Reviewerly/exHarmony huggingface dataset:
InDEX: Indonesian Idiom and Expression Dataset for Cloze Test

We propose InDEX, an Indonesian Idiom and Expression dataset for cloze test. The dataset contains 10438 unique sentences for 289 idioms and expressions for which we generate 15 different types of distractors, resulting in a large cloze-style corpus. Many baseline models of cloze test reading comprehension apply BERT with random initialization to learn embedding representation. But idioms and fixed expressions are different such that the literal meaning of the phrases may or may not be consistent with their contextual meaning. Therefore, we explore different ways to combine static and contextual representations for a stronger baseline model. Experimentations show that combining definition and random initialization will better support cloze test model performance for idioms whether independently or mixed with fixed expressions. While for fixed expressions with no special meaning, static embedding with random initialization is sufficient for cloze test model.
Minimax Gaussian Mixture Particle Filtering

Minimax Gaussian mixture particle filtering (MGPF) is developed for nonlinear non-Gaussian estimation in this paper. The minimax strategy is adopted to approximate the modified prior. Then, the latest spatial measurement and temporal evolution are introduced into the establishment of minimax Gaussian mixture importance distribution for PF, so as to update and correct the importance samples. Simulation results show the effectiveness of the proposed method.
Meeting Record Generation Based on Hierarchical Decomposition Encoding

Meeting record generation is the conversion of ASR transcriptions into written version reports, which aims to help people understand and quickly locate the meeting content. Recent researches on Chinese style transfer mainly focus on between formal and informal texts, text emotional style transfer, etc. In the field of meetings, there is a temporarily lack of public datasets of style transfer between ASR transcriptions and written reports. In order to handle these challenges, this paper constructs a Cantonese meeting style transfer dataset, and proposes an improved BART model based on hierarchical decomposition encoding that can process longer texts and effectively utilize contextual information. The experimental results show that the introduction of hierarchical decomposition encoding can help BART model improve the quality of meeting record generation.
Mortality Prediction with Bidirectional Coupled and Gumbel Subset Network on Irregularly Multivariate Time Series

Mortality prediction is a crucial challenge because of multivariate time series (MTS) complexity, which are sparse, irregularly, asynchronous and hold missing values for various reasons in a single acquisition. Various methods are proposed to deal with missing values for the final mortality prediction. However, existing models only capture the temporal dependencies within a time series and are inefficient to capture the dependencies between time series to rebuild missing values for mortality prediction. To address these challenges, in this paper, we present an end-to-end imputation and mortality prediction model, named bidirectional coupled and Gumbel subset network (BiCGSN), for mortality prediction with such irregularly multivariate time series. Our proposed model (BiCGSN) uses a recurrent network to learn the temporal dependencies (intra-time series couplings) and uses a Gumbel selector on multi-head attention to obtain the relationship between the variables (inter-time series couplings) in the forward and backward directions. Then the learned bidirectional inter-and intra-time series couplings are fused to impute missing values for further mortality prediction. We evaluate our model on PhysioNet2012 and COVID-19 datasets to imputation and predict mortality. Experiments show that BiCGSN obtains the AUC 0.869 and 0.911 on two real-world datasets respectively and outperforms all the baselines.
Research on recommendation system for railway intelligent monitoring

With the increasing development of the railway industry, the railway transportation is becoming intelligent and digital. In order to ensure the safety of railway transport tasks, station monitoring is an indispensable guarantee means. However, with the increasing establishment of the train station, and the increasing complex environment, the number of cameras in the station has dramaticlly growing. Traditional human monitoring methods have difficult to bear the increasingly heavy monitoring tasks, so we propose a recommendation system for intelligent monitoring of railway transport. Unlike the recommendation systems in other fields, the railway oriented recommendation system has a limited number of users, and the user’s viewing focus is highly related to the train arrival and departure schedule. Therefore, we propose a recommendation system based on the user’s behaviors and the train arrival and departure schedule in the station. To simplify the work of monitoring staff and accurately recommend the key monitoring pictures that are worth paying attention to at present.
Multiscale Image Deblurring Network Using Dual Attention Mechanism

The existing image de-blurring network based on deep learning is complex in structure, which has a large number of parameters. How to improve the efficiency of the network model without adding too many parameters is a key research direction. In this paper, a novel Dual Attention mechanism is proposed to improve the performance of Multiscale Image Deblurring Network (DA-MIDN). Here, dual attention mechanism is designed by combining spatial and channel attention to solve the problem that the convolution operation is limited by the size of the convolution kernel and the correlation between remote pixel features cannot be obtained. Specifically, the spatial attention module can make good use of the dependency between the distant pixels and capture the region of interest in the image information. Channel attention module calculates the correlation between the channel dimensions of the feature maps and suppresses the useless features, which can improve the training efficiency of the model. The experimental results show that the proposed method can achieve better de-blurring effect with a small number of parameters.
Language Code-Switching Detection Based on BERT-LID

Language identification (LID) is the task of automatically determining the identity of a language conveyed by a spoken segment. As more and more people speak more than one language, the phenomenon of code-switching (CS), where multiple languages appear in the same sentence in everyday conversations, occurs frequently. In this paper, the BERT-LID model, which uses the Bidirectional Encoder Representation from Transformers (BERT) model to complete the LID task, combined with post-processing is employed to detect language switching points in code-switching speech. We leverage the excellent performance of the BERT-LID model for short speech segments to complete the CS detection task. We used the sliding window method as the baseline and proposed a potential switching point detection method. Experiments show that our system can find the potential switching points and achieve an accuracy of 93.2%.
Semantic Memory Neural Network Model Based on Two-Dimension Coding Method

Recently, many artificial intelligence network models have achieved excellent performance on image classification tasks. However, these models do not take advantage of knowledge about how memory works in the human brain. Most deep learning methods are designed and run based on primary visual networks. With the further deepening of cognition in the field of brain science, the biological mechanism of the neocortex, such as the hippocampus and perirhinal cortex with long-term semantic memory, has been found to play an essential role in the human recognition of images. Therefore, we propose a semantic memory neural network model based on two-dimension coding method(SMNN-TDC), which is used to simulate the long-term semantic memory operation mechanism of the human brain to recognize visual images and obtain key target semantics, further improving the recognition of important image features. Specifically, the method improves the residual network and adds an attention module to simulate the operation process of the memory mechanism of the human brain. The improvement of the residual network is based on the mechanism of the interconnection of the various brain subsystems, and the attention mechanism is added because each visual cortex has the function of perfecting edge information. Final image classification experiments show that our framework can capture good target semantic features and perform well on benchmark datasets.
Gravel Extraction from FMI Based on DSAM-DeepLabV3+ Network

Accurate representation of the gravels plays a fundamental role for fine characterization and comprehensive evaluation of glutenite reservoir. Formation microscanner images (FMI) is an advanced well log method, which can provide intuitionistic 2D images of the rocks underground. The existing applications of FMI for reservoir evaluation relies on manual fitting or subjective judgment by experts, which is inefficient and lack of accuracy. Several attempts so far have been made to extract useful information from FMI such as grayscale threshold segmentation, connected domain technology and wavelet transform image segmentation technology. However, the above methods are either sensitive to noise or easy to cause over-segmentation, so the segmentation results are always unsatisfactory. Recently, in image processing area, DeepLabV3+ network has become a powerful tool for image semantic segmentation by combining spatial pyramid pooling with dilated convolutions to capture the high-level semantic information. To this end, in this paper, we propose to apply DeepLabV3+ network for gravel extraction by segmenting the FMI. In order to achieve better pixel-level recognition, we design a dual serial attention module (DSAM) and embed it into the conventional DeepLabV3+network, which can capture a large number of low-level image statistical information. The improved DeepLabV3+ network is called DSAM-DeepLabV3+. Experimental results show that DSAM-DeepLabV3+ pushes the performance of gravel extraction further.
PosWSD: Low-Resource Word Sense Disambiguation Model using Part Of Speech Information

Word Sense Disambiguation(WSD) is a long-standing problem in Natural Language Processing(NLP), which aims to find the exact meaning of the target word in a given context. Current WSD methods mainly rely on pre-trained models. They use large-scale annotated data to take advantage of additional knowledge for disambiguation. Due to the limitations of annotated data and open source knowledge base, current WSD methods mainly disambiguate the data of WordNet annotation system. For the situation where there is no open source knowledge base, little annotated data and few computational resources, we propose PosWSD: a WSD model using part of speech(POS) information. The model learns the context and candidate word meanings based on the idea of treating the Word Sense Disambiguation task as a binary task, and then introduces POS of the context which improves its performance. It can be used for the English WSD task of non-WordNet annotation system. The experimental results show that incorporating POS information is necessary for the Word Sense Disambiguation task, which is beneficial for the model to understand the semantics.
Multi-Instance Classification of Histopathological Breast Cancer Images with Visual Explanation

Breast cancer is a big concern for women due to its higher mortality compared to other cancers. Objective and accurate early diagnosis is primordial for the treatment and survival improvement of patients. Histopathological image classification is considered the gold standard and is usually the last and most dependent diagnosis approach for doctors to make patient treatment proposals. In particular, recent deep learning-based methods provide remarkable classification results. However, these methods ignore rationale or logical explanation that is important for diagnosis reliability and human-level understanding. This paper proposes a multi-instance classification network (MICNet) with the mechanism of visual explanation to achieve the explainable classification of histopathological breast cancer images. The method uses a simple two-dimensional convolution kernel to generate explanation maps (i.e., visual explanation) through features coming from the end of the feature extractor in the VGG11 model pre-trained by ImageNet. Multiple instance learning (MIL) based on mirror padding and overlap cropping is adopted to improve the network’s classification performance. We also design a weighted average pooling method to encourage the network to learn more accurate visual explanation. Experiments on BreakHis and Camelyon16 patch-based datasets demonstrate that our MICNet outperforms other CNN models in classification and is able to provide a logical visual explanation that supports the network’s prediction.
ITMix: Image-Text Mix Augmentation for Transferring CLIP to Image Classification

The success of cross-modal models like CLIP has sparked researchers’ interest in better understanding the interaction between different modalities recently. Inspired by the valuable zero-shot image classification experiment of CLIP, we focus on data augmentation when transferring CLIP for finetuning downstream classification tasks in this paper. Mix series like Mixup or CutMix is an effective data augmentation method that generates new images by interpolating between different samples. Different from the common mix series which only concentrates on augmentation of image modality, we intend to mix image and text modalities simultaneously, named ITMix. In this way, more abundant matched image-text pairs would be created. For the implementation of ITMix, effective fine-tuning with match loss and soft one-to-more mapping are proposed. The experimental results verify the outperformance of our proposed method in terms of accuracy on different image classification benchmarks: CIFAR-10, CIFAR-100, Food-101, etc.
Domain Specific Intent Classification with BiLSTM

In this work, we propose an Automatic Speech Recognition System (ASR) with Bi-directional Long-Short-Term Memory Network (BiLSTM) that uses transfer learning and end-to-end deep learning for the healthcare domain. The domain-specific classification solutions have gained more attention in recent years as the research of the open domain is really costly as creating a good quality dataset comes with a high cost. This is one of the main challenges faced by low-resource languages like Sinhala or domains like healthcare due to privacy issues. Most of the existing research has used a text-based intermediate form of the spoken clip and performs the classification on top of the converted text. However, the proposed solution uses end-to-end deep learning and transfer-learning as fine-tuning to improve the performance alone with data augmentation. We achieved the best performance on Sinhala banking dataset [1] with 98.53% accuracy and the performance with only using half of the epochs, unlike the benchmark. When applied to the publicly available dataset AudioMNist [21] has recorded 98.54% of accuracy over 5-fold cross-validation.
Improving the Process of Evaluating User Stories Using the Paraconsistent Annotated Evidential Logic Eτ

Software developers need to be agile to meet users’ needs, delivering software on tight, quality deadlines. User history is a technique used in agile methods to elicit requirements. However, this process is performed with the developers and the user, and there may be contradictions between them, resulting in inaccurate metrics. This article presents a model of validation of user history using the Para-analyzer algorithm, based on the Paraconsistent Annotated Evidential Logic Eτ to assist in improving the evaluation, prioritization, and estimation process of user stories. A survey was conducted with a team of developers working with agile methods. The model uses the degrees of favourable and contrary evidence for each INVEST criterion as input variables. The application of this model allows considering extremely relevant issues when it comes to supporting decision-making based on a mathematical model and serving as a support tool for teams, Product Owners, Project Managers, and others. Four user stories were analyzed by nine experts, who evaluated the criteria for each user story. The interpretation of the evaluations performed by the experts was through the global analysis in the unit square of the Cartesian plane, which indicated the degrees of favourable evidence and contrary evidence for the data used. Two stories that could not be developed in a Sprint were verified and, therefore, should be refactored and resubmitted to the opinion of experts. The other two stories had favourable evidence to be used in a Sprint.
Improved TrustChain for Lightweight Devices

In recent years, Blockchain technology has been used in many fields, including Machine Learning, Cloud Computing, and the Internet of Things (IoT). Integrating Blockchain and IoT with its lightweight devices has benefits such as enhanced security and reduced costs. In this field, critical challenges that need to be addressed are providing security and performance guarantees. An available solution to meet the mentioned challenges is TrustChain in which each node has a local chain, built and maintained by that node. There are, however, some drawbacks. TrustChain cannot resist Whitewashing attacks and results in the node’s high recovery time once a fatal error occurs. In this article, we propose the usage of a smart contract-based authentication system on top of TrustChain to address the Whitewashing attack. Moreover, we connected the TrustChain to a distributed cloud (i.e., IPFS), with the aim of significantly reducing the recovery time of nodes (up to 90%) in the fatal errors. Finally, we evaluate improved TrustChain with PoW Blockchains simulator (As the most secure and basic Blockchain) and MIB (As a PoW-based Blockchain for smartphone) from two aspects of security and performance and prove that the improved TrustChain propagation delay is less than Pow-based Blockchains and it is more secure than these systems against main attacks.
Weakly Supervised Convolutional Neural Network for Automatic Gleason Grading of Prostate Cancer

Digital histopathology is based on the analysis of digitized biopsy slides. Prostate cancer is an usual disease among men. Process of analyzing histopathology images and manually determining the Gleason grades by expert pathologists takes a lot of time. Therefore, development of a system based on machine learning can provide an accurate method for grading prostate cancer. But development of these systems are difficult because they need significant amounts of pixel level annotated data. Since the pathologists’ clinical reports often contain only slide-level labels, this type of data is rarely available. Therefore, the development of methods that can learn using only slide-level labels and do not require manual pixel level annotation would be a considerable advance in this field. In this paper, we design a weakly supervised convolutional neural network for Gleason grading in tissue microarrays, without using pixel level annotations. We explored different pre-trained models as a backbone of our models, namely ResNet-50, VGG-19 and MobileNet. We used class-wise data augmentation method to face the imbalance problem in our dataset. The best network architecture was ResNet-50 as backbone. In the test cohort, ResNet-50 as backbone with class-wise data augmentation achieved an accuracy of 80% for the Gleason grades.
The Effect of Network Environment on Traffic Classification

One of the challenges of network traffic classification and mobile app identification is model generalization. The accuracy and efficiency of classification models are strongly influenced by the network environment and user behavior. By changing the network environmental parameters such as mobile type or app version, the accuracy of the trained classification model may be reduced. In this paper, we investigate “how different network environments can affect the performance of a classification model?” and To this end, we have collected a dataset of 60 popular apps in Iran using different network environments.
A Survey on Semi-Automated and Automated Approaches for Video Annotation

Video analytics systems have recently gained intensive attention due to the fact that they play a practical role in a broad range of topics, including understanding scenes in autonomous driving and processing huge volumes of video data. Nevertheless, labeling frame-by-frame of video datasets is extremely cost-prohibiting and time-consuming. Therefore, the development of effective annotation systems will be crucial for the generation of proper annotations for large-scale datasets of video. This survey paper briefly reviews novel representative literature and systematizes the commonalities in different machine learning techniques such as supervised learning, active learning, transfer learning, and neural networks. We consider some reviews that introduce annotation tools as well. Ultimately, we summarized the performances of all the mentioned approaches applied to various datasets and demonstrated the experimental results.
Andragogy reception in school education during the COVID-19 pandemic in Peru

The article addresses how the application of the principles of andragogy is perceived in basic education during the COVID-19 pandemic in Peru. This study has a naturist view, follows a research methodology with a phenomenological design and a quality approach. The analysis is engaged from an interview application semi structured as an instrument applied to twelve teachers in public and private institutions of basic regular education in Peru. Among the results it was found in the teachers training process changing from formal education to online had many issues in its execution due to lack of strategies and technological resources. Besides, most teachers expressed their unconformity with the loaded work process and training they received although they had improvements within their media capabilities. In this sense, teachers manifested the lack of the horizontal principle from the andragogy principle, opposite to this they developed the participation and synergy between professors to face challenges. Furthermore, its manifested in this study that due to the training and transitioning's process complexities some teachers decided to drop out of their educational facilities. The answers obtained offer information to forecast the teachers training in events of worldwide disaster such as COVID-19. According to the results obtained in this study we can have the teachers training process general view highlighting social inequity, educational and digital that has been installed for a long time in these educational systems, both public and private sectors.
Constructivist Didactics in the Teaching-Learning Process

This paper reflects on the applicability of didactics in the constructivist approach, derived from different ideologies and worldviews associated with the teaching-learning process, based on the analysis of the tradition of educational practices in the classroom, as well as the characteristics adopted by teachers in the applicability of the curriculum, within the comprehensive training. The methodology was based on the systemic bibliographic review and in which the performance responds to the questions what, how and when to teach. The results show that there are different didactic strategies aimed at constructivism. Reference is also made to constructivist didactics as an organized and complex activity, in which the learner constructs their own learning, through interest, previous learning and transformations. It is concluded that the evolution of education has undergone significant transformations, where the passivity of the learner assumes a role in innovation and creativity.