Reviewerly/exHarmony huggingface dataset:
Digital Visual 3D Teaching Materials Dynamic Resources on Account of Mobile Terminal

Digital education resource is a very important resource. Using mobile terminals, using digital visual means to transform the form of teaching material resources, the original rigid theory and knowledge in the teaching material into rich forms of dynamic information resources. Digital education resources have a lot of value, many of which cannot be used reasonably, so that the phenomenon of “Resource Island” and “digital ruins” is generated. At this point, the use of mobile terminals, the use of digital visualization to process the teaching material resources, the teaching material resources for digital transformation, then the knowledge and theory of the teaching material will be presented to the public in a new way of network. This paper studies the construction of digital visual 3D teaching material dynamic resources on account of mobile terminals, and expounds the construction principle of digital visual 3D teaching material dynamic resources. The test shows that the construction of digital visual 3D teaching material dynamic resources on account of mobile terminal has efficient performance in the aspect of digital visual 3D teaching material dynamic resources.
Computer-aided Breast Cancer Diagnosis using Deep Convolutional Neural Networks

In the contemporary period, breast cancer has become one of the leading causes of cancer-related mortality globally. It is referred to as asymptomatic cancer because it has no symptoms at all when it is first diagnosed. As a result, radiology professionals periodically evaluate mammograms to check for unusual lesions and pinpoint the location, kind, and shape of any troublesome breast areas. For the segmentation and detection of breast cancers, image analysis techniques are vital because they can accurately depict key morphological features that are necessary for a conclusive diagnosis. This paper presents the image segmentation of a breast tumour using U-Net model. The proposed model attained a high accuracy of 94.29% on our training dataset. The complete empirical analysis along with the exhaustive literature review is presented in the paper.
Multi-Person Key Points Detection for Abnormal Human Behavior Analysis Using The ConvLSTM-AE Method

The detection of abnormal human behavior is an interesting issue to consider in computer vision. The problem of detecting abnormal activities carried out in this study is a problem formulated in the process of monitoring human activities. In understanding the nature of human activities, a system is needed that is applied to data training which is specifically proposed based on ConvLSTM-AE for the detection of moving objects in motion-based events. Detection of anomalies and localization that arise due to camera jitter and movement of objects in the area. With the unusual motion of the video frame, connected component analysis is exploited to provide bounding boxing to find out whether human movements whose activities are based on human poses will be classified as normal or abnormal in a set of activity data. The learning and training process so that the proposed model can capture different duration of time and more optimal processes.
Optimized Test Pattern Generation for Single Missing Gate Fault Detection in Reversible Arithmetic Circuits

In this paper, optimized test pattern generation technique for single missing gate fault detection of reversible arithmetic circuits have been shown. Here the optimized test patterns, which are generated, are for reversible adder, subtractor and adder/ subtractor units. A generalized algorithm has been shown for generating the test patterns of different reversible arithmetic circuits.
Intelligent Mining Method of Massive Digital Archives Based on Artificial Intelligence

With the rapid development of artificial intelligence, a series of changes have taken place in the development practice of archives department, which provides an implementation path for the effective development of massive digital archives resources. Therefore, the research on the development of digital archives resources has practical significance for modern archives management. The wide application of artificial intelligence has brought great opportunities and challenges to the development of digital archives resources. How to use artificial intelligence to make digital archives resources “live” has become an urgent task for archives departments. Based on the intelligent mining method of massive digital archives based on artificial intelligence, this paper starts from a multidisciplinary perspective, draws lessons from the existing theories, analyzes the key elements of the development of digital archives resources, combs the characteristics of China's digital archives resources and the problems existing in the mining process, and puts forward the intelligent mining method of massive digital archives based on artificial intelligence.
Covid19 Unknown Risks—Using AI for Disaster Recovery

Artificial intelligence (AI) is a potentially powerful tool to mitigate the unknown unknown risk. Unknown-Unknown risks are unknown risks and accordingly unknown consequences. This research’s objective is investigating the impact of AI on mitigating unknown unknown risk. For all six cases analyzed, it was possible to extract that unknown risk is hard to be reported in advance. Therefore, since the outbreak of the Coronavirus COVID-19, organizations have been eager to use AI in response to the consequences caused by this pandemic. As a result, AI played a significant role in rapid response, decision-making, early prediction, automated processes, and detection and tracking. This means AI has the potential to be used as a solution to mitigate the post event impacts and study the patterns for future prediction.
Target Segmentation Based Adversarial Attack for SAR Images

With the increasing increments on the Deep Learning (DL) in these years, increasing attentions have been arose on the adversarial attack on DL models for Synthetic Aperture Radar (SAR) target recognition. However, most of the previous methods transfer exisiting adversarial attack methods from computer vision area which typically attack DL models by generating background-located perturbation of certain size. With respect to the physical realizability in SAR target recognition tasks, the perturbation is only feasibly generated in the target region. Therefore, in this paper, we propose a Target Segmentation based Adversarial Attack (TSAA) method for SAR images. The proposed TSAA method tends to only generate the perturbation on the target region by incorporating the target segmentation technique. Meanwhile, the TSAA approach reformulates the perturbation generation process to achieve an automatic perturbation region searching instead of manual design. Thus, the attack performance towards DL models and imperceptibility for visualization both present significantly better results than previous methods in extensive simulation results including white and black box experiments for eight popular DL models.
How to Build a Risk Management Culture that Supports Diffusion of Innovation? A Systematic Review

This research paper aimed to produce a systematic review of two of the critical terms in any organisation: risk management (RM) and diffusion of innovation (DOINV). The research sought to examine the relationship between RM and DOINV. The systematic review comprised 27 articles published from 2007 to 2020 sourced from different databases, such as; ProQuest, Emerald, ScienceDirect, IEEE, Scopus, and Google Scholar. A set of inclusion and exclusion criteria were used to filter the results, which were then assessed using a quality assessment process. Factors that appeared more frequently were highlighted as the main findings for future study. A proposed framework for future study was also defined at that point. Next, a comprehensive analysis was conducted on the 27 selected articles to answer the five research questions. This researches’ main finding of this research was that a risk management culture positively supported the diffusion of innovation in firms. Several factors affected this relationship, such as; organisation learning, intellectual capital, human resources management, information technology, soft total quality management, knowledge management practice, strategy and structure.
Distributed Multi-Sensor Multi-View Fusion of PHD Filter for Maneuvering Targets

This paper addresses the problem of maneuvering multi-target tracking by a network of sensors having different and limited fields of view (FoV s). Each local sensor runs the Gaussian Mixture Probability Hypothetical Density (GMPHD) filter. Due to the target maneuver, based on a single motion model, severe tracking performance degradation can be observed. We propose to use multi-model (MM) method to realize the adaptation to motion characteristic so as to overcome maneuverability of targets. Then considering FoV s of sensors in the network are different and limited, the standard weighted arithmetic average (WAA) fusion is no longer applicable and leads to an underestimation of the target number. Therefore, we use state-dependent WAA (SD-WAA) fusion rule, which performs the WAA in a more robust way by calculating a set of state-related fusion weights. Numerical experiment is designed to demonstrate the efficacy of the proposed method.
Combating Mutuality with Difficulty Factors in Multi-class Imbalanced Data: A Similarity-based Hybrid Sampling

Multi-class imbalanced problem widely exists in real-life applications and has been a challenging issue. Existing sampling methods including decomposition approaches and dedicated approaches have limitations in handling the complex mutual relationships along with data difficulty factors. Actually, the relative minorities are critical in mutual relationship, and the data difficulty factors are harmful for these minority classes. In this paper, we propose SHSampler, a similarity-based hybrid sampling to combat the mutuality by addressing data difficulty factors in multi-class imbalanced data. Specifically, SHSampler firstly utilizes a sample similarity and dissimilarity estimation to identify data difficulty factors. Then, SHSampler conducts a relative majority weakening undersampling and a relative minority strengthening oversampling to reduce the negative impact of data difficulty factors and highlight the importance of the minorities. Extensive experiments over 20 typical datasets demonstrate the superiority of SHSampler in terms of MAUC and mGM when compared with 6 state-of-the-art methods.
Multi-aspect Matrix Factorization based Visualization of Convolutional Neural Networks

What does the space learned by a Convolutional neural network look like? Can we automatically extract high-level concepts that concisely summarize this space in a human-understandable manner? Can we, then, use those concepts for neural network interpretability? In this work, we define a concept to be a co-cluster of data instances (e.g., images), raw features (e.g., pixels), and neuron activations per hidden layer. Such a co-clusters links human-understandable characteristics like data instances and raw features with the architectural elements like neurons of the neural network. In order to extract such multi dimensional concepts, we propose a framework based on regularized and constrained coupled matrix factorization, where the goal of regularization is to force the latent factors to correspond to the sought-after concepts. Our proposed framework is unsupervised since it only requires unlabeled data instances and their activations as an input. Through extensive qualitative and quantitative experimentation on a number of datasets and architectures we show that our proposed framework is able to extract coherent and human-understandable concepts. Finally, we demonstrate the flexibility and versatility of our proposed framework in its ability to be leveraged as an additional tool which complements the existing state-of-the-art neural network interpretability methods.
Information Systems Solutions for the Database Problems

This paper has attempted to build on an explanation of solutions for database problems in information systems. The main focal area for this paper is majorly on enterprise resource planning systems and their effects and characteristics that make them a primary software tool which is used by businesses worldwide whether they be at large scales or work at smaller scales compared to the big-name enterprises out there. The paper has given a basic introduction into what an enterprise resource planning system is and defined its key characteristics that make it exactly an ERP. It also points out how businesses and enterprises make use of such systems and the methodologies that they use to implement ERP systems into their business practices. This paper also attempts to characterize ERP in relation to the solutions that they provide for the specific industry that they are used in. This is mostly done through categorizing the ERP through characteristics such as according to size, technology, risk factor, cost effectiveness and more.
Social Media Benefit for Advancing Language Ability in E-learning Environment: A Systematic Literature Review

E-learning has made extensive use of social media for language learning. Prior studies have identified trends in social media research in learning, but social media in language learning has not received as much attention. Consequently, the purpose of this study is to review scholarly articles on social media and language learning published in Scopus-indexed journals between 2012 and 2022. Fifteen publications meeting the selection criteria were reviewed and organized into five categories for research. This study examines the crucial function of social media in language instruction. This study found that the social media widely used in language learning are Facebook, WhatsApp, and YouTube. Language learning generally utilizes social media with a target foreign language and a second language. Social media applications in e-learning improve all language skills, namely writing, speaking, reading, and listening, but the most dominant is writing. The contribution of social media also occurs in the mastery of linguistic units, such as spelling/pronunciation, orthography, vocabulary, grammar, and discourse, but the most dominant is grammar. It is because writing is a complex language process that requires adequate grammar knowledge.
The Sentiment Analysis on Mental Health Awareness by Non-Governmental Organisation's Twitter

The Ministry of Health Malaysia's statistical analysis showed increasing mental health problems among Malaysians. However, Malaysian society’s stigma causes ignorance of mental health problems and lack awareness of the issues. The main purpose of this study was to determine the Malaysian community's awareness of mental health issues using data from the Non-Governmental Organization Twitter. The data were taken from the Twitter application for NGOs related to mental health in Malaysia. NGOs often disseminate information such as health statistics, causes, and ways to manage mental health on their Twitter application. The data collected from the Twitter API application require permission and application from Twitter even though the data are accessible publicly. The study was implemented using experimental methods from which the sentiment analysis is an appropriate way to study the Malaysian community's awareness of mental health problems. A few experiments were conducted, such as data collection, preprocessing, Sentiment Analysis, machine learning techniques, Support Vector Machine (SVM), Neural Network (NN), and Naive Bayes (NB). The analysis showed that each NGO's total number of positive tweets was more than the number of negative snippets. The analysis of machine learning using the three techniques showed the highest percentage of positive data for Precision, Recall, and F1-Score. Therefore, the awareness of mental health problems should be created using more positive text posts by the NGOs on social media to educate people.
GPUCSL: GPU-Based Library for Causal Structure Learning

GPUCSL is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$a$</tex> maintainable and extensible Python library for GPU-accelerated causal structure learning (CSL) based on the PC algorithm. The library supports multivariate normal and discrete distributed data, and implements multi-GPU support for multivariate normal distributed data. GPUCSL combines several stand-alone independent research implementations to allow a unified entry point into G PU -accelerated CSL. The library outperforms CPU -based implementations with an average speedup factor of 9.5 against pcalg and 19.8 against bnlearn and remains within the order of magnitude of existing G PU -accelerated CSL research. The source code is available at https://github.com/hpi-epic/gpucsl.
A study of the Dream Net model robustness across continual learning scenarios

Continual learning is one of the major challenges of deep learning. For decades, many studies have proposed efficient models overcoming catastrophic forgetting when learning new data. However, as they were focused on providing the best reduce-forgetting performance, studies have moved away from real-life applications where algorithms need to adapt to changing environments and perform, no matter the type of data arrival. Therefore, there is a growing need to define new scenarios to assess the robustness of existing methods with those challenges in mind. The issue of data availability during training is another essential point in the development of solid continual learning algorithms. Depending on the streaming formulation, the model needs in the more extreme scenarios to be able to adapt to new data as soon as it arrives and without the possibility to review it afterwards. In this study, we propose a review of existing continual learning scenarios and their associated terms. Those existing terms and definitions are synthesized in an atlas in order to provide a better overview. Based on two of the main categories defined in the atlas, “Class-IL.” and “Domain-IL”, we define eight different scenarios with data streams of varying complexity that allow to test the models robustness in changing data arrival scenarios. We choose to evaluate Dream Net - Data Free, a privacy-preserving continual learning algorithm, in each proposed scenario and demonstrate that this model is robust enough to succeed in every proposed scenario, regardless of how the data is presented. We also show that it is competitive with other continual learning literature algorithms that are not privacy preserving which is a clear advantage for real-life human-centered applications.
CorrectSpeech: A Fully Automated System for Speech Correction and Accent Reduction

This study propose a fully automated system for speech correction and accent reduction. Consider the application scenario that a recorded speech audio contains certain errors, e.g., inappropriate words, mispronunciations, that need to be corrected. The proposed system, named CorrectSpeech, performs the correction in three steps: recognizing the recorded speech and converting it into time-stamped symbol sequence, aligning recognized symbol sequence with target text to determine locations and types of required edit operations, and generating the corrected speech. Experiments show that the quality and naturalness of corrected speech depend on the performance of speech recognition and alignment modules, as well as the granularity level of editing operations. The proposed system is evaluated on two corpora: a manually perturbed version of VCTK and L2-ARCTIC. The results demonstrate that our system is able to correct mispronunciation and reduce accent in speech recordings. Audio samples are available online for demonstration <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://daxintan-cuhk.github.io/CorrectSpeech/
Multi-purpose Recommender Platform using Perceiver IO

Web services usually require many different types of recommender systems using large amount of user log and content data, in order to provide personalized content to their customers. Different recommenders may share the same customer-base or cross-use models/data. It is challenging to design different models for each recommendation task. In this work, we propose a general-purpose framework for various recommendation tasks based on Perceiver IO model. Perceiver lOis a general ma-chine learning architecture based on transformer-style attention modules, which helps eliminating feature engineering for various tasks. Different type of recommenders can be developed with minimal modifications and models can be transferred among dif- ferent tasks. Our experiments with a variety of recommendation scenarios confirm that our framework is able to handle those tasks while achieving state-of-the-art accuracy.
Development of Data Mining Framework Cardiovascular Disease Prediction

One of the highest shares of data-driven technology of health sector happens for private insurance stakeholders. It is therefore clear that private insurance companies can only survive being competitive in covering different medical stages such as surgery, intervention and other clinical trials in a high-risk environment. Estimation of expected costs and coverage is also important for both patient and insurer. In this case study we as a Data Mining and Artificial Business consultant want to explore different techniques of data mining to find out business risks for patients. We have asked the insurer to provide us a sizable medical history to watch those features. We would like to predict if given biographical profile of the patient along with exam results can predict CVD so he can cover his costs with this Insurer. On the other hand, in case of higher error of misclassified CVD what kind of decision should be taken by risk holder and insurer. Which one of these attributes causing this cost and what other stakeholders like target group of patients can be suffered from the loss? The ultimate goal is to develop a model that can predict the gap between those patients’ perception of their disease and their real disease. This can further help stakeholders to develop specific insurance policy.
Sentiment Analysis On Covid-19 Outbreak Awareness Using Naïve Bayes Algorithm

Sentiment analysis has gained much attention nowadays among the researchers especially during the Covid-19 pandemic. Due to the increasing volume of data coming from the social media platforms, researchers have been using sentiment analysis to analyse topics regarding commercial products, daily issues among the society and also to detect important events from the community. Since the social media users are consisting of the community, content that are shared could also be used to detect possible situational hazard such as the outbreak of Covid-19 in advanced. The result from the sentiment analysis could be beneficial to government organizations in order to contain the outbreaks and public health crisis related to Covid-19. The objective of this research is to explore Naive Bayes algorithm for the sentiment analysis on the Covid-19 outbreak awareness based on Twitter data. In this research, the data were collected during the Malaysia's second lock down, which was between the months of April to June 2021 using the Twitter API Tweepy. After the pre-processing and feature extraction stages, the data have been divided into the training and testing dataset for the Naive Bayes sentiment classification. The result has shown that Naive Bayes has been able to generate high performance with more than 90% accuracy for this classification problem. Future work would include the improvement of data preprocessing, more balance of dataset, enhancement of the algorithm and also comparing the performance with other well-known classification algorithms.