Reviewerly/exHarmony huggingface dataset:
Eliciting group judgements about replicability: a technical implementation of the IDEA Protocol

In recent years there has been increased interest in replicating prior research.One of the biggest challenges to assessing replicability is the cost in resources and time that it takes to repeat studies.Thus there is an impetus to develop rapid elicitation protocols that can, in a practical manner, estimate the likelihood that research findings will successfully replicate.We employ a novel implementation of the IDEA ('Investigate', 'Discuss', 'Estimate' and 'Aggregate) protocol, realised through the repliCATS platform.The repliCATS platform is designed to scalably elicit expert opinion about replicability of social and behavioural science research.The IDEA protocol provides a structured methodology for eliciting judgements and reasoning from groups.This paper describes the repliCATS platform as a multi-user cloud-based software platform featuring (1) a technical implementation of the IDEA protocol for eliciting expert opinion on research replicability, (2) capture of consent and demographic data, (3) on-line training on replication concepts, and (4) exporting of completed judgements.The platform has, to date, evaluated 3432 social and behavioural science research claims from 637 participants.
Research on Named Entity Recognition of Doctor-Patient Question Answering Community Based on BiLSTM-CRF Model

As more and more patients choose online consultation, the information in the Question and Answering(Q&A) community is widely studied. Through automatic extraction of medical inquiry information entities, and establishing a neural network model, the input of basic patient information and disease information can be extracted. In order to provide an effective method for exploring medical named entity recognition in the medical community. Entity annotation is performed for the medical community Q&A pairs, and a BiLSTM-CRF network model is established for training to realize the identification of medical entities which are crawled from Q&A community. This paper introduces the principles and process of BiLSTM-CRF model, illustrates our experiment, including the data sources, experiment environment, evaluation index. This paper chooses four models of HMM, CRF, BiLSTM and BiLSTM-CRF for experiments. Finally, the experimental results are compared which verifies the effectiveness of the BiLSTM-CRF model. Realizing the recognition and extraction of diabetes-related entities in the medical field.
Wavelet Transform for Signal Processing in Internet-of-Things (IoT)

The primary contribution of this chapter is to provide an overview of different denoising methods used for signal processing in IoT networks from the perspectives of physical layer in the network. The chapter starts with the introduction to different kinds of noise that can be encountered in any kind of wireless communication networks, different kinds of wavelet transform and wavelet packet transform methods that can be used for denoising sensor signals in IoT networks and the different processing steps that are needed to be followed to accomplish wavelet packet transform for the sensor signals. Finally, a universal framework based on energy correlation analysis has been presented for denoising sensor signals in IoT networks, and such a framework can achieve considerable improvement in denoising performance reducing the effective noise correlation coefficient to 0.00001 or lower. Moreover, this method is found to be equally effective for Gaussian or impact noise or both.
Online Public Access Catalogues and Library Discovery Systems

This article provides an overview of computer based catalogue systems designed for use by library clients, seeing present day ‘discovery systems’ on the same trajectory as the older ‘online public access catalogues’ (OPACs) which they are gradually replacing, both in technical development and their approach to client use scenarios. It traces the history of the OPAC/discovery system from its origins in the library automation of the 1960s through to the present and discusses the main technical standards which have formed its development. The article goes on to consider questions relating to the usability of electronic library catalogues and highlights semiotic and ethical issues inherent to their design. It concludes with reflections on the future of the OPAC/discovery system in an information universe apparently dominated by the internet search engine.
Single-shot discrimination of coherent states beyond the standard quantum limit

The discrimination of coherent states is a key task in optical communication and quantum key distribution protocols. In this work, we use a photon-number-resolving detector, the transition-edge sensor, to discriminate binary-phase-shifted coherent states at a telecom wavelength. Owing to its dynamic range and high efficiency, we achieve a bit error probability that unconditionally exceeds the standard quantum limit (SQL) by up to 7.7 dB. The improvement to the SQL persists for signals containing up to approximately seven photons on average and is achieved in a single shot (i.e., without measurement feedback), thus making our approach compatible with larger bandwidths.
CorGAN: Context aware Recurrent Generative Adversarial Network for Medical Image Generation

Multi-modal imaging plays a critical role in various clinical applications. However, due to the associated high cost and potential risk, the acquisition of multi-modal images could be limited. To address this issue, many cross-modality image synthesis methods have been proposed. The state-of-the-art methods are mainly based on traditional convolutional generative adversarial networks (GANs) for generating target images. In 3D medical image synthesis, an open problem is how to efficiently exploit the spatial correlations of the 3D image sequence to resolve the inter-slice discontinuity and unevenness artifacts. In this paper, we propose a novel Context aware Residual Recurrent Generative Adversarial Network (short for CorGAN) for sequential medical image generation, which jointly exploits the spatial dependencies of the sequences as well as the peer image generation with GANs. Experimental results show the robustness and accuracy of our method, which outperforms the-state-of-the-art methods in synthesizing target 3D images from the corresponding source images.
Towards POS Tagging Methods for Bengali Language: A Comparative Analysis

Part of Speech (POS) tagging is recognized as a significant research problem in the field of Natural Language Processing (NLP). It has considerable importance in several NLP technologies. However, developing an efficient POS tagger is a challenging task for resource-scarce languages like Bengali. This paper presents an empirical investigation of various POS tagging techniques concerning the Bengali language. An extensively annotated corpus of around 7390 sentences has been used for 16 POS tagging techniques, including eight stochastic based methods and eight transformation-based methods. The stochastic methods are uni-gram, bi-gram, tri-gram, unigram+bigram, unigram+bigram+trigram, Hidden Markov Model (HMM), Conditional Random Forest (CRF), Trigrams ‘n’ Tags (TnT) whereas the transformation methods are Brill with the combination of previously mentioned stochastic techniques. A comparative analysis of the tagging methods is performed using two tagsets (30-tag and 11-tag) with accuracy measures. Brill combined with CRF shows the highest accuracy of 91.83% (for 11 tagset) and 84.5% (for 30 tagset) among all the tagging techniques.
End-to-end multispectral image compression framework based on adaptive multiscale feature extraction

Multispectral image compression can considerably reduce the volume of data and promote their application. However, conventional single-scale compression schemes, such as JPEG2000 and three-dimensional set partitioning in hierarchical tree (3D-SPIHT), do not accurately preserve the features of images due to the complex features of multispectral images. A compression framework based on adaptive multiscale feature extraction with a convolutional neural network is proposed. First, an adaptive multiscale feature extraction module, which is the basic component of the compression framework, is designed to extract the multiscale spatial–spectral features of the multispectral images and adaptively adjust the weights of the features according to the content of the images. Second, the encoder, which is composed of multiscale feature extraction modules, extracts the multiscale spatial–spectral features of the multispectral images, and the extracted features are quantized and encoded by the quantizer and the entropy coder to generate a compressed bitstream. Third, the decoder, which is structurally similar to the encoder, is utilized to recover the images. The rate-distortion optimizer is embedded in the encoder to control the trade-off between the rate loss and the distortion. The results of these experiments on multispectral images of the Landsat 8 satellite and the WorldView-3 satellite validate the better performance of our compression framework compared with the performances of conventional schemes, including JPEG2000 and 3D-SPIHT. In order to further verify the effectiveness of multiscale features, the framework is compared with a single-scale compression algorithm based on deep learning, the experimental results validate that the performance of the single-scale compression algorithm superior to the conventional schemes but inferior to our multiscale algorithm, which indicates that the multiscale features can significantly improve the performance of the compression algorithm.
Neural image compression for non-small cell lung cancer subtype classification in H&amp;E stained whole-slide images

Classification of non-small-cell lung cancer (NSCLC) into adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) via histopathology is a vital prerequisite to select the appropriate treatment for lung cancer patients. Most machine learning approaches rely on manually annotating large numbers of whole slide images (WSI) for training. However, manually delineating cancer areas or even single cancer cells on hundreds or thousands of slides is tedious, subjective and requires highly trained pathologists. We propose to use Neural Image Compression (NIC), which requires only slide-level labels, to classify NSCLC into LUSC and LUAD. NIC consists of two phases/networks. In the first phase the slides are compressed with a convolutional neural network (CNN) acting as an encoder. In the second phase the compressed slides are classified with a second CNN. We trained our classification model on &gt;2,000 NIC-compressed slides from the TCGA and TCIA databases and evaluated the model performance additionally on several internal and external cohorts. We show that NIC approaches state of the art performance on lung cancer classification, with an average AUC of 0.94 on the TCGA and TCIA testdata, and AUCs between 0.84 and 0.98 on other independent datasets.
Generating Diversified Comments via Reader-Aware Topic Modeling and Saliency Detection

Automatic comment generation is a special and challenging task to verify the model ability on news content comprehension and language generation. Comments not only convey salient and interesting information in news articles, but also imply various and different reader characteristics which we treat as the essential clues for diversity. However, most of the comment generation approaches only focus on saliency information extraction, while the reader-aware factors implied by comments are neglected. To address this issue, we propose a unified reader-aware topic modeling and saliency information detection framework to enhance the quality of generated comments. For reader-aware topic modeling, we design a variational generative clustering algorithm for latent semantic learning and topic mining from reader comments. For saliency information detection, we introduce Bernoulli distribution estimating on news content to select saliency information. The obtained topic representations as well as the selected saliency information are incorporated into the decoder to generate diversified and informative comments. Experimental results on three datasets show that our framework outperforms existing baseline methods in terms of both automatic metrics and human evaluation. The potential ethical issues are also discussed in detail.
Interactive 360-Degree Virtual Reality into e-Learning Content Design

The techniques and methods essential in creating 2D and 3D virtual reality images that can be displayed in multimedia devices are the main aims of the study. Tools such as desktops, laptops, tablets, smartphones, and other multimedia devices that display such content are the primary concern in the study. Such devices communicate the content through videos, images, or sound which are realistic and useful to the user. Such contents can be captured from different locations in virtual imaginary sites through the abovenamed electronic devices. These are beneficial e-learning instructional techniques for students, especially for higher learning [1]. Considering architectural learners who rely on such images to develop the expected simple designs useful in real constructions, 360-degree imaging has to be considered in e-learning for their benefits. The primary forms through which the content can be transformed into a virtual reality include YouTube and Facebook websites, all of which can display 360-degree virtual environment content. Through this, the learners will interact with virtual reality in such setups, thus enhancing their studies.
RETRACTED: The Innovative Reform of Public English Teaching in Higher Vocational Education Based on Computer Information Technology

Abstract We will find a phenomenon that many parents in China are very concerned about their children’s public English performance. Although both mathematics and Chinese are major subjects, English has a very high status in people’s hearts. On the whole, this phenomenon is caused by the trend that our country is moving towards internationalization. English has become a necessary subject for every student. Even many parents think that the importance of English should be higher than that of Chinese. Although this argument is one-sided, it also reflects the importance of parents on English. In view of today’s educational forms, the situation where computers are used in classroom teaching is very extensive. How to use computer technology to innovate in public English teaching has become a concern of every higher vocational college. I believe its reform and innovation will write a new chapter in China’s English education.
Bayesian Feature Fusion Using Factor Graph in Reduced Normal Form

In this work, we investigate an Information Fusion architecture based on a Factor Graph in Reduced Normal Form. This paradigm permits to describe the fusion in a completely probabilistic framework and the information related to the different features are represented as messages that flow in a probabilistic network. In this way we build a sort of context for observed features conferring to the solution a great flexibility for managing different type of features with wrong and missing values as required by many real applications. Moreover, modifying opportunely the messages that flow into the network, we obtain an effective way to condition the inference based on the different reliability of each information source or in presence of single unreliable signal. The proposed architecture has been used to fuse different detectors for an identity document classification task but its flexibility, extendibility and robustness make it suitable to many real scenarios where the signal can be wrongly received or completely missing.
RETRACTED: Strategy of Diversified Music Education in Our Country Based on the Analysis of Big Data

Abstract Diversified music education has already been the major way of music teaching in nowadays society of our country and the related government also make the guidelines and slogans that we should carry forward national music and have a better understanding of the diversification of music to set the clear direction for the future of music education. Targeting at the diversification of music education, scholars analyze and study from different aspects to deepen the understanding of the diversification of music education in the big data environment. The paper herein will mainly research aspects of strengthening the basic education music, cultivating the diversified abilities of teachers and constructing multiple teaching forms to promote the development of the diversification of music education.
Research on the Application of Computer Aided Multimedia Teaching Technology in Japanese Teaching

Abstract Japanese has always been a minority language, but there are a large number of students and high social needs in China. Refer to the past teaching methods and means has important value in basic talent education. However, the continuous improvement of employment demand and the requirements of the development of the times put forward higher requirements for the effectiveness of Japanese teaching and training. Therefore, the study of strengthening the effect of Japanese teaching has important practical value. Computer-assisted multimedia teaching technology has a wide range of application value in Japanese teaching, but there are certain limitations in the actual use process, which affects the effectiveness of Japanese teaching in the multimedia environment. Therefore, it needs to be improved and optimized. This paper conducts research from three aspects: the application of computer-assisted multimedia teaching technology in Japanese teaching, teaching difficulties and promotion strategies [1-3] .
TAES: Two-factor Authentication with End-to-End Security against VoIP Phishing

In the current state of communication technology, the abuse of VoIP has led to the emergence of telecommunications fraud. We urgently need an end-to-end identity authentication mechanism to verify the identity of the caller. This paper proposes an end-to-end, dual identity authentication mechanism to solve the problem of telecommunications fraud. Our first technique is to use the Hermes algorithm of data transmission technology on an unknown voice channel to transmit the certificate, thereby authenticating the caller’s phone number. Our second technique uses voice-print recognition technology and a Gaussian mixture model (a general background probabilistic model) to establish a model of the speaker to verify the caller’s voice to ensure the speaker’s identity. Our solution is implemented on the Android platform, and simultaneously tests and evaluates transmission efficiency and speaker recognition. Experiments conducted on Android phones show that the error rate of the voice channel transmission signature certificate is within 3.247 %, and the certificate signature verification mechanism is feasible. The accuracy of the voice-print recognition is 72%, making it effective as a reference for identity authentication.
Adaptive Reservation of Network Resources According to Video Classification Scenes

Video quality evaluation needs a combined approach that includes subjective and objective metrics, testing, and monitoring of the network. This paper deals with the novel approach of mapping quality of service (QoS) to quality of experience (QoE) using QoE metrics to determine user satisfaction limits, and applying QoS tools to provide the minimum QoE expected by users. Our aim was to connect objective estimations of video quality with the subjective estimations. A comprehensive tool for the estimation of the subjective evaluation is proposed. This new idea is based on the evaluation and marking of video sequences using the sentinel flag derived from spatial information (SI) and temporal information (TI) in individual video frames. The authors of this paper created a video database for quality evaluation, and derived SI and TI from each video sequence for classifying the scenes. Video scenes from the database were evaluated by objective and subjective assessment. Based on the results, a new model for prediction of subjective quality is defined and presented in this paper. This quality is predicted using an artificial neural network based on the objective evaluation and the type of video sequences defined by qualitative parameters such as resolution, compression standard, and bitstream. Furthermore, the authors created an optimum mapping function to define the threshold for the variable bitrate setting based on the flag in the video, determining the type of scene in the proposed model. This function allows one to allocate a bitrate dynamically for a particular segment of the scene and maintains the desired quality. Our proposed model can help video service providers with the increasing the comfort of the end users. The variable bitstream ensures consistent video quality and customer satisfaction, while network resources are used effectively. The proposed model can also predict the appropriate bitrate based on the required quality of video sequences, defined using either objective or subjective assessment.
Relationship between Cognitive Learning Psychological Classification and Neural Network Design Elements

This article first analyzes the research background of the design elements of cognitive psychology and neural networks at home and abroad, roughly understands the research status and research background of these two courses at home and abroad, and discusses the application of cognitive psychology to neural networks. The design method has not yet formed a systematic theoretical system. Then, a systematic theoretical analysis of the research in this article is carried out to analyze the relationship between the various characteristics of cognitive psychology and the design elements of the neural network, and it uses these relationships to guide the design practice. Second, it analyzes the relationship between the influence and interaction of cognitive psychology on neural network design and connects cognitive psychology with neural network design. Finally, according to the theoretical analysis and research of the system, the application of cognitive psychology in neural network design, design practice, and the relationship between the two are systematically reviewed. Through the exploratory research on cognitive psychology in neural network design, we can see that the combination of neural network design and psychology, art aesthetics, and other cross-disciplinary and multidisciplinary research is necessary, which can promote the scientific and technological progress of neural network design in the context of the information age and the improvement of public mental health. Under the background of the era in which the neural network design becomes the link between people's emotions and culture, we must fully understand the essential role of each element in neural network design and build a design concept based on cognitive psychology and emotional experience. It is hoped that the content of this topic can provide a certain reference value for the future development of neural network design and cognitive psychology and clarify the new development direction.