Reviewerly/exHarmony huggingface dataset:
Research on Pre-training Model of Natural Language Processing Based on Recurrent Neural Network

In NLP(Natural Language Processing), the direct object of computer processing is not the actual natural language, but its computational model. Therefore, to truly understand the problems of NLP and find out the solutions, we need to discuss these problems from the perspective of language processing modeling. In this paper, the characteristics of NLP are analyzed, and the language model, word segmentation, part-of-speech tagging and named entity recognition in NLP are studied by using the method based on RNN(Recursive Neural Network). A dependency parsing model based on LSTM is proposed. The model is based on the feedforward neural network model mentioned above, and it is used as a feature extractor. In this paper, Minibatch method is used to optimize the training process of Embedding RNN, and this network is successfully applied to the construction of NLP pre-training model. Experimental results show that compared with baseline method, the performance of the model is greatly improved.
End-to-End Learning-based Two-Way AF Relay Networks with I/Q Imbalance

In this work, we design an end-to-end (E2E) learning-based two-way amplify-and-forward (TWAF) relay network in the presence of I/Q imbalance (IQI) by maximizing the generalized mutual information between the input-output bits. The proposed system employs neural network (NN)-based terminal nodes impacted by the IQI and a TWAF relay node that only retransmits the amplified received signals (no additional processing at the relay nodes). Also, we propose four specific lambda layers at the NN decoders to pre-process the received signals. In particular, we propose to design coded-modulation and decoded-demodulation for the NN encoders and NN decoders of both the terminal nodes jointly, to tackle the interference of simultaneously received signals at the TWAF relay node and remove the deteriorating impacts of IQI at the terminal nodes. The simulation results show that the proposed E2E learning framework outperforms the maximum likelihood detector with no IQI by at least 3 dB.
Virtual and Open Integration of Culture for Education (VOICE) with Science Teacher Candidates from Korea during COVID-19

Abstract Cultural competencies and confidence in teaching diverse students are needed to close achievement gaps between mainstream and minority students in science. In this study, an online teacher education program, Virtual and Open Integration of Culture for Education ( VOICE ), was designed to help retain teacher candidates’ science affinities and cultural competency during the COVID -19 pandemic. Thirty-two Korean teacher candidates were selected as culturally responsive instructors who were able to reduce their cultural bias and preferences about Korean culture and develop culturally responsive instruction. VOICE proceeded through (1) “collaborating,” by connecting seminar speakers worldwide; (2) “designing,” by creating culturally responsive science activities based on Korean culture; and (3) “performing,” by communicating with American students, teacher candidates, teachers, and parents. Pre- and post-survey results show that VOICE increased the teacher candidates’ cultural proficiency and interest in science. Guidelines for research on designing online programs to enhance teacher candidates’ science affinities and cultural competencies are provided.
Keeping ahead of the phishing curve

The Covid-19 pandemic flipped the world on its head and triggered major shifts in day-to-day activity. However, it also caused noticeable acceleration of the digital transformation already underway, forcing companies to develop and speed up their online presence in order to survive the volatile climate.
Virtual Companion for the Elderly: Conceptual Framework

As the proportion of global elderly population increases, there is an accompanying demand for improved care for ageing individuals. Technological developments in fields of machine learning and mobile applications can be leveraged to improve quality of life for elderly individuals. We explore development of virtual companions and chatbots, conducting a detailed literature review. Ultimately, we propose a novel virtual companion that combines emotion, personality, and external knowledge into one virtual companion system. We extend the objective to include design considerations to tailor the proposed virtual companion for needs specific to elderly users. Additional features such as mental health monitoring, cognitive impairment detection, design of a virtual avatar, error recovery and latency, and more are included.
Risk Metrics to Measure Safety Performance of the National Airspace System: Implementation Using Machine Learning

Metrics are the key tools to monitor the safety performance of complex systems like air traffic control. Developing risk metrics is a complex process, which involves prediction of risk and identification of different potential outcomes of undesired safety events. For instance, a metric that helps monitor the risk on the surface in an airport environment needs to detect and identify events such as runway excursions, runway incursions, and taxiway incidents and assign appropriate numerical indices proportional to the outcome of each event. An accident with a fatality, injury and aircraft damage will have a corresponding weight for each outcome and incident that involves no outcome can be assigned a severity weight based on its probability of becoming an accident. Models that support such metrics needs to be able to process data from diverse sources. In this paper, we discuss key aspects of two comprehensive metrics that the office of Safety in the Air Traffic Organization has deployed recently: 1) How to employ an automation to detect all relevant events from different data sources and 2) how to assign severity weights for different types of events (accidents and incidents).1. Detection of Relevant Events: one key element of supporting a comprehensive metrics is an automated detection of relevant events by categories. Unlike in aviation, the use of AI and ML have permeated in most industries. Aviation is a safety-critical domain in which there is very little tolerance for failures. The stringent requirements of aviation have contributed to the slow adoption of AI and ML in aviation, in general. However, many aviation sectors are increasingly adopting AI systems, ranging from automating simple, yet tedious and repetitive tasks to a more sophisticated application of a complex autonomous air traffic control system to de-conflict traffic. In this paper, we outline how machine learning models were employed to identify relevant accidents and incidents to support two metrics, a surface safety metric and an airborne safety metric.2. Severity Weighting Scheme: for a metric to be a comprehensive measure and indicate the overall performance of the system, it needs to account for various types of accidents and incidents (precursors) that occur in the system. This paper shows how a weighting scheme we developed to measure the outcome of accidents, such as injuries to people and damage to property, as well as probabilistically determine the severity of incidents with no outcome. The aggregation of all weights along with the frequency of occurrence in a given period represent the overall safety performance of the system.
Decentralised and Scalable Security for IoT Devices

Advancements in IoT technology has provided great benefits, unfortunately, IoT adoption also causes an increase in the attack surface which intensify security risks. As a consequence, different types of IoT smart devices have become the main targets of many high-profile cyber-attacks. To safeguard against threats such as introduction of fake IoT nodes and identity theft, the IoT needs scalable and resilient device authentication management. Contrary to existing mechanisms for IoT device authentication which are unsuitable for huge number of devices, my research focuses on decentralised and distributed security mechanisms that will improve current protocols such as Oauth2, GDOI and GNAP.
Efficient Tunstall Decoder for Deep Neural Network Compression

Power and area-efficient deep neural network (DNN) designs are key in edge applications. Compact DNNs, via compression or quantization, enable such designs by significantly reducing memory footprint. Lossless entropy coding can further reduce the size of networks. It is then critical to provide hardware support for such entropy coding module to fully benefit from the resulted reduced memory requirement. In this work, we introduce Tunstall coding to compress the quantized weights. Tunstall coding can achieve high compression ratio as well as very fast decoding speed on various deep networks. We present two hardware-accelerated decoding techniques that provide streamlined decoding capabilities. We synthesize these designs targeting on FPGA. Results show that we achieve up to 6× faster decoding time versus state-of-the-art decoding methods.
Perceiving informative key-points: A self-attention approach for person search

Though person re-identification has witnessed significant progress, person search, as a more practical task considering the unavailability of annotations of pedestrian bounding boxes, has progressed much slower because of less discriminative feature representation. To this end, we propose a novel self-attention based person search approach by perceiving informative implicit key-points with weak supervision. Firstly, we design the Self-attention Slice Part (SSP) module, to implicitly localize informative key-points by only taking a pre-defined number of points as supervision. Concretely, this module utilizes both channel-attention and spatial attention with weak supervision on partitioned pedestrian slices to get the most discriminative key-points. After that, we strengthen the self-attention weight for these cardinal key-points, and then, more robust feature representations for conducting person search can be obtained from these self-mined key-points. Meanwhile, the SSP also provides semantic alignment with the horizontally partitioned slices. Besides, to focus more on reducing the inner-class margin rather than enlarging inter-class distance, the Random Label Smooth (RLS) loss is defined for more robust classification. The RLS loss not only provides a larger margin hyperplane but also enhances the training efficiency. Therefore, all in all: (1) We propose an end-to-end person search framework by fully exploiting current object detection and person re-identification techniques jointly with weak supervision. (2) Our proposed weakly-supervised self-attention module is generic and can be plugged into any related tasks to improve the performance. (3) We conduct extensive experiments on popular benchmarks, including the dataset of CUHK-SYSU and PRW, and our approach outperforms most current state-of-the-art methods according to mAP and top-1 evaluation metrics.
Geospatial Forecasting and Social Media Exploration Based on Sentiment Analysis: Application to Flood Forecasting

The objective of this article was to propose a geospatial forecasting approach to analyze the textual content of social media using geospatial components and Natural Language Processing (NLP) tools. This approach has been applied to flood forecasting to mitigate future risks and flood damage since the dynamics of real-world events such as floods prompt users to discuss the topic. The approach is based on the appropriate filtering and preprocessing of information from the Twitter exchange platform. A textual preprocessing method and a new sentiment analysis method (also called opinion extraction method) have been developed to gradually build the database provided by humans. Another method was associated with sentiment analysis, the polarity method which was developed to identify the good or bad feeling attributed to a word in a sentence (positive or negative polarity). The work of this paper offers a different approach of geospatial flood prediction. The method relies on social behavior displayed and made available by users of the platform Twitter. A sentiment analysis method is developed in order to identify users’ reactions to inundations according to their geospatial location, while considering how users randomly interact on social media. This paper is innovative compared to the existing approaches, for it uses a social network to extract geospatial components, pairs them with a new data preprocessing and a sentiment analysis method, in order to predict floods on a map, by selecting relevant data. The system thus proposed in this exploration on social media allows the generation of flood forecasting maps to aid the alerting decision-making process. The flood occurrence probabilities provided by this system allow the simulation of the flood forecast distribution map for each month of the year.
Evolution of Intellectual Structure of Data Mining Research Based on Keywords

Data mining has made rapid progress in the past decade and detecting intellectual structure of data mining research is of great help to researchers. The purpose of this study is to detect the evolutional intellectual structure of data mining from the aspect of keywords. This study takes the 5,380 papers, published between 2007 and 2016 retrieved from 11 leading data mining journals defined by Google Scholar Metrics as the dataset. After data pre-processing, keyword frequency analysis is caried out to detect the three different developing patterns of keywords, which indicates that the research focus of data mining has shifted from such topics as association rule mining to large-scale complex networks. Then this paper constructs co-word matrices of high-frequency keywords of different time periods, namely 2007 to 2016 for the whole picture during these years, 2007 to 2011 and 2012 to 2016 for two periods. Clustering results show that there are four main data mining topics, and the attention has been paid more to graph data mining and complex network analysis in the past five years.
Hardware Implementation of SM2 ECC Protocols on FPGAs

The SM2 elliptic curve public key cryptography (ECC) is a Chinese commercial cryptography standard similar to National Institute of Standards and Technology (NIST) ECC. The recommended SM2 ECC parameter refers to a 256-bit prime field with different field characteristic from that with NIST ECC. This work proposes a new approach to perform modular reduction over the SM2 ECC modulus. In addition, the famous plus-minus algorithm for modular division is corrected to deal with universal data. Finally, the results for hardware implementation of the three SM2 ECC protocols are given, while the acceleration by the elliptic curve order is introduced.
Research on Embedded Startup Method of Trusted Module

In order to meet the requirements of secure start-up of embedded devices, this paper designs a secure and trusted circuit to realize the secure and trusted start-up of the system. This paper analyzes the principle and method of the circuit design, and verifies the preset information of the embedded device before the start of the embedded device, so as to ensure that the start process of the embedded device is carried out according to the predetermined way, and then uses the security module to measure the integrity of the data in the start process, so as to realize a trusted embedded system. The experimental results show that the security module has stronger security features and low latency. The integrity measurement is implemented in the trusted embedded system to realize the safe startup of embedded devices.
Data Sovereignty in the Cloud - Wishful Thinking or Reality?

The idea of data sovereignty has been at the core of various research activities over the last years, especially in Europe. The topic gained additional traction through various regulations and initiatives such as the EU General Data Protection Regulation (GDPR), the European Cybersecurity Certification Scheme for Cloud Services (EUCS) and lastly, Gaia-X.
Surficial redistribution of gold and arsenic from the Rise and Shine Shear Zone, Otago, New Zealand

The historic Come-In-Time gold mine was developed in a ∼100*100 m area of the >7 km long orogenic Rise and Shine Shear Zone in Otago Schist. The oxidation of hydrothermal ankeritic and auriferous arsenopyrite and pyrite-bearing Otago Schist beneath a regional Miocene unconformity resulted in replacement of sulphides and ankerite by arsenic (As)-bearing ferric oxyhydroxide. The As-bearing ferric oxyhydroxide is mainly concentrated in and near the original mineralisation sites, especially shears, but mobilisation into post-mineralisation joints and fractures has redistributed As on a metre scale. Gold was liberated from sulphides during oxidation and aggregated by supergene oxidation processes into a smaller number of larger particles (mm scale) that are intimately intergrown with As-bearing ferric oxyhydroxide, especially in shears. Eluvial gold in proximal immature debris includes variably rounded supergene particles eroded from nearby, and some externally derived detrital flakes from previously overlying Miocene sediments. Some eluvial gold is trapped in fine sediments by lichen on outcrops. The hydrothermal As-Au relationship has therefore been broadly maintained through oxidation of rocks at the metre scale but Au concentrations have been locally enhanced in the surficial environment.
Overfitting Problem in Images Classification for Egg Incubator Using Convolutional Neural Network

Chickens can be hatched using an incubator in addition to hatching naturally through direct hatching by the hen. The incubator process necessitates regular monitoring, including the recording of temperature and humidity, as well as monitoring the images on the incubator, such as the position of the eggs and whether or not chicks have hatched, so that all images on the incubator can be classified. Image classification research for this hatching machine object is divided into three categories: chicks, eggs, and hatched eggs. This study has issues with overfitting the model. To address the issues, several methods were used, including image augmentation, image reshaping, and Dropout regularization on the convolution layer. The researchers made several models with different CNN architectures to see if they could recognize improvements from overfitting. This research obtained an accuracy of 0.8687 after making several models with different CNN architectures.
MODENN: A Shallow Broad Neural Network Model Based on Multi-Order Descartes Expansion

Deep neural networks have achieved great success in almost every field of artificial intelligence. However, several weaknesses keep bothering researchers due to its hierarchical structure, particularly when large-scale parallelism, faster learning, better performance, and high reliability are required. Inspired by the parallel and large-scale information processing structures in the human brain, a shallow broad neural network model is proposed on a specially designed multi-order Descartes expansion operation. Such Descartes expansion acts as an efficient feature extraction method for the network, improve the separability of the original pattern by transforming the raw data pattern into a high-dimensional feature space, the multi-order Descartes expansion space. As a result, a single-layer perceptron network will be able to accomplish the classification task. The multi-order Descartes expansion neural network (MODENN) is thus created by combining the multi-order Descartes expansion operation and the single-layer perceptron together, and its capacity is proved equivalent to the traditional multi-layer perceptron and the deep neural networks. Three kinds of experiments were implemented, the results showed that the proposed MODENN model retains great potentiality in many aspects, including implementability, parallelizability, performance, robustness, and interpretability, indicating MODENN would be an excellent alternative to mainstream neural networks.
Shadow Detection from Real Images and Removal Using Image Processing

Automatic shadow detection and removal have been used in many image processing systems such as video surveillance, scene interpretations, and object recognition. Ignoring the presence of shadows in images can cause serious problems such as object merging, object loss, misinterpretation, and alteration makeup in visual processing applications such as segment, group analysis, and follow-up. Many algorithms had it proposed to books, related to the acquisition and removal of images and videos. Comparative testing and capacity building of existing methods in the video has already been reported, but we do not have the same in case the images are still standing. This paper provides the complete existing dignity detection survey and removal technique reported in the current situation image. The test metrics involved in strategies for finding and removing strategies are also discussed with the inefficiencies of common metrics such as the accuracy of the pixel, precision, recall, and F-score in the acquisition phase which is also checked. Plenty and quantity of the selected methods are also tested. Ku to our knowledge all of this is a special first article that discusses ways to detect and remove shadows from real photos.
Decentralized Cloud Storage using Unutilized Storage in PC

Cloud Storage is emerging tremendously. Cloud Storage service is provided as an Infrastructure as a Service (IaaS). We can access the data stored in the cloud whenever and wherever we want it. Cloud Storage providers require high resources to maintain the storage servers in their data centres. Nowadays in most of our personal computers (PCs), we find a lot of storage space left unused and we have good internet connectivity. So, our idea is to use our PCs as storage servers by storing the data in the unutilized storage space. We also focus on the security of the data. So, along with some practices followed by the existing cloud storage services, we implement some additional methods to protect the data in our decentralized cloud storage model.
Breast Cancer Prediction and Trail Using Machine Learning and Image Processing

As per WHO, there are more than 2.09 million cases furthermore 627,000 deaths globally women are diagnosed with breast cancer worldwide annually. It is one of the common cancer in women in India, occurring at any age however in India rise in the early thirties and peak at 50−64 years of age. Only small number of accurate prognostic and predictive factors is used clinically for managing patients with breast cancer. Early detection of this fatal disease is very important which helps in decreasing the morality rate and increasing the survival period of breast cancer patients. This work exploits Mammography for screening and premature identification, analysis as well as processing are solution to improving breast cancer prognosis. To detect breast cancer with mammogram, segmentation of image is performed with the help of Fuzzy C-Means (FCM) technique. As the regions are segmented the required features are extracted, and trained. Finally trained images are classified by the efficient classifier of different classes in mammogram. Texture features are extracted using a feature extraction technique like Gray-Level Co-occurrence Matrix (GLCM), Multi-level Discrete Wavelet Transform and Principal Component Analysis (PCA). Morphological operators are used to make a distinction of micro-calcifications and masses from background tissue also for classification KNN algorithm are exercised. The boundaries of tumor affected region in mammogram are marked and displayed to the doctor, along with area of tumor.