Reviewerly/exHarmony huggingface dataset:
Organisational Reputation and Innovation in the Higher Education Industry: A Correlation Study based on the RepTrakTM Model in the Context of Corporate Communication

Although there is ample literature in organisational innovation, not enough attention is accorded to the correlation between innovation and corporate reputation in the context of corporate communication in the higher education industry.This study was prompted by the urge to close this research gap.Three hundred and thirty-one (331) international, or foreign students from the Infrastructure University of Kuala Lumpur (IUKL), Malaysia were recruited as respondents through a simple random sampling technique who completed a 21-item questionnaire with a very high reliability (α = 0.92).Only one correlation hypothesis was tested which was formulated on the innovation dimension of the RepTrack TM model and it was accepted.Data analysis was performed using SPSS version 23, and a moderate correlation (Person's p = 0.00, r = 0.51) was found between innovation and reputation.In addition, reputation is expressed as high esteem, love, respect, and trust.The study concludes that a university is more likely to be held in high regard when it has strong drive towards innovation, both managerial and technological.The article recommends that for IUKL's reputation to keep soring locally and internationally, it should double its commitment to adopt both managerial and technical innovative strategies.Future research should explore the differential influence of managerial and technical innovation in IUKL and other institutions of higher learning.
Application of Machine Learning Algorithms in Medical Image Analysis: A case study for Breast Cancer detection

Image processing is a very vital part of many medical diagnosis. With the advent of more technologically advanced devices, machine learning implementation has also proven to be boon in the medical world for imaging related diagnosis. This paper aims to utilize different machine learning models to highlight its efficacy in the field of medical image analysis. The paper uses machine learning classifiers to classify breast cancer into malignant and benign using 31 attributes of the Wisconsin Breast Cancer Diagnostic dataset. Five different classifier models – Decision Trees, SVM, Naive Bayes, KNN and ANN were used to classify the tumors and it was observed and concluded that SVM model performed better with an accuracy of approximately 97.4% followed by ANN model with an accuracy of approximately 96.5%.
Cyberbullying Detection using Machine Learning Algorithms

Abstract: In today's digital age, the internet has become an integral part of our lives, resulting in an exponential increase in the volume of data generated. However, this surge in online activity has also led to the emergence of cyberbullying as a major concern in web 4.0. Cyberbullying refers to the use of technology to intimidate, harass or threaten an individual, and is considered a form of cybercrime. Given the lack of available datasets, anonymous identities of perpetrators and the privacy of victims, previous research in cyberbullying detection has been limited. To address this issue, a new approach based on text mining and machine learning algorithms is proposed to proactively detect bullying text. Unlike previous research, which only considered textual features, the current study extracts three types of features: textual, behavioural and demographic features. Textual features include specific words commonly used in cyberbullying, which may indicate the presence of bullying behaviour. Behavioural features are based on personality traits and are extracted to determine the likelihood of a user engaging in bullying behaviour in the future. Demographic features, such as age, gender and location, are also extracted from the dataset. Overall, this text mining approach using machine learning algorithms can effectively detect cyberbullying, providing a valuable tool to combat this growing concern in the cyber world.
Block-level dependency syntax based model for end-to-end aspect-based sentiment analysis

End-to-End aspect-based sentiment analysis (E2E-ABSA) aims to jointly extract aspect terms and identify their sentiment polarities. Although previous research has demonstrated that syntax knowledge can be beneficial for E2E-ABSA, standard syntax dependency parsing struggles to capture the block-level relation between aspect and opinion terms, which hinders the role of syntax in E2E-ABSA. To address this issue, this paper proposes a block-level dependency syntax parsing (BDEP) based model to enhance the performance of E2E-ABSA. BDEP is constructed by incorporating routine dependency syntax parsing and part-of-speech tagging, which enables the capture of block-level relations. Subsequently. the BDEP-guided interactive attention module (BDEP-IAM) is used to obtain the aspect-aware representation of each word. Finally the adaptive fusion module is leveraged to combine the semantic-syntactic representation to simultaneously extract the aspect term and identify aspect-orient sentiment polarity. The model is evaluated on five benchmark datasets, including Laptop14, Rest _ALL, Restaurant14, Restaurant15, and TWITTER, with F1 scores of 62.67%, 76.53%, 75.42%, 62.21%, and 58.03%, respectively. The results show that our model outperforms the other compared state-of-the-art (SOTA) methods on all datasets. Additionally, ablation experiments confirm the efficacy of BDEP and IAM in improving aspect-level sentiment analysis.
Intelligent language analysis method for multi‐sensor data fusion

Abstract Language intelligence analysis oriented to multi‐sensor data fusion is of great significance for language analysis in real scenarios. On the one hand, intelligent language analysis technology can greatly improve the performance of applications such as information retrieval and machine translation, and provide technical support for semantic‐level applications. On the other hand, each language has its own unique characteristics, and the advancement of the language system through language analysis technology is of great benefit to natural language analysis. In this letter, an intelligent language analysis method for multi‐sensor data fusion is elaborated. Specifically, the Kalman filter algorithm is combined to perform the first preprocessing filter fusion on multi‐sensor data. Then, the deep learning model is used to design a language analysis model using Bidirectional Long‐Short Memory Neural Networks (Bi‐LSTM) to obtain deep fusion of multi‐sensor data. In the experiment, the multi‐sensors are used to collect real language data and public language datasets for verification, and the results show the effectiveness of the method proposed in this letter in terms of syntactic label classification.
Analysis of Cross-cultural Education in Japanese Teaching based on Multimedia Technology

In order to improve the effect of sports strategy formulation, this paper combines artificial intelligence assisted agile Internet of Things to conduct research on sports simulation scheme, and builds an intelligent sports simulation system.The sports simulation model designed in this paper can change the mechanism parameters according to the actual situation of the moving object, establish the fixed coordinate system and local coordinate system of the parallel platform model, and establish the mathematical model of the parallel platform.Moreover, according to the principle of vector closure, the inverse kinematics position solution is analyzed, and the inverse kinematic velocity solution is derived by using the equation of the inverse position solution.The experimental research results show that the sports simulation system based on artificial intelligence-assisted agile Internet of Things proposed in this paper has a good performance in sports simulation.
FPGA Implementation for Elliptic Curve Cryptography Algorithm and Circuit with High Efficiency and Low Delay for IoT Applications

The Internet of Things requires greater attention to the security and privacy of the network. Compared to other public-key cryptosystems, elliptic curve cryptography can provide better security and lower latency with shorter keys, rendering it more suitable for IoT security. This paper presents a high-efficiency and low-delay elliptic curve cryptographic architecture based on the NIST-p256 prime field for IoT security applications. A modular square unit utilizes a fast partial Montgomery reduction algorithm, demanding just a mere four clock cycles to complete a modular square operation. The modular square unit can be computed simultaneously with the modular multiplication unit, consequently improving the speed of point multiplication operations. Synthesized on the Xilinx Virtex-7 FPGA platform, the proposed architecture completes one PM operation in 0.08 ms using 23.1 k LUTs at 105.3 MHz. These results show significantly better performance compared to that in previous works.
EDUCATIONAL TECHNOLOGIES USED TO TEACH SELF-MANAGEMENT AFTER HEMATOPOIETIC STEM CELL TRANSPLANTATION: A SCOPING REVIEW

Objective: to map the scientific evidence on the educational technologies used to teach self-management in hematopoietic stem cell post-transplantation. Method: a scoping review, based on JBI recommendations.The searches took place between January and February 2022, in databases and repositories of dissertations and theses.The PCC strategy was used, namely: P (Population) -patients (patient participation); C (Concept) -educational technologies and selfmanagement (instructional technology, self-management); and C (Context) -post hematopoietic stem cell transplantation (bone marrow transplantation).Studies that discussed the educational technologies used to teach self-management after hematopoietic stem cell transplantation, available in full electronically, were included.Editorials, letters to the editor and opinion articles were excluded.Duplicate studies were considered only once.The data are presented in figures and chart format.Results: sixteen studies were selected to compose the final sample, most of which showed that the most used educational technologies in the context of hospital discharge after hematopoietic stem cell transplantation are websites, software, movies, online videos or not, care plans, posters, books and booklets aimed at teaching.Conclusion: the use of educational technologies in teaching and patient health education is a reality present in services at any level of health care.The highlight of the approach to this topic is anchored in how these technologies are used and whether they are properly defined for each patient, according to the results of this study.
GIGAN: Self‐supervised GAN for generating the invisible using cycle transformation and conditional normalization

Abstract Objects in a real scene often occlude each other and inferring a complete appearance from the visible part is an important and challenging task. In this paper, the authors propose a self‐supervised generative adversarial network GIGAN (GAN for generating the invisible), which can generate the complete appearance of objects without labelled invisible part information. The authors build two cycle transformation networks CycleIncomplete (CycleI) and CycleComplete (CycleC) that share parameters to improve the accuracy of mask completion. This design does not require well‐matched training images and can make better use of the limited labelled samples. In addition, the authors propose a conditional normalization module and combine it with the inferred complete mask output. The combination not only enhances the content recovery ability and obtains more realistic outputs, but also improves the efficiency of the generation process. Experimental results show that compared with existing self‐supervised learning models, our method achieves l 1 error, mean intersection‐over‐union (mIOU), and Fréchet inception distance (FID) improvements on the COCOA and KINS datasets.
Cataloguing and classifi cation in the era of artifi cial intelligence

Goal: This study has examined cataloguing and classiﬁcation in the era of artiﬁcial intelligence, beneﬁts, and challenges from the perspective of cataloguing librarians in Oyo state, Nigeria. Methodology: The study adopted a purely qualitative method using interviews for data collection. The target population was the cataloguing and classiﬁcation librarians in academic libraries in Southwest Nigeria. The sample comprised 21 cataloguing and classiﬁcation librarians working in the cataloguing and classiﬁcation section of 10 university libraries in Southwest Nigeria. Four research questions were developed and answered. Findings/Results: The ﬁndings revealed that cataloguing librarians are aware of the application of artiﬁcial intelligence for cataloguing and classiﬁcation. Artiﬁcial intelligence enhances automatic cataloguing and classiﬁcation using optical character recognition, assists in undertaking stressful work that humans may struggle to do; completes tasks faster than a human being and enables libraries to provide accurate cataloguing and classiﬁcation services with fewer errors; saving time and money, assisting libraries to create metadata for digital resources and allowing cataloguing to increase the speed of metadata generation. The challenges associated with the application of AI to cataloguing and classiﬁcation practices from the perspective of cataloguing and classiﬁcation librarians are funding, irregular power supply and inadequate skilled personnel to handle AI. Currently, there is no strategic plan in place for the implementation of AI for cataloguing and classiﬁcation in academic libraries in Nigeria. Originality and Values: Based on the ﬁndings, the study recommends that a strategic plan should be initiated in all academic libraries in Nigeria to allow the real-life practice of using AI for cataloguing and classiﬁcation.
Learning Program Representations with a Tree-Structured Transformer

Learning vector representations for programs is a critical step in applying deep learning techniques for program understanding tasks. Various neural network models are proposed to learn from tree-structured program representations, e.g., abstract syntax tree (AST) and concrete syntax tree (CST). However, most neural architectures either fail to capture long-range dependencies which are ubiquitous in programs, or cannot learn effective representations for syntax tree nodes, making them incapable of performing the node-level prediction tasks, e.g., bug localization. In this paper, we propose Tree-Transformer, a novel recursive tree-structured neural network to learn the vector representations for source codes. We propose a multi-head attention mechanism to model the dependency between siblings and parent-children node pairs. Moreover, we propose a bi-directional propagation strategy to allow node information passing in two directions, bottom-up and top-down along trees. In this way, Tree-Transformer can learn the information of the node features as well as the global contextual information. The extensive experimental results show that our Tree-Transformer significantly outperforms the existing tree-based and graph-based program representation learning approaches in both the tree-level and node-level prediction tasks.
SharpFormer: Learning Local Feature Preserving Global Representations for Image Deblurring

The goal of dynamic scene deblurring is to remove the motion blur presented in a given image. To recover the details from the severe blurs, conventional convolutional neural networks (CNNs) based methods typically increase the number of convolution layers, kernel-size, or different scale images to enlarge the receptive field. However, these methods neglect the non-uniform nature of blurs, and cannot extract varied local and global information. Unlike the CNNs-based methods, we propose a Transformer-based model for image deblurring, named SharpFormer, that directly learns long-range dependencies via a novel Transformer module to overcome large blur variations. Transformer is good at learning global information but is poor at capturing local information. To overcome this issue, we design a novel Locality preserving Transformer (LTransformer) block to integrate sufficient local information into global features. In addition, to effectively apply LTransformer to the medium-resolution features, a hybrid block is introduced to capture intermediate mixed features. Furthermore, we use a dynamic convolution (DyConv) block, which aggregates multiple parallel convolution kernels to handle the non-uniform blur of inputs. We leverage a powerful two-stage attentive framework composed of the above blocks to learn the global, hybrid, and local features effectively. Extensive experiments on the GoPro and REDS datasets show that the proposed SharpFormer performs favourably against the state-of-the-art methods in blurred image restoration.
Neural networks based on attention architecture are robust to data missingness for early predicting hospital mortality in intensive care unit patients

Background Although the machine learning model developed on electronic health records has become a promising method for early predicting hospital mortality, few studies focus on the approaches for handling missing data in electronic health records and evaluate model robustness to data missingness. This study proposes an attention architecture that shows excellent predictive performance and is robust to data missingness. Methods Two public intensive care unit databases were used for model training and external validation, respectively. Three neural networks (masked attention model, attention model with imputation, attention model with missing indicator) based on the attention architecture were developed, using masked attention mechanism, multiple imputation, and missing indicator to handle missing data, respectively. Model interpretability was analyzed by attention allocations. Extreme gradient boosting, logistic regression with multiple imputation and missing indicator (logistic regression with imputation, logistic regression with missing indicator) were used as baseline models. Model discrimination and calibration were evaluated by area under the receiver operating characteristic curve, area under precision-recall curve, and calibration curve. In addition, model robustness to data missingness in both model training and validation was evaluated by three analyses. Results In total, 65,623 and 150,753 intensive care unit stays were respectively included in the training set and the test set, with mortality of 10.1% and 8.5%, and overall missing rate of 10.3% and 19.7%. attention model with missing indicator had the highest area under the receiver operating characteristic curve (0.869; 95% CI: 0.865 to 0.873) in external validation; attention model with imputation had the highest area under precision-recall curve (0.497; 95% CI: 0.480–0.513). Masked attention model and attention model with imputation showed better calibration than other models. The three neural networks showed different patterns of attention allocation. In terms of robustness to data missingness, masked attention model and attention model with missing indicator are more robust to missing data in model training; while attention model with imputation is more robust to missing data in model validation. Conclusions The attention architecture has the potential to become an excellent model architecture for clinical prediction task with data missingness.
Chatbot Development using APIs and Integration into the MOOC

&#x0D; In recent years, chatbot technologies have evolved into modern information and communication technology applications that perform many virtual tasks, including learning. One of the challenges in improving the chatbot is the insufficient knowledge base of chatbots, including education-oriented conversational agents, the challenges in connecting the chatbot with course content on Massive Open Online Course platforms. In this study, a chatbot was developed to answer questions using publicly available technologies, specifically Application Program Interfaces (APIs) that promise convenient user accessibility via APIs, such as the Facebook Messenger platform along with wit.ai API, Canvas MOOC API, and Wikipedia API. API technologies were used to connect the chatbot to selected course content on the MOOC platform as well as to large knowledge bases such as Wikipedia to expand the knowledge base of the Conversational Agent. The course selected for the chatbot integration was on general informatics topics. Most course participants interacted with the chatbot via the Facebook Messenger platform using their handheld devices. Thus, integrating the chatbot into a widely used platform such as Facebook Messenger is a convenient and effective way for reaching learners. The API technology enabled an efficient connection between the chatbot and third-party apps, including the Messenger app, wit.ai, Canvas MOOC, and Wikipedia. This was due to the variety, richness, manipulation capabilities, and format of data that an API can transfer. In addition, the Wikipedia API seemed to be a vast source of information for expanding the chatbot's knowledge base. Not all of the queries posed to the chatbot were part of the course content. Some participants questioned the personality of the chatbot and were curious about the persona of the conversational agent. This suggests that a chatbot that has been endowed with some personality traits is stimulating and more likely to be accepted by learners.&#x0D;
Succinct Computational Secret Sharing

A secret-sharing scheme enables a dealer to share a secret s among n parties such that only authorized subsets of parties, specified by a monotone access structure f:{0,1}n→{0,1}, can reconstruct s from their shares. Other subsets of parties learn nothing about s.
Breast Cancer Prediction Based on Feature Extraction using Hybrid Methodologies

The breast cancer prediction is essential for effective treatment and management of the disease. Using data mining techniques to develop predictive models can assist in identifying patients at high risk of developing breast cancer, allowing for early detection and treatment. Early detection has been shown to improve patient outcomes and survival rates. The proposed system for breast cancer prediction involves two main techniques: Linear Discriminant Analysis (LDA) based feature extraction and hyperparameter tuned LSTM-XGBoost based hybrid modelling. The LDA is used to extract the features from the input data that can be trained using a hybrid model such as LSTM and XGBoost. The hyperparameters of both models are optimized using cross-validation techniques to achieve high accuracy in breast cancer prediction. Overall, this proposed system has achieved an accuracy and efficiency of breast cancer prediction than existing.
Online measurement system of slab front-end bending in hot rough rolling based on line structured light vision

Abstract Front-end bending is one of the most significant defects of slabs in hot rough rolling. To address the lack of effective methods to detect slab front-end bending in hot rolling, a measurement system based on line structured light vision is proposed in this paper. Line structured light lasers and area-scan cameras are arranged on the supports on both the exit and entrance of a roughing mill. First, the calibration of the measurement system is completed by camera calibration and the random sampling consistency algorithm. Second, multiple regions of interest gray thresholds and the Steger algorithm are used to realize laser centerline extraction on the surface of a large-scale and high-temperature slab. Last, the actual value of the slab front-end bending is obtained and calculated through coordinate transformation. The proposed measurement system can detect slab front-end bending in real time and the relative mean deviation is 0.93%.