Reviewerly/exHarmony huggingface dataset:
Chinese Named Entity Recognition based on Multi-level Information Extraction

Chinese named entity recognition (CNER) is a challenging issue, since CNER has the characteristics of flexible part of speech and no separator between words. ALBERT is a pre-training language representation model proposed by Google and has been widely used because of its good performance. This work proposed a new neural network model, which combines Albert, bidirectional LSTM, CNN and CRF to recognize Chinese named entities. The model first trains character level word embedding through using Albert, then feeds the word embedding results into CNN and BiLSTM to obtain local information and remote information, respectively, and finally outputs the sequence through CRF. According to the experimental evaluation over real datasets, the proposed model achieves better performance than some new existing approaches, such as: Lattice-LSTM-CRF and Collaborative-Graph-Network.
Virtual Immersive English Teaching System Based on VR Intelligence

Information technology is more and more widely used in the education industry, and the development of network technology enables students to realize online independent learning. There are various English teaching platforms based on the Internet, which provide functions such as knowledge point learning and online testing. But judging from the current situation, there is a problem of inattention of students. In response to the current problems in the English learning system, VR intelligent technology is introduced into the design of the English teaching system, and the student user is the center, simulating a virtual English teaching environment, providing students with a full range of services, so that students can devote their energy put it completely on classroom teaching to achieve immersive English teaching, so as to improve the efficiency of English teaching.
IoT based Attendance Management System (AMS) with Smartwatches' Compatibility

The technological evolution and recent advances in machine learning have transformed how ordinary tasks are performed. Due to many technological, cultural and health related changes (such as Covid 19 pandemic), the means for managing attendance has been transformed with Internet of Things (IoT) based technologies. Attendance management system (AMS) is a system that documents and keeps track of employee and student hours and stores them on local repository or in the cloud. Manual approach to recording and keeping track of attendance is prone to human errors and time consuming. Although many studies have proposed new IoT biometric based solutions to enhance this process, achieving accuracy, efficiency and expense affordability can be a challenging task. The most used biometric approach recently is face recognition IoT solutions. Face recognition can be challenging during the Covid 19 pandemic because of face masks. Taking these issues into consideration, we propose a GPS-enabled Iris-based biometric approach for the attendance management system with smartwatches' compatibility feature. The system performs two main tasks: identification and real time localization. The identification is achieved with iris-based identification while localization is using GPS technology and smart watches. The proposed system addresses many fundamental issues such as the expense factors of manufacturing dedicated tracking wearable devices. It also provides an efficient means of identification using iris-based biometric identification which provides many advantages such as accuracy and enhanced friendly experience without relying on face recognition. The proposed IoT Attendance management systems will be designed to provide better automation for managing attendance and reduce many human errors resulting from manual approaches.
STANN: Spatio-Temporal Attention-based Neural Network for Epidemic Prediction

Accurately forecasting the future trend of an epidemic plays an essential role in making effective and efficient public health policies for disease prevention and control. In reality, the local transmission of an epidemic depends not only on the cumulative number of infections at the same location, but also on the geographical spread of the disease from nearby locations. Therefore, the epidemic data usually show high nonlinearity and certain spatio-temporal patterns. Most existing methods lack the ability to simultaneously characterize the dynamic spatio-temporal patterns, thus cannot make satisfactory prediction results. In this paper, we propose a spatio-temporal attention-based neural network (STANN) to solve the epidemic prediction problem, where attention mechanisms are adopted to effectively capture the dynamic correlations of epidemic data in both spatial and temporal dimensions. The architecture of the network consists of three modules: a temporal attention module, a spatial attention module, and a temporal convolution module. Experimental results on the epidemic prediction of malaria cases in Yunnan Province, China, demonstrate that the STANN model outperforms the state-of-the-art baselines.
Prediction of Movie Genres Based on Its Synopsis and Title Using BiLSTM

This research paper aims to perform the classification of movies into their corresponding genres. To achieve this goal, three methods have been used; the first method uses plots or summaries of movies to perform the multi-label classification, the second method uses the movie titles and finally the third approach uses both the movie summaries and the titles too, to classify the movies into the genres it defines. To achieve this, various Natural Language Processing and Machine Learning techniques have been used. The data used includes the movie summaries, the movie titles and the tags (i.e., the genres), to which we applied pre-processing techniques. Later, this data was passed into a Bidirectional Long Short-Term Memory (BiLSTM) model which could efficiently classify the movies into its genres.
Attention-Based Comparison of Automatic Image Caption Generation Encoders

Generating captions to images has still been a challenging task. Image captioning is a combination of both computer vision and natural language processing (NLP) which has many applications in social networking and is advantageous to people who are impaired visually. There are different encoders (CNN) for feature extraction from the input image and decoders (RNN) for the language model and attention mechanisms which concentrate on relevant data to improve the model’s performance. In this paper, for the comparison of encoders, VGG19 and ResNet152 are used and LSTM as a decoder to generate captions. Along with the decoder, visual attention mechanism is used which allows the human or a system to concentrate on the essential parts from the input data. Visual attention mechanism is also widely used in video analytics. The proposed work uses the MSCOCO dataset for both architectures. The generated captions are then compared with the actual captions using the BLEU score. From the proposed models, the generated captions are 80 per cent accurate.
Promoting Students� Reading Achievement Using Hamburger Strategy

This research aimed to promote the reading achievement of the first STIK Bina Husada Palembang midwifery students using the Hamburger strategy. A quasi-experimental method was used, with a non-equivalent group design for the pretest and posttest. The samples consisted of 38 students from STIK Bina Husada Palembang's first-semester midwifery students. A written test with multiple-choice questions was utilized to collect data. The test was found to be both valid and reliable. The value of t-obtained (5.57) was greater than t-table (2.02) at the significant level of p0.05, according to the results of the independent t-test. It was deduced that the Hamburger strategy could promote students� reading achievement. It meant that the Hamburger strategy could help students improve their reading skills, particularly in recounting text. The Hamburger strategy helped students to stimulate their memory to learn, retain a text, and store information from the text in their long-term memory so that they could remember the information and comprehend the text properly.
Attack-based Automation of Security Testing for IoT Applications with Genetic Algorithms and Fuzzing

The Internet of Things (IoT) has increased the connectivity of systems and fueled the digitalization. However, IoT systems are prone to various security vulnerabilities that may lead to serious incidents that can cause physical harm and financial damage. The security of IoT systems is a major challenge for the their success. If vulnerabilities remain undetected, an attacker can exploit them remotely and thus, gain full access to confidential data, control the connected IoT devices with magnitudes of consequences. This paper presents an approach that exploits information on attacks detected and recorded at runtime to facilitate the development of security patches and uses the information to detect further vulnerabilities. This paper presents work in progress from an ongoing research project.
To Design a Mammogram Edge Detection Algorithm Using an Artificial Neural Network (ANN)

An artificial neural network (ANN) is used to resolve problems related to complex scenarios and logical thinking. Nowadays, a cause for concern is the mortality rate among women due to cancer. Generally, women to around 45 years old are the most vulnerable to this disease. Early detection is the only hope for the patient to survive, otherwise it may reach an unrecoverable stage. Currently, there are numerous techniques available for the diagnosis of such diseases out of which mammography is the most trustworthy method for detecting early stage cancer. The analysis of these mammogram images is always difficult to analyze due to low contrast and non-uniform background. The mammogram images are scanned, digitized for processing, nut that further reduces the contrast between region of interest (ROI) and the background. Furthermore, presence of noise, glands, and muscles leads to background contrast variations. The boundaries of the suspected tumor area are always fuzzy and improper. The aim of this article is to develop a robust edge detection technique which works optimally on mammogram images to segment a tumor area.
Track Correlation Algorithm Based on Improved SOM Neural Network

An improved-self organizing mapping neural network (SOM) track correlation algorithm is proposed to overcome the disadvantages of the traditional track correlation algorithm in practical application, which has low correlation accuracy and complex calculation process. The algorithm uses all the feature data after Kalman filtering as the input vector to improve the learning rules of SOM neural network, so that the same neuron in the competition layer can only win once at the same time when the data of the same sensor is input. The feature data of multi-sensor at the same time are input into SOM neural network for self-organizing clustering. Experiments show that the SOM neural network indirectly transforms the track correlation problem into the track clustering problem. Thus, the problem of increasing computation caused by the increase of the number of targets and sensors is well solved. At the same time, improved SOM neural network can use the multidimensional characteristic information of the targets, and can effectively solve the problems of low correlation accuracy and slow convergence rate when the dense targets track crosses and bifurcates.
A Recommendation Model of Deeply Fusion Rating matrix and Review Text

With the development of deep learning technology, some work applies deep learning to recommendation system. In order to further improve the quality of recommendation, we propose a recommendation model of deeply fusion rating matrix and review text. In this paper, the variable convolution neural network is used to process the review text, and the attention mechanism is introduced to extract the representative reviews. The depth neural network is used to process the rating, extract the depth features, and fuse the features to predict the user&#x0027;s rating. In this paper, three real data sets are verified, and the mean square error MSE is used as the evaluation index. The results show that our proposed model can achieve better recommendation effect than multiple baseline models.
The Evaluation and Application of BP Neural Network in the Multiple Quality Assurance Paths of Higher Education

China's higher education, with colleges and universities as its main part, provides public services, performs public functions and realizes public interests, so it has public attributes. Since the development of China's higher education, it has been popularized nationwide with a gross enrollment rate of higher education over 50 percent. The quality of higher education is related to the level of talents for the society in the future. Therefore, colleges and universities need to improve the level of teaching and the quality of talent cultivation. In this context, the multi-quality teaching of higher education has become a hot topic of discussion. This paper uses the BP neural network evaluation model to evaluate the teaching quality. The principal component analysis method is used in the research evaluation index, and the main components are extracted from the evaluation index to calculate the evaluation score. Through quantitative research, the school education can be obtained. The quality evaluation results and the test results prove that the selection of evaluation indicators is reasonable and can accurately reflect the quality of school education, thus reflecting the scientificity and feasibility of teaching quality evaluation.
An Evaluation of the Quality of Answers to Academic Questions Relating to COVID-19 on Academic Social Q&amp;A Platforms

Academic social network sites have become an important channel by which scholars obtain academic information. In the context of the COVID-19 pandemic, the use of academic social question and answer (Q&A) online platforms is a fast and efficient means of gathering information needed to solve research problems relating to the study of COVID-19. The question then is how to provide scholars with high-quality answers. Therefore, this research focuses on studying the characteristics of high-quality answers to COVID-19 questions on academic social Q&A platforms in terms of the answer content. By analyzing 6791 answers to 349 questions about COVID-19 on ResearchGate Q&A, high-quality academic answers on this topic should be rich in content, contain more negative emotions, be fluent in the use of language, and propose conjectures or hypotheses. This research helps to improve the provision of satisfactory academic information services for scholars during public health emergencies.
An Image Stitching Technique for Images Blurred by Camera Shake Based on FAST Algorithm

research-article Share on An Image Stitching Technique for Images Blurred by Camera Shake Based on FAST Algorithm Authors: Yongxin Chang school of electrical engineering and information, Southwest Petroleum University, China school of electrical engineering and information, Southwest Petroleum University, ChinaView Profile , Zhe Luo school of electrical engineering and information, Southwest Petroleum University, China school of electrical engineering and information, Southwest Petroleum University, ChinaView Profile , Hao Shu school of electrical engineering and information, Southwest Petroleum University, China school of electrical engineering and information, Southwest Petroleum University, ChinaView Profile Authors Info & Claims ICVIP 2021: 2021 The 5th International Conference on Video and Image ProcessingDecember 2021 Pages 49–55https://doi.org/10.1145/3511176.3511185Online:12 March 2022Publication History 0citation9DownloadsMetricsTotal Citations0Total Downloads9Last 12 Months9Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Integration Of Consortium Blockchain Model In The Nigerian Banking Sector

Conventionally, Nigerian banks rely on a less secure and time-consuming centralized clearinghouse where transactions are sorted out and balances are made. This paper presents a model for integrating permission DLT into the Nigerian banking sector. The proposed model has utilized Corda – permissioned DLT, to log transactions between parties on the chain such that those transactions are only visible to the participants of the transaction, thus, combatting fraudulent claims. Participants' details are also verified upon provision of their National Identification Number (NIN) and Bank Verification Number (BVN) as stipulated in the smart contract. This work has also avoided the problems inherent in the public blockchain that requires miners to spend gigantic computer resources and time validating and verifying blocks before being added to the chain. The result obtained is a developed DLT model for logging real-time transactions between banks and their clients.
SemUserProfiling: A Hybrid Knowledge Centric Approach for Semantically Driven User Profiling

Information is shared by a large number of people around the globe in the form of chats, posts, blogs, tweets, and news. For information to reach the right audience and for companies to target the right people, user profiling and term profiling are much needed. In this paper, SemUserProfiling which is an entity enrichment and structural topic modeling (STM)-based approach is proposed. The proposed approach uses the Twitter dataset, and user profile Up as an input. The Twitter dataset is preprocessed, and scenario is retrieved for each term in the preprocessed dataset. Furthermore, the entity enrichment takes place using linked open data (LOD) cloud and classification of the entity set happens using eXtreme gradient boosting (XGBoost) algorithm using categorical domain ontologies. Similarly, user profile Up is preprocessed and subjected to topic modeling using STM, and entity integration takes place using LOD cloud. Finally, the semantic similarity is calculated between the enriched user profile terms and classified enriched entity set using entropy and normalized Google distance (NGD) under frog leap algorithm. The proposed SemUserProfiling yields a high accuracy of 99.02% and a negligible false discovery rate of 0.011.
Research on Information Collection and Recommendation System of Learning Resources

The research of learning resource information collection and recommendation system uses collaborative filtering and data mining technology to realize the negative and efficiency reduction and personalized learning. The intelligent algorithm recommendation model is adopted to design “one platform and four modules”. The framework is the form of combination of spring MVC, hibernate and spring. MVC layered design idea is adopted. The service of personalized portrait, learning feedback and learning material recommendation is provided for users according to the mixed recommendation mechanism, so as to facilitate learners to query, recommend and share the learning content in a wide range.
The Journey of Task-Based Learning and TPACK in Higher Education in the UAE

Online educational tools are shaping the education of today. Research has shown that students respond to the language they speak. Nowadays, the use of tablets, phones, and other forms of media is prevailing in real-time practices as well as in a typical student’s life. Therefore, educators are encouraged to use online educational tools in their classrooms to produce a desired output in learning and to support technology, pedagogy, and content. In this study, the author will highlight the teaching methodology (Task-based teaching and learning) she uses in her ESL classroom in the UAE, using TPACK and three online educational tools. Moreover, the purpose of this study is to shed light on the importance of task-based learning in the context of online education and manifest the different phases and online tools utilized in a typical ESL classroom. Also discussed in this chapter is a methodological framework for implementing TBLT and a pedagogical and theoretical framework (TPACK). Later, the author will share her input on how she used TBLT in her classrooms, with emphasis on the online educational tools she used (mainly Padlet, Plickers, and Think-Pair-Share). This chapter also discusses unique challenges teachers and instructors face when implementing TBLT in its online form and propose solutions for how these can be overcome to maximize language learning.
Research on Armature Loading Control Accuracy Based on Cloud Model Algorithm

An electromagnetic launch propulsion system is developed, using the timer and interrupt function of STM32 chip, the output signals of displacement sensor and pressure sensor are collected and processed, and transmitted to the host computer through two USB channels for real-time online observation. Then, the expected displacement of the armature is set by the developed loading control system, and the command is sent to the controller through the serial port to stop the push rod. In order to reduce the sampling error of displacement and thrust, a protection circuit is added to the hardware circuit design, and the designed NFC conversion circuit is experimentally verified. The relationship between voltage and frequency in NFC circuit is determined by fitting, and the average error of acquisition system is analyzed to be 4 Hz. Finally, the control system uses the cloud model, which can accurately control the armature position and detect the displacement and pressure changes in the armature loading process. The developed electromagnetic rail gun loading control system has a propulsion range of 0–1000 mm and a pressure range of 0–5 t. The armature displacement error is less than 5 mm.
Security Challenges in Mobile Cloud Computing

Mobile Cloud Computing might be a computing which has mobile and cloud computation, as MCC is true now within the growing stages of development. the utilization of mobile cloud computing beats performing-associated hurdles e.g., bandwidth, storage volume and mobile life, and environmental problems e.g., availability, distribution, and heterogeneity. during this paper we discussed about different Cloud service models and about the various loopholes also as there are challenges which exists within the security policies. However, the mobile devices don’t seem to be inadequate in resources provided by Cloud. This research paper concludes surveys on the infrastructure of MCC whereas describing concern of knowledge exploitation and hence the issues of security.
A fine-grained classification method based on self-attention Siamese network

Compared with other fine-grained image classifications, the classification of wild snakes is more difficult and complicated. This is because snakes have different postures, move very fast, and are often coiled. Judging and classifying according to the local characteristics of snakes is difficult. To solve this problem, this paper applies the self-attention mechanism to fine-grained wild snake image classification, to solve the problem of convolutional neural networks that focus on the local part and ignore the global information due to the deepening of the number of layers. Use Swin Transformer for transfer learning to obtain a fine-grained feature extraction model. To further study the performance of the self-attention mechanism in the field of meta-learning, this paper improves the feature extraction model to build a Siamese network and construct a classifier to learn and classify a small number of samples. Compared with other methods, this method reduces the time and space consumption caused by feature extraction, improves the accuracy and efficiency of meta-learning classification, and increases the autonomous learning of meta-learning.