Reviewerly/exHarmony huggingface dataset:
A comparison of few-shot and traditional named entity recognition models for medical text

Many research problems involving medical texts have limited amounts of annotated data available (e.g., expressions of rare diseases). Traditional supervised machine learning algorithms, particularly those based on deep neural networks, require large volumes of annotated data, and they underperform when only small amounts of labeled data are available. Few-shot learning (FSL) is a category of machine learning models that are designed with the intent of solving problems that have small annotated datasets available. However, there is no current study that compares the performances of FSL models with traditional models (e.g., conditional random fields) for medical text at different training set sizes. In this paper, we attempted to fill this gap in research by comparing multiple FSL models with traditional models for the task of named entity recognition (NER) from medical texts. Using five health-related annotated NER datasets, we benchmarked three traditional NER models based on BERT-BERT-Linear Classifier (BLC), BERT-CRF (BC) and SANER; and three FSL NER models-StructShot & NNShot, Few-Shot Slot Tagging (FS-ST) and ProtoNER. Our benchmarking results show that almost all models, whether traditional or FSL, achieve significantly lower performances compared to the state-of-the-art with small amounts of training data. For the NER experiments we executed, the F1-scores were very low with small training sets, typically below 30 %. FSL models that were reported to perform well on non-medical texts significantly underperformed, compared to their reported best, on medical texts. Our experiments also suggest that FSL methods tend to perform worse on data sets from noisy sources of medical texts, such as social media (which includes misspellings and colloquial expressions), compared to less noisy sources such as medical literature. Our experiments demonstrate that the current state-of-the-art FSL systems are not yet suitable for effective NER in medical natural language processing tasks, and further research needs to be carried out to improve their performances. Creation of specialized, standardized datasets replicating real-world scenarios may help to move this category of methods forward.
Identification research and realization of DMI dynamic display in high-speed railway onboard equipment

The Driver Machine Interface(DMI) is a peripheral interface device of the onboard equipment of the high-speed railway train control system. It receives and processes the key control information such as train speed, alarm, level, and mode updated by the onboard equipment in real-time, meanwhile it outputs dynamic display and instructions to the driver, and thereby transmits the automatic protection status of the onboard equipment. To promote the automatic function test of onboard and DMI equipment, this research is proposed for the identification of the dynamic displays of DMI. Different schemes are mainly used for feature extraction and classification recognition of DMI so that reducing artificial recognition errors and assist in-vehicle equipment fault diagnosis.
Design of evaluation system for higher vocational institutions based on AHP hierarchical analysis

The development of higher vocational education is inseparable from a country's economic and social development and industrial structure development. Strengthening scientific evaluation of the development status of higher vocational education is an important prerequisite and foundation for scientifically promoting the development of higher vocational education. Educational evaluation is an effective scientific method for discovering the shortcomings of institutions' development and promoting their improvement. Based on this, it is important to construct a systematic, rich, measurable, forward-looking and robust as an evaluation index system. This paper uses a combination of subjective expert scoring and objective AHP hierarchical analysis to construct an evaluation system for higher vocational institutions, and comprehensively analyses the relative strength indicators and relative weakness indicators of each vocational institution, with a view to helping relevant institutions understand their overall development situation more comprehensively and objectively, and promoting the sustainable development of higher vocational education.
Memberikan Pendidikan Pentingnya Ilmu Komputer dan Manajemen, Semangat Anti Narkoba dan Berprestasi Siswa di Masa Pandemi untuk Masa Depan yang Gemilang di SMAN 2 Medan

Education Computing and management knowledge is very important related to the human ability to access the internet and self-management to know about the dangers of drugs as early possible we inform children, students and neighbors in the surrounding environment that drugs have a negative impact on human health and survival, there are many types of drugs that have been circulating throughout Indonesia which are not only consumed by the public, artists , government officials and even state official of areas North Sumatra. This is very influential and has a negative impact on the development of self and the soul of adolescent children characterized by frequent truancy during study hours, often sleepy while studying in class, declining grades because they do not care about school and never study again, changes in attitudes and personality such as irritability , likes to steal justifies all means and does not care about personal health. Meanwhile, the dangers for students in the short term when consuming illegal drugs are characterized by increased heart rate, sleeplessness or insomnia, no appetite, unclear speech, changes in cognitive abilities, and a sense of euphoria and loss of coordination of the student's body parts if consuming drugs this means it can damage the moral life of the younger generation. The population in this service is 33 students majoring in social studies with the technique of determining the choice is the census technique. Data collection by interview, question and answer and documentation.
Feature extraction of public English vocabulary text based on wavelet transform

Aiming at the problems of low extraction accuracy and long time-consuming in the existing public English vocabulary text feature extraction methods, a public English vocabulary text feature extraction method based on wavelet transform is proposed. Set the existence space of public English vocabulary text, convert the text into vector mode, and cluster the text vector by clustering and fusion method. The distance between lexical text vectors in space is calculated, the similarity between vectors is determined, and the noise in text data is removed by information gain method; divide several small bands of different sizes, determine the change frequency of small band vocabulary text data, integrate the eigenvalues of different bands of text, and carry out wavelet transform on the integrated data features to obtain the transformation result, which is the text feature. The results show that the accuracy of the proposed method is 96%.
Global Best Guided Binary Crow Search Algorithm for Feature Selection

Feature selection is a universal combinatorial optimization problem that is used to enhance the characteristics of high-dimensional datasets by eliminating redundant data and selecting prominent features to generate acceptable classification performance. In optimization problems, the objective function can have several local optima, but the ultimate aim is to identify global optima or values close to global optimum. The Crow Search Algorithm (CSA) is a recently suggested metaheuristic algorithm, implemented to feature selection issues systematically. It is witnessed that the CSA solution search equation is suitable in exploration but poor in exploitation. In this paper, a global best-guided solution to CSA (G-CSA) is proposed and applied to pick the optimum feature subset in a wrapper mode to boost exploitation for classification purposes. The efficiency of the proposed methodology is examined on twelve standard UCI datasets. When comparing experimental results, it is clear that the proposed algorithm is superior to its challengers.
Optimization of 2D-Wavelet Filters Based on Taylor Hybrid BAT Algorithm

The aim of image compression is to minimize the redundancy of the image data in order to process or transfer the images quickly. It also decreases the file size and allows small space for additional files to be stored. In lossy wavelet based image compression, it is not possible to fully recover the input image, since the quantization error effect is greater. But it can be minimized by using an optimized filter bank. The regular wavelet filter coefficients and its inverse filter were optimized in this proposed novel Taylor hybrid bat Algorithm. The optimized wavelet filters are utilized by SPIHT encoder/decoder for picture compression. Performance metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measures (SSIM) are utilized in image restoration during the decompression process for performance of optimized filters. The obtained results show that by restricting the errors between the input and the decompressed image, the proposed filters beat the traditional wavelet filters.
A Local Descriptor and Histogram of Oriented Gradients for Makeup Invariant Face Recognition Under Uncontrolled Environment

Makeup is an important cosmetic which is used for beautification and for hiding their identity by the people or criminals. To recognize the face of a people or criminal after makeup is difficult due to the changes in the appearance of the face. This paper presents a descriptor which works on local region for extracting the important features for finding similarity against intra-class variation. The proposed pixel intensity difference is calculated from the local regions of the input image to generate the feature vector. Then, histogram of oriented gradient is also computed for shape feature and combined with the proposed descriptor feature. A machine learning classifier Multi-class SVM has been utilized and five-fold cross validation is used for calculating the rank-1 accuracy. The evaluation has been done carried on two standard datasets-VMU and YMU. The fusion approach performs better and outperforms to the most of the existing approaches of the standard datasets.
Generating tripartite entanglement via spatial overlap of identical particles

We report experimental generation of all the genuine three-party entangled states, i.e., GHZ and W states, of identical particles via spatial overlap. It shows that the indistinguishability is crucial for generating entanglement of identical particles.
Optical Teleportation using Electro-Optic Plasmonic Graphene Waveguide

We demonstrate that optical teleportation can be realized by using two interacting optical fields in an electrically driven graphene waveguide. The simulations show that the proposed system can achieve high-fidelity teleportation over significant transmission distances.
Detection of anomaly over streams using isolation forest

Machine learning algorithms provide useful methods for detecting anomaly over streaming data. There are two major categories of machine learning algorithms, namely supervised and unsupervised algorithms for detecting anomaly. Of these two categories, supervised algorithms need to be trained using huge collection of &apos;training data&apos;, prior to the detection of anomaly in unforeseen data. Here, the training data is required to be labelled data where the label refers to the predefined class. But in reality, labelled data may not always be available to train the machine learning algorithms. In such situations, unsupervised algorithms provide a mechanism of detecting anomaly. In this chapter, the usefulness of unsupervised (clustering) algorithms for anomaly detection over streams is analysed.
Detection of anomaly over streams using big data technologies

Anomaly detection serves as a method for identifying and recognizing abnormal events that may occur over data in various application domains. Anomaly detection is very useful as it provides valuable and actionable information such as detection of fraud in financial domain, detection of intrusion in networking etc. Detecting anomaly over streaming data requires efficient tools and techniques as streaming data is continuously flowing one with no start or end. As streaming data is associated with speed, big data-based platforms provide the fundamental base over which machine learning algorithms can be employed so that the detection of anomaly over streaming data can be performed efficiently. This chapter describes Apache Kafka-based architecture in which detection of anomaly over streams is being done using machine learning algorithm.
Event processing platforms and streaming databases for event-driven enterprises

The Internet of things (IoT) devices and sensors are found everywhere generating tons of multi-structured data every second. These tiny, trendy, networked and embedded computers communicate and correspond to produce a mammoth amount of poly-structured data. The real beauty is here. When the generated data gets collected, cleansed, and subjected to a variety of deeper and decisive investigations, it is possible to extract and emit out actionable insights out of voluminous IoT data. The discovered knowledge comes handy for product, solution and service that are provided to visualize and realize a plethora of next-generation, multifaceted, state-of-the-art, and intelligent devices, systems, and networks. These smart entities and elements are deployed in critical junctions and environments ranging from industrial plants, manufacturing floors, retail stores, airports to people residences to derive people-centric, event-driven, service-oriented, knowledge-filled, process-optimized, mission-critical, situation-aware, time-sensitive and composite services, and applications.Thus, the world is tending to be deeply and decisively connected and cognitive. Such an extreme integration results in streams of event data and messages. To bring forth premium use cases, businesses have to be event streaming, storage, and processing systems in place to readily make sense out of the event data. The event-driven architecture (EDA) style toward event-driven applications is maturing and stabilizing. This chapter is dedicated to convey how streaming databases combine well with other EDA components to build and run futuristic streaming applications and services.
A survey on supervised and unsupervised algorithmic techniques to handle streaming Big Data

The more data an association has, the more troublesome it is to process, store, and break down; however, on the other hand, the more data the association has, the more precise its expectations can be. Too Big Data accompanies big duty. Big Data computing is typically classified into two sorts based on the process necessities, which are Big Data batch computing and Big Data stream computing. Big Data requires military-grade encryption keys to keep data sheltered and private. This is the place data science comes in. Numerous associations, confronted with the issue of having the option to gauge, channel, and dissect data, are going to data science for arrangements - recruiting data researchers, individuals who are authorities in seeming well and good out of a tremendous measure of data. By and large, this implies utilizing measurable models to make calculations to sort, characterize, and process data. In this paper, an audit of different algorithms essential for dealing with such enormous data streams for classification and clustering is given. These algorithms give us different techniques executed to deal with Big Data.
Performing streaming analytics on tweets (text and images) data

In today&apos;s world, Twitter is one of the most popular and powerful social networking platforms. People interact, voice out their opinions, express their thoughts, start conversations about various issues through "tweets" and the market has started analyzing these tweets to get a better understanding of how the general public feels about their products, schemes, or new policies. This helps them to get their perspective directly and work towards making them more people-friendly. Different organizations and consumers are using various mechanisms to perform the sentiment analysis. In this work, two different methods are proposed to perform Twitter sentiment analysis. One of them is using Node-RED and IBM services where real-time data is harvested i.e. tweets from Twitter using Twitter&apos;s API from its developer tool. Data is then stored into the IBM cloudant and then the tweets are classified into positive, negative or neutral based on their sentiment score which lies between −5 and +5. Using IBM Watson&apos;s text to speech service, the tweets are converted to the audio format so that the user could listen to them one by one and the IBM Watson image analyzer is used to analyze what the image represents and it also shows the confidence and different features of the image. Using the visual recognition service, the tone of the tweet is analyzed among emotions like joy, anger, disgust, etc. and the Node-RED dashboard can be created to see the sentiment scores of various tweets. The other proposed method is using Python in machine learning where the sentiment analysis of the tweets is performed with the sentiment score using different classifiers and those classifiers are compared based on their precision, recall, f1 score, and accuracy to conclude which one of them is the most appropriate one for this work. Real-time data is harvested from Twitter, converted into a CSV file and analyzed into positive, negative, or neutral according to their sentiment scores using the classifiers.
Scalable and real-time prediction on streaming data - the role of Kafka and streaming frameworks

Increasingly data gets generated and streamed from different and distributed data sources. In order to make sense out of any streaming data, the market is flooded with a variety of features-rich streaming data analytics platforms and frameworks. Lately, the role and responsibility of Kafka, an open source streaming data platform, are growing steadily. In this chapter, we have written about the noteworthy contributions of Kafka in producing timely, trendsetting and predictive insights out of streaming data.A new breed of "Fast Data" architectures has evolved to be stream-oriented, where data is processed as it arrives, providing businesses with a competitive advantage. The demand for stream processing is increasing every day in the digital era. The main reason behind it is that processing only volumes of data is not sufficient but processing data at faster rates and making insights out of it in real time is very essential so that organization can react to changing business conditions/sentiments in real time. And hence, there is a need to understand the concept "stream processing" and technology behind it. This collateral is prepared with the noble intention of articulating the need for scalable and real-time prediction on streaming data. There are competent technologies, tools, and techniques for real-time prediction out of streaming data in a highly elastic manner. All these details are covered in this document in order to enlighten our readers. The major topics illustrated here include; 1. Streaming concepts; 2. Apache Kafka; 3. Apache Kafka streams; 4. Apache Spark; 5. A sample machine learning (ML) application for real-time prediction out of streaming data in a scalable fashion.
Person Re-identification Method Based on Multi-view and Attention Mechanism

For person re-identification the appearance of photographs taken by different cameras at different viewpoints varies considerably. In this paper, a person re-identification method based on multi-view and attention mechanism is proposed. Firstly, IBN-Net is introduced as the backbone network based on ResNet50, and CA attention mechanism is embedded into the backbone network to obtain person information with more discriminative features. Then, global, spatial and channel features are combined in order to obtain multi-scale feature information and local features of people. It is also considered that the person is a non-rigid structure, and its pose and viewpoint changes can cause huge differences in person appearance features from one camera to another and can ultimately affect the accuracy of recognition. Therefore, a mesh-based view generator is introduced to generate multi-view person images, and multi-view image enhancement methods are utilized to reduce the sensitivity of the network to viewpoint. Finally, the model is optimized by introducing three loss functions. Experiments conducted on three publicly available benchmark datasets Market-1501, DukeMTMC-ReID and CUHK03, the experimental results show that our proposed model is at a high level in both single-domain and cross-domain tasks.
End-to-End Multi-speaker Speech Synthesis with Controllable Stress

In this paper, an end-to-end multi-speaker speech synthesis with controllable stress method is proposed to make the synthetic speech more prominent and expressive, so as to improve the naturalness of synthetic speech. Specifically, we first recorded a small parallel corpus of stress and neutral audio, and labeled the corpora based on three levels of stress: the enhancement of pitch, the stretch of duration, and both. Secondly, based on the multi-speaker acoustic model, the features of the speaker identity and stress are modeled respectively to realize the transfer of stress between different speakers. Finally, we use the LPCNet to convert the spectrum from the target speaker with controllable stress into audio. At the end of the experiment, confusion matrix and Mean Opinion Score (MOS) are used as our evaluation criteria. In addition, we train the basemodel with 100 speakers, so that for any target speaker with only half an hour neutral corpus, can be used to synthesize the stress audio, which greatly improves the efficiency of speech synthesis. Experimental results indicate that the proposed method does not reduce the quality and correctness of synthetic speech, and meanwhile improves the naturalness, expressiveness and similarity of speech synthesis by at least 5% from MOS result.
A Speech Recognition Method Based on Transfer Learning for PSC Topic Speaking Section

Many automatic speech scoring systems employ an Automatic Speech Recognition (ASR) system to get the speech’s transcription, then carry out the other scoring process. The accuracy of the ASR system output, which will be used to calculate the scoring model’s features, significantly impacts achieving high scoring accuracy. The National Putonghua Proficiency Test (NPPT) is an official proficiency test in China. The NPPT has four sections: monosyllabic-word reading, multisyllabic-word reading, short text reading, and topic speaking. At present, the topic speaking part still needs to be scored by two certificated human testers. In order to develop an automatic speech scoring system for the NPPT, a robust ASR system is needed. Although the general mandarin ASR systems could also get higher accuracy, most popular general ASR systems have significantly lower performance on the topic of speaking speeches, which contains more mistakes than the general level. This paper builds a speech recognition corpus to develop a specialized ASR for the NPPT topic speaking speech scoring task and adopts the transformer and conformer model to implement the ASR. We also implemented a transfer-learning-based ASR model based on the popular pre-trained Mandarin ASR framework WeNet. The experimental results show that our model could significantly reduce the error rate of lower proficiency speech while achieving higher performance than popular Mandarin ASR systems for general-purpose speech recognition.