Model

Recent years have witnessed an explosion of user-generated content (UGC) shared and streamed over the Internet. Accordingly, there is a great need for accurate video quality assessment (VQA) models for consumer or UGC videos to monitor, control, and optimize this vast content. Here we contribute to advancing the UGC-VQA problem by conducting a comprehensive evaluation of leading blind VQA (BVQA) models. Besides, we also created a new fusion-based BVQA model, which we dub the VIDeo quality EVALuator (VIDEVAL), that effectively balances the trade-off between performance and efficiency. Our experimental results show that VIDEVAL achieves state-of-the-art performance at a lower computational cost. We believe our reliable and reproducible benchmark will facilitate further research on deep learning-based BVQA modeling. An implementation of VIDEVAL has been made available online <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/vztu/VIDEVAL_release
Are Word Embedding Methods Stable and Should We Care About It?

A representation learning method is considered stable if it consistently generates similar representation of the given data across multiple runs. Word Embedding Methods (WEMs) are a class of representation learning methods that generate dense vector representation for each word in the given text data. The central idea of this paper is to explore the stability measurement of WEMs using intrinsic evaluation based on word similarity. We experiment with three popular WEMs: Word2Vec, GloVe, and fastText. For stability measurement, we investigate the effect of five parameters involved in training these models. We perform experiments using four real-world datasets from different domains: Wikipedia, News, Song lyrics, and European parliament proceedings. We also observe the effect of WEM stability on two downstream tasks: Clustering and Fairness evaluation. Our experiments indicate that amongst the three WEMs, fastText is the most stable, followed by GloVe and Word2Vec.
Optimizing AlexNet using Swarm Intelligence for Cervical Cancer Classification

In this study, we optimized a convolutional neural network model i.e. AlexNet to classify images of cervical cancer cells. Although having canonical CNN architecture, AlexNet is only equipped with few hidden layers and thus makes it less efficient for complex objects such as cervical images. To overcome this limitation, we optimized AlexNet using a swarm-based approach (particle swarm optimization). The dataset used is the Intel & MobileODT Cervical Cancer Screening dataset. Firstly, we optimize standard AlexNet based on epoch, data subsets during training (minibatch), learning rate, input image resolution, and training-testing ratio. After having the best parameter values, we derive 3 models of AlexNet based on the number of convolutional layers. Using this approach, AlexNet with a double convolutional layer produces 60.14%, almost as good as the standard residual network on cervical images. However, when AlexNet optimized by swarm-based intelligence (particle swarm optimization) and an additional dropout layer, the accuracy can attain about 67% which is can surpass the standard residual network by 6.22%.
LAMAR: LiDAR based Multi-inhabitant Activity Recognition

Human activity recognition (HAR) is a an important field of study concerned with identifying the specific movement or action of a person based on sensor data. Movements are often typical activities, such as walking, lying, standing, and sitting which has high significance in assessing health related vital signs. While accuracy of HAR been improved significantly using advanced deep learning techniques, employment of LiDAR (Light detection and Ranging) has been rarely explored due to the high cost of sensor device as well as the complexity of LiDAR point-cloud data. In this paper, we propose LAMAR (LiDAR-based Adaptive Multi-inhabitant Activity Recognition), a low-resolution low-cost (<300$) solid-state LiDAR-based multiple-inhabitant activity recognition system. LAMAR incorporates a series of existing signal processing techniques and a novel adaptive deep learning model to improve multi-inhabitant HAR performance. In this regard, at first, LAMAR posits a voxelized feature representation and clustering based multiple person tracking method to separate individual person related point-clouds. Then, it employes a novel variational autoencoder based domain adaptation technique to improve activity recognition performance by transferring domain knowledge from high resolution to low resolution LiDAR data. Our experimental evaluation on two datasets (one real-time collected data and another publicly available available data) provided significant improvement of HAR performance (94%) in multiple-inhabitant scenario with 63% improvement of multiple person tracking than existing work.
Identification of digitally modulated signal used in cognitive radio network -A survey

To analyze any communication signal, knowing the type of modulation is an important topic. Hence, Adaptive modulation identification has gained great importance in various communication systems such as military communication systems, software radios, and cognitive radios (CR) for various purposes including adaptive communication and spectrum monitoring. Applying a machine learning algorithm and subsequently computing the distinguishing features of an input data set is a well-known way to differentiate these signals. What is proposed, therefore, is a recognition system capable of classifying digital modulation signals (QAM and QPSK). As a matter of fact, it is an artificial neural network (ANN) that is used to distinguish between the two types mentioned above.
A Developmental Method that Computes Optimal Networks without Post-Selections

This work is the theory of Post-Selection practices that have been rarely studied. Post-Selections mean selections of systems after the systems have been trained. Post-Selections Using Validation Sets (PSUVS) are wasteful and Post-Selections Using Test Sets (PSUTS) are wasteful and unethical. Both result in systems whose generalization powers are weak. The PSUTS fall into two kinds, machine PSUTS and human PSUTS. The connectionist AI school received criticisms for its “scruffiness” due to a huge number of network parameters and now the machine PSUTS; but the seemingly “clean” symbolic AI school seems more brittle because of its human PSUTS. This paper analyzes why, in deep learning, error-backprop methods with random initial weights suffer from severe local minima, why PSUTS violates well-established research ethics, and publications that used PSUTS should have transparently reported PSUTS. This paper proposes a Developmental Methodology that trains only a single but optimal network for each application lifetime using a new standard for performance evaluation in machine learning, called developmental errors for all networks trained in a project that the selection of the luckiest network depends on, along with Three Learning Conditions: (1) framework restrictions, (2) training experience and (3) computational resources. This paper also discusses how the brain-inspired Developmental Networks (DNs) avoid PSUTS by reporting developmental errors and its maximum likelihood (ML) optimality under the Three Learning Conditions. DNs are not “scruffy” because they are ML-estimators of the observed Emergent Turing Machines at each time during their “lives”. This implies best performance given a limited amount of overall available computational resources for a project.
Comparative Analysis of Resources Utilization in Some Open-Source Videoconferencing Applications based on WebRTC

Since Covid19, using video conferencing applications is increasing. We have dependent on videoconference meetings in work, education, socialization, etc. Most of the recent videoconference applications are created based on Web Real-Time Communication (WebRTC) technology. WebRTC technology provides peer-to-peer communication between browsers across different types of networks. WebRTC is an open-source project available for browser makers. It is a collection of protocols and standards that are built in modern browsers. So, the complexity of WebRTC is behind the browser's interface. Furthermore, browsers provide JavaScript APIs for web developers to deal with. Through using those APIs web developers can, for example, manage the client camera and microphone, enabling sharing data, transferring voice and video, and recording. In another hand, there are many opensource videoconference applications available for web developers to be reused, deployed, and developed. Organization and companies have a need to implement a videoconference application on premises due to security and business needs. The cost will be held on the utilization of resources. In this paper, a comparative analysis of several open-source videoconference applications created based on WebRTC technology. Then, we choose one of the compared applications depending on the best utilization of resources.